Research on On-Line Modeling of Fed-Batch Fermentation Process Based on v-SVR

NASA Astrophysics Data System (ADS)

Ma, Yongjun

The fermentation process is very complex and non-linear, many parameters are not easy to measure directly on line, soft sensor modeling is a good solution. This paper introduces v-support vector regression (v-SVR) for soft sensor modeling of fed-batch fermentation process. v-SVR is a novel type of learning machine. It can control the accuracy of fitness and prediction error by adjusting the parameter v. An on-line training algorithm is discussed in detail to reduce the training complexity of v-SVR. The experimental results show that v-SVR has low error rate and better generalization with appropriate v.

A Short-Term and High-Resolution System Load Forecasting Approach Using Support Vector Regression with Hybrid Parameters Optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang

This work proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of themore » hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system.« less

Fuzzy classifier based support vector regression framework for Poisson ratio determination

NASA Astrophysics Data System (ADS)

Asoodeh, Mojtaba; Bagheripour, Parisa

2013-09-01

Poisson ratio is considered as one of the most important rock mechanical properties of hydrocarbon reservoirs. Determination of this parameter through laboratory measurement is time, cost, and labor intensive. Furthermore, laboratory measurements do not provide continuous data along the reservoir intervals. Hence, a fast, accurate, and inexpensive way of determining Poisson ratio which produces continuous data over the whole reservoir interval is desirable. For this purpose, support vector regression (SVR) method based on statistical learning theory (SLT) was employed as a supervised learning algorithm to estimate Poisson ratio from conventional well log data. SVR is capable of accurately extracting the implicit knowledge contained in conventional well logs and converting the gained knowledge into Poisson ratio data. Structural risk minimization (SRM) principle which is embedded in the SVR structure in addition to empirical risk minimization (EMR) principle provides a robust model for finding quantitative formulation between conventional well log data and Poisson ratio. Although satisfying results were obtained from an individual SVR model, it had flaws of overestimation in low Poisson ratios and underestimation in high Poisson ratios. These errors were eliminated through implementation of fuzzy classifier based SVR (FCBSVR). The FCBSVR significantly improved accuracy of the final prediction. This strategy was successfully applied to data from carbonate reservoir rocks of an Iranian Oil Field. Results indicated that SVR predicted Poisson ratio values are in good agreement with measured values.

Load forecast method of electric vehicle charging station using SVR based on GA-PSO

NASA Astrophysics Data System (ADS)

Lu, Kuan; Sun, Wenxue; Ma, Changhui; Yang, Shenquan; Zhu, Zijian; Zhao, Pengfei; Zhao, Xin; Xu, Nan

2017-06-01

This paper presents a Support Vector Regression (SVR) method for electric vehicle (EV) charging station load forecast based on genetic algorithm (GA) and particle swarm optimization (PSO). Fuzzy C-Means (FCM) clustering is used to establish similar day samples. GA is used for global parameter searching and PSO is used for a more accurately local searching. Load forecast is then regressed using SVR. The practical load data of an EV charging station were taken to illustrate the proposed method. The result indicates an obvious improvement in the forecasting accuracy compared with SVRs based on PSO and GA exclusively.

Accurate Descriptions of Hot Flow Behaviors Across β Transus of Ti-6Al-4V Alloy by Intelligence Algorithm GA-SVR

NASA Astrophysics Data System (ADS)

Wang, Li-yong; Li, Le; Zhang, Zhi-hua

2016-09-01

Hot compression tests of Ti-6Al-4V alloy in a wide temperature range of 1023-1323 K and strain rate range of 0.01-10 s-1 were conducted by a servo-hydraulic and computer-controlled Gleeble-3500 machine. In order to accurately and effectively characterize the highly nonlinear flow behaviors, support vector regression (SVR) which is a machine learning method was combined with genetic algorithm (GA) for characterizing the flow behaviors, namely, the GA-SVR. The prominent character of GA-SVR is that it with identical training parameters will keep training accuracy and prediction accuracy at a stable level in different attempts for a certain dataset. The learning abilities, generalization abilities, and modeling efficiencies of the mathematical regression model, ANN, and GA-SVR for Ti-6Al-4V alloy were detailedly compared. Comparison results show that the learning ability of the GA-SVR is stronger than the mathematical regression model. The generalization abilities and modeling efficiencies of these models were shown as follows in ascending order: the mathematical regression model < ANN < GA-SVR. The stress-strain data outside experimental conditions were predicted by the well-trained GA-SVR, which improved simulation accuracy of the load-stroke curve and can further improve the related research fields where stress-strain data play important roles, such as speculating work hardening and dynamic recovery, characterizing dynamic recrystallization evolution, and improving processing maps.

Modeling daily soil temperature over diverse climate conditions in Iran—a comparison of multiple linear regression and support vector regression techniques

NASA Astrophysics Data System (ADS)

Delbari, Masoomeh; Sharifazari, Salman; Mohammadi, Ehsan

2018-02-01

The knowledge of soil temperature at different depths is important for agricultural industry and for understanding climate change. The aim of this study is to evaluate the performance of a support vector regression (SVR)-based model in estimating daily soil temperature at 10, 30 and 100 cm depth at different climate conditions over Iran. The obtained results were compared to those obtained from a more classical multiple linear regression (MLR) model. The correlation sensitivity for the input combinations and periodicity effect were also investigated. Climatic data used as inputs to the models were minimum and maximum air temperature, solar radiation, relative humidity, dew point, and the atmospheric pressure (reduced to see level), collected from five synoptic stations Kerman, Ahvaz, Tabriz, Saghez, and Rasht located respectively in the hyper-arid, arid, semi-arid, Mediterranean, and hyper-humid climate conditions. According to the results, the performance of both MLR and SVR models was quite well at surface layer, i.e., 10-cm depth. However, SVR performed better than MLR in estimating soil temperature at deeper layers especially 100 cm depth. Moreover, both models performed better in humid climate condition than arid and hyper-arid areas. Further, adding a periodicity component into the modeling process considerably improved the models' performance especially in the case of SVR.

Hybrid Support Vector Regression and Autoregressive Integrated Moving Average Models Improved by Particle Swarm Optimization for Property Crime Rates Forecasting with Economic Indicators

PubMed Central

Alwee, Razana; Hj Shamsuddin, Siti Mariyam; Sallehuddin, Roselina

2013-01-01

Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models. PMID:23766729

Hybrid support vector regression and autoregressive integrated moving average models improved by particle swarm optimization for property crime rates forecasting with economic indicators.

PubMed

Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Sallehuddin, Roselina

2013-01-01

Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.

Support vector regression-guided unravelling: antioxidant capacity and quantitative structure-activity relationship predict reduction and promotion effects of flavonoids on acrylamide formation

PubMed Central

Huang, Mengmeng; Wei, Yan; Wang, Jun; Zhang, Yu

2016-01-01

We used the support vector regression (SVR) approach to predict and unravel reduction/promotion effect of characteristic flavonoids on the acrylamide formation under a low-moisture Maillard reaction system. Results demonstrated the reduction/promotion effects by flavonoids at addition levels of 1–10000 μmol/L. The maximal inhibition rates (51.7%, 68.8% and 26.1%) and promote rates (57.7%, 178.8% and 27.5%) caused by flavones, flavonols and isoflavones were observed at addition levels of 100 μmol/L and 10000 μmol/L, respectively. The reduction/promotion effects were closely related to the change of trolox equivalent antioxidant capacity (ΔTEAC) and well predicted by triple ΔTEAC measurements via SVR models (R: 0.633–0.900). Flavonols exhibit stronger effects on the acrylamide formation than flavones and isoflavones as well as their O-glycosides derivatives, which may be attributed to the number and position of phenolic and 3-enolic hydroxyls. The reduction/promotion effects were well predicted by using optimized quantitative structure-activity relationship (QSAR) descriptors and SVR models (R: 0.926–0.994). Compared to artificial neural network and multi-linear regression models, SVR models exhibited better fitting performance for both TEAC-dependent and QSAR descriptor-dependent predicting work. These observations demonstrated that the SVR models are competent for predicting our understanding on the future use of natural antioxidants for decreasing the acrylamide formation. PMID:27586851

Support vector regression-guided unravelling: antioxidant capacity and quantitative structure-activity relationship predict reduction and promotion effects of flavonoids on acrylamide formation

NASA Astrophysics Data System (ADS)

Huang, Mengmeng; Wei, Yan; Wang, Jun; Zhang, Yu

2016-09-01

We used the support vector regression (SVR) approach to predict and unravel reduction/promotion effect of characteristic flavonoids on the acrylamide formation under a low-moisture Maillard reaction system. Results demonstrated the reduction/promotion effects by flavonoids at addition levels of 1-10000 μmol/L. The maximal inhibition rates (51.7%, 68.8% and 26.1%) and promote rates (57.7%, 178.8% and 27.5%) caused by flavones, flavonols and isoflavones were observed at addition levels of 100 μmol/L and 10000 μmol/L, respectively. The reduction/promotion effects were closely related to the change of trolox equivalent antioxidant capacity (ΔTEAC) and well predicted by triple ΔTEAC measurements via SVR models (R: 0.633-0.900). Flavonols exhibit stronger effects on the acrylamide formation than flavones and isoflavones as well as their O-glycosides derivatives, which may be attributed to the number and position of phenolic and 3-enolic hydroxyls. The reduction/promotion effects were well predicted by using optimized quantitative structure-activity relationship (QSAR) descriptors and SVR models (R: 0.926-0.994). Compared to artificial neural network and multi-linear regression models, SVR models exhibited better fitting performance for both TEAC-dependent and QSAR descriptor-dependent predicting work. These observations demonstrated that the SVR models are competent for predicting our understanding on the future use of natural antioxidants for decreasing the acrylamide formation.

[Extraction Optimization of Rhizome of Curcuma longa by Response Surface Methodology and Support Vector Regression].

PubMed

Zhou, Pei-pei; Shan, Jin-feng; Jiang, Jian-lan

2015-12-01

To optimize the optimal microwave-assisted extraction method of curcuminoids from Curcuma longa. On the base of single factor experiment, the ethanol concentration, the ratio of liquid to solid and the microwave time were selected for further optimization. Support Vector Regression (SVR) and Central Composite Design-Response Surface Methodology (CCD) algorithm were utilized to design and establish models respectively, while Particle Swarm Optimization (PSO) was introduced to optimize the parameters of SVR models and to search optimal points of models. The evaluation indicator, the sum of curcumin, demethoxycurcumin and bisdemethoxycurcumin by HPLC, were used. The optimal parameters of microwave-assisted extraction were as follows: ethanol concentration of 69%, ratio of liquid to solid of 21 : 1, microwave time of 55 s. On those conditions, the sum of three curcuminoids was 28.97 mg/g (per gram of rhizomes powder). Both the CCD model and the SVR model were credible, for they have predicted the similar process condition and the deviation of yield were less than 1.2%.

Use of multivariate linear regression and support vector regression to predict functional outcome after surgery for cervical spondylotic myelopathy.

PubMed

Hoffman, Haydn; Lee, Sunghoon I; Garst, Jordan H; Lu, Derek S; Li, Charles H; Nagasawa, Daniel T; Ghalehsari, Nima; Jahanforouz, Nima; Razaghy, Mehrdad; Espinal, Marie; Ghavamrezaii, Amir; Paak, Brian H; Wu, Irene; Sarrafzadeh, Majid; Lu, Daniel C

2015-09-01

This study introduces the use of multivariate linear regression (MLR) and support vector regression (SVR) models to predict postoperative outcomes in a cohort of patients who underwent surgery for cervical spondylotic myelopathy (CSM). Currently, predicting outcomes after surgery for CSM remains a challenge. We recruited patients who had a diagnosis of CSM and required decompressive surgery with or without fusion. Fine motor function was tested preoperatively and postoperatively with a handgrip-based tracking device that has been previously validated, yielding mean absolute accuracy (MAA) results for two tracking tasks (sinusoidal and step). All patients completed Oswestry disability index (ODI) and modified Japanese Orthopaedic Association questionnaires preoperatively and postoperatively. Preoperative data was utilized in MLR and SVR models to predict postoperative ODI. Predictions were compared to the actual ODI scores with the coefficient of determination (R(2)) and mean absolute difference (MAD). From this, 20 patients met the inclusion criteria and completed follow-up at least 3 months after surgery. With the MLR model, a combination of the preoperative ODI score, preoperative MAA (step function), and symptom duration yielded the best prediction of postoperative ODI (R(2)=0.452; MAD=0.0887; p=1.17 × 10(-3)). With the SVR model, a combination of preoperative ODI score, preoperative MAA (sinusoidal function), and symptom duration yielded the best prediction of postoperative ODI (R(2)=0.932; MAD=0.0283; p=5.73 × 10(-12)). The SVR model was more accurate than the MLR model. The SVR can be used preoperatively in risk/benefit analysis and the decision to operate. Copyright © 2015 Elsevier Ltd. All rights reserved.

Support vector machine regression (SVR/LS-SVM)--an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data.

PubMed

Balabin, Roman M; Lomakina, Ekaterina I

2011-04-21

In this study, we make a general comparison of the accuracy and robustness of five multivariate calibration models: partial least squares (PLS) regression or projection to latent structures, polynomial partial least squares (Poly-PLS) regression, artificial neural networks (ANNs), and two novel techniques based on support vector machines (SVMs) for multivariate data analysis: support vector regression (SVR) and least-squares support vector machines (LS-SVMs). The comparison is based on fourteen (14) different datasets: seven sets of gasoline data (density, benzene content, and fractional composition/boiling points), two sets of ethanol gasoline fuel data (density and ethanol content), one set of diesel fuel data (total sulfur content), three sets of petroleum (crude oil) macromolecules data (weight percentages of asphaltenes, resins, and paraffins), and one set of petroleum resins data (resins content). Vibrational (near-infrared, NIR) spectroscopic data are used to predict the properties and quality coefficients of gasoline, biofuel/biodiesel, diesel fuel, and other samples of interest. The four systems presented here range greatly in composition, properties, strength of intermolecular interactions (e.g., van der Waals forces, H-bonds), colloid structure, and phase behavior. Due to the high diversity of chemical systems studied, general conclusions about SVM regression methods can be made. We try to answer the following question: to what extent can SVM-based techniques replace ANN-based approaches in real-world (industrial/scientific) applications? The results show that both SVR and LS-SVM methods are comparable to ANNs in accuracy. Due to the much higher robustness of the former, the SVM-based approaches are recommended for practical (industrial) application. This has been shown to be especially true for complicated, highly nonlinear objects.

Application of support vector regression for optimization of vibration flow field of high-density polyethylene melts characterized by small angle light scattering

NASA Astrophysics Data System (ADS)

Xian, Guangming

2018-03-01

In this paper, the vibration flow field parameters of polymer melts in a visual slit die are optimized by using intelligent algorithm. Experimental small angle light scattering (SALS) patterns are shown to characterize the processing process. In order to capture the scattered light, a polarizer and an analyzer are placed before and after the polymer melts. The results reported in this study are obtained using high-density polyethylene (HDPE) with rotation speed at 28 rpm. In addition, support vector regression (SVR) analytical method is introduced for optimization the parameters of vibration flow field. This work establishes the general applicability of SVR for predicting the optimal parameters of vibration flow field.

An integrated fiber-optic probe combined with support vector regression for fast estimation of optical properties of turbid media.

PubMed

Zhou, Yang; Fu, Xiaping; Ying, Yibin; Fang, Zhenhuan

2015-06-23

A fiber-optic probe system was developed to estimate the optical properties of turbid media based on spatially resolved diffuse reflectance. Because of the limitations in numerical calculation of radiative transfer equation (RTE), diffusion approximation (DA) and Monte Carlo simulations (MC), support vector regression (SVR) was introduced to model the relationship between diffuse reflectance values and optical properties. The SVR models of four collection fibers were trained by phantoms in calibration set with a wide range of optical properties which represented products of different applications, then the optical properties of phantoms in prediction set were predicted after an optimal searching on SVR models. The results indicated that the SVR model was capable of describing the relationship with little deviation in forward validation. The correlation coefficient (R) of reduced scattering coefficient μ'(s) and absorption coefficient μ(a) in the prediction set were 0.9907 and 0.9980, respectively. The root mean square errors of prediction (RMSEP) of μ'(s) and μ(a) in inverse validation were 0.411 cm(-1) and 0.338 cm(-1), respectively. The results indicated that the integrated fiber-optic probe system combined with SVR model were suitable for fast and accurate estimation of optical properties of turbid media based on spatially resolved diffuse reflectance. Copyright © 2015 Elsevier B.V. All rights reserved.

The application of SVR model in the improvement of QbD: a case study of the extraction of podophyllotoxin.

PubMed

Zhai, Chun-Hui; Xuan, Jian-Bang; Fan, Hai-Liu; Zhao, Teng-Fei; Jiang, Jian-Lan

2018-05-03

In order to make a further optimization of process design via increasing the stability of design space, we brought in the model of Support Vector Regression (SVR). In this work, the extraction of podophyllotoxin was researched as a case study based on Quality by Design (QbD). We compared the fitting effect of SVR and the most used quadratic polynomial model (QPM) in QbD, and an analysis was made between the two design spaces obtained by SVR and QPM. As a result, the SVR stayed ahead of QPM in prediction accuracy, the stability of model and the generalization ability. The introduction of SVR into QbD made the extraction process of podophyllotoxin well designed and easier to control. The better fitting effect of SVR improved the application effect of QbD and the universal applicability of SVR, especially for non-linear, complicated and weak-regularity problems, widened the application field of QbD.

Real time flaw detection and characterization in tube through partial least squares and SVR: Application to eddy current testing

NASA Astrophysics Data System (ADS)

Ahmed, Shamim; Miorelli, Roberto; Calmon, Pierre; Anselmi, Nicola; Salucci, Marco

2018-04-01

This paper describes Learning-By-Examples (LBE) technique for performing quasi real time flaw localization and characterization within a conductive tube based on Eddy Current Testing (ECT) signals. Within the framework of LBE, the combination of full-factorial (i.e., GRID) sampling and Partial Least Squares (PLS) feature extraction (i.e., GRID-PLS) techniques are applied for generating a suitable training set in offine phase. Support Vector Regression (SVR) is utilized for model development and inversion during offine and online phases, respectively. The performance and robustness of the proposed GIRD-PLS/SVR strategy on noisy test set is evaluated and compared with standard GRID/SVR approach.

A short-term and high-resolution distribution system load forecasting approach using support vector regression with hybrid parameters optimization

DOE PAGES

Jiang, Huaiguang; Zhang, Yingchen; Muljadi, Eduard; ...

2016-01-01

This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of themore » hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization (PSO) is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of the paper.« less

Estimation of Electrically-Evoked Knee Torque from Mechanomyography Using Support Vector Regression.

PubMed

Ibitoye, Morufu Olusola; Hamzaid, Nur Azah; Abdul Wahab, Ahmad Khairi; Hasnan, Nazirah; Olatunji, Sunday Olusanya; Davis, Glen M

2016-07-19

The difficulty of real-time muscle force or joint torque estimation during neuromuscular electrical stimulation (NMES) in physical therapy and exercise science has motivated recent research interest in torque estimation from other muscle characteristics. This study investigated the accuracy of a computational intelligence technique for estimating NMES-evoked knee extension torque based on the Mechanomyographic signals (MMG) of contracting muscles that were recorded from eight healthy males. Simulation of the knee torque was modelled via Support Vector Regression (SVR) due to its good generalization ability in related fields. Inputs to the proposed model were MMG amplitude characteristics, the level of electrical stimulation or contraction intensity, and knee angle. Gaussian kernel function, as well as its optimal parameters were identified with the best performance measure and were applied as the SVR kernel function to build an effective knee torque estimation model. To train and test the model, the data were partitioned into training (70%) and testing (30%) subsets, respectively. The SVR estimation accuracy, based on the coefficient of determination (R²) between the actual and the estimated torque values was up to 94% and 89% during the training and testing cases, with root mean square errors (RMSE) of 9.48 and 12.95, respectively. The knee torque estimations obtained using SVR modelling agreed well with the experimental data from an isokinetic dynamometer. These findings support the realization of a closed-loop NMES system for functional tasks using MMG as the feedback signal source and an SVR algorithm for joint torque estimation.

Support vector regression and artificial neural network models for stability indicating analysis of mebeverine hydrochloride and sulpiride mixtures in pharmaceutical preparation: A comparative study

NASA Astrophysics Data System (ADS)

Naguib, Ibrahim A.; Darwish, Hany W.

2012-02-01

A comparison between support vector regression (SVR) and Artificial Neural Networks (ANNs) multivariate regression methods is established showing the underlying algorithm for each and making a comparison between them to indicate the inherent advantages and limitations. In this paper we compare SVR to ANN with and without variable selection procedure (genetic algorithm (GA)). To project the comparison in a sensible way, the methods are used for the stability indicating quantitative analysis of mixtures of mebeverine hydrochloride and sulpiride in binary mixtures as a case study in presence of their reported impurities and degradation products (summing up to 6 components) in raw materials and pharmaceutical dosage form via handling the UV spectral data. For proper analysis, a 6 factor 5 level experimental design was established resulting in a training set of 25 mixtures containing different ratios of the interfering species. An independent test set consisting of 5 mixtures was used to validate the prediction ability of the suggested models. The proposed methods (linear SVR (without GA) and linear GA-ANN) were successfully applied to the analysis of pharmaceutical tablets containing mebeverine hydrochloride and sulpiride mixtures. The results manifest the problem of nonlinearity and how models like the SVR and ANN can handle it. The methods indicate the ability of the mentioned multivariate calibration models to deconvolute the highly overlapped UV spectra of the 6 components' mixtures, yet using cheap and easy to handle instruments like the UV spectrophotometer.

Multiple kernel SVR based on the MRE for remote sensing water depth fusion detection

NASA Astrophysics Data System (ADS)

Wang, Jinjin; Ma, Yi; Zhang, Jingyu

2018-03-01

Remote sensing has an important means of water depth detection in coastal shallow waters and reefs. Support vector regression (SVR) is a machine learning method which is widely used in data regression. In this paper, SVR is used to remote sensing multispectral bathymetry. Aiming at the problem that the single-kernel SVR method has a large error in shallow water depth inversion, the mean relative error (MRE) of different water depth is retrieved as a decision fusion factor with single kernel SVR method, a multi kernel SVR fusion method based on the MRE is put forward. And taking the North Island of the Xisha Islands in China as an experimentation area, the comparison experiments with the single kernel SVR method and the traditional multi-bands bathymetric method are carried out. The results show that: 1) In range of 0 to 25 meters, the mean absolute error(MAE)of the multi kernel SVR fusion method is 1.5m,the MRE is 13.2%; 2) Compared to the 4 single kernel SVR method, the MRE of the fusion method reduced 1.2% (1.9%) 3.4% (1.8%), and compared to traditional multi-bands method, the MRE reduced 1.9%; 3) In 0-5m depth section, compared to the single kernel method and the multi-bands method, the MRE of fusion method reduced 13.5% to 44.4%, and the distribution of points is more concentrated relative to y=x.

[A novel approach to NIR spectral quantitative analysis: semi-supervised least-squares support vector regression machine].

PubMed

Li, Lin; Xu, Shuo; An, Xin; Zhang, Lu-Da

2011-10-01

In near infrared spectral quantitative analysis, the precision of measured samples' chemical values is the theoretical limit of those of quantitative analysis with mathematical models. However, the number of samples that can obtain accurately their chemical values is few. Many models exclude the amount of samples without chemical values, and consider only these samples with chemical values when modeling sample compositions' contents. To address this problem, a semi-supervised LS-SVR (S2 LS-SVR) model is proposed on the basis of LS-SVR, which can utilize samples without chemical values as well as those with chemical values. Similar to the LS-SVR, to train this model is equivalent to solving a linear system. Finally, the samples of flue-cured tobacco were taken as experimental material, and corresponding quantitative analysis models were constructed for four sample compositions' content(total sugar, reducing sugar, total nitrogen and nicotine) with PLS regression, LS-SVR and S2 LS-SVR. For the S2 LS-SVR model, the average relative errors between actual values and predicted ones for the four sample compositions' contents are 6.62%, 7.56%, 6.11% and 8.20%, respectively, and the correlation coefficients are 0.974 1, 0.973 3, 0.923 0 and 0.948 6, respectively. Experimental results show the S2 LS-SVR model outperforms the other two, which verifies the feasibility and efficiency of the S2 LS-SVR model.

The application of artificial neural networks and support vector regression for simultaneous spectrophotometric determination of commercial eye drop contents

NASA Astrophysics Data System (ADS)

Valizadeh, Maryam; Sohrabi, Mahmoud Reza

2018-03-01

In the present study, artificial neural networks (ANNs) and support vector regression (SVR) as intelligent methods coupled with UV spectroscopy for simultaneous quantitative determination of Dorzolamide (DOR) and Timolol (TIM) in eye drop. Several synthetic mixtures were analyzed for validating the proposed methods. At first, neural network time series, which one type of network from the artificial neural network was employed and its efficiency was evaluated. Afterwards, the radial basis network was applied as another neural network. Results showed that the performance of this method is suitable for predicting. Finally, support vector regression was proposed to construct the Zilomole prediction model. Also, root mean square error (RMSE) and mean recovery (%) were calculated for SVR method. Moreover, the proposed methods were compared to the high-performance liquid chromatography (HPLC) as a reference method. One way analysis of variance (ANOVA) test at the 95% confidence level applied to the comparison results of suggested and reference methods that there were no significant differences between them. Also, the effect of interferences was investigated in spike solutions.

TWSVR: Regression via Twin Support Vector Machine.

PubMed

Khemchandani, Reshma; Goyal, Keshav; Chandra, Suresh

2016-02-01

Taking motivation from Twin Support Vector Machine (TWSVM) formulation, Peng (2010) attempted to propose Twin Support Vector Regression (TSVR) where the regressor is obtained via solving a pair of quadratic programming problems (QPPs). In this paper we argue that TSVR formulation is not in the true spirit of TWSVM. Further, taking motivation from Bi and Bennett (2003), we propose an alternative approach to find a formulation for Twin Support Vector Regression (TWSVR) which is in the true spirit of TWSVM. We show that our proposed TWSVR can be derived from TWSVM for an appropriately constructed classification problem. To check the efficacy of our proposed TWSVR we compare its performance with TSVR and classical Support Vector Regression(SVR) on various regression datasets. Copyright © 2015 Elsevier Ltd. All rights reserved.

Support vector regression to predict porosity and permeability: Effect of sample size

NASA Astrophysics Data System (ADS)

Al-Anazi, A. F.; Gates, I. D.

2012-02-01

Porosity and permeability are key petrophysical parameters obtained from laboratory core analysis. Cores, obtained from drilled wells, are often few in number for most oil and gas fields. Porosity and permeability correlations based on conventional techniques such as linear regression or neural networks trained with core and geophysical logs suffer poor generalization to wells with only geophysical logs. The generalization problem of correlation models often becomes pronounced when the training sample size is small. This is attributed to the underlying assumption that conventional techniques employing the empirical risk minimization (ERM) inductive principle converge asymptotically to the true risk values as the number of samples increases. In small sample size estimation problems, the available training samples must span the complexity of the parameter space so that the model is able both to match the available training samples reasonably well and to generalize to new data. This is achieved using the structural risk minimization (SRM) inductive principle by matching the capability of the model to the available training data. One method that uses SRM is support vector regression (SVR) network. In this research, the capability of SVR to predict porosity and permeability in a heterogeneous sandstone reservoir under the effect of small sample size is evaluated. Particularly, the impact of Vapnik's ɛ-insensitivity loss function and least-modulus loss function on generalization performance was empirically investigated. The results are compared to the multilayer perception (MLP) neural network, a widely used regression method, which operates under the ERM principle. The mean square error and correlation coefficients were used to measure the quality of predictions. The results demonstrate that SVR yields consistently better predictions of the porosity and permeability with small sample size than the MLP method. Also, the performance of SVR depends on both kernel function type and loss functions used.

Estimation of diffusion coefficients from voltammetric signals by support vector and gaussian process regression

PubMed Central

2014-01-01

Background Support vector regression (SVR) and Gaussian process regression (GPR) were used for the analysis of electroanalytical experimental data to estimate diffusion coefficients. Results For simulated cyclic voltammograms based on the EC, Eqr, and EqrC mechanisms these regression algorithms in combination with nonlinear kernel/covariance functions yielded diffusion coefficients with higher accuracy as compared to the standard approach of calculating diffusion coefficients relying on the Nicholson-Shain equation. The level of accuracy achieved by SVR and GPR is virtually independent of the rate constants governing the respective reaction steps. Further, the reduction of high-dimensional voltammetric signals by manual selection of typical voltammetric peak features decreased the performance of both regression algorithms compared to a reduction by downsampling or principal component analysis. After training on simulated data sets, diffusion coefficients were estimated by the regression algorithms for experimental data comprising voltammetric signals for three organometallic complexes. Conclusions Estimated diffusion coefficients closely matched the values determined by the parameter fitting method, but reduced the required computational time considerably for one of the reaction mechanisms. The automated processing of voltammograms according to the regression algorithms yields better results than the conventional analysis of peak-related data. PMID:24987463

Determining Cutoff Point of Ensemble Trees Based on Sample Size in Predicting Clinical Dose with DNA Microarray Data.

PubMed

Yılmaz Isıkhan, Selen; Karabulut, Erdem; Alpar, Celal Reha

2016-01-01

Background/Aim . Evaluating the success of dose prediction based on genetic or clinical data has substantially advanced recently. The aim of this study is to predict various clinical dose values from DNA gene expression datasets using data mining techniques. Materials and Methods . Eleven real gene expression datasets containing dose values were included. First, important genes for dose prediction were selected using iterative sure independence screening. Then, the performances of regression trees (RTs), support vector regression (SVR), RT bagging, SVR bagging, and RT boosting were examined. Results . The results demonstrated that a regression-based feature selection method substantially reduced the number of irrelevant genes from raw datasets. Overall, the best prediction performance in nine of 11 datasets was achieved using SVR; the second most accurate performance was provided using a gradient-boosting machine (GBM). Conclusion . Analysis of various dose values based on microarray gene expression data identified common genes found in our study and the referenced studies. According to our findings, SVR and GBM can be good predictors of dose-gene datasets. Another result of the study was to identify the sample size of n = 25 as a cutoff point for RT bagging to outperform a single RT.

Applying different independent component analysis algorithms and support vector regression for IT chain store sales forecasting.

PubMed

Dai, Wensheng; Wu, Jui-Yu; Lu, Chi-Jie

2014-01-01

Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting.

Applying Different Independent Component Analysis Algorithms and Support Vector Regression for IT Chain Store Sales Forecasting

PubMed Central

Dai, Wensheng

2014-01-01

Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting. PMID:25165740

Hybrid approach of selecting hyperparameters of support vector machine for regression.

PubMed

Jeng, Jin-Tsong

2006-06-01

To select the hyperparameters of the support vector machine for regression (SVR), a hybrid approach is proposed to determine the kernel parameter of the Gaussian kernel function and the epsilon value of Vapnik's epsilon-insensitive loss function. The proposed hybrid approach includes a competitive agglomeration (CA) clustering algorithm and a repeated SVR (RSVR) approach. Since the CA clustering algorithm is used to find the nearly "optimal" number of clusters and the centers of clusters in the clustering process, the CA clustering algorithm is applied to select the Gaussian kernel parameter. Additionally, an RSVR approach that relies on the standard deviation of a training error is proposed to obtain an epsilon in the loss function. Finally, two functions, one real data set (i.e., a time series of quarterly unemployment rate for West Germany) and an identification of nonlinear plant are used to verify the usefulness of the hybrid approach.

PM2.5 forecasting using SVR with PSOGSA algorithm based on CEEMD, GRNN and GCA considering meteorological factors

NASA Astrophysics Data System (ADS)

Zhu, Suling; Lian, Xiuyuan; Wei, Lin; Che, Jinxing; Shen, Xiping; Yang, Ling; Qiu, Xuanlin; Liu, Xiaoning; Gao, Wenlong; Ren, Xiaowei; Li, Juansheng

2018-06-01

The PM2.5 is the culprit of air pollution, and it leads to respiratory system disease when the fine particles are inhaled. Therefore, it is increasingly significant to develop an effective model for PM2.5 forecasting and warnings that informs people to foresee the air quality. People can reduce outdoor activities and take preventive measures if they know the air quality is bad ahead of time. In addition, reliable forecasting results can remind the relevant departments to control and reduce pollutants discharge. According to our knowledge, the current hybrid forecasting techniques of PM2.5 do not take the meteorological factors into consideration. Actually, meteorological factors affect the concentrations of air pollution, but it is unclear whether meteorological factors are helpful for improving the PM2.5 forecasting results or not. This paper proposes a hybrid model called CEEMD-PSOGSA-SVR-GRNN, based on complementary ensemble empirical mode decomposition (CEEMD), particle swarm optimization and gravitational search algorithm (PSOGSA), support vector regression (SVR), generalized regression neural network (GRNN) and grey correlation analysis (GCA), for the daily PM2.5 concentrations forecasting. The main steps of proposed model are described as follows: the original PM2.5 data decomposition with CEEMD, optimal SVR selection with PSOGCA, meteorological factors selection with GCA, residual revision by GRNN and forecasting results analysis. Three cities (Chongqing, Harbin and Jinan) in China with different characteristics of climate, terrain and pollution sources are selected to verify the effectiveness of proposed model, and CEEMD-PSOGSA-SVR*, EEMD-PSOGSA-SVR, PSOGSA-SVR, CEEMD-PSO-SVR, CEEMD-GSA-SVR, CEEMD-GWO-SVR are considered to be compared models. The experimental results show that the hybrid CEEMD-PSOGSA-SVR-GRNN model outperforms other six compared models. Therefore, the proposed CEEMD-PSOGSA-SVR-GRNN model can be used to develop air quality forecasting and warnings.

Modeling and Predicting the Electrical Conductivity of Composite Cathode for Solid Oxide Fuel Cell by Using Support Vector Regression

NASA Astrophysics Data System (ADS)

Tang, J. L.; Cai, C. Z.; Xiao, T. T.; Huang, S. J.

2012-07-01

The electrical conductivity of solid oxide fuel cell (SOFC) cathode is one of the most important indices affecting the efficiency of SOFC. In order to improve the performance of fuel cell system, it is advantageous to have accurate model with which one can predict the electrical conductivity. In this paper, a model utilizing support vector regression (SVR) approach combined with particle swarm optimization (PSO) algorithm for its parameter optimization was established to modeling and predicting the electrical conductivity of Ba0.5Sr0.5Co0.8Fe0.2 O3-δ-xSm0.5Sr0.5CoO3-δ (BSCF-xSSC) composite cathode under two influence factors, including operating temperature (T) and SSC content (x) in BSCF-xSSC composite cathode. The leave-one-out cross validation (LOOCV) test result by SVR strongly supports that the generalization ability of SVR model is high enough. The absolute percentage error (APE) of 27 samples does not exceed 0.05%. The mean absolute percentage error (MAPE) of all 30 samples is only 0.09% and the correlation coefficient (R2) as high as 0.999. This investigation suggests that the hybrid PSO-SVR approach may be not only a promising and practical methodology to simulate the properties of fuel cell system, but also a powerful tool to be used for optimal designing or controlling the operating process of a SOFC system.

Acoustic emission localization based on FBG sensing network and SVR algorithm

NASA Astrophysics Data System (ADS)

Sai, Yaozhang; Zhao, Xiuxia; Hou, Dianli; Jiang, Mingshun

2017-03-01

In practical application, carbon fiber reinforced plastics (CFRP) structures are easy to appear all sorts of invisible damages. So the damages should be timely located and detected for the safety of CFPR structures. In this paper, an acoustic emission (AE) localization system based on fiber Bragg grating (FBG) sensing network and support vector regression (SVR) is proposed for damage localization. AE signals, which are caused by damage, are acquired by high speed FBG interrogation. According to the Shannon wavelet transform, time differences between AE signals are extracted for localization algorithm based on SVR. According to the SVR model, the coordinate of AE source can be accurately predicted without wave velocity. The FBG system and localization algorithm are verified on a 500 mm×500 mm×2 mm CFRP plate. The experimental results show that the average error of localization system is 2.8 mm and the training time is 0.07 s.

Nonparametric methods for drought severity estimation at ungauged sites

NASA Astrophysics Data System (ADS)

Sadri, S.; Burn, D. H.

2012-12-01

The objective in frequency analysis is, given extreme events such as drought severity or duration, to estimate the relationship between that event and the associated return periods at a catchment. Neural networks and other artificial intelligence approaches in function estimation and regression analysis are relatively new techniques in engineering, providing an attractive alternative to traditional statistical models. There are, however, few applications of neural networks and support vector machines in the area of severity quantile estimation for drought frequency analysis. In this paper, we compare three methods for this task: multiple linear regression, radial basis function neural networks, and least squares support vector regression (LS-SVR). The area selected for this study includes 32 catchments in the Canadian Prairies. From each catchment drought severities are extracted and fitted to a Pearson type III distribution, which act as observed values. For each method-duration pair, we use a jackknife algorithm to produce estimated values at each site. The results from these three approaches are compared and analyzed, and it is found that LS-SVR provides the best quantile estimates and extrapolating capacity.

Soft-sensing model of temperature for aluminum reduction cell on improved twin support vector regression

NASA Astrophysics Data System (ADS)

Li, Tao

2018-06-01

The complexity of aluminum electrolysis process leads the temperature for aluminum reduction cells hard to measure directly. However, temperature is the control center of aluminum production. To solve this problem, combining some aluminum plant's practice data, this paper presents a Soft-sensing model of temperature for aluminum electrolysis process on Improved Twin Support Vector Regression (ITSVR). ITSVR eliminates the slow learning speed of Support Vector Regression (SVR) and the over-fit risk of Twin Support Vector Regression (TSVR) by introducing a regularization term into the objective function of TSVR, which ensures the structural risk minimization principle and lower computational complexity. Finally, the model with some other parameters as auxiliary variable, predicts the temperature by ITSVR. The simulation result shows Soft-sensing model based on ITSVR has short time-consuming and better generalization.

Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter

NASA Astrophysics Data System (ADS)

Dong, Hancheng; Jin, Xiaoning; Lou, Yangbing; Wang, Changhong

2014-12-01

Lithium-ion batteries are used as the main power source in many electronic and electrical devices. In particular, with the growth in battery-powered electric vehicle development, the lithium-ion battery plays a critical role in the reliability of vehicle systems. In order to provide timely maintenance and replacement of battery systems, it is necessary to develop a reliable and accurate battery health diagnostic that takes a prognostic approach. Therefore, this paper focuses on two main methods to determine a battery's health: (1) Battery State-of-Health (SOH) monitoring and (2) Remaining Useful Life (RUL) prediction. Both of these are calculated by using a filter algorithm known as the Support Vector Regression-Particle Filter (SVR-PF). Models for battery SOH monitoring based on SVR-PF are developed with novel capacity degradation parameters introduced to determine battery health in real time. Moreover, the RUL prediction model is proposed, which is able to provide the RUL value and update the RUL probability distribution to the End-of-Life cycle. Results for both methods are presented, showing that the proposed SOH monitoring and RUL prediction methods have good performance and that the SVR-PF has better monitoring and prediction capability than the standard particle filter (PF).

Modeling and Prediction of Solvent Effect on Human Skin Permeability using Support Vector Regression and Random Forest.

PubMed

Baba, Hiromi; Takahara, Jun-ichi; Yamashita, Fumiyoshi; Hashida, Mitsuru

2015-11-01

The solvent effect on skin permeability is important for assessing the effectiveness and toxicological risk of new dermatological formulations in pharmaceuticals and cosmetics development. The solvent effect occurs by diverse mechanisms, which could be elucidated by efficient and reliable prediction models. However, such prediction models have been hampered by the small variety of permeants and mixture components archived in databases and by low predictive performance. Here, we propose a solution to both problems. We first compiled a novel large database of 412 samples from 261 structurally diverse permeants and 31 solvents reported in the literature. The data were carefully screened to ensure their collection under consistent experimental conditions. To construct a high-performance predictive model, we then applied support vector regression (SVR) and random forest (RF) with greedy stepwise descriptor selection to our database. The models were internally and externally validated. The SVR achieved higher performance statistics than RF. The (externally validated) determination coefficient, root mean square error, and mean absolute error of SVR were 0.899, 0.351, and 0.268, respectively. Moreover, because all descriptors are fully computational, our method can predict as-yet unsynthesized compounds. Our high-performance prediction model offers an attractive alternative to permeability experiments for pharmaceutical and cosmetic candidate screening and optimizing skin-permeable topical formulations.

Modeling and forecasting US presidential election using learning algorithms

NASA Astrophysics Data System (ADS)

Zolghadr, Mohammad; Niaki, Seyed Armin Akhavan; Niaki, S. T. A.

2017-09-01

The primary objective of this research is to obtain an accurate forecasting model for the US presidential election. To identify a reliable model, artificial neural networks (ANN) and support vector regression (SVR) models are compared based on some specified performance measures. Moreover, six independent variables such as GDP, unemployment rate, the president's approval rate, and others are considered in a stepwise regression to identify significant variables. The president's approval rate is identified as the most significant variable, based on which eight other variables are identified and considered in the model development. Preprocessing methods are applied to prepare the data for the learning algorithms. The proposed procedure significantly increases the accuracy of the model by 50%. The learning algorithms (ANN and SVR) proved to be superior to linear regression based on each method's calculated performance measures. The SVR model is identified as the most accurate model among the other models as this model successfully predicted the outcome of the election in the last three elections (2004, 2008, and 2012). The proposed approach significantly increases the accuracy of the forecast.

A hybrid sales forecasting scheme by combining independent component analysis with K-means clustering and support vector regression.

PubMed

Lu, Chi-Jie; Chang, Chi-Chang

2014-01-01

Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting.

A Hybrid Sales Forecasting Scheme by Combining Independent Component Analysis with K-Means Clustering and Support Vector Regression

PubMed Central

2014-01-01

Sales forecasting plays an important role in operating a business since it can be used to determine the required inventory level to meet consumer demand and avoid the problem of under/overstocking. Improving the accuracy of sales forecasting has become an important issue of operating a business. This study proposes a hybrid sales forecasting scheme by combining independent component analysis (ICA) with K-means clustering and support vector regression (SVR). The proposed scheme first uses the ICA to extract hidden information from the observed sales data. The extracted features are then applied to K-means algorithm for clustering the sales data into several disjoined clusters. Finally, the SVR forecasting models are applied to each group to generate final forecasting results. Experimental results from information technology (IT) product agent sales data reveal that the proposed sales forecasting scheme outperforms the three comparison models and hence provides an efficient alternative for sales forecasting. PMID:25045738

Monthly evaporation forecasting using artificial neural networks and support vector machines

NASA Astrophysics Data System (ADS)

Tezel, Gulay; Buyukyildiz, Meral

2016-04-01

Evaporation is one of the most important components of the hydrological cycle, but is relatively difficult to estimate, due to its complexity, as it can be influenced by numerous factors. Estimation of evaporation is important for the design of reservoirs, especially in arid and semi-arid areas. Artificial neural network methods and support vector machines (SVM) are frequently utilized to estimate evaporation and other hydrological variables. In this study, usability of artificial neural networks (ANNs) (multilayer perceptron (MLP) and radial basis function network (RBFN)) and ɛ-support vector regression (SVR) artificial intelligence methods was investigated to estimate monthly pan evaporation. For this aim, temperature, relative humidity, wind speed, and precipitation data for the period 1972 to 2005 from Beysehir meteorology station were used as input variables while pan evaporation values were used as output. The Romanenko and Meyer method was also considered for the comparison. The results were compared with observed class A pan evaporation data. In MLP method, four different training algorithms, gradient descent with momentum and adaptive learning rule backpropagation (GDX), Levenberg-Marquardt (LVM), scaled conjugate gradient (SCG), and resilient backpropagation (RBP), were used. Also, ɛ-SVR model was used as SVR model. The models were designed via 10-fold cross-validation (CV); algorithm performance was assessed via mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R 2). According to the performance criteria, the ANN algorithms and ɛ-SVR had similar results. The ANNs and ɛ-SVR methods were found to perform better than the Romanenko and Meyer methods. Consequently, the best performance using the test data was obtained using SCG(4,2,2,1) with R 2 = 0.905.

Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine

NASA Astrophysics Data System (ADS)

Ebrahimi, Hadi; Rajaee, Taher

2017-01-01

Simulation of groundwater level (GWL) fluctuations is an important task in management of groundwater resources. In this study, the effect of wavelet analysis on the training of the artificial neural network (ANN), multi linear regression (MLR) and support vector regression (SVR) approaches was investigated, and the ANN, MLR and SVR along with the wavelet-ANN (WNN), wavelet-MLR (WLR) and wavelet-SVR (WSVR) models were compared in simulating one-month-ahead of GWL. The only variable used to develop the models was the monthly GWL data recorded over a period of 11 years from two wells in the Qom plain, Iran. The results showed that decomposing GWL time series into several sub-time series, extremely improved the training of the models. For both wells 1 and 2, the Meyer and Db5 wavelets produced better results compared to the other wavelets; which indicated wavelet types had similar behavior in similar case studies. The optimal number of delays was 6 months, which seems to be due to natural phenomena. The best WNN model, using Meyer mother wavelet with two decomposition levels, simulated one-month-ahead with RMSE values being equal to 0.069 m and 0.154 m for wells 1 and 2, respectively. The RMSE values for the WLR model were 0.058 m and 0.111 m, and for WSVR model were 0.136 m and 0.060 m for wells 1 and 2, respectively.

A regression approach to the mapping of bio-physical characteristics of surface sediment using in situ and airborne hyperspectral acquisitions

NASA Astrophysics Data System (ADS)

Ibrahim, Elsy; Kim, Wonkook; Crawford, Melba; Monbaliu, Jaak

2017-02-01

Remote sensing has been successfully utilized to distinguish and quantify sediment properties in the intertidal environment. Classification approaches of imagery are popular and powerful yet can lead to site- and case-specific results. Such specificity creates challenges for temporal studies. Thus, this paper investigates the use of regression models to quantify sediment properties instead of classifying them. Two regression approaches, namely multiple regression (MR) and support vector regression (SVR), are used in this study for the retrieval of bio-physical variables of intertidal surface sediment of the IJzermonding, a Belgian nature reserve. In the regression analysis, mud content, chlorophyll a concentration, organic matter content, and soil moisture are estimated using radiometric variables of two airborne sensors, namely airborne hyperspectral sensor (AHS) and airborne prism experiment (APEX) and and using field hyperspectral acquisitions by analytical spectral device (ASD). The performance of the two regression approaches is best for the estimation of moisture content. SVR attains the highest accuracy without feature reduction while MR achieves good results when feature reduction is carried out. Sediment property maps are successfully obtained using the models and hyperspectral imagery where SVR used with all bands achieves the best performance. The study also involves the extraction of weights identifying the contribution of each band of the images in the quantification of each sediment property when MR and principal component analysis are used.

Study on the medical meteorological forecast of the number of hypertension inpatient based on SVR

NASA Astrophysics Data System (ADS)

Zhai, Guangyu; Chai, Guorong; Zhang, Haifeng

2017-06-01

The purpose of this study is to build a hypertension prediction model by discussing the meteorological factors for hypertension incidence. The research method is selecting the standard data of relative humidity, air temperature, visibility, wind speed and air pressure of Lanzhou from 2010 to 2012(calculating the maximum, minimum and average value with 5 days as a unit ) as the input variables of Support Vector Regression(SVR) and the standard data of hypertension incidence of the same period as the output dependent variables to obtain the optimal prediction parameters by cross validation algorithm, then by SVR algorithm learning and training, a SVR forecast model for hypertension incidence is built. The result shows that the hypertension prediction model is composed of 15 input independent variables, the training accuracy is 0.005, the final error is 0.0026389. The forecast accuracy based on SVR model is 97.1429%, which is higher than statistical forecast equation and neural network prediction method. It is concluded that SVR model provides a new method for hypertension prediction with its simple calculation, small error as well as higher historical sample fitting and Independent sample forecast capability.

Automated Scoring of Chinese Engineering Students' English Essays

ERIC Educational Resources Information Center

Liu, Ming; Wang, Yuqi; Xu, Weiwei; Liu, Li

2017-01-01

The number of Chinese engineering students has increased greatly since 1999. Rating the quality of these students' English essays has thus become time-consuming and challenging. This paper presents a novel automatic essay scoring algorithm called PSOSVR, based on a machine learning algorithm, Support Vector Machine for Regression (SVR), and a…

Estimation of the laser cutting operating cost by support vector regression methodology

NASA Astrophysics Data System (ADS)

Jović, Srđan; Radović, Aleksandar; Šarkoćević, Živče; Petković, Dalibor; Alizamir, Meysam

2016-09-01

Laser cutting is a popular manufacturing process utilized to cut various types of materials economically. The operating cost is affected by laser power, cutting speed, assist gas pressure, nozzle diameter and focus point position as well as the workpiece material. In this article, the process factors investigated were: laser power, cutting speed, air pressure and focal point position. The aim of this work is to relate the operating cost to the process parameters mentioned above. CO2 laser cutting of stainless steel of medical grade AISI316L has been investigated. The main goal was to analyze the operating cost through the laser power, cutting speed, air pressure, focal point position and material thickness. Since the laser operating cost is a complex, non-linear task, soft computing optimization algorithms can be used. Intelligent soft computing scheme support vector regression (SVR) was implemented. The performance of the proposed estimator was confirmed with the simulation results. The SVR results are then compared with artificial neural network and genetic programing. According to the results, a greater improvement in estimation accuracy can be achieved through the SVR compared to other soft computing methodologies. The new optimization methods benefit from the soft computing capabilities of global optimization and multiobjective optimization rather than choosing a starting point by trial and error and combining multiple criteria into a single criterion.

A semisupervised support vector regression method to estimate biophysical parameters from remotely sensed images

NASA Astrophysics Data System (ADS)

Castelletti, Davide; Demir, Begüm; Bruzzone, Lorenzo

2014-10-01

This paper presents a novel semisupervised learning (SSL) technique defined in the context of ɛ-insensitive support vector regression (SVR) to estimate biophysical parameters from remotely sensed images. The proposed SSL method aims to mitigate the problems of small-sized biased training sets without collecting any additional samples with reference measures. This is achieved on the basis of two consecutive steps. The first step is devoted to inject additional priors information in the learning phase of the SVR in order to adapt the importance of each training sample according to distribution of the unlabeled samples. To this end, a weight is initially associated to each training sample based on a novel strategy that defines higher weights for the samples located in the high density regions of the feature space while giving reduced weights to those that fall into the low density regions of the feature space. Then, in order to exploit different weights for training samples in the learning phase of the SVR, we introduce a weighted SVR (WSVR) algorithm. The second step is devoted to jointly exploit labeled and informative unlabeled samples for further improving the definition of the WSVR learning function. To this end, the most informative unlabeled samples that have an expected accurate target values are initially selected according to a novel strategy that relies on the distribution of the unlabeled samples in the feature space and on the WSVR function estimated at the first step. Then, we introduce a restructured WSVR algorithm that jointly uses labeled and unlabeled samples in the learning phase of the WSVR algorithm and tunes their importance by different values of regularization parameters. Experimental results obtained for the estimation of single-tree stem volume show the effectiveness of the proposed SSL method.

Hybrid PSO-ASVR-based method for data fitting in the calibration of infrared radiometer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Sen; Li, Chengwei, E-mail: heikuanghit@163.com

2016-06-15

The present paper describes a hybrid particle swarm optimization-adaptive support vector regression (PSO-ASVR)-based method for data fitting in the calibration of infrared radiometer. The proposed hybrid PSO-ASVR-based method is based on PSO in combination with Adaptive Processing and Support Vector Regression (SVR). The optimization technique involves setting parameters in the ASVR fitting procedure, which significantly improves the fitting accuracy. However, its use in the calibration of infrared radiometer has not yet been widely explored. Bearing this in mind, the PSO-ASVR-based method, which is based on the statistical learning theory, is successfully used here to get the relationship between the radiationmore » of a standard source and the response of an infrared radiometer. Main advantages of this method are the flexible adjustment mechanism in data processing and the optimization mechanism in a kernel parameter setting of SVR. Numerical examples and applications to the calibration of infrared radiometer are performed to verify the performance of PSO-ASVR-based method compared to conventional data fitting methods.« less

Mixed kernel function support vector regression for global sensitivity analysis

NASA Astrophysics Data System (ADS)

Cheng, Kai; Lu, Zhenzhou; Wei, Yuhao; Shi, Yan; Zhou, Yicheng

2017-11-01

Global sensitivity analysis (GSA) plays an important role in exploring the respective effects of input variables on an assigned output response. Amongst the wide sensitivity analyses in literature, the Sobol indices have attracted much attention since they can provide accurate information for most models. In this paper, a mixed kernel function (MKF) based support vector regression (SVR) model is employed to evaluate the Sobol indices at low computational cost. By the proposed derivation, the estimation of the Sobol indices can be obtained by post-processing the coefficients of the SVR meta-model. The MKF is constituted by the orthogonal polynomials kernel function and Gaussian radial basis kernel function, thus the MKF possesses both the global characteristic advantage of the polynomials kernel function and the local characteristic advantage of the Gaussian radial basis kernel function. The proposed approach is suitable for high-dimensional and non-linear problems. Performance of the proposed approach is validated by various analytical functions and compared with the popular polynomial chaos expansion (PCE). Results demonstrate that the proposed approach is an efficient method for global sensitivity analysis.

Developing a dengue forecast model using machine learning: A case study in China.

PubMed

Guo, Pi; Liu, Tao; Zhang, Qin; Wang, Li; Xiao, Jianpeng; Zhang, Qingying; Luo, Ganfeng; Li, Zhihao; He, Jianfeng; Zhang, Yonghui; Ma, Wenjun

2017-10-01

In China, dengue remains an important public health issue with expanded areas and increased incidence recently. Accurate and timely forecasts of dengue incidence in China are still lacking. We aimed to use the state-of-the-art machine learning algorithms to develop an accurate predictive model of dengue. Weekly dengue cases, Baidu search queries and climate factors (mean temperature, relative humidity and rainfall) during 2011-2014 in Guangdong were gathered. A dengue search index was constructed for developing the predictive models in combination with climate factors. The observed year and week were also included in the models to control for the long-term trend and seasonality. Several machine learning algorithms, including the support vector regression (SVR) algorithm, step-down linear regression model, gradient boosted regression tree algorithm (GBM), negative binomial regression model (NBM), least absolute shrinkage and selection operator (LASSO) linear regression model and generalized additive model (GAM), were used as candidate models to predict dengue incidence. Performance and goodness of fit of the models were assessed using the root-mean-square error (RMSE) and R-squared measures. The residuals of the models were examined using the autocorrelation and partial autocorrelation function analyses to check the validity of the models. The models were further validated using dengue surveillance data from five other provinces. The epidemics during the last 12 weeks and the peak of the 2014 large outbreak were accurately forecasted by the SVR model selected by a cross-validation technique. Moreover, the SVR model had the consistently smallest prediction error rates for tracking the dynamics of dengue and forecasting the outbreaks in other areas in China. The proposed SVR model achieved a superior performance in comparison with other forecasting techniques assessed in this study. The findings can help the government and community respond early to dengue epidemics.

SVR versus neural-fuzzy network controllers for the sagittal balance of a biped robot.

PubMed

Ferreira, João P; Crisóstomo, Manuel M; Coimbra, A Paulo

2009-12-01

The real-time balance control of an eight-link biped robot using a zero moment point (ZMP) dynamic model is difficult due to the processing time of the corresponding equations. To overcome this limitation, two alternative intelligent computing control techniques were compared: one based on support vector regression (SVR) and another based on a first-order Takagi-Sugeno-Kang (TSK)-type neural-fuzzy (NF) network. Both methods use the ZMP error and its variation as inputs and the output is the correction of the robot's torso necessary for its sagittal balance. The SVR and the NF were trained based on simulation data and their performance was verified with a real biped robot. Two performance indexes are proposed to evaluate and compare the online performance of the two control methods. The ZMP is calculated by reading four force sensors placed under each robot's foot. The gait implemented in this biped is similar to a human gait that was acquired and adapted to the robot's size. Some experiments are presented and the results show that the implemented gait combined either with the SVR controller or with the TSK NF network controller can be used to control this biped robot. The SVR and the NF controllers exhibit similar stability, but the SVR controller runs about 50 times faster.

Potential of the Sentinel-2 Red Edge Spectral Bands for Estimation of Eco-Physiological Plant Parameters

NASA Astrophysics Data System (ADS)

Malenovsky, Zbynek; Homolova, Lucie; Janoutova, Ruzena; Landier, Lucas; Gastellu-Etchegorry, Jean-Philippe; Berthelot, Beatrice; Huck, Alexis

2016-08-01

In this study we investigated importance of the space- borne instrument Sentinel-2 red edge spectral bands and reconstructed red edge position (REP) for retrieval of the three eco-physiological plant parameters, leaf and canopy chlorophyll content and leaf area index (LAI), in case of maize agricultural fields and beech and spruce forest stands. Sentinel-2 spectral bands and REP of the investigated vegetation canopies were simulated in the Discrete Anisotropic Radiative Transfer (DART) model. Their potential for estimation of the plant parameters was assessed through training support vector regressions (SVR) and examining their P-vector matrices indicating significance of each input. The trained SVR were then applied on Sentinel-2 simulated images and the acquired estimates were cross-compared with results from high spatial resolution airborne retrievals. Results showed that contribution of REP was significant for canopy chlorophyll content, but less significant for leaf chlorophyll content and insignificant for leaf area index estimations. However, the red edge spectral bands contributed strongly to the retrievals of all parameters, especially canopy and leaf chlorophyll content. Application of SVR on Sentinel-2 simulated images demonstrated, in general, an overestimation of leaf chlorophyll content and an underestimation of LAI when compared to the reciprocal airborne estimates. In the follow-up investigation, we will apply the trained SVR algorithms on real Sentinel-2 multispectral images acquired during vegetation seasons 2015 and 2016.

Predicting residue-wise contact orders in proteins by support vector regression.

PubMed

Song, Jiangning; Burrage, Kevin

2006-10-03

The residue-wise contact order (RWCO) describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR), starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC) between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE) of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and amino acid composition we could further improve the prediction performance with the CC to 0.57 and an RMSE of 0.79. In addition, combining the predicted secondary structure by PSIPRED was found to significantly improve the prediction performance and could yield the best prediction accuracy with a CC of 0.60 and RMSE of 0.78, which provided at least comparable performance compared with the other existing methods. The SVR method shows a prediction performance competitive with or at least comparable to the previously developed linear regression-based methods for predicting RWCO values. In contrast to support vector classification (SVC), SVR is very good at estimating the raw value profiles of the samples. The successful application of the SVR approach in this study reinforces the fact that support vector regression is a powerful tool in extracting the protein sequence-structure relationship and in estimating the protein structural profiles from amino acid sequences.

Urban air quality forecasting based on multi-dimensional collaborative Support Vector Regression (SVR): A case study of Beijing-Tianjin-Shijiazhuang

PubMed Central

Liu, Bing-Chun; Binaykia, Arihant; Chang, Pei-Chann; Tiwari, Manoj Kumar; Tsao, Cheng-Chin

2017-01-01

Today, China is facing a very serious issue of Air Pollution due to its dreadful impact on the human health as well as the environment. The urban cities in China are the most affected due to their rapid industrial and economic growth. Therefore, it is of extreme importance to come up with new, better and more reliable forecasting models to accurately predict the air quality. This paper selected Beijing, Tianjin and Shijiazhuang as three cities from the Jingjinji Region for the study to come up with a new model of collaborative forecasting using Support Vector Regression (SVR) for Urban Air Quality Index (AQI) prediction in China. The present study is aimed to improve the forecasting results by minimizing the prediction error of present machine learning algorithms by taking into account multiple city multi-dimensional air quality information and weather conditions as input. The results show that there is a decrease in MAPE in case of multiple city multi-dimensional regression when there is a strong interaction and correlation of the air quality characteristic attributes with AQI. Also, the geographical location is found to play a significant role in Beijing, Tianjin and Shijiazhuang AQI prediction. PMID:28708836

Real time groove characterization combining partial least squares and SVR strategies: application to eddy current testing

NASA Astrophysics Data System (ADS)

Ahmed, S.; Salucci, M.; Miorelli, R.; Anselmi, N.; Oliveri, G.; Calmon, P.; Reboud, C.; Massa, A.

2017-10-01

A quasi real-time inversion strategy is presented for groove characterization of a conductive non-ferromagnetic tube structure by exploiting eddy current testing (ECT) signal. Inversion problem has been formulated by non-iterative Learning-by-Examples (LBE) strategy. Within the framework of LBE, an efficient training strategy has been adopted with the combination of feature extraction and a customized version of output space filling (OSF) adaptive sampling in order to get optimal training set during offline phase. Partial Least Squares (PLS) and Support Vector Regression (SVR) have been exploited for feature extraction and prediction technique respectively to have robust and accurate real time inversion during online phase.

A glucose model based on support vector regression for the prediction of hypoglycemic events under free-living conditions.

PubMed

Georga, Eleni I; Protopappas, Vasilios C; Ardigò, Diego; Polyzos, Demosthenes; Fotiadis, Dimitrios I

2013-08-01

The prevention of hypoglycemic events is of paramount importance in the daily management of insulin-treated diabetes. The use of short-term prediction algorithms of the subcutaneous (s.c.) glucose concentration may contribute significantly toward this direction. The literature suggests that, although the recent glucose profile is a prominent predictor of hypoglycemia, the overall patient's context greatly impacts its accurate estimation. The objective of this study is to evaluate the performance of a support vector for regression (SVR) s.c. glucose method on hypoglycemia prediction. We extend our SVR model to predict separately the nocturnal events during sleep and the non-nocturnal (i.e., diurnal) ones over 30-min and 60-min horizons using information on recent glucose profile, meals, insulin intake, and physical activities for a hypoglycemic threshold of 70 mg/dL. We also introduce herein additional variables accounting for recurrent nocturnal hypoglycemia due to antecedent hypoglycemia, exercise, and sleep. SVR predictions are compared with those from two other machine learning techniques. The method is assessed on a dataset of 15 patients with type 1 diabetes under free-living conditions. Nocturnal hypoglycemic events are predicted with 94% sensitivity for both horizons and with time lags of 5.43 min and 4.57 min, respectively. As concerns the diurnal events, when physical activities are not considered, the sensitivity is 92% and 96% for a 30-min and 60-min horizon, respectively, with both time lags being less than 5 min. However, when such information is introduced, the diurnal sensitivity decreases by 8% and 3%, respectively. Both nocturnal and diurnal predictions show a high (>90%) precision. Results suggest that hypoglycemia prediction using SVR can be accurate and performs better in most diurnal and nocturnal cases compared with other techniques. It is advised that the problem of hypoglycemia prediction should be handled differently for nocturnal and diurnal periods as regards input variables and interpretation of results.

[Screen potential CYP450 2E1 inhibitors from Chinese herbal medicine based on support vector regression and molecular docking method].

PubMed

Chen, Xi; Lu, Fang; Jiang, Lu-di; Cai, Yi-Lian; Li, Gong-Yu; Zhang, Yan-Ling

2016-07-01

Inhibition of cytochrome P450 (CYP450) enzymes is the most common reasons for drug interactions, so the study on early prediction of CYPs inhibitors can help to decrease the incidence of adverse reactions caused by drug interactions.CYP450 2E1(CYP2E1), as a key role in drug metabolism process, has broad spectrum of drug metabolism substrate. In this study, 32 CYP2E1 inhibitors were collected for the construction of support vector regression (SVR) model. The test set data were used to verify CYP2E1 quantitative models and obtain the optimal prediction model of CYP2E1 inhibitor. Meanwhile, one molecular docking program, CDOCKER, was utilized to analyze the interaction pattern between positive compounds and active pocket to establish the optimal screening model of CYP2E1 inhibitors.SVR model and molecular docking prediction model were combined to screen traditional Chinese medicine database (TCMD), which could improve the calculation efficiency and prediction accuracy. 6 376 traditional Chinese medicine (TCM) compounds predicted by SVR model were obtained, and in further verification by using molecular docking model, 247 TCM compounds with potential inhibitory activities against CYP2E1 were finally retained. Some of them have been verified by experiments. The results demonstrated that this study could provide guidance for the virtual screening of CYP450 inhibitors and the prediction of CYPs-mediated DDIs, and also provide references for clinical rational drug use. Copyright© by the Chinese Pharmaceutical Association.

SMOS salinity retrieval by using Support Vector Regression (SVR)

NASA Astrophysics Data System (ADS)

Katagis, Thomas; Fernández-Prieto, Diego; Marconcini, Mattia; Sabia, Roberto; Martinez, Justino

2013-04-01

The Soil Moisture and Ocean Salinity (SMOS) mission was launched in November 2009 within the framework of the European Space Agency (ESA) Living Planet programme. Over the oceans, it aims at providing Sea Surface Salinity (SSS) maps with spatial and temporal coverage adequate for large scale oceanography. A comprehensive inversion scheme has been defined and implemented in the operational retrieval chain to allow proper SSS estimates in a single satellite overpass (L2 product) from the multi-angular brightness temperatures (TBs) measured by SMOS. Such SMOS operational L2 salinity processor minimizes the difference between the measured and modeled TBs, including additional constraints on Sea Surface Temperature (SST) and wind speed auxiliary fields. In particular, by adopting a maximum-likelihood Bayesian approach, the inversion scheme retrieves salinity under an iterative convergence loop. However, despite the implemented iterative technique is well established and robust, it is still prone to limitations; for instance, the presence of local minima in the cost function cannot be excluded. Moreover, previous studies have demonstrated that the background and observational terms of the cost function are not properly balanced and this is likely to introduce errors in the retrieval procedure. In order to overcome such potential drawbacks, in this study it is proposed a novel approach for the SSS estimation based on the ɛ-insensitive Support Vector Regression (SVR), where both SMOS L1 measurements and auxiliary parameters are used as input. The SVR technique already proved capable of high generalization and robustness in a variety of different applications, with a limited complexity in handling the learning phase. Notably, instead of minimizing the observed training error, it attempts to minimize the generalization error bound so as to achieve generalized performance. For this purpose, the original input domain is mapped into a higher dimensionality space (where the function underlying the data is supposed to have increased flatness) and linear regression is performed. The SVR training is performed using suitable in situ SSS data (i.e., ARGO buoys data) collected in a representative region of the ocean. So far, in situ data coming from a match-up ARGO database in November 2010 over the South Pacific constitute the preliminary benchmark of the study. Ongoing activities point at extending this spatial and temporal frame to assess the robustness of the method. The in situ data have been collocated with SMOS TB measurements and additional parameters (e.g., SST and wind speed) in the learning phase of the SVR under various training/testing configurations. Afterwards, the SSS regression has been performed out of the SMOS TBs or emissivities. Estimated SVR salinity fields are in general (very) well correlated with ARGO data. The analysis of the different impact of the various features has been performed once a rigorous data filtering/flagging is applied, and misfit (SSSSVR-SSSARGO) statistics have been computed. For assessing the effectiveness of the proposed method, final results will be compared to those obtained using the official SMOS SSS retrieval algorithm.

Linear regression models for solvent accessibility prediction in proteins.

PubMed

Wagner, Michael; Adamczak, Rafał; Porollo, Aleksey; Meller, Jarosław

2005-04-01

The relative solvent accessibility (RSA) of an amino acid residue in a protein structure is a real number that represents the solvent exposed surface area of this residue in relative terms. The problem of predicting the RSA from the primary amino acid sequence can therefore be cast as a regression problem. Nevertheless, RSA prediction has so far typically been cast as a classification problem. Consequently, various machine learning techniques have been used within the classification framework to predict whether a given amino acid exceeds some (arbitrary) RSA threshold and would thus be predicted to be "exposed," as opposed to "buried." We have recently developed novel methods for RSA prediction using nonlinear regression techniques which provide accurate estimates of the real-valued RSA and outperform classification-based approaches with respect to commonly used two-class projections. However, while their performance seems to provide a significant improvement over previously published approaches, these Neural Network (NN) based methods are computationally expensive to train and involve several thousand parameters. In this work, we develop alternative regression models for RSA prediction which are computationally much less expensive, involve orders-of-magnitude fewer parameters, and are still competitive in terms of prediction quality. In particular, we investigate several regression models for RSA prediction using linear L1-support vector regression (SVR) approaches as well as standard linear least squares (LS) regression. Using rigorously derived validation sets of protein structures and extensive cross-validation analysis, we compare the performance of the SVR with that of LS regression and NN-based methods. In particular, we show that the flexibility of the SVR (as encoded by metaparameters such as the error insensitivity and the error penalization terms) can be very beneficial to optimize the prediction accuracy for buried residues. We conclude that the simple and computationally much more efficient linear SVR performs comparably to nonlinear models and thus can be used in order to facilitate further attempts to design more accurate RSA prediction methods, with applications to fold recognition and de novo protein structure prediction methods.

Developing a dengue forecast model using machine learning: A case study in China

PubMed Central

Zhang, Qin; Wang, Li; Xiao, Jianpeng; Zhang, Qingying; Luo, Ganfeng; Li, Zhihao; He, Jianfeng; Zhang, Yonghui; Ma, Wenjun

2017-01-01

Background In China, dengue remains an important public health issue with expanded areas and increased incidence recently. Accurate and timely forecasts of dengue incidence in China are still lacking. We aimed to use the state-of-the-art machine learning algorithms to develop an accurate predictive model of dengue. Methodology/Principal findings Weekly dengue cases, Baidu search queries and climate factors (mean temperature, relative humidity and rainfall) during 2011–2014 in Guangdong were gathered. A dengue search index was constructed for developing the predictive models in combination with climate factors. The observed year and week were also included in the models to control for the long-term trend and seasonality. Several machine learning algorithms, including the support vector regression (SVR) algorithm, step-down linear regression model, gradient boosted regression tree algorithm (GBM), negative binomial regression model (NBM), least absolute shrinkage and selection operator (LASSO) linear regression model and generalized additive model (GAM), were used as candidate models to predict dengue incidence. Performance and goodness of fit of the models were assessed using the root-mean-square error (RMSE) and R-squared measures. The residuals of the models were examined using the autocorrelation and partial autocorrelation function analyses to check the validity of the models. The models were further validated using dengue surveillance data from five other provinces. The epidemics during the last 12 weeks and the peak of the 2014 large outbreak were accurately forecasted by the SVR model selected by a cross-validation technique. Moreover, the SVR model had the consistently smallest prediction error rates for tracking the dynamics of dengue and forecasting the outbreaks in other areas in China. Conclusion and significance The proposed SVR model achieved a superior performance in comparison with other forecasting techniques assessed in this study. The findings can help the government and community respond early to dengue epidemics. PMID:29036169

A study of machine learning regression methods for major elemental analysis of rocks using laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Boucher, Thomas F.; Ozanne, Marie V.; Carmosino, Marco L.; Dyar, M. Darby; Mahadevan, Sridhar; Breves, Elly A.; Lepore, Kate H.; Clegg, Samuel M.

2015-05-01

The ChemCam instrument on the Mars Curiosity rover is generating thousands of LIBS spectra and bringing interest in this technique to public attention. The key to interpreting Mars or any other types of LIBS data are calibrations that relate laboratory standards to unknowns examined in other settings and enable predictions of chemical composition. Here, LIBS spectral data are analyzed using linear regression methods including partial least squares (PLS-1 and PLS-2), principal component regression (PCR), least absolute shrinkage and selection operator (lasso), elastic net, and linear support vector regression (SVR-Lin). These were compared against results from nonlinear regression methods including kernel principal component regression (K-PCR), polynomial kernel support vector regression (SVR-Py) and k-nearest neighbor (kNN) regression to discern the most effective models for interpreting chemical abundances from LIBS spectra of geological samples. The results were evaluated for 100 samples analyzed with 50 laser pulses at each of five locations averaged together. Wilcoxon signed-rank tests were employed to evaluate the statistical significance of differences among the nine models using their predicted residual sum of squares (PRESS) to make comparisons. For MgO, SiO2, Fe2O3, CaO, and MnO, the sparse models outperform all the others except for linear SVR, while for Na2O, K2O, TiO2, and P2O5, the sparse methods produce inferior results, likely because their emission lines in this energy range have lower transition probabilities. The strong performance of the sparse methods in this study suggests that use of dimensionality-reduction techniques as a preprocessing step may improve the performance of the linear models. Nonlinear methods tend to overfit the data and predict less accurately, while the linear methods proved to be more generalizable with better predictive performance. These results are attributed to the high dimensionality of the data (6144 channels) relative to the small number of samples studied. The best-performing models were SVR-Lin for SiO2, MgO, Fe2O3, and Na2O, lasso for Al2O3, elastic net for MnO, and PLS-1 for CaO, TiO2, and K2O. Although these differences in model performance between methods were identified, most of the models produce comparable results when p ≤ 0.05 and all techniques except kNN produced statistically-indistinguishable results. It is likely that a combination of models could be used together to yield a lower total error of prediction, depending on the requirements of the user.

A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework

NASA Astrophysics Data System (ADS)

Liang, Zhongmin; Li, Yujie; Hu, Yiming; Li, Binquan; Wang, Jun

2017-06-01

Accurate and reliable long-term forecasting plays an important role in water resources management and utilization. In this paper, a hybrid model called SVR-HUP is presented to predict long-term runoff and quantify the prediction uncertainty. The model is created based on three steps. First, appropriate predictors are selected according to the correlations between meteorological factors and runoff. Second, a support vector regression (SVR) model is structured and optimized based on the LibSVM toolbox and a genetic algorithm. Finally, using forecasted and observed runoff, a hydrologic uncertainty processor (HUP) based on a Bayesian framework is used to estimate the posterior probability distribution of the simulated values, and the associated uncertainty of prediction was quantitatively analyzed. Six precision evaluation indexes, including the correlation coefficient (CC), relative root mean square error (RRMSE), relative error (RE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE), and qualification rate (QR), are used to measure the prediction accuracy. As a case study, the proposed approach is applied in the Han River basin, South Central China. Three types of SVR models are established to forecast the monthly, flood season and annual runoff volumes. The results indicate that SVR yields satisfactory accuracy and reliability at all three scales. In addition, the results suggest that the HUP cannot only quantify the uncertainty of prediction based on a confidence interval but also provide a more accurate single value prediction than the initial SVR forecasting result. Thus, the SVR-HUP model provides an alternative method for long-term runoff forecasting.

Portal pressure and liver stiffness measurements in the prediction of fibrosis regression after sustained virological response in recurrent hepatitis C.

PubMed

Mauro, Ezequiel; Crespo, Gonzalo; Montironi, Carla; Londoño, Maria-Carlota; Hernández-Gea, Virginia; Ruiz, Pablo; Sastre, Lydia; Lombardo, Julissa; Mariño, Zoe; Díaz, Alba; Colmenero, Jordi; Rimola, Antoni; Garcia-Pagán, Juan Carlos; Brunet, Mercé; Forns, Xavier; Navasa, Miquel

2018-05-01

Sustained virological response (SVR) improves survival in post-liver transplant (LT) recurrent hepatitis C. However, the impact of SVR on fibrosis regression is not well defined. In addition, the performance of noninvasive methods to evaluate the presence of fibrosis and portal hypertension (PH) post-SVR has been scarcely evaluated. We aimed to investigate the degree of fibrosis regression (decrease ≥1 METAVIR stage) after-SVR and its associated factors in recurrent hepatitis C, as well as the diagnostic capacity of noninvasive methods in the assessment of liver fibrosis and PH after viral clearance. We evaluated 112 hepatitis C virus-infected LT recipients who achieved SVR between 2001 and 2015. A liver biopsy was performed before treatment and 12 months post-SVR. Hepatic venous pressure gradient (HVPG), liver stiffness measurement (LSM), and Enhanced Liver Fibrosis (ELF) score were also determined at the same time points. Sixty-seven percent of the cohort presented fibrosis regression: 43% in recipients with cirrhosis and 72%-85% in the remaining stages (P = 0.002). HVPG, LSM, and ELF significantly decreased post-SVR. Liver function significantly improved, and survival was significantly better in patients achieving fibrosis regression. Baseline HVPG and LSM as well as decompensations before therapy were independent predictors of fibrosis regression. One year post-SVR, LSM had a high diagnostic accuracy to discard the presence of advanced fibrosis (AF) and clinically significant PH (AUROC, 0.902 and 0.888). In conclusion, SVR post-LT induces fibrosis regression in most patients, leading to significant clinical benefits. Pretreatment HVPG and LSM are significant determinants of the likelihood of fibrosis regression. Finally, LSM accurately predicts the presence of AF and PH 1 year after SVR and thus can be used to determine monitoring strategies. (Hepatology 2018;67:1683-1694). © 2017 by the American Association for the Study of Liver Diseases.

A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers

PubMed Central

2009-01-01

Background Genomic selection (GS) uses molecular breeding values (MBV) derived from dense markers across the entire genome for selection of young animals. The accuracy of MBV prediction is important for a successful application of GS. Recently, several methods have been proposed to estimate MBV. Initial simulation studies have shown that these methods can accurately predict MBV. In this study we compared the accuracies and possible bias of five different regression methods in an empirical application in dairy cattle. Methods Genotypes of 7,372 SNP and highly accurate EBV of 1,945 dairy bulls were used to predict MBV for protein percentage (PPT) and a profit index (Australian Selection Index, ASI). Marker effects were estimated by least squares regression (FR-LS), Bayesian regression (Bayes-R), random regression best linear unbiased prediction (RR-BLUP), partial least squares regression (PLSR) and nonparametric support vector regression (SVR) in a training set of 1,239 bulls. Accuracy and bias of MBV prediction were calculated from cross-validation of the training set and tested against a test team of 706 young bulls. Results For both traits, FR-LS using a subset of SNP was significantly less accurate than all other methods which used all SNP. Accuracies obtained by Bayes-R, RR-BLUP, PLSR and SVR were very similar for ASI (0.39-0.45) and for PPT (0.55-0.61). Overall, SVR gave the highest accuracy. All methods resulted in biased MBV predictions for ASI, for PPT only RR-BLUP and SVR predictions were unbiased. A significant decrease in accuracy of prediction of ASI was seen in young test cohorts of bulls compared to the accuracy derived from cross-validation of the training set. This reduction was not apparent for PPT. Combining MBV predictions with pedigree based predictions gave 1.05 - 1.34 times higher accuracies compared to predictions based on pedigree alone. Some methods have largely different computational requirements, with PLSR and RR-BLUP requiring the least computing time. Conclusions The four methods which use information from all SNP namely RR-BLUP, Bayes-R, PLSR and SVR generate similar accuracies of MBV prediction for genomic selection, and their use in the selection of immediate future generations in dairy cattle will be comparable. The use of FR-LS in genomic selection is not recommended. PMID:20043835

Improved Measurement of Blood Pressure by Extraction of Characteristic Features from the Cuff Oscillometric Waveform

PubMed Central

Lim, Pooi Khoon; Ng, Siew-Cheok; Jassim, Wissam A.; Redmond, Stephen J.; Zilany, Mohammad; Avolio, Alberto; Lim, Einly; Tan, Maw Pin; Lovell, Nigel H.

2015-01-01

We present a novel approach to improve the estimation of systolic (SBP) and diastolic blood pressure (DBP) from oscillometric waveform data using variable characteristic ratios between SBP and DBP with mean arterial pressure (MAP). This was verified in 25 healthy subjects, aged 28 ± 5 years. The multiple linear regression (MLR) and support vector regression (SVR) models were used to examine the relationship between the SBP and the DBP ratio with ten features extracted from the oscillometric waveform envelope (OWE). An automatic algorithm based on relative changes in the cuff pressure and neighbouring oscillometric pulses was proposed to remove outlier points caused by movement artifacts. Substantial reduction in the mean and standard deviation of the blood pressure estimation errors were obtained upon artifact removal. Using the sequential forward floating selection (SFFS) approach, we were able to achieve a significant reduction in the mean and standard deviation of differences between the estimated SBP values and the reference scoring (MLR: mean ± SD = −0.3 ± 5.8 mmHg; SVR and −0.6 ± 5.4 mmHg) with only two features, i.e., Ratio2 and Area3, as compared to the conventional maximum amplitude algorithm (MAA) method (mean ± SD = −1.6 ± 8.6 mmHg). Comparing the performance of both MLR and SVR models, our results showed that the MLR model was able to achieve comparable performance to that of the SVR model despite its simplicity. PMID:26087370

Predict the fatigue life of crack based on extended finite element method and SVR

NASA Astrophysics Data System (ADS)

Song, Weizhen; Jiang, Zhansi; Jiang, Hui

2018-05-01

Using extended finite element method (XFEM) and support vector regression (SVR) to predict the fatigue life of plate crack. Firstly, the XFEM is employed to calculate the stress intensity factors (SIFs) with given crack sizes. Then predicetion model can be built based on the function relationship of the SIFs with the fatigue life or crack length. Finally, according to the prediction model predict the SIFs at different crack sizes or different cycles. Because of the accuracy of the forward Euler method only ensured by the small step size, a new prediction method is presented to resolve the issue. The numerical examples were studied to demonstrate the proposed method allow a larger step size and have a high accuracy.

Estimating atmospheric visibility using synergy of MODIS data and ground-based observations

NASA Astrophysics Data System (ADS)

Komeilian, H.; Mohyeddin Bateni, S.; Xu, T.; Nielson, J.

2015-05-01

Dust events are intricate climatic processes, which can have adverse effects on human health, safety, and the environment. In this study, two data mining approaches, namely, back-propagation artificial neural network (BP ANN) and supporting vector regression (SVR), were used to estimate atmospheric visibility through the synergistic use of Moderate Resolution Imaging Spectroradiometer (MODIS) Level 1B (L1B) data and ground-based observations at fourteen stations in the province of Khuzestan (southwestern Iran), during 2009-2010. Reflectance and brightness temperature in different bands (from MODIS) along with in situ meteorological data were input to the models to estimate atmospheric visibility. The results show that both models can accurately estimate atmospheric visibility. The visibility estimates from the BP ANN network had a root-mean-square error (RMSE) and Pearson's correlation coefficient (R) of 0.67 and 0.69, respectively. The corresponding RMSE and R from the SVR model were 0.59 and 0.71, implying that the SVR approach outperforms the BP ANN.

SVR-based prediction of carbon emissions from energy consumption in Henan Province

NASA Astrophysics Data System (ADS)

Gou, Guohua

2018-02-01

This paper analyzes the advantage of support vector regression (SVR) in the prediction of carbon emission and establishes the SVR-based carbon emission prediction model. The model is established using the data of Henan’s carbon emissions and influence factors from the 1991 to 2016 to train and test and then predict the carbon emissions from 2017 to 2021. The results show that: from the perspective of carbon emission from energy consumption, it raised 224.876 million tons of carbon dioxide from 1991 to 2016, and the predicted increment from 2017 to 2021 is 30.5563million tons with an average annual growth rate at 3%. From the perspective of growth rate among the six factors related to carbon emissions it is proved that population urbanization rate per capital GDP and energy consumption per unit of GDP influences the growth rate of carbon emissions less than the proportion of secondary industry and coal consumption ratio of carbon. Finally some suggestions are proposed for the carbon emission reduction of Henan Province.

Massive-training support vector regression and Gaussian process for false-positive reduction in computer-aided detection of polyps in CT colonography

PubMed Central

Xu, Jian-Wu; Suzuki, Kenji

2011-01-01

Purpose: A massive-training artificial neural network (MTANN) has been developed for the reduction of false positives (FPs) in computer-aided detection (CADe) of polyps in CT colonography (CTC). A major limitation of the MTANN is the long training time. To address this issue, the authors investigated the feasibility of two state-of-the-art regression models, namely, support vector regression (SVR) and Gaussian process regression (GPR) models, in the massive-training framework and developed massive-training SVR (MTSVR) and massive-training GPR (MTGPR) for the reduction of FPs in CADe of polyps. Methods: The authors applied SVR and GPR as volume-processing techniques in the distinction of polyps from FP detections in a CTC CADe scheme. Unlike artificial neural networks (ANNs), both SVR and GPR are memory-based methods that store a part of or the entire training data for testing. Therefore, their training is generally fast and they are able to improve the efficiency of the massive-training methodology. Rooted in a maximum margin property, SVR offers excellent generalization ability and robustness to outliers. On the other hand, GPR approaches nonlinear regression from a Bayesian perspective, which produces both the optimal estimated function and the covariance associated with the estimation. Therefore, both SVR and GPR, as the state-of-the-art nonlinear regression models, are able to offer a performance comparable or potentially superior to that of ANN, with highly efficient training. Both MTSVR and MTGPR were trained directly with voxel values from CTC images. A 3D scoring method based on a 3D Gaussian weighting function was applied to the outputs of MTSVR and MTGPR for distinction between polyps and nonpolyps. To test the performance of the proposed models, the authors compared them to the original MTANN in the distinction between actual polyps and various types of FPs in terms of training time reduction and FP reduction performance. The authors’ CTC database consisted of 240 CTC data sets obtained from 120 patients in the supine and prone positions. The training set consisted of 27 patients, 10 of which had 10 polyps. The authors selected 10 nonpolyps (i.e., FP sources) from the training set. These ten polyps and ten nonpolyps were used for training the proposed models. The testing set consisted of 93 patients, including 19 polyps in 7 patients and 86 negative patients with 474 FPs produced by an original CADe scheme. Results: With the MTSVR, the training time was reduced by a factor of 190, while a FP reduction performance [by-polyp sensitivity of 94.7% (18∕19) with 2.5 (230∕93) FPs∕patient] comparable to that of the original MTANN [the same sensitivity with 2.6 (244∕93) FPs∕patient] was achieved. The classification performance in terms of the area under the receiver-operating-characteristic curve value of the MTGPR (0.82) was statistically significantly higher than that of the original MTANN (0.77), with a two-sided p-value of 0.03. The MTGPR yielded a 94.7% (18∕19) by-polyp sensitivity at a FP rate of 2.5 (235∕93) per patient and reduced the training time by a factor of 1.3. Conclusions: Both MTSVR and MTGPR improve the efficiency of the training in the massive-training framework while maintaining a comparable performance. PMID:21626922

Determination of total iron-reactive phenolics, anthocyanins and tannins in wine grapes of skins and seeds based on near-infrared hyperspectral imaging.

PubMed

Zhang, Ni; Liu, Xu; Jin, Xiaoduo; Li, Chen; Wu, Xuan; Yang, Shuqin; Ning, Jifeng; Yanne, Paul

2017-12-15

Phenolics contents in wine grapes are key indicators for assessing ripeness. Near-infrared hyperspectral images during ripening have been explored to achieve an effective method for predicting phenolics contents. Principal component regression (PCR), partial least squares regression (PLSR) and support vector regression (SVR) models were built, respectively. The results show that SVR behaves globally better than PLSR and PCR, except in predicting tannins content of seeds. For the best prediction results, the squared correlation coefficient and root mean square error reached 0.8960 and 0.1069g/L (+)-catechin equivalents (CE), respectively, for tannins in skins, 0.9065 and 0.1776 (g/L CE) for total iron-reactive phenolics (TIRP) in skins, 0.8789 and 0.1442 (g/L M3G) for anthocyanins in skins, 0.9243 and 0.2401 (g/L CE) for tannins in seeds, and 0.8790 and 0.5190 (g/L CE) for TIRP in seeds. Our results indicated that NIR hyperspectral imaging has good prospects for evaluation of phenolics in wine grapes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Design optimization of tailor-rolled blank thin-walled structures based on ɛ-support vector regression technique and genetic algorithm

NASA Astrophysics Data System (ADS)

Duan, Libin; Xiao, Ning-cong; Li, Guangyao; Cheng, Aiguo; Chen, Tao

2017-07-01

Tailor-rolled blank thin-walled (TRB-TH) structures have become important vehicle components owing to their advantages of light weight and crashworthiness. The purpose of this article is to provide an efficient lightweight design for improving the energy-absorbing capability of TRB-TH structures under dynamic loading. A finite element (FE) model for TRB-TH structures is established and validated by performing a dynamic axial crash test. Different material properties for individual parts with different thicknesses are considered in the FE model. Then, a multi-objective crashworthiness design of the TRB-TH structure is constructed based on the ɛ-support vector regression (ɛ-SVR) technique and non-dominated sorting genetic algorithm-II. The key parameters (C, ɛ and σ) are optimized to further improve the predictive accuracy of ɛ-SVR under limited sample points. Finally, the technique for order preference by similarity to the ideal solution method is used to rank the solutions in Pareto-optimal frontiers and find the best compromise optima. The results demonstrate that the light weight and crashworthiness performance of the optimized TRB-TH structures are superior to their uniform thickness counterparts. The proposed approach provides useful guidance for designing TRB-TH energy absorbers for vehicle bodies.

New data-driven estimation of terrestrial CO2 fluxes in Asia using a standardized database of eddy covariance measurements, remote sensing data, and support vector regression

NASA Astrophysics Data System (ADS)

Ichii, Kazuhito; Ueyama, Masahito; Kondo, Masayuki; Saigusa, Nobuko; Kim, Joon; Alberto, Ma. Carmelita; Ardö, Jonas; Euskirchen, Eugénie S.; Kang, Minseok; Hirano, Takashi; Joiner, Joanna; Kobayashi, Hideki; Marchesini, Luca Belelli; Merbold, Lutz; Miyata, Akira; Saitoh, Taku M.; Takagi, Kentaro; Varlagin, Andrej; Bret-Harte, M. Syndonia; Kitamura, Kenzo; Kosugi, Yoshiko; Kotani, Ayumi; Kumar, Kireet; Li, Sheng-Gong; Machimura, Takashi; Matsuura, Yojiro; Mizoguchi, Yasuko; Ohta, Takeshi; Mukherjee, Sandipan; Yanagi, Yuji; Yasuda, Yukio; Zhang, Yiping; Zhao, Fenghua

2017-04-01

The lack of a standardized database of eddy covariance observations has been an obstacle for data-driven estimation of terrestrial CO2 fluxes in Asia. In this study, we developed such a standardized database using 54 sites from various databases by applying consistent postprocessing for data-driven estimation of gross primary productivity (GPP) and net ecosystem CO2 exchange (NEE). Data-driven estimation was conducted by using a machine learning algorithm: support vector regression (SVR), with remote sensing data for 2000 to 2015 period. Site-level evaluation of the estimated CO2 fluxes shows that although performance varies in different vegetation and climate classifications, GPP and NEE at 8 days are reproduced (e.g., r2 = 0.73 and 0.42 for 8 day GPP and NEE). Evaluation of spatially estimated GPP with Global Ozone Monitoring Experiment 2 sensor-based Sun-induced chlorophyll fluorescence shows that monthly GPP variations at subcontinental scale were reproduced by SVR (r2 = 1.00, 0.94, 0.91, and 0.89 for Siberia, East Asia, South Asia, and Southeast Asia, respectively). Evaluation of spatially estimated NEE with net atmosphere-land CO2 fluxes of Greenhouse Gases Observing Satellite (GOSAT) Level 4A product shows that monthly variations of these data were consistent in Siberia and East Asia; meanwhile, inconsistency was found in South Asia and Southeast Asia. Furthermore, differences in the land CO2 fluxes from SVR-NEE and GOSAT Level 4A were partially explained by accounting for the differences in the definition of land CO2 fluxes. These data-driven estimates can provide a new opportunity to assess CO2 fluxes in Asia and evaluate and constrain terrestrial ecosystem models.

Improved Correction of Atmospheric Pressure Data Obtained by Smartphones through Machine Learning

PubMed Central

Kim, Yong-Hyuk; Ha, Ji-Hun; Kim, Na-Young; Im, Hyo-Hyuc; Sim, Sangjin; Choi, Reno K. Y.

2016-01-01

A correction method using machine learning aims to improve the conventional linear regression (LR) based method for correction of atmospheric pressure data obtained by smartphones. The method proposed in this study conducts clustering and regression analysis with time domain classification. Data obtained in Gyeonggi-do, one of the most populous provinces in South Korea surrounding Seoul with the size of 10,000 km2, from July 2014 through December 2014, using smartphones were classified with respect to time of day (daytime or nighttime) as well as day of the week (weekday or weekend) and the user's mobility, prior to the expectation-maximization (EM) clustering. Subsequently, the results were analyzed for comparison by applying machine learning methods such as multilayer perceptron (MLP) and support vector regression (SVR). The results showed a mean absolute error (MAE) 26% lower on average when regression analysis was performed through EM clustering compared to that obtained without EM clustering. For machine learning methods, the MAE for SVR was around 31% lower for LR and about 19% lower for MLP. It is concluded that pressure data from smartphones are as good as the ones from national automatic weather station (AWS) network. PMID:27524999

Comparison of Nine Statistical Model Based Warfarin Pharmacogenetic Dosing Algorithms Using the Racially Diverse International Warfarin Pharmacogenetic Consortium Cohort Database

PubMed Central

Liu, Rong; Li, Xi; Zhang, Wei; Zhou, Hong-Hao

2015-01-01

Objective Multiple linear regression (MLR) and machine learning techniques in pharmacogenetic algorithm-based warfarin dosing have been reported. However, performances of these algorithms in racially diverse group have never been objectively evaluated and compared. In this literature-based study, we compared the performances of eight machine learning techniques with those of MLR in a large, racially-diverse cohort. Methods MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied in warfarin dose algorithms in a cohort from the International Warfarin Pharmacogenetics Consortium database. Covariates obtained by stepwise regression from 80% of randomly selected patients were used to develop algorithms. To compare the performances of these algorithms, the mean percentage of patients whose predicted dose fell within 20% of the actual dose (mean percentage within 20%) and the mean absolute error (MAE) were calculated in the remaining 20% of patients. The performances of these techniques in different races, as well as the dose ranges of therapeutic warfarin were compared. Robust results were obtained after 100 rounds of resampling. Results BART, MARS and SVR were statistically indistinguishable and significantly out performed all the other approaches in the whole cohort (MAE: 8.84–8.96 mg/week, mean percentage within 20%: 45.88%–46.35%). In the White population, MARS and BART showed higher mean percentage within 20% and lower mean MAE than those of MLR (all p values < 0.05). In the Asian population, SVR, BART, MARS and LAR performed the same as MLR. MLR and LAR optimally performed among the Black population. When patients were grouped in terms of warfarin dose range, all machine learning techniques except ANN and LAR showed significantly higher mean percentage within 20%, and lower MAE (all p values < 0.05) than MLR in the low- and high- dose ranges. Conclusion Overall, machine learning-based techniques, BART, MARS and SVR performed superior than MLR in warfarin pharmacogenetic dosing. Differences of algorithms’ performances exist among the races. Moreover, machine learning-based algorithms tended to perform better in the low- and high- dose ranges than MLR. PMID:26305568

Highly predictive and interpretable models for PAMPA permeability.

PubMed

Sun, Hongmao; Nguyen, Kimloan; Kerns, Edward; Yan, Zhengyin; Yu, Kyeong Ri; Shah, Pranav; Jadhav, Ajit; Xu, Xin

2017-02-01

Cell membrane permeability is an important determinant for oral absorption and bioavailability of a drug molecule. An in silico model predicting drug permeability is described, which is built based on a large permeability dataset of 7488 compound entries or 5435 structurally unique molecules measured by the same lab using parallel artificial membrane permeability assay (PAMPA). On the basis of customized molecular descriptors, the support vector regression (SVR) model trained with 4071 compounds with quantitative data is able to predict the remaining 1364 compounds with the qualitative data with an area under the curve of receiver operating characteristic (AUC-ROC) of 0.90. The support vector classification (SVC) model trained with half of the whole dataset comprised of both the quantitative and the qualitative data produced accurate predictions to the remaining data with the AUC-ROC of 0.88. The results suggest that the developed SVR model is highly predictive and provides medicinal chemists a useful in silico tool to facilitate design and synthesis of novel compounds with optimal drug-like properties, and thus accelerate the lead optimization in drug discovery. Copyright © 2016 Elsevier Ltd. All rights reserved.

Oil Formation Volume Factor Determination Through a Fused Intelligence

NASA Astrophysics Data System (ADS)

Gholami, Amin

2016-12-01

Volume change of oil between reservoir condition and standard surface condition is called oil formation volume factor (FVF), which is very time, cost and labor intensive to determine. This study proposes an accurate, rapid and cost-effective approach for determining FVF from reservoir temperature, dissolved gas oil ratio, and specific gravity of both oil and dissolved gas. Firstly, structural risk minimization (SRM) principle of support vector regression (SVR) was employed to construct a robust model for estimating FVF from the aforementioned inputs. Subsequently, an alternating conditional expectation (ACE) was used for approximating optimal transformations of input/output data to a higher correlated data and consequently developing a sophisticated model between transformed data. Eventually, a committee machine with SVR and ACE was constructed through the use of hybrid genetic algorithm-pattern search (GA-PS). Committee machine integrates ACE and SVR models in an optimal linear combination such that makes benefit of both methods. A group of 342 data points was used for model development and a group of 219 data points was used for blind testing the constructed model. Results indicated that the committee machine performed better than individual models.

Multiple local feature representations and their fusion based on an SVR model for iris recognition using optimized Gabor filters

NASA Astrophysics Data System (ADS)

He, Fei; Liu, Yuanning; Zhu, Xiaodong; Huang, Chun; Han, Ye; Dong, Hongxing

2014-12-01

Gabor descriptors have been widely used in iris texture representations. However, fixed basic Gabor functions cannot match the changing nature of diverse iris datasets. Furthermore, a single form of iris feature cannot overcome difficulties in iris recognition, such as illumination variations, environmental conditions, and device variations. This paper provides multiple local feature representations and their fusion scheme based on a support vector regression (SVR) model for iris recognition using optimized Gabor filters. In our iris system, a particle swarm optimization (PSO)- and a Boolean particle swarm optimization (BPSO)-based algorithm is proposed to provide suitable Gabor filters for each involved test dataset without predefinition or manual modulation. Several comparative experiments on JLUBR-IRIS, CASIA-I, and CASIA-V4-Interval iris datasets are conducted, and the results show that our work can generate improved local Gabor features by using optimized Gabor filters for each dataset. In addition, our SVR fusion strategy may make full use of their discriminative ability to improve accuracy and reliability. Other comparative experiments show that our approach may outperform other popular iris systems.

Prediction of GWL with the help of GRACE TWS for unevenly spaced time series data in India : Analysis of comparative performances of SVR, ANN and LRM

NASA Astrophysics Data System (ADS)

Mukherjee, Amritendu; Ramachandran, Parthasarathy

2018-03-01

Prediction of Ground Water Level (GWL) is extremely important for sustainable use and management of ground water resource. The motivations for this work is to understand the relationship between Gravity Recovery and Climate Experiment (GRACE) derived terrestrial water change (ΔTWS) data and GWL, so that ΔTWS could be used as a proxy measurement for GWL. In our study, we have selected five observation wells from different geographic regions in India. The datasets are unevenly spaced time series data which restricts us from applying standard time series methodologies and therefore in order to model and predict GWL with the help of ΔTWS, we have built Linear Regression Model (LRM), Support Vector Regression (SVR) and Artificial Neural Network (ANN). Comparative performances of LRM, SVR and ANN have been evaluated with the help of correlation coefficient (ρ) and Root Mean Square Error (RMSE) between the actual and fitted (for training dataset) or predicted (for test dataset) values of GWL. It has been observed in our study that ΔTWS is highly significant variable to model GWL and the amount of total variations in GWL that could be explained with the help of ΔTWS varies from 36.48% to 74.28% (0.3648 ⩽R2 ⩽ 0.7428) . We have found that for the model GWL ∼ Δ TWS, for both training and test dataset, performances of SVR and ANN are better than that of LRM in terms of ρ and RMSE. It also has been found in our study that with the inclusion of meteorological variables along with ΔTWS as input parameters to model GWL, the performance of SVR improves and it performs better than ANN. These results imply that for modelling irregular time series GWL data, ΔTWS could be very useful.

Efficient Resources Provisioning Based on Load Forecasting in Cloud

PubMed Central

Hu, Rongdong; Jiang, Jingfei; Liu, Guangming; Wang, Lixin

2014-01-01

Cloud providers should ensure QoS while maximizing resources utilization. One optimal strategy is to timely allocate resources in a fine-grained mode according to application's actual resources demand. The necessary precondition of this strategy is obtaining future load information in advance. We propose a multi-step-ahead load forecasting method, KSwSVR, based on statistical learning theory which is suitable for the complex and dynamic characteristics of the cloud computing environment. It integrates an improved support vector regression algorithm and Kalman smoother. Public trace data taken from multitypes of resources were used to verify its prediction accuracy, stability, and adaptability, comparing with AR, BPNN, and standard SVR. Subsequently, based on the predicted results, a simple and efficient strategy is proposed for resource provisioning. CPU allocation experiment indicated it can effectively reduce resources consumption while meeting service level agreements requirements. PMID:24701160

Aircraft Engine Thrust Estimator Design Based on GSA-LSSVM

NASA Astrophysics Data System (ADS)

Sheng, Hanlin; Zhang, Tianhong

2017-08-01

In view of the necessity of highly precise and reliable thrust estimator to achieve direct thrust control of aircraft engine, based on support vector regression (SVR), as well as least square support vector machine (LSSVM) and a new optimization algorithm - gravitational search algorithm (GSA), by performing integrated modelling and parameter optimization, a GSA-LSSVM-based thrust estimator design solution is proposed. The results show that compared to particle swarm optimization (PSO) algorithm, GSA can find unknown optimization parameter better and enables the model developed with better prediction and generalization ability. The model can better predict aircraft engine thrust and thus fulfills the need of direct thrust control of aircraft engine.

Improved RMR Rock Mass Classification Using Artificial Intelligence Algorithms

NASA Astrophysics Data System (ADS)

Gholami, Raoof; Rasouli, Vamegh; Alimoradi, Andisheh

2013-09-01

Rock mass classification systems such as rock mass rating (RMR) are very reliable means to provide information about the quality of rocks surrounding a structure as well as to propose suitable support systems for unstable regions. Many correlations have been proposed to relate measured quantities such as wave velocity to rock mass classification systems to limit the associated time and cost of conducting the sampling and mechanical tests conventionally used to calculate RMR values. However, these empirical correlations have been found to be unreliable, as they usually overestimate or underestimate the RMR value. The aim of this paper is to compare the results of RMR classification obtained from the use of empirical correlations versus machine-learning methodologies based on artificial intelligence algorithms. The proposed methods were verified based on two case studies located in northern Iran. Relevance vector regression (RVR) and support vector regression (SVR), as two robust machine-learning methodologies, were used to predict the RMR for tunnel host rocks. RMR values already obtained by sampling and site investigation at one tunnel were taken into account as the output of the artificial networks during training and testing phases. The results reveal that use of empirical correlations overestimates the predicted RMR values. RVR and SVR, however, showed more reliable results, and are therefore suggested for use in RMR classification for design purposes of rock structures.

New analysis methods to push the boundaries of diagnostic techniques in the environmental sciences

NASA Astrophysics Data System (ADS)

Lungaroni, M.; Murari, A.; Peluso, E.; Gelfusa, M.; Malizia, A.; Vega, J.; Talebzadeh, S.; Gaudio, P.

2016-04-01

In the last years, new and more sophisticated measurements have been at the basis of the major progress in various disciplines related to the environment, such as remote sensing and thermonuclear fusion. To maximize the effectiveness of the measurements, new data analysis techniques are required. First data processing tasks, such as filtering and fitting, are of primary importance, since they can have a strong influence on the rest of the analysis. Even if Support Vector Regression is a method devised and refined at the end of the 90s, a systematic comparison with more traditional non parametric regression methods has never been reported. In this paper, a series of systematic tests is described, which indicates how SVR is a very competitive method of non-parametric regression that can usefully complement and often outperform more consolidated approaches. The performance of Support Vector Regression as a method of filtering is investigated first, comparing it with the most popular alternative techniques. Then Support Vector Regression is applied to the problem of non-parametric regression to analyse Lidar surveys for the environments measurement of particulate matter due to wildfires. The proposed approach has given very positive results and provides new perspectives to the interpretation of the data.

Evaluation of facial attractiveness for patients with malocclusion: a machine-learning technique employing Procrustes.

PubMed

Yu, Xiaonan; Liu, Bin; Pei, Yuru; Xu, Tianmin

2014-05-01

To establish an objective method for evaluating facial attractiveness from a set of orthodontic photographs. One hundred eight malocclusion patients randomly selected from six universities in China were randomly divided into nine groups, with each group containing an equal number of patients with Class I, II, and III malocclusions. Sixty-nine expert Chinese orthodontists ranked photographs of the patients (frontal, lateral, and frontal smiling photos) before and after orthodontic treatment from "most attractive" to "least attractive" in each group. A weighted mean ranking was then calculated for each patient, based on which a three-point scale was created. Procrustes superimposition was conducted on 101 landmarks identified on the photographs. A support vector regression (SVR) function was set up according to the coordinate values of identified landmarks of each photographic set and its corresponding grading. Its predictive ability was tested for each group in turn. The average coincidence rate obtained for comparisons of the subjective ratings with the SVR evaluation was 71.8% according to 18 verification tests. Geometric morphometrics combined with SVR may be a prospective method for objective comprehensive evaluation of facial attractiveness in the near future.

Soft computing techniques toward modeling the water supplies of Cyprus.

PubMed

Iliadis, L; Maris, F; Tachos, S

2011-10-01

This research effort aims in the application of soft computing techniques toward water resources management. More specifically, the target is the development of reliable soft computing models capable of estimating the water supply for the case of "Germasogeia" mountainous watersheds in Cyprus. Initially, ε-Regression Support Vector Machines (ε-RSVM) and fuzzy weighted ε-RSVMR models have been developed that accept five input parameters. At the same time, reliable artificial neural networks have been developed to perform the same job. The 5-fold cross validation approach has been employed in order to eliminate bad local behaviors and to produce a more representative training data set. Thus, the fuzzy weighted Support Vector Regression (SVR) combined with the fuzzy partition has been employed in an effort to enhance the quality of the results. Several rational and reliable models have been produced that can enhance the efficiency of water policy designers. Copyright © 2011 Elsevier Ltd. All rights reserved.

Discrete wavelength selection for the optical readout of a metamaterial biosensing system for glucose concentration estimation via a support vector regression model.

PubMed

Teutsch, T; Mesch, M; Giessen, H; Tarin, C

2015-01-01

In this contribution, a method to select discrete wavelengths that allow an accurate estimation of the glucose concentration in a biosensing system based on metamaterials is presented. The sensing concept is adapted to the particular application of ophthalmic glucose sensing by covering the metamaterial with a glucose-sensitive hydrogel and the sensor readout is performed optically. Due to the fact that in a mobile context a spectrometer is not suitable, few discrete wavelengths must be selected to estimate the glucose concentration. The developed selection methods are based on nonlinear support vector regression (SVR) models. Two selection methods are compared and it is shown that wavelengths selected by a sequential forward feature selection algorithm achieves an estimation improvement. The presented method can be easily applied to different metamaterial layouts and hydrogel configurations.

Determination of foodborne pathogenic bacteria by multiplex PCR-microchip capillary electrophoresis with genetic algorithm-support vector regression optimization.

PubMed

Li, Yongxin; Li, Yuanqian; Zheng, Bo; Qu, Lingli; Li, Can

2009-06-08

A rapid and sensitive method based on microchip capillary electrophoresis with condition optimization of genetic algorithm-support vector regression (GA-SVR) was developed and applied to simultaneous analysis of multiplex PCR products of four foodborne pathogenic bacteria. Four pairs of oligonucleotide primers were designed to exclusively amplify the targeted gene of Vibrio parahemolyticus, Salmonella, Escherichia coli (E. coli) O157:H7, Shigella and the quadruplex PCR parameters were optimized. At the same time, GA-SVR was employed to optimize the separation conditions of DNA fragments in microchip capillary electrophoresis. The proposed method was applied to simultaneously detect the multiplex PCR products of four foodborne pathogenic bacteria under the optimal conditions within 8 min. The levels of detection were as low as 1.2 x 10(2) CFU mL(-1) of Vibrio parahemolyticus, 2.9 x 10(2) CFU mL(-1) of Salmonella, 8.7 x 10(1) CFU mL(-1) of E. coli O157:H7 and 5.2 x 10(1) CFU mL(-1) of Shigella, respectively. The relative standard deviation of migration time was in the range of 0.74-2.09%. The results demonstrated that the good resolution and less analytical time were achieved due to the application of the multivariate strategy. This study offers an efficient alternative to routine foodborne pathogenic bacteria detection in a fast, reliable, and sensitive way.

Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking.

PubMed

Zhou, Ping; Guo, Dongwei; Wang, Hong; Chai, Tianyou

2017-09-29

Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on a reliable measurement of molten iron quality (MIQ) indices, which are not feasible using the conventional sensors. This paper proposes a novel data-driven robust modeling method for the online estimation and control of MIQ indices. First, a nonlinear autoregressive exogenous (NARX) model is constructed for the MIQ indices to completely capture the nonlinear dynamics of the BF process. Then, considering that the standard least-squares support vector regression (LS-SVR) cannot directly cope with the multioutput problem, a multitask transfer learning is proposed to design a novel multioutput LS-SVR (M-LS-SVR) for the learning of the NARX model. Furthermore, a novel M-estimator is proposed to reduce the interference of outliers and improve the robustness of the M-LS-SVR model. Since the weights of different outlier data are properly given by the weight function, their corresponding contributions on modeling can properly be distinguished, thus a robust modeling result can be achieved. Finally, a novel multiobjective evaluation index on the modeling performance is developed by comprehensively considering the root-mean-square error of modeling and the correlation coefficient on trend fitting, based on which the nondominated sorting genetic algorithm II is used to globally optimize the model parameters. Both experiments using industrial data and industrial applications illustrate that the proposed method can eliminate the adverse effect caused by the fluctuation of data in BF process efficiently. This indicates its stronger robustness and higher accuracy. Moreover, control testing shows that the developed model can be well applied to realize data-driven control of the BF process.

Examination of tapered plastic multimode fiber-based sensor performance with silver coating for different concentrations of calcium hypochlorite by soft computing methodologies--a comparative study.

PubMed

Zakaria, Rozalina; Sheng, Ong Yong; Wern, Kam; Shamshirband, Shahaboddin; Wahab, Ainuddin Wahid Abdul; Petković, Dalibor; Saboohi, Hadi

2014-05-01

A soft methodology study has been applied on tapered plastic multimode sensors. This study basically used tapered plastic multimode fiber [polymethyl methacrylate (PMMA)] optics as a sensor. The tapered PMMA fiber was fabricated using an etching method involving deionized water and acetone to achieve a waist diameter and length of 0.45 and 10 mm, respectively. In addition, a tapered PMMA probe, which was coated by silver film, was fabricated and demonstrated using a calcium hypochlorite (G70) solution. The working mechanism of such a device is based on the observation increment in the transmission of the sensor that is immersed in solutions at high concentrations. As the concentration was varied from 0 to 6 ppm, the output voltage of the sensor increased linearly. The silver film coating increased the sensitivity of the proposed sensor because of the effective cladding refractive index, which increases with the coating and thus allows more light to be transmitted from the tapered fiber. In this study, the polynomial and radial basis function (RBF) were applied as the kernel function of the support vector regression (SVR) to estimate and predict the output voltage response of the sensors with and without silver film according to experimental tests. Instead of minimizing the observed training error, SVR_poly and SVR_rbf were used in an attempt to minimize the generalization error bound so as to achieve generalized performance. An adaptive neuro-fuzzy interference system (ANFIS) approach was also investigated for comparison. The experimental results showed that improvements in the predictive accuracy and capacity for generalization can be achieved by the SVR_poly approach in comparison to the SVR_rbf methodology. The same testing errors were found for the SVR_poly approach and the ANFIS approach.

Management of health care expenditure by soft computing methodology

NASA Astrophysics Data System (ADS)

Maksimović, Goran; Jović, Srđan; Jovanović, Radomir; Aničić, Obrad

2017-01-01

In this study was managed the health care expenditure by soft computing methodology. The main goal was to predict the gross domestic product (GDP) according to several factors of health care expenditure. Soft computing methodologies were applied since GDP prediction is very complex task. The performances of the proposed predictors were confirmed with the simulation results. According to the results, support vector regression (SVR) has better prediction accuracy compared to other soft computing methodologies. The soft computing methods benefit from the soft computing capabilities of global optimization in order to avoid local minimum issues.

Daily air quality index forecasting with hybrid models: A case in China.

PubMed

Zhu, Suling; Lian, Xiuyuan; Liu, Haixia; Hu, Jianming; Wang, Yuanyuan; Che, Jinxing

2017-12-01

Air quality is closely related to quality of life. Air pollution forecasting plays a vital role in air pollution warnings and controlling. However, it is difficult to attain accurate forecasts for air pollution indexes because the original data are non-stationary and chaotic. The existing forecasting methods, such as multiple linear models, autoregressive integrated moving average (ARIMA) and support vector regression (SVR), cannot fully capture the information from series of pollution indexes. Therefore, new effective techniques need to be proposed to forecast air pollution indexes. The main purpose of this research is to develop effective forecasting models for regional air quality indexes (AQI) to address the problems above and enhance forecasting accuracy. Therefore, two hybrid models (EMD-SVR-Hybrid and EMD-IMFs-Hybrid) are proposed to forecast AQI data. The main steps of the EMD-SVR-Hybrid model are as follows: the data preprocessing technique EMD (empirical mode decomposition) is utilized to sift the original AQI data to obtain one group of smoother IMFs (intrinsic mode functions) and a noise series, where the IMFs contain the important information (level, fluctuations and others) from the original AQI series. LS-SVR is applied to forecast the sum of the IMFs, and then, S-ARIMA (seasonal ARIMA) is employed to forecast the residual sequence of LS-SVR. In addition, EMD-IMFs-Hybrid first separately forecasts the IMFs via statistical models and sums the forecasting results of the IMFs as EMD-IMFs. Then, S-ARIMA is employed to forecast the residuals of EMD-IMFs. To certify the proposed hybrid model, AQI data from June 2014 to August 2015 collected from Xingtai in China are utilized as a test case to investigate the empirical research. In terms of some of the forecasting assessment measures, the AQI forecasting results of Xingtai show that the two proposed hybrid models are superior to ARIMA, SVR, GRNN, EMD-GRNN, Wavelet-GRNN and Wavelet-SVR. Therefore, the proposed hybrid models can be used as effective and simple tools for air pollution forecasting and warning as well as for management. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice.

PubMed

Jacquin, Laval; Cao, Tuong-Vi; Ahmadi, Nourollah

2016-01-01

One objective of this study was to provide readers with a clear and unified understanding of parametric statistical and kernel methods, used for genomic prediction, and to compare some of these in the context of rice breeding for quantitative traits. Furthermore, another objective was to provide a simple and user-friendly R package, named KRMM, which allows users to perform RKHS regression with several kernels. After introducing the concept of regularized empirical risk minimization, the connections between well-known parametric and kernel methods such as Ridge regression [i.e., genomic best linear unbiased predictor (GBLUP)] and reproducing kernel Hilbert space (RKHS) regression were reviewed. Ridge regression was then reformulated so as to show and emphasize the advantage of the kernel "trick" concept, exploited by kernel methods in the context of epistatic genetic architectures, over parametric frameworks used by conventional methods. Some parametric and kernel methods; least absolute shrinkage and selection operator (LASSO), GBLUP, support vector machine regression (SVR) and RKHS regression were thereupon compared for their genomic predictive ability in the context of rice breeding using three real data sets. Among the compared methods, RKHS regression and SVR were often the most accurate methods for prediction followed by GBLUP and LASSO. An R function which allows users to perform RR-BLUP of marker effects, GBLUP and RKHS regression, with a Gaussian, Laplacian, polynomial or ANOVA kernel, in a reasonable computation time has been developed. Moreover, a modified version of this function, which allows users to tune kernels for RKHS regression, has also been developed and parallelized for HPC Linux clusters. The corresponding KRMM package and all scripts have been made publicly available.

Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq

NASA Astrophysics Data System (ADS)

Yaseen, Zaher Mundher; Jaafar, Othman; Deo, Ravinesh C.; Kisi, Ozgur; Adamowski, Jan; Quilty, John; El-Shafie, Ahmed

2016-11-01

Monthly stream-flow forecasting can yield important information for hydrological applications including sustainable design of rural and urban water management systems, optimization of water resource allocations, water use, pricing and water quality assessment, and agriculture and irrigation operations. The motivation for exploring and developing expert predictive models is an ongoing endeavor for hydrological applications. In this study, the potential of a relatively new data-driven method, namely the extreme learning machine (ELM) method, was explored for forecasting monthly stream-flow discharge rates in the Tigris River, Iraq. The ELM algorithm is a single-layer feedforward neural network (SLFNs) which randomly selects the input weights, hidden layer biases and analytically determines the output weights of the SLFNs. Based on the partial autocorrelation functions of historical stream-flow data, a set of five input combinations with lagged stream-flow values are employed to establish the best forecasting model. A comparative investigation is conducted to evaluate the performance of the ELM compared to other data-driven models: support vector regression (SVR) and generalized regression neural network (GRNN). The forecasting metrics defined as the correlation coefficient (r), Nash-Sutcliffe efficiency (ENS), Willmott's Index (WI), root-mean-square error (RMSE) and mean absolute error (MAE) computed between the observed and forecasted stream-flow data are employed to assess the ELM model's effectiveness. The results revealed that the ELM model outperformed the SVR and the GRNN models across a number of statistical measures. In quantitative terms, superiority of ELM over SVR and GRNN models was exhibited by ENS = 0.578, 0.378 and 0.144, r = 0.799, 0.761 and 0.468 and WI = 0.853, 0.802 and 0.689, respectively and the ELM model attained lower RMSE value by approximately 21.3% (relative to SVR) and by approximately 44.7% (relative to GRNN). Based on the findings of this study, several recommendations were suggested for further exploration of the ELM model in hydrological forecasting problems.

Integrating machine learning to achieve an automatic parameter prediction for practical continuous-variable quantum key distribution

NASA Astrophysics Data System (ADS)

Liu, Weiqi; Huang, Peng; Peng, Jinye; Fan, Jianping; Zeng, Guihua

2018-02-01

For supporting practical quantum key distribution (QKD), it is critical to stabilize the physical parameters of signals, e.g., the intensity, phase, and polarization of the laser signals, so that such QKD systems can achieve better performance and practical security. In this paper, an approach is developed by integrating a support vector regression (SVR) model to optimize the performance and practical security of the QKD system. First, a SVR model is learned to precisely predict the time-along evolutions of the physical parameters of signals. Second, such predicted time-along evolutions are employed as feedback to control the QKD system for achieving the optimal performance and practical security. Finally, our proposed approach is exemplified by using the intensity evolution of laser light and a local oscillator pulse in the Gaussian modulated coherent state QKD system. Our experimental results have demonstrated three significant benefits of our SVR-based approach: (1) it can allow the QKD system to achieve optimal performance and practical security, (2) it does not require any additional resources and any real-time monitoring module to support automatic prediction of the time-along evolutions of the physical parameters of signals, and (3) it is applicable to any measurable physical parameter of signals in the practical QKD system.

Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms

NASA Astrophysics Data System (ADS)

Salcedo-Sanz, S.; Deo, R. C.; Carro-Calvo, L.; Saavedra-Moreno, B.

2016-07-01

Long-term air temperature prediction is of major importance in a large number of applications, including climate-related studies, energy, agricultural, or medical. This paper examines the performance of two Machine Learning algorithms (Support Vector Regression (SVR) and Multi-layer Perceptron (MLP)) in a problem of monthly mean air temperature prediction, from the previous measured values in observational stations of Australia and New Zealand, and climate indices of importance in the region. The performance of the two considered algorithms is discussed in the paper and compared to alternative approaches. The results indicate that the SVR algorithm is able to obtain the best prediction performance among all the algorithms compared in the paper. Moreover, the results obtained have shown that the mean absolute error made by the two algorithms considered is significantly larger for the last 20 years than in the previous decades, in what can be interpreted as a change in the relationship among the prediction variables involved in the training of the algorithms.

Data mining-based coefficient of influence factors optimization of test paper reliability

NASA Astrophysics Data System (ADS)

Xu, Peiyao; Jiang, Huiping; Wei, Jieyao

2018-05-01

Test is a significant part of the teaching process. It demonstrates the final outcome of school teaching through teachers' teaching level and students' scores. The analysis of test paper is a complex operation that has the characteristics of non-linear relation in the length of the paper, time duration and the degree of difficulty. It is therefore difficult to optimize the coefficient of influence factors under different conditions in order to get text papers with clearly higher reliability with general methods [1]. With data mining techniques like Support Vector Regression (SVR) and Genetic Algorithm (GA), we can model the test paper analysis and optimize the coefficient of impact factors for higher reliability. It's easy to find that the combination of SVR and GA can get an effective advance in reliability from the test results. The optimal coefficient of influence factors optimization has a practicability in actual application, and the whole optimizing operation can offer model basis for test paper analysis.

Intelligent Optimization of the Film-to-Fiber Ratio of a Degradable Braided Bicomponent Ureteral Stent

PubMed Central

Liu, Xiaoyan; Li, Feng; Ding, Yongsheng; Zou, Ting; Wang, Lu; Hao, Kuangrong

2015-01-01

A hierarchical support vector regression (SVR) model (HSVRM) was employed to correlate the compositions and mechanical properties of bicomponent stents composed of poly(lactic-co-glycolic acid) (PGLA) film and poly(glycolic acid) (PGA) fibers for urethral repair for the first time. PGLA film and PGA fibers could provide ureteral stents with good compressive and tensile properties, respectively. In bicomponent stents, high film content led to high stiffness, while high fiber content resulted in poor compressional properties. To simplify the procedures to optimize the ratio of PGLA film and PGA fiber in the stents, a hierarchical support vector regression model (HSVRM) and particle swarm optimization (PSO) algorithm were used to construct relationships between the film-to-fiber weight ratio and the measured compressional/tensile properties of the stents. The experimental data and simulated data fit well, proving that the HSVRM could closely reflect the relationship between the component ratio and performance properties of the ureteral stents. PMID:28793658

Compound analysis via graph kernels incorporating chirality.

PubMed

Brown, J B; Urata, Takashi; Tamura, Takeyuki; Arai, Midori A; Kawabata, Takeo; Akutsu, Tatsuya

2010-12-01

High accuracy is paramount when predicting biochemical characteristics using Quantitative Structural-Property Relationships (QSPRs). Although existing graph-theoretic kernel methods combined with machine learning techniques are efficient for QSPR model construction, they cannot distinguish topologically identical chiral compounds which often exhibit different biological characteristics. In this paper, we propose a new method that extends the recently developed tree pattern graph kernel to accommodate stereoisomers. We show that Support Vector Regression (SVR) with a chiral graph kernel is useful for target property prediction by demonstrating its application to a set of human vitamin D receptor ligands currently under consideration for their potential anti-cancer effects.

Quantitative prediction of ionization effect on human skin permeability.

PubMed

Baba, Hiromi; Ueno, Yusuke; Hashida, Mitsuru; Yamashita, Fumiyoshi

2017-04-30

Although skin permeability of an active ingredient can be severely affected by its ionization in a dose solution, most of the existing prediction models cannot predict such impacts. To provide reliable predictors, we curated a novel large dataset of in vitro human skin permeability coefficients for 322 entries comprising chemically diverse permeants whose ionization fractions can be calculated. Subsequently, we generated thousands of computational descriptors, including LogD (octanol-water distribution coefficient at a specific pH), and analyzed the dataset using nonlinear support vector regression (SVR) and Gaussian process regression (GPR) combined with greedy descriptor selection. The SVR model was slightly superior to the GPR model, with externally validated squared correlation coefficient, root mean square error, and mean absolute error values of 0.94, 0.29, and 0.21, respectively. These models indicate that Log D is effective for a comprehensive prediction of ionization effects on skin permeability. In addition, the proposed models satisfied the statistical criteria endorsed in recent model validation studies. These models can evaluate virtually generated compounds at any pH; therefore, they can be used for high-throughput evaluations of numerous active ingredients and optimization of their skin permeability with respect to permeant ionization. Copyright © 2017 Elsevier B.V. All rights reserved.

Development of a coal quality analyzer for application to power plants based on laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Zhang, Lei; Gong, Yao; Li, Yufang; Wang, Xin; Fan, Juanjuan; Dong, Lei; Ma, Weiguang; Yin, Wangbao; Jia, Suotang

2015-11-01

It is vitally important for a power plant to determine the coal property rapidly to optimize the combustion process. In this work, a fully software-controlled laser-induced breakdown spectroscopy (LIBS) based coal quality analyzer comprising a LIBS apparatus, a sampling equipment, and a control module, has been designed for possible application to power plants for offering rapid and precise coal quality analysis results. A closed-loop feedback pulsed laser energy stabilization technology is proposed to stabilize the Nd: YAG laser output energy to a preset interval by using the detected laser energy signal so as to enhance the measurement stability and applied in a month-long monitoring experiment. The results show that the laser energy stability has been greatly reduced from ± 5.2% to ± 1.3%. In order to indicate the complex relationship between the concentrations of the analyte of interest and the corresponding plasma spectra, the support vector regression (SVR) is employed as a non-linear regression method. It is shown that this SVR method combined with principal component analysis (PCA) enables a significant improvement in cross-validation accuracy by using the calibration set of coal samples. The root mean square error for prediction of ash content, volatile matter content, and calorific value decreases from 2.74% to 1.82%, 1.69% to 1.22%, and 1.23 MJ/kg to 0.85 MJ/kg, respectively. Meanwhile, the corresponding average relative error of the predicted samples is reduced from 8.3% to 5.48%, 5.83% to 4.42%, and 5.4% to 3.68%, respectively. The enhanced levels of accuracy obtained with the SVR combined with PCA based calibration models open up avenues for prospective prediction in coal properties.

Estimation of elimination half-lives of organic chemicals in humans using gradient boosting machine.

PubMed

Lu, Jing; Lu, Dong; Zhang, Xiaochen; Bi, Yi; Cheng, Keguang; Zheng, Mingyue; Luo, Xiaomin

2016-11-01

Elimination half-life is an important pharmacokinetic parameter that determines exposure duration to approach steady state of drugs and regulates drug administration. The experimental evaluation of half-life is time-consuming and costly. Thus, it is attractive to build an accurate prediction model for half-life. In this study, several machine learning methods, including gradient boosting machine (GBM), support vector regressions (RBF-SVR and Linear-SVR), local lazy regression (LLR), SA, SR, and GP, were employed to build high-quality prediction models. Two strategies of building consensus models were explored to improve the accuracy of prediction. Moreover, the applicability domains (ADs) of the models were determined by using the distance-based threshold. Among seven individual models, GBM showed the best performance (R(2)=0.820 and RMSE=0.555 for the test set), and Linear-SVR produced the inferior prediction accuracy (R(2)=0.738 and RMSE=0.672). The use of distance-based ADs effectively determined the scope of QSAR models. However, the consensus models by combing the individual models could not improve the prediction performance. Some essential descriptors relevant to half-life were identified and analyzed. An accurate prediction model for elimination half-life was built by GBM, which was superior to the reference model (R(2)=0.723 and RMSE=0.698). Encouraged by the promising results, we expect that the GBM model for elimination half-life would have potential applications for the early pharmacokinetic evaluations, and provide guidance for designing drug candidates with favorable in vivo exposure profile. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang. Copyright © 2016 Elsevier B.V. All rights reserved.

A novel strategy for forensic age prediction by DNA methylation and support vector regression model

PubMed Central

Xu, Cheng; Qu, Hongzhu; Wang, Guangyu; Xie, Bingbing; Shi, Yi; Yang, Yaran; Zhao, Zhao; Hu, Lan; Fang, Xiangdong; Yan, Jiangwei; Feng, Lei

2015-01-01

High deviations resulting from prediction model, gender and population difference have limited age estimation application of DNA methylation markers. Here we identified 2,957 novel age-associated DNA methylation sites (P < 0.01 and R2 > 0.5) in blood of eight pairs of Chinese Han female monozygotic twins. Among them, nine novel sites (false discovery rate < 0.01), along with three other reported sites, were further validated in 49 unrelated female volunteers with ages of 20–80 years by Sequenom Massarray. A total of 95 CpGs were covered in the PCR products and 11 of them were built the age prediction models. After comparing four different models including, multivariate linear regression, multivariate nonlinear regression, back propagation neural network and support vector regression, SVR was identified as the most robust model with the least mean absolute deviation from real chronological age (2.8 years) and an average accuracy of 4.7 years predicted by only six loci from the 11 loci, as well as an less cross-validated error compared with linear regression model. Our novel strategy provides an accurate measurement that is highly useful in estimating the individual age in forensic practice as well as in tracking the aging process in other related applications. PMID:26635134

Estimating Soil Moisture Using Polsar Data: a Machine Learning Approach

NASA Astrophysics Data System (ADS)

Khedri, E.; Hasanlou, M.; Tabatabaeenejad, A.

2017-09-01

Soil moisture is an important parameter that affects several environmental processes. This parameter has many important functions in numerous sciences including agriculture, hydrology, aerology, flood prediction, and drought occurrence. However, field procedures for moisture calculations are not feasible in a vast agricultural region territory. This is due to the difficulty in calculating soil moisture in vast territories and high-cost nature as well as spatial and local variability of soil moisture. Polarimetric synthetic aperture radar (PolSAR) imaging is a powerful tool for estimating soil moisture. These images provide a wide field of view and high spatial resolution. For estimating soil moisture, in this study, a model of support vector regression (SVR) is proposed based on obtained data from AIRSAR in 2003 in C, L, and P channels. In this endeavor, sequential forward selection (SFS) and sequential backward selection (SBS) are evaluated to select suitable features of polarized image dataset for high efficient modeling. We compare the obtained data with in-situ data. Output results show that the SBS-SVR method results in higher modeling accuracy compared to SFS-SVR model. Statistical parameters obtained from this method show an R2 of 97% and an RMSE of lower than 0.00041 (m3/m3) for P, L, and C channels, which has provided better accuracy compared to other feature selection algorithms.

Correlation between external and internal respiratory motion: a validation study.

PubMed

Ernst, Floris; Bruder, Ralf; Schlaefer, Alexander; Schweikard, Achim

2012-05-01

In motion-compensated image-guided radiotherapy, accurate tracking of the target region is required. This tracking process includes building a correlation model between external surrogate motion and the motion of the target region. A novel correlation method is presented and compared with the commonly used polynomial model. The CyberKnife system (Accuray, Inc., Sunnyvale/CA) uses a polynomial correlation model to relate externally measured surrogate data (optical fibres on the patient's chest emitting red light) to infrequently acquired internal measurements (X-ray data). A new correlation algorithm based on ɛ -Support Vector Regression (SVR) was developed. Validation and comparison testing were done with human volunteers using live 3D ultrasound and externally measured infrared light-emitting diodes (IR LEDs). Seven data sets (5:03-6:27 min long) were recorded from six volunteers. Polynomial correlation algorithms were compared to the SVR-based algorithm demonstrating an average increase in root mean square (RMS) accuracy of 21.3% (0.4 mm). For three signals, the increase was more than 29% and for one signal as much as 45.6% (corresponding to more than 1.5 mm RMS). Further analysis showed the improvement to be statistically significant. The new SVR-based correlation method outperforms traditional polynomial correlation methods for motion tracking. This method is suitable for clinical implementation and may improve the overall accuracy of targeted radiotherapy.

Identification and Severity Determination of Wheat Stripe Rust and Wheat Leaf Rust Based on Hyperspectral Data Acquired Using a Black-Paper-Based Measuring Method.

PubMed

Wang, Hui; Qin, Feng; Ruan, Liu; Wang, Rui; Liu, Qi; Ma, Zhanhong; Li, Xiaolong; Cheng, Pei; Wang, Haiguang

2016-01-01

It is important to implement detection and assessment of plant diseases based on remotely sensed data for disease monitoring and control. Hyperspectral data of healthy leaves, leaves in incubation period and leaves in diseased period of wheat stripe rust and wheat leaf rust were collected under in-field conditions using a black-paper-based measuring method developed in this study. After data preprocessing, the models to identify the diseases were built using distinguished partial least squares (DPLS) and support vector machine (SVM), and the disease severity inversion models of stripe rust and the disease severity inversion models of leaf rust were built using quantitative partial least squares (QPLS) and support vector regression (SVR). All the models were validated by using leave-one-out cross validation and external validation. The diseases could be discriminated using both distinguished partial least squares and support vector machine with the accuracies of more than 99%. For each wheat rust, disease severity levels were accurately retrieved using both the optimal QPLS models and the optimal SVR models with the coefficients of determination (R2) of more than 0.90 and the root mean square errors (RMSE) of less than 0.15. The results demonstrated that identification and severity evaluation of stripe rust and leaf rust at the leaf level could be implemented based on the hyperspectral data acquired using the developed method. A scientific basis was provided for implementing disease monitoring by using aerial and space remote sensing technologies.

Identification and Severity Determination of Wheat Stripe Rust and Wheat Leaf Rust Based on Hyperspectral Data Acquired Using a Black-Paper-Based Measuring Method

PubMed Central

Ruan, Liu; Wang, Rui; Liu, Qi; Ma, Zhanhong; Li, Xiaolong; Cheng, Pei; Wang, Haiguang

2016-01-01

It is important to implement detection and assessment of plant diseases based on remotely sensed data for disease monitoring and control. Hyperspectral data of healthy leaves, leaves in incubation period and leaves in diseased period of wheat stripe rust and wheat leaf rust were collected under in-field conditions using a black-paper-based measuring method developed in this study. After data preprocessing, the models to identify the diseases were built using distinguished partial least squares (DPLS) and support vector machine (SVM), and the disease severity inversion models of stripe rust and the disease severity inversion models of leaf rust were built using quantitative partial least squares (QPLS) and support vector regression (SVR). All the models were validated by using leave-one-out cross validation and external validation. The diseases could be discriminated using both distinguished partial least squares and support vector machine with the accuracies of more than 99%. For each wheat rust, disease severity levels were accurately retrieved using both the optimal QPLS models and the optimal SVR models with the coefficients of determination (R2) of more than 0.90 and the root mean square errors (RMSE) of less than 0.15. The results demonstrated that identification and severity evaluation of stripe rust and leaf rust at the leaf level could be implemented based on the hyperspectral data acquired using the developed method. A scientific basis was provided for implementing disease monitoring by using aerial and space remote sensing technologies. PMID:27128464

Appraisal of adaptive neuro-fuzzy computing technique for estimating anti-obesity properties of a medicinal plant.

PubMed

Kazemipoor, Mahnaz; Hajifaraji, Majid; Radzi, Che Wan Jasimah Bt Wan Mohamed; Shamshirband, Shahaboddin; Petković, Dalibor; Mat Kiah, Miss Laiha

2015-01-01

This research examines the precision of an adaptive neuro-fuzzy computing technique in estimating the anti-obesity property of a potent medicinal plant in a clinical dietary intervention. Even though a number of mathematical functions such as SPSS analysis have been proposed for modeling the anti-obesity properties estimation in terms of reduction in body mass index (BMI), body fat percentage, and body weight loss, there are still disadvantages of the models like very demanding in terms of calculation time. Since it is a very crucial problem, in this paper a process was constructed which simulates the anti-obesity activities of caraway (Carum carvi) a traditional medicine on obese women with adaptive neuro-fuzzy inference (ANFIS) method. The ANFIS results are compared with the support vector regression (SVR) results using root-mean-square error (RMSE) and coefficient of determination (R(2)). The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the ANFIS approach. The following statistical characteristics are obtained for BMI loss estimation: RMSE=0.032118 and R(2)=0.9964 in ANFIS testing and RMSE=0.47287 and R(2)=0.361 in SVR testing. For fat loss estimation: RMSE=0.23787 and R(2)=0.8599 in ANFIS testing and RMSE=0.32822 and R(2)=0.7814 in SVR testing. For weight loss estimation: RMSE=0.00000035601 and R(2)=1 in ANFIS testing and RMSE=0.17192 and R(2)=0.6607 in SVR testing. Because of that, it can be applied for practical purposes. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, Ping; Guo, Dongwei; Wang, Hong

Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on a reliable measurement of molten iron quality (MIQ) indices, which are not feasible using the conventional sensors. This paper proposes a novel data-driven robust modeling method for the online estimation and control of MIQ indices. First, a nonlinear autoregressive exogenous (NARX) model is constructed for the MIQ indices to completely capture the nonlinear dynamics of the BF process. Then, considering that the standard least-squares support vector regression (LS-SVR) cannot directly cope with the multioutput problem, a multitask transfer learning is proposed to design a novel multioutput LS-SVRmore » (M-LS-SVR) for the learning of the NARX model. Furthermore, a novel M-estimator is proposed to reduce the interference of outliers and improve the robustness of the M-LS-SVR model. Since the weights of different outlier data are properly given by the weight function, their corresponding contributions on modeling can properly be distinguished, thus a robust modeling result can be achieved. Finally, a novel multiobjective evaluation index on the modeling performance is developed by comprehensively considering the root-mean-square error of modeling and the correlation coefficient on trend fitting, based on which the nondominated sorting genetic algorithm II is used to globally optimize the model parameters. Both experiments using industrial data and industrial applications illustrate that the proposed method can eliminate the adverse effect caused by the fluctuation of data in BF process efficiently. In conclusion, this indicates its stronger robustness and higher accuracy. Moreover, control testing shows that the developed model can be well applied to realize data-driven control of the BF process.« less

Data-Driven Robust M-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking

DOE PAGES

Zhou, Ping; Guo, Dongwei; Wang, Hong; ...

2017-09-29

Optimal operation of an industrial blast furnace (BF) ironmaking process largely depends on a reliable measurement of molten iron quality (MIQ) indices, which are not feasible using the conventional sensors. This paper proposes a novel data-driven robust modeling method for the online estimation and control of MIQ indices. First, a nonlinear autoregressive exogenous (NARX) model is constructed for the MIQ indices to completely capture the nonlinear dynamics of the BF process. Then, considering that the standard least-squares support vector regression (LS-SVR) cannot directly cope with the multioutput problem, a multitask transfer learning is proposed to design a novel multioutput LS-SVRmore » (M-LS-SVR) for the learning of the NARX model. Furthermore, a novel M-estimator is proposed to reduce the interference of outliers and improve the robustness of the M-LS-SVR model. Since the weights of different outlier data are properly given by the weight function, their corresponding contributions on modeling can properly be distinguished, thus a robust modeling result can be achieved. Finally, a novel multiobjective evaluation index on the modeling performance is developed by comprehensively considering the root-mean-square error of modeling and the correlation coefficient on trend fitting, based on which the nondominated sorting genetic algorithm II is used to globally optimize the model parameters. Both experiments using industrial data and industrial applications illustrate that the proposed method can eliminate the adverse effect caused by the fluctuation of data in BF process efficiently. In conclusion, this indicates its stronger robustness and higher accuracy. Moreover, control testing shows that the developed model can be well applied to realize data-driven control of the BF process.« less

α-fetoprotein levels after interferon therapy predict regression of liver fibrosis in patients with sustained virological response.

PubMed

Tachi, Yoshihiko; Hirai, Takanori; Ishizu, Youji; Honda, Takashi; Kuzuya, Teiji; Hayashi, Kazuhiko; Ishigami, Masatoshi; Goto, Hidemi

2016-05-01

Eradicating chronic hepatitis C virus (HCV) infection improves liver fibrosis and reduces hepatocellular carcinoma (HCC) incidence in chronic HCV patients. We evaluated the relationship between fibrosis regression, as assessed by sequential biopsies, and clinical factors of patients with sustained virological response (SVR). We retrospectively enrolled 130 patients (74 men; 60.1 ± 8.1 years) with chronic HCV treated with interferon and ribavirin therapy who achieved SVR. To evaluate the change in fibrosis stage over time, all patients underwent a pre-therapy initial biopsy and a second biopsy after achieving SVR. The mean time between biopsies was 5.5 ± 1.2 years. Fibrosis stage regressed in 55 patients (42.3%), remained stable in 69 (53.1%), and progressed in 6 (4.6%). The mean fibrosis stage significantly decreased, from 2.01 ± 0.99 units to 1.61 ± 1.24 units (P < 0.001). Aspartate aminotransferase, γ-glutamyltransferase, and α-fetoprotein (AFP) levels at 24 weeks after the end of treatment (EOT) were significantly lower, and the platelet count at 24 weeks after the EOT was significantly higher in patients with fibrosis regression than in those without. Logistic regression analysis confirmed that lower AFP levels (< 5.4 ng/mL) at 24 weeks after the EOT (odds ratio [OR], 4.626; 95% confidence interval [CI], 1.557-13.153; P = 0.006) and HCV genotype 2 (OR, 2.198; 95% CI, 1.010-4.786; P = 0.047) were significant independent predictive factors for regressed fibrosis after SVR. Lower post-treatment AFP levels and HCV genotype 2 significantly correlated with liver fibrosis regression after SVR. © 2015 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

Evaluation of laser cutting process with auxiliary gas pressure by soft computing approach

NASA Astrophysics Data System (ADS)

Lazov, Lyubomir; Nikolić, Vlastimir; Jovic, Srdjan; Milovančević, Miloš; Deneva, Heristina; Teirumenieka, Erika; Arsic, Nebojsa

2018-06-01

Evaluation of the optimal laser cutting parameters is very important for the high cut quality. This is highly nonlinear process with different parameters which is the main challenge in the optimization process. Data mining methodology is one of most versatile method which can be used laser cutting process optimization. Support vector regression (SVR) procedure is implemented since it is a versatile and robust technique for very nonlinear data regression. The goal in this study was to determine the optimal laser cutting parameters to ensure robust condition for minimization of average surface roughness. Three cutting parameters, the cutting speed, the laser power, and the assist gas pressure, were used in the investigation. As a laser type TruLaser 1030 technological system was used. Nitrogen as an assisted gas was used in the laser cutting process. As the data mining method, support vector regression procedure was used. Data mining prediction accuracy was very high according the coefficient (R2) of determination and root mean square error (RMSE): R2 = 0.9975 and RMSE = 0.0337. Therefore the data mining approach could be used effectively for determination of the optimal conditions of the laser cutting process.

Blood glucose level prediction based on support vector regression using mobile platforms.

PubMed

Reymann, Maximilian P; Dorschky, Eva; Groh, Benjamin H; Martindale, Christine; Blank, Peter; Eskofier, Bjoern M

2016-08-01

The correct treatment of diabetes is vital to a patient's health: Staying within defined blood glucose levels prevents dangerous short- and long-term effects on the body. Mobile devices informing patients about their future blood glucose levels could enable them to take counter-measures to prevent hypo or hyper periods. Previous work addressed this challenge by predicting the blood glucose levels using regression models. However, these approaches required a physiological model, representing the human body's response to insulin and glucose intake, or are not directly applicable to mobile platforms (smart phones, tablets). In this paper, we propose an algorithm for mobile platforms to predict blood glucose levels without the need for a physiological model. Using an online software simulator program, we trained a Support Vector Regression (SVR) model and exported the parameter settings to our mobile platform. The prediction accuracy of our mobile platform was evaluated with pre-recorded data of a type 1 diabetes patient. The blood glucose level was predicted with an error of 19 % compared to the true value. Considering the permitted error of commercially used devices of 15 %, our algorithm is the basis for further development of mobile prediction algorithms.

Support vector machine firefly algorithm based optimization of lens system.

PubMed

Shamshirband, Shahaboddin; Petković, Dalibor; Pavlović, Nenad T; Ch, Sudheer; Altameem, Torki A; Gani, Abdullah

2015-01-01

Lens system design is an important factor in image quality. The main aspect of the lens system design methodology is the optimization procedure. Since optimization is a complex, nonlinear task, soft computing optimization algorithms can be used. There are many tools that can be employed to measure optical performance, but the spot diagram is the most useful. The spot diagram gives an indication of the image of a point object. In this paper, the spot size radius is considered an optimization criterion. Intelligent soft computing scheme support vector machines (SVMs) coupled with the firefly algorithm (FFA) are implemented. The performance of the proposed estimators is confirmed with the simulation results. The result of the proposed SVM-FFA model has been compared with support vector regression (SVR), artificial neural networks, and generic programming methods. The results show that the SVM-FFA model performs more accurately than the other methodologies. Therefore, SVM-FFA can be used as an efficient soft computing technique in the optimization of lens system designs.

Factors Associated with Failure to Achieve SVR in Hepatitis C Genotype 3 Patients Within an Integrated Care Delivery System.

PubMed

Cheetham, T Craig; Niu, Fang; Chiang, Kevin; Yuan, Yong; Kalsekar, Anu; Hechter, Rulin; Hay, Joel W; Nyberg, Lisa

2015-08-01

Achievement of sustained virologic response (SVR) and factors associated with treatment failure in hepatitis C virus (HCV) genotype 3 have been described in tertiary and referral care settings, with rates of SVR reported to range between 72% and 89%. Fewer data exist on SVR outside of these settings. To describe rates of SVR and characterize factors associated with achievement of SVR within an integrated health care delivery system. A retrospective cohort study of genotype 3 HCV patients treated with dual therapy (pegylated interferon-alpha plus ribavirin) was conducted at Kaiser Permanente Southern California. Adult patients diagnosed with HCV and testing positive for HCV-RNA genotype 3 were identified from electronic medical records. Data were collected on patient demographics, baseline health status, and comorbid conditions. A multivariate logistic regression model was used to determine the association between baseline patient factors and SVR. A total of 484 HCV genotype 3 patients met the eligibility criteria. The median age was 49 years, and 65.7% were male. Overall, 252 (52.1%) achieved SVR. Aged ≥ 45 years and male gender were associated with lower rates of SVR; cirrhosis and chronic diseases (diabetes and chronic obstructive pulmonary disease) were also associated with lower rates of SVR. SVR was lower in patients within an integrated care delivery system than in those in tertiary and referral centers. Males and older patients had lower rates of SVR, as well as patients with cirrhosis, diabetes, and chronic obstructive pulmonary disease.

Multivariate Time Series Forecasting of Crude Palm Oil Price Using Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Kanchymalay, Kasturi; Salim, N.; Sukprasert, Anupong; Krishnan, Ramesh; Raba'ah Hashim, Ummi

2017-08-01

The aim of this paper was to study the correlation between crude palm oil (CPO) price, selected vegetable oil prices (such as soybean oil, coconut oil, and olive oil, rapeseed oil and sunflower oil), crude oil and the monthly exchange rate. Comparative analysis was then performed on CPO price forecasting results using the machine learning techniques. Monthly CPO prices, selected vegetable oil prices, crude oil prices and monthly exchange rate data from January 1987 to February 2017 were utilized. Preliminary analysis showed a positive and high correlation between the CPO price and soy bean oil price and also between CPO price and crude oil price. Experiments were conducted using multi-layer perception, support vector regression and Holt Winter exponential smoothing techniques. The results were assessed by using criteria of root mean square error (RMSE), means absolute error (MAE), means absolute percentage error (MAPE) and Direction of accuracy (DA). Among these three techniques, support vector regression(SVR) with Sequential minimal optimization (SMO) algorithm showed relatively better results compared to multi-layer perceptron and Holt Winters exponential smoothing method.

Comparison between Two Linear Supervised Learning Machines' Methods with Principle Component Based Methods for the Spectrofluorimetric Determination of Agomelatine and Its Degradants.

PubMed

Elkhoudary, Mahmoud M; Naguib, Ibrahim A; Abdel Salam, Randa A; Hadad, Ghada M

2017-05-01

Four accurate, sensitive and reliable stability indicating chemometric methods were developed for the quantitative determination of Agomelatine (AGM) whether in pure form or in pharmaceutical formulations. Two supervised learning machines' methods; linear artificial neural networks (PC-linANN) preceded by principle component analysis and linear support vector regression (linSVR), were compared with two principle component based methods; principle component regression (PCR) as well as partial least squares (PLS) for the spectrofluorimetric determination of AGM and its degradants. The results showed the benefits behind using linear learning machines' methods and the inherent merits of their algorithms in handling overlapped noisy spectral data especially during the challenging determination of AGM alkaline and acidic degradants (DG1 and DG2). Relative mean squared error of prediction (RMSEP) for the proposed models in the determination of AGM were 1.68, 1.72, 0.68 and 0.22 for PCR, PLS, SVR and PC-linANN; respectively. The results showed the superiority of supervised learning machines' methods over principle component based methods. Besides, the results suggested that linANN is the method of choice for determination of components in low amounts with similar overlapped spectra and narrow linearity range. Comparison between the proposed chemometric models and a reported HPLC method revealed the comparable performance and quantification power of the proposed models.

Estimation of Global Subsurface Thermal Structure from Satellite Remote Sensing Observations Based on Machine Learning

NASA Astrophysics Data System (ADS)

Su, H.; Yan, X. H.

2017-12-01

Subsurface thermal structure of the global ocean is a key factor that reflects the impact of the global climate variability and change. Accurately determining and describing the global subsurface and deeper ocean thermal structure from satellite measurements is becoming even more important for understanding the ocean interior anomaly and dynamic processes during recent global warming and hiatus. It is essential but challenging to determine the extent to which such surface remote sensing observations can be used to develop information about the global ocean interior. This study proposed a Support Vector Regression (SVR) method to estimate Subsurface Temperature Anomaly (STA) in the global ocean. The SVR model can well estimate the global STA upper 1000 m through a suite of satellite remote sensing observations of sea surface parameters (including Sea Surface Height Anomaly (SSHA), Sea Surface Temperature Anomaly (SSTA), Sea Surface Salinity Anomaly (SSSA) and Sea Surface Wind Anomaly (SSWA)) with in situ Argo data for training and testing at different depth levels. Here, we employed the MSE and R2 to assess SVR performance on the STA estimation. The results from the SVR model were validated for the accuracy and reliability using the worldwide Argo STA data. The average MSE and R2 of the 15 levels are 0.0090 / 0.0086 / 0.0087 and 0.443 / 0.457 / 0.485 for 2-attributes (SSHA, SSTA) / 3-attributes (SSHA, SSTA, SSSA) / 4-attributes (SSHA, SSTA, SSSA, SSWA) SVR, respectively. The estimation accuracy was improved by including SSSA and SSWA for SVR input (MSE decreased by 0.4% / 0.3% and R2 increased by 1.4% / 4.2% on average). While, the estimation accuracy gradually decreased with the increase of the depth from 500 m. The results showed that SSSA and SSWA, in addition to SSTA and SSHA, are useful parameters that can help estimate the subsurface thermal structure, as well as improve the STA estimation accuracy. In future, we can figure out more potential and useful sea surface parameters from satellite remote sensing as input attributes so as to further improve the STA sensing accuracy from machine learning. This study can provide a helpful technique for studying thermal variability in the ocean interior which has played an important role in recent global warming and hiatus from satellite observations over global scale.

Genetic Variants in the Apoptosis Gene BCL2L1 Improve Response to Interferon-Based Treatment of Hepatitis C Virus Genotype 3 Infection

PubMed Central

Clausen, Louise Nygaard; Weis, Nina; Ladelund, Steen; Madsen, Lone; Lunding, Suzanne; Tarp, Britta; Christensen, Peer Brehm; Krarup, Henrik Bygum; Møller, Axel; Gerstoft, Jan; Clausen, Mette Rye; Benfield, Thomas

2015-01-01

Genetic variation upstream of the apoptosis pathway has been associated with outcome of hepatitis C virus (HCV) infection. We investigated genetic polymorphisms in the intrinsic apoptosis pathway to assess their influence on sustained virological response (SVR) to pegylated interferon-α and ribavirin (pegIFN/RBV) treatment of HCV genotypes 1 and 3 infections. We conducted a candidate gene association study in a prospective cohort of 201 chronic HCV-infected individuals undergoing treatment with pegIFN/RBV. Differences between groups were compared in logistic regression adjusted for age, HCV viral load and interleukin 28B genotypes. Four single nucleotide polymorphisms (SNPs) located in the B-cell lymphoma 2-like 1 (BCL2L1) gene were significantly associated with SVR. SVR rates were significantly higher for carriers of the beneficial rs1484994 CC genotypes. In multivariate logistic regression, the rs1484994 SNP combined CC + TC genotypes were associated with a 3.4 higher odds ratio (OR) in SVR for the HCV genotype 3 (p = 0.02). The effect estimate was similar for genotype 1, but the association did not reach statistical significance. In conclusion, anti-apoptotic SNPs in the BCL2L1 gene were predictive of SVR to pegIFN/RBV treatment in HCV genotypes 1 and 3 infected individuals. These SNPs may be used in prediction of SVR, but further studies are needed. PMID:25648321

Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients

NASA Astrophysics Data System (ADS)

Tang, Jie; Liu, Rong; Zhang, Yue-Li; Liu, Mou-Ze; Hu, Yong-Fang; Shao, Ming-Jie; Zhu, Li-Jun; Xin, Hua-Wen; Feng, Gui-Wen; Shang, Wen-Jun; Meng, Xiang-Guang; Zhang, Li-Rong; Ming, Ying-Zi; Zhang, Wei

2017-02-01

Tacrolimus has a narrow therapeutic window and considerable variability in clinical use. Our goal was to compare the performance of multiple linear regression (MLR) and eight machine learning techniques in pharmacogenetic algorithm-based prediction of tacrolimus stable dose (TSD) in a large Chinese cohort. A total of 1,045 renal transplant patients were recruited, 80% of which were randomly selected as the “derivation cohort” to develop dose-prediction algorithm, while the remaining 20% constituted the “validation cohort” to test the final selected algorithm. MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied and their performances were compared in this work. Among all the machine learning models, RT performed best in both derivation [0.71 (0.67-0.76)] and validation cohorts [0.73 (0.63-0.82)]. In addition, the ideal rate of RT was 4% higher than that of MLR. To our knowledge, this is the first study to use machine learning models to predict TSD, which will further facilitate personalized medicine in tacrolimus administration in the future.

Structural features that predict real-value fluctuations of globular proteins.

PubMed

Jamroz, Michal; Kolinski, Andrzej; Kihara, Daisuke

2012-05-01

It is crucial to consider dynamics for understanding the biological function of proteins. We used a large number of molecular dynamics (MD) trajectories of nonhomologous proteins as references and examined static structural features of proteins that are most relevant to fluctuations. We examined correlation of individual structural features with fluctuations and further investigated effective combinations of features for predicting the real value of residue fluctuations using the support vector regression (SVR). It was found that some structural features have higher correlation than crystallographic B-factors with fluctuations observed in MD trajectories. Moreover, SVR that uses combinations of static structural features showed accurate prediction of fluctuations with an average Pearson's correlation coefficient of 0.669 and a root mean square error of 1.04 Å. This correlation coefficient is higher than the one observed in predictions by the Gaussian network model (GNM). An advantage of the developed method over the GNMs is that the former predicts the real value of fluctuation. The results help improve our understanding of relationships between protein structure and fluctuation. Furthermore, the developed method provides a convienient practial way to predict fluctuations of proteins using easily computed static structural features of proteins. Copyright © 2012 Wiley Periodicals, Inc.

A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting

NASA Astrophysics Data System (ADS)

Niu, Mingfei; Wang, Yufang; Sun, Shaolong; Li, Yongwu

2016-06-01

To enhance prediction reliability and accuracy, a hybrid model based on the promising principle of "decomposition and ensemble" and a recently proposed meta-heuristic called grey wolf optimizer (GWO) is introduced for daily PM2.5 concentration forecasting. Compared with existing PM2.5 forecasting methods, this proposed model has improved the prediction accuracy and hit rates of directional prediction. The proposed model involves three main steps, i.e., decomposing the original PM2.5 series into several intrinsic mode functions (IMFs) via complementary ensemble empirical mode decomposition (CEEMD) for simplifying the complex data; individually predicting each IMF with support vector regression (SVR) optimized by GWO; integrating all predicted IMFs for the ensemble result as the final prediction by another SVR optimized by GWO. Seven benchmark models, including single artificial intelligence (AI) models, other decomposition-ensemble models with different decomposition methods and models with the same decomposition-ensemble method but optimized by different algorithms, are considered to verify the superiority of the proposed hybrid model. The empirical study indicates that the proposed hybrid decomposition-ensemble model is remarkably superior to all considered benchmark models for its higher prediction accuracy and hit rates of directional prediction.

A system methodology for optimization design of the structural crashworthiness of a vehicle subjected to a high-speed frontal crash

NASA Astrophysics Data System (ADS)

Xia, Liang; Liu, Weiguo; Lv, Xiaojiang; Gu, Xianguang

2018-04-01

The structural crashworthiness design of vehicles has become an important research direction to ensure the safety of the occupants. To effectively improve the structural safety of a vehicle in a frontal crash, a system methodology is presented in this study. The surrogate model of Online support vector regression (Online-SVR) is adopted to approximate crashworthiness criteria and different kernel functions are selected to enhance the accuracy of the model. The Online-SVR model is demonstrated to have the advantages of solving highly nonlinear problems and saving training costs, and can effectively be applied for vehicle structural crashworthiness design. By combining the non-dominated sorting genetic algorithm II and Monte Carlo simulation, both deterministic optimization and reliability-based design optimization (RBDO) are conducted. The optimization solutions are further validated by finite element analysis, which shows the effectiveness of the RBDO solution in the structural crashworthiness design process. The results demonstrate the advantages of using RBDO, resulting in not only increased energy absorption and decreased structural weight from a baseline design, but also a significant improvement in the reliability of the design.

Water Quality Sensing and Spatio-Temporal Monitoring Structure with Autocorrelation Kernel Methods.

PubMed

Vizcaíno, Iván P; Carrera, Enrique V; Muñoz-Romero, Sergio; Cumbal, Luis H; Rojo-Álvarez, José Luis

2017-10-16

Pollution on water resources is usually analyzed with monitoring campaigns, which consist of programmed sampling, measurement, and recording of the most representative water quality parameters. These campaign measurements yields a non-uniform spatio-temporal sampled data structure to characterize complex dynamics phenomena. In this work, we propose an enhanced statistical interpolation method to provide water quality managers with statistically interpolated representations of spatial-temporal dynamics. Specifically, our proposal makes efficient use of the a priori available information of the quality parameter measurements through Support Vector Regression (SVR) based on Mercer's kernels. The methods are benchmarked against previously proposed methods in three segments of the Machángara River and one segment of the San Pedro River in Ecuador, and their different dynamics are shown by statistically interpolated spatial-temporal maps. The best interpolation performance in terms of mean absolute error was the SVR with Mercer's kernel given by either the Mahalanobis spatial-temporal covariance matrix or by the bivariate estimated autocorrelation function. In particular, the autocorrelation kernel provides with significant improvement of the estimation quality, consistently for all the six water quality variables, which points out the relevance of including a priori knowledge of the problem.

Water Quality Sensing and Spatio-Temporal Monitoring Structure with Autocorrelation Kernel Methods

PubMed Central

Vizcaíno, Iván P.; Muñoz-Romero, Sergio; Cumbal, Luis H.

2017-01-01

Pollution on water resources is usually analyzed with monitoring campaigns, which consist of programmed sampling, measurement, and recording of the most representative water quality parameters. These campaign measurements yields a non-uniform spatio-temporal sampled data structure to characterize complex dynamics phenomena. In this work, we propose an enhanced statistical interpolation method to provide water quality managers with statistically interpolated representations of spatial-temporal dynamics. Specifically, our proposal makes efficient use of the a priori available information of the quality parameter measurements through Support Vector Regression (SVR) based on Mercer’s kernels. The methods are benchmarked against previously proposed methods in three segments of the Machángara River and one segment of the San Pedro River in Ecuador, and their different dynamics are shown by statistically interpolated spatial-temporal maps. The best interpolation performance in terms of mean absolute error was the SVR with Mercer’s kernel given by either the Mahalanobis spatial-temporal covariance matrix or by the bivariate estimated autocorrelation function. In particular, the autocorrelation kernel provides with significant improvement of the estimation quality, consistently for all the six water quality variables, which points out the relevance of including a priori knowledge of the problem. PMID:29035333

Validation of the Simple View of Reading in Hebrew--A Semitic Language

ERIC Educational Resources Information Center

Joshi, R. Malatesha; Ji, Xuejun Ryan; Breznitz, Zvia; Amiel, Meirav; Yulia, Astri

2015-01-01

The Simple View of Reading (SVR) in Hebrew was tested by administering decoding, listening comprehension, and reading comprehension measures to 1,002 students from Grades 2 to 10 in the northern part of Israel. Results from hierarchical regression analyses supported the SVR in Hebrew with decoding and listening comprehension measures explaining…

Canopy Spectral Reflectance as a Predictor of Soil Water Potential in Rice

NASA Astrophysics Data System (ADS)

Panigrahi, N.; Das, B. S.

2018-04-01

Soil water potential (SWP) is a key parameter for characterizing water stress. Typically, a tensiometer is used to measure SWP. However, the measurement range for commercially available tensiometers is limited to -90 kPa and a tensiometer can only provide estimate of SWP at a single location. In this study, a new approach was developed for estimating SWP from spectral reflectance data of a standing rice crop over the visible to shortwave-infrared region (wavelength: 350-2,500 nm). Five water stress treatments corresponding to targeted SWP of -30, -50, -70, -120, and -140 kPa were examined by withholding irrigation during the vegetative growth stage of three rice varieties. Tensiometers and mechanistic water flow model were used for monitoring SWP. Spectral models for SWP were developed using partial-least-squares regression (PLSR), support vector regression (SVR), and coupled PLSR and feature selection (PLSRFS) approaches. Results showed that the SVR approach was the best model for estimating SWP from spectral reflectance data with the coefficient of determination values of 0.71 and 0.55 for the calibration and validation data sets, respectively. Observed root-mean-squared residuals for the predicted SWPs were in the range of -7 to -19 kPa. A new spectral water stress index was also developed using the reflectance values at 745 and 2,002 nm, which showed strong correlation with relative water contents and electrolyte leakage. This new approach is rapid and noninvasive and may be used for estimating SWP over large areas.

Catchments as non-linear filters: evaluating data-driven approaches for spatio-temporal predictions in ungauged basins

NASA Astrophysics Data System (ADS)

Bellugi, D. G.; Tennant, C.; Larsen, L.

2016-12-01

Catchment and climate heterogeneity complicate prediction of runoff across time and space, and resulting parameter uncertainty can lead to large accumulated errors in hydrologic models, particularly in ungauged basins. Recently, data-driven modeling approaches have been shown to avoid the accumulated uncertainty associated with many physically-based models, providing an appealing alternative for hydrologic prediction. However, the effectiveness of different methods in hydrologically and geomorphically distinct catchments, and the robustness of these methods to changing climate and changing hydrologic processes remain to be tested. Here, we evaluate the use of machine learning techniques to predict daily runoff across time and space using only essential climatic forcing (e.g. precipitation, temperature, and potential evapotranspiration) time series as model input. Model training and testing was done using a high quality dataset of daily runoff and climate forcing data for 25+ years for 600+ minimally-disturbed catchments (drainage area range 5-25,000 km2, median size 336 km2) that cover a wide range of climatic and physical characteristics. Preliminary results using Support Vector Regression (SVR) suggest that in some catchments this nonlinear-based regression technique can accurately predict daily runoff, while the same approach fails in other catchments, indicating that the representation of climate inputs and/or catchment filter characteristics in the model structure need further refinement to increase performance. We bolster this analysis by using Sparse Identification of Nonlinear Dynamics (a sparse symbolic regression technique) to uncover the governing equations that describe runoff processes in catchments where SVR performed well and for ones where it performed poorly, thereby enabling inference about governing processes. This provides a robust means of examining how catchment complexity influences runoff prediction skill, and represents a contribution towards the integration of data-driven inference and physically-based models.

Mapping of macro and micro nutrients of mixed pastures using airborne AisaFENIX hyperspectral imagery

NASA Astrophysics Data System (ADS)

Pullanagari, R. R.; Kereszturi, Gábor; Yule, I. J.

2016-07-01

On-farm assessment of mixed pasture nutrient concentrations is important for animal production and pasture management. Hyperspectral imaging is recognized as a potential tool to quantify the nutrient content of vegetation. However, it is a great challenge to estimate macro and micro nutrients in heterogeneous mixed pastures. In this study, canopy reflectance data was measured by using a high resolution airborne visible-to-shortwave infrared (Vis-SWIR) imaging spectrometer measuring in the wavelength region 380-2500 nm to predict nutrient concentrations, nitrogen (N) phosphorus (P), potassium (K), sulfur (S), zinc (Zn), sodium (Na), manganese (Mn) copper (Cu) and magnesium (Mg) in heterogeneous mixed pastures across a sheep and beef farm in hill country, within New Zealand. Prediction models were developed using four different methods which are included partial least squares regression (PLSR), kernel PLSR, support vector regression (SVR), random forest regression (RFR) algorithms and their performance compared using the test data. The results from the study revealed that RFR produced highest accuracy (0.55 ⩽ R2CV ⩽ 0.78; 6.68% ⩽ nRMSECV ⩽ 26.47%) compared to all other algorithms for the majority of nutrients (N, P, K, Zn, Na, Cu and Mg) described, and the remaining nutrients (S and Mn) were predicted with high accuracy (0.68 ⩽ R2CV ⩽ 0.86; 13.00% ⩽ nRMSECV ⩽ 14.64%) using SVR. The best training models were used to extrapolate over the whole farm with the purpose of predicting those pasture nutrients and expressed through pixel based spatial maps. These spatially registered nutrient maps demonstrate the range and geographical location of often large differences in pasture nutrient values which are normally not measured and therefore not included in decision making when considering more effective ways to utilized pasture.

Hemodynamics and Vascular Hypertrophy in African Americans and Caucasians With High Blood Pressure.

PubMed

Hill, LaBarron K; Sherwood, Andrew; Blumenthal, James A; Hinderliter, Alan L

2016-12-01

Hypertension in African Americans is characterized by greater systemic vascular resistance (SVR) compared with Caucasian Americans, but the responsible mechanisms are not known. The present study sought to determine if peripheral vascular hypertrophy is a potential mechanism contributing to elevated SVR in African Americans with high blood pressure (BP). In a biracial sample of 80 men and women between the ages of 25 and 45 years, with clinic BP in the range 130/85-160/99mm Hg, we assessed cardiac output and SVR, in addition to BP. Minimum forearm vascular resistance (MFVR), a marker of vascular hypertrophy, also was assessed. SVR was elevated in African Americans compared with Caucasians (P < 0.001). Regression models indicated that age, body mass index, 24-hour diastolic BP, and ethnicity were significant predictors of SVR. There was also a significant interaction between ethnicity and MFVR in explaining SVR in the study sample. In particular, there was a significant positive association between MFVR and SVR among African Americans (P = 0.002), whereas the association was inverse and not statistically significant among Caucasians (P = 0.601). Hypertrophy of the systemic microvasculature may contribute to the elevated SVR that is characteristic of the early stages of hypertension in African American compared with Caucasians. © American Journal of Hypertension, Ltd 2016. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Stagnation of histopathological improvement is a predictor of hepatocellular carcinoma development after hepatitis C virus eradication

PubMed Central

Motoyama, Hiroyuki; Tamori, Akihiro; Kubo, Shoji; Uchida-Kobayashi, Sawako; Takemura, Shigekazu; Tanaka, Shogo; Ohfuji, Satoko; Teranishi, Yuga; Kozuka, Ritsuzo; Kawamura, Etsushi; Hagihara, Atsushi; Morikawa, Hiroyasu; Enomoto, Masaru; Murakami, Yoshiki; Kawada, Norifumi

2018-01-01

Background Hepatocellular carcinoma (HCC) develops in some patients who achieve sustained virological response (SVR) against hepatitis C virus (HCV) infection via anti-HCV therapy. To examine the pathogenesis of HCC development after HCV eradication, histopathological changes and clinical markers were evaluated in SVR patients. Methods Of 654 SVR patients treated with interferon (IFN)-based therapies, 34 patients who had undergone liver biopsy before initiating IFN therapy and after SVR achievement were enrolled: 11 patients with HCC and 23 patients without HCC (male/female, 9/2 and 8/15, respectively: age, 58 ± 5 and 54 ± 11 years, respectively). We compared the clinical and histopathological factors between the two groups. Immunohistochemistry for Cytoglobin (CYGB) and α smooth muscle actin (α-SMA) was also performed. Results At baseline, prior to initiating the IFN-based therapy, there were significant differences between the SVR-non-HCC and SVR-HCC groups in the male gender, HBc antibody positivity, prothrombin activity, and histological inflammatory grade. Histopathological evaluation, using the new Inuyama classification system, revealed an improvement in the inflammatory grade, from 2.1 ± 0.6 to 1.0 ± 0.6 (p < 0.0001), whereas the fibrosis stage remained unchanged, from 2.3 ± 0.9 to 2.0 ± 1.2 (p = 0.2749), during the 97 ± 72-month observation period in the SVR-HCC group. Both the grade and stage scores were significantly improved in the SVR-non-HCC group. The area of collagen deposition, evaluated using Sirius red staining, showed a marked decrease, from 18.6 ± 7.6% to 7.7 ± 4.6%, in the SVR-non-HCC group, with no change in the SVR-HCC group. CYGB- and α-SMA-positive hepatic stellate cells (HSCs), indicative of the HSC activated phenotype, remained in the fibrotic tissue of livers among patients in the SVR-HCC group. Conclusion Stagnation of fibrosis regression is associated with a high risk for HCC after SVR. HSC activation may inhibit improvement in fibrosis after SVR and potentially contribute to hepatocarcinogenesis. PMID:29534101

Prediction of shear wave velocity using empirical correlations and artificial intelligence methods

NASA Astrophysics Data System (ADS)

Maleki, Shahoo; Moradzadeh, Ali; Riabi, Reza Ghavami; Gholami, Raoof; Sadeghzadeh, Farhad

2014-06-01

Good understanding of mechanical properties of rock formations is essential during the development and production phases of a hydrocarbon reservoir. Conventionally, these properties are estimated from the petrophysical logs with compression and shear sonic data being the main input to the correlations. This is while in many cases the shear sonic data are not acquired during well logging, which may be for cost saving purposes. In this case, shear wave velocity is estimated using available empirical correlations or artificial intelligent methods proposed during the last few decades. In this paper, petrophysical logs corresponding to a well drilled in southern part of Iran were used to estimate the shear wave velocity using empirical correlations as well as two robust artificial intelligence methods knows as Support Vector Regression (SVR) and Back-Propagation Neural Network (BPNN). Although the results obtained by SVR seem to be reliable, the estimated values are not very precise and considering the importance of shear sonic data as the input into different models, this study suggests acquiring shear sonic data during well logging. It is important to note that the benefits of having reliable shear sonic data for estimation of rock formation mechanical properties will compensate the possible additional costs for acquiring a shear log.

Development of a hybrid model to predict construction and demolition waste: China as a case study.

PubMed

Song, Yiliao; Wang, Yong; Liu, Feng; Zhang, Yixin

2017-01-01

Construction and demolition waste (C&DW) is currently a worldwide issue, and the situation is the worst in China due to a rapid increase in the construction industry and the short life span of China's buildings. To create an opportunity out of this problem, comprehensive prevention measures and effective management strategies are urgently needed. One major gap in the literature of waste management is a lack of estimations on future C&DW generation. Therefore, this paper presents a forecasting procedure for C&DW in China that can forecast the quantity of each component in such waste. The proposed approach is based on a GM-SVR model that improves the forecasting effectiveness of the gray model (GM), which is achieved by adjusting the residual series by a support vector regression (SVR) method and a transition matrix that aims to estimate the discharge of each component in the C&DW. Through the proposed method, future C&DW volume are listed and analyzed containing their potential components and distribution in different provinces in China. Besides, model testing process provides mathematical evidence to validate the proposed model is an effective way to give future information of C&DW for policy makers. Copyright © 2016 Elsevier Ltd. All rights reserved.

A medical cost estimation with fuzzy neural network of acute hepatitis patients in emergency room.

PubMed

Kuo, R J; Cheng, W C; Lien, W C; Yang, T J

2015-10-01

Taiwan is an area where chronic hepatitis is endemic. Liver cancer is so common that it has been ranked first among cancer mortality rates since the early 1980s in Taiwan. Besides, liver cirrhosis and chronic liver diseases are the sixth or seventh in the causes of death. Therefore, as shown by the active research on hepatitis, it is not only a health threat, but also a huge medical cost for the government. The estimated total number of hepatitis B carriers in the general population aged more than 20 years old is 3,067,307. Thus, a case record review was conducted from all patients with diagnosis of acute hepatitis admitted to the Emergency Department (ED) of a well-known teaching-oriented hospital in Taipei. The cost of medical resource utilization is defined as the total medical fee. In this study, a fuzzy neural network is employed to develop the cost forecasting model. A total of 110 patients met the inclusion criteria. The computational results indicate that the FNN model can provide more accurate forecasts than the support vector regression (SVR) or artificial neural network (ANN). In addition, unlike SVR and ANN, FNN can also provide fuzzy IF-THEN rules for interpretation. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Robust prediction of three-dimensional spinal curve from back surface for non-invasive follow-up of scoliosis

NASA Astrophysics Data System (ADS)

Bergeron, Charles; Labelle, Hubert; Ronsky, Janet; Zernicke, Ronald

2005-04-01

Spinal curvature progression in scoliosis patients is monitored from X-rays, and this serial exposure to harmful radiation increases the incidence of developing cancer. With the aim of reducing the invasiveness of follow-up, this study seeks to relate the three-dimensional external surface to the internal geometry, having assumed that that the physiological links between these are sufficiently regular across patients. A database was used of 194 quasi-simultaneous acquisitions of two X-rays and a 3D laser scan of the entire trunk. Data was processed to sets of datapoints representing the trunk surface and spinal curve. Functional data analyses were performed using generalized Fourier series using a Haar basis and functional minimum noise fractions. The resulting coefficients became inputs and outputs, respectively, to an array of support vector regression (SVR) machines. SVR parameters were set based on theoretical results, and cross-validation increased confidence in the system's performance. Predicted lateral and frontal views of the spinal curve from the back surface demonstrated average L2-errors of 6.13 and 4.38 millimetres, respectively, across the test set; these compared favourably with measurement error in data. This constitutes a first robust prediction of the 3D spinal curve from external data using learning techniques.

A prediction model of short-term ionospheric foF2 based on AdaBoost

NASA Astrophysics Data System (ADS)

Zhao, Xiukuan; Ning, Baiqi; Liu, Libo; Song, Gangbing

2014-02-01

In this paper, the AdaBoost-BP algorithm is used to construct a new model to predict the critical frequency of the ionospheric F2-layer (foF2) one hour ahead. Different indices were used to characterize ionospheric diurnal and seasonal variations and their dependence on solar and geomagnetic activity. These indices, together with the current observed foF2 value, were input into the prediction model and the foF2 value at one hour ahead was output. We analyzed twenty-two years' foF2 data from nine ionosonde stations in the East-Asian sector in this work. The first eleven years' data were used as a training dataset and the second eleven years' data were used as a testing dataset. The results show that the performance of AdaBoost-BP is better than those of BP Neural Network (BPNN), Support Vector Regression (SVR) and the IRI model. For example, the AdaBoost-BP prediction absolute error of foF2 at Irkutsk station (a middle latitude station) is 0.32 MHz, which is better than 0.34 MHz from BPNN, 0.35 MHz from SVR and also significantly outperforms the IRI model whose absolute error is 0.64 MHz. Meanwhile, AdaBoost-BP prediction absolute error at Taipei station from the low latitude is 0.78 MHz, which is better than 0.81 MHz from BPNN, 0.81 MHz from SVR and 1.37 MHz from the IRI model. Finally, the variety characteristics of the AdaBoost-BP prediction error along with seasonal variation, solar activity and latitude variation were also discussed in the paper.

A machine learning approach for real-time modelling of tissue deformation in image-guided neurosurgery.

PubMed

Tonutti, Michele; Gras, Gauthier; Yang, Guang-Zhong

2017-07-01

Accurate reconstruction and visualisation of soft tissue deformation in real time is crucial in image-guided surgery, particularly in augmented reality (AR) applications. Current deformation models are characterised by a trade-off between accuracy and computational speed. We propose an approach to derive a patient-specific deformation model for brain pathologies by combining the results of pre-computed finite element method (FEM) simulations with machine learning algorithms. The models can be computed instantaneously and offer an accuracy comparable to FEM models. A brain tumour is used as the subject of the deformation model. Load-driven FEM simulations are performed on a tetrahedral brain mesh afflicted by a tumour. Forces of varying magnitudes, positions, and inclination angles are applied onto the brain's surface. Two machine learning algorithms-artificial neural networks (ANNs) and support vector regression (SVR)-are employed to derive a model that can predict the resulting deformation for each node in the tumour's mesh. The tumour deformation can be predicted in real time given relevant information about the geometry of the anatomy and the load, all of which can be measured instantly during a surgical operation. The models can predict the position of the nodes with errors below 0.3mm, beyond the general threshold of surgical accuracy and suitable for high fidelity AR systems. The SVR models perform better than the ANN's, with positional errors for SVR models reaching under 0.2mm. The results represent an improvement over existing deformation models for real time applications, providing smaller errors and high patient-specificity. The proposed approach addresses the current needs of image-guided surgical systems and has the potential to be employed to model the deformation of any type of soft tissue. Copyright © 2017 Elsevier B.V. All rights reserved.

Wavelength selection-based nonlinear calibration for transcutaneous blood glucose sensing using Raman spectroscopy

PubMed Central

Dingari, Narahara Chari; Barman, Ishan; Kang, Jeon Woong; Kong, Chae-Ryon; Dasari, Ramachandra R.; Feld, Michael S.

2011-01-01

While Raman spectroscopy provides a powerful tool for noninvasive and real time diagnostics of biological samples, its translation to the clinical setting has been impeded by the lack of robustness of spectroscopic calibration models and the size and cumbersome nature of conventional laboratory Raman systems. Linear multivariate calibration models employing full spectrum analysis are often misled by spurious correlations, such as system drift and covariations among constituents. In addition, such calibration schemes are prone to overfitting, especially in the presence of external interferences that may create nonlinearities in the spectra-concentration relationship. To address both of these issues we incorporate residue error plot-based wavelength selection and nonlinear support vector regression (SVR). Wavelength selection is used to eliminate uninformative regions of the spectrum, while SVR is used to model the curved effects such as those created by tissue turbidity and temperature fluctuations. Using glucose detection in tissue phantoms as a representative example, we show that even a substantial reduction in the number of wavelengths analyzed using SVR lead to calibration models of equivalent prediction accuracy as linear full spectrum analysis. Further, with clinical datasets obtained from human subject studies, we also demonstrate the prospective applicability of the selected wavelength subsets without sacrificing prediction accuracy, which has extensive implications for calibration maintenance and transfer. Additionally, such wavelength selection could substantially reduce the collection time of serial Raman acquisition systems. Given the reduced footprint of serial Raman systems in relation to conventional dispersive Raman spectrometers, we anticipate that the incorporation of wavelength selection in such hardware designs will enhance the possibility of miniaturized clinical systems for disease diagnosis in the near future. PMID:21895336

Sustained virological response and baseline predictors in HIV-HCV coinfected patients retreated with pegylated interferon and ribavirin after failing a previous interferon-based therapy: systematic review and meta-analysis.

PubMed

Basso, Monica; Parisi, Saverio Giuseppe; Mengoli, Carlo; Gentilini, Valeria; Menegotto, Nicola; Monticelli, Jacopo; Nicolè, Stefano; Cruciani, Mario; Palù, Giorgio

2013-01-01

Published data on retreatment with pegylated interferon and ribavirin of previously failing HIV-HCV coinfected patients are sparse and limited to observational study. We aimed to evaluate efficacy and pretreatment predictors. Systematic review and meta-analysis of observational studies. The overall and genotype-related success rate was investigated. A direct comparison was performed between genotypes 1/4 and 2/3 by evaluating the sustained virological response (SVR) rate ratio (RR). The effect of study level variables on the effect size was investigated by meta-regression. Variables that were analyzed included age, gender, advanced hepatic fibrosis, pretreatment of HCV RNA and CD4, and successful antiretroviral treatment (ART). The available evidence was from 5 open-label, cohort studies (275 patients). The overall SVR rate was 0.280 (95% CI,0.171-0.425). The SVR rate in genotype 1/4 infections was 0.174 (95% CI, 0.129-0.230), and in genotype 2/3 infections it was 0.474 (95% CI, 0.286-0.670). The pooled RR comparing the SVR of genotype 1/4 to 2/3 was 0.369 (95% CI, 0.239-0.568), with a decreased probability of response for genotype 1/4 (P < .001). HIV RNA suppression had a significant effect on SVR (P = .005). The other covariates had no effect on the overall SVR rate. The overall SVR rate was 28%, consistent with the rate reported in the retreatment of mono-infected patients with the same schedule. A substantial relative reduction in the SVR rate of about one-third, when treating genotypes 1/4, was found, with a low SVR rate of 17%. Successful HIV suppression by ART predicted a higher rate of treatment success.

Shear wave elastography predicts hepatocellular carcinoma risk in hepatitis C patients after sustained virological response.

PubMed

Hamada, Koichi; Saitoh, Satoshi; Nishino, Noriyuki; Fukushima, Daizo; Horikawa, Yoshinori; Nishida, Shinya; Honda, Michitaka

2018-01-01

To evaluate the relationship between fibrosis and HCC after sustained virological response (SVR) to treatment for chronic hepatitis C (HCV). This single-center study retrospectively evaluated 196 patients who achieved SVR after HCV infection. The associations of risk factors with HCC development after HCV eradication were evaluated using univariate and multivariate Cox proportional hazards regression models. Among the 196 patients, 8 patients (4.1%) developed HCC after SVR during a median follow-up of 26 months. Multivariate analyses revealed that HCC development was independently associated with age of ≥75 years (risk ratio [RR] = 35.16), α- fetoprotein levels of ≥6 ng/mL (RR = 40.30), and SWE results of ≥11 kPa (RR = 28.71). Our findings indicate that SWE may facilitate HCC surveillance after SVR and the identification of patients who have an increased risk of HCC after HCV clearance.

Load Forecasting Based Distribution System Network Reconfiguration -- A Distributed Data-Driven Approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang; Zhang, Yingchen; Muljadi, Eduard

In this paper, a short-term load forecasting approach based network reconfiguration is proposed in a parallel manner. Specifically, a support vector regression (SVR) based short-term load forecasting approach is designed to provide an accurate load prediction and benefit the network reconfiguration. Because of the nonconvexity of the three-phase balanced optimal power flow, a second-order cone program (SOCP) based approach is used to relax the optimal power flow problem. Then, the alternating direction method of multipliers (ADMM) is used to compute the optimal power flow in distributed manner. Considering the limited number of the switches and the increasing computation capability, themore » proposed network reconfiguration is solved in a parallel way. The numerical results demonstrate the feasible and effectiveness of the proposed approach.« less

Deep learning architecture for air quality predictions.

PubMed

Li, Xiang; Peng, Ling; Hu, Yuan; Shao, Jing; Chi, Tianhe

2016-11-01

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.

Factors associated with success of telaprevir- and boceprevir-based triple therapy for hepatitis C virus infection

PubMed Central

Bichoupan, Kian; Tandon, Neeta; Martel-Laferriere, Valerie; Patel, Neal M; Sachs, David; Ng, Michel; Schonfeld, Emily A; Pappas, Alexis; Crismale, James; Stivala, Alicia; Khaitova, Viktoriya; Gardenier, Donald; Linderman, Michael; Olson, William; Perumalswami, Ponni V; Schiano, Thomas D; Odin, Joseph A; Liu, Lawrence U; Dieterich, Douglas T; Branch, Andrea D

2017-01-01

AIM To evaluate new therapies for hepatitis C virus (HCV), data about real-world outcomes are needed. METHODS Outcomes of 223 patients with genotype 1 HCV who started telaprevir- or boceprevir-based triple therapy (May 2011-March 2012) at the Mount Sinai Medical Center were analyzed. Human immunodeficiency virus-positive patients and patients who received a liver transplant were excluded. Factors associated with sustained virological response (SVR24) and relapse were analyzed by univariable and multivariable logistic regression as well as classification and regression trees. Fast virological response (FVR) was defined as undetectable HCV RNA at week-4 (telaprevir) or week-8 (boceprevir). RESULTS The median age was 57 years, 18% were black, 44% had advanced fibrosis/cirrhosis (FIB-4 ≥ 3.25). Only 42% (94/223) of patients achieved SVR24 on an intention-to-treat basis. In a model that included platelets, SVR24 was associated with white race [odds ratio (OR) = 5.92, 95% confidence interval (CI): 2.34-14.96], HCV sub-genotype 1b (OR = 2.81, 95%CI: 1.45-5.44), platelet count (OR = 1.10, per x 104 cells/μL, 95%CI: 1.05-1.16), and IL28B CC genotype (OR = 3.54, 95%CI: 1.19-10.53). Platelet counts > 135 x 103/μL were the strongest predictor of SVR by classification and regression tree. Relapse occurred in 25% (27/104) of patients with an end-of-treatment response and was associated with non-FVR (OR = 4.77, 95%CI: 1.68-13.56), HCV sub-genotype 1a (OR = 5.20; 95%CI: 1.40-18.97), and FIB-4 ≥ 3.25 (OR = 2.77; 95%CI: 1.07-7.22). CONCLUSION The SVR rate was 42% with telaprevir- or boceprevir-based triple therapy in real-world practice. Low platelets and advanced fibrosis were associated with treatment failure and relapse. PMID:28469811

Factors associated with success of telaprevir- and boceprevir-based triple therapy for hepatitis C virus infection.

PubMed

Bichoupan, Kian; Tandon, Neeta; Martel-Laferriere, Valerie; Patel, Neal M; Sachs, David; Ng, Michel; Schonfeld, Emily A; Pappas, Alexis; Crismale, James; Stivala, Alicia; Khaitova, Viktoriya; Gardenier, Donald; Linderman, Michael; Olson, William; Perumalswami, Ponni V; Schiano, Thomas D; Odin, Joseph A; Liu, Lawrence U; Dieterich, Douglas T; Branch, Andrea D

2017-04-18

To evaluate new therapies for hepatitis C virus (HCV), data about real-world outcomes are needed. Outcomes of 223 patients with genotype 1 HCV who started telaprevir- or boceprevir-based triple therapy (May 2011-March 2012) at the Mount Sinai Medical Center were analyzed. Human immunodeficiency virus-positive patients and patients who received a liver transplant were excluded. Factors associated with sustained virological response (SVR24) and relapse were analyzed by univariable and multivariable logistic regression as well as classification and regression trees. Fast virological response (FVR) was defined as undetectable HCV RNA at week-4 (telaprevir) or week-8 (boceprevir). The median age was 57 years, 18% were black, 44% had advanced fibrosis/cirrhosis (FIB-4 ≥ 3.25). Only 42% (94/223) of patients achieved SVR24 on an intention-to-treat basis. In a model that included platelets, SVR24 was associated with white race [odds ratio (OR) = 5.92, 95% confidence interval (CI): 2.34-14.96], HCV sub-genotype 1b (OR = 2.81, 95%CI: 1.45-5.44), platelet count (OR = 1.10, per x 10 4 cells/μL, 95%CI: 1.05-1.16), and IL28B CC genotype (OR = 3.54, 95%CI: 1.19-10.53). Platelet counts > 135 x 10 3 /μL were the strongest predictor of SVR by classification and regression tree. Relapse occurred in 25% (27/104) of patients with an end-of-treatment response and was associated with non-FVR (OR = 4.77, 95%CI: 1.68-13.56), HCV sub-genotype 1a (OR = 5.20; 95%CI: 1.40-18.97), and FIB-4 ≥ 3.25 (OR = 2.77; 95%CI: 1.07-7.22). The SVR rate was 42% with telaprevir- or boceprevir-based triple therapy in real-world practice. Low platelets and advanced fibrosis were associated with treatment failure and relapse.

Incorporating Canopy Cover for Airborne-Derived Assessments of Forest Biomass in the Tropical Forests of Cambodia

PubMed Central

Singh, Minerva; Evans, Damian; Coomes, David A.; Friess, Daniel A.; Suy Tan, Boun; Samean Nin, Chan

2016-01-01

This research examines the role of canopy cover in influencing above ground biomass (AGB) dynamics of an open canopied forest and evaluates the efficacy of individual-based and plot-scale height metrics in predicting AGB variation in the tropical forests of Angkor Thom, Cambodia. The AGB was modeled by including canopy cover from aerial imagery alongside with the two different canopy vertical height metrics derived from LiDAR; the plot average of maximum tree height (Max_CH) of individual trees, and the top of the canopy height (TCH). Two different statistical approaches, log-log ordinary least squares (OLS) and support vector regression (SVR), were used to model AGB variation in the study area. Ten different AGB models were developed using different combinations of airborne predictor variables. It was discovered that the inclusion of canopy cover estimates considerably improved the performance of AGB models for our study area. The most robust model was log-log OLS model comprising of canopy cover only (r = 0.87; RMSE = 42.8 Mg/ha). Other models that approximated field AGB closely included both Max_CH and canopy cover (r = 0.86, RMSE = 44.2 Mg/ha for SVR; and, r = 0.84, RMSE = 47.7 Mg/ha for log-log OLS). Hence, canopy cover should be included when modeling the AGB of open-canopied tropical forests. PMID:27176218

Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method

NASA Astrophysics Data System (ADS)

Ouyang, Qi; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Li, Shuai; Luo, Jiannan

2017-05-01

In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability.

Incorporating Canopy Cover for Airborne-Derived Assessments of Forest Biomass in the Tropical Forests of Cambodia.

PubMed

Singh, Minerva; Evans, Damian; Coomes, David A; Friess, Daniel A; Suy Tan, Boun; Samean Nin, Chan

2016-01-01

This research examines the role of canopy cover in influencing above ground biomass (AGB) dynamics of an open canopied forest and evaluates the efficacy of individual-based and plot-scale height metrics in predicting AGB variation in the tropical forests of Angkor Thom, Cambodia. The AGB was modeled by including canopy cover from aerial imagery alongside with the two different canopy vertical height metrics derived from LiDAR; the plot average of maximum tree height (Max_CH) of individual trees, and the top of the canopy height (TCH). Two different statistical approaches, log-log ordinary least squares (OLS) and support vector regression (SVR), were used to model AGB variation in the study area. Ten different AGB models were developed using different combinations of airborne predictor variables. It was discovered that the inclusion of canopy cover estimates considerably improved the performance of AGB models for our study area. The most robust model was log-log OLS model comprising of canopy cover only (r = 0.87; RMSE = 42.8 Mg/ha). Other models that approximated field AGB closely included both Max_CH and canopy cover (r = 0.86, RMSE = 44.2 Mg/ha for SVR; and, r = 0.84, RMSE = 47.7 Mg/ha for log-log OLS). Hence, canopy cover should be included when modeling the AGB of open-canopied tropical forests.

Detection of Independent Associations of Plasma Lipidomic Parameters with Insulin Sensitivity Indices Using Data Mining Methodology.

PubMed

Kopprasch, Steffi; Dheban, Srirangan; Schuhmann, Kai; Xu, Aimin; Schulte, Klaus-Martin; Simeonovic, Charmaine J; Schwarz, Peter E H; Bornstein, Stefan R; Shevchenko, Andrej; Graessler, Juergen

2016-01-01

Glucolipotoxicity is a major pathophysiological mechanism in the development of insulin resistance and type 2 diabetes mellitus (T2D). We aimed to detect subtle changes in the circulating lipid profile by shotgun lipidomics analyses and to associate them with four different insulin sensitivity indices. The cross-sectional study comprised 90 men with a broad range of insulin sensitivity including normal glucose tolerance (NGT, n = 33), impaired glucose tolerance (IGT, n = 32) and newly detected T2D (n = 25). Prior to oral glucose challenge plasma was obtained and quantitatively analyzed for 198 lipid molecular species from 13 different lipid classes including triacylglycerls (TAGs), phosphatidylcholine plasmalogen/ether (PC O-s), sphingomyelins (SMs), and lysophosphatidylcholines (LPCs). To identify a lipidomic signature of individual insulin sensitivity we applied three data mining approaches, namely least absolute shrinkage and selection operator (LASSO), Support Vector Regression (SVR) and Random Forests (RF) for the following insulin sensitivity indices: homeostasis model of insulin resistance (HOMA-IR), glucose insulin sensitivity index (GSI), insulin sensitivity index (ISI), and disposition index (DI). The LASSO procedure offers a high prediction accuracy and and an easier interpretability than SVR and RF. After LASSO selection, the plasma lipidome explained 3% (DI) to maximal 53% (HOMA-IR) variability of the sensitivity indexes. Among the lipid species with the highest positive LASSO regression coefficient were TAG 54:2 (HOMA-IR), PC O- 32:0 (GSI), and SM 40:3:1 (ISI). The highest negative regression coefficient was obtained for LPC 22:5 (HOMA-IR), TAG 51:1 (GSI), and TAG 58:6 (ISI). Although a substantial part of lipid molecular species showed a significant correlation with insulin sensitivity indices we were able to identify a limited number of lipid metabolites of particular importance based on the LASSO approach. These few selected lipids with the closest connection to sensitivity indices may help to further improve disease risk prediction and disease and therapy monitoring.

Retrieving Tract Variables From Acoustics: A Comparison of Different Machine Learning Strategies.

PubMed

Mitra, Vikramjit; Nam, Hosung; Espy-Wilson, Carol Y; Saltzman, Elliot; Goldstein, Louis

2010-09-13

Many different studies have claimed that articulatory information can be used to improve the performance of automatic speech recognition systems. Unfortunately, such articulatory information is not readily available in typical speaker-listener situations. Consequently, such information has to be estimated from the acoustic signal in a process which is usually termed "speech-inversion." This study aims to propose and compare various machine learning strategies for speech inversion: Trajectory mixture density networks (TMDNs), feedforward artificial neural networks (FF-ANN), support vector regression (SVR), autoregressive artificial neural network (AR-ANN), and distal supervised learning (DSL). Further, using a database generated by the Haskins Laboratories speech production model, we test the claim that information regarding constrictions produced by the distinct organs of the vocal tract (vocal tract variables) is superior to flesh-point information (articulatory pellet trajectories) for the inversion process.

Real-world effectiveness for 12 weeks of ledipasvir-sofosbuvir for genotype 1 hepatitis C: the Trio Health study.

PubMed

Tapper, E B; Bacon, B R; Curry, M P; Dieterich, D T; Flamm, S L; Guest, L E; Kowdley, K V; Lee, Y; Tsai, N C; Younossi, Z M; Afdhal, N H

2017-01-01

Early data regarding the "real-world" experience with novel therapies for hepatitis C (HCV) are encouraging. Data are still limited, however, regarding real-world rates of sustained virologic response (SVR) for ledipasvir-sofosbuvir (LDV-SOF), particularly for patients with prior treatment failure. We performed a retrospective cohort study of 1597 patients with chronic genotype 1 HCV who were treated using 12 weeks of the following regimens LDV-SOF±ribavirin (RBV) (n=1521 without RBV, n=76 with RBV). The primary outcome was SVR-determined at 12 weeks in an intention-to-treat design. Prescription according to Food and Drug Administration (FDA) approved labelling (adding RBV for patients with cirrhosis and treatment failure) was assessed in multivariate models. The study population was aged 60 years on average (range 19-89), 60% male, 50% Caucasian, 43% cared for at an academic centre and 30% cirrhotic. Overall, LDV-SOF resulted in a 94% SVR rate. Only 44 (2.9%) patients relapsed. LDV-SOF+RBV yielded SVR in 97% with 0 viral relapses. While cirrhosis and thrombocytopenia were associated with lower odds of SVR, in a multivariable regression model, only treatment at an academic centre and prescriptions contrary to FDA labelling were significantly associated with lower SVR-odds ratios, 0.56 95% CI (0.35-0.87) and 0.29 95% CI(0.12-0.68), respectively. The real-world experience with LDV-SOF mirrors the SVR rates observed in clinical trials. Efforts to promote prescription within FDA recommendations are warranted. © 2016 John Wiley & Sons Ltd.

The complete title: The effect of interleukin-28B rs12979860 polymorphism on the therapeutic response of Moroccan patients with chronic hepatitis C.

PubMed

Nadia, Kandoussi; Hicham, Elannaz; Reda, Tagajdid Mohamed; Nadia, Touil; Elarbi, Bouaiti; Saâd, Elkabbaj; Mimoun, Zouhdi; Saâd, Mrani

2015-08-15

There is increasing evidence for the effect of rs12979860 IL28B polymorphism in response to the standard treatment PEG-IFN/RBV (i.e. combination of pegylated interferon and ribavirin) in chronic hepatitis C virus (HCV) infection. The present study aimed to determine the impact of IL28B associations in interferon responsiveness in 187 Moroccan patients with chronic HCV infection. HCV RNA levels were measured with a real-time RT-PCR assay and treatment efficacy was assessed by sustained virological response (SVR) and patients were classified as responders or non-responders. IL28B rs12979860 polymorphism genotyping was achieved by PCR-HRM technique. The results demonstrated that SVR was achieved in 102 patients (55%); while 69 were non-responders (37%) and 16 relapsed (8%). Genotype 1 was the predominant HCV genotype detected in 112 patients followed by genotype 2 in 56 patients. The genotype CC was observed in 42 cases (25%); CT in 69 (41%) and TT in 57 (34%) demonstrating a C allele frequency of 46%. The SVR was observed in 32 patients with genotype CC accounting for 76%. The frequencies of rs12979860 CC type in infected individuals with HCV genotype 1 were 47% and 12% respectively in SVR and non-SVR groups. A highly statistically significant association between this SNP and SVR was found (p<0.001). Using multivariate logistic regression analysis, CC genotype was an independent factor for SVR. In the group of patients infected with genotype 2, SVR rate was 79%. The frequency of rs12979860 CC type in SVR group (n=4) was 9% and rs12979860 non-CC genotype was highly associated with SVR (p=0.001). This finding adds evidence that genotyping for the IL-28B rs12979860 SNP can be a good parameter for the prediction of treatment success in patients with chronic hepatitis C before initiation of antiviral therapy in Morocco. Copyright © 2015. Published by Elsevier B.V.

Speech Signal and Facial Image Processing for Obstructive Sleep Apnea Assessment

PubMed Central

Espinoza-Cuadros, Fernando; Fernández-Pozo, Rubén; Toledano, Doroteo T.; Alcázar-Ramírez, José D.; López-Gonzalo, Eduardo; Hernández-Gómez, Luis A.

2015-01-01

Obstructive sleep apnea (OSA) is a common sleep disorder characterized by recurring breathing pauses during sleep caused by a blockage of the upper airway (UA). OSA is generally diagnosed through a costly procedure requiring an overnight stay of the patient at the hospital. This has led to proposing less costly procedures based on the analysis of patients' facial images and voice recordings to help in OSA detection and severity assessment. In this paper we investigate the use of both image and speech processing to estimate the apnea-hypopnea index, AHI (which describes the severity of the condition), over a population of 285 male Spanish subjects suspected to suffer from OSA and referred to a Sleep Disorders Unit. Photographs and voice recordings were collected in a supervised but not highly controlled way trying to test a scenario close to an OSA assessment application running on a mobile device (i.e., smartphones or tablets). Spectral information in speech utterances is modeled by a state-of-the-art low-dimensional acoustic representation, called i-vector. A set of local craniofacial features related to OSA are extracted from images after detecting facial landmarks using Active Appearance Models (AAMs). Support vector regression (SVR) is applied on facial features and i-vectors to estimate the AHI. PMID:26664493

Speech Signal and Facial Image Processing for Obstructive Sleep Apnea Assessment.

PubMed

Espinoza-Cuadros, Fernando; Fernández-Pozo, Rubén; Toledano, Doroteo T; Alcázar-Ramírez, José D; López-Gonzalo, Eduardo; Hernández-Gómez, Luis A

2015-01-01

Obstructive sleep apnea (OSA) is a common sleep disorder characterized by recurring breathing pauses during sleep caused by a blockage of the upper airway (UA). OSA is generally diagnosed through a costly procedure requiring an overnight stay of the patient at the hospital. This has led to proposing less costly procedures based on the analysis of patients' facial images and voice recordings to help in OSA detection and severity assessment. In this paper we investigate the use of both image and speech processing to estimate the apnea-hypopnea index, AHI (which describes the severity of the condition), over a population of 285 male Spanish subjects suspected to suffer from OSA and referred to a Sleep Disorders Unit. Photographs and voice recordings were collected in a supervised but not highly controlled way trying to test a scenario close to an OSA assessment application running on a mobile device (i.e., smartphones or tablets). Spectral information in speech utterances is modeled by a state-of-the-art low-dimensional acoustic representation, called i-vector. A set of local craniofacial features related to OSA are extracted from images after detecting facial landmarks using Active Appearance Models (AAMs). Support vector regression (SVR) is applied on facial features and i-vectors to estimate the AHI.

Retrieval and Mapping of Heavy Metal Concentration in Soil Using Time Series Landsat 8 Imagery

NASA Astrophysics Data System (ADS)

Fang, Y.; Xu, L.; Peng, J.; Wang, H.; Wong, A.; Clausi, D. A.

2018-04-01

Heavy metal pollution is a critical global environmental problem which has always been a concern. Traditional approach to obtain heavy metal concentration relying on field sampling and lab testing is expensive and time consuming. Although many related studies use spectrometers data to build relational model between heavy metal concentration and spectra information, and then use the model to perform prediction using the hyperspectral imagery, this manner can hardly quickly and accurately map soil metal concentration of an area due to the discrepancies between spectrometers data and remote sensing imagery. Taking the advantage of easy accessibility of Landsat 8 data, this study utilizes Landsat 8 imagery to retrieve soil Cu concentration and mapping its distribution in the study area. To enlarge the spectral information for more accurate retrieval and mapping, 11 single date Landsat 8 imagery from 2013-2017 are selected to form a time series imagery. Three regression methods, partial least square regression (PLSR), artificial neural network (ANN) and support vector regression (SVR) are used to model construction. By comparing these models unbiasedly, the best model are selected to mapping Cu concentration distribution. The produced distribution map shows a good spatial autocorrelation and consistency with the mining area locations.

A New Approach for Mobile Advertising Click-Through Rate Estimation Based on Deep Belief Nets.

PubMed

Chen, Jie-Hao; Zhao, Zi-Qian; Shi, Ji-Yun; Zhao, Chong

2017-01-01

In recent years, with the rapid development of mobile Internet and its business applications, mobile advertising Click-Through Rate (CTR) estimation has become a hot research direction in the field of computational advertising, which is used to achieve accurate advertisement delivery for the best benefits in the three-side game between media, advertisers, and audiences. Current research on the estimation of CTR mainly uses the methods and models of machine learning, such as linear model or recommendation algorithms. However, most of these methods are insufficient to extract the data features and cannot reflect the nonlinear relationship between different features. In order to solve these problems, we propose a new model based on Deep Belief Nets to predict the CTR of mobile advertising, which combines together the powerful data representation and feature extraction capability of Deep Belief Nets, with the advantage of simplicity of traditional Logistic Regression models. Based on the training dataset with the information of over 40 million mobile advertisements during a period of 10 days, our experiments show that our new model has better estimation accuracy than the classic Logistic Regression (LR) model by 5.57% and Support Vector Regression (SVR) model by 5.80%.

A New Approach for Mobile Advertising Click-Through Rate Estimation Based on Deep Belief Nets

PubMed Central

Zhao, Zi-Qian; Shi, Ji-Yun; Zhao, Chong

2017-01-01

In recent years, with the rapid development of mobile Internet and its business applications, mobile advertising Click-Through Rate (CTR) estimation has become a hot research direction in the field of computational advertising, which is used to achieve accurate advertisement delivery for the best benefits in the three-side game between media, advertisers, and audiences. Current research on the estimation of CTR mainly uses the methods and models of machine learning, such as linear model or recommendation algorithms. However, most of these methods are insufficient to extract the data features and cannot reflect the nonlinear relationship between different features. In order to solve these problems, we propose a new model based on Deep Belief Nets to predict the CTR of mobile advertising, which combines together the powerful data representation and feature extraction capability of Deep Belief Nets, with the advantage of simplicity of traditional Logistic Regression models. Based on the training dataset with the information of over 40 million mobile advertisements during a period of 10 days, our experiments show that our new model has better estimation accuracy than the classic Logistic Regression (LR) model by 5.57% and Support Vector Regression (SVR) model by 5.80%. PMID:29209363

Body Fat Percentage Prediction Using Intelligent Hybrid Approaches

PubMed Central

Shao, Yuehjen E.

2014-01-01

Excess of body fat often leads to obesity. Obesity is typically associated with serious medical diseases, such as cancer, heart disease, and diabetes. Accordingly, knowing the body fat is an extremely important issue since it affects everyone's health. Although there are several ways to measure the body fat percentage (BFP), the accurate methods are often associated with hassle and/or high costs. Traditional single-stage approaches may use certain body measurements or explanatory variables to predict the BFP. Diverging from existing approaches, this study proposes new intelligent hybrid approaches to obtain fewer explanatory variables, and the proposed forecasting models are able to effectively predict the BFP. The proposed hybrid models consist of multiple regression (MR), artificial neural network (ANN), multivariate adaptive regression splines (MARS), and support vector regression (SVR) techniques. The first stage of the modeling includes the use of MR and MARS to obtain fewer but more important sets of explanatory variables. In the second stage, the remaining important variables are served as inputs for the other forecasting methods. A real dataset was used to demonstrate the development of the proposed hybrid models. The prediction results revealed that the proposed hybrid schemes outperformed the typical, single-stage forecasting models. PMID:24723804

Changes in liver stiffness measurement using acoustic radiation force impulse elastography after antiviral therapy in patients with chronic hepatitis C.

PubMed

Chen, Sheng-Hung; Lai, Hsueh-Chou; Chiang, I-Ping; Su, Wen-Pang; Lin, Chia-Hsin; Kao, Jung-Ta; Chuang, Po-Heng; Hsu, Wei-Fan; Wang, Hung-Wei; Chen, Hung-Yao; Huang, Guan-Tarn; Peng, Cheng-Yuan

2018-01-01

To compare on-treatment and off-treatment parameters acquired using acoustic radiation force impulse elastography, the Fibrosis-4 (FIB-4) index, and aspartate aminotransferase-to-platelet ratio index (APRI) in patients with chronic hepatitis C (CHC). Patients received therapies based on pegylated interferon or direct-acting antiviral agents. The changes in paired patient parameters, including liver stiffness (LS) values, the FIB-4 index, and APRI, from baseline to sustained virologic response (SVR) visit (24 weeks after the end of treatment) were compared. Multiple regression models were used to identify significant factors that explained the correlations with LS, FIB-4, and APRI values and SVR. A total of 256 patients were included, of which 219 (85.5%) achieved SVR. The paired LS values declined significantly from baseline to SVR visit in all groups and subgroups except the nonresponder subgroup (n = 10). Body mass index (P = 0.0062) and baseline LS (P < 0.0001) were identified as independent factors that explained the LS declines. Likewise, the baseline FIB-4 (P < 0.0001) and APRI (P < 0.0001) values independently explained the declines in the FIB-4 index and APRI, respectively. Moreover, interleukin-28B polymorphisms, baseline LS, and rapid virologic response were identified as independent correlates with SVR. Paired LS measurements in patients treated for CHC exhibited significant declines comparable to those in FIB-4 and APRI values. These declines may have correlated with the resolution of necroinflammation. Baseline LS values predicted SVR.

A new analytical method for quantification of olive and palm oil in blends with other vegetable edible oils based on the chromatographic fingerprints from the methyl-transesterified fraction.

PubMed

Jiménez-Carvelo, Ana M; González-Casado, Antonio; Cuadros-Rodríguez, Luis

2017-03-01

A new analytical method for the quantification of olive oil and palm oil in blends with other vegetable edible oils (canola, safflower, corn, peanut, seeds, grapeseed, linseed, sesame and soybean) using normal phase liquid chromatography, and applying chemometric tools was developed. The procedure for obtaining of chromatographic fingerprint from the methyl-transesterified fraction from each blend is described. The multivariate quantification methods used were Partial Least Square-Regression (PLS-R) and Support Vector Regression (SVR). The quantification results were evaluated by several parameters as the Root Mean Square Error of Validation (RMSEV), Mean Absolute Error of Validation (MAEV) and Median Absolute Error of Validation (MdAEV). It has to be highlighted that the new proposed analytical method, the chromatographic analysis takes only eight minutes and the results obtained showed the potential of this method and allowed quantification of mixtures of olive oil and palm oil with other vegetable oils. Copyright © 2016 Elsevier B.V. All rights reserved.

Impact of interferon-free antivirus therapy on lipid profiles in patients with chronic hepatitis C genotype 1b

PubMed Central

Endo, Daisuke; Satoh, Kenichi; Shimada, Noritomo; Hokari, Atsushi; Aizawa, Yoshio

2017-01-01

AIM To investigate the influence of interferon-free antivirus therapy on lipid profiles in chronic hepatitis C virus genotype 1b (HCV1b) infection. METHODS Interferon-free antiviral agents were used to treat 276 patients with chronic HCV1b infection, and changes in serum lipids of those who achieved sustained virologic response (SVR) were examined. The treatment regimen included 24 wk of daclatasvir plus asunaprevir (DCV + ASV) or 12 wk of sofosbuvir plus ledipasvir (SOF + LDV). SVR was achieved in 121 (85.8%) of 141 patients treated with DCV + ASV and 132 (97.8%) of 135 patients treated with SOF + LDV. In the two patient groups (DCV + ASV-SVR and SOF + LDV-SVR), serum total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides were measured at baseline during treatment and at 4 and 12 wk after treatment. Then, longitudinal changes in lipid profiles were analyzed. RESULTS Serum levels of TC, LDL-C, and HDL-C were significantly increased throughout the observation period in both the DCV + ASV-SVR and SOF + LDV-SVR groups. During antivirus treatment, the increases in TC and LDL-C were significantly greater in the SOF + LDV-SVR group than in the DCV + ASV-SVR group (P < 0.001). At 4 and 12 wk after the therapy, serum levels of TC and LDL-C were similar between the two groups and were significantly greater than those at baseline. Approximately 75%-80% of the increase in TC was derived from an increased LDL-C. In multiple regression analysis, the difference in therapy protocol (DCA + ASV or SOF + LDV) was an independent predictor that was significantly associated with the increase in TC and LDL-C at 4 wk of therapy. CONCLUSION Serum cholesterol significantly increased during SOF + LDV treatment. After treatment, HCV elimination was associated with a similar increase in cholesterol regardless of the therapy protocol. PMID:28428715

In Vivo and Ex Vivo Transcutaneous Glucose Detection Using Surface-Enhanced Raman Spectroscopy

NASA Astrophysics Data System (ADS)

Ma, Ke

Diabetes mellitus is widely acknowledged as a large and growing health concern. The lack of practical methods for continuously monitoring glucose levels causes significant difficulties in successful diabetes management. Extensive validation work has been carried out using surface-enhanced Raman spectroscopy (SERS) for in vivo glucose sensing. This dissertation details progress made towards a Raman-based glucose sensor for in vivo, transcutaneous glucose detection. The first presented study combines spatially offset Raman spectroscopy (SORS) with SERS (SESORS) to explore the possibility of in vivo, transcutaneous glucose sensing. A SERS-based glucose sensor was implanted subcutaneously in Sprague-Dawley rats. SERS spectra were acquired transcutaneously and analyzed using partial least-squares (PLS). Highly accurate and consistent results were obtained, especially in the hypoglycemic range. Additionally, the sensor demonstrated functionality at least17 days after implantation. A subsequent study further extends the application of SESORS to the possibility of in vivo detection of glucose in brain through skull. Specifically, SERS nanoantennas were buried in an ovine tissue behind a bone with 8 mm thickness and detected by using SESORS. In addition, quantitative detection through bones by using SESORS was also demonstrated. A device that could measure glucose continuously as well as noninvasively would be of great use to patients with diabetes. The inherent limitation of the SESORS approach may prevent this technique from becoming a noninvasive method. Therefore, the prospect of using normal Raman spectroscopy for glucose detection was re-examined. Quantitative detection of glucose and lactate in the clinically relevant range was demonstrated by using normal Raman spectroscopy with low power and short acquisition time. Finally, a nonlinear calibration method called least-squares support vector machine regression (LS-SVR) was investigated for analyzing spectroscopic data sets of glucose detection. Comparison studies were demonstrated between LS-SVR and PLS. LS-SVR demonstrated significant improvements in accuracy over PLS for glucose detection, especially when a global calibration model was required. The improvements imparted by LS-SVR open up the possibility of developing an accurate prediction algorithm for Raman-based glucose sensing applicable to a large human population. Overall, these studies show the high promise held by the Raman-based sensor for the challenge of optimal glycemic control.

The Additional Contribution of White Matter Hyperintensity Location to Post-stroke Cognitive Impairment: Insights From a Multiple-Lesion Symptom Mapping Study.

PubMed

Zhao, Lei; Wong, Adrian; Luo, Yishan; Liu, Wenyan; Chu, Winnie W C; Abrigo, Jill M; Lee, Ryan K L; Mok, Vincent; Shi, Lin

2018-01-01

White matter hyperintensities (WMH) are common in acute ischemic stroke patients. Although WMH volume has been reported to influence post-stroke cognition, it is still not clear whether WMH location, independent of acute ischemic lesion (AIL) volume and location, contributes to cognitive impairment after stroke. Here, we proposed a multiple-lesion symptom mapping model that considers both the presence of WMH and AIL to measure the additional contribution of WMH locations to post-stroke cognitive impairment. Seventy-six first-ever stroke patients with AILs in the left hemisphere were examined by Montreal Cognitive Assessment (MoCA) at baseline and 1 year after stroke. The association between the location of AIL and WMH and global cognition was investigated by a multiple-lesion symptom mapping (MLSM) model based on support vector regression (SVR). To explore the relative merits of MLSM over the existing lesion-symptom mapping approaches with only AIL considered (mass-univariate VLSM and SVR-LSM), we measured the contribution of the significant AIL and/or WMH clusters from these models to post-stroke cognitive impairment. In addition, we compared the significant WMH locations identified by the optimal SVR-MLSM model for cognitive impairment at baseline and 1 year post stroke. The identified strategic locations of WMH significantly contributed to the prediction of MoCA at baseline (short-term) and 1 year (long-term) after stroke independent of the strategic locations of AIL. The significant clusters of WMH for short-term and long-term post-stroke cognitive impairment were mainly in the corpus callosum, corona radiata, and posterior thalamic radiation. We noted that in some regions, the AIL clusters that were significant for short-term outcome were no longer significant for long-term outcome, and interestingly more WMH clusters in these regions became significant for long-term outcome compared to short-term outcome. This indicated that there are some regions where local WMH burden has larger impact than AIL burden on the long-term post-stroke cognitive impairment. In consequence, SVR-MLSM was effective in identifying the WMH locations that have additional impact on post-stroke cognition on top of AIL locations. Such a method can also be applied to other lesion-behavior studies where multiple types of lesions may have potential contributions to a specific behavior.

Discovery of Antibiotics-derived Polymers for Gene Delivery using Combinatorial Synthesis and Cheminformatics Modeling

PubMed Central

Potta, Thrimoorthy; Zhen, Zhuo; Grandhi, Taraka Sai Pavan; Christensen, Matthew D.; Ramos, James; Breneman, Curt M.; Rege, Kaushal

2014-01-01

We describe the combinatorial synthesis and cheminformatics modeling of aminoglycoside antibiotics-derived polymers for transgene delivery and expression. Fifty-six polymers were synthesized by polymerizing aminoglycosides with diglycidyl ether cross-linkers. Parallel screening resulted in identification of several lead polymers that resulted in high transgene expression levels in cells. The role of polymer physicochemical properties in determining efficacy of transgene expression was investigated using Quantitative Structure-Activity Relationship (QSAR) cheminformatics models based on Support Vector Regression (SVR) and ‘building block’ polymer structures. The QSAR model exhibited high predictive ability, and investigation of descriptors in the model, using molecular visualization and correlation plots, indicated that physicochemical attributes related to both, aminoglycosides and diglycidyl ethers facilitated transgene expression. This work synergistically combines combinatorial synthesis and parallel screening with cheminformatics-based QSAR models for discovery and physicochemical elucidation of effective antibiotics-derived polymers for transgene delivery in medicine and biotechnology. PMID:24331709

A computational visual saliency model based on statistics and machine learning.

PubMed

Lin, Ru-Je; Lin, Wei-Song

2014-08-01

Identifying the type of stimuli that attracts human visual attention has been an appealing topic for scientists for many years. In particular, marking the salient regions in images is useful for both psychologists and many computer vision applications. In this paper, we propose a computational approach for producing saliency maps using statistics and machine learning methods. Based on four assumptions, three properties (Feature-Prior, Position-Prior, and Feature-Distribution) can be derived and combined by a simple intersection operation to obtain a saliency map. These properties are implemented by a similarity computation, support vector regression (SVR) technique, statistical analysis of training samples, and information theory using low-level features. This technique is able to learn the preferences of human visual behavior while simultaneously considering feature uniqueness. Experimental results show that our approach performs better in predicting human visual attention regions than 12 other models in two test databases. © 2014 ARVO.

Production of Low Cost Carbon-Fiber through Energy Optimization of Stabilization Process.

PubMed

Golkarnarenji, Gelayol; Naebe, Minoo; Badii, Khashayar; Milani, Abbas S; Jazar, Reza N; Khayyam, Hamid

2018-03-05

To produce high quality and low cost carbon fiber-based composites, the optimization of the production process of carbon fiber and its properties is one of the main keys. The stabilization process is the most important step in carbon fiber production that consumes a large amount of energy and its optimization can reduce the cost to a large extent. In this study, two intelligent optimization techniques, namely Support Vector Regression (SVR) and Artificial Neural Network (ANN), were studied and compared, with a limited dataset obtained to predict physical property (density) of oxidative stabilized PAN fiber (OPF) in the second zone of a stabilization oven within a carbon fiber production line. The results were then used to optimize the energy consumption in the process. The case study can be beneficial to chemical industries involving carbon fiber manufacturing, for assessing and optimizing different stabilization process conditions at large.

Production of Low Cost Carbon-Fiber through Energy Optimization of Stabilization Process

PubMed Central

Golkarnarenji, Gelayol; Naebe, Minoo; Badii, Khashayar; Milani, Abbas S.; Jazar, Reza N.; Khayyam, Hamid

2018-01-01

To produce high quality and low cost carbon fiber-based composites, the optimization of the production process of carbon fiber and its properties is one of the main keys. The stabilization process is the most important step in carbon fiber production that consumes a large amount of energy and its optimization can reduce the cost to a large extent. In this study, two intelligent optimization techniques, namely Support Vector Regression (SVR) and Artificial Neural Network (ANN), were studied and compared, with a limited dataset obtained to predict physical property (density) of oxidative stabilized PAN fiber (OPF) in the second zone of a stabilization oven within a carbon fiber production line. The results were then used to optimize the energy consumption in the process. The case study can be beneficial to chemical industries involving carbon fiber manufacturing, for assessing and optimizing different stabilization process conditions at large. PMID:29510592

No-reference image quality assessment based on statistics of convolution feature maps

NASA Astrophysics Data System (ADS)

Lv, Xiaoxin; Qin, Min; Chen, Xiaohui; Wei, Guo

2018-04-01

We propose a Convolutional Feature Maps (CFM) driven approach to accurately predict image quality. Our motivation bases on the finding that the Nature Scene Statistic (NSS) features on convolution feature maps are significantly sensitive to distortion degree of an image. In our method, a Convolutional Neural Network (CNN) is trained to obtain kernels for generating CFM. We design a forward NSS layer which performs on CFM to better extract NSS features. The quality aware features derived from the output of NSS layer is effective to describe the distortion type and degree an image suffered. Finally, a Support Vector Regression (SVR) is employed in our No-Reference Image Quality Assessment (NR-IQA) model to predict a subjective quality score of a distorted image. Experiments conducted on two public databases demonstrate the promising performance of the proposed method is competitive to state of the art NR-IQA methods.

Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites.

PubMed

Betel, Doron; Koppal, Anjali; Agius, Phaedra; Sander, Chris; Leslie, Christina

2010-01-01

mirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly, the method identifies a significant number of experimentally determined non-canonical and non-conserved sites.

Quantifying biomass consumption and carbon release from the California Rim fire by integrating airborne LiDAR and Landsat OLI data.

PubMed

Garcia, Mariano; Saatchi, Sassan; Casas, Angeles; Koltunov, Alexander; Ustin, Susan; Ramirez, Carlos; Garcia-Gutierrez, Jorge; Balzter, Heiko

2017-02-01

Quantifying biomass consumption and carbon release is critical to understanding the role of fires in the carbon cycle and air quality. We present a methodology to estimate the biomass consumed and the carbon released by the California Rim fire by integrating postfire airborne LiDAR and multitemporal Landsat Operational Land Imager (OLI) imagery. First, a support vector regression (SVR) model was trained to estimate the aboveground biomass (AGB) from LiDAR-derived metrics over the unburned area. The selected model estimated AGB with an R 2 of 0.82 and RMSE of 59.98 Mg/ha. Second, LiDAR-based biomass estimates were extrapolated to the entire area before and after the fire, using Landsat OLI reflectance bands, Normalized Difference Infrared Index, and the elevation derived from LiDAR data. The extrapolation was performed using SVR models that resulted in R 2 of 0.73 and 0.79 and RMSE of 87.18 (Mg/ha) and 75.43 (Mg/ha) for the postfire and prefire images, respectively. After removing bias from the AGB extrapolations using a linear relationship between estimated and observed values, we estimated the biomass consumption from postfire LiDAR and prefire Landsat maps to be 6.58 ± 0.03 Tg (10 12  g), which translate into 12.06 ± 0.06 Tg CO2 e released to the atmosphere, equivalent to the annual emissions of 2.57 million cars.

MRI-based intelligence quotient (IQ) estimation with sparse learning.

PubMed

Wang, Liye; Wee, Chong-Yaw; Suk, Heung-Il; Tang, Xiaoying; Shen, Dinggang

2015-01-01

In this paper, we propose a novel framework for IQ estimation using Magnetic Resonance Imaging (MRI) data. In particular, we devise a new feature selection method based on an extended dirty model for jointly considering both element-wise sparsity and group-wise sparsity. Meanwhile, due to the absence of large dataset with consistent scanning protocols for the IQ estimation, we integrate multiple datasets scanned from different sites with different scanning parameters and protocols. In this way, there is large variability in these different datasets. To address this issue, we design a two-step procedure for 1) first identifying the possible scanning site for each testing subject and 2) then estimating the testing subject's IQ by using a specific estimator designed for that scanning site. We perform two experiments to test the performance of our method by using the MRI data collected from 164 typically developing children between 6 and 15 years old. In the first experiment, we use a multi-kernel Support Vector Regression (SVR) for estimating IQ values, and obtain an average correlation coefficient of 0.718 and also an average root mean square error of 8.695 between the true IQs and the estimated ones. In the second experiment, we use a single-kernel SVR for IQ estimation, and achieve an average correlation coefficient of 0.684 and an average root mean square error of 9.166. All these results show the effectiveness of using imaging data for IQ prediction, which is rarely done in the field according to our knowledge.

Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method.

PubMed

Ouyang, Qi; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Li, Shuai; Luo, Jiannan

2017-05-01

In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability. Copyright © 2017 Elsevier B.V. All rights reserved.

Quantifying biomass consumption and carbon release from the California Rim fire by integrating airborne LiDAR and Landsat OLI data

PubMed Central

Saatchi, Sassan; Casas, Angeles; Koltunov, Alexander; Ustin, Susan; Ramirez, Carlos; Garcia‐Gutierrez, Jorge; Balzter, Heiko

2017-01-01

Abstract Quantifying biomass consumption and carbon release is critical to understanding the role of fires in the carbon cycle and air quality. We present a methodology to estimate the biomass consumed and the carbon released by the California Rim fire by integrating postfire airborne LiDAR and multitemporal Landsat Operational Land Imager (OLI) imagery. First, a support vector regression (SVR) model was trained to estimate the aboveground biomass (AGB) from LiDAR‐derived metrics over the unburned area. The selected model estimated AGB with an R 2 of 0.82 and RMSE of 59.98 Mg/ha. Second, LiDAR‐based biomass estimates were extrapolated to the entire area before and after the fire, using Landsat OLI reflectance bands, Normalized Difference Infrared Index, and the elevation derived from LiDAR data. The extrapolation was performed using SVR models that resulted in R 2 of 0.73 and 0.79 and RMSE of 87.18 (Mg/ha) and 75.43 (Mg/ha) for the postfire and prefire images, respectively. After removing bias from the AGB extrapolations using a linear relationship between estimated and observed values, we estimated the biomass consumption from postfire LiDAR and prefire Landsat maps to be 6.58 ± 0.03 Tg (1012 g), which translate into 12.06 ± 0.06 Tg CO2e released to the atmosphere, equivalent to the annual emissions of 2.57 million cars. PMID:28405539

Hepatitis C treatment among racial and ethnic groups in the IDEAL trial.

PubMed

Muir, A J; Hu, K-Q; Gordon, S C; Koury, K; Boparai, N; Noviello, S; Albrecht, J K; Sulkowski, M S; McCone, J

2011-04-01

Previous studies of chronic hepatitis C virus (HCV) treatment have demonstrated variations in response among racial and ethnic groups including poorer efficacy rates among African American and Hispanic patients. The individualized dosing efficacy vs flat dosing to assess optimaL pegylated interferon therapy (IDEAL) trial enrolled 3070 patients from 118 United States centres to compare treatment with peginterferon (PEG-IFN) alfa-2a and ribavirin (RBV) and two doses of PEG-IFN alfa-2b and RBV. This analysis examines treatment response among the major racial and ethnic groups in the trial. Overall, sustained virologic response (SVR) rates were 44% for white, 22% for African American, 38% for Hispanic and 59% for Asian American patients. For patients with undetectable HCV RNA at treatment week 4, the positive predictive value of SVR was 86% for white, 92% for African American, 83% for Hispanic and 89% for Asian American patients. The positive predictive values of SVR in those with undetectable HCV RNA at treatment week 12 ranged from 72% to 81%. Multivariate regression analysis using baseline characteristics demonstrated that treatment regimen was not a predictor of SVR. Despite wide-ranging SVR rates among the different racial and ethnic groups, white and Hispanic patients had similar SVR rates. In all groups, treatment response was largely determined by antiviral activity in the first 12 weeks of treatment. Therefore, decisions regarding HCV treatment should consider the predictive value of the early on-treatment response, not just baseline characteristics, such as race and ethnicity. © 2010 Blackwell Publishing Ltd.

Prediction of Sustained Virological Response to Peginterferon-based Therapy for Chronic Hepatitis C: Regression Analysis of a Cohort from Rio Grande do Sul, Brazil

PubMed Central

Fendt, Lúcia; Amaral, Karine; D Picon, Paulo

2017-01-01

Aim: Peginterferon plus ribavirin (peg-IFN/RBV) is still the standard of care for treatment of hepatitis C virus (HCV) in many countries. Given the high toxicity of this regimen, our study aimed to develop a prediction tool that can identify which patients are unlikely to benefit from peg-IFN/RBV and could thus postpone treatment in favor of new-generation direct-acting antivirals. Materials and methods: Binary regression was performed using demographic, clinical, and laboratory covariates and sustained virological response (SVR) outcomes from a prospective cohort of individuals referred for therapy from 2003 to 2008 in a public HCV treatment center in Rio Grande do Sul, Brazil. Results: Of the 743 participants analyzed, 489 completed 48 weeks of treatment (65.8%). A total of 202 of those who completed peg-IFN/RBV therapy achieved SVR (27.2% responders), 196 did not (26.4%), and 91 had missing viral load (VL) at week 72 (12.2% loss to follow-up). The remainder discontinued therapy (n = 254 [34.2%]), 78 (30.7%) doing so due to adverse effects. Baseline covariates included in the regression model were sex, age, human immunodeficiency virus, infection status, aspartate transaminase, alanine transaminase, hemoglobin, platelets, serum creatinine, prothrombin time, pretreatment VL, cirrhosis on liver biopsy, and treatment naivety. A predicted SVR of 17.9% had 90.0% sensitivity for detecting true nonresponders. The negative likelihood ratio at a predicted SVR of 17.9% was 0.16, and the negative predictive value was 92.6%. Conclusion: Easily obtainable variables can identify patients that will likely not benefit from peg-IFN-based therapy. This prediction model might be useful to clinicians. Clinical significance: To our knowledge, this is the only prediction tool that can reliably help clinicians to postpone peg-IFN/RBV therapy for HCV genotype 1 patients. How to cite this article: Picon RV, Fendt L, Amaral K, Picon PD. Prediction of Sustained Virological Response to Peginterferon-based Therapy for Chronic Hepatitis C: Regression Analysis of a Cohort from Rio Grande do Sul, Brazil. Euroasian J Hepato-Gastroenterol 2017;7(1):27-33. PMID:29201768

Prediction of Sustained Virological Response to Peginterferon-based Therapy for Chronic Hepatitis C: Regression Analysis of a Cohort from Rio Grande do Sul, Brazil.

PubMed

V Picon, Rafael; Fendt, Lúcia; Amaral, Karine; D Picon, Paulo

2017-01-01

Peginterferon plus ribavirin (peg-IFN/RBV) is still the standard of care for treatment of hepatitis C virus (HCV) in many countries. Given the high toxicity of this regimen, our study aimed to develop a prediction tool that can identify which patients are unlikely to benefit from peg-IFN/RBV and could thus postpone treatment in favor of new-generation direct-acting antivirals. Binary regression was performed using demographic, clinical, and laboratory covariates and sustained virological response (SVR) outcomes from a prospective cohort of individuals referred for therapy from 2003 to 2008 in a public HCV treatment center in Rio Grande do Sul, Brazil. Of the 743 participants analyzed, 489 completed 48 weeks of treatment (65.8%). A total of 202 of those who completed peg-IFN/RBV therapy achieved SVR (27.2% responders), 196 did not (26.4%), and 91 had missing viral load (VL) at week 72 (12.2% loss to follow-up). The remainder discontinued therapy (n = 254 [34.2%]), 78 (30.7%) doing so due to adverse effects. Baseline covariates included in the regression model were sex, age, human immunodeficiency virus, infection status, aspartate transaminase, alanine transaminase, hemoglobin, platelets, serum creatinine, prothrombin time, pretreatment VL, cirrhosis on liver biopsy, and treatment naivety. A predicted SVR of 17.9% had 90.0% sensitivity for detecting true nonresponders. The negative likelihood ratio at a predicted SVR of 17.9% was 0.16, and the negative predictive value was 92.6%. Easily obtainable variables can identify patients that will likely not benefit from peg-IFN-based therapy. This prediction model might be useful to clinicians. To our knowledge, this is the only prediction tool that can reliably help clinicians to postpone peg-IFN/RBV therapy for HCV genotype 1 patients. How to cite this article: Picon RV, Fendt L, Amaral K, Picon PD. Prediction of Sustained Virological Response to Peginterferon-based Therapy for Chronic Hepatitis C: Regression Analysis of a Cohort from Rio Grande do Sul, Brazil. Euroasian J Hepato-Gastroenterol 2017;7(1):27-33.

Fruit fly optimization based least square support vector regression for blind image restoration

NASA Astrophysics Data System (ADS)

Zhang, Jiao; Wang, Rui; Li, Junshan; Yang, Yawei

2014-11-01

The goal of image restoration is to reconstruct the original scene from a degraded observation. It is a critical and challenging task in image processing. Classical restorations require explicit knowledge of the point spread function and a description of the noise as priors. However, it is not practical for many real image processing. The recovery processing needs to be a blind image restoration scenario. Since blind deconvolution is an ill-posed problem, many blind restoration methods need to make additional assumptions to construct restrictions. Due to the differences of PSF and noise energy, blurring images can be quite different. It is difficult to achieve a good balance between proper assumption and high restoration quality in blind deconvolution. Recently, machine learning techniques have been applied to blind image restoration. The least square support vector regression (LSSVR) has been proven to offer strong potential in estimating and forecasting issues. Therefore, this paper proposes a LSSVR-based image restoration method. However, selecting the optimal parameters for support vector machine is essential to the training result. As a novel meta-heuristic algorithm, the fruit fly optimization algorithm (FOA) can be used to handle optimization problems, and has the advantages of fast convergence to the global optimal solution. In the proposed method, the training samples are created from a neighborhood in the degraded image to the central pixel in the original image. The mapping between the degraded image and the original image is learned by training LSSVR. The two parameters of LSSVR are optimized though FOA. The fitness function of FOA is calculated by the restoration error function. With the acquired mapping, the degraded image can be recovered. Experimental results show the proposed method can obtain satisfactory restoration effect. Compared with BP neural network regression, SVR method and Lucy-Richardson algorithm, it speeds up the restoration rate and performs better. Both objective and subjective restoration performances are studied in the comparison experiments.

Applying machine learning methods for characterization of hexagonal prisms from their 2D scattering patterns - an investigation using modelled scattering data

NASA Astrophysics Data System (ADS)

Salawu, Emmanuel Oluwatobi; Hesse, Evelyn; Stopford, Chris; Davey, Neil; Sun, Yi

2017-11-01

Better understanding and characterization of cloud particles, whose properties and distributions affect climate and weather, are essential for the understanding of present climate and climate change. Since imaging cloud probes have limitations of optical resolution, especially for small particles (with diameter < 25 μm), instruments like the Small Ice Detector (SID) probes, which capture high-resolution spatial light scattering patterns from individual particles down to 1 μm in size, have been developed. In this work, we have proposed a method using Machine Learning techniques to estimate simulated particles' orientation-averaged projected sizes (PAD) and aspect ratio from their 2D scattering patterns. The two-dimensional light scattering patterns (2DLSP) of hexagonal prisms are computed using the Ray Tracing with Diffraction on Facets (RTDF) model. The 2DLSP cover the same angular range as the SID probes. We generated 2DLSP for 162 hexagonal prisms at 133 orientations for each. In a first step, the 2DLSP were transformed into rotation-invariant Zernike moments (ZMs), which are particularly suitable for analyses of pattern symmetry. Then we used ZMs, summed intensities, and root mean square contrast as inputs to the advanced Machine Learning methods. We created one random forests classifier for predicting prism orientation, 133 orientation-specific (OS) support vector classification models for predicting the prism aspect-ratios, 133 OS support vector regression models for estimating prism sizes, and another 133 OS Support Vector Regression (SVR) models for estimating the size PADs. We have achieved a high accuracy of 0.99 in predicting prism aspect ratios, and a low value of normalized mean square error of 0.004 for estimating the particle's size and size PADs.

Biological mechanisms of premature ovarian failure caused by psychological stress based on support vector regression

PubMed Central

Wang, Xiu-Feng; Zhang, Lei; Wu, Qing-Hua; Min, Jian-Xin; Ma, Na; Luo, Lai-Cheng

2015-01-01

Psychological stress has become a common and important cause of premature ovarian failure (POF). Therefore, it is very important to explore the mechanisms of POF resulting from psychological stress. Sixty SD rats were randomly divided into control and model groups. Biomolecules associated with POF (β-EP, IL-1, NOS, NO, GnRH, CRH, FSH, LH, E2, P, ACTH, and CORT) were measured in the control and psychologically stressed rats. The regulation relationships of the biomolecules were explored in the psychologically stressed state using support vector regression (SVR). The values of β-EP, IL-1, NOS, and GnRH in the hypothalamus decreased significantly, and the value of NO changed slightly, when the values of 3 biomolecules in the hypothalamic-pituitary-adrenal axis decreased. The values of E2 and P in the hypothalamic-pituitary-ovarian axis decreased significantly, while the values of FSH and LH changed slightly, when the values of the biomolecules in the hypothalamus decreased. The values of FSH and LH in the pituitary layer of the hypothalamic-pituitary-ovarian axis changed slightly when the values of E2 and P in the target gland layer of the hypothalamic-pituitary-ovarian axis decreased. An Imbalance in the neuroendocrine-immune bimolecular network, particularly the failure of the feedback action of the target gland layer to pituitary layer in the pituitary-ovarian axis, is possibly one of the pathogenic mechanisms of POF. PMID:26885082

Combining semiquantitative measures of fibrosis and qualitative features of parenchymal remodelling to identify fibrosis regression in hepatitis C: a multiple biopsy study.

PubMed

Pattullo, Venessa; Thein, Hla-Hla; Heathcote, Elizabeth Jenny; Guindi, Maha

2012-09-01

A fall in hepatic fibrosis stage may be observed in patients with chronic hepatitis C (CHC); however, parenchymal architectural changes may also signify hepatic remodelling associated with fibrosis regression. The aim of this study was to utilize semiquantitative and qualitative methods to report the prevalence and factors associated with fibrosis regression in CHC. Paired liver biopsies were scored for fibrosis (Ishak), and for the presence of eight qualitative features of parenchymal remodelling, to derive a qualitative regression score (QR score). Combined fibrosis regression was defined as ≥2-stage fall in Ishak stage (Reg-I) or <2-stage fall in Ishak stage with a rise in QR score (Reg-Qual). Among 159 patients (biopsy interval 5.4 ± 3.1 years), Reg-I was observed in 12 (7.5%) and Reg-Qual in 26 (16.4%) patients. The combined diagnostic criteria increased the diagnosis rate for fibrosis regression (38 patients, 23.9%) compared with use of Reg-I alone (P < 0.001). Combined fibrosis regression was observed in nine patients (50%) who achieved sustained virological response (SVR), and in 29 of 141 (21%) patients despite persistent viraemia. SVR was the only clinical factor associated independently with combined fibrosis regression (odds ratio 3.05). The combination of semiquantitative measures and qualitative features aids the identification of fibrosis regression in CHC. © 2012 Blackwell Publishing Ltd.

An Open-Label Trial of 12-Week Simeprevir plus Peginterferon/Ribavirin (PR) in Treatment-Naïve Patients with Hepatitis C Virus (HCV) Genotype 1 (GT1)

PubMed Central

Asselah, Tarik; Moreno, Christophe; Sarrazin, Christoph; Gschwantler, Michael; Foster, Graham R.; Craxí, Antonio; Buggisch, Peter; Ryan, Robert; Lenz, Oliver; Scott, Jane; Van Dooren, Gino; Lonjon-Domanec, Isabelle; Schlag, Michael; Buti, Maria

2016-01-01

Background Shortening duration of peginterferon-based HCV treatment reduces associated burden for patients. Primary objectives of this study were to assess the efficacy against the minimally acceptable response rate 12 weeks post-treatment (SVR12) and safety of simeprevir plus PR in treatment-naïve HCV GT1 patients treated for 12 weeks. Additional objectives included the investigation of potential associations of rapid viral response and baseline factors with SVR12. Methods In this Phase III, open-label study in treatment-naïve HCV GT1 patients with F0–F2 fibrosis, patients with HCV-RNA <25 IU/mL (detectable/undetectable) at Week 2, and undetectable HCV-RNA at Weeks 4 and 8, stopped all treatment at Week 12. All other patients continued PR for a further 12 weeks. Baseline factors significantly associated with SVR12 were identified through logistic regression. Results Of 163 patients who participated in the study, 123 (75%) qualified for 12-week treatment; of these, 81 (66%) achieved SVR12. Baseline factors positively associated with SVR12 rates in patients receiving the 12-week regimen were: IL28B CC genotype: (94% SVR12); HCV RNA ≤800,000 IU/mL (82%); F0–F1 fibrosis (74%). Among all 163 patients, 94% experienced ≥1 adverse event (AE), 4% a serious AE, and 2.5% discontinued due to an AE. Reduced impairment in patient-reported outcomes was observed in the 12-week vs >12-week regimen. Conclusions Overall SVR12 rate (66%) was below the target of 80%, indicating that shortening of treatment with simeprevir plus PR to 12 weeks based on very early response is not effective. However, baseline factors associated with higher SVR12 rates were identified. Therefore, while Week 2 response alone is insufficient to predict efficacy, GT1 patients with favourable baseline factors may benefit from a shortened simeprevir plus PR regimen. Trial Registration ClinicalTrials.gov NCT01846832 PMID:27428331

Liver Stiffness Decreases Rapidly in Response to Successful Hepatitis C Treatment and Then Plateaus.

PubMed

Chekuri, Sweta; Nickerson, Jillian; Bichoupan, Kian; Sefcik, Roberta; Doobay, Kamini; Chang, Sanders; DelBello, David; Harty, Alyson; Dieterich, Douglas T; Perumalswami, Ponni V; Branch, Andrea D

2016-01-01

To investigate the impact of a sustained virological response (SVR) to hepatitis C virus (HCV) treatment on liver stiffness (LS). LS, measured by transient elastography (FibroScan), demographic and laboratory data of patients treated with interferon (IFN)-containing or IFN-free regimens who had an SVR24 (undetectable HCV viral load 24 weeks after the end of treatment) were analyzed using two-tailed paired t-tests, Mann-Whitney Wilcoxon Signed-rank tests and linear regression. Two time intervals were investigated: pre-treatment to SVR24 and SVR24 to the end of follow-up. LS scores ≥ 12.5 kPa indicated LS-defined cirrhosis. A p-value below 0.05 was considered statistically significant. The median age of the patients (n = 100) was 60 years [IQR (interquartile range) 54-64); 72% were male; 60% were Caucasian; and 42% had cirrhosis pre-treatment according to the FibroScan measurement. The median LS score dropped from 10.40 kPa (IQR: 7.25-18.60) pre-treatment to 7.60 kPa (IQR: 5.60-12.38) at SVR24, p <0.01. Among the 42 patients with LS-defined cirrhosis pre-treatment, 25 (60%) of patients still had LS scores ≥ 12.5 kPa at SVR24, indicating the persistence of cirrhosis. The median change in LS was similar in patients receiving IFN-containing and IFN-free regimens: -1.95 kPa (IQR: -5.75 --0.38) versus -2.40 kPa (IQR: -7.70 --0.23), p = 0.74. Among 56 patients with a post-SVR24 LS measurement, the LS score changed by an additional -0.90 kPa (IQR: -2.98-0.5) during a median follow-up time of 1.17 (IQR: 0.88-1.63) years, which was not a statistically significant decrease (p = 0.99). LS decreased from pre-treatment to SVR24, but did not decrease significantly during additional follow-up. Earlier treatment may be needed to reduce the burden of liver disease.

Risk Factors for Hepatitis C Virus Reinfection After Sustained Virologic Response in Patients Coinfected With HIV

PubMed Central

Young, Jim; Rossi, Carmine; Gill, John; Walmsley, Sharon; Cooper, Curtis; Cox, Joseph; Martel-Laferriere, Valerie; Conway, Brian; Pick, Neora; Vachon, Marie-Louise

2017-01-01

Abstract Background. Highly effective hepatitis C virus (HCV) therapies have spurred a scale-up of treatment to populations at greater risk of reinfection after sustained virologic response (SVR). Reinfection may be higher in HIV–HCV coinfection, but prior studies have considered small selected populations. We assessed risk factors for reinfection after SVR in a representative cohort of Canadian coinfected patients in clinical care. Methods. All patients achieving SVR after HCV treatment were followed with HCV RNA measurements every 6 months in a prospective cohort study. We used Bayesian Cox regression to estimate reinfection rates according to patient reported injection drug use (IDU) and sexual activity among men who have sex with men (MSM). Results. Of 497 patients treated for HCV, 257 achieved SVR and had at least 1 subsequent RNA measurement. During 589 person-years of follow-up (PYFU) after SVR, 18 (7%) became HCV RNA positive. The adjusted reinfection rate (per 1000 PYFU) in the first year after SVR was highest in those who reported high-frequency IDU (58; 95% credible interval [CrI], 18–134) followed by MSM reporting high-risk sexual activity (26; 95% CrI, 6–66) and low-frequency IDU (22; 95% CrI, 4–68). The rate in low-risk MSM (16; 95% CrI, 4–38) was similar to that in reference patients (10; 95% CrI, 4–20). Reinfection rates did not diminish with time. Conclusions. HCV reinfection rates varied according to risk. Measures are needed to reduce risk behaviors and increase monitoring in high-risk IDU and MSM if HCV elimination targets are to be realized. PMID:28199495

Pre-treatment prediction of response to peginterferon plus ribavirin in chronic hepatitis C genotype 3

PubMed Central

Marciano, Sebastián; Borzi, Silvia M; Dirchwolf, Melisa; Ridruejo, Ezequiel; Mendizabal, Manuel; Bessone, Fernando; Sirotinsky, María E; Giunta, Diego H; Trinks, Julieta; Olivera, Pablo A; Galdame, Omar A; Silva, Marcelo O; Fainboim, Hugo A; Gadano, Adrián C

2015-01-01

AIM: To evaluate pre-treatment factors associated with sustained virological response (SVR) in patients with hepatitis C virus (HCV) genotype 3 treated with peginterferon and ribavirin (RBV). METHODS: We retrospectively analyzed treatment naive, mono-infected HCV genotype 3 patients treated with peginterferon and RBV. Exclusion criteria included presence of other liver disease, alcohol consumption and African American or Asian ethnicity. The variables collected and compared between patients who achieved an SVR and patients who did not were as follows: gender, age, fibrosis stage, diabetes, body mass index, steatosis, INFL3 polymorphism, pre-treatment HCV-RNA, type of peginterferon, RBV dose and adherence. RESULTS: A total of 107 patients treated between June, 2004 and March, 2013 were included. Mean treatment duration was 25.1 (± 1.8) wk. Overall, 58% (62/107) of the patients achieved an SVR and 42% (45/107) did not. In the multivariate logistic regression analysis, pre-treatment HCV-RNA ≥ 600000 UI/mL (OR = 0.375, 95%CI: 0.153-0.919, P = 0.032) and advanced fibrosis (OR = 0.278, 95%CI: 0.113-0.684, P = 0.005) were significantly associated with low SVR rates. In patients with pre-treatment HCV-RNA ≥ 600000 UI/mL and advanced fibrosis, the probability of achieving an SVR was 29% (95%CI: 13.1-45.2). In patients with pre-treatment HCV-RNA < 600000 UI/mL and mild to moderate fibrosis, the probability of achieving an SVR was 81% (95%CI: 68.8-93.4). CONCLUSION: In patients with HCV genotype 3 infections the presence of advance fibrosis and high pre-treatment viral load might be associated with poor response to peginterferon plus RBV. These patients could benefit the most from new direct antiviral agents-based regimes. PMID:25866607

Diabetes and Cirrhosis Are Risk Factors for Hepatocellular Carcinoma After Successful Treatment of Chronic Hepatitis C.

PubMed

Hedenstierna, Magnus; Nangarhari, Ali; Weiland, Ola; Aleman, Soo

2016-09-15

Successful treatment of hepatitis C virus (HCV) infection reduces the risk for hepatocellular carcinoma (HCC), but a risk remains. Current guidelines recommend continued HCC surveillance after sustained virologic response (SVR) has been achieved. This study aimed to investigate risk factors and incidence rates for HCC after SVR in HCV patients with pretreatment advanced liver disease (Metavir stage F3/F4). All patients with advanced liver disease successfully treated for HCV at Karolinska University Hospital during 1992-2013 (n = 399) were followed up for a median of 7.8 years. Data from national registries were used to minimize loss to follow-up. Incidence rates and hazard ratios (HRs) for development of HCC were calculated by Cox regression analysis. Seventeen patients developed HCC during 3366 person-years (PY) of follow-up. The HCC incidence rate was 0.95 (95% confidence interval [CI], .57-1.6) and 0.15 (95% CI, .05-.49) per 100 PY for patients with pretreatment F4 and F3, respectively. Patients with pretreatment cirrhosis and diabetes had a HR to develop HCC of 6.3, and an incidence rate of 7.9 per 100 PY (95% CI, 3.3-19) during the first 2 years of follow-up. The risk for HCC decreased significantly 2 years after SVR had been achieved. Diabetes mellitus and cirrhosis are strong risk factors for HCC development after SVR has been achieved. The risk to develop HCC diminishes significantly 2 years after SVR. Patients without cirrhosis have a low risk to develop HCC after SVR, and the benefit of HCC surveillance for this group is questionable. © The Author 2016. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.

Melamine detection by mid- and near-infrared (MIR/NIR) spectroscopy: a quick and sensitive method for dairy products analysis including liquid milk, infant formula, and milk powder.

PubMed

Balabin, Roman M; Smirnov, Sergey V

2011-07-15

Melamine (2,4,6-triamino-1,3,5-triazine) is a nitrogen-rich chemical implicated in the pet and human food recalls and in the global food safety scares involving milk products. Due to the serious health concerns associated with melamine consumption and the extensive scope of affected products, rapid and sensitive methods to detect melamine's presence are essential. We propose the use of spectroscopy data-produced by near-infrared (near-IR/NIR) and mid-infrared (mid-IR/MIR) spectroscopies, in particular-for melamine detection in complex dairy matrixes. None of the up-to-date reported IR-based methods for melamine detection has unambiguously shown its wide applicability to different dairy products as well as limit of detection (LOD) below 1 ppm on independent sample set. It was found that infrared spectroscopy is an effective tool to detect melamine in dairy products, such as infant formula, milk powder, or liquid milk. ALOD below 1 ppm (0.76±0.11 ppm) can be reached if a correct spectrum preprocessing (pretreatment) technique and a correct multivariate (MDA) algorithm-partial least squares regression (PLS), polynomial PLS (Poly-PLS), artificial neural network (ANN), support vector regression (SVR), or least squares support vector machine (LS-SVM)-are used for spectrum analysis. The relationship between MIR/NIR spectrum of milk products and melamine content is nonlinear. Thus, nonlinear regression methods are needed to correctly predict the triazine-derivative content of milk products. It can be concluded that mid- and near-infrared spectroscopy can be regarded as a quick, sensitive, robust, and low-cost method for liquid milk, infant formula, and milk powder analysis. Copyright © 2011 Elsevier B.V. All rights reserved.

Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms.

PubMed

Barzegar, Rahim; Moghaddam, Asghar Asghari; Deo, Ravinesh; Fijani, Elham; Tziritis, Evangelos

2018-04-15

Constructing accurate and reliable groundwater risk maps provide scientifically prudent and strategic measures for the protection and management of groundwater. The objectives of this paper are to design and validate machine learning based-risk maps using ensemble-based modelling with an integrative approach. We employ the extreme learning machines (ELM), multivariate regression splines (MARS), M5 Tree and support vector regression (SVR) applied in multiple aquifer systems (e.g. unconfined, semi-confined and confined) in the Marand plain, North West Iran, to encapsulate the merits of individual learning algorithms in a final committee-based ANN model. The DRASTIC Vulnerability Index (VI) ranged from 56.7 to 128.1, categorized with no risk, low and moderate vulnerability thresholds. The correlation coefficient (r) and Willmott's Index (d) between NO 3 concentrations and VI were 0.64 and 0.314, respectively. To introduce improvements in the original DRASTIC method, the vulnerability indices were adjusted by NO 3 concentrations, termed as the groundwater contamination risk (GCR). Seven DRASTIC parameters utilized as the model inputs and GCR values utilized as the outputs of individual machine learning models were served in the fully optimized committee-based ANN-predictive model. The correlation indicators demonstrated that the ELM and SVR models outperformed the MARS and M5 Tree models, by virtue of a larger d and r value. Subsequently, the r and d metrics for the ANN-committee based multi-model in the testing phase were 0.8889 and 0.7913, respectively; revealing the superiority of the integrated (or ensemble) machine learning models when compared with the original DRASTIC approach. The newly designed multi-model ensemble-based approach can be considered as a pragmatic step for mapping groundwater contamination risks of multiple aquifer systems with multi-model techniques, yielding the high accuracy of the ANN committee-based model. Copyright © 2017 Elsevier B.V. All rights reserved.

Reviewing the connection between speech and obstructive sleep apnea.

PubMed

Espinoza-Cuadros, Fernando; Fernández-Pozo, Rubén; Toledano, Doroteo T; Alcázar-Ramírez, José D; López-Gonzalo, Eduardo; Hernández-Gómez, Luis A

2016-02-20

Sleep apnea (OSA) is a common sleep disorder characterized by recurring breathing pauses during sleep caused by a blockage of the upper airway (UA). The altered UA structure or function in OSA speakers has led to hypothesize the automatic analysis of speech for OSA assessment. In this paper we critically review several approaches using speech analysis and machine learning techniques for OSA detection, and discuss the limitations that can arise when using machine learning techniques for diagnostic applications. A large speech database including 426 male Spanish speakers suspected to suffer OSA and derived to a sleep disorders unit was used to study the clinical validity of several proposals using machine learning techniques to predict the apnea-hypopnea index (AHI) or classify individuals according to their OSA severity. AHI describes the severity of patients' condition. We first evaluate AHI prediction using state-of-the-art speaker recognition technologies: speech spectral information is modelled using supervectors or i-vectors techniques, and AHI is predicted through support vector regression (SVR). Using the same database we then critically review several OSA classification approaches previously proposed. The influence and possible interference of other clinical variables or characteristics available for our OSA population: age, height, weight, body mass index, and cervical perimeter, are also studied. The poor results obtained when estimating AHI using supervectors or i-vectors followed by SVR contrast with the positive results reported by previous research. This fact prompted us to a careful review of these approaches, also testing some reported results over our database. Several methodological limitations and deficiencies were detected that may have led to overoptimistic results. The methodological deficiencies observed after critically reviewing previous research can be relevant examples of potential pitfalls when using machine learning techniques for diagnostic applications. We have found two common limitations that can explain the likelihood of false discovery in previous research: (1) the use of prediction models derived from sources, such as speech, which are also correlated with other patient characteristics (age, height, sex,…) that act as confounding factors; and (2) overfitting of feature selection and validation methods when working with a high number of variables compared to the number of cases. We hope this study could not only be a useful example of relevant issues when using machine learning for medical diagnosis, but it will also help in guiding further research on the connection between speech and OSA.

Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure.

PubMed

Song, Jiangning; Yuan, Zheng; Tan, Hao; Huber, Thomas; Burrage, Kevin

2007-12-01

Disulfide bonds are primary covalent crosslinks between two cysteine residues in proteins that play critical roles in stabilizing the protein structures and are commonly found in extracy-toplasmatic or secreted proteins. In protein folding prediction, the localization of disulfide bonds can greatly reduce the search in conformational space. Therefore, there is a great need to develop computational methods capable of accurately predicting disulfide connectivity patterns in proteins that could have potentially important applications. We have developed a novel method to predict disulfide connectivity patterns from protein primary sequence, using a support vector regression (SVR) approach based on multiple sequence feature vectors and predicted secondary structure by the PSIPRED program. The results indicate that our method could achieve a prediction accuracy of 74.4% and 77.9%, respectively, when averaged on proteins with two to five disulfide bridges using 4-fold cross-validation, measured on the protein and cysteine pair on a well-defined non-homologous dataset. We assessed the effects of different sequence encoding schemes on the prediction performance of disulfide connectivity. It has been shown that the sequence encoding scheme based on multiple sequence feature vectors coupled with predicted secondary structure can significantly improve the prediction accuracy, thus enabling our method to outperform most of other currently available predictors. Our work provides a complementary approach to the current algorithms that should be useful in computationally assigning disulfide connectivity patterns and helps in the annotation of protein sequences generated by large-scale whole-genome projects. The prediction web server and Supplementary Material are accessible at http://foo.maths.uq.edu.au/~huber/disulfide

The effect of geographical indices on left ventricular structure in healthy Han Chinese population

NASA Astrophysics Data System (ADS)

Cen, Minyi; Ge, Miao; Liu, Yonglin; Wang, Congxia; Yang, Shaofang

2017-02-01

The left ventricular posterior wall thickness (LVPWT) and interventricular septum thickness (IVST) are generally regarded as the functional parts of the left ventricular (LV) structure. This paper aims to examine the effects of geographical indices on healthy Han adults' LV structural indices and to offer a scientific basis for developing a unified standard for the reference values of adults' LV structural indices in China. Fifteen terrain, climate, and soil indices were examined as geographical explanatory variables. Statistical analysis was performed using correlation analysis. Moreover, a back propagation neural network (BPNN) and a support vector regression (SVR) were applied to developing models to predict the values of two indices. After the prediction models were built, distribution maps were produced. The results show that LV structural indices are characteristically associated with latitude, longitude, altitude, average temperature, average wind velocity, topsoil sand fraction, topsoil silt fraction, topsoil organic carbon, and topsoil sodicity. The model test analyses show the BPNN model possesses better simulative and predictive ability in comparison with the SVR model. The distribution maps of the LV structural indices show that, in China, the values are higher in the west and lower in the east. These results demonstrate that the reference values of the adults' LV structural indices will be different affected by different geographical environment. The reference values of LV structural indices in one region can be calculated by setting up a BPNN, which showed better applicability in this study. The distribution of the reference values of the LV structural indices can be seen clearly on the geographical distribution map.

Comparative study of surrogate models for groundwater contamination source identification at DNAPL-contaminated sites

NASA Astrophysics Data System (ADS)

Hou, Zeyu; Lu, Wenxi

2018-05-01

Knowledge of groundwater contamination sources is critical for effectively protecting groundwater resources, estimating risks, mitigating disaster, and designing remediation strategies. Many methods for groundwater contamination source identification (GCSI) have been developed in recent years, including the simulation-optimization technique. This study proposes utilizing a support vector regression (SVR) model and a kernel extreme learning machine (KELM) model to enrich the content of the surrogate model. The surrogate model was itself key in replacing the simulation model, reducing the huge computational burden of iterations in the simulation-optimization technique to solve GCSI problems, especially in GCSI problems of aquifers contaminated by dense nonaqueous phase liquids (DNAPLs). A comparative study between the Kriging, SVR, and KELM models is reported. Additionally, there is analysis of the influence of parameter optimization and the structure of the training sample dataset on the approximation accuracy of the surrogate model. It was found that the KELM model was the most accurate surrogate model, and its performance was significantly improved after parameter optimization. The approximation accuracy of the surrogate model to the simulation model did not always improve with increasing numbers of training samples. Using the appropriate number of training samples was critical for improving the performance of the surrogate model and avoiding unnecessary computational workload. It was concluded that the KELM model developed in this work could reasonably predict system responses in given operation conditions. Replacing the simulation model with a KELM model considerably reduced the computational burden of the simulation-optimization process and also maintained high computation accuracy.

MRI-Based Intelligence Quotient (IQ) Estimation with Sparse Learning

PubMed Central

Wang, Liye; Wee, Chong-Yaw; Suk, Heung-Il; Tang, Xiaoying; Shen, Dinggang

2015-01-01

In this paper, we propose a novel framework for IQ estimation using Magnetic Resonance Imaging (MRI) data. In particular, we devise a new feature selection method based on an extended dirty model for jointly considering both element-wise sparsity and group-wise sparsity. Meanwhile, due to the absence of large dataset with consistent scanning protocols for the IQ estimation, we integrate multiple datasets scanned from different sites with different scanning parameters and protocols. In this way, there is large variability in these different datasets. To address this issue, we design a two-step procedure for 1) first identifying the possible scanning site for each testing subject and 2) then estimating the testing subject’s IQ by using a specific estimator designed for that scanning site. We perform two experiments to test the performance of our method by using the MRI data collected from 164 typically developing children between 6 and 15 years old. In the first experiment, we use a multi-kernel Support Vector Regression (SVR) for estimating IQ values, and obtain an average correlation coefficient of 0.718 and also an average root mean square error of 8.695 between the true IQs and the estimated ones. In the second experiment, we use a single-kernel SVR for IQ estimation, and achieve an average correlation coefficient of 0.684 and an average root mean square error of 9.166. All these results show the effectiveness of using imaging data for IQ prediction, which is rarely done in the field according to our knowledge. PMID:25822851

The effect of geographical indices on left ventricular structure in healthy Han Chinese population.

PubMed

Cen, Minyi; Ge, Miao; Liu, Yonglin; Wang, Congxia; Yang, Shaofang

2017-02-01

The left ventricular posterior wall thickness (LVPWT) and interventricular septum thickness (IVST) are generally regarded as the functional parts of the left ventricular (LV) structure. This paper aims to examine the effects of geographical indices on healthy Han adults' LV structural indices and to offer a scientific basis for developing a unified standard for the reference values of adults' LV structural indices in China. Fifteen terrain, climate, and soil indices were examined as geographical explanatory variables. Statistical analysis was performed using correlation analysis. Moreover, a back propagation neural network (BPNN) and a support vector regression (SVR) were applied to developing models to predict the values of two indices. After the prediction models were built, distribution maps were produced. The results show that LV structural indices are characteristically associated with latitude, longitude, altitude, average temperature, average wind velocity, topsoil sand fraction, topsoil silt fraction, topsoil organic carbon, and topsoil sodicity. The model test analyses show the BPNN model possesses better simulative and predictive ability in comparison with the SVR model. The distribution maps of the LV structural indices show that, in China, the values are higher in the west and lower in the east. These results demonstrate that the reference values of the adults' LV structural indices will be different affected by different geographical environment. The reference values of LV structural indices in one region can be calculated by setting up a BPNN, which showed better applicability in this study. The distribution of the reference values of the LV structural indices can be seen clearly on the geographical distribution map.

Robust design optimization method for centrifugal impellers under surface roughness uncertainties due to blade fouling

NASA Astrophysics Data System (ADS)

Ju, Yaping; Zhang, Chuhua

2016-03-01

Blade fouling has been proved to be a great threat to compressor performance in operating stage. The current researches on fouling-induced performance degradations of centrifugal compressors are based mainly on simplified roughness models without taking into account the realistic factors such as spatial non-uniformity and randomness of the fouling-induced surface roughness. Moreover, little attention has been paid to the robust design optimization of centrifugal compressor impellers with considerations of blade fouling. In this paper, a multi-objective robust design optimization method is developed for centrifugal impellers under surface roughness uncertainties due to blade fouling. A three-dimensional surface roughness map is proposed to describe the nonuniformity and randomness of realistic fouling accumulations on blades. To lower computational cost in robust design optimization, the support vector regression (SVR) metamodel is combined with the Monte Carlo simulation (MCS) method to conduct the uncertainty analysis of fouled impeller performance. The analyzed results show that the critical fouled region associated with impeller performance degradations lies at the leading edge of blade tip. The SVR metamodel has been proved to be an efficient and accurate means in the detection of impeller performance variations caused by roughness uncertainties. After design optimization, the robust optimal design is found to be more efficient and less sensitive to fouling uncertainties while maintaining good impeller performance in the clean condition. This research proposes a systematic design optimization method for centrifugal compressors with considerations of blade fouling, providing a practical guidance to the design of advanced centrifugal compressors.

A comparative research of different ensemble surrogate models based on set pair analysis for the DNAPL-contaminated aquifer remediation strategy optimization.

PubMed

Hou, Zeyu; Lu, Wenxi; Xue, Haibo; Lin, Jin

2017-08-01

Surrogate-based simulation-optimization technique is an effective approach for optimizing the surfactant enhanced aquifer remediation (SEAR) strategy for clearing DNAPLs. The performance of the surrogate model, which is used to replace the simulation model for the aim of reducing computation burden, is the key of corresponding researches. However, previous researches are generally based on a stand-alone surrogate model, and rarely make efforts to improve the approximation accuracy of the surrogate model to the simulation model sufficiently by combining various methods. In this regard, we present set pair analysis (SPA) as a new method to build ensemble surrogate (ES) model, and conducted a comparative research to select a better ES modeling pattern for the SEAR strategy optimization problems. Surrogate models were developed using radial basis function artificial neural network (RBFANN), support vector regression (SVR), and Kriging. One ES model is assembling RBFANN model, SVR model, and Kriging model using set pair weights according their performance, and the other is assembling several Kriging (the best surrogate modeling method of three) models built with different training sample datasets. Finally, an optimization model, in which the ES model was embedded, was established to obtain the optimal remediation strategy. The results showed the residuals of the outputs between the best ES model and simulation model for 100 testing samples were lower than 1.5%. Using an ES model instead of the simulation model was critical for considerably reducing the computation time of simulation-optimization process and maintaining high computation accuracy simultaneously. Copyright © 2017 Elsevier B.V. All rights reserved.

ASTEROSEISMIC-BASED ESTIMATION OF THE SURFACE GRAVITY FOR THE LAMOST GIANT STARS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Chao; Wu, Yue; Deng, Li-Cai

2015-07-01

Asteroseismology is one of the most accurate approaches to estimate the surface gravity of a star. However, most of the data from the current spectroscopic surveys do not have asteroseismic measurements, which is very expensive and time consuming. In order to improve the spectroscopic surface gravity estimates for a large amount of survey data with the help of the small subset of the data with seismic measurements, we set up a support vector regression (SVR) model for the estimation of the surface gravity supervised by 1374 Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST) giant stars with Kepler seismic surfacemore » gravity. The new approach can reduce the uncertainty of the estimates down to about 0.1 dex, which is better than the LAMOST pipeline by at least a factor of 2, for the spectra with signal-to-noise ratio higher than 20. Compared with the log g estimated from the LAMOST pipeline, the revised log g values provide a significantly improved match to the expected distribution of red clump and red giant branch stars from stellar isochrones. Moreover, even the red bump stars, which extend to only about 0.1 dex in log g, can be discriminated from the new estimated surface gravity. The method is then applied to about 350,000 LAMOST metal-rich giant stars to provide improved surface gravity estimates. In general, the uncertainty of the distance estimate based on the SVR surface gravity can be reduced to about 12% for the LAMOST data.« less

A prediction model of short-term ionospheric foF2 Based on AdaBoost

NASA Astrophysics Data System (ADS)

Zhao, Xiukuan; Liu, Libo; Ning, Baiqi

Accurate specifications of spatial and temporal variations of the ionosphere during geomagnetic quiet and disturbed conditions are critical for applications, such as HF communications, satellite positioning and navigation, power grids, pipelines, etc. Therefore, developing empirical models to forecast the ionospheric perturbations is of high priority in real applications. The critical frequency of the F2 layer, foF2, is an important ionospheric parameter, especially for radio wave propagation applications. In this paper, the AdaBoost-BP algorithm is used to construct a new model to predict the critical frequency of the ionospheric F2-layer one hour ahead. Different indices were used to characterize ionospheric diurnal and seasonal variations and their dependence on solar and geomagnetic activity. These indices, together with the current observed foF2 value, were input into the prediction model and the foF2 value at one hour ahead was output. We analyzed twenty-two years’ foF2 data from nine ionosonde stations in the East-Asian sector in this work. The first eleven years’ data were used as a training dataset and the second eleven years’ data were used as a testing dataset. The results show that the performance of AdaBoost-BP is better than those of BP Neural Network (BPNN), Support Vector Regression (SVR) and the IRI model. For example, the AdaBoost-BP prediction absolute error of foF2 at Irkutsk station (a middle latitude station) is 0.32 MHz, which is better than 0.34 MHz from BPNN, 0.35 MHz from SVR and also significantly outperforms the IRI model whose absolute error is 0.64 MHz. Meanwhile, AdaBoost-BP prediction absolute error at Taipei station from the low latitude is 0.78 MHz, which is better than 0.81 MHz from BPNN, 0.81 MHz from SVR and 1.37 MHz from the IRI model. Finally, the variety characteristics of the AdaBoost-BP prediction error along with seasonal variation, solar activity and latitude variation were also discussed in the paper.

Displacement prediction of Baijiabao landslide based on empirical mode decomposition and long short-term memory neural network in Three Gorges area, China

NASA Astrophysics Data System (ADS)

Xu, Shiluo; Niu, Ruiqing

2018-02-01

Every year, landslides pose huge threats to thousands of people in China, especially those in the Three Gorges area. It is thus necessary to establish an early warning system to help prevent property damage and save peoples' lives. Most of the landslide displacement prediction models that have been proposed are static models. However, landslides are dynamic systems. In this paper, the total accumulative displacement of the Baijiabao landslide is divided into trend and periodic components using empirical mode decomposition. The trend component is predicted using an S-curve estimation, and the total periodic component is predicted using a long short-term memory neural network (LSTM). LSTM is a dynamic model that can remember historical information and apply it to the current output. Six triggering factors are chosen to predict the periodic term using the Pearson cross-correlation coefficient and mutual information. These factors include the cumulative precipitation during the previous month, the cumulative precipitation during a two-month period, the reservoir level during the current month, the change in the reservoir level during the previous month, the cumulative increment of the reservoir level during the current month, and the cumulative displacement during the previous month. When using one-step-ahead prediction, LSTM yields a root mean squared error (RMSE) value of 6.112 mm, while the support vector machine for regression (SVR) and the back-propagation neural network (BP) yield values of 10.686 mm and 8.237 mm, respectively. Meanwhile, the Elman network (Elman) yields an RMSE value of 6.579 mm. In addition, when using multi-step-ahead prediction, LSTM obtains an RMSE value of 8.648 mm, while SVR, BP and the Elman network obtains RSME values of 13.418 mm, 13.014 mm, and 13.370 mm. The predicted results indicate that, to some extent, the dynamic model (LSTM) achieves results that are more accurate than those of the static models (i.e., SVR and BP). LSTM even displays better performance than the Elman network, which is also a dynamic method.

Determination of relationship between sensory viscosity rating and instrumental flow behaviour of soluble dietary fibers.

PubMed

Arora, Simran Kaur; Patel, A A; Kumar, Naveen; Chauhan, O P

2016-04-01

The shear-thinning low, medium and high-viscosity fiber preparations (0.15-1.05 % psyllium husk, 0.07-0.6 % guar gum, 0.15-1.20 % gum tragacanth, 0.1-0.8 % gum karaya, 0.15-1.05 % high-viscosity Carboxy Methyl Cellulose and 0.1-0.7 % xanthan gum) showed that the consistency coefficient (k) was a function of concentration, the relationship being exponential (R(2), 0.87-0.96; P < 0.01). The flow behaviour index (n) (except for gum karaya and CMC) was exponentially related to concentration (R(2), 0.61-0.98). The relationship between k and sensory viscosity rating (SVR) was essentially linear in nearly all cases. The SVR could be predicted from the consistency coefficient using the regression equations developed. Also, the relationship of k with fiber concentration would make it possible to identify the concentration of a particular gum required to have desired consistency in terms of SVR.

Rapid determination of total protein and wet gluten in commercial wheat flour using siSVR-NIR.

PubMed

Chen, Jia; Zhu, Shipin; Zhao, Guohua

2017-04-15

The determination of total protein and wet gluten is of critical importance when screening commercial flour for desired processing suitability. To this end, a near-infrared spectroscopy (NIR) method with support vector regression was developed in the present study. The effects of spectral preprocessing and the synergy interval on model performance were investigated. The results showed that the models from raw spectra were not acceptable, but they were substantially improved by properly applying spectral preprocessing methods. Meanwhile, the synergy interval was validated with a good ability to improve the performance of models based on the whole spectrum. The coefficient of determination (R 2 ), the root mean square error of prediction (RMSEP) and the standard deviation ratio (SDR) of the best models for total protein (wet gluten) were 0.906 (0.850), 0.425 (1.024) and 3.065 (2.482), respectively. These two best models have similar and lower relative errors (approximately 8.8%), which indicates their feasibility. Copyright © 2016 Elsevier Ltd. All rights reserved.

Use seismic colored inversion and power law committee machine based on imperial competitive algorithm for improving porosity prediction in a heterogeneous reservoir

NASA Astrophysics Data System (ADS)

Ansari, Hamid Reza

2014-09-01

In this paper we propose a new method for predicting rock porosity based on a combination of several artificial intelligence systems. The method focuses on one of the Iranian carbonate fields in the Persian Gulf. Because there is strong heterogeneity in carbonate formations, estimation of rock properties experiences more challenge than sandstone. For this purpose, seismic colored inversion (SCI) and a new approach of committee machine are used in order to improve porosity estimation. The study comprises three major steps. First, a series of sample-based attributes is calculated from 3D seismic volume. Acoustic impedance is an important attribute that is obtained by the SCI method in this study. Second, porosity log is predicted from seismic attributes using common intelligent computation systems including: probabilistic neural network (PNN), radial basis function network (RBFN), multi-layer feed forward network (MLFN), ε-support vector regression (ε-SVR) and adaptive neuro-fuzzy inference system (ANFIS). Finally, a power law committee machine (PLCM) is constructed based on imperial competitive algorithm (ICA) to combine the results of all previous predictions in a single solution. This technique is called PLCM-ICA in this paper. The results show that PLCM-ICA model improved the results of neural networks, support vector machine and neuro-fuzzy system.

Periscope: quantitative prediction of soluble protein expression in the periplasm of Escherichia coli

NASA Astrophysics Data System (ADS)

Chang, Catherine Ching Han; Li, Chen; Webb, Geoffrey I.; Tey, Bengti; Song, Jiangning; Ramanan, Ramakrishnan Nagasundara

2016-03-01

Periplasmic expression of soluble proteins in Escherichia coli not only offers a much-simplified downstream purification process, but also enhances the probability of obtaining correctly folded and biologically active proteins. Different combinations of signal peptides and target proteins lead to different soluble protein expression levels, ranging from negligible to several grams per litre. Accurate algorithms for rational selection of promising candidates can serve as a powerful tool to complement with current trial-and-error approaches. Accordingly, proteomics studies can be conducted with greater efficiency and cost-effectiveness. Here, we developed a predictor with a two-stage architecture, to predict the real-valued expression level of target protein in the periplasm. The output of the first-stage support vector machine (SVM) classifier determines which second-stage support vector regression (SVR) classifier to be used. When tested on an independent test dataset, the predictor achieved an overall prediction accuracy of 78% and a Pearson’s correlation coefficient (PCC) of 0.77. We further illustrate the relative importance of various features with respect to different models. The results indicate that the occurrence of dipeptide glutamine and aspartic acid is the most important feature for the classification model. Finally, we provide access to the implemented predictor through the Periscope webserver, freely accessible at http://lightning.med.monash.edu/periscope/.

Implementing and scaling up HCV treatment services for people who inject drugs and other high risk groups in Ukraine: An evaluation of programmatic and treatment outcomes.

PubMed

Mazhnaya, Alyona; Meteliuk, Anna; Barnard, Tetiana; Zelenev, Alexei; Filippovych, Sergii; Altice, Frederick L

2017-09-01

HCV prevalence estimates among people who inject drugs (PWID) in Ukraine is high (60-90%), yet barriers to HCV treatment and care remain substantial including limited access to direct acting antiviral (DAA) medications. A feasibility scale-up project implemented HCV treatment in community-based settings to improve access to DAA treatment for key populations in this context. Using program-level data and verified medical records, we describe the development, implementation processes and outcomes for HCV treatment for PWID and other risks groups. Most participants (76%) received a combination of sofosbuvir, pegylated interferon, and ribavirin for 12 weeks. Treatment enrollment started in June 2015; the first two waves are reported. Data on demographics, HIV characteristics, HCV genotype and RNA levels, including sustained virologic response (SVR) were obtained from verified medical records. We used logistic regression to examine the independent correlates of achieving a SVR. The project was implemented in 19 healthcare institutions from 16 regions of Ukraine, mainly within AIDS specialty centers. Our analytical sample included 1126 participants who were mostly men (73%) and the majority were HIV co-infected (79%). Treatment retention was 97.7%; the proportions of participants who achieved SVR for the overall sample and for those with complete data (N=1029) were 86.2% (95% CI 84.08-88.19%) and 94.3% (95% CI 92.8-95.7%) respectively. The analysis of data restricted to only those with SVR data available showed that PWID who were currently injecting had comparable SVR rates (89.2%, 95% CI 81.5-94.5%) to PWID not injecting (94.4%, 95% CI 92.4-96.1), PWID on methadone (94.4%, 95%CI 92.4-96.1), and 'other' risk groups (95.2%, 95% CI 91.3-97.7). Independent factors associated with achieving a SVR were female sex (AOR: 3.44, 95% CI 1.45-8.14), HCV genotype 3 (AOR: 4.57, 95% CI 1.97-10.59) compared to genotype 1. SVR rates in PWID actively injecting did not differ significantly from any other group. Both patient-level and structural factors influence HCV treatment scale-up in Ukraine, but patient-level outcomes confirm high levels of achieving SVR in PWID, irrespective of injection and treatment status. Copyright © 2017. Published by Elsevier B.V.

Protein pathway activation associated with sustained virologic response in patients with chronic hepatitis C treated with pegylated interferon (PEG-IFN) and ribavirin (RBV).

PubMed

Younossi, Zobair M; Limongi, Dolores; Stepanova, Maria; Pierobon, Mariaelena; Afendy, Arian; Mehta, Rohini; Baranova, Ancha; Liotta, Lance; Petricoin, Emanuel

2011-02-04

Only half of chronic hepatitis C (CH-C) patients treated with pegylated interferon and ribavirin (PEG-IFN+RBV) achieve sustained virologic response) SVR. In addition to known factors, we postulated that activation of key protein signaling networks in the peripheral blood mononuclear cells (PBMCs) may contribute to SVR due to inherent patient-specific basal immune cell signaling architecture. In this study, we included 92 patients with CH-C. PBMCs were collected while patients were not receiving treatment and used for phosphoprotein-based network profiling. Patients received a full course of PEG-IFN+RBV with overall SVR of 55%. From PBMC, protein lysates were extracted and then used for Reverse Phase Protein Microarray (RPMA) analysis, which quantitatively measured the levels of cytokines and activation levels of 25 key protein signaling molecules involved in immune cell regulation and interferon alpha signaling. Regression models for predicting SVR were generated by stepwise bidirectional selection. Both clinical-laboratory and RPMA parameters were used as predictor variables. Model accuracies were estimated using 10-fold cross-validation. Our results show that by comparing patients who achieved SVR to those who did not, phosphorylation levels of 6 proteins [AKT(T308), JAK1(Y1022/1023), p70 S6 Kinase (S371), PKC zeta/lambda(T410/403), TYK2(Y1054/1055), ZAP-70(Y319)/Syk(Y352)] and overall levels of 6 unmodified proteins [IL2, IL10, IL4, IL5, TNF-alpha, CD5L] were significantly different (P < 0.05). For SVR, the model based on a combination of clinical and proteome parameters was developed, with an AUC = 0.914, sensitivity of 92.16%, and specificity of 85.0%. This model included the following parameters: viral genotype, previous treatment status, BMI, phosphorylated states of STAT2, AKT, LCK, and TYK2 kinases as well as steady state levels of IL4, IL5, and TNF-alpha. In conclusion, SVR could be predicted by a combination of clinical, cytokine, and protein signaling activation profiles. Signaling events elucidated in the study may shed some light into molecular mechanisms of response to anti-HCV treatment.

Clinical utility of host genetic IL-28B variants in hepatitis C virus genotype 1 Asian patients retreated with pegylated interferon plus ribavirin.

PubMed

Huang, Chung-Feng; Yeh, Ming-Lun; Hsieh, Meng-Hsuan; Hsieh, Ming-Yen; Lin, Zu-Yau; Chen, Shinn-Cherng; Wang, Liang-Yen; Huang, Jee-Fu; Juo, Suh-Hang Hank; Lin, Yi-Ching; Dai, Chia-Yen; Chuang, Wan-Long; Yu, Ming-Lung

2013-09-01

Host interleukin-28B (IL-28B) genetic variants determine a sustained virological response (SVR) in hepatitis C virus genotype 1 (HCV-1) treatment-naïve patients. Its impact on treatment-experienced Asian patients with peginterferon/ribavirin in is to be elucidated. IL-28B rs8099917 genotype was determined in 70 HCV-1 treatment-experienced patients retreated with 48-week peginterferon/ribavirin. The SVR rate was 60.0% and was significantly higher in previous relapsers than in nonresponders (72.7% and 13.3%, P < 0.001). Multivariate analysis revealed that the most important factor predictive of an SVR was previous relapse (Odds ratio [OR]/95% confidence interval [CI]: 14.76/2.72-80.06, P = 0.002), followed by the carriage of rs8099917 TT genotype (OR/95% C.I.: 7.67/1.27-46.49, P = 0.03). Comparing to patients with TG/GG genotype, those with TT genotype had significantly higher rates of rapid virological response (29.3% vs 0%, P = 0.03), end-of-treatment virological response (86.2% vs 50.0%, P = 0.01), SVR (69.0% vs 16.7%, P = 0.002), and lower relapse rate (22.0 % vs 66.7%, P = 0.04). The SVR rate was similarly low between previous nonresponders with different rs8099917 genotypes (12.5% vs 14.3%, P = 1). On the contrary, previous relapsers with rs8099917 TT genotype had a significantly higher SVR rate than those who carried rs8099917 TG/GG genotype (78.0 % vs 20.0%, P = 0.02). Stepwise logistic regression analysis revealed that the only factor predictive of an SVR in previous relapsers was the carriage of rs809997 TT genotype (OR/95% CI:18.50/1.82-188.39, P = 0.014). Host IL-28B genetic variants played a role in Asian relapsers but not nonresponders retreated with peginterferon/ribavirin. Direct antiviral agents might be possibly avoidable in Asian relapsers with favorable IL-28B genotype. © 2013 Journal of Gastroenterology and Hepatology Foundation and Wiley Publishing Asia Pty Ltd.

The effect of sustained virological response on the risk of extrahepatic manifestations of hepatitis C virus infection.

PubMed

Mahale, Parag; Engels, Eric A; Li, Ruosha; Torres, Harrys A; Hwang, Lu-Yu; Brown, Eric L; Kramer, Jennifer R

2018-03-01

Chronic HCV infection is associated with several extrahepatic manifestations (EHMs). Data on the effect of sustained virological response (SVR) on the risk of EHMs are limited. We conducted a retrospective cohort study using data of patients from the US Veterans Affairs HCV Clinical Case Registry who had a positive HCV RNA test (10/1999-08/2009). Patients receiving interferon-based antiviral therapy (AVT) were identified. SVR was defined as negative HCV RNA at least 12 weeks after end of AVT. Risks of eight incident EHMs were evaluated in Cox regression models. Of the 160 875 HCV-infected veterans, 31 143 (19.4%) received AVT, of whom 10 575 (33.9%) experienced SVR. EHM risk was reduced in the SVR group compared with untreated patients for mixed cryoglobulinaemia (adjusted HR (aHR)=0.61; 95% CI 0.39 to 0.94), glomerulonephritis (aHR=0.62; 95% CI 0.48 to 0.79), porphyria cutanea tarda (PCT) (aHR=0.41; 95% CI 0.20 to 0.83), non-Hodgkin's lymphoma (NHL) (aHR=0.64; 95% CI 0.43 to 0.95), diabetes (aHR=0.82; 95% CI 0.76 to 0.88) and stroke (aHR=0.84; 95% CI 0.74 to 0.94), but not for lichen planus (aHR=1.11; 95% CI 0.78 to 1.56) or coronary heart disease (aHR=1.12; 95% CI 0.81 to 1.56). Risk reductions were also observed when patients with SVR were compared with treated patients without SVR for mixed cryoglobulinaemia, glomerulonephritis, PCT and diabetes. Significant reductions in the magnitude of aHRs towards the null with increasing time to initiation of AVT after HCV diagnosis were observed for glomerulonephritis, NHL and stroke. Risks of several EHMs of HCV infection are reduced after AVT with SVR. However, early initiation of AVT may be required to reduce the risk of glomerulonephritis, NHL and stroke. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Improvement of liver stiffness measurement, acoustic radiation force impulse measurements, and noninvasive fibrosis markers after direct-acting antivirals for hepatitis C virus G4 recurrence post living donor liver transplantation: Egyptian cohort.

PubMed

Alem, Shereen Abdel; Said, Mohamed; Anwar, Ismail; Abdellatif, Zeinab; Elbaz, Tamer; Eletreby, Rasha; AbouElKhair, Mahmoud; El-Serafy, Magdy; Mogawer, Sherif; El-Amir, Mona; El-Shazly, Mostafa; Hosny, Adel; Yosry, Ayman

2018-05-02

Progression of recurrent hepatitis C is accelerated in liver transplant (LT) recipients. Direct-acting antivirals (DAAs) have recently emerged as a promising therapeutic regimen for the treatment of hepatitis C virus infection. Rates of sustained virological response (SVR) have drastically improved since the introduction of DAAs. The aim is to elucidate the changes in liver stiffness measurement (LSM) by transient elastography (TE) as well as acoustic radiation force impulse (ARFI) elastography and fibrosis scores after DAA treatment in LT recipients with hepatitis C virus recurrence. A single-center, prospective study including 58 LT recipients with hepatitis C recurrence who received different sofosbuvir-based treatment regimens. Transient elastography and ARFI elastography values were recorded as well as fibrosis 4 score (FIB-4) and aspartate aminotransferase-to-platelet ratio index were calculated at baseline and SVR at week 24 (SVR24). The outcome was improvement in LSM and at least a 20% decrease in LSM at SVR24 compared with baseline. The sustained virological response was 98.1%. There was improvement of platelet counts, alanine aminotransferase, and aspartate aminotransferase, which in turn caused improvement in fibrosis scores at SVR24. LSM by TE and ARFI elastography decreased from the baseline median value of 6.3 kPa (interquartile range [IQR]; 4.6 to 8.8 kPa) and 1.28 m/s (IQR; 1.07 to 1.53 m/s) to an SVR24 median value of 6.2 kPa (IQR; 4.85 to 8.9 kPa) and 1.12 (IQR; 0.97 to 1.30 m/s), respectively. Logistic regression analysis showed that baseline viral load was the only significant predictor of improvement in LS after DAA therapy at SVR24. Sofosbuvir-based treatment resulted in an early improvement in parameters of liver fibrosis in post-LT patients with hepatitis C recurrence. © 2018 Wiley Periodicals, Inc.

Association of sustained virologic response with reduced progression to liver cirrhosis in elderly patients with chronic hepatitis C.

PubMed

Tseng, Chih-Wei; Chang, Ting-Tsung; Tzeng, Shinn-Jia; Hsieh, Yu-Hsi; Hung, Tsung-Hsing; Huang, Hsiang-Ting; Wu, Shu-Fen; Tseng, Kuo-Chih

2016-01-01

We studied the effect of sustained virologic response (SVR) after treatment with pegylated-interferon (PEG-IFN) plus ribavirin on the development of liver cirrhosis in elderly patients with chronic hepatitis C (CHC). This retrospective study enrolled 145 elderly CHC patients (aged ≥65 years) who were treatment-naïve and were treated with PEG-IFN plus ribavirin for 6 months between January 2005 and December 2011. Abdominal sonography was performed and liver biochemistry was studied at baseline, at the end of treatment, and every 3-6 months thereafter. The development of liver cirrhosis and related complications was evaluated at the follow-ups. The aspartate aminotransferase-to-platelet ratio index was used as a noninvasive maker for fibrosis. The mean patient age was 69.1±3.3 years, and the average follow-up time was 5.5 years (standard deviation: 2.5 years, range: 1.1-12.3 years). Ninety-five patients (65.5%) achieved SVR, and 26 (17.9%) discontinued treatment. Twenty-seven patients (18.6%) developed liver cirrhosis after treatment. Patients without SVR had significantly greater risk of liver cirrhosis than those with SVR (hazard ratio [HR]: 3.39, 95% confidence interval [CI]: 1.312-8.761, P=0.012). The difference in 3-year cumulative incidence of liver cirrhosis was 24.8% greater for patients without SVR (35.2%, 95% CI: 13.0-57.5, P=0.012) compared with those with SVR (10.4%, 95% CI: 3.1-17.7). There was a trend of a higher baseline aspartate aminotransferase-to-platelet ratio index score in patients who progressed to liver cirrhosis compared with those who did not progress (2.1±1.2 vs 1.6±1.3, P=0.055), but the difference failed to reach significance by Cox regression (adjusted HR: 1.285, 95% CI: 0.921-1.791, P=0.14). An SVR following PEG-IFN combination treatment can reduce the risk of liver cirrhosis in elderly CHC patients.

A Bayesian least squares support vector machines based framework for fault diagnosis and failure prognosis

NASA Astrophysics Data System (ADS)

Khawaja, Taimoor Saleem

A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classification for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to find a good trade-off between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data is able to distinguish between normal behavior and any abnormal or novel data during real-time operation. The results of the scheme are interpreted as a posterior probability of health (1 - probability of fault). As shown through two case studies in Chapter 3, the scheme is well suited for diagnosing imminent faults in dynamical non-linear systems. Finally, the failure prognosis scheme is based on an incremental weighted Bayesian LS-SVR machine. It is particularly suited for online deployment given the incremental nature of the algorithm and the quick optimization problem solved in the LS-SVR algorithm. By way of kernelization and a Gaussian Mixture Modeling (GMM) scheme, the algorithm can estimate "possibly" non-Gaussian posterior distributions for complex non-linear systems. An efficient regression scheme associated with the more rigorous core algorithm allows for long-term predictions, fault growth estimation with confidence bounds and remaining useful life (RUL) estimation after a fault is detected. The leading contributions of this thesis are (a) the development of a novel Bayesian Anomaly Detector for efficient and reliable Fault Detection and Identification (FDI) based on Least Squares Support Vector Machines, (b) the development of a data-driven real-time architecture for long-term Failure Prognosis using Least Squares Support Vector Machines, (c) Uncertainty representation and management using Bayesian Inference for posterior distribution estimation and hyper-parameter tuning, and finally (d) the statistical characterization of the performance of diagnosis and prognosis algorithms in order to relate the efficiency and reliability of the proposed schemes.

Increased duration of dual pegylated interferon and ribavirin therapy for genotype 1 hepatitis C post-liver transplantation increases sustained virologic response: a retrospective review.

PubMed

Wells, Malcolm M; Roth, Lee S; Marotta, Paul; Levstik, Mark; Mason, Andrew L; Bain, Vincent G; Chandok, Natasha; Aljudaibi, Bandar M

2013-01-01

In patients with advanced post-transplant hepatitis C virus (HCV) recurrence, antiviral treatment (AVT) with interferon and ribavirin is indicated to prevent graft failure. The aim of this study was to determine and report Canadian data with respect to the safety, efficacy, and spontaneous virologic response (SVR) predictors of AVT among transplanted patients with HCV recurrence. A retrospective chart review was performed on patients transplanted in London, Ontario and Edmonton, Alberta from 2002 to 2012 who were treated for HCV. Demographic, medical, and treatment information was collected and analyzed. A total of 85 patients with HCV received pegylated interferon with ribavirin post-liver transplantation and 28 of the 65 patients (43%) with genotype 1 achieved SVR. Of the patients having genotype 1 HCV who achieved SVR, there was a significantly lower stage of fibrosis (1.37 ± 0.88 vs. 1.89 ± 0.96; P = 0.03), increased ribavirin dose (total daily dose 1057 ± 230 vs. 856 ± 399 mg; P = 0.02), increased rapid virologic response (RVR) (6/27 vs. 0/31; P = 0.05), increased early virologic response (EVR) (28/28 vs. 18/35; P = 0.006), and longer duration of therapy (54.7 ± 13.4 weeks vs. 40.2 ± 18.7; P = 0.001). A logistic regression model using gender, age, RVR, EVR, anemia, duration of therapy, viral load, years' post-transplant, and type of organ (donation after cardiac death vs. donation after brain death) significantly predicted SVR (P < 0.001), with duration of therapy having a significant odds ratio of 1.078 (P = 0.007). This study identified factors that predict SVR in HCV-positive patients who received dual therapy post-transplantation. Extending therapy from 48 weeks to 72 weeks of dual therapy is associated with increased SVR rates. Future studies examining the role of extended therapy are needed to confirm these findings, since the current study is a retrospective one.

Predictive value of FIB-4 and APRI versus METAVIR on sustained virologic response in genotype 1 hepatitis C patients.

PubMed

Ferenci, Peter; Aires, Rodrigo; Beavers, Kimberly L; Curescu, Manuela; Abrão Ferreira, Paulo R; Gschwantler, Michael; Ion, Stefan; Larrey, Dominique; Maticic, Mojca; Puoti, Massimo; Schuller, János; Tornai, Istvan; Tusnádi, Anna; Messinger, Diethelm; Tatsch, Fernando; Horban, Andrzej

2014-01-01

Advanced liver fibrosis is a negative predictor of virologic response in genotype 1 chronic hepatitis C (CHC) patients. Biopsy, however, is invasive, costly, and carries some risk of complications. Using data from the prospective, international cohort study PROPHESYS, we assessed two alternative noninvasive measures of fibrosis, the FIB-4 and AST-to-platelet ratio index (APRI), to predict virologic response in CHC patients. CHC genotype 1, monoinfected, treatment-naive patients prescribed peginterferon alfa-2a (40 KD)/ribavirin in accordance with country-specific legal and regulatory requirements and who had baseline METAVIR, FIB-4, and APRI scores (N = 1,592) were included in this analysis. Patients were stratified according to the baseline METAVIR, FIB-4, or APRI score to assess virologic response [hepatitis C virus (HCV) RNA <50 IU/mL] by week 4 of treatment (rapid virologic response) and 24 weeks after untreated follow-up ]sustained virologic response (SVR)]. Baseline predictors of SVR were explored by multiple logistic regression, and the strength of the association between each fibrosis measure and SVR was evaluated. Both FIB-4 and APRI scores increased with increasing levels of biopsy-assessed fibrosis. The association between FIB-4 and SVR (p < 0.1 × 10(-30)) was stronger than that between METAVIR (p = 3.86 × 10(-13)) or APRI (p = 5.48 × 10(-6)) and SVR. Baseline factors significantly associated with SVR included male gender, lower HCV RNA, lower FIB-4 score, no steatosis, and higher alanine aminotransferase ratio. The FIB-4 index provides a valuable, noninvasive measure of fibrosis and can be used to predict virologic response in patients treated with peginterferon alfa-2a (40  KD)/ribavirin.

The natural history and long-term outcomes in patients with chronic hepatitis C genotype 4 after interferon-based therapy.

PubMed

Alfaleh, Faleh Z; Alswat, Khalid; Helmy, Ahmed; Al-hamoudi, Waleed; El-sharkawy, Mohamed; Omar, Mohanned; Shalaby, Ahmed; Bedewi, Mohaned A; Hadad, Qais; Ali, Safiyya M; Alfaleh, Ahmad; Abdo, Ayman A

2013-07-01

Hepatitis C virus (HCV) genotype 4 (G4) infection is common in the Middle East. Post-treatment long-term outcomes have not been reported in these patients. This study evaluates these outcomes in patients after interferon-based therapy. A total of 157 patients were followed from June 2001 to February 2012. Descriptive and analytical statistics, cumulative outcomes and the independent predictors of disease progression were calculated. The overall age was 48.0 ± 11.8 years, 75 (47.8%) were males and 53 (70.7%) of 75 who were genotyped had G4. The follow-up period was 63.8 ± 32.8 months. Sustained virological response (SVR) was achieved in 62 (39.5%) and 24 (45.3%) patients in the whole group and the G4 subgroup respectively. Among the whole cohort and the G4 subgroup, disease progressed in 59 (37.6%) and 21 (39.6%), respectively, with less progression in the SVR groups; 15/62 (24.2%) and 3/24 (12.5%) compared with non-responders; 44 (46.3%) and 18 (62.1%) with P = 0.01 and 0.001 respectively. Multivariate logistic regression analysis showed that having diabetes mellitus (P = 0.03), higher baseline APRI score (P = 0.00) and non-SVR (P = 0.00) were independent predictors of disease progression. G4 patients showed similar results, but 'non-SVR' (P = 0.00) was the only independent predictor of progression. Eight patients died and four developed HCC all among the non-SVR group only. This study describes, for the first time, the natural history and demonstrates the beneficial long-term effects of interferon-based therapy in HCV G4 patients. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Is response guided therapy dead? Low cure rates in patients with detectable hepatitis C virus at week 4 of treatment.

PubMed

Thornton, Karla; Deming, Paulina; Manch, Richard A; Moore, Ann; Kohli, Anita; Gish, Robert; Sussman, Norman L; Khaderi, Saira; Scott, John; Mera, Jorge; Box, Terry; Qualls, Clifford; Sedillo, Miranda; Arora, Sanjeev

2016-07-01

Historically, chronic hepatitis C virus (HCV) treatment was response-guided. Clinical trials with sofosbuvir indicated on-treatment virologic response was not predictive of sustained virologic response (SVR) and hence response-guided therapy (RGT) was abandoned. The purpose of this study is to examine the association between on-treatment 4-week HCV RNA and SVR in patients treated in real-world practice. The study is a retrospective analysis of consecutive patients started on treatment with a sofosbuvir-containing regimen, January 1, 2014 through August 20, 2014, for HCV genotype 1-6 infection. Patients were treated by HCV specialists at 6 centers in the Project ECHO (Extension for Community Healthcare Outcomes) HCV Collaborative or in the community by primary care clinicians mentored by HCV specialists through Project ECHO. Patients were included if they were over 18 years, had evidence of chronic HCV, and were started on a sofosbuvir-containing regimen. The aspartate aminotransferase:platelet ratio index (APRI) was used to estimate fibrosis. The main outcome measures were 4-week HCV RNA and SVR. Overall SVR was 82.5 %. At week 4, HCV RNA was detected in 27.4 % of patients. Stepwise multivariable logistic-regression analyses identified APRI > 1.0, male sex, genotype 3, and detectable on treatment 4-week HCV RNA as independent predictors of failure to achieve SVR. In a real-world setting, a significant proportion of sofosbuvir treated patients have detectable on-treatment 4-week HCV RNA. Detectable on-treatment 4-week HCV RNA is associated with virologic failure. More data are needed to formulate guidance for RGT with newly available HCV therapies.

Semi-supervised Machine Learning for Analysis of Hydrogeochemical Data and Models

NASA Astrophysics Data System (ADS)

Vesselinov, Velimir; O'Malley, Daniel; Alexandrov, Boian; Moore, Bryan

2017-04-01

Data- and model-based analyses such as uncertainty quantification, sensitivity analysis, and decision support using complex physics models with numerous model parameters and typically require a huge number of model evaluations (on order of 10^6). Furthermore, model simulations of complex physics may require substantial computational time. For example, accounting for simultaneously occurring physical processes such as fluid flow and biogeochemical reactions in heterogeneous porous medium may require several hours of wall-clock computational time. To address these issues, we have developed a novel methodology for semi-supervised machine learning based on Non-negative Matrix Factorization (NMF) coupled with customized k-means clustering. The algorithm allows for automated, robust Blind Source Separation (BSS) of groundwater types (contamination sources) based on model-free analyses of observed hydrogeochemical data. We have also developed reduced order modeling tools, which coupling support vector regression (SVR), genetic algorithms (GA) and artificial and convolutional neural network (ANN/CNN). SVR is applied to predict the model behavior within prior uncertainty ranges associated with the model parameters. ANN and CNN procedures are applied to upscale heterogeneity of the porous medium. In the upscaling process, fine-scale high-resolution models of heterogeneity are applied to inform coarse-resolution models which have improved computational efficiency while capturing the impact of fine-scale effects at the course scale of interest. These techniques are tested independently on a series of synthetic problems. We also present a decision analysis related to contaminant remediation where the developed reduced order models are applied to reproduce groundwater flow and contaminant transport in a synthetic heterogeneous aquifer. The tools are coded in Julia and are a part of the MADS high-performance computational framework (https://github.com/madsjulia/Mads.jl).

Quantitative analysis of multi-component gas mixture based on AOTF-NIR spectroscopy

NASA Astrophysics Data System (ADS)

Hao, Huimin; Zhang, Yong; Liu, Junhua

2007-12-01

Near Infrared (NIR) spectroscopy analysis technology has attracted many eyes and has wide application in many domains in recent years because of its remarkable advantages. But the NIR spectrometer can only be used for liquid and solid analysis by now. In this paper, a new quantitative analysis method of gas mixture by using new generation NIR spectrometer is explored. To collect the NIR spectra of gas mixtures, a vacuumable gas cell was designed and assembled to Luminar 5030-731 Acousto-Optic Tunable Filter (AOTF)-NIR spectrometer. Standard gas samples of methane (CH 4), ethane (C IIH 6) and propane (C 3H 8) are diluted with super pure nitrogen via precision volumetric gas flow controllers to obtain gas mixture samples of different concentrations dynamically. The gas mixtures were injected into the gas cell and the spectra of wavelength between 1100nm-2300nm were collected. The feature components extracted from gas mixture spectra by using Partial Least Squares (PLS) were used as the inputs of the Support Vector Regress Machine (SVR) to establish the quantitative analysis model. The effectiveness of the model is tested by the samples of predicting set. The prediction Root Mean Square Error (RMSE) of CH 4, C IIH 6 and C 3H 8 is respectively 1.27%, 0.89%, and 1.20% when the concentrations of component gas are over 0.5%. It shows that the AOTF-NIR spectrometer with gas cell can be used for gas mixture analysis. PLS combining with SVR has a good performance in NIR spectroscopy analysis. This paper provides the bases for extending the application of NIR spectroscopy analysis to gas detection.

A Machine-Learning and Filtering Based Data Assimilation Framework for Geologic Carbon Sequestration Monitoring Optimization

NASA Astrophysics Data System (ADS)

Chen, B.; Harp, D. R.; Lin, Y.; Keating, E. H.; Pawar, R.

2017-12-01

Monitoring is a crucial aspect of geologic carbon sequestration (GCS) risk management. It has gained importance as a means to ensure CO2 is safely and permanently stored underground throughout the lifecycle of a GCS project. Three issues are often involved in a monitoring project: (i) where is the optimal location to place the monitoring well(s), (ii) what type of data (pressure, rate and/or CO2 concentration) should be measured, and (iii) What is the optimal frequency to collect the data. In order to address these important issues, a filtering-based data assimilation procedure is developed to perform the monitoring optimization. The optimal monitoring strategy is selected based on the uncertainty reduction of the objective of interest (e.g., cumulative CO2 leak) for all potential monitoring strategies. To reduce the computational cost of the filtering-based data assimilation process, two machine-learning algorithms: Support Vector Regression (SVR) and Multivariate Adaptive Regression Splines (MARS) are used to develop the computationally efficient reduced-order-models (ROMs) from full numerical simulations of CO2 and brine flow. The proposed framework for GCS monitoring optimization is demonstrated with two examples: a simple 3D synthetic case and a real field case named Rock Spring Uplift carbon storage site in Southwestern Wyoming.

Moisture Damage Modeling in Lime and Chemically Modified Asphalt at Nanolevel Using Ensemble Computational Intelligence

PubMed Central

2018-01-01

This paper measures the adhesion/cohesion force among asphalt molecules at nanoscale level using an Atomic Force Microscopy (AFM) and models the moisture damage by applying state-of-the-art Computational Intelligence (CI) techniques (e.g., artificial neural network (ANN), support vector regression (SVR), and an Adaptive Neuro Fuzzy Inference System (ANFIS)). Various combinations of lime and chemicals as well as dry and wet environments are used to produce different asphalt samples. The parameters that were varied to generate different asphalt samples and measure the corresponding adhesion/cohesion forces are percentage of antistripping agents (e.g., Lime and Unichem), AFM tips K values, and AFM tip types. The CI methods are trained to model the adhesion/cohesion forces given the variation in values of the above parameters. To achieve enhanced performance, the statistical methods such as average, weighted average, and regression of the outputs generated by the CI techniques are used. The experimental results show that, of the three individual CI methods, ANN can model moisture damage to lime- and chemically modified asphalt better than the other two CI techniques for both wet and dry conditions. Moreover, the ensemble of CI along with statistical measurement provides better accuracy than any of the individual CI techniques. PMID:29849551

Detection of Buried Objects by Means of a SAP Technique: Comparing MUSIC- and SVR-Based Approaches

NASA Astrophysics Data System (ADS)

Meschino, S.; Pajewski, L.; Pastorino, M.; Randazzo, A.; Schettini, G.

2012-04-01

This work is focused on the application of a Sub-Array Processing (SAP) technique to the detection of metallic cylindrical objects embedded in a dielectric half-space. The identification of buried cables, pipes, conduits, and other cylindrical utilities, is an important problem that has been extensively studied in the last years. Most commonly used approaches are based on the use of electromagnetic sensing: a set of antennas illuminates the ground and the collected echo is analyzed in order to extract information about the scenario and to localize the sought objects [1]. In a SAP approach, algorithms for the estimation of Directions of Arrival (DOAs) are employed [2]: they assume that the sources (in this paper, currents induced on buried targets) are in the far-field region of the receiving array, so that the received wavefront can be considered as planar, and the main angular direction of the field can be estimated. However, in electromagnetic sensing of buried objects, the scatterers are usually quite near to the antennas. Nevertheless, by dividing the whole receiving array in a suitable number of sub-arrays, and by finding a dominant DOA for each one, it is possible to localize objects that are in the far-field of the sub-array, although being in the near-field of the array. The DOAs found by the sub-arrays can be triangulated, obtaining a set of crossings with intersections condensed around object locations. In this work, the performances of two different DOA algorithms are compared. In particular, a MUltiple SIgnal Classification (MUSIC)-type method [3] and Support Vector Regression (SVR) based approach [4] are employed. The results of a Cylindrical-Wave Approach forward solver are used as input data of the detection procedure [5]. To process the crossing pattern, the region of interest is divided in small windows, and a Poisson model is adopted for the statistical distribution of intersections in the windows. Hypothesis testing procedures are used (imposing a suitable threshold from a desired false-alarm rate), to ascribe each window to the ground or to the sought objects. Numerical results are presented, for a test scenario with a circular-section cylinder in a dielectric half-space. Different values of the ground permittivity, target size, and its position with respect to the receiving array, are considered. Preliminary results on the application of MUSIC and SVR to multiple-object localization are reported. [1] H. Jol, Ground Penetrating Radar: Theory and Applications, Elsevier, Amsterdam, NL, 2009. [2] Gross F.B., Smart Antennas for Wireless Communications, McGraw-Hill, New York 2005. [3] S. Meschino, L. Pajewski, G. Schettini, "Use of a Sub-Array Statistical Approach for the Detection of a Buried Object", Near Surface Geophysics, vol. 8(5), pp. 365-375, 2010. [4] M. Pastorino, A. Randazzo, "A Smart Antenna System for Direction of Arrival Estimation based on a Support Vector Regression," IEEE Trans. Antennas Propagat., vol. 53(7), pp. 2161-2168, 2005. [5] M. Di Vico, F. Frezza, L. Pajewski, G. Schettini, "Scattering by a Finite Set of Perfectly Conducting Cylinders Buried in a Dielectric Half-Space: a Spectral-Domain Solution," IEEE Trans. Antennas Propagat., vol. 53(2), pp. 719-727, 2005.

Changes in liver stiffness and steatosis among patients with hepatitis C virus infection who received direct-acting antiviral therapy and achieved sustained virological response.

PubMed

Kobayashi, Natsuko; Iijima, Hiroko; Tada, Toshifumi; Kumada, Takashi; Yoshida, Masahiro; Aoki, Tomoko; Nishimura, Takashi; Nakano, Chikage; Takata, Ryo; Yoh, Kazunori; Ishii, Akio; Takashima, Tomoyuki; Sakai, Yoshiyuki; Aizawa, Nobuhiro; Nishikawa, Hiroki; Ikeda, Naoto; Iwata, Yoshinori; Enomoto, Hirayuki; Hirota, Seiichi; Fujimoto, Jiro; Nishiguchi, Shuhei

2018-05-01

Whether direct-acting antiviral (DAA) therapy can reduce liver fibrosis and steatosis in patients with chronic hepatitis C virus (HCV) infection remains unclear. We evaluated sequential changes in liver stiffness and steatosis using transient elastography (TE) and the TE-based controlled attenuation parameter (CAP) in patients with HCV who received DAA therapy. A total of 57 patients with HCV who received DAA therapy and achieved sustained virological response (SVR) were analyzed. Liver stiffness as evaluated with TE, steatosis as evaluated with CAP, and laboratory data were assessed before treatment (baseline), at end of treatment (EOT), 24 weeks after EOT (SVR24), and 48 weeks after EOT (SVR48). Alanine aminotransferase levels, corresponding to the presence of necroinflammatory activity, significantly decreased overall, with significant differences between baseline and EOT, EOT, and SVR24, and baseline and SVR48. However, alanine aminotransferase levels showed no significant changes between SVR24 and SVR48. Median (interquartile range) liver stiffness values at baseline, EOT, SVR24, and SVR48 were 8.3 (5.0-14.8), 7.4 (4.6-14.7), 5.3 (4.1-11.8), and 5.4 (4.0-13.4) kPa, respectively (baseline vs. EOT, P=0.044; EOT vs. SVR24, P=0.011; and SVR24 vs. SVR48, P=0.054). In patients with fatty liver (CAP≥236 dB/m, n=14), CAP values at baseline and SVR48 were 253 (245-278) and 229 (209-249) dB/m, respectively (P=0.020). Liver stiffness at SVR24 might reflect liver fibrosis in the patients who received DAA therapy and achieved SVR. In addition, liver steatosis reduces in the same cohort with fatty liver.

Influence of insulin resistance on the development of hepatocellular carcinoma after antiviral treatment for non-cirrhotic patients with chronic hepatitis C.

PubMed

Hayashi, Takeo; Ogawa, Eiichi; Furusyo, Norihiro; Murata, Masayuki; Hayashi, Jun

2016-01-01

Insulin resistance is considered to be an important factor in the progression of fibrosis and the enhancement of the risk of hepatocellular carcinoma (HCC) for chronic hepatitis C patients. The aim of this study was to assess the effect of insulin resistance on the development of HCC by non-cirrhotic chronic hepatitis C patients treated with pegylated interferon alpha-2b (PEG-IFNα2b) and ribavirin. This retrospective study consisted of 474 Japanese non-cirrhotic patients with chronic hepatitis C. The cumulative incidence of HCC was estimated using the Kaplan-Meier method, according to insulin resistance by the homeostasis model assessment of insulin resistance (HOMA-IR) and treatment outcome. The overall sustained virological response (SVR) rate was 45.1 % (214/474, genotype 1: 35.4 % [129/364] and genotype 2: 77.3 % [85/110]). Twenty-one (4.4 %) patients developed HCC during the follow-up period. The 5-year cumulative incidence of HCC of the SVR group (2.6 %) was significantly lower than that of the non-SVR group (9.7 %) (log-rank test: P = 0.025). In multivariable logistic regression analysis, HOMA-IR (≥2.5) (hazard ratio [HR] 12.8, P = 0.0006), fibrosis status (F3) (HR 8.85, P < 0.0001), and post-treatment alanine aminotransferase (ALT) level (≥40 U/L) (HR 4.33, P = 0.036) were independently correlated to the development of HCC. Receiver operating characteristic analysis to determine the optimal threshold value of HOMA-IR for predicting the development of HCC in the non-SVR group showed that the areas under the curve was high (0.80, cutoff value: 3.0). Only three patients (1.4 %) who achieved SVR developed HCC. Two of them had severe insulin resistance and did not show improvement in HOMA-IR after achieving SVR. Insulin resistance has a strong impact on the development of HCC by non-cirrhotic patients who have PEG-IFNα2b and ribavirin treatment failure.

Effectiveness of Ledipasvir-Sofosbuvir Combination in Patients With Hepatitis C Virus Infection and Factors Associated With Sustained Virologic Response

PubMed Central

Terrault, Norah A.; Zeuzem, Stefan; Di Bisceglie, Adrian M.; Lim, Joseph K.; Pockros, Paul J.; Frazier, Lynn M.; Kuo, Alexander; Lok, Anna S.; Shiffman, Mitchell L.; Ben Ari, Ziv; Akushevich, Lucy; Vainorius, Monika; Sulkowski, Mark S.; Fried, Michael W.; Nelson, David R.

2017-01-01

BACKGROUND & AIMS The combination of ledipasvir and sofosbuvir has been approved for treatment of genotype 1 hepatitis C virus (HCV) infection, including an 8-week regimen for treatment-naïve patients without cirrhosis and a baseline level of HCV RNA <6 million IU/mL. We analyzed data from a multicenter, prospective, observational study to determine real-world sustained virologic responses 12 weeks after treatment (SVR12) with regimens containing ledipasvir and sofosbuvir and identify factors associated with treatment failure. METHODS We collected data from 2099 participants in the HCV-TARGET study with complete virologic data (per-protocol population). We analyzed data from 1788 patients receiving ledipasvir-sofosbuvir (282 for 8 weeks, 910 for 12 weeks, 510 for 24 weeks, and 86 for a different duration) and 311 receiving ledipasvir-sofosbuvir plus ribavirin (212 for 12 weeks and 81 for 24 weeks, 18 for other duration) to estimate SVR12 (with 95% confidence interval [CI]), and logistic regression methods to identify factors that predicted an SVR12. RESULTS The overall study population was 25% black, 66% with HCV genotype 1A infection, 41% with cirrhosis, 50% treatment-experienced, and 30% receiving proton pump inhibitors at start of treatment. In the per-protocol population, SVR12s were achieved by 96% of patients receiving ledipasvir-sofosbuvir for 8 weeks (95% CI, 93%–98%), 97% receiving the drugs for 12 weeks (95% CI, 96%–98%), and 95% receiving the drugs for 24 weeks (95% CI, 93%–97%). Among patients also receiving ribavirin, SVR12 was achieved by 97% of the patients receiving the drugs for 12 weeks (95% CI, 94%–99%) and 95% receiving the drugs for 24 weeks (95% CI, 88%–99%). Of the 586 patients who qualified for 8 weeks of treatment, only 255 (44%) received the drugs for 8 weeks. The rate of SVR12 among those who qualified for and received 8 weeks of therapy was similar in those who qualified for 8 weeks but received 12 weeks therapy (96%; 95% CI, 92%–99% vs 98%; 95% CI, 95%–99%). Factors that predicted SVR12 were higher albumin (≤3.5 g/dL), lower total bilirubin (≥1.2 g/dL), absence of cirrhosis, and absence of proton pump inhibitor use. CONCLUSIONS Regimens containing ledipasvir and sofosbuvir are highly effective for a broad spectrum of patients with HCV genotype 1 infection treated in different clinical practice settings. Expanded use of 8-week treatment regimens for eligible patients is supported by these real-world results. Modification of proton pump inhibitor use may increase rates of SVR. ClinicalTrials.gov no. NCT01474811. PMID:27565882

Effectiveness of Ledipasvir-Sofosbuvir Combination in Patients With Hepatitis C Virus Infection and Factors Associated With Sustained Virologic Response.

PubMed

Terrault, Norah A; Zeuzem, Stefan; Di Bisceglie, Adrian M; Lim, Joseph K; Pockros, Paul J; Frazier, Lynn M; Kuo, Alexander; Lok, Anna S; Shiffman, Mitchell L; Ben Ari, Ziv; Akushevich, Lucy; Vainorius, Monika; Sulkowski, Mark S; Fried, Michael W; Nelson, David R

2016-12-01

The combination of ledipasvir and sofosbuvir has been approved for treatment of genotype 1 hepatitis C virus (HCV) infection, including an 8-week regimen for treatment-naïve patients without cirrhosis and a baseline level of HCV RNA <6 million IU/mL. We analyzed data from a multicenter, prospective, observational study to determine real-world sustained virologic responses 12 weeks after treatment (SVR12) with regimens containing ledipasvir and sofosbuvir and identify factors associated with treatment failure. We collected data from 2099 participants in the HCV-TARGET study with complete virologic data (per-protocol population). We analyzed data from 1788 patients receiving ledipasvir-sofosbuvir (282 for 8 weeks, 910 for 12 weeks, 510 for 24 weeks, and 86 for a different duration) and 311 receiving ledipasvir-sofosbuvir plus ribavirin (212 for 12 weeks and 81 for 24 weeks, 18 for other duration) to estimate SVR12 (with 95% confidence interval [CI]), and logistic regression methods to identify factors that predicted an SVR12. The overall study population was 25% black, 66% with HCV genotype 1A infection, 41% with cirrhosis, 50% treatment-experienced, and 30% receiving proton pump inhibitors at start of treatment. In the per-protocol population, SVR12s were achieved by 96% of patients receiving ledipasvir-sofosbuvir for 8 weeks (95% CI, 93%-98%), 97% receiving the drugs for 12 weeks (95% CI, 96%-98%), and 95% receiving the drugs for 24 weeks (95% CI, 93%-97%). Among patients also receiving ribavirin, SVR12 was achieved by 97% of the patients receiving the drugs for 12 weeks (95% CI, 94%-99%) and 95% receiving the drugs for 24 weeks (95% CI, 88%-99%). Of the 586 patients who qualified for 8 weeks of treatment, only 255 (44%) received the drugs for 8 weeks. The rate of SVR12 among those who qualified for and received 8 weeks of therapy was similar in those who qualified for 8 weeks but received 12 weeks therapy (96%; 95% CI, 92%-99% vs 98%; 95% CI, 95%-99%). Factors that predicted SVR12 were higher albumin (≥3.5 g/dL), lower total bilirubin (≤1.2 g/dL), absence of cirrhosis, and absence of proton pump inhibitor use. Regimens containing ledipasvir and sofosbuvir are highly effective for a broad spectrum of patients with HCV genotype 1 infection treated in different clinical practice settings. Expanded use of 8-week treatment regimens for eligible patients is supported by these real-world results. Modification of proton pump inhibitor use may increase rates of SVR. ClinicalTrials.gov no. NCT01474811. Copyright © 2016 AGA Institute. Published by Elsevier Inc. All rights reserved.

Phosphorylated Smad2 and Smad3 signaling: Shifting between tumor suppression and fibro-carcinogenesis in chronic hepatitis C.

PubMed

Yamaguchi, Takashi; Matsuzaki, Koichi; Inokuchi, Ryosuke; Kawamura, Rinako; Yoshida, Katsunori; Murata, Miki; Fujisawa, Junichi; Fukushima, Nobuyoshi; Sata, Michio; Kage, Masayoshi; Nakashima, Osamu; Tamori, Akihiro; Kawada, Norifumi; Tsuneyama, Koichi; Dooley, Steven; Seki, Toshihito; Okazaki, Kazuichi

2013-12-01

Insight into hepatic fibrogenesis and carcinogenesis (fibro-carcinogenesis) caused by hepatitis C virus (HCV) infection has come from recent analyses of transforming growth factor (TGF)-β signaling. TGF-β type I receptor and pro-inflammatory cytokine-activated kinases differentially phosphorylate Smad2 and Smad3 to create C-terminally (C), linker (L) or dually (L/C) phosphorylated (p) isoforms. This study aimed to elucidate how HCV infection affected hepatic fibro-carcinogenesis, particularly via phospho-Smad signaling. We first studied phospho-Smad2/3 positivity of 100 patients in different stages of HCV-related chronic liver disease. To examine changes in phospho-Smad2/3 after HCV clearance, we analyzed 32 paired liver biopsy samples obtained before and after sustained virological response (SVR), dividing patients into two groups: 20 patients not developing hepatocellular carcinoma (HCC) after attaining SVR (non-HCC group), and 12 patients who developed HCC despite SVR (HCC group). Hepatocytic tumor-suppressive pSmad3C signaling shifted to carcinogenic pSmad3L and fibrogenic pSmad2L/C signaling as liver diseases progressed. In the non-HCC group, 13 patients (65%) displayed fibrotic regression and inflammation reduction after SVR. Interestingly, SVR restored cytostatic pSmad3C signaling in hepatocytes, while eliminating prior carcinogenic pSmad3L and fibrogenic pSmad2L/C signaling. In the HCC group, seven patients (58%) displayed unchanged or even progressed fibrosis despite smoothened inflammatory activity, reflecting persistently high numbers of hepatocytes with pSmad3L- and pSmad2L/C-signaling and low pSmad3C-signaling. HCV clearance limits fibrosis and reduces HCC incidence by switching inflammation-dependent phospho-Smad signaling from fibro-carcinogenesis to tumor suppression. However, progression to HCC would occur in severely fibrotic livers if an inflammation-independent fibro-carcinogenic process has already begun before HCV clearance. © 2013 The Japan Society of Hepatology.

Predictors of functional benefit of hepatitis C therapy in a ‘real-life’ cohort

PubMed Central

Steinebrunner, Niels; Stein, Kerstin; Sandig, Catharina; Bruckner, Thomas; Stremmel, Wolfgang; Pathil, Anita

2018-01-01

AIM To define predictors of functional benefit of direct-acting antivirals (DAAs) in patients with chronic hepatitis C virus (HCV) infection and liver cirrhosis. METHODS We analysed a cohort of 199 patients with chronic HCV genotype 1, 2, 3 and 4 infection involving previously treated and untreated patients with compensated (76%) and decompensated (24%) liver cirrhosis at two tertiary centres in Germany. Patients were included with treatment initiation between February 2014 and August 2016. All patients received a combination regimen of one or more DAAs for either 12 or 24 wk. Predictors of functional benefit were assessed in a univariable as well as multivariable model by binary logistic regression analysis. RESULTS Viral clearance was achieved in 88% (175/199) of patients. Sustained virological response (SVR) 12 rates were as follows: among 156 patients with genotype 1 infection the SVR 12 rate was 90% (n = 141); among 7 patients with genotype 2 infection the SVR 12 rate was 57% (n = 4); among 30 patients with genotype 3 infection the SVR 12 rate was 87% (n = 26); and among 6 patients with genotype 4 infection the SVR 12 rate was 67% (n = 4). Follow-up MELD scores were available for 179 patients. A MELD score improvement was observed in 37% (65/179) of patients, no change of MELD score in 41% (74/179) of patients, and an aggravation was observed in 22% (40/179) of patients. We analysed predictors of functional benefit from antiviral therapy in our patients beyond viral eradication. We identified the Child-Pugh score, the MELD score, the number of platelets and the levels of albumin and bilirubin as significant factors for functional benefit. CONCLUSION Our data may contribute to the discussion of potential risks and benefits of antiviral therapy with individual patients infected with HCV and with advanced liver disease. PMID:29467555

What is the impact of a country-wide scale-up in antiviral therapy on the characteristics and sustained viral response rates of patients treated for hepatitis C?

PubMed

McDonald, Scott A; Innes, Hamish A; Hayes, Peter C; Dillon, John F; Mills, Peter R; Goldberg, David J; Barclay, Stephen; Allen, Sam; Fox, Ray; Fraser, Andrew; Kennedy, Nicholas; Bhattacharyya, Diptendu; Hutchinson, Sharon J

2015-02-01

The global burden associated with hepatitis C virus (HCV) infection has prompted a scale-up of antiviral therapy. Hitherto, no data exist on the impact of scaling-up, on the characteristics of treated populations, or on sustained viral response (SVR) rates. We assessed the country-wide scale-up of antiviral therapy in Scotland, a country which nationally monitors uptake of and response to HCV treatment. Data for patients, initiated on combined pegylated interferon and ribavirin therapy at 13 specialist HCV clinics in 2001-2010, were extracted from the Scottish HCV Clinical Database (n=3895). Patient characteristics included age, genotype, PWID (people who inject drugs) status, prison referral, and diagnosed cirrhosis. Temporal trends in covariates and adjusted effects on a SVR were examined via mixed-effects regression. The number of patients starting treatment increased from 237 in 2001-2002 to 1560 in 2009-2010, with an increasing trend in SVR from 44% to 57% over this period. For a given clinic, between 2001/2 and 2010 there was a decrease in the odds of those treated being diagnosed with cirrhosis (odds ratio [OR]=0.84 per year), and increasing temporal trends for those treated being PWID (OR=1.08) and prison referrals (OR=1.06). Adjusting for covariates, the proportion of a given clinic's patients achieving SVR was positively associated with the percentage of PWID (OR=1.01 per percent increase; 95% confidence interval [CI]: 1.00-1.02) and genotype 2/3 (OR=1.03; 95% CI: 1.02-1.04). Despite changes in patient characteristics, a country-wide scale-up of antiviral therapy did not compromise SVR rates. Results are highly relevant to countries planning on scaling-up treatment, given the forthcoming availability of new interferon-free therapies. Copyright © 2014 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.

Cotton Leaf Curl Multan Betasatellite DNA as a Tool to Deliver and Express the Human B-Cell Lymphoma 2 (Bcl-2) Gene in Plants.

PubMed

Kharazmi, Sara; Ataie Kachoie, Elham; Behjatnia, Seyed Ali Akbar

2016-05-01

The betasatellite DNA associated with Cotton leaf curl Multan virus (CLCuMB) contains a single complementary-sense ORF, βC1, which is a pathogenicity determinant. CLCuMB was able to replicate in plants in the presence of diverse helper geminiviruses, including Tomato leaf curl virus-Australia (TLCV-Au), Iranian isolate of Tomato yellow leaf curl virus (TYLCV-[Ab]), and Beet curly top virus (BCTV-Svr), and can be used as a plant gene delivery vector. To test the hypothesis that CLCuMB has the potential to act as an animal gene delivery vector, a specific insertion construct was produced by the introduction of a human B-cell lymphoma 2 (Bcl-2) cDNA into a mutant DNA of CLCuMB in which the βC1 was deleted (β∆C1). The recombinant βΔC1-Bcl-2 construct was successfully replicated in tomato and tobacco plants in the presence of TLCV-Au, BCTV-Svr and TYLCV-[Ab]. Real-time PCR and Western blot analyses of plants containing the replicative forms of recombinant βΔC1-Bcl-2 DNA showed that Bcl-2 gene was expressed in an acceptable level in these plants, indicating that β∆C1 can be used as a tool to deliver and express animal genes in plants. This CLCuMB-based system, having its own promoter activity, offers the possibility of production of animal recombinant proteins in plants.

Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine

NASA Astrophysics Data System (ADS)

Maimaitijiang, Maitiniyazi; Ghulam, Abduwasit; Sidike, Paheding; Hartling, Sean; Maimaitiyiming, Matthew; Peterson, Kyle; Shavers, Ethan; Fishman, Jack; Peterson, Jim; Kadam, Suhas; Burken, Joel; Fritschi, Felix

2017-12-01

Estimating crop biophysical and biochemical parameters with high accuracy at low-cost is imperative for high-throughput phenotyping in precision agriculture. Although fusion of data from multiple sensors is a common application in remote sensing, less is known on the contribution of low-cost RGB, multispectral and thermal sensors to rapid crop phenotyping. This is due to the fact that (1) simultaneous collection of multi-sensor data using satellites are rare and (2) multi-sensor data collected during a single flight have not been accessible until recent developments in Unmanned Aerial Systems (UASs) and UAS-friendly sensors that allow efficient information fusion. The objective of this study was to evaluate the power of high spatial resolution RGB, multispectral and thermal data fusion to estimate soybean (Glycine max) biochemical parameters including chlorophyll content and nitrogen concentration, and biophysical parameters including Leaf Area Index (LAI), above ground fresh and dry biomass. Multiple low-cost sensors integrated on UASs were used to collect RGB, multispectral, and thermal images throughout the growing season at a site established near Columbia, Missouri, USA. From these images, vegetation indices were extracted, a Crop Surface Model (CSM) was advanced, and a model to extract the vegetation fraction was developed. Then, spectral indices/features were combined to model and predict crop biophysical and biochemical parameters using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extreme Learning Machine based Regression (ELR) techniques. Results showed that: (1) For biochemical variable estimation, multispectral and thermal data fusion provided the best estimate for nitrogen concentration and chlorophyll (Chl) a content (RMSE of 9.9% and 17.1%, respectively) and RGB color information based indices and multispectral data fusion exhibited the largest RMSE 22.6%; the highest accuracy for Chl a + b content estimation was obtained by fusion of information from all three sensors with an RMSE of 11.6%. (2) Among the plant biophysical variables, LAI was best predicted by RGB and thermal data fusion while multispectral and thermal data fusion was found to be best for biomass estimation. (3) For estimation of the above mentioned plant traits of soybean from multi-sensor data fusion, ELR yields promising results compared to PLSR and SVR in this study. This research indicates that fusion of low-cost multiple sensor data within a machine learning framework can provide relatively accurate estimation of plant traits and provide valuable insight for high spatial precision in agriculture and plant stress assessment.

Do changes in climate and land use pose a risk to the future water availability of Mediterranean Lakes?

NASA Astrophysics Data System (ADS)

Bucak, T.; Trolle, D.; Andersen, H. E.; Thodsen, H.; Erdoğan, Ş.; Levi, E. E.; Filiz, N.; Jeppesen, E.; Beklioğlu, M.

2016-12-01

Inter- and intra-annual water level fluctuations and change in water flow regime are intrinsic characteristics of Mediterranean lakes. However, considering the climate change projections for the water-limited Mediterranean region where potential evapotranspiration exceeds precipitation and with increased air temperatures and decreased precipitation, more dramatic water level declines in lakes and severe water scarcity problems are expected to occur in the future. Our study lake, Lake Beyşehir, the largest freshwater lake in the Mediterranean basin, is - like other Mediterranean lakes - under pressure due to water abstraction for irrigated crop farming and climatic changes, and integrated water level management is therefore required. We used an integrated modeling approach to predict the future lake water level of Lake Beyşehir in response to the future changes in both climate and, potentially, land use by linking the catchment model Soil and Water Assessment Tool (SWAT) with a Support Vector Machine Regression model (ɛ-SVR). We found that climate change projections caused enhanced potential evapotranspiration and reduced total runoff, whereas the effects of various land use scenarios within the catchment were comparatively minor. In all climate scenarios applied in the ɛ-SVR model, changes in hydrological processes caused a water level reduction, predicting that the lake may dry out already in the 2040s with the current outflow regulation considering the most pessimistic scenario. Based on model runs with optimum outflow management, a 9-60% reduction in outflow withdrawal is needed to prevent the lake from drying out by the end of this century. Our results indicate that shallow Mediterranean lakes may face a severe risk of drying out and loss of ecosystem value in near future if the current intense water abstraction is maintained. Therefore, we conclude that outflow management in water-limited regions in a warmer and drier future and sustainable use of water sources are vitally important to sustain lake ecosystems and their ecosystem services.

Estimating Influenza Outbreaks Using Both Search Engine Query Data and Social Media Data in South Korea.

PubMed

Woo, Hyekyung; Cho, Youngtae; Shim, Eunyoung; Lee, Jong-Koo; Lee, Chang-Gun; Kim, Seong Hwan

2016-07-04

As suggested as early as in 2006, logs of queries submitted to search engines seeking information could be a source for detection of emerging influenza epidemics if changes in the volume of search queries are monitored (infodemiology). However, selecting queries that are most likely to be associated with influenza epidemics is a particular challenge when it comes to generating better predictions. In this study, we describe a methodological extension for detecting influenza outbreaks using search query data; we provide a new approach for query selection through the exploration of contextual information gleaned from social media data. Additionally, we evaluate whether it is possible to use these queries for monitoring and predicting influenza epidemics in South Korea. Our study was based on freely available weekly influenza incidence data and query data originating from the search engine on the Korean website Daum between April 3, 2011 and April 5, 2014. To select queries related to influenza epidemics, several approaches were applied: (1) exploring influenza-related words in social media data, (2) identifying the chief concerns related to influenza, and (3) using Web query recommendations. Optimal feature selection by least absolute shrinkage and selection operator (Lasso) and support vector machine for regression (SVR) were used to construct a model predicting influenza epidemics. In total, 146 queries related to influenza were generated through our initial query selection approach. A considerable proportion of optimal features for final models were derived from queries with reference to the social media data. The SVR model performed well: the prediction values were highly correlated with the recent observed influenza-like illness (r=.956; P<.001) and virological incidence rate (r=.963; P<.001). These results demonstrate the feasibility of using search queries to enhance influenza surveillance in South Korea. In addition, an approach for query selection using social media data seems ideal for supporting influenza surveillance based on search query data.

Estimating Influenza Outbreaks Using Both Search Engine Query Data and Social Media Data in South Korea

PubMed Central

Woo, Hyekyung; Shim, Eunyoung; Lee, Jong-Koo; Lee, Chang-Gun; Kim, Seong Hwan

2016-01-01

Background As suggested as early as in 2006, logs of queries submitted to search engines seeking information could be a source for detection of emerging influenza epidemics if changes in the volume of search queries are monitored (infodemiology). However, selecting queries that are most likely to be associated with influenza epidemics is a particular challenge when it comes to generating better predictions. Objective In this study, we describe a methodological extension for detecting influenza outbreaks using search query data; we provide a new approach for query selection through the exploration of contextual information gleaned from social media data. Additionally, we evaluate whether it is possible to use these queries for monitoring and predicting influenza epidemics in South Korea. Methods Our study was based on freely available weekly influenza incidence data and query data originating from the search engine on the Korean website Daum between April 3, 2011 and April 5, 2014. To select queries related to influenza epidemics, several approaches were applied: (1) exploring influenza-related words in social media data, (2) identifying the chief concerns related to influenza, and (3) using Web query recommendations. Optimal feature selection by least absolute shrinkage and selection operator (Lasso) and support vector machine for regression (SVR) were used to construct a model predicting influenza epidemics. Results In total, 146 queries related to influenza were generated through our initial query selection approach. A considerable proportion of optimal features for final models were derived from queries with reference to the social media data. The SVR model performed well: the prediction values were highly correlated with the recent observed influenza-like illness (r=.956; P<.001) and virological incidence rate (r=.963; P<.001). Conclusions These results demonstrate the feasibility of using search queries to enhance influenza surveillance in South Korea. In addition, an approach for query selection using social media data seems ideal for supporting influenza surveillance based on search query data. PMID:27377323

Sequential optimization of a terrestrial biosphere model constrained by multiple satellite based products

NASA Astrophysics Data System (ADS)

Ichii, K.; Kondo, M.; Wang, W.; Hashimoto, H.; Nemani, R. R.

2012-12-01

Various satellite-based spatial products such as evapotranspiration (ET) and gross primary productivity (GPP) are now produced by integration of ground and satellite observations. Effective use of these multiple satellite-based products in terrestrial biosphere models is an important step toward better understanding of terrestrial carbon and water cycles. However, due to the complexity of terrestrial biosphere models with large number of model parameters, the application of these spatial data sets in terrestrial biosphere models is difficult. In this study, we established an effective but simple framework to refine a terrestrial biosphere model, Biome-BGC, using multiple satellite-based products as constraints. We tested the framework in the monsoon Asia region covered by AsiaFlux observations. The framework is based on the hierarchical analysis (Wang et al. 2009) with model parameter optimization constrained by satellite-based spatial data. The Biome-BGC model is separated into several tiers to minimize the freedom of model parameter selections and maximize the independency from the whole model. For example, the snow sub-model is first optimized using MODIS snow cover product, followed by soil water sub-model optimized by satellite-based ET (estimated by an empirical upscaling method; Support Vector Regression (SVR) method; Yang et al. 2007), photosynthesis model optimized by satellite-based GPP (based on SVR method), and respiration and residual carbon cycle models optimized by biomass data. As a result of initial assessment, we found that most of default sub-models (e.g. snow, water cycle and carbon cycle) showed large deviations from remote sensing observations. However, these biases were removed by applying the proposed framework. For example, gross primary productivities were initially underestimated in boreal and temperate forest and overestimated in tropical forests. However, the parameter optimization scheme successfully reduced these biases. Our analysis shows that terrestrial carbon and water cycle simulations in monsoon Asia were greatly improved, and the use of multiple satellite observations with this framework is an effective way for improving terrestrial biosphere models.

Hemodynamic effects of nitroglycerin ointment in emergency department patients.

PubMed

Mumma, Bryn E; Dhingra, Kapil R; Kurlinkus, Charley; Diercks, Deborah B

2014-08-01

Nitroglycerin ointment is commonly used in the treatment of emergency department (ED) patients with suspected acute heart failure (AHF) or suspected acute coronary syndrome (ACS), but its hemodynamic effects in this population are not well described. Our objective was to assess the effect of nitroglycerin ointment on mean arterial pressure (MAP) and systemic vascular resistance (SVR) in ED patients receiving nitroglycerin. We hypothesized that nitroglycerin ointment would result in a reduction of MAP and SVR in the acute treatment of patients. We conducted a prospective, observational pilot study in a convenience sample of adult patients from a single ED who were treated with nitroglycerin ointment. Impedance cardiography was used to measure MAP, SVR, cardiac output (CO), stroke volume (SV), and thoracic fluid content (TFC) at baseline and at 30, 60, and 120 min after application of nitroglycerin ointment. Mixed effects regression models with random slope and random intercept were used to analyze changes in hemodynamic parameters from baseline to 30, 60, and 120 min after adjusting for age, sex, and final ED diagnosis of AHF. Sixty-four subjects with mean age of 55 years (interquartile range, 48-67 years) were enrolled; 59% were male. In the adjusted analysis, MAP and TFC decreased after application of nitroglycerin ointment (p=0.001 and p=0.043, respectively). Cardiac index, CO, SVR, and SV showed no change (p=0.113, p=0.085, p=0.570, and p=0.076, respectively) over time. Among ED patients who are treated with nitroglycerin ointment, MAP and TFC decrease over time. However, other hemodynamic parameters do not change after application of nitroglycerin ointment in these patients. Copyright © 2014 Elsevier Inc. All rights reserved.

Hemodynamic Effects of Nitroglycerin Ointment in Emergency Department Patients

PubMed Central

Mumma, Bryn E.; Dhingra, Kapil R.; Kurlinkus, Charley; Diercks, Deborah B.

2014-01-01

Background Nitroglycerin ointment is commonly used in the treatment of emergency department (ED) patients with suspected acute heart failure (AHF) or suspected acute coronary syndrome (ACS), but its hemodynamic effects in this population are not well described. Objectives Our objective was to assess effect of nitroglycerin ointment on mean arterial pressure (MAP) and systemic vascular resistance (SVR) in ED patients receiving nitroglycerin. We hypothesized that nitroglycerin ointment would result in a reduction of MAP and SVR in the acute treatment of patients. Methods We conducted a prospective, observational pilot study in a convenience sample of adult patients from a single ED who were treated with nitroglycerin ointment. Impedance cardiography was used to measure MAP, SVR, cardiac output (CO), stroke volume (SV), and thoracic fluid content (TFC) at baseline and at 30, 60, and 120 minutes following application of nitroglycerin ointment. Mixed effects regression models with random slope and random intercept were used to analyze changes in hemodynamic parameters from baseline to 30, 60, and 120 minutes after adjusting for age, sex, and final ED diagnosis of AHF. Results Sixty-four subjects with mean age 55 years (IQR 48-67) were enrolled; 59% were male. In the adjusted analysis, MAP and TFC decreased following application of nitroglycerin ointment (p=0.001 and p=0.043, respectively). CI, CO, SVR, and SV showed no change (p=0.113, p=0.085, p=0.570, and p=0.076, respectively) over time. Conclusions Among ED patients who are treated with nitroglycerin ointment, MAP and TFC decrease over time. However, other hemodynamic parameters do not change following application of nitroglycerin ointment in these patients. PMID:24698507

Impact of hepatitis C virus genotype-4 eradication following direct acting antivirals on liver stiffness measurement.

PubMed

Tag-Adeen, Mohammed; Sabra, Ahlam Mohamed; Akazawa, Yuko; Ohnita, Ken; Nakao, Kazuhiko

2017-01-01

Liver fibrosis is the most important prognostic factor in chronic hepatitis C virus (HCV) patients, and Egypt shows the highest worldwide HCV prevalence with genotype-4 pre-dominance. The aim of this study was to investigate the degree of liver stiffness measurement (LSM) improvement after successful HCV eradication. The study included 84 chronic HCV Egyptian patients, and was conducted at Qena University Hospital from November 1, 2015 till October 31, 2016. LSM was obtained by FibroScan® before starting direct acting antiviral (DAA) treatment and after achieving sustained virologic response-24 (SVR-24). Based on baseline LSM, patients were stratified into F0-F1, F2, F3 and F4 groups (METAVIR). LSM and laboratory data after achieving SVR-24 was compared with that before starting therapy in each fibrosis group (F0-F4), p -value <0.05 was statistically significant. Following DAA treatment, 80 patients achieved SVR-24; of these, 50 were males (62.5%), mean age: 54.2±7.6 years, and mean body mass index: 28.6±2.2 kg/m 2 . Mean baseline LSM dropped from 15.6±10.8 to 12.1±8.7 kPa post-SVR; the maximum change of -5.8 occurred in F4 versus -2.79, -1.28 and +0.08 in F3, F2 and F0-F1 respectively ( p <0.0001). At baseline, 41 patients were in the F4 group; only 16 (39%) regressed to non-cirrhotic range (<12.5 kPa), while 25 (61%) were still cirrhotic despite achieving SVR-24 ( p <0.0001). Patients who achieved LSM improvement (n=64) have had significantly higher baseline aspartate transferase (AST) and alanine transaminase (ALT). Also, those patients showed significant improvement in AST, AST/platelets ratio index (APRI) and fibrosis-4 index (Fib-4) after achieving SVR; 91% showed AST improvement ( p =0.01) and APRI improvement ( p =0.01) and 81% showed Fib-4 improvement ( p =0.04). Females, diabetics, patients with S3 steatosis and patients older than 50 years showed less LSM improvements than their counterparts. Baseline LSM ≥9 kPa, bilirubin ≥1 mg/dl, ALT ≥36 U/L and AST ≥31 U/L were significant predictors for LSM improvement. Successful HCV genotype-4 eradication results in significant LSM improvement; the best improvement occurs in F4 patients. But as the majority of cirrhotics are still at risk for liver decompensation and hepatocellular carcinoma development despite achieving SVR-24, early detection and treatment are highly recommended.

Conservative strategy-based ensemble surrogate model for optimal groundwater remediation design at DNAPLs-contaminated sites

NASA Astrophysics Data System (ADS)

Ouyang, Qi; Lu, Wenxi; Lin, Jin; Deng, Wenbing; Cheng, Weiguo

2017-08-01

The surrogate-based simulation-optimization techniques are frequently used for optimal groundwater remediation design. When this technique is used, surrogate errors caused by surrogate-modeling uncertainty may lead to generation of infeasible designs. In this paper, a conservative strategy that pushes the optimal design into the feasible region was used to address surrogate-modeling uncertainty. In addition, chance-constrained programming (CCP) was adopted to compare with the conservative strategy in addressing this uncertainty. Three methods, multi-gene genetic programming (MGGP), Kriging (KRG) and support vector regression (SVR), were used to construct surrogate models for a time-consuming multi-phase flow model. To improve the performance of the surrogate model, ensemble surrogates were constructed based on combinations of different stand-alone surrogate models. The results show that: (1) the surrogate-modeling uncertainty was successfully addressed by the conservative strategy, which means that this method is promising for addressing surrogate-modeling uncertainty. (2) The ensemble surrogate model that combines MGGP with KRG showed the most favorable performance, which indicates that this ensemble surrogate can utilize both stand-alone surrogate models to improve the performance of the surrogate model.

Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang; Ding, Fei; Zhang, Yingchen

In the traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of load forecasting technique can provide accurate prediction of load power that will happen in future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during the longer time period instead of using the snapshot of load at the time when the reconfiguration happens, and thus it can provide information to the distribution systemmore » operator (DSO) to better operate the system reconfiguration to achieve optimal solutions. Thus, this paper proposes a short-term load forecasting based approach for automatically reconfiguring distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with support vector regression (SVR) based forecaster and parallel parameters optimization. And the network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum loss at the future time. The simulation results validate and evaluate the proposed approach.« less

Development of a rapid method for the automatic classification of biological agents' fluorescence spectral signatures

NASA Astrophysics Data System (ADS)

Carestia, Mariachiara; Pizzoferrato, Roberto; Gelfusa, Michela; Cenciarelli, Orlando; Ludovici, Gian Marco; Gabriele, Jessica; Malizia, Andrea; Murari, Andrea; Vega, Jesus; Gaudio, Pasquale

2015-11-01

Biosecurity and biosafety are key concerns of modern society. Although nanomaterials are improving the capacities of point detectors, standoff detection still appears to be an open issue. Laser-induced fluorescence of biological agents (BAs) has proved to be one of the most promising optical techniques to achieve early standoff detection, but its strengths and weaknesses are still to be fully investigated. In particular, different BAs tend to have similar fluorescence spectra due to the ubiquity of biological endogenous fluorophores producing a signal in the UV range, making data analysis extremely challenging. The Universal Multi Event Locator (UMEL), a general method based on support vector regression, is commonly used to identify characteristic structures in arrays of data. In the first part of this work, we investigate fluorescence emission spectra of different simulants of BAs and apply UMEL for their automatic classification. In the second part of this work, we elaborate a strategy for the application of UMEL to the discrimination of different BAs' simulants spectra. Through this strategy, it has been possible to discriminate between these BAs' simulants despite the high similarity of their fluorescence spectra. These preliminary results support the use of SVR methods to classify BAs' spectral signatures.

Assessing Wheat Traits by Spectral Reflectance: Do We Really Need to Focus on Predicted Trait-Values or Directly Identify the Elite Genotypes Group?

PubMed Central

Garriga, Miguel; Romero-Bravo, Sebastián; Estrada, Félix; Escobar, Alejandro; Matus, Iván A.; del Pozo, Alejandro; Astudillo, Cesar A.; Lobos, Gustavo A.

2017-01-01

Phenotyping, via remote and proximal sensing techniques, of the agronomic and physiological traits associated with yield potential and drought adaptation could contribute to improvements in breeding programs. In the present study, 384 genotypes of wheat (Triticum aestivum L.) were tested under fully irrigated (FI) and water stress (WS) conditions. The following traits were evaluated and assessed via spectral reflectance: Grain yield (GY), spikes per square meter (SM2), kernels per spike (KPS), thousand-kernel weight (TKW), chlorophyll content (SPAD), stem water soluble carbohydrate concentration and content (WSC and WSCC, respectively), carbon isotope discrimination (Δ13C), and leaf area index (LAI). The performances of spectral reflectance indices (SRIs), four regression algorithms (PCR, PLSR, ridge regression RR, and SVR), and three classification methods (PCA-LDA, PLS-DA, and kNN) were evaluated for the prediction of each trait. For the classification approaches, two classes were established for each trait: The lower 80% of the trait variability range (Class 1) and the remaining 20% (Class 2 or elite genotypes). Both the SRIs and regression methods performed better when data from FI and WS were combined. The traits that were best estimated by SRIs and regression methods were GY and Δ13C. For most traits and conditions, the estimations provided by RR and SVR were the same, or better than, those provided by the SRIs. PLS-DA showed the best performance among the categorical methods and, unlike the SRI and regression models, most traits were relatively well-classified within a specific hydric condition (FI or WS), proving that classification approach is an effective tool to be explored in future studies related to genotype selection. PMID:28337210

Assessing Wheat Traits by Spectral Reflectance: Do We Really Need to Focus on Predicted Trait-Values or Directly Identify the Elite Genotypes Group?

PubMed

Garriga, Miguel; Romero-Bravo, Sebastián; Estrada, Félix; Escobar, Alejandro; Matus, Iván A; Del Pozo, Alejandro; Astudillo, Cesar A; Lobos, Gustavo A

2017-01-01

Phenotyping, via remote and proximal sensing techniques, of the agronomic and physiological traits associated with yield potential and drought adaptation could contribute to improvements in breeding programs. In the present study, 384 genotypes of wheat ( Triticum aestivum L.) were tested under fully irrigated (FI) and water stress (WS) conditions. The following traits were evaluated and assessed via spectral reflectance: Grain yield (GY), spikes per square meter (SM2), kernels per spike (KPS), thousand-kernel weight (TKW), chlorophyll content (SPAD), stem water soluble carbohydrate concentration and content (WSC and WSCC, respectively), carbon isotope discrimination (Δ 13 C), and leaf area index (LAI). The performances of spectral reflectance indices (SRIs), four regression algorithms (PCR, PLSR, ridge regression RR, and SVR), and three classification methods (PCA-LDA, PLS-DA, and k NN) were evaluated for the prediction of each trait. For the classification approaches, two classes were established for each trait: The lower 80% of the trait variability range (Class 1) and the remaining 20% (Class 2 or elite genotypes). Both the SRIs and regression methods performed better when data from FI and WS were combined. The traits that were best estimated by SRIs and regression methods were GY and Δ 13 C. For most traits and conditions, the estimations provided by RR and SVR were the same, or better than, those provided by the SRIs. PLS-DA showed the best performance among the categorical methods and, unlike the SRI and regression models, most traits were relatively well-classified within a specific hydric condition (FI or WS), proving that classification approach is an effective tool to be explored in future studies related to genotype selection.

25-Hydroxy vitamin D suppresses hepatitis C virus replication and contributes to rapid virological response of treatment efficacy.

PubMed

Huang, Jee-Fu; Ko, Yu-Min; Huang, Chung-Feng; Yeh, Ming-Lun; Dai, Chia-Yen; Hsieh, Meng-Hsuan; Huang, Ching-I; Yang, Hua-Ling; Wang, Shu-Chi; Lin, Zu-Yau; Chen, Shinn-Chern; Yu, Ming-Lung; Chuang, Wan-Long

2017-12-01

25-Hydroxy vitamin D (Vit D) plays a role in treatment outcomes in chronic hepatitis C virus (HCV) infection. We aimed to clarify whether HCV replication is inhibited by Vit D in HCV replicon cells. Clinical implication was assessed for rapid virological response (RVR) and sustained virological response (SVR) among those patients receiving antiviral therapy. Cell survival and viral loads were observed in Con1 (genotype 1b) and J6/JFH (genotype 2a) cells treated with different doses of Vit D. Three groups of patients with different treatment responses were recruited to assess their Vit D levels: group A, RVR-/SVR-; group B, RVR+/SVR-; and group C, RVR+/SVR+. The viral load of Con1 cells decreased by 69%, 80%, and 86% following treatment with 1 μM, 5 μM, and 10 μM Vit D, respectively (P < 0.0001). In J6/JFH cells, it decreased by 12%, 55%, and 80.5% following treatment with 1 μM, 5 μM, and 10 μM Vit D, respectively (P < 0.0001). There was a significant increase of Vit D between chronic hepatitis C groups, ranging from 4.4 ± 5.6 ng/mL in group A (n = 44), to 17.2 ± 11.6 ng/mL in group B (n = 44), and 32.5 ± 37.5 ng/mL of group C (n = 44) (P < 0.001). Advanced fibrosis (odds ratio = 0.13, 95% confidence interval = 0.04-0.41, P < 0.001) and Vit D deficiency (<10 ng/mL) (odds ratio = 0.11, 95% confidence interval = 0.03-0.43, P = 0.001) were predictive of SVR in the multivariate regression analysis. Vitamin D decreases HCV replication and also contributes to early treatment viral kinetics. © 2017 The Japan Society of Hepatology.

Undetectable HCV-RNA at treatment-week 8 results in high-sustained virological response in HCV G1 treatment-experienced patients with advanced liver disease: the International Italian/Spanish Boceprevir/Peginterferon/Ribavirin Name Patients Program.

PubMed

Bruno, S; Bollani, S; Zignego, A L; Pascasio, J M; Magni, C; Ciancio, A; Caremani, M; Mangia, A; Marenco, S; Piovesan, S; Chemello, L; Babudieri, S; Moretti, A; Gea, F; Colletta, C; Perez-Alvarez, R; Forns, X; Larrubia, J R; Arenas, J; Crespo, J; Calvaruso, V; Ceccherini Silberstein, F; Maisonneuve, P; Craxì, A; Calleja, J L

2015-05-01

In many countries, first-generation protease inhibitors (PIs)/peginterferon/ribavirin (P/R) still represent the only treatment option for HCV-infected patients. Subjects with advanced disease and previous failure to P/R urgently need therapy, but they are under-represented in clinical trials. All treatment-experienced F3/4 Metavir patients who received boceprevir (BOC)+P/R in the Italian-Spanish Name Patient Program have been included in this study. Multivariate logistic regression analysis (MLR) was used to identify baseline and on-treatment predictors of SVR and adverse events (AEs). Four hundred and sixteen patients, mean age 57.7 (range 25-78 years), 70% males, 69.5% (289/416) F4, 14% (41/289) Child-Pugh class A6, 24% (70/289) with varices and 42% (173/416) prior null responders to P/R, were analysed. Overall, SVR rate (all 381 patients who received one dose of BOC) was 49%, (58% in F3, 45% in F4, 61% in relapsers, 51% in partial, 38% in null responders, and 72% in subjects with undetectable HCV-RNA at treatment-week (TW)8. Among patients with TW8 HCV-RNA ≥ 1000 IU/L, SVR was 8% (negative predictive value = 92%). Death occurred in 3 (0.8%) patients, while decompensation and infections were observed in 2.9% and 11%, respectively. At MLR, SVR predictors were TW4 HCV-RNA ≥ 1log10 -decline from baseline, undetectable TW8 HCV-RNA, prior relapse, albumin levels ≥3.5 g/dL and platelet counts ≥100 000/μL. Metavir F4, Child-Pugh A6, albumin, platelets, age and female gender were associated with serious and haematological AEs. Among treatment-experienced patients with advanced liver disease eligible for IFN-based therapy, TW8 HCV-RNA characterised the subset with either high or poor likelihood of achieving SVR. Using TW8 HCV-RNA as a futility rule, BOC/P/R appears to have a favourable benefit-risk profile. © 2014 John Wiley & Sons Ltd.

Comparison of the Predictive Performance and Interpretability of Random Forest and Linear Models on Benchmark Data Sets.

PubMed

Marchese Robinson, Richard L; Palczewska, Anna; Palczewski, Jan; Kidley, Nathan

2017-08-28

The ability to interpret the predictions made by quantitative structure-activity relationships (QSARs) offers a number of advantages. While QSARs built using nonlinear modeling approaches, such as the popular Random Forest algorithm, might sometimes be more predictive than those built using linear modeling approaches, their predictions have been perceived as difficult to interpret. However, a growing number of approaches have been proposed for interpreting nonlinear QSAR models in general and Random Forest in particular. In the current work, we compare the performance of Random Forest to those of two widely used linear modeling approaches: linear Support Vector Machines (SVMs) (or Support Vector Regression (SVR)) and partial least-squares (PLS). We compare their performance in terms of their predictivity as well as the chemical interpretability of the predictions using novel scoring schemes for assessing heat map images of substructural contributions. We critically assess different approaches for interpreting Random Forest models as well as for obtaining predictions from the forest. We assess the models on a large number of widely employed public-domain benchmark data sets corresponding to regression and binary classification problems of relevance to hit identification and toxicology. We conclude that Random Forest typically yields comparable or possibly better predictive performance than the linear modeling approaches and that its predictions may also be interpreted in a chemically and biologically meaningful way. In contrast to earlier work looking at interpretation of nonlinear QSAR models, we directly compare two methodologically distinct approaches for interpreting Random Forest models. The approaches for interpreting Random Forest assessed in our article were implemented using open-source programs that we have made available to the community. These programs are the rfFC package ( https://r-forge.r-project.org/R/?group_id=1725 ) for the R statistical programming language and the Python program HeatMapWrapper [ https://doi.org/10.5281/zenodo.495163 ] for heat map generation.

An integrated simulation and optimization approach for managing human health risks of atmospheric pollutants by coal-fired power plants.

PubMed

Dai, C; Cai, X H; Cai, Y P; Guo, H C; Sun, W; Tan, Q; Huang, G H

2014-06-01

This research developed a simulation-aided nonlinear programming model (SNPM). This model incorporated the consideration of pollutant dispersion modeling, and the management of coal blending and the related human health risks within a general modeling framework In SNPM, the simulation effort (i.e., California puff [CALPUFF]) was used to forecast the fate of air pollutants for quantifying the health risk under various conditions, while the optimization studies were to identify the optimal coal blending strategies from a number of alternatives. To solve the model, a surrogate-based indirect search approach was proposed, where the support vector regression (SVR) was used to create a set of easy-to-use and rapid-response surrogates for identifying the function relationships between coal-blending operating conditions and health risks. Through replacing the CALPUFF and the corresponding hazard quotient equation with the surrogates, the computation efficiency could be improved. The developed SNPM was applied to minimize the human health risk associated with air pollutants discharged from Gaojing and Shijingshan power plants in the west of Beijing. Solution results indicated that it could be used for reducing the health risk of the public in the vicinity of the two power plants, identifying desired coal blending strategies for decision makers, and considering a proper balance between coal purchase cost and human health risk. A simulation-aided nonlinear programming model (SNPM) is developed. It integrates the advantages of CALPUFF and nonlinear programming model. To solve the model, a surrogate-based indirect search approach based on the combination of support vector regression and genetic algorithm is proposed. SNPM is applied to reduce the health risk caused by air pollutants discharged from Gaojing and Shijingshan power plants in the west of Beijing. Solution results indicate that it is useful for generating coal blending schemes, reducing the health risk of the public, reflecting the trade-offbetween coal purchase cost and health risk.

Delivering direct acting antiviral therapy for hepatitis C to highly marginalised and current drug injecting populations in a targeted primary health care setting.

PubMed

Read, Phillip; Lothian, Rebecca; Chronister, Karen; Gilliver, Rosie; Kearley, John; Dore, Gregory J; van Beek, Ingrid

2017-09-01

The Kirketon Road Centre (KRC) is a community-based public health facility in Sydney, Australia, that provides healthcare to people who inject drugs (PWID), including hepatitis C virus (HCV) treatment. From March 2016, the Australian Government has provided access to direct-acting antivirals (DAA) for adults with chronic HCV, without liver disease stage or drug and alcohol use restrictions. The aim of this study was to report DAA treatment outcomes among highly marginalised PWID treated at KRC. All individuals initiating DAA treatment at KRC and due for sustained virological response (SVR12) testing by end 2016 were included. Demographic, drug use behaviour, clinical parameters, adherence support and HCV treatment outcomes, including SVR12 were recorded. Factors associated with SVR12, loss-to-follow-up (LTFU) and delayed SVR12 testing (>SVR16) were assessed by multivariate analysis. SVR12 was assessed by intention-to-treat (ITT) and modified ITT, the latter excluding individuals with an end-of-treatment response (ETR) but no SVR12 assessment, or who postponed their SVR12 date due to treatment interruption. A total of 72 individuals commencing DAAs were included, of whom 67% were male, 30% homeless, and 32% Aboriginal. All had a lifetime history of injecting drug use, with 75% having injected within the last six months, and 44% injecting at least weekly; 25% were also enrolled in opioid substitution therapy. Twenty-five (35%) individuals elected to receive an enhanced adherence-support package. Fifty-nine of 72 (82%) individuals due for SVR12 attended for testing, of whom 59/59 (100%) achieved SVR, providing an ITT SVR of 82%. A further six individuals had undetectable HCV RNA at ETR, but no SVR12 assessment, and one interrupted treatment, providing a mITT SVR of 91%. Homelessness was associated with delayed SVR12 testing (OR 24.9 95%CI 2.9-212.8, p=0.003). There was no association between LTFU and frequency of drug injection, last drug injected, or planned treatment duration. This study confirms that PWID can be successfully treated for HCV in a real-world setting using an integrated primary health care model. It also demonstrates feasibility to upscale DAA therapy in high-risk PWID populations, with potential individual and population-level public health benefits. Enhanced efforts are required to optimise post-treatment follow-up. Copyright © 2017 Elsevier B.V. All rights reserved.

Support vector regression model of wastewater bioreactor performance using microbial community diversity indices: effect of stress and bioaugmentation.

PubMed

Seshan, Hari; Goyal, Manish K; Falk, Michael W; Wuertz, Stefan

2014-04-15

The relationship between microbial community structure and function has been examined in detail in natural and engineered environments, but little work has been done on using microbial community information to predict function. We processed microbial community and operational data from controlled experiments with bench-scale bioreactor systems to predict reactor process performance. Four membrane-operated sequencing batch reactors treating synthetic wastewater were operated in two experiments to test the effects of (i) the toxic compound 3-chloroaniline (3-CA) and (ii) bioaugmentation targeting 3-CA degradation, on the sludge microbial community in the reactors. In the first experiment, two reactors were treated with 3-CA and two reactors were operated as controls without 3-CA input. In the second experiment, all four reactors were additionally bioaugmented with a Pseudomonas putida strain carrying a plasmid with a portion of the pathway for 3-CA degradation. Molecular data were generated from terminal restriction fragment length polymorphism (T-RFLP) analysis targeting the 16S rRNA and amoA genes from the sludge community. The electropherograms resulting from these T-RFs were used to calculate diversity indices - community richness, dynamics and evenness - for the domain Bacteria as well as for ammonia-oxidizing bacteria in each reactor over time. These diversity indices were then used to train and test a support vector regression (SVR) model to predict reactor performance based on input microbial community indices and operational data. Considering the diversity indices over time and across replicate reactors as discrete values, it was found that, although bioaugmentation with a bacterial strain harboring a subset of genes involved in the degradation of 3-CA did not bring about 3-CA degradation, it significantly affected the community as measured through all three diversity indices in both the general bacterial community and the ammonia-oxidizer community (α = 0.5). The impact of bioaugmentation was also seen qualitatively in the variation of community richness and evenness over time in each reactor, with overall community richness falling in the case of bioaugmented reactors subjected to 3-CA and community evenness remaining lower and more stable in the bioaugmented reactors as opposed to the unbioaugmented reactors. Using diversity indices, 3-CA input, bioaugmentation and time as input variables, the SVR model successfully predicted reactor performance in terms of the removal of broad-range contaminants like COD, ammonia and nitrate as well as specific contaminants like 3-CA. This work was the first to demonstrate that (i) bioaugmentation, even when unsuccessful, can produce a change in community structure and (ii) microbial community information can be used to reliably predict process performance. However, T-RFLP may not result in the most accurate representation of the microbial community itself, and a much more powerful prediction tool can potentially be developed using more sophisticated molecular methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

Survival benefits of interferon-based therapy in patients with recurrent hepatitis C after orthotopic liver transplantation

PubMed Central

Zanaga, L.P.; Vigani, A.G.; Angerami, R.N.; Giorgetti, A.; Escanhoela, C.A.F.; Ataíde, E.C.; Boin, I.F.S.F.; Stucchi, R.S.B.

2017-01-01

Recurrent hepatitis C after orthotopic liver transplantation (OLT) is universal and can lead to graft failure and, consequently, reduced survival. Hepatitis C treatment can be used to prevent these detrimental outcomes. The aim of this study was to describe rates of hepatitis C recurrence and sustained virological response (SVR) to interferon-based treatment after OLT and its relationship to survival and progression of liver disease through retrospective analysis of medical records of 127 patients who underwent OLT due to cirrhosis or hepatocellular carcinoma secondary to chronic hepatitis C between January 2002 and December 2013. Fifty-six patients were diagnosed with recurrent disease, 42 started interferon-based therapy and 37 completed treatment. Demographic, treatment- and outcome-related variables were compared between SVR and non-responders (non-SVR). There was an overall 54.1% SVR rate with interferon-based therapies. SVR was associated with longer follow-up after treatment (median 66.5 vs 37 months for non-SVR, P=0.03) and after OLT (median 105 vs 72 months, P=0.074), and lower rates of disease progression (15 vs 64.7%, P=0.0028) and death (5 vs 35.3%, P=0.033). Regardless of the result of therapy (SVR or non-SVR), there was a significant difference between treated and untreated patients regarding the occurrence of death (P<0.001) and months of survival (P<0.001). Even with suboptimal interferon-based therapies (compared to the new direct-acting antivirals) there is a 54.1% SVR rate to treatment. SVR is associated with improved survival and reduced risks of clinical decompensation, loss of the liver graft and death. PMID:28076451

The impact of pegylated interferon and ribavirin combination treatment on lipid metabolism and insulin resistance in chronic hepatitis C patients.

PubMed

Jung, Hee Jae; Kim, Young Seok; Kim, Sang Gyune; Lee, Yun Nah; Jeong, Soung Won; Jang, Jae Young; Lee, Sae Hwan; Kim, Hong Soo; Kim, Boo Sung

2014-03-01

Lipid profile and insulin resistance (IR) are associated with hepatitis C virus (HCV) and may predict the chronic hepatitis C (CHC) treatment response. The aim of this study was to determine the association between CHC treatment response and lipid profile and IR change during treatment. In total, 203 CHC patients were reviewed retrospectively between January 2005 and December 2011 at Soon Chun Hyang University Hospital. The lipid profile, homeostasis model for assessment (HOMA) of IR (HOMA-IR), and HOMA of β cells (HOMA-β) were evaluated before interferon plus ribavirin therapy (BTx), at the end of treatment (DTx), and 24 weeks after the end of treatment (ATx). A sustained virologic response (SVR) was achieved by 81% of all patients (49/60), 60% (n=36) of whom possessed genotype 1, with the remainder being non-genotype-1 (40%, n=24). Apart from age, which was significantly higher in the non-SVR group (SVR, 48.0 ± 11.2 years, mean ± SD; non-SVR, 56.6 ± 9.9 years; P<0.01), there were no significant differences in the baseline characteristics between the SVR and non-SVR groups. In the SVR group, low density lipoprotein-cholesterol (LDL-C) had significantly changed at DTx and ATx compared to BTx. In addition, HOMA-IR and HOMA-β were significantly changed at DTx in the SVR group. Among those with a high baseline insulin resistance (HOMA-IR >2.5), HOMA-IR was significantly changed at DTx in the SVR group. LDL-C appears to be associated with HCV treatment in SVR patients. Furthermore, eradication of HCV may improve whole-body IR and insulin hypersecretion, as well as high baseline insulin resistance (HOMA-IR >2.5).

High resolution melting curve assay for detecting rs12979860 IL28B polymorphisms involved in response of Iranian patients to chronic hepatitis C treatment.

PubMed

Fateh, Abolfazl; Aghasadeghi, Mohammad Reza; Keyvani, Hossein; Mollaie, Hamid Reza; Yari, Shamsi; Hadizade Tasbiti, Ali Reza; Ghazanfari, Morteza; Monavari, Seyed Hamid Reza

2015-01-01

A recent genome-wide association study (GWAS) on patients with chronic hepatitis C (CHC) treated with peginterferon and ribavirin (pegIFN-α/RBV) identified a single nucleotide polymorphism (SNP) on chromosome 19 (rs12979860) which was strongly associated with a sustained virological response (SVR). The aim of this study was twofold: to study the relationship between IL28B rs12979860 and sustained virological response (SVR) to pegIFN-α/RVB therapy among CHC patients and to detect the rs12979860 polymorphism by high resolution melting curve (HRM) assay as a simple, fast, sensitive, and inexpensive method. The study examined outcomes in 100 patients with chronic hepatitis C in 2 provinces of Iran from December 2011 to June 2013. Two methods were applied to detect IL28B polymorphisms: PCR-sequencing as a gold standard method and HRM as a simple, fast, sensitive, and inexpensive method. The frequencies of IL28B rs12979860 CC, CT, and TT alleles in chronic hepatitis C genotype 1a patients were 10% (10/100), 35% (35/100), and 6% (6/100) and in genotype 3a were 13% (13/100), 31% (31/100), and 5% (5/100), respectively. In genotype 3a infected patients, rs12979860 (CC and CT alleles) and in genotype 1a infected patients (CC allele) were significantly associated with a sustained virological response (SVR). The SVR rates for CC, CT and TT (IL28B rs12979860) were 18%, 34% and 4%, respectively. Multiple logistic regression analysis identified two independent factors that were significantly associated with SVR: IL-28B genotype (rs 12979860 CC vs TT and CT; odds ratio [ORs], 7.86 and 4.084, respectively), and HCV subtype 1a (OR, 7.46). In the present study, an association between SVR rates and IL28B polymorphisms was observed. The HRM assay described herein is rapid, inexpensive, sensitive and accurate for detecting rs12979860 alleles in CHC patients. This method can be readily adopted by any molecular diagnostic laboratory with HRM capability and will be clinically beneficial in predicting treatment response in HCV genotype 1 and 3 infected patients. In addition, it was demonstrated that CC and CT alleles in HCV-3a and the CC allele in HCV-1a were significantly associated with response to pegIFN-α/RBV treatment. The present results may help identify subjects for whom the therapy might be successful.

The Reduced Predictive Value of Interleukin 28b Gene Polymorphisms in a Cohort of Patients With Thyroiditis Developed During Antiviral Therapy for Chronic Hepatitis C: A Preliminary Study

PubMed Central

Tran, Huy A; Jones, Tracey L; Ianna, Elizabeth A; Gibson, Robert A; Reeves, Glenn E M

2012-01-01

Background: Single nucleotide polymorphism in the interleukin28B (IL28B) gene was recently shown to be associated with a significant increase in response to interferon-α and ribavirin treatment in patients with chronic hepatitis C. Similarly, thyroid disease (TD) occurring during treatment confer an improved sustained virologic response (SVR). Objectives: To determine the role of IL28B genotypes in a cohort of hepatitis C patients who develop TD during treatment and its relationship to SVR. Patients and Methods: IL28B gene profiles including rs12979860, rs12980275 and rs 8099917 and their genotypes were determined in a cohort of 23 hepatitis C patients who developed TD during treatment and their relationship to SVR. Results: Out of 23 studies cases, 19 has one or more favorable genotypes, of which 15 (78.9%) achieved SVR. Eleven has all three unfavorable genotypes and yet achieved 72.7 % SVR. The presence of more than one favorable genotype only correctly predicts SVR vs. non- SVR in ~50 % of cases, i.e. by chance. Conclusions: Despite the small number of subjects, the presence of one or more unfavorable IL28B genotype does not portend a poor SVR prognostic outcome. This suggests that TD in this clinical context may be a critical factor in the achievement of SVR, probably above that of the genetic predisposition. PMID:23087747

Insulin Sensitivity Determines Effects of Insulin and Meal Ingestion on Systemic Vascular Resistance in Healthy Subjects.

PubMed

Woerdeman, Jorn; Meijer, Rick I; Eringa, Etto C; Hoekstra, Trynke; Smulders, Yvo M; Serné, Erik H

2016-01-01

In addition to insulin's metabolic actions, insulin can dilate arterioles which increase blood flow to metabolically active tissues. This effect is blunted in insulin-resistant subjects. Insulin's effect on SVR, determined by resistance arterioles, has, however, rarely been examined directly. We determined the effects of both hyperinsulinemia and a mixed meal on SVR and its relationship with insulin sensitivity. Thirty-seven lean and obese women underwent a hyperinsulinemic-euglycemic clamp, and 24 obese volunteers underwent a mixed-meal test. SVR was assessed using CPP before and during hyperinsulinemia as well as before and 60 and 120 minutes after a meal. SVR decreased significantly during hyperinsulinemia (-13%; p < 0.001) and after the meal (-11%; p < 0.001). Insulin decreased SVR more strongly in insulin-sensitive individuals (standardized β: -0.44; p = 0.01). In addition, SVR at 60 minutes after meal ingestion was inversely related to the Matsuda index (β: -0.39; p = 0.04) and the change in postprandial SVR was directly related to postprandial glycemia (β: 0.53; p < 0.01). Hyperinsulinemia and meal ingestion decrease SVR, which is directly associated with metabolic insulin resistance. This suggests that resistance to insulin-induced vasodilatation contributes to regulation of vascular resistance. © 2015 John Wiley & Sons Ltd.

Predicting early and sustained virological responses in prior nonresponders to pegylated interferon alpha-2b plus ribavirin retreated with peginterferon alpha-2a plus ribavirin and the benefit-risk ratio of retreatment.

PubMed

Marcellin, Patrick; Craxi, Antonio; Brandao-Mello, Carlos E; Di Bisceglie, Adrian M; Andreone, Pietro; Freilich, Bradley; Rajender Reddy, K; Olveira Martín, Antonio; Teuber, Gerlinde; Messinger, Diethelm; Hooper, Greg; Wat, Cynthia; Tatsch, Fernando; Jensen, Donald M

2013-10-01

To evaluate the predictive value of complete early virological response (cEVR) on sustained virological response (SVR) following retreatment with peginterferon alpha-2a (40 kDa) plus ribavirin in previous nonresponders to peginterferon alpha-2b (12 kDa). In the randomized multinational retreatment with Pegasys in patients not responding to PegIntron therapy study, a 72-week regimen of peginterferon alpha-2a (40 kDa) plus ribavirin improved SVR rates over a standard 48-week regimen in previous nonresponders to peginterferon alpha-2b (12 kDa). cEVR, defined as hepatitis C virus RNA <50 IU/mL at treatment week 12, was an important predictor of SVR. We conducted an exploratory analysis of the retreatment with Pegasys in patients not responding to PegIntron therapy study data to better define the predictive value of cEVR for SVR in this patient population. In total, 157 of the 942 patients achieved a cEVR (16.7%). SVR rates were higher with 72 versus 48 weeks of retreatment in patients with a cEVR (57% vs. 35%), whereas SVR rates were <5% in patients without cEVR in both groups. The relative adverse event (AE) burden was lower with 72 weeks of treatment (8.1 vs. 10.1 AEs/y of treatment) as was the estimated number of AEs per SVR achieved (55 vs. 100). Cumulative treatment duration required to achieve 1 SVR was lower with 72 weeks of treatment (6.7 vs. 10.0 y/SVR) and lower still assuming that treatment was stopped at week 12 for non-cEVR patients (3.6 vs. 7.1 y/SVR). cEVR is a reliable predictor of SVR in patients retreated with peginterferon alpha-2a (40 kDa) plus ribavirin. Seventy-two-week retreatment has a more favorable benefit-risk ratio than 48 weeks, especially when cEVR is used to identify patients most likely to be cured.

Ultrasensitive HCV RNA Quantification in Antiviral Triple Therapy: New Insight on Viral Clearance Dynamics and Treatment Outcome Predictors

PubMed Central

Garbuglia, Anna Rosa; Visco-Comandini, Ubaldo; Lionetti, Raffaella; Lapa, Daniele; Castiglione, Filippo; D’Offizi, Gianpiero; Taibi, Chiara; Montalbano, Marzia; Capobianchi, Maria Rosaria; Paci, Paola

2016-01-01

Objectives Identifying the predictive factors of Sustained Virological Response (SVR) represents an important challenge in new interferon-based DAA therapies. Here, we analyzed the kinetics of antiviral response associated with a triple drug regimen, and the association between negative residual viral load at different time points during treatment. Methods Twenty-three HCV genotype 1 (GT 1a n = 11; GT1b n = 12) infected patients were included in the study. Linear Discriminant Analysis (LDA) was used to establish possible association between HCV RNA values at days 1 and 4 from start of therapy and SVR. Principal component analysis (PCA) was applied to analyze the correlation between HCV RNA slope and SVR. A ultrasensitive (US) method was established to measure the residual HCV viral load in those samples which resulted “detected <12IU/ml” or undetectable with ABBOTT standard assay, and was retrospectively used on samples collected at different time points to establish its predictive power for SVR. Results According to LDA, there was no association between SVR and viral kinetics neither at time points earlier than 1 week (days 1 and 4) after therapy initiation nor later. The slopes were not relevant for classifying patients as SVR or no-SVR. No significant differences were observed in the median HCV RNA values at T0 among SVR and no-SVR patients. HCV RNA values with US protocol (LOD 1.2 IU/ml) after 1 month of therapy were considered; the area under the ROC curve was 0.70. Overall, PPV and NPV of undetectable HCV RNA with the US method for SVR was 100% and 46.7%, respectively; sensitivity and specificity were 38.4% and 100% respectively. Conclusion HCV RNA “not detected” by the US method after 1 month of treatment is predictive of SVR in first generation Protease inhibitor (PI)-based triple therapy. The US method could have clinical utility for advanced monitoring of virological response in new interferon based DAA combination regimens. PMID:27560794

Efficacy of ledipasvir/sofosbuvir with or without ribavirin for 12 weeks in genotype 1b HCV patients previously treated with a nonstructural protein 5A inhibitor-containing regimen.

PubMed

Ikeda, Hiroki; Watanabe, Tsunamasa; Shimizu, Hirohito; Hiraishi, Tetsuya; Kaneko, Rena; Baba, Toshiyuki; Takahashi, Hideaki; Matsunaga, Kotaro; Matsumoto, Nobuyuki; Yasuda, Hiroshi; Okuse, Chiaki; Iwabuchi, Shogo; Suzuki, Michihiro; Itoh, Fumio

2018-03-05

The therapeutic benefit of adding ribavirin (RBV) to 12 weeks of ledipasvir/sofosbuvir (LDV/SOF) for patients who experienced failure of a previous nonstructural protein (NS) 5A inhibitor-containing regimen is unclear. A total of 29 genotype 1b HCV patients who had failed prior daclatasvir (DCV) plus asunaprevir (ASV) treatment were retreated for 12 weeks of LDV/SOF, with or without RBV. Antiviral efficacy and predictive factors associating with a sustained virological response at 24 weeks (SVR24) were evaluated retrospectively. SVR24 was achieved in 67% (10/15) of patients who received LDV/SOF with, and 64% (9/14) without, RBV. The SVR24 rates were 80% in patients with, and 58% without, mild fibrosis (FIB-4 < 3.25). The SVR24 rate was lower with unfavorable IL28B rs8099917 SNP genotypes; specifically, the TT, TG and GG had SVR24 rates of 78%, 50% and 40%. The SVR24 rate was lower with a poor response to prior DCV plus ASV, where relapse, viral breakthrough and no response had SVR24 rates 71%, 58% and 0%. The SVR24 rate was lower with the number of NS5A resistance-associated substitutions (RAS), where 2, 3, 4 and 5 RAS had SVR24 rates of 78%, 67%, 50% and 0%. A patient with an NS5A-P32 deletion, which shows resistance to next-generation NS5A inhibitors, was retreated with LDV/SOF with RBV and achieved SVR24. The addition of RBV to 12 weeks of LDV/SOF has little therapeutic benefit when retreating patients in whom a prior NS5A inhibitor-containing regimen had failed. © 2018 The Japan Society of Hepatology.

Incidence of DAA failure and the clinical impact of retreatment in real-life patients treated in the advanced stage of liver disease: Interim evaluations from the PITER network

PubMed Central

Gaeta, Giovanni Battista; Brunetto, Maurizia Rossana; Di Leo, Alfredo; Iannone, Andrea; Santantonio, Teresa Antonia; Giammario, Adele; Raimondo, Giovanni; Filomia, Roberto; Coppola, Carmine; Amoruso, Daniela Caterina; Blanc, Pierluigi; Del Pin, Barbara; Chemello, Liliana; Cavalletto, Luisa; Morisco, Filomena; Donnarumma, Laura; Rumi, Maria Grazia; Gasbarrini, Antonio; Siciliano, Massimo; Massari, Marco; Corsini, Romina; Coco, Barbara; Madonia, Salvatore; Cannizzaro, Marco; Zignego, Anna Linda; Monti, Monica; Russo, Francesco Paolo; Zanetto, Alberto; Persico, Marcello; Masarone, Mario; Villa, Erica; Bernabucci, Veronica; Taliani, Gloria; Biliotti, Elisa; Chessa, Luchino; Pasetto, Maria Cristina; Andreone, Pietro; Margotti, Marzia; Brancaccio, Giuseppina; Ieluzzi, Donatella; Borgia, Guglielmo; Zappulo, Emanuela; Calvaruso, Vincenza; Petta, Salvatore; Falzano, Loredana; Quaranta, Maria Giovanna; Weimer, Liliana Elena; Rosato, Stefano; Vella, Stefano; Giannini, Edoardo Giovanni

2017-01-01

Background Few data are available on the virological and clinical outcomes of advanced liver disease patients retreated after first-line DAA failure. Aim To evaluate DAA failure incidence and the retreatment clinical impact in patients treated in the advanced liver disease stage. Methods Data on HCV genotype, liver disease severity, and first and second line DAA regimens were prospectively collected in consecutive patients who reached the 12-week post-treatment and retreatment evaluations from January 2015 to December 2016 in 23 of the PITER network centers. Results Among 3,830 patients with advanced fibrosis (F3) or cirrhosis, 139 (3.6%) failed to achieve SVR. Genotype 3, bilirubin levels >1.5mg/dl, platelet count <120,000/mm3 and the sofosbuvir+ribavirin regimen were independent predictors of failure by logistic regression analysis. The failure rate was 7.6% for patients treated with regimens that are no longer recommended or considered suboptimal (sofosbuvir+ribavirin or simeprevir+sofosbuvir±ribavirin), whereas 1.4% for regimens containing sofosbuvir combined with daclatasvir or ledipasvir or other DAAs. Of the patients who failed to achieve SVR, 72 (51.8%) were retreated with a second DAA regimen, specifically 38 (52.7%) with sofosbuvir+daclatasvir, 27 (37.5%) with sofosbuvir+ledipasvir, and 7 (9.7%) with other DAAs ±ribavirin. Among these, 69 (96%) patients achieved SVR12 and 3 (4%) failed. During a median time of 6 months (range: 5–14 months) between failure and the second DAA therapy, the Child-Pugh class worsened in 12 (16.7%) patients: from A to B in 10 patients (19.6%) and from B to C in 2 patients (10.5%), whereas it did not change in the remaining 60 patients. Following the retreatment SVR12 (median time of 6 months; range: 3–12 months), the Child-Pugh class improved in 17 (23.6%) patients: from B to A in 14 (19.4%) patients, from C to A in 1 patient (1.4%) and from C to B in 2 (2.9%) patients; it remained unchanged in 53 patients (73.6%) and worsened in 2 (2.8%) patients. Of patients who were retreated, 3 (4%) had undergone OLT before retreatment (all reached SVR12 following retreatment) and 2 (2.8%) underwent OLT after having achieved retreatment SVR12. Two (70%) of the 3 patients who failed to achieve SVR12 after retreatment, and 2 (2.8%) of the 69 patients who achieved retreatment SVR12 died from liver failure (Child-Pugh class deteriorated from B to C) or HCC complications. Conclusions Failure rate following the first DAA regimen in patients with advanced disease is similar to or lower than that reported in clinical trials, although the majority of patients were treated with suboptimal regimens. Interim findings showed that worsening of liver function after failure, in terms of Child Pugh class deterioration, was improved by successful retreatment in about one third of retreated patients within a short follow-up period; however, in some advanced liver disease patients, clinical outcomes (Child Pugh class, HCC development, liver failure and death) were independent of viral eradication. PMID:28977040

Incidence of DAA failure and the clinical impact of retreatment in real-life patients treated in the advanced stage of liver disease: Interim evaluations from the PITER network.

PubMed

Kondili, Loreta A; Gaeta, Giovanni Battista; Brunetto, Maurizia Rossana; Di Leo, Alfredo; Iannone, Andrea; Santantonio, Teresa Antonia; Giammario, Adele; Raimondo, Giovanni; Filomia, Roberto; Coppola, Carmine; Amoruso, Daniela Caterina; Blanc, Pierluigi; Del Pin, Barbara; Chemello, Liliana; Cavalletto, Luisa; Morisco, Filomena; Donnarumma, Laura; Rumi, Maria Grazia; Gasbarrini, Antonio; Siciliano, Massimo; Massari, Marco; Corsini, Romina; Coco, Barbara; Madonia, Salvatore; Cannizzaro, Marco; Zignego, Anna Linda; Monti, Monica; Russo, Francesco Paolo; Zanetto, Alberto; Persico, Marcello; Masarone, Mario; Villa, Erica; Bernabucci, Veronica; Taliani, Gloria; Biliotti, Elisa; Chessa, Luchino; Pasetto, Maria Cristina; Andreone, Pietro; Margotti, Marzia; Brancaccio, Giuseppina; Ieluzzi, Donatella; Borgia, Guglielmo; Zappulo, Emanuela; Calvaruso, Vincenza; Petta, Salvatore; Falzano, Loredana; Quaranta, Maria Giovanna; Weimer, Liliana Elena; Rosato, Stefano; Vella, Stefano; Giannini, Edoardo Giovanni

2017-01-01

Few data are available on the virological and clinical outcomes of advanced liver disease patients retreated after first-line DAA failure. To evaluate DAA failure incidence and the retreatment clinical impact in patients treated in the advanced liver disease stage. Data on HCV genotype, liver disease severity, and first and second line DAA regimens were prospectively collected in consecutive patients who reached the 12-week post-treatment and retreatment evaluations from January 2015 to December 2016 in 23 of the PITER network centers. Among 3,830 patients with advanced fibrosis (F3) or cirrhosis, 139 (3.6%) failed to achieve SVR. Genotype 3, bilirubin levels >1.5mg/dl, platelet count <120,000/mm3 and the sofosbuvir+ribavirin regimen were independent predictors of failure by logistic regression analysis. The failure rate was 7.6% for patients treated with regimens that are no longer recommended or considered suboptimal (sofosbuvir+ribavirin or simeprevir+sofosbuvir±ribavirin), whereas 1.4% for regimens containing sofosbuvir combined with daclatasvir or ledipasvir or other DAAs. Of the patients who failed to achieve SVR, 72 (51.8%) were retreated with a second DAA regimen, specifically 38 (52.7%) with sofosbuvir+daclatasvir, 27 (37.5%) with sofosbuvir+ledipasvir, and 7 (9.7%) with other DAAs ±ribavirin. Among these, 69 (96%) patients achieved SVR12 and 3 (4%) failed. During a median time of 6 months (range: 5-14 months) between failure and the second DAA therapy, the Child-Pugh class worsened in 12 (16.7%) patients: from A to B in 10 patients (19.6%) and from B to C in 2 patients (10.5%), whereas it did not change in the remaining 60 patients. Following the retreatment SVR12 (median time of 6 months; range: 3-12 months), the Child-Pugh class improved in 17 (23.6%) patients: from B to A in 14 (19.4%) patients, from C to A in 1 patient (1.4%) and from C to B in 2 (2.9%) patients; it remained unchanged in 53 patients (73.6%) and worsened in 2 (2.8%) patients. Of patients who were retreated, 3 (4%) had undergone OLT before retreatment (all reached SVR12 following retreatment) and 2 (2.8%) underwent OLT after having achieved retreatment SVR12. Two (70%) of the 3 patients who failed to achieve SVR12 after retreatment, and 2 (2.8%) of the 69 patients who achieved retreatment SVR12 died from liver failure (Child-Pugh class deteriorated from B to C) or HCC complications. Failure rate following the first DAA regimen in patients with advanced disease is similar to or lower than that reported in clinical trials, although the majority of patients were treated with suboptimal regimens. Interim findings showed that worsening of liver function after failure, in terms of Child Pugh class deterioration, was improved by successful retreatment in about one third of retreated patients within a short follow-up period; however, in some advanced liver disease patients, clinical outcomes (Child Pugh class, HCC development, liver failure and death) were independent of viral eradication.

Impact of HIV infection on sustained virological response to treatment against hepatitis C virus with pegylated interferon plus ribavirin.

PubMed

Monje-Agudo, P; Castro-Iglesias, A; Rivero-Juárez, A; Martínez-Marcos, F; Ortega-González, E; Real, L M; Pernas, B; Merchante, N; Cid, P; Macías, J; Merino, M D; Rivero, A; Mena, A; Neukam, K; Pineda, J A

2015-10-01

It is commonly accepted that human immunodeficiency (HIV) coinfection negatively impacts on the rates of sustained virological response (SVR) to therapy with pegylated interferon plus ribavirin (PR). However, this hypothesis is derived from comparing different studies. The aim of this study was to determine the impact of HIV coinfection on SVR to PR in one single population. In a multicentric, prospective study conducted between 2000 and 2013, all previously naïve hepatitis C virus (HCV)-infected patients who started PR in five Spanish hospitals were analyzed. SVR was evaluated 24 weeks after the scheduled end of therapy. Of the 1046 patients included in this study, 413 (39%) were coinfected with HIV. Three hundred and forty-one (54%) HCV-monoinfected versus 174 (42%) HIV/HCV-coinfected patients achieved SVR (p < 0.001). The corresponding figures for undetectable HCV RNA at treatment week 4 were 86/181 (47%) versus 59/197 (30%), p < 0.001. SVR was observed in 149 (69%) HCV genotype 2/3-monoinfected subjects versus 91 (68%) HIV/HCV genotype 2/3-coinfected subjects (p = 0.785). In the HCV genotype 1/4-infected population, 188 (46%) monoinfected patients versus 82 (30%) with HIV coinfection (p < 0.001) achieved SVR. In this subgroup, absence of HIV coinfection was independently associated with higher SVR [adjusted odds ratio (95% confidence interval): 2.127 (1.135-3.988); p = 0.019] in a multivariate analysis adjusted for age, sex, baseline HCV RNA load, IL28B genotype, fibrosis stage, and type of pegylated interferon. HIV coinfection impacts on the rates of SVR to PR only in HCV genotype 1/4-infected patients, while it has no effect on SVR in the HCV genotype 2/3-infected subpopulation.

The cost of treatment failure: resource use and costs incurred by hepatitis C virus genotype 1-infected patients who do or do not achieve sustained virological response to therapy.

PubMed

Backx, M; Lewszuk, A; White, J R; Cole, J; Sreedharan, A; van Sanden, S; Diels, J; Lawson, A; Neal, K R; Wiselka, M J; Ito, T; Irving, W L

2014-03-01

Chronic hepatitis C virus (HCV) infection places a considerable economic burden on health services. Cost-effectiveness analyses of antiviral treatment for patients with chronic HCV infection are dependent on assumptions about cost reductions following sustained virological response (SVR) to therapy. This study quantified the medium-term difference in health resource usage and costs depending on treatment outcome. Retrospective chart review of patients with HCV genotype 1 infection who had received at least 2 months pegylated interferon and ribavirin therapy, with known treatment outcome was conducted. Disease status was categorized as chronic hepatitis, cirrhosis or decompensated liver disease. Health resource use was documented for each patient in each disease state. Unit costs were from the NHS 'Payment by Results' database and the British National Formulary. One hundred and ninety three patients (108 SVR, 85 non-SVR) with mean follow-up of 3.5 (SVR) and 4.9 (non-SVR) years were enrolled. No SVR patient progressed to a more severe liver disease state. Annual transition rates for non-SVR patients were 7.4% (chronic hepatitis to cirrhosis) and 4.9% (cirrhosis to decompensated liver disease). By extrapolation of modelled data over a 5-year post-treatment period, failure of patients with chronic hepatitis to achieve SVR was associated with a 13-fold increase (roughly £2300) in costs, whilst for patients who were retreated, the increase was 56-fold, equating to more than £10 000. Achievement of an SVR has significant effects on health service usage and costs. This work provides real-life data for future cost-effectiveness analyses related to the treatment for chronic HCV infection. © 2013 John Wiley & Sons Ltd.

Impact of surgical ventricular reconstruction on sphericity index in patients with ischaemic cardiomyopathy: follow-up from the STICH trial.

PubMed

Choi, Jin-Oh; Daly, Richard C; Lin, Grace; Lahr, Brian D; Wiste, Heather J; Beaver, Thomas M; Iacovoni, Attilio; Malinowski, Marcin; Friedrich, Ivar; Rouleau, Jean L; Favaloro, Roberto R; Sopko, George; Lang, Irene M; White, Harvey D; Milano, Carmelo A; Jones, Robert H; Lee, Kerry L; Velazquez, Eric J; Oh, Jae K

2015-04-01

We sought to evaluate associations between baseline sphericity index (SI) and clinical outcome, and changes in SI after coronary artery bypass graft (CABG) surgery with or without surgical ventricular reconstruction (SVR) in ischaemic cardiomyopathy patients enrolled in the SVR study (Hypothesis 2) of the Surgical Treatment for Ischemic Heart Failure (STICH) trial. Among 1000 patients in the STICH SVR study, we evaluated 546 patients (255 randomized to CABG alone and 291 to CABG + SVR) whose baseline SI values were available. SI was not significantly different between treatment groups at baseline. After 4 months, SI had increased in the CABG + SVR group, but was unchanged in the CABG alone group (0.69 ± 0.10 to 0.77 ± 0.12 vs. 0.67 ± 0.07 to 0.66 ± 0.09, respectively; P < 0.001). SI did not significantly change from 4 months to 2 years in either group. Although LV end-systolic volume and EF improved significantly more in the CABG + SVR group compared with CABG alone, the severity of mitral regurgitation significantly improved only in the CABG alone group, and the estimated LV filling pressure (E/A ratio) increased only in the CABG + SVR group. Higher baseline SI was associated with worse survival after surgery (hazard ratio 1.21, 95% confidence interval 1.02 - 1.43; P = 0.026). Survival was not significantly different by treatment strategy. Although SVR was designed to improve LV geometry, SI worsened after SVR despite improved LVEF and smaller LV volume. Survival was significantly better in patients with lower SI regardless of treatment strategy. © 2015 The Authors. European Journal of Heart Failure © 2015 European Society of Cardiology.

Viral eradication reduces both liver stiffness and steatosis in patients with chronic hepatitis C virus infection who received direct-acting anti-viral therapy.

PubMed

Tada, T; Kumada, T; Toyoda, H; Sone, Y; Takeshima, K; Ogawa, S; Goto, T; Wakahata, A; Nakashima, M; Nakamuta, M; Tanaka, J

2018-04-01

Whether direct-acting anti-viral therapy can reduce liver fibrosis and steatosis in patients with chronic hepatitis C virus (HCV) infection is unclear. To evaluate changes in liver stiffness and steatosis in patients with HCV who received direct-acting anti-viral therapy and achieved sustained virological response (SVR). A total of 198 patients infected with HCV genotype 1 or 2 who achieved SVR after direct-acting anti-viral therapy were analysed. Liver stiffness as evaluated by magnetic resonance elastography, steatosis as evaluated by magnetic resonance imaging-determined proton density fat fraction (PDFF), insulin resistance, and laboratory data were assessed before treatment (baseline) and at 24 weeks after the end of treatment (SVR24). Alanine aminotransferase and homeostatic model assessment-insulin resistance levels decreased significantly from baseline to SVR24. Conversely, platelet count, which is inversely associated with liver fibrosis, increased significantly from baseline to SVR24. In patients with high triglyceride levels (≥150 mg/dL), triglyceride levels significantly decreased from baseline to SVR24 (P = 0.004). The median (interquartile range) liver stiffness values at baseline and SVR24 were 3.10 (2.70-4.18) kPa and 2.80 (2.40-3.77) kPa respectively (P < 0.001). The PDFF values at baseline and SVR 24 were 2.4 (1.7-3.4)% and 1.9 (1.3-2.8)% respectively (P < 0.001). In addition, 68% (19/28) of patients with fatty liver at baseline (PDFF ≥5.2%; n = 28) no longer had fatty liver (PDFF <5.2%) at SVR24. Viral eradication reduces both liver stiffness and steatosis in patients with chronic HCV who received direct-acting anti-viral therapy (UMIN000017020). © 2018 John Wiley & Sons Ltd.

Effective 2D-3D medical image registration using Support Vector Machine.

PubMed

Qi, Wenyuan; Gu, Lixu; Zhao, Qiang

2008-01-01

Registration of pre-operative 3D volume dataset and intra-operative 2D images gradually becomes an important technique to assist radiologists in diagnosing complicated diseases easily and quickly. In this paper, we proposed a novel 2D/3D registration framework based on Support Vector Machine (SVM) to compensate the disadvantages of generating large number of DRR images in the stage of intra-operation. Estimated similarity metric distribution could be built up from the relationship between parameters of transform and prior sparse target metric values by means of SVR method. Based on which, global optimal parameters of transform are finally searched out by an optimizer in order to guide 3D volume dataset to match intra-operative 2D image. Experiments reveal that our proposed registration method improved performance compared to conventional registration method and also provided a precise registration result efficiently.

Interferon-based anti-viral therapy for hepatitis C virus infection after renal transplantation: an updated meta-analysis.

PubMed

Wei, Fang; Liu, Junying; Liu, Fen; Hu, Huaidong; Ren, Hong; Hu, Peng

2014-01-01

Hepatitis C virus (HCV) infection is highly prevalent in renal transplant (RT) recipients. Currently, interferon-based (IFN-based) antiviral therapies are the standard approach to control HCV infection. In a post-transplantation setting, however, IFN-based therapies appear to have limited efficacy and their use remains controversial. The present study aimed to evaluate the efficacy and safety of IFN-based therapies for HCV infection post RT. We searched Pubmed, Embase, Web of Knowledge, and The Cochrane Library (1997-2013) for clinical trials in which transplant patients were given Interferon (IFN), pegylated interferon (PEG), interferon plus ribavirin (IFN-RIB), or pegylated interferon plus ribavirin (PEG-RIB). The Sustained Virological Response (SVR) and/or drop-out rates were the primary outcomes. Summary estimates were calculated using the random-effects model of DerSimonian and Laird, with heterogeneity and sensitivity analysis. We identified 12 clinical trials (140 patients in total). The summary estimate for SVR rate, drop-out rate and graft rejection rate was 26.6% (95%CI, 15.0-38.1%), 21.1% (95% CI, 10.9-31.2%) and 4% (95%CI: 0.8%-7.1%), respectively. The overall SVR rate in PEG-based and standard IFN-based therapy was 40.6% (24/59) and 20.9% (17/81), respectively. The most frequent side-effect requiring discontinuation of treatment was graft dysfunction (14 cases, 45.1%). Meta-regression analysis showed the covariates included contribute to the heterogeneity in the SVR logit rate, but not in the drop-out logit rate. The sensitivity analyses by the random model yielded very similar results to the fixed-effects model. IFN-based therapy for HCV infection post RT has poor efficacy and limited safety. PEG-based therapy is a more effective approach for treating HCV infection post-RT than standard IFN-based therapy. Future research is required to develop novel strategies to improve therapeutic efficacy and tolerability, and reduce the liver-related morbidity and mortality in this important patient population.

Emergence of drug resistance-associated variants and changes in serum lipid profiles in sofosbuvir plus ledipasvir-treated chronic hepatitis C patients.

PubMed

Kan, Hiromi; Imamura, Michio; Kawakami, Yoshiiku; Daijo, Kana; Teraoka, Yuji; Honda, Fumi; Nakamura, Yuki; Morio, Kei; Kobayashi, Tomoki; Nakahara, Takashi; Nagaoki, Yuko; Kawaoka, Tomokazu; Tsuge, Masataka; Aikata, Hiroshi; Hayes, Clair Nelson; Miki, Daiki; Ochi, Hidenori; Honda, Yoji; Mori, Nami; Takaki, Shintaro; Tsuji, Keiji; Chayama, Kazuaki

2017-11-01

Combination of sofosbuvir plus ledipasvir therapy has been expected to enhance sustained virological response (SVR) rates in hepatitis C virus (HCV) genotype 1 chronic infected patients. We analyzed the emergence of drug resistance-associated variants (RAVs) in treatment failure and changes in lipid profiles in sofosbuvir/ledipasvir-treated patients. A total of 176 patients with chronic HCV genotype 1 infection without decompensated liver cirrhosis were treated with sofosbuvir/ledipasvir for 12 weeks. NS5A and NS5B RAVs were determined by either Invader assay or direct sequencing. Serum lipid-related markers were measured at the start of treatment and at week 4 in patients who received sofosbuvir/ledipasvir and ombitasvir/paritaprevir/ritonavir therapies. SVR was achieved in 94.9% (167 out of 176) of patients. SVR12 rate was 97.1% for patietns with low frequncy (<25%) of baseline NS5A RAVs, but 82.8% for patients with high frequency (>75%) of NS5A RAVs. In multivariate regression analysis, higher albumin (odds ratio [OR] = 0.020 for presence; P = 0.007), and NS5A-L31/Y93 RAVs with a population frequency <75% (OR = 29.860 for presence; P = 0.023) were identified as significant independent predictors for SVR12. NS5A-Y93H substitutions were detected in all nine treatment failures at HCV relapse, and three out of six patients with NS5A inhibitor-naïve patients achieved additional NS5A RAVs. Serum low-density lipoprotein cholesterol and apolipoprotein B levels were significantly elevated at week 4 in sofosbuvir/ledipasvir-treated patients. These elevations were greater than in ombitasvir/paritaprevir/ritonavir-treated patients. In conclusion, NS5A multi-RAVs are likely to develop in patients who fail to respond to sofosbuvir/ledipasvir therapy. Inhibition of HCV replication with sofosbuvir might affect lipid metabolism. © 2017 Wiley Periodicals, Inc.

Ombitasvir, paritaprevir, and ritonavir, with or without dasabuvir, plus ribavirin for patients with hepatitis C virus genotype 1 or 4 infection with cirrhosis (ABACUS): a prospective observational study.

PubMed

Petta, Salvatore; Marzioni, Marco; Russo, Pierluigi; Aghemo, Alessio; Alberti, Alfredo; Ascione, Antonio; Antinori, Andrea; Bruno, Raffaele; Bruno, Savino; Chirianni, Antonio; Gaeta, Giovanni Battista; Giannini, Edoardo G; Merli, Manuela; Messina, Vincenzo; Montilla, Simona; Perno, Carlo Federico; Puoti, Massimo; Raimondo, Giovanni; Rendina, Maria; Silberstein, Francesca Ceccherini; Villa, Erica; Zignego, Anna Linda; Pani, Luca; Craxì, Antonio

2017-06-01

We ran a compassionate use nationwide programme (ABACUS) to provide access to ombitasvir, paritaprevir, and ritonavir, with dasabuvir, plus ribavirin for hepatitis C virus (HCV) genotype 1 infection and ombitasvir, paritaprevir, and ritonavir, plus ribavirin for HCV genotype 4 infection in patients with cirrhosis at high risk of decompensation while approval of these regimens was pending in Italy. In this prospective observational study, we collected data from a compassionate use nationwide programme from March 17, 2014, to May 28, 2015. Patients with HCV genotype 1 infection and cirrhosis at high risk of decompensation were given coformulated ombitasvir (25 mg), paritaprevir (150 mg), and ritonavir (100 mg) once daily and dasabuvir (250 mg) twice daily for 12 weeks (patients with HCV genotype 1b infection) or 24 weeks (patients with HCV genotype 1a infection). Patients with HCV genotype 4 infection were given coformulated ombitasvir (25 mg), paritaprevir (150 mg), and ritonavir (100 mg) once per day for 24 weeks. All patients were given weight-based ribavirin. The primary efficacy endpoint was sustained virological response at week 12 after the end of treatment (SVR12), analysed by intention-to-treat. Univariate and multivariate logistic regression analyses were used to identify baseline characteristics associated with SVR12. Adverse events were recorded throughout the study. 728 (96%) of 762 patients with cirrhosis who were given ombitasvir, paritaprevir, and ritonavir, with or without dasabuvir, plus ribavirin therapy for 12 or 24 weeks achieved SVR12. Logistic regression analyses identified that bilirubin concentrations of less than 2 mg/dL were associated with SVR12 (odds ratio [OR] 4·76 [95% CI 1·83-12·3]; p=0·001). 166 (23%) of 734 patients included in safety analyses had an adverse event. 25 (3%) patients discontinued treatment because of adverse events. Asthenia was the most commonly reported adverse event, occurring in 36 (5%) patients. Our findings suggest that the safety and effectiveness of ombitasvir, paritaprevir, and ritonavir, with or without dasabuvir, plus ribavirin in patients with HCV genotype 1 or 4 infection and cirrhosis at high risk of decompensation in a real-life setting are similar to those reported in clinical trials. The concordance with clinical trials provides reassurance that the reported efficacy of this treatment in clinical trials will translate to its use in routine clinical practice. Dipartimento Biomedico di Medicina Interna e Specialistica dell'Universita di Palermo. Copyright © 2017 Elsevier Ltd. All rights reserved.

Early gene expression profiles of patients with chronic hepatitis C treated with pegylated interferon-alfa and ribavirin.

PubMed

Younossi, Zobair M; Baranova, Ancha; Afendy, Arian; Collantes, Rochelle; Stepanova, Maria; Manyam, Ganiraju; Bakshi, Anita; Sigua, Christopher L; Chan, Joanne P; Iverson, Ayuko A; Santini, Christopher D; Chang, Sheng-Yung P

2009-03-01

Responsiveness to hepatitis C virus (HCV) therapy depends on viral and host factors. Our aim was to assess sustained virologic response (SVR)-associated early gene expression in patients with HCV receiving pegylated interferon-alpha2a (PEG-IFN-alpha2a) or PEG-IFN-alpha2b and ribavirin with the duration based on genotypes. Blood samples were collected into PAXgene tubes prior to treatment as well as 1, 7, 28, and 56 days after treatment. From the peripheral blood cells, total RNA was extracted, quantified, and used for one-step reverse transcription polymerase chain reaction to profile 154 messenger RNAs. Expression levels of messenger RNAs were normalized with six "housekeeping" genes and a reference RNA. Multiple regression and stepwise selection were performed to assess differences in gene expression at different time points, and predictive performance was evaluated for each model. A total of 68 patients were enrolled in the study and treated with combination therapy. The results of gene expression showed that SVR could be predicted by the gene expression of signal transducer and activator of transcription-6 (STAT-6) and suppressor of cytokine signaling-1 in the pretreatment samples. After 24 hours, SVR was predicted by the expression of interferon-dependent genes, and this dependence continued to be prominent throughout the treatment. Early gene expression during anti-HCV therapy may elucidate important molecular pathways that may be influencing the probability of achieving virologic response.

Apolipoprotein B-associated cholesterol is a determinant of treatment outcome in patients with chronic hepatitis C virus infection receiving anti-viral agents interferon-alpha and ribavirin.

PubMed

Sheridan, D A; Price, D A; Schmid, M L; Toms, G L; Donaldson, P; Neely, D; Bassendine, M F

2009-06-15

Hepatitis C virus (HCV) co-opts very-low-density lipoprotein (VLDL) pathways for replication, secretion and entry into hepatocytes and associates with apolipoprotein B (apoB) in plasma. Each VLDL contains apoB-100 and variable amounts of apolipoproteins E and C, cholesterol and triglycerides. To determine whether baseline lipid levels predicted treatment outcome. Retrospective analysis was performed of 250 chronic hepatitis C (CHC) patients who had received anti-viral agents interferon-alpha and ribavirin; 165 had a sustained virological response (SVR). Pre- and post-treatment nonfasting lipid profiles were measured and non-high-density lipoprotein (non-HDL) cholesterol (i.e. apoB-associated) was calculated. Binary logistic regression analysis assessed factors independently associated with treatment outcome. There was an independent association between higher apoB-associated cholesterol (non-HDL-C) and increased odds of SVR (odds ratio 2.09, P = 0.042). In multivariate analysis, non-HDL-C was significantly lower in HCV genotype 3 (g3) than genotype 1 (P = 0.007); this was reversible upon eradication of HCVg3 (pre-treatment non-HDL-C = 2.8 mmol/L, SVR = 3.6 mmol/L, P < 0.001). Higher apoB-associated cholesterol is positively associated with treatment outcome in CHC patients receiving anti-viral therapy, possibly due to competition between apoB-containing lipoproteins and infectious low-density HCV lipo-viral particles for hepatocyte entry via shared lipoprotein receptors.

Computational identification of post-translational modification-based nuclear import regulations by characterizing nuclear localization signal-import receptor interaction.

PubMed

Lin, Jhih-Rong; Liu, Zhonghao; Hu, Jianjun

2014-10-01

The binding affinity between a nuclear localization signal (NLS) and its import receptor is closely related to corresponding nuclear import activity. PTM-based modulation of the NLS binding affinity to the import receptor is one of the most understood mechanisms to regulate nuclear import of proteins. However, identification of such regulation mechanisms is challenging due to the difficulty of assessing the impact of PTM on corresponding nuclear import activities. In this study we proposed NIpredict, an effective algorithm to predict nuclear import activity given its NLS, in which molecular interaction energy components (MIECs) were used to characterize the NLS-import receptor interaction, and the support vector regression machine (SVR) was used to learn the relationship between the characterized NLS-import receptor interaction and the corresponding nuclear import activity. Our experiments showed that nuclear import activity change due to NLS change could be accurately predicted by the NIpredict algorithm. Based on NIpredict, we developed a systematic framework to identify potential PTM-based nuclear import regulations for human and yeast nuclear proteins. Application of this approach has identified the potential nuclear import regulation mechanisms by phosphorylation of two nuclear proteins including SF1 and ORC6. © 2014 Wiley Periodicals, Inc.

Beef quality parameters estimation using ultrasound and color images

PubMed Central

2015-01-01

Background Beef quality measurement is a complex task with high economic impact. There is high interest in obtaining an automatic quality parameters estimation in live cattle or post mortem. In this paper we set out to obtain beef quality estimates from the analysis of ultrasound (in vivo) and color images (post mortem), with the measurement of various parameters related to tenderness and amount of meat: rib eye area, percentage of intramuscular fat and backfat thickness or subcutaneous fat. Proposal An algorithm based on curve evolution is implemented to calculate the rib eye area. The backfat thickness is estimated from the profile of distances between two curves that limit the steak and the rib eye, previously detected. A model base in Support Vector Regression (SVR) is trained to estimate the intramuscular fat percentage. A series of features extracted on a region of interest, previously detected in both ultrasound and color images, were proposed. In all cases, a complete evaluation was performed with different databases including: color and ultrasound images acquired by a beef industry expert, intramuscular fat estimation obtained by an expert using a commercial software, and chemical analysis. Conclusions The proposed algorithms show good results to calculate the rib eye area and the backfat thickness measure and profile. They are also promising in predicting the percentage of intramuscular fat. PMID:25734452

Soil Moisture Retrieval Using Convolutional Neural Networks: Application to Passive Microwave Remote Sensing

NASA Astrophysics Data System (ADS)

Hu, Z.; Xu, L.; Yu, B.

2018-04-01

A empirical model is established to analyse the daily retrieval of soil moisture from passive microwave remote sensing using convolutional neural networks (CNN). Soil moisture plays an important role in the water cycle. However, with the rapidly increasing of the acquiring technology for remotely sensed data, it's a hard task for remote sensing practitioners to find a fast and convenient model to deal with the massive data. In this paper, the AMSR-E brightness temperatures are used to train CNN for the prediction of the European centre for medium-range weather forecasts (ECMWF) model. Compared with the classical inversion methods, the deep learning-based method is more suitable for global soil moisture retrieval. It is very well supported by graphics processing unit (GPU) acceleration, which can meet the demand of massive data inversion. Once the model trained, a global soil moisture map can be predicted in less than 10 seconds. What's more, the method of soil moisture retrieval based on deep learning can learn the complex texture features from the big remote sensing data. In this experiment, the results demonstrates that the CNN deployed to retrieve global soil moisture can achieve a better performance than the support vector regression (SVR) for soil moisture retrieval.

EMG-Based Estimation of Limb Movement Using Deep Learning With Recurrent Convolutional Neural Networks.

PubMed

Xia, Peng; Hu, Jie; Peng, Yinghong

2017-10-25

A novel model based on deep learning is proposed to estimate kinematic information for myoelectric control from multi-channel electromyogram (EMG) signals. The neural information of limb movement is embedded in EMG signals that are influenced by all kinds of factors. In order to overcome the negative effects of variability in signals, the proposed model employs the deep architecture combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The EMG signals are transformed to time-frequency frames as the input to the model. The limb movement is estimated by the model that is trained with the gradient descent and backpropagation procedure. We tested the model for simultaneous and proportional estimation of limb movement in eight healthy subjects and compared it with support vector regression (SVR) and CNNs on the same data set. The experimental studies show that the proposed model has higher estimation accuracy and better robustness with respect to time. The combination of CNNs and RNNs can improve the model performance compared with using CNNs alone. The model of deep architecture is promising in EMG decoding and optimization of network structures can increase the accuracy and robustness. © 2017 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

Retrieving Temperature Anomaly in the Global Subsurface and Deeper Ocean From Satellite Observations

NASA Astrophysics Data System (ADS)

Su, Hua; Li, Wene; Yan, Xiao-Hai

2018-01-01

Retrieving the subsurface and deeper ocean (SDO) dynamic parameters from satellite observations is crucial for effectively understanding ocean interior anomalies and dynamic processes, but it is challenging to accurately estimate the subsurface thermal structure over the global scale from sea surface parameters. This study proposes a new approach based on Random Forest (RF) machine learning to retrieve subsurface temperature anomaly (STA) in the global ocean from multisource satellite observations including sea surface height anomaly (SSHA), sea surface temperature anomaly (SSTA), sea surface salinity anomaly (SSSA), and sea surface wind anomaly (SSWA) via in situ Argo data for RF training and testing. RF machine-learning approach can accurately retrieve the STA in the global ocean from satellite observations of sea surface parameters (SSHA, SSTA, SSSA, SSWA). The Argo STA data were used to validate the accuracy and reliability of the results from the RF model. The results indicated that SSHA, SSTA, SSSA, and SSWA together are useful parameters for detecting SDO thermal information and obtaining accurate STA estimations. The proposed method also outperformed support vector regression (SVR) in global STA estimation. It will be a useful technique for studying SDO thermal variability and its role in global climate system from global-scale satellite observations.

Crysalis: an integrated server for computational analysis and design of protein crystallization.

PubMed

Wang, Huilin; Feng, Liubin; Zhang, Ziding; Webb, Geoffrey I; Lin, Donghai; Song, Jiangning

2016-02-24

The failure of multi-step experimental procedures to yield diffraction-quality crystals is a major bottleneck in protein structure determination. Accordingly, several bioinformatics methods have been successfully developed and employed to select crystallizable proteins. Unfortunately, the majority of existing in silico methods only allow the prediction of crystallization propensity, seldom enabling computational design of protein mutants that can be targeted for enhancing protein crystallizability. Here, we present Crysalis, an integrated crystallization analysis tool that builds on support-vector regression (SVR) models to facilitate computational protein crystallization prediction, analysis, and design. More specifically, the functionality of this new tool includes: (1) rapid selection of target crystallizable proteins at the proteome level, (2) identification of site non-optimality for protein crystallization and systematic analysis of all potential single-point mutations that might enhance protein crystallization propensity, and (3) annotation of target protein based on predicted structural properties. We applied the design mode of Crysalis to identify site non-optimality for protein crystallization on a proteome-scale, focusing on proteins currently classified as non-crystallizable. Our results revealed that site non-optimality is based on biases related to residues, predicted structures, physicochemical properties, and sequence loci, which provides in-depth understanding of the features influencing protein crystallization. Crysalis is freely available at http://nmrcen.xmu.edu.cn/crysalis/.

Crysalis: an integrated server for computational analysis and design of protein crystallization

PubMed Central

Wang, Huilin; Feng, Liubin; Zhang, Ziding; Webb, Geoffrey I.; Lin, Donghai; Song, Jiangning

2016-01-01

The failure of multi-step experimental procedures to yield diffraction-quality crystals is a major bottleneck in protein structure determination. Accordingly, several bioinformatics methods have been successfully developed and employed to select crystallizable proteins. Unfortunately, the majority of existing in silico methods only allow the prediction of crystallization propensity, seldom enabling computational design of protein mutants that can be targeted for enhancing protein crystallizability. Here, we present Crysalis, an integrated crystallization analysis tool that builds on support-vector regression (SVR) models to facilitate computational protein crystallization prediction, analysis, and design. More specifically, the functionality of this new tool includes: (1) rapid selection of target crystallizable proteins at the proteome level, (2) identification of site non-optimality for protein crystallization and systematic analysis of all potential single-point mutations that might enhance protein crystallization propensity, and (3) annotation of target protein based on predicted structural properties. We applied the design mode of Crysalis to identify site non-optimality for protein crystallization on a proteome-scale, focusing on proteins currently classified as non-crystallizable. Our results revealed that site non-optimality is based on biases related to residues, predicted structures, physicochemical properties, and sequence loci, which provides in-depth understanding of the features influencing protein crystallization. Crysalis is freely available at http://nmrcen.xmu.edu.cn/crysalis/. PMID:26906024

Hepatitis C virus therapy with peg-interferon and ribavirin in Myanmar: A resource-constrained country

PubMed Central

Hlaing, Naomi Khaing Than; Banerjee, Debolina; Mitrani, Robert; Arker, Soe Htet; Win, Kyaw San; Tun, Nyan Lin; Thant, Zaw; Win, Khin Maung; Reddy, K Rajender

2016-01-01

AIM To investigate peg-interferon (peg-IFN) and ribavirin (RBV) therapy in Myanmar and to predict sustained virologic response (SVR). METHODS This single-center, open-label, study was conducted in Myanmar between 2009 and 2014. A total of 288 patients infected with HCV genotypes 1, 2, 3 and 6 were treated with peg-IFN alpha-2a (180 μg/wk) or alpha-2b (50 to 100 μg as a weight-based dose) and RBV as a weight-based dose (15 mg/kg/d). Treatment duration was 48 wk for genotypes 1 and 6, 24 wk for genotype 2, and 24 or 48 wk for genotype 3 based on rapid virologic response (RVR). Those co-infected with hepatitis B received 48 wk of therapy. RESULTS Overall, SVR was achieved for 82% of patients and the therapy was well tolerated. All patients achieved SVR at equivalent rates regardless of HCV genotype (P = 0.314). Low fibrosis scores (P < 0.001), high baseline albumin levels (P = 0.028) and low baseline viral loads (P = 0.029) all independently predicted SVR. On the other hand, IL-28B TT and CC genotypes were not found to significantly predict SVR (P = 0.634; P = 0.618). Among those who completed treatment, the occurrence of RVR showed a > 96% positive predictive value for achieving SVR. Treatment duration did not significantly impact the likelihood of achieving SVR for patients infected with genotype 3 HCV (P = 0.371). The most common adverse events were fatigue (71%) and poor appetite (60%). Among patients with genotype 3 HCV, more patients in the 48-wk treatment group required erythropoietin than in the 24-wk treatment group (61.1% vs 49.2%). CONCLUSION SVR rates were high with peg-IFN and RBV therapy in Myanmar. Fibrosis scores, baseline albumin, HCV RNA levels and RVR independently predicted SVR. PMID:27920482

Hepatitis C virus therapy with peg-interferon and ribavirin in Myanmar: A resource-constrained country.

PubMed

Hlaing, Naomi Khaing Than; Banerjee, Debolina; Mitrani, Robert; Arker, Soe Htet; Win, Kyaw San; Tun, Nyan Lin; Thant, Zaw; Win, Khin Maung; Reddy, K Rajender

2016-11-21

To investigate peg-interferon (peg-IFN) and ribavirin (RBV) therapy in Myanmar and to predict sustained virologic response (SVR). This single-center, open-label, study was conducted in Myanmar between 2009 and 2014. A total of 288 patients infected with HCV genotypes 1, 2, 3 and 6 were treated with peg-IFN alpha-2a (180 μg/wk) or alpha-2b (50 to 100 μg as a weight-based dose) and RBV as a weight-based dose (15 mg/kg/d). Treatment duration was 48 wk for genotypes 1 and 6, 24 wk for genotype 2, and 24 or 48 wk for genotype 3 based on rapid virologic response (RVR). Those co-infected with hepatitis B received 48 wk of therapy. Overall, SVR was achieved for 82% of patients and the therapy was well tolerated. All patients achieved SVR at equivalent rates regardless of HCV genotype ( P = 0.314). Low fibrosis scores ( P < 0.001), high baseline albumin levels ( P = 0.028) and low baseline viral loads ( P = 0.029) all independently predicted SVR. On the other hand, IL-28B TT and CC genotypes were not found to significantly predict SVR ( P = 0.634; P = 0.618). Among those who completed treatment, the occurrence of RVR showed a > 96% positive predictive value for achieving SVR. Treatment duration did not significantly impact the likelihood of achieving SVR for patients infected with genotype 3 HCV ( P = 0.371). The most common adverse events were fatigue (71%) and poor appetite (60%). Among patients with genotype 3 HCV, more patients in the 48-wk treatment group required erythropoietin than in the 24-wk treatment group (61.1% vs 49.2%). SVR rates were high with peg-IFN and RBV therapy in Myanmar. Fibrosis scores, baseline albumin, HCV RNA levels and RVR independently predicted SVR.

Improvement of liver stiffness in patients with hepatitis C virus infection who received direct-acting antiviral therapy and achieved sustained virological response.

PubMed

Tada, Toshifumi; Kumada, Takashi; Toyoda, Hidenori; Mizuno, Kazuyuki; Sone, Yasuhiro; Kataoka, Saki; Hashinokuchi, Shinichi

2017-12-01

There is insufficient research on whether direct-acting antiviral (DAA) therapy can improve liver fibrosis in patients with chronic hepatitis C virus (HCV). We evaluated sequential changes in liver stiffness using shear wave elastography in patients with HCV who received DAA therapy. A total of 210 patients with HCV who received daclatasvir and asunaprevir therapy and achieved sustained virological response (SVR) were analyzed. Liver stiffness, as evaluated by shear wave elastography, and laboratory data were assessed before treatment (baseline), at end of treatment (EOT), and at 24 weeks after EOT (SVR24). Alanine aminotransferase levels (ALT) decreased over time, and there were significant differences between baseline and EOT and between EOT and SVR24. Although platelet counts did not significantly differ between baseline and EOT, they increased significantly from EOT to SVR24. The median (interquartile range) liver stiffness values at baseline, EOT, and SVR24 were 10.2 (7.7-14.7), 8.8 (7.1-12.1), and 7.6 (6.3-10.3) kPa, respectively (P < 0.001, baseline vs EOT; P < 0.001, EOT vs SVR24). Additionally, in patients with ALT ≤ 30 (indicating low necroinflammatory activity in the liver) and Fibrosis-4 index > 2.0 (n = 75), the liver stiffness values at baseline, EOT, and SVR24 were 9.6 (7.7-15.2), 9.2 (7.3-12.1), and 7.7 (6.3-10.1) kPa, respectively (P < 0.001, baseline vs EOT; P < 0.001, EOT vs SVR24). These results suggest that early improvement of liver stiffness starts during the administration of DAAs in patients who achieve SVR, and this effect is particularly pronounced in patients with progressive liver fibrosis. © 2017 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

Improving the Enterprise Requirements and Acquisition Model’s Developmental Test and Evaluation Process Fidelity

DTIC Science & Technology

2014-03-27

and excluded from the model. The “Check SVR Loop” prevents programs from failing the SVR a second time. If a program has not previously failed the SVR...and Acquisition Management Plan Initiative. Briefing, Peterson AFB, CO: HQ AFSPC/A5X, 2011. Gilmore, Michael J., Key Issues Causing Prgram Delays

Can the Simple View Deal with the Complexities of Reading?

ERIC Educational Resources Information Center

Kirby, John R.; Savage, Robert S.

2008-01-01

We review the Simple View of Reading (SVR) model and examine its nature, applicability and validity. We describe the SVR as an abstract framework for understanding the relationship between global linguistic comprehension and word-reading abilities in reading comprehension (RC). We argue that the SVR is neither a full theory of reading nor a…

Significance of day-1 viral response of hepatitis C virus in patients with chronic hepatitis C receiving direct-acting antiviral therapy.

PubMed

Toyoda, Hidenori; Kumada, Takashi; Tada, Toshifumi; Yama, Tsuyoki; Mizuno, Kazuyuki

2018-06-01

On-treatment response of serum hepatitis C virus (HCV) is reportedly less useful to predict the outcome of anti-HCV therapy with interferon (IFN)-free regimen with direct-acting antivirals than with IFN-based regimens in clinical trials. We evaluated the significance of very early viral response after the start of therapy, which indicates direct HCV response to the drugs, on therapeutic outcome. Reductions in serum HCV-RNA levels were measured at 1 day after the start of therapy in 544 patients who underwent IFN-free direct-acting antiviral regimens. The association between these reductions and the achievement or failure of sustained virologic response (SVR) was evaluated. Patient characteristics did not influence 1-day reduction in serum HCV-RNA except for liver fibrosis. There was no difference in 1-day HCV reduction between SVR and non-SVR patients treated with a 24-week regimen. In contrast, in patients treated with a 12-week regimen, 1-day reduction was significantly greater in SVR than in non-SVR patients (P = 0.0013) and was predictive of SVR versus non-SVR (area under the receiver-operating characteristics curve: 0.80). Whereas the reduction in serum HCV-RNA levels at 1 day after the start of therapy was not associated with treatment outcomes in patients who underwent a 24-week regimen of IFN-free therapy, there was an association in patients receiving a 12-week regimen, and this reduction was predictive of SVR, thus potentially serving as a factor to identify patients at risk of treatment failure. © 2017 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.

Daclatasvir and asunaprevir combination therapy for patients with chronic hepatitis C virus genotype 1b infection in real world.

PubMed

Oh, Jae Young; Kim, Byung Seok; Lee, Chang Hyeong; Song, Jeong Eun; Lee, Heon Ju; Park, Jung Gil; Hwang, Jae Seok; Chung, Woo Jin; Jang, Byoung Kuk; Kweon, Young Oh; Tak, Won Young; Park, Soo Young; Jang, Se Young; Suh, Jeong Ill; Kwak, Sang Gyu

2018-05-25

Previous studies have reported a high rate of sustained virologic response (SVR) and a low rate of serious adverse events with the use of daclatasvir (DCV) and asunaprevir (ASV) combination therapy. We evaluated the efficacy and safety of DCV and ASV combination therapy for patients with chronic hepatitis C virus (HCV) genotype 1b infection in real world. We enrolled 278 patients (184 treatment-naïve patients) from five hospitals in Daegu and Gyeongsangbuk-do. We evaluated the rates of rapid virologic response (RVR), end-of-treatment response (ETR), and SVR at 12 weeks after completion of treatment (SVR12). Furthermore, we investigated the rate of adverse events and predictive factors of SVR12 failure. The mean age of patients was 59.5 ± 10.6 years, and 140 patients (50.2%) were men. Seventy-seven patients had cirrhosis. Baseline information regarding nonstructural protein 5A (NS5A) sequences was available in 268 patients. Six patients presented with pretreatment NS5A resistance-associated variants. The RVR and the ETR rates were 96.6% (258/267) and 95.2% (223/232), respectively. The overall SVR12 rate was 91.6% (197/215). Adverse events occurred in 17 patients (7.9%). Six patients discontinued treatment because of liver enzyme elevation (n = 4) and severe nausea (n = 2). Among these, four achieved SVR12. Other adverse events observed were fatigue, headache, diarrhea, dizziness, loss of appetite, skin rash, and dyspnea. Univariate analysis did not show significant predictive factors of SVR12 failure. DCV and ASV combination therapy showed high rates of RVR, ETR, and SVR12 in chronic HCV genotype 1b-infected patients in real world and was well tolerated without serious adverse events.

Costs of telaprevir-based triple therapy for hepatitis C: $189,000 per sustained virological response.

PubMed

Bichoupan, Kian; Martel-Laferriere, Valerie; Sachs, David; Ng, Michel; Schonfeld, Emily A; Pappas, Alexis; Crismale, James; Stivala, Alicia; Khaitova, Viktoriya; Gardenier, Donald; Linderman, Michael; Perumalswami, Ponni V; Schiano, Thomas D; Odin, Joseph A; Liu, Lawrence; Moskowitz, Alan J; Dieterich, Douglas T; Branch, Andrea D

2014-10-01

In registration trials, triple therapy with telaprevir (TVR), pegylated interferon (Peg-IFN), and ribavirin (RBV) achieved sustained virological response (SVR) rates between 64% and 75%, but the clinical effectiveness and economic burdens of this treatment in real-world practice remain to be determined. Records of 147 patients who initiated TVR-based triple therapy at the Mount Sinai Medical Center (May-December 2011) were reviewed. Direct medical costs for pretreatment, on-treatment, and posttreatment care were calculated using data from Medicare reimbursement databases, RED Book, and the Healthcare Cost and Utilization Project database. Costs are presented in 2012 U.S. dollars. SVR (undetectable hepatitis C virus [HCV] RNA 24 weeks after the end of treatment) was determined on an intention-to-treat basis. Cost per SVR was calculated by dividing the median cost by the SVR rate. Median age of the 147 patients was 56 years (interquartile range [IQR] = 51-61), 68% were male, 19% were black, 11% had human immunodeficiency virus/HCV coinfection, 36% had advanced fibrosis/cirrhosis (FIB-4 scores ≥3.25), and 44% achieved an SVR. The total cost of care was $11.56 million. Median cost of care was $83,721 per patient (IQR = $66,652-$98,102). The median cost per SVR was $189,338 (IQR = $150,735-$221,860). Total costs were TVR (61%), IFN (24%), RBV (4%), adverse event management (8%), professional fees (2%), and laboratory tests (1%). TVR and Peg-IFN accounted for 85% of costs. Pharmaceutical prices and the low (44%) SVR rate, in this real-world study, were major contributors to the high cost per SVR. © 2014 by the American Association for the Study of Liver Diseases.

Persistence of hepatocellular carcinoma risk in hepatitis C patients with a response to IFN and cirrhosis regression.

PubMed

D'Ambrosio, Roberta; Aghemo, Alessio; Rumi, Maria Grazia; Degasperi, Elisabetta; Sangiovanni, Angelo; Maggioni, Marco; Fraquelli, Mirella; Perbellini, Riccardo; Rosenberg, William; Bedossa, Pierre; Colombo, Massimo; Lampertico, Pietro

2018-01-27

In patients with HCV-related cirrhosis, a sustained virological response may lead to cirrhosis regression. Whether histological changes translate into prevention of long-term complications, particularly hepatocellular carcinoma is still unknown. This was investigated in a cohort of histological cirrhotics who had been prospectively followed-up for 10 years after the achievement of a sustained virological response to IFN. In all, 38 sustained virological response cirrhotics who underwent a liver biopsy 5 years post-SVR were prospectively followed to assess the impact of cirrhosis regression on clinical endpoints. During a follow-up of 86 (30-96) months from liver biopsy, no patients developed clinical decompensation, whilst 5 (13%) developed hepatocellular carcinoma after 79 (7-88) months. The 8-year cumulative probability of hepatocellular carcinoma was 17%, without differences between patients with or without cirrhosis regression (19% [95% CI 6%-50%] vs 14% [95% CI 4%-44%], P = .88). Patients who developed or did not an hepatocellular carcinoma had similar rates of residual cirrhosis (P = 1.0), collagen content (P = .48), METAVIR activity (P = .34), portal inflammation (P = .06) and steatosis (P = .17). At baseline, patients who developed an hepatocellular carcinoma had higher γGT (HR 1.03, 95% CI 1.00-1.06; P = .014) and glucose (HR 1.02, 95% CI 1.00-1.02; P = .012) values; moreover, they had increased Forns Score (HR 12.8, 95% CI 1.14-143.9; P = .039), Lok Index (HR 6.24, 95% CI 1.03-37.6; P = .046) and PLF (HR 19.3, 95% CI 1.72-217.6; P = .016) values. One regressor died of lung cancer. The 8-year cumulative survival probability was 97%, independently on cirrhosis regression (96% vs 100%, P = 1.0) or hepatocellular carcinoma (100% vs 97%, P = 1.0). Post-SVR cirrhosis regression does not prevent hepatocellular carcinoma occurrence. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

[Clinical benefit of HCV core antigen assay in patients receiving interferon and ribavirin combination therapy].

PubMed

Higashimoto, Makiko; Takahashi, Masahiko; Jokyu, Ritsuko; Saito, Hidetsugu

2006-02-01

A highly sensitive second generation HCV core antigen assay has recently been developed. We compared viral disappearance and kinetics data between commercially available core antigen assays, Lumipulse Ortho HCV Ag, and a quantitative HCV RNA PCR assay, Cobas Amplicor HCV Monitor Test, Version 2 to estimate the predictive benefit of sustained viral response (SVR) and non-SVR in 59 patients treated with interferon and ribavirin combination therapy. We found a good correlation between HCV core Ag and HCV RNA level regardless of genotype. Although the sensitivity of the core antigen assay was lower than PCR, the dynamic range was broader than that of the PCR assay, so that we did not need to dilute the samples in 59 patients. We detected serial decline of core Ag levels in 24 hrs, 7 days and 14 days after interferon combination therapy. The decline of core antigen levels was significant in SVR patients compared to non-SVR as well as in genotype 2a, 2b patients compared to 1b. Core antigen-negative on day 1 could predict all 10 SVR patients (PPV = 100%), whereas RNA-negative could predict 22 SVR out of 25 on day 14 (PPV = 88.0%). None of the patients who had detectable serum core antigen on day 14 became SVR(NPV = 100%), although NPV was 91.2% on RNA negativity. An easy, simple, low cost new HCV core antigen detecting system seems to be useful for assessing and monitoring IFN treatment for HCV.

The treatment of HCV in patients with haemoglobinopathy in Kurdistan Region, Iraq: a single centre experience.

PubMed

Hussein, N R; Tunjel, I; Basharat, Z; Taha, A; Irving, W

2016-06-01

Various variables that might influence the rapid and sustained virological response to recombinant PEG-IFN-α-2a were explored in Iraqi HCV-infected patients with haemoglobinopathy. Forty-three patients were evaluated for the relationship between rapid virological response (RVR), IL-28B polymorphism, viral load, liver enzyme levels, blood group, ultrasound findings, or HCV genotype and the sustained virological response (SVR) achievement. The overall RVR was 55·81% while the overall SVR was 53·49%. SVR in patients that achieved RVR was 82·61% (P = 0·0004). A significant association was found between initial alanine transaminase levels and viral load with SVR achievement (P = 0·025) and (P = 0·004), respectively. Thirty-two (74%) out of 43 of our samples were host genotyped at the IL-28B locus as CC, a significant association was found between CC group and SVR achievement (P = 0·04). Of our samples, 23/43 (53%) were typed as HCV genotype 4, 10/43 (23%) as genotype 1, 9/43 (20·9%) as genotype 3 and 1/43 (2·3%) as genotype 2. A significant association was found between genotype 3 and SVR achievement (P = 0·006). Multivariate analysis showed that only RVR achievement independently associated with SVR in the Iraqi population (P = 0·00). These results can be used to classify the patients requiring the more expensive new direct-acting antiviral drugs.

Weight loss, leukopenia and thrombocytopenia associated with sustained virologic response to Hepatitis C treatment

PubMed Central

Suwantarat, Nuntra; Tice, Alan D.; Khawcharoenporn, Thana; Chow, Dominic C.

2010-01-01

OBJECTIVE: To identify apparent adverse effects of treatment of chronic hepatitis C and their relationship to sustained virologic response (SVR). METHODS: A retrospective study was conducted of all Hepatitis C virus (HCV)-infected patients treated with pegylated interferon and ribavirin in an academic ambulatory infectious disease practice. Clinical and laboratory characteristics were compared between patients with SVR and without SVR. RESULTS: Fifty-four patients completed therapy with the overall SVR rate of 76%. SVR was associated with genotype non-1 (P=0.01), weight loss more than 5 kilograms (P=0.04), end of treatment leukopenia (P=0.02) and thrombocytopenia (P=0.05). In multivariate analysis, SVR was significant associated with HCV genotype non-1 (Adjusted Odd Ratio [AOR] 15.22; CI 1.55 to 149.72; P=0.02), weight loss more than 5 kilograms, (AOR 5.74; CI 1.24 to 26.32; P=0.04), and end of treatment white blood cell count level less than 3 X 103 cells/µl (AOR 9.09; CI 1.59 to 52.63; P=0.02). Thrombocytopenia was not significant after adjustment. Other factors including age, gender, ethnicity, injection drug use, viral load, anemia, alanine transaminase level, and liver histology did not reach statistical significance. CONCLUSION: Besides non-1 genotype, SVR was found to be independently associated with weight loss during therapy, and leukopenia at the end of HCV treatment. These correlations suggest continuation of therapy despite adverse effects, may be of benefit. PMID:20107528

Hepatitis C Virus Infection Outcomes Among Immigrants to Canada: A Retrospective Cohort Analysis.

PubMed

Cooper, Curtis L; Thavorn, Kednapa; Damian, Ecaterina; Corsi, Daniel J

2017-01-01

HCV-infected immigrants contribute to the total prevalence in Canada and other developed nations. Little is known about engagement in care, access to service, and treatment outcomes in recipients of Direct Acting Antiviral (DAA) HCV therapies among immigrants living with HCV. HCV patients assessed at The Ottawa Hospital Viral Hepatitis Clinic between 2000-2016 were identified. Immigration history, race, socioeconomic status, HCV work-up, treatment and outcome data were evaluated. HCV fibrosis assessment, treatment and sustained virologic response (SVR) were compared using logistic regression. 2,335 HCV-infected patients were analyzed with 91% (2114) having data on immigration (23% immigrants). A median 16 years (Quartiles: 5, 29) passed from immigration to referral. Access to diagnostic procedures (Fibroscan/liver biopsy) was greater among immigrants compared to Canadian-born (78% vs. 68%, p = 0.001) and immigrants had an odds ratio of 1.72 (95% CI: 1.18-2.51) of receiving a FibroScan compared to Canadians after adjustment for demographic characteristics, HCV risk factors, and socioeconomic status. No differences in SVR were found between immigrants for IFN recipients. Among DAA recipients, rates of SVR were > 94% among all patients, 93% in Canadian-born and 98% among immigrants (p = 0.14). Nearly 80% of immigrants in this setting had access to fibrosis assessment which was higher than Canadian-born patients. Under half of both groups had initiated HCV therapy. Delays in accessing HCV care represent a missed opportunity to engage, treat and cure HCV patients. HCV screening and health care engagement at the time of immigration would optimize HCV care and therapeutic outcomes.

Serological Tests Do Not Predict Residual Fibrosis in Hepatitis C Cirrhotics with a Sustained Virological Response to Interferon.

PubMed

D'Ambrosio, Roberta; Degasperi, Elisabetta; Aghemo, Alessio; Fraquelli, Mirella; Lampertico, Pietro; Rumi, Maria Grazia; Facchetti, Floriana; Grassi, Eleonora; Casazza, Giovanni; Rosenberg, William; Bedossa, Pierre; Colombo, Massimo

2016-01-01

Liver biopsy (LB) has lost popularity to stage liver fibrosis in the era of highly effective anti-hepatitis C virus (HCV) therapy, yet diagnosis of persistent cirrhosis may have important implications following HCV eradication. As performance of serological non-invasive tests (NITs) to predict residual fibrosis in non-viremic HCV patients is unknown, we investigated accuracy of NITs to predict residual fibrosis in cirrhotics after a sustained virological response (SVR) to interferon (IFN). Thirty-eight patients with a pre-treatment histological diagnosis of cirrhosis and a 48-104 months post-SVR LB were tested with APRI, CDS, FIB-4, FibroQ, Forns Score, GUCI Index, King Score, Lok Index, PLF, ELF. In 23 (61%) patients, cirrhosis had histologically regressed. All NITs values declined after SVR without any significant difference between regressors and non-regressors (AUROC 0.52-0.75). Using viremic cut-offs, PPV ranged from 34% to 100%, with lower NPV (63% - 68%). NITs performance did not improve using derived cut-offs (PPV: 40% - 80%; NPV: 66% - 100%). PLF, which combines several NITs with transient elastography, had the best diagnostic performance (AUROC 0.75, Sn 61%, Sp 90%, PPV 80%, NPV 78%). After treatment, none of the NITs resulted significantly associated with any of the histological features (activity grade, fibrosis stage, area of fibrosis). The diagnostic estimates obtained using both viremic and derived cut-off values of NITs were suboptimal, indicating that none of these tests helps predicting residual fibrosis and that LB remains the gold standard for this purpose.

Short-Term Distribution System State Forecast Based on Optimal Synchrophasor Sensor Placement and Extreme Learning Machine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Huaiguang; Zhang, Yingchen

This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster. Specifically, considering the sensor installation cost and measurement error, an OSSP algorithm is proposed to reduce the number of synchrophasor sensor and keep the whole distribution system numerically and topologically observable. Then, the weighted least square (WLS) based system state estimator is used to produce the training data for the proposed forecaster. Traditionally, the artificial neural network (ANN) and support vectormore » regression (SVR) are widely used in forecasting due to their nonlinear modeling capabilities. However, the ANN contains heavy computation load and the best parameters for SVR are difficult to obtain. In this paper, the ELM, which overcomes these drawbacks, is used to forecast the future system states with the historical system states. The proposed approach is effective and accurate based on the testing results.« less

Real-world effectiveness of ledipasvir/sofosbuvir in 4,365 treatment-naive, genotype 1 hepatitis C-infected patients.

PubMed

Backus, Lisa I; Belperio, Pamela S; Shahoumian, Troy A; Loomis, Timothy P; Mole, Larry A

2016-08-01

Real-world effectiveness data are needed to inform hepatitis C virus (HCV) treatment decisions. The uptake of ledipasvir/sofosbuvir (LDV/SOF) regimens across health care settings has been rapid, but variations often occur in clinical practice. The aim of this study was to assess sustained virologic response (SVR) of LDV/SOF±ribavirin (RBV) in routine medical practice. This observational, intent-to-treat cohort was comprised of 4,365 genotype 1, treatment-naive, HCV-infected veterans treated with LDV/SOF±RBV. SVR rates were 91.3% (3,191/3,495) for LDV/SOF and 92.0% (527/573) for LDV/SOF+RBV (P = 0.65). African American race (odds ratio 0.70, 95% confidence interval 0.54-0.90, P  = 0.004) and FIB-4 >3.25 (odds ratio 0.56, 95% confidence interval 0.43-0.71, P < 0.001) were independently associated with decreased likelihood of SVR; age, sex, body mass index, decompensated liver disease, diabetes, genotype 1 subtype, and regimen did not predict SVR. In models limited to those who completed 12 weeks of treatment, African American race was no longer a significant predictor of SVR but FIB-4 >3.25 (odds ratio 0.35, 95% confidence interval 0.24-0.50, P < 0.001) remained. Among those without cirrhosis (defined by FIB-4 ≤3.25) and with baseline HCV RNA<6,000,000 IU/mL, SVR rates were 93.2% (1,020/1,094) for those who completed 8 weeks of therapy and 96.6% (875/906) for those who completed 12 weeks of therapy (P = 0.001). In this real-world cohort, SVR rates with LDV/SOF±RBV nearly matched the rates reported in clinical trials and were consistently high across all subgroups; those without cirrhosis but with HCV RNA<6,000,000 IU/mL were less likely to achieve SVR with 8 weeks compared to 12 weeks of therapy, although the numeric difference in SVR rates was small. (Hepatology 2016;64:405-414). Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Eight-week ledipasvir/sofosbuvir in non-cirrhotic, treatment-naïve hepatitis C genotype-1 patients with hepatitis C virus-RNA < 6 million: Single center, real world effectiveness and safety.

PubMed

Latt, Nyan L; Yanny, Beshoy T; Gharibian, Derenik; Gevorkyan, Rita; Sahota, Amandeep K

2017-07-14

To evaluate sustained viral response (SVR) of 8-wk ledipasvir/sofosbuvir therapy among non-cirrhotic, genotype-1 hepatitis C virus (HCV) patients with RNA < 6 million IU/mL. We performed a retrospective cohort study to examine SVR rates, predictors of treatment failure and safety analysis of 8-wk ledipasvir/sofosbuvir (LDV/SOF) therapy among non-cirrhotic, genotype 1 HCV patients with viral load < 6 million IU/mL. Primary outcome was an achievement of SVR at 12 wk after treatment. Secondary outcomes were identifying predictors of treatment failure and adverse events during treatment. Total 736 patients: 55% males, 51% Caucasians and 65% were genotype 1a. Non-cirrhotic state of 53% was determined by clinical judgment (imaging, AST, platelet count) and 47% had documented liver fibrosis testing (biopsy, vibration-controlled transient elastography, serum biomarkers). Overall SVR12 was 96%. No difference in SVR12 was seen between patients whose non-cirrhotic state was determined by clinical judgment and patients who had fibrosis testing. Age groups, gender, ethnicity and genotype 1 subtype did not predict SVR. Non-cirrhotic state determined by clinical judgment based on simple, non-invasive tests were not associated with lower SVR [OR = 1.02, 95%CI: 0.48-2.17, P = 0.962]. The AUROC for hepatitis C RNA viral load was 0.734 ( P < 0.001, 95%CI: 0.66-0.82). HCV RNA 2.2 million IU/mL was identified as the cutoff value with sensitivity 73% and specificity 64%. HCV RNA < 2.2 million IU/mL was associated with significantly higher SVR 98% with OR = 0.22 (95%CI: 0.1-0.49, P < 0.001) compared to SVR 92% in HCV RNA ≥ 2.2 million IU/mL. No death or morbidities were reported. Our outcomes validate safety and effectiveness of 8-wk LDV/SOF therapy in non-cirrhotic, untreated HCV genotype 1 patients with HCV RNA < 6 million IU/mL.

Eight-week ledipasvir/sofosbuvir in non-cirrhotic, treatment-naïve hepatitis C genotype-1 patients with hepatitis C virus-RNA < 6 million: Single center, real world effectiveness and safety

PubMed Central

Latt, Nyan L; Yanny, Beshoy T; Gharibian, Derenik; Gevorkyan, Rita; Sahota, Amandeep K

2017-01-01

AIM To evaluate sustained viral response (SVR) of 8-wk ledipasvir/sofosbuvir therapy among non-cirrhotic, genotype-1 hepatitis C virus (HCV) patients with RNA < 6 million IU/mL. METHODS We performed a retrospective cohort study to examine SVR rates, predictors of treatment failure and safety analysis of 8-wk ledipasvir/sofosbuvir (LDV/SOF) therapy among non-cirrhotic, genotype 1 HCV patients with viral load < 6 million IU/mL. Primary outcome was an achievement of SVR at 12 wk after treatment. Secondary outcomes were identifying predictors of treatment failure and adverse events during treatment. RESULTS Total 736 patients: 55% males, 51% Caucasians and 65% were genotype 1a. Non-cirrhotic state of 53% was determined by clinical judgment (imaging, AST, platelet count) and 47% had documented liver fibrosis testing (biopsy, vibration-controlled transient elastography, serum biomarkers). Overall SVR12 was 96%. No difference in SVR12 was seen between patients whose non-cirrhotic state was determined by clinical judgment and patients who had fibrosis testing. Age groups, gender, ethnicity and genotype 1 subtype did not predict SVR. Non-cirrhotic state determined by clinical judgment based on simple, non-invasive tests were not associated with lower SVR [OR = 1.02, 95%CI: 0.48-2.17, P = 0.962]. The AUROC for hepatitis C RNA viral load was 0.734 (P < 0.001, 95%CI: 0.66-0.82). HCV RNA 2.2 million IU/mL was identified as the cutoff value with sensitivity 73% and specificity 64%. HCV RNA < 2.2 million IU/mL was associated with significantly higher SVR 98% with OR = 0.22 (95%CI: 0.1-0.49, P < 0.001) compared to SVR 92% in HCV RNA ≥ 2.2 million IU/mL. No death or morbidities were reported. CONCLUSION Our outcomes validate safety and effectiveness of 8-wk LDV/SOF therapy in non-cirrhotic, untreated HCV genotype 1 patients with HCV RNA < 6 million IU/mL. PMID:28765697

Drug Pricing Evolution in Hepatitis C.

PubMed

Vernaz, Nathalie; Girardin, François; Goossens, Nicolas; Brügger, Urs; Riguzzi, Marco; Perrier, Arnaud; Negro, Francesco

2016-01-01

We aimed to determine the association between the stepwise increase in the sustained viral response (SVR) and Swiss and United States (US) market prices of drug regimens for treatment-naive, genotype 1 chronic hepatitis C virus (HCV) infection in the last 25 years. We identified the following five steps in the development of HCV treatment regimens: 1) interferon (IFN)-α monotherapy in the early '90s, 2) IFN-α in combination with ribavirin (RBV), 3) pegylated (peg) IFN-α in combination with RBV, 4) the first direct acting antivirals (DAAs) (telaprevir and boceprevir) in combination with pegIFN-α and RBV, and 5) newer DAA-based regimens, such as sofosbuvir (which is or is not combined with ledipasvir) and fixed-dose combination of ritonavir-boosted paritaprevir and ombitasvir in combination with dasabuvir. We performed a linear regression and mean cost analysis to test for an association between SVRs and HCV regimen prices. We conducted a sensitivity analysis using US prices at the time of US drug licensing. We selected randomized clinical trials of drugs approved for use in Switzerland from 1997 to July 2015 including treatment-naïve patients with HCV genotype 1 infection. We identified a statistically significant positive relationship between the proportion of patients achieving SVRs and the costs of HCV regimens in Switzerland (with a bivariate ordinary least square regression yielding an R2 measure of 0.96) and the US (R2 = 0.95). The incremental cost per additional percentage of SVR was 597.14 USD in Switzerland and 1,063.81 USD in the US. The pricing of drugs for HCV regimens follows a value-based model, which has a stable ratio of costs per achieved SVR over 25 years. Health care systems are struggling with the high resource use of these new agents despite their obvious long-term advantages for the overall health of the population. Therefore, the pharmaceutical industry, health care payers and other stakeholders are challenged with finding new drug pricing schemes to treat the entire population infected with HCV.

Drug Pricing Evolution in Hepatitis C

PubMed Central

Vernaz, Nathalie; Girardin, François; Goossens, Nicolas; Brügger, Urs; Riguzzi, Marco; Perrier, Arnaud; Negro, Francesco

2016-01-01

Objective We aimed to determine the association between the stepwise increase in the sustained viral response (SVR) and Swiss and United States (US) market prices of drug regimens for treatment-naive, genotype 1 chronic hepatitis C virus (HCV) infection in the last 25 years. We identified the following five steps in the development of HCV treatment regimens: 1) interferon (IFN)-α monotherapy in the early '90s, 2) IFN-α in combination with ribavirin (RBV), 3) pegylated (peg) IFN-α in combination with RBV, 4) the first direct acting antivirals (DAAs) (telaprevir and boceprevir) in combination with pegIFN-α and RBV, and 5) newer DAA-based regimens, such as sofosbuvir (which is or is not combined with ledipasvir) and fixed-dose combination of ritonavir-boosted paritaprevir and ombitasvir in combination with dasabuvir. Design We performed a linear regression and mean cost analysis to test for an association between SVRs and HCV regimen prices. We conducted a sensitivity analysis using US prices at the time of US drug licensing. We selected randomized clinical trials of drugs approved for use in Switzerland from 1997 to July 2015 including treatment-naïve patients with HCV genotype 1 infection. Results We identified a statistically significant positive relationship between the proportion of patients achieving SVRs and the costs of HCV regimens in Switzerland (with a bivariate ordinary least square regression yielding an R2 measure of 0.96) and the US (R2 = 0.95). The incremental cost per additional percentage of SVR was 597.14 USD in Switzerland and 1,063.81 USD in the US. Conclusion The pricing of drugs for HCV regimens follows a value-based model, which has a stable ratio of costs per achieved SVR over 25 years. Health care systems are struggling with the high resource use of these new agents despite their obvious long-term advantages for the overall health of the population. Therefore, the pharmaceutical industry, health care payers and other stakeholders are challenged with finding new drug pricing schemes to treat the entire population infected with HCV. PMID:27310294

Real-World Effectiveness of Simeprevir-containing Regimens Among Patients With Chronic Hepatitis C Virus: The SONET Study

PubMed Central

Brown, Kimberley; Donovan, Cynthia; Forlenza, Jamie; Lauwers, Kris; Mah’moud, Mitchell A.; Manch, Richard; Mohanty, Smruti R.; Prabhakar, Avinash; Reindollar, Robert; DeMasi, Ralph; Slim, Jihad; Tandon, Neeta; Villadiego, Shirley; Naggie, Susanna

2017-01-01

Abstract Background. The Simeprevir ObservatioNal Effectiveness across practice seTtings (SONET) study evaluated the real-world effectiveness of simeprevir-based treatment for hepatitis C virus (HCV) infection. Methods. The SONET study was a phase 4, prospective, observational, United States–based study enrolling patients ≥18 years of age with chronic genotype 1 HCV infection. The primary endpoint was the proportion of patients who achieved sustained virologic response 12 weeks after the end of treatment (SVR12), defined as HCV ribonucleic acid undetectable ≥12 weeks after the end of all HCV treatments. Results. Of 315 patients (intent-to-treat [ITT] population), 275 (87.3%) completed the study. Overall, 291 were treated with simeprevir + sofosbuvir, 17 with simeprevir + sofosbuvir + ribavirin, and 7 with simeprevir + peginterferon + ribavirin. The majority of patients were male (63.2%) and white (60.6%); median age was 58 years, 71.7% had genotype/subtype 1a, and 39.4% had cirrhosis. The SVR12 was achieved by 81.2% (255 of 314) of ITT patients (analysis excluded 1 patient who completed the study but was missing SVR12 data); 2 had viral breakthrough and 18 had viral relapse. The SVR12 was achieved by 92.4% (255 of 276) of patients in the modified ITT (mITT) population, which excluded patients who discontinued treatment for nonvirologic reasons before the SVR12 time point or were missing SVR12 assessment data. Among mITT patients, higher SVR12 rates were associated with factors including age ≥65 years, non-Hispanic/Latino ethnicity, and employment status, but not genotype/subtype nor presence of cirrhosis. Simeprevir-based treatment was well tolerated; no serious adverse events were considered related to simeprevir. Conclusions. In the real-world setting, simeprevir + sofosbuvir treatment was common and 92% of mITT patients achieved SVR12. Simeprevir-based treatment was effective and well tolerated in this cohort, including patients with cirrhosis. PMID:28480251

Boceprevir early-access for advanced-fibrosis/cirrhosis in Asia-pacific hepatitis C virus genotype 1 non-responders/relapsers

PubMed Central

Sukeepaisarnjaroen, Wattana; Pham, Tri; Tanwandee, Tewesak; Nazareth, Saroja; Galhenage, Sam; Mollison, Lindsay; Totten, Leanne; Wigg, Alan; Altus, Rosalie; Colman, Anton; Morales, Brenda; Mason, Sue; Jones, Tracey; Leembruggen, Nadine; Fragomelli, Vince; Sendall, Cheryl; Guan, Richard; Sutedja, Dede; Tan, Soek Siam; Dan, Yock Young; Lee, Yin Mei; Luman, Widjaja; Teo, Eng Kiong; Than, Yin Min; Piratvisuth, Teerha; Lim, Seng Gee

2015-01-01

AIM: To examined the efficacy and safety of treatment with boceprevir, PEGylated-interferon and ribavirin (PR) in hepatitis C virus genotype 1 (HCVGT1) PR treatment-failures in Asia. METHODS: The Boceprevir Named-Patient Program provided boceprevir to HCVGT1 PR treatment-failures. Participating physicians were invited to contribute data from their patients: baseline characteristics, on-treatment responses, sustained virological response at week 12 (SVR12), and safety were collected and analysed. Multivariate analysis was performed to determine predictors of response. RESULTS: 150 patients were enrolled from Australia, Malaysia, Singapore and Thailand (Asians = 86, Caucasians = 63). Overall SVR12 was 61% (Asians = 59.3%, Caucasians = 63.5%). SVR12 was higher in relapsers (78%) compared with non-responders (34%). On-treatment responses predicted SVR, with undetectable HCVRNA at week 4, 8 and 12 leading to SVR12s of 100%, 87%, and 82% respectively, and detectable HCVRNA at week 4, 8 and 12, leading to SVR12s of 58%, 22% and 6% respectively. Asian patients were similar to Caucasian patients with regards to on-treatment responses. Patients with cirrhosis (n = 69) also behaved in the same manner with regards to on-treatment responses. Those with the IL28B CC genotype (80%) had higher SVRs than those with the CT/TT (56%) genotype (P = 0.010). Multivariate analysis showed that TW8 and TW12 responses were independent predictors of SVR. Serious adverse events occurred in 18.6%: sepsis (2%), decompensation (2.7%) and blood transfusion (14%). Discontinuations occurred in 30.7%, with 18.6% fulfilling stopping rules. CONCLUSION: Boceprevir can be used successfully in PR treatment failures with a SVR12 > 80% if they have good on-treatment responses; however, discontinuations occurred in 30% because of virological failure or adverse events. PMID:26229408

Very Fast Estimation of Epicentral Distance and Magnitude from a Single Three Component Seismic Station Using Machine Learning Techniques

NASA Astrophysics Data System (ADS)

Ochoa Gutierrez, L. H.; Niño Vasquez, L. F.; Vargas-Jimenez, C. A.

2012-12-01

To minimize adverse effects originated by high magnitude earthquakes, early warning has become a powerful tool to anticipate a seismic wave arrival to an specific location and lets to bring people and government agencies opportune information to initiate a fast response. To do this, a very fast and accurate characterization of the event must be done but this process is often made using seismograms recorded in at least 4 stations where processing time is usually greater than the wave travel time to the interest area, mainly in coarse networks. A faster process can be done if only one three component seismic station is used that is the closest unsaturated station respect to the epicenter. Here we present a Support Vector Regression algorithm which calculates Magnitude and Epicentral Distance using only 5 seconds of signal since P wave onset. This algorithm was trained with 36 records of historical earthquakes where the input were regression parameters of an exponential function estimated by least squares, corresponding to the waveform envelope and the maximum value of the observed waveform for each component in one single station. A 10 fold Cross Validation was applied for a Normalized Polynomial Kernel obtaining the mean absolute error for different exponents and complexity parameters. Magnitude could be estimated with 0.16 of mean absolute error and the distance with an error of 7.5 km for distances within 60 to 120 km. This kind of algorithm is easy to implement in hardware and can be used directly in the field station to make possible the broadcast of estimations of this values to generate fast decisions at seismological control centers, increasing the possibility to have an effective reactiontribute and Descriptors calculator for SVR model training and test

Position Information Encoded by Population Activity in Hierarchical Visual Areas

PubMed Central

Majima, Kei; Horikawa, Tomoyasu

2017-01-01

Abstract Neurons in high-level visual areas respond to more complex visual features with broader receptive fields (RFs) compared to those in low-level visual areas. Thus, high-level visual areas are generally considered to carry less information regarding the position of seen objects in the visual field. However, larger RFs may not imply loss of position information at the population level. Here, we evaluated how accurately the position of a seen object could be predicted (decoded) from activity patterns in each of six representative visual areas with different RF sizes [V1–V4, lateral occipital complex (LOC), and fusiform face area (FFA)]. We collected functional magnetic resonance imaging (fMRI) responses while human subjects viewed a ball randomly moving in a two-dimensional field. To estimate population RF sizes of individual fMRI voxels, RF models were fitted for individual voxels in each brain area. The voxels in higher visual areas showed larger estimated RFs than those in lower visual areas. Then, the ball’s position in a separate session was predicted by maximum likelihood estimation using the RF models of individual voxels. We also tested a model-free multivoxel regression (support vector regression, SVR) to predict the position. We found that regardless of the difference in RF size, all visual areas showed similar prediction accuracies, especially on the horizontal dimension. Higher areas showed slightly lower accuracies on the vertical dimension, which appears to be attributed to the narrower spatial distributions of the RF centers. The results suggest that much position information is preserved in population activity through the hierarchical visual pathway regardless of RF sizes and is potentially available in later processing for recognition and behavior. PMID:28451634

Identifying pollution sources and predicting urban air quality using ensemble learning methods

NASA Astrophysics Data System (ADS)

Singh, Kunwar P.; Gupta, Shikha; Rai, Premanjali

2013-12-01

In this study, principal components analysis (PCA) was performed to identify air pollution sources and tree based ensemble learning models were constructed to predict the urban air quality of Lucknow (India) using the air quality and meteorological databases pertaining to a period of five years. PCA identified vehicular emissions and fuel combustion as major air pollution sources. The air quality indices revealed the air quality unhealthy during the summer and winter. Ensemble models were constructed to discriminate between the seasonal air qualities, factors responsible for discrimination, and to predict the air quality indices. Accordingly, single decision tree (SDT), decision tree forest (DTF), and decision treeboost (DTB) were constructed and their generalization and predictive performance was evaluated in terms of several statistical parameters and compared with conventional machine learning benchmark, support vector machines (SVM). The DT and SVM models discriminated the seasonal air quality rendering misclassification rate (MR) of 8.32% (SDT); 4.12% (DTF); 5.62% (DTB), and 6.18% (SVM), respectively in complete data. The AQI and CAQI regression models yielded a correlation between measured and predicted values and root mean squared error of 0.901, 6.67 and 0.825, 9.45 (SDT); 0.951, 4.85 and 0.922, 6.56 (DTF); 0.959, 4.38 and 0.929, 6.30 (DTB); 0.890, 7.00 and 0.836, 9.16 (SVR) in complete data. The DTF and DTB models outperformed the SVM both in classification and regression which could be attributed to the incorporation of the bagging and boosting algorithms in these models. The proposed ensemble models successfully predicted the urban ambient air quality and can be used as effective tools for its management.

Study on the influence of ground and satellite observations on the numerical air-quality for PM10 over Romanian territory

NASA Astrophysics Data System (ADS)

Dumitrache, Rodica Claudia; Iriza, Amalia; Maco, Bogdan Alexandru; Barbu, Cosmin Danut; Hirtl, Marcus; Mantovani, Simone; Nicola, Oana; Irimescu, Anisoara; Craciunescu, Vasile; Ristea, Alina; Diamandi, Andrei

2016-10-01

The numerical forecast of particulate matter concentrations in general, and PM10 in particular is a theme of high socio-economic relevance. The aim of this study was to investigate the impact of ground and satellite data assimilation of PM10 observations into the Weather Research and Forecasting model coupled with Chemistry (WRF-CHEM) numerical air quality model for Romanian territory. This is the first initiative of the kind for this domain of interest. Assimilation of satellite information - e.g. AOT's in air quality models is of interest due to the vast spatial coverage of the observations. Support Vector Regression (SVR) techniques are used to estimate the PM content from heterogeneous data sources, including EO products (Aerosol Optical Thickness), ground measurements and numerical model data (temperature, humidity, wind, etc.). In this study we describe the modeling framework employed and present the evaluation of the impact from the data assimilation of PM10 observations on the forecast of the WRF-CHEM model. Integrations of the WRF-CHEM model in data assimilation enabled/disabled configurations allowed the evaluation of satellite and ground data assimilation impact on the PM10 forecast performance for the Romanian territory. The model integration and evaluation were performed for two months, one in winter conditions (January 2013) and one in summer conditions (June 2013).

A new solar power output prediction based on hybrid forecast engine and decomposition model.

PubMed

Zhang, Weijiang; Dang, Hongshe; Simoes, Rolando

2018-06-12

Regarding to the growing trend of photovoltaic (PV) energy as a clean energy source in electrical networks and its uncertain nature, PV energy prediction has been proposed by researchers in recent decades. This problem is directly effects on operation in power network while, due to high volatility of this signal, an accurate prediction model is demanded. A new prediction model based on Hilbert Huang transform (HHT) and integration of improved empirical mode decomposition (IEMD) with feature selection and forecast engine is presented in this paper. The proposed approach is divided into three main sections. In the first section, the signal is decomposed by the proposed IEMD as an accurate decomposition tool. To increase the accuracy of the proposed method, a new interpolation method has been used instead of cubic spline curve (CSC) fitting in EMD. Then the obtained output is entered into the new feature selection procedure to choose the best candidate inputs. Finally, the signal is predicted by a hybrid forecast engine composed of support vector regression (SVR) based on an intelligent algorithm. The effectiveness of the proposed approach has been verified over a number of real-world engineering test cases in comparison with other well-known models. The obtained results prove the validity of the proposed method. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

An interval-possibilistic basic-flexible programming method for air quality management of municipal energy system through introducing electric vehicles.

PubMed

Yu, L; Li, Y P; Huang, G H; Shan, B G

2017-09-01

Contradictions of sustainable transportation development and environmental issues have been aggravated significantly and been one of the major concerns for energy systems planning and management. A heavy emphasis is placed on stimulation of electric vehicles (EVs) to handle these problems associated with various complexities and uncertainties in municipal energy system (MES). In this study, an interval-possibilistic basic-flexible programming (IPBFP) method is proposed for planning MES of Qingdao, where uncertainties expressed as interval-flexible variables and interval-possibilistic parameters can be effectively reflected. Support vector regression (SVR) is used for predicting electricity demand of the city under various scenarios. Solutions of EVs stimulation levels and satisfaction levels in association with flexible constraints and predetermined necessity degrees are analyzed, which can help identify the optimized energy-supply patterns that could plunk for improvement of air quality and hedge against violation of soft constraints. Results disclose that largely developing EVs can help facilitate the city's energy system with an environment-effective way. However, compared to the rapid growth of transportation, the EVs' contribution of improving the city's air quality is limited. It is desired that, to achieve an environmentally sustainable MES, more concerns should be focused on the integration of increasing renewable energy resources, stimulating EVs as well as improving energy transmission, transport and storage. Copyright © 2017 Elsevier B.V. All rights reserved.

Developing reservoir monthly inflow forecasts using artificial intelligence and climate phenomenon information

NASA Astrophysics Data System (ADS)

Yang, Tiantian; Asanjan, Ata Akbari; Welles, Edwin; Gao, Xiaogang; Sorooshian, Soroosh; Liu, Xiaomang

2017-04-01

Reservoirs are fundamental human-built infrastructures that collect, store, and deliver fresh surface water in a timely manner for many purposes. Efficient reservoir operation requires policy makers and operators to understand how reservoir inflows are changing under different hydrological and climatic conditions to enable forecast-informed operations. Over the last decade, the uses of Artificial Intelligence and Data Mining [AI & DM] techniques in assisting reservoir streamflow subseasonal to seasonal forecasts have been increasing. In this study, Random Forest [RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR) are employed and compared with respect to their capabilities for predicting 1 month-ahead reservoir inflows for two headwater reservoirs in USA and China. Both current and lagged hydrological information and 17 known climate phenomenon indices, i.e., PDO and ENSO, etc., are selected as predictors for simulating reservoir inflows. Results show (1) three methods are capable of providing monthly reservoir inflows with satisfactory statistics; (2) the results obtained by Random Forest have the best statistical performances compared with the other two methods; (3) another advantage of Random Forest algorithm is its capability of interpreting raw model inputs; (4) climate phenomenon indices are useful in assisting monthly or seasonal forecasts of reservoir inflow; and (5) different climate conditions are autocorrelated with up to several months, and the climatic information and their lags are cross correlated with local hydrological conditions in our case studies.

The prevalence and impact of thrombocytopenia, anaemia and leucopenia on sustained virological response in patients receiving hepatitis C therapy: evidence from a large 'real world' cohort.

PubMed

Wang, Huan; Innes, Hamish; Hutchinson, Sharon J; Goldberg, David J; Allen, Samuel; Barclay, Stephen T; Bramley, Peter; Fox, Raymond; Fraser, Andrew; Hayes, Peter C; Kennedy, Nicholas; Mills, Peter R; Dillon, John F

2016-04-01

The aim of the study was to explore the extent of thrombocytopenia (TCP), anaemia and leucopenia in patients with hepatitis C and evaluate how they impact the management of antiviral therapy, the attainment of sustained virological response (SVR), and some therapy-related adverse events. The Scottish Hepatitis C Clinical Database was used in this retrospective study. The prevalence of TCP, anaemia and leucopenia was evaluated. The impact of the three deficiencies on antiviral therapy management, serious adverse events and SVR attainment was assessed in patients who received therapy. The prevalence of TCP, anaemia and leucopenia was 18.5, 0.9 and 0.2% among 4907 treated patients at baseline, increasing to 72, 25.8 and 5.4% during treatment, respectively. Dose reduction occurred in 29.3% of the patients without TCP; this percentage was higher in those with baseline TCP (53%) and in those who acquired it during treatment (35%). Similar results were found for anaemia and leucopenia. Baseline TCP (odds ratio=0.67, P<0.001) and baseline anaemia (odds ratio=0.43, P=0.03) were identified as risk factors associated with lower SVR rate; acquired TCP and anaemia were not associated with reduced SVR. Baseline TCP or anaemia increased the risk of dose cessation. Patients who acquired TCP, anaemia or leucopenia during treatment did not exhibit compromised SVR rates, whereas patients with TCP or anaemia at baseline did. The potential benefit of growth factors in maintaining SVR rate is likely to be confined to those with baseline TCP or anaemia rather than to those who acquire it during therapy, where dose reduction does not appear to reduce the chance of SVR.

Improved survival of patients with hepatocellular carcinoma and compensated hepatitis C virus-related cirrhosis who attained sustained virological response.

PubMed

Bruno, Savino; Di Marco, Vito; Iavarone, Massimo; Roffi, Luigi; Boccaccio, Vincenzo; Crosignani, Andrea; Cabibbo, Giuseppe; Rossi, Sonia; Calvaruso, Vincenza; Aghemo, Alessio; Giacomelli, Luca; Craxì, Antonio; Colombo, Massimo; Maisonneuve, Patrick

2017-10-01

Few studies examined the outcome of patients with hepatitis C virus (HCV)-related cirrhosis who developed hepatocellular carcinoma (HCC). The relative weight as determinant of death for cancer vs end-stage liver disease (ESLD) and the benefit of HCV eradication remain undefined. This multicentre, retrospective analysis evaluates overall survival (OS), rate of decompensation and tumour recurrence in compensated HCC patients treated with interferon (IFN) according to HCV status since HCC diagnosis. Two groups of patients with HCV-related cirrhosis and HCC were followed since HCC diagnosis: (i) compensated cirrhotics with prior sustained virological response (SVR) on IFN-based regimens (N=19); (ii) compensated cirrhotics without SVR (viraemic) (N=156). Over a median follow-up of 3.0 years since the onset of HCC, OS was longer for HCC patients with SVR than for viraemic patients (log-rank P=.004). The 5-year OS rate was 65.9% in patients with SVR vs 31.9% in viraemic patients. Similar trends were reported for hepatic decompensation (log-rank P=.01) and tumour recurrence (log-rank P=.01). These findings were confirmed at multivariable and propensity score analysis. At propensity analysis, 0/19 compensated patients with SVR died for ESLD vs 7/19 (37%) viraemic patients (P=.004). HCC mortality was similar in the two groups. Hepatocellular carcinoma patients with prior SVR and compensated cirrhosis at the time of tumour diagnosis have prolonged OS than viraemic patients. Given the lack of cirrhosis progression, no SVR patient ultimately died for ESLD while this condition appears the main cause of death among viraemic patients. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

[Subjective sensations indicating simulator sickness and fatigue after exposure to virtual reality].

PubMed

Malińska, Marzena; Zuzewicz, Krystyna; Bugajska, Joanna; Grabowski, Andrzej

2014-01-01

The study assessed the incidence and intensity of subjective symptoms indicating simulator sickness among the persons with no inclination to motion sickness, immersed in virtual reality (VR) by watching an hour long movie in the stereoscopic (three-dimensional - 3D) and non-stereoscopic (two-dimensional - 2D) versions and after an hour long training using virtual reality, called sVR. The sample comprised 20 healthy young men with no inclination to motion sickness. The participants' subjective sensations, indicating symptoms of simulator sickness were assessed using the questionnaire completed by the participants immediately, 20 min and 24 h following the test. Grandjean's scale was used to assess fatigue and mood. The symptoms were observed immediately after the exposure to sVR. Their intensity was higher than after watching the 2D and 3D movies. A significant relationship was found between the eye pain and the type of exposure (2D, 3D and sVR) (Chi2)(2) = 6.225, p < or = 0.05); the relationship between excessive perspiration and the exposure to 31) movie and sVR was also noted (Chi2(1) = 9.173, p < or = 0.01). Some symptoms were still observed 20 min after exposure to sVR. The comparison of Grandjean's scale results before and after the training in sVR handing showed significant differences in 11 out of 14 subscales. Before and after exposure to 3D movie, the differences were significant only for the "tired-fatigued" subscale (Z = 2.501, p < or = 0.012) in favor of "fatigued". Based on the subjective sensation of discomfort after watching 2D and 3D movies it is impossible to predict symptoms of simulator sickness after training using sVR.

Modeling how reversal of immune exhaustion elicits cure of chronic hepatitis C after the end of treatment with direct-acting antiviral agents.

PubMed

Baral, Subhasish; Roy, Rahul; Dixit, Narendra M

2018-05-09

A fraction of chronic hepatitis C patients treated with direct-acting antivirals (DAAs) achieved sustained virological responses (SVR), or cure, despite having detectable viremia at the end of treatment (EOT). This observation, termed EOT + /SVR, remains puzzling and precludes rational optimization of treatment durations. One hypothesis to explain EOT + /SVR, the immunologic hypothesis, argues that the viral decline induced by DAAs during treatment reverses the exhaustion of cytotoxic T lymphocytes (CTLs), which then clear the infection after treatment. Whether the hypothesis is consistent with data of viral load changes in patients who experienced EOT + /SVR is unknown. Here, we constructed a mathematical model of viral kinetics incorporating the immunologic hypothesis and compared its predictions with patient data. We found the predictions to be in quantitative agreement with patient data. Using the model, we unraveled an underlying bistability that gives rise to EOT + /SVR and presents a new avenue to optimize treatment durations. Infected cells trigger both activation and exhaustion of CTLs. CTLs in turn kill infected cells. Due to these competing interactions, two stable steady states, chronic infection and viral clearance, emerge, separated by an unstable steady state with intermediate viremia. When treatment during chronic infection drives viremia sufficiently below the unstable state, spontaneous viral clearance results post-treatment, marking EOT + /SVR. The duration to achieve this desired reduction in viremia defines the minimum treatment duration required for ensuring SVR, which our model can quantify. Estimating parameters defining the CTL response of individuals to HCV infection would enable the application of our model to personalize treatment durations. © 2018 The Authors Immunology & Cell Biology published by John Wiley & Sons Australia, Ltd on behalf of Australasian Society for Immunology Inc.

Sample registration of vital events with verbal autopsy: a renewed commitment to measuring and monitoring vital statistics.

PubMed Central

Setel, Philip W.; Sankoh, Osman; Rao, Chalapati; Velkoff, Victoria A.; Mathers, Colin; Gonghuan, Yang; Hemed, Yusuf; Jha, Prabhat; Lopez, Alan D.

2005-01-01

Registration of births, recording deaths by age, sex and cause, and calculating mortality levels and differentials are fundamental to evidence-based health policy, monitoring and evaluation. Yet few of the countries with the greatest need for these data have functioning systems to produce them despite legislation providing for the establishment and maintenance of vital registration. Sample vital registration (SVR), when applied in conjunction with validated verbal autopsy procedures and implemented in a nationally representative sample of population clusters represents an affordable, cost-effective, and sustainable short- and medium-term solution to this problem. SVR complements other information sources by producing age-, sex-, and cause-specific mortality data that are more complete and continuous than those currently available. The tools and methods employed in an SVR system, however, are imperfect and require rigorous validation and continuous quality assurance; sampling strategies for SVR are also still evolving. Nonetheless, interest in establishing SVR is rapidly growing in Africa and Asia. Better systems for reporting and recording data on vital events will be sustainable only if developed hand-in-hand with existing health information strategies at the national and district levels; governance structures; and agendas for social research and development monitoring. If the global community wishes to have mortality measurements 5 or 10 years hence, the foundation stones of SVR must be laid today. PMID:16184280

Treatment Response and Long-Term Outcome of Peginterferon α and Ribavirin Therapy in Korean Patients with Chronic Hepatitis C.

PubMed

Jung, Chang Ho; Um, Soon Ho; Kim, Tae Hyung; Yim, Sun Young; Suh, Sang Jun; Yim, Hyung Joon; Seo, Yeon Seok; Choi, Hyuk Soon; Chun, Hoon Jai

2016-09-15

Peginterferon plus ribavirin remains a standard therapy for patients with chronic hepatitis C (CHC) in Korea. We investigated the efficacy and long-term outcome of peginterferon and ribavirin therapy in Korean patients with CHC, particularly in relation to the stage of liver fibrosis. The incidence of sustained virological response (SVR), hepatic decompensation, hepatocellular carcinoma, and liver-related death was analyzed in 304 patients with CHC; the patients were followed up for a median of 54 months. Among patients with HCV genotype 1, the SVR rate was 36.7% (18/49) and 67% (69/103) for patients with and without cirrhosis, respectively (p<0.001). For patients with non-1 HCV genotypes, the SVR rates were 86.0% (37/43) in cirrhotic patients and 86.2% (94/109) in noncirrhotic patients. SVR significantly reduced the risk of liverrelated death, hepatic decompensation, and hepatocellular carcinoma, which had hazard ratios of 0.27, 0.16, and 0.22, respectively (all p<0.05). However, despite the SVR rate, patients with advanced fibrosis were still at risk of developing liver-related complications. A relatively high SVR rate was achieved by peginterferon plus ribavirin therapy in Korean patients with CHC, which improved their long-term outcomes. However, all CHC patients with advanced hepatic fibrosis should receive close follow-up observations, even after successful antiviral treatment.

Impact of liver steatosis on response to pegylated interferon therapy in patients with chronic hepatitis B

PubMed Central

Ateş, Fehmi; Yalnız, Mehmet; Alan, Saadet

2011-01-01

AIM: To evaluate the impact of liver steatosis upon response to given therapy in chronic hepatitis B (CHB) patients. METHODS: 84 consecutive CHB patients treated with 48-wk PEGylated interferon (PEG-IFN) therapy were enrolled. Baseline characteristics and sustained viral response (SVR) to PEG-IFN therapy were evaluated. RESULTS: Mean body mass index (BMI) was 27.36 ± 4.4 kg/m2. Six (7.1%) had hypertension and three (3.5%) had diabetes mellitus. Steatosis was present in 22.6% (19/84) of liver biopsy samples. Age, BMI, and triglyceride levels of the patients with hepatic steatosis were significantly higher than those without hepatic steatosis (P < 0.05). SVR to PEG-IFN therapy was 21.4% (18/84). Sixteen of these 18 CHB patients with SVR (88.9%) did not have any histopathologically determined steatosis. On the other hand, only two of the 19 CHB patients with hepatic steatosis had SVR (10.5%). Although the SVR rate observed in patients without steatosis (16/65, 24.6%) was higher compared to those with steatosis (2/19, 10.5%), the difference was not statistically significant (P > 0.05). CONCLUSION: Occurrence of hepatic steatosis is significantly high in CHB patients and this association leads to a trend of decreased, but statistically insignificant, SVR rates to PEG-IFN treatment. PMID:22110283

Efficacy of Second Generation Direct-Acting Antiviral Agents for Treatment Naïve Hepatitis C Genotype 1: A Systematic Review and Network Meta-Analysis

PubMed Central

Sobhonslidsuk, Abhasnee; Thakkinstian, Ammarin; Teerawattananon, Yot

2015-01-01

Background The treatment of hepatitis C (HCV) infections has significantly changed in the past few years due to the introduction of direct-acting antiviral agents (DAAs). DAAs could improve the sustained virological response compared to pegylated interferon with ribavirin (PR). However, there has been no evidence from randomized controlled trials (RCTs) that directly compare the efficacy among the different regimens of DAAs. Aim Therefore, we performed a systematic review and network meta-analysis aiming to compare the treatment efficacy between different DAA regimens for treatment naïve HCV genotype 1. Methods Medline and Scopus were searched up to 25th May 2015. RCTs investigating the efficacy of second generation DAA regimens for treatment naïve HCV genotype 1 were eligible for the review. Due to the lower efficacy and more side effects of first generation DAAs, this review included only second generation DAAs approved by the US or EU Food and Drug Administration, that comprised of simeprevir (SMV), sofosbuvir (SOF), daclatasvir (DCV), ledipasvir (LDV), and paritaprevir/ritonavir/ombitasvir plus dasabuvir (PrOD). Primary outcomes were sustained virological response at weeks 12 (SVR12) and 24 (SVR24) after the end of treatment and adverse drug events (i.e. serious adverse events, anemia, and fatigue). Efficacy of all treatment regimens were compared by applying a multivariate random effect meta-analysis. Incidence rates of SVR12 and SVR24, and adverse drug events of each treatment regimen were pooled using ‘pmeta’ command in STATA program. Results Overall, 869 studies were reviewed and 16 studies were eligible for this study. Compared with the PR regimen, SOF plus PR, SMV plus PR, and DVC plus PR regimens yielded significantly higher probability of having SVR24 with pooled risk ratios (RR) of 1.98 (95% CI 1.24, 3.14), 1.46 (95% CI: 1.22, 1.75), and 1.68 (95% CI: 1.14, 2.46), respectively. Pooled incidence rates of SVR12 and SVR24 in all treatment regimens without PR, i.e. SOF plus LDV with/without ribavirin, SOF plus SMV with/without ribavirin, SOF plus DCV with/without ribavirin, and PrOD with/without ribavirin, (pooled incidence of SVR12 ranging from 93% to 100%, and pooled incidence of SVR24 ranging from 89% to 96%) were much higher than the pooled incidence rates of SVR12 (51%) and SVR24 (48%) in PR alone. In comparing SOF plus LDV with ribavirin and SOF plus LDV without ribavirin, the chance of having SVR12 was not significantly different between these two regimens, with the pooled RR of 0.99 (95% CI: 0.97, 1.01). Regarding adverse drug events, risk of serious adverse drug events, anemia and fatigue were relatively higher in treatment regimens with PR than the treatment regimens without PR. The main limitation of our study is that a subgroup analysis according to dosages and duration of treatment could not be performed. Therefore, the dose and duration of recommended treatment have been suggested in range and not in definite value. Conclusions Both DAA plus PR and dual DAA regimens should be included in the first line drug for treatment naïve HCV genotype 1 because of the significant clinical benefits over PR alone. However, due to high drug costs, an economic evaluation should be conducted in order to assess the value of the investment when making coverage decisions. PMID:26720298

Sofosbuvir-based treatment regimens: real life results of 14 409 chronic HCV genotype 4 patients in Egypt.

PubMed

Elsharkawy, A; Fouad, R; El Akel, W; El Raziky, M; Hassany, M; Shiha, G; Said, M; Motawea, I; El Demerdash, T; Seif, S; Gaballah, A; El Shazly, Y; Makhlouf, M A M; Waked, I; Abdelaziz, A O; Yosry, A; El Serafy, M; Thursz, M; Doss, W; Esmat, G

2017-03-01

Chronic hepatitis C virus infection is one of the most important health problems in Egypt. The Ministry of Health's National Treatment Programme introduced sofosbuvir-based therapy in October 2014. To assess the clinical effectiveness and predictors of response to SOF-based treatment regimens, either dual therapy, with SOF/ribavirin (RBV) for 6 months or triple therapy with SOF/peg-IFN-alfa-2a/RBV for 3 months, in a cohort of patients treated in National Treatment Programme affiliated centres in Egypt. Between October 2014 and end of 2014, patients who were eligible for treatment were classified according to their eligibility for interferon therapy: Group 1 (interferon eligible) were treated with triple therapy for 12 weeks and Group 2 (interferon ineligible) were treated with dual therapy for 24 weeks. Difficult to treat patients included those with F3-F4 on Metavir score, Fib-4 >3.25, albumin ≤3.5, total Bilirubin >1.2 mg/dL, INR >1.2 and platelet count <150 000 mm 3 . Twelve weeks post-treatment data were available on 14 409 patients; 8742 in group 1 and 5667 in group 2. In group 1, the sustained virological response at week 12 (SVR12) was 94% and in group 2 the SVR12 was 78.7%. Multivariate logistic regression analysis in which treatment failure is the dependent variable was done. Male gender, being a difficult to treat patient and previous interferon therapy were significant predictors of nonresponse in both treatment groups. Results of sofosbuvir-based therapies in Egypt achieved similar rates of SVR12 as seen in phase III efficacy studies. © 2017 John Wiley & Sons Ltd.

Homozygosity for HLA group 2 alleles predicts treatment failure with interferon-α and ribavirin in chronic hepatitis C virus genotype 1 infection.

PubMed

Collison, Meadhbh; Chin, Jun Liong; Abu Shanab, Ahmed; Mac Nicholas, Ross; Segurado, Ricardo; Coughlan, Suzie; Connell, Jeff; Carr, Michael J; Merriman, Raphael B; McCormick, P Aiden; Hall, William W

2015-02-01

Host genetic factors influence treatment responses to antiviral therapy in chronic hepatitis C virus (HCV) infection. We retrospectively investigated associations between host genetic markers and treatment-induced virologic responses to dual therapy with interferon-α and ribavirin in chronically infected HCV genotype 1 (g1)- and genotype 3 (g3)-infected individuals. A total of 171 patients (89 HCV g1 and 82 HCV g3 infected) were investigated for genetic markers influencing treatment-induced sustained virologic response (SVR). Overall, SVR was observed for 46/89 (52%) HCV g1- and 57/82 (70%) HCV g3-infected patients. Of the 4 interleukin 28B (IL28B) single-nucleotide polymorphisms (SNPs), rs12979860 was the host genetic marker most significantly associated with failure to achieve an SVR in HCV g1-infected individuals [P=3.83×10(-4); odds ratio (OR)=5.61; confidence interval (CI)=2.07-15.18] and gave a positive predictive value for treatment failure of 81.3% for minor homozygotes (TT). Using additive (P=3.54×10(-4)) and dominant models (P=3.83×10(-4)), a dosage effect of the T allele was observed, with the dominance term not significant for this SNP. Logistic regression showed an association between HLA-C1/C1 and rapid virologic response in HCV g1 infections with an OR relative to the heterozygote of 10.0 (95% CI: 1.6-62.5, P=0.014). HLA-C2 homozygosity was a significant predictor of nonresponse to treatment in HCV g1-infected individuals (P=0.023).

Outcomes of chronic hepatitis C therapy in patients treated in community versus academic centres in Canada: final results of APPROACH (a prospective study of peginterferon alfa-2a and ribavirin at academic and community centres in Canada).

PubMed

Myers, Robert P; Cooper, Curtis; Sherman, Morris; Lalonde, Richard; Witt-Sullivan, Helga; Elkashab, Magdy; Harris, Paul; Balshaw, Robert; Usaty, Chistopher; Marrotta, Paul J

2011-09-01

In patients chronically infected with the hepatitis C virus (HCV), it is not established whether viral outcomes or health-related quality of life (HRQoL) differ between individuals treated at academic or community centres. In the present observational study, adults with chronic HCV were treated with peginterferon alfa-2a 180 ìg⁄week plus ribavirin at 45 Canadian centres (16 academic, 29 community). The primary efficacy end point was sustained virological response (SVR). Other outcome measures included HRQoL (assessed using the 36-item Short-Form Health Survey), heath resource use, and workplace productivity and absences within a 60-day interval. In treatment-naive patients infected with HCV genotype 1, significantly higher SVR rates were achieved in those treated at academic (n=54) compared with community (n=125) centres (52% versus 32% [P=0.01]), although rates of dosage reduction and treatment discontinuation were similar across settings. SVR rates among patients infected with genotype 2⁄3 were similar between academic (n=59) and community (n=100) centres (64% versus 67% [P=0.73]). Following antiviral therapy, patients with genotype 1 who achieved an SVR (n=67) had significantly higher mean scores on the physical (P=0.005) and mental components of the 36-item Short-Form Health Survey (P=0.043) compared with those without an SVR (n=111). In contrast, HRQoL scores were similar in HCV genotype 2⁄3 patients with and without an SVR. There were no differences in workplace productivity or absences between patients with and without an SVR. The most frequently used health care resources by all patients were visits and phone calls to hepatitis nurses, and general practice or walk-in clinics. Patients infected with HCV genotype 1 achieved higher SVR rates when treated at academic rather than community centres in Canada. The reasons for this difference require additional investigation.

Outcomes of chronic hepatitis C therapy in patients treated in community versus academic centres in Canada: Final results of APPROACH (A Prospective study of Peginterferon alfa-2a and Ribavirin at Academic and Community Centres in Canada)

PubMed Central

Myers, Robert P; Cooper, Curtis; Sherman, Morris; Lalonde, Richard; Witt-Sullivan, Helga; Elkashab, Magdy; Harris, Paul; Balshaw, Rob; Usaty, Christopher; Marotta, Paul J

2011-01-01

BACKGROUND: In patients chronically infected with the hepatitis C virus (HCV), it is not established whether viral outcomes or health-related quality of life (HRQoL) differ between individuals treated at academic or community centres. METHODS: In the present observational study, adults with chronic HCV were treated with peginterferon alfa-2a 180 μg/week plus ribavirin at 45 Canadian centres (16 academic, 29 community). The primary efficacy end point was sustained virological response (SVR). Other outcome measures included HRQoL (assessed using the 36-item Short-Form Health Survey), heath resource use, and workplace productivity and absences within a 60-day interval. RESULTS: In treatment-naive patients infected with HCV genotype 1, significantly higher SVR rates were achieved in those treated at academic (n=54) compared with community (n=125) centres (52% versus 32% [P=0.01]), although rates of dosage reduction and treatment discontinuation were similar across settings. SVR rates among patients infected with genotype 2/3 were similar between academic (n=59) and community (n=100) centres (64% versus 67% [P=0.73]). Following antiviral therapy, patients with genotype 1 who achieved an SVR (n=67) had significantly higher mean scores on the physical (P=0.005) and mental components of the 36-item Short-Form Health Survey (P=0.043) compared with those without an SVR (n=111). In contrast, HRQoL scores were similar in HCV genotype 2/3 patients with and without an SVR. There were no differences in workplace productivity or absences between patients with and without an SVR. The most frequently used health care resources by all patients were visits and phone calls to hepatitis nurses, and general practice or walk-in clinics. CONCLUSION: Patients infected with HCV genotype 1 achieved higher SVR rates when treated at academic rather than community centres in Canada. The reasons for this difference require additional investigation. PMID:21912762

Sustained virological response and its treatment predictors in hepatitis C virus genotype 4 compared to genotypes 1, 2, and 3: a meta-analysis.

PubMed

Yee, Brittany E; Nguyen, Nghia H; Zhang, Bing; Lin, Derek; Vutien, Philip; Wong, Carrie R; Lutchman, Glen A; Nguyen, Mindie H

2015-01-01

Pegylated interferon and ribavirin (PEG-IFN+RBV) may be more cost-effective than direct-acting antivirals in resource-limited settings. Current literature suggests sustained virological response (SVR) in hepatitis C virus genotype 4 (HCV-4) is similar to genotype 1 (HCV-1), but worse than 2 and 3 (HCV-2/3). However, few studies have compared treatment response between these groups and these have been limited by small sample sizes with heterogeneous designs. We performed a meta-analysis of SVR predictors in HCV-4 versus HCV-1, 2, and 3 patients treated with PEG-IFN+RBV. In November 2013, we searched for 'genotype 4' in MEDLINE/EMBASE databases and scientific conferences. We included original articles with ≥25 treatment-naïve HCV-4 and comparisons to HCV-1, 2, and/or 3 patients treated with PEG-IFN+RBV. Random effects modelling was used with heterogeneity defined by Cochrane Q-test (p value<0.10) and I(2) statistic (>50%). Five studies with 20 014 patients (899 HCV-4; 12 033 HCV-1; and 7082 HCV-2/3 patients) were included. SVR was 53% (CI 43% to 62%) for HCV-4, 44% (CI 40% to 47%) for HCV-1; and 73% (CI 58% to 84%) for HCV-2/3. SVR with EVR (early virological response) was 75% (CI 61% to 86%) in HCV-4; 64% (CI 46% to 79%) in HCV-1; and 85% (CI 71% to 93%) in HCV-2/3. SVR without EVR was 10% (CI 6% to 17%) for HCV-4; 13% (CI 12% to 15%) for HCV-1; and 23% (CI 16% to 33%) for HCV-2/3. SVR rates are similar in HCV-4 (∼50%) and HCV-1 (∼40%). Lack of EVR is a good stopping rule for HCV-4 and HCV-1 since only 10% subsequently achieve SVR. In HCV-4 patients with EVR, three-quarters can expect to achieve SVR with PEG-IFN+RBV.

Evolution of multi-drug resistant HCV clones from pre-existing resistant-associated variants during direct-acting antiviral therapy determined by third-generation sequencing

NASA Astrophysics Data System (ADS)

Takeda, Haruhiko; Ueda, Yoshihide; Inuzuka, Tadashi; Yamashita, Yukitaka; Osaki, Yukio; Nasu, Akihiro; Umeda, Makoto; Takemura, Ryo; Seno, Hiroshi; Sekine, Akihiro; Marusawa, Hiroyuki

2017-03-01

Resistance-associated variant (RAV) is one of the most significant clinical challenges in treating HCV-infected patients with direct-acting antivirals (DAAs). We investigated the viral dynamics in patients receiving DAAs using third-generation sequencing technology. Among 283 patients with genotype-1b HCV receiving daclatasvir + asunaprevir (DCV/ASV), 32 (11.3%) failed to achieve sustained virological response (SVR). Conventional ultra-deep sequencing of HCV genome was performed in 104 patients (32 non-SVR, 72 SVR), and detected representative RAVs in all non-SVR patients at baseline, including Y93H in 28 (87.5%). Long contiguous sequences spanning NS3 to NS5A regions of each viral clone in 12 sera from 6 representative non-SVR patients were determined by third-generation sequencing, and showed the concurrent presence of several synonymous mutations linked to resistance-associated substitutions in a subpopulation of pre-existing RAVs and dominant isolates at treatment failure. Phylogenetic analyses revealed close genetic distances between pre-existing RAVs and dominant RAVs at treatment failure. In addition, multiple drug-resistant mutations developed on pre-existing RAVs after DCV/ASV in all non-SVR cases. In conclusion, multi-drug resistant viral clones at treatment failure certainly originated from a subpopulation of pre-existing RAVs in HCV-infected patients. Those RAVs were selected for and became dominant with the acquisition of multiple resistance-associated substitutions under DAA treatment pressure.

Optimal efficacy of interferon-free HCV retreatment after protease inhibitor failure in real life.

PubMed

Cento, V; Barbaliscia, S; Lenci, I; Ruggiero, T; Magni, C F; Paolucci, S; Babudieri, S; Siciliano, M; Pasquazzi, C; Ciancio, A; Perno, C F; Ceccherini-Silberstein, F

2017-10-01

First-generation protease-inhibitors (PIs) have suboptimal efficacy in GT-1 patients with advanced liver disease, and patients experiencing treatment failure may require urgent retreatment. Our objective was to analyse the real-life efficacy of interferon (IFN)-free retreatment after PI-failure, and the role of genotypic-resistance-testing (GRT) in guiding retreatment choice. In this multi-centre observational study, patients retreated with IFN-free regimens after first-generation PI-failure (telaprevir-boceprevir-simeprevir) were included. Sustained-virological-response (SVR) was evaluated at week 12 of follow-up. GRT was performed by population-sequencing. After PI-failure, 121 patients (cirrhotic=86.8%) were retreated following three different strategies: A) with 'GRT-guided' regimens (N=18); B) with 'AASLD/EASL recommended, not GRT-guided' regimens (N=72); C) with 'not recommended, not GRT-guided' regimens (N=31). Overall SVR rate was 91%, but all 18 patients treated with 'GRT-guided' regimens reached SVR (100%), despite heterogeneity in treatment duration, use of PI and ribavirin, versus 68/72 patients (94.4%) receiving 'AASLD/EASL recommended, not GRT-guided' regimens. SVR was strongly reduced (77.4%) among the 31 patients who received a 'not recommended, not GRT-guided regimen' (p <0.01). Among 37 patients retreated with a PI, SVR rate was 89.2% (33/37). Four GT-1a cirrhotic patients failed an option (C) simeprevir-containing treatment; three out of four had a baseline R155K NS3-RAS. All seven patients treated with paritaprevir-containing regimens reached SVR, regardless of treatment duration and performance of a baseline-GRT. Retreatment of PI-experienced patients can induce maximal SVR rates in real life. Baseline-GRT could help to optimize retreatment strategy, allowing PIs to be reconsidered when chosen after a RASs evaluation. Copyright © 2017 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

IL-28B Polymorphisms Correlated with Treatment Response in HCV-4 Mono-Infected Patients: A Meta-Analysis

PubMed Central

Liu, Tonggang; Sha, Kaihui; Yang, Luhua; Wang, Yun; Zhang, Liguo; Liu, Xianxian; Yang, Fang

2014-01-01

Background The role of interleukin 28B (IL-28B) polymorphisms played in hepatitis C virus (HCV) infection has been gradually explicit, especially in HCV genotype 1, 2 and 3. However, no confirmative conclusion was acquired in genotype 4 HCV patients. Thus we conducted this meta-analysis. Methods We searched the commonly used databases both in English and Chinese. Meta-analysis was performed in fixed/random effects models using STATA 12.0 or R software. Publication bias was examined through Egger's test and Begg's funnel plot. Results In total, 11 studies were included in this meta-analysis, encompassing 1284 patients who were mono-infected with HCV-4 and received Peg-interferon (Peg-IFN) plus Ribavirin (Rbv). Around 53.0% patients would achieve sustained virologic response (SVR), 36.6% achieve rapid virologic response (RVR) and 62.4% achieve end of treatment response (ETR). Egyptian patients had a higher rate achieving SVR than non-Egyptian patients (56.3% vs. 47.8%). IL-28B rs12979860 CC genotype not only favored SVR (OR = 3.95, 95%CI = 3.03–5.16), regardless of citizenship, but also favored RVR (OR = 3.82, 95%CI = 2.46–5.95) and ETR (OR = 4.22, 95%CI = 2.81–6.34). IL-28B rs8099917 genotype TT also correlated with SVR (OR = 3.41, 95%CI = 1.92–6.07), but might not with RVR. IL-28B rs12980275 might still correlate with SVR, but warrant more studies to validate. Conclusions The favorable IL-28B rs12979860 genotype is a statistically significant predictor of SVR, RVR and ETR in HCV-4 monoinfected patients treated with Peg-IFN plus Rbv. Rs8099917 might predict SVR but not RVR. Egyptian HCV-4 patients would achieve better outcomes than non-Egyptian patients when treated with standard care. PMID:24642705

Addition of ribavirin to daclatasvir plus asunaprevir for chronic hepatitis C 1b patients with baseline NS5A resistance-associated variants improved response.

PubMed

Hong, Chun-Ming; Liu, Chun-Jen; Yeh, Shiou-Hwei; Chen, Pei-Jer

2017-04-01

Daclatasvir is a nonstructural protein 5A inhibitor with potent activity against hepatitis C virus genotypes 1-6 in vitro, and asunaprevir is a nonstructural protein 3 protease inhibitor with activity against genotypes 1, 4, 5, and 6. Despite a 90% sustained virologic response (SVR) rate, the SVR rate in patients with baseline NS5A-L31/Y93H polymorphisms decreased to around 40%. Therefore, an alternative regimen under the consideration of cost-effectiveness would be important. Whether the addition of ribavirin could improve the SVR rate among this group of patients remains unknown and hence our case series was reported. For six adult chronic hepatitis C 1b patients with a pre-existing NS5A-Y93H (>20%) polymorphism, we added ribavirin (800 mg/d) to daclatasvir/asunaprevir for 24 weeks and followed through 12-weeks post-treatment. Four of these patients received interferon/ribavirin treatment before but relapsed, while the other two were naïve cases. Two of them had liver cirrhosis and one had hepatocellular carcinoma postcurative therapy. The primary efficacy end-point was undetectable hepatitis C virus RNA (hepatitis C virus RNA level of<25 IU/mL) at 12 weeks after the end of the treatment (SVR12). In total, five cases reached SVR12 eventually (SVR rate: 83%; 95% confidence interval: 18.6-99.1%). However, the viral load of one remaining patient rebounded from the 24 th week of treatment. No patients developed significant adverse effects during and after the treatment. In genotype 1b chronic hepatitis C patients with NS5A-Y93H polymorphism, the addition of ribavirin to daclatasvir/asunaprevir may increase the SVR12 rate with minimal side effects, and thus deserves more comprehensive trials in resource-limited areas. Copyright © 2016. Published by Elsevier B.V.

Daclatasvir/asunaprevir/beclabuvir fixed-dose combination in Japanese patients with HCV genotype 1 infection.

PubMed

Toyota, Joji; Karino, Yoshiyasu; Suzuki, Fumitaka; Ikeda, Fusao; Ido, Akio; Tanaka, Katsuaki; Takaguchi, Koichi; Naganuma, Atsushi; Tomita, Eiichi; Chayama, Kazuaki; Fujiyama, Shigetoshi; Inada, Yukiko; Yoshiji, Hitoshi; Watanabe, Hideaki; Ishikawa, Hiroki; Hu, Wenhua; McPhee, Fiona; Linaberry, Misti; Yin, Philip D; Swenson, Eugene Scott; Kumada, Hiromitsu

2017-03-01

DCV-TRIO, a fixed-dose combination of daclatasvir (pangenotypic NS5A inhibitor), asunaprevir (NS3/4A protease inhibitor), and beclabuvir (non-nucleoside NS5B inhibitor), has achieved high rates of sustained virologic response at post-treatment Week 12 (SVR12) in phase 3 studies. In this phase 3 study, DCV-TRIO for 12 weeks and daclatasvir plus asunaprevir (DUAL) for 24 weeks were studied in Japanese patients infected with HCV genotype 1 (99 % genotype 1b). SVR12 rates ≥95 % were achieved in both treatment-naive (N = 152) and interferon-experienced (N = 65) cohorts treated with DCV-TRIO for 12 weeks and were comparable across patient subgroups, including patients aged ≥65 years and those with cirrhosis. DUAL recipients (N = 75) had an SVR12 rate of 87 %. In the absence of baseline resistance-associated polymorphisms at positions NS5A-Y93H or -L31, SVR12 rates were 98 % with DCV-TRIO or DUAL. Among genotype 1b-infected patients with baseline Y93H or L31 polymorphisms, 35/38 (92 %) DCV-TRIO recipients, and 7/16 (44 %) DUAL recipients achieved SVR12. Adverse events, mostly liver related, led to treatment discontinuation in 10 % of DCV-TRIO recipients. In this group, SVR12 was achieved by 3/9 patients who discontinued before Week 4 and by 12/12 patients who completed ≥4 weeks of DCV-TRIO. Treatment-related serious adverse events occurred in 4 and 3 % of DCV-TRIO and DUAL recipients, respectively. Seven patients (9 %) discontinued DUAL due to adverse events. No deaths occurred. SVR12 was achieved by 96 % of Japanese patients with HCV genotype 1 infection after 12 weeks of treatment with the DCV-TRIO regimen. DCV-TRIO and DUAL exhibited comparable safety profiles.

Direct-Acting Antivirals in Chronic Hepatitis C Genotype 4 Infection in Community Care Setting.

PubMed

Gayam, Vijay; Khalid, Mazin; Mandal, Amrendra Kumar; Hussain, Muhammad Rajib; Mukhtar, Osama; Gill, Arshpal; Garlapati, Pavani; Shrestha, Binav; Guss, Debra; Sherigar, Jagannath; Mansour, Mohammed; Mohanty, Smruti

2018-04-01

Limited data exists comparing the safety, tolerability, and efficacy of direct-acting antivirals (DAAs) in patients with chronic hepatitis C genotype 4 (HCV GT-4) in the community practice setting. We aim to evaluate the treatment response of DAAs in these patients. All the HCV GT-4 patients treated with DAAs between January 2014 and October 2017 in a community clinic setting were retrospectively analyzed. Pretreatment baseline patient characteristics, treatment efficacy with sustained virologic response (SVR) at 12 weeks post treatment (SVR12), and adverse reactions were assessed. Fifty-two patients of Middle Eastern (primarily Egyptian) descent were included in the study. Thirty-two patients were treated with ledipasvir/sofosbuvir (Harvoni ® ) ± ribavirin, 12 patients were treated with ombitasvir/paritaprevir/ritonavir/dasabuvir (ViekiraPak ® ) ± ribavirin, and eight patients were treated with sofosbuvir/Velpatasvir (Epclusa ® ). Ten patients (19.2%) had compensated cirrhosis. Overall, SVR at 12 weeks was achieved in 94% in patients who received one of the three DAA regimens (93.8% in Harvoni ® group, 91.7 % in ViekiraPak ® group and 100% in Epclusa ® group). Prior treatment status and type of regimen used in the presence of compensated cirrhosis had no statistical significance on overall SVR achievement (P value = 0.442 and P value = 0.091, respectively). The most common adverse effect was fatigue (27%). In the real-world setting, DAAs are effective and well tolerated in patients with chronic HCV GT-4 infection with a high overall SVR rate of 94%. Large-scale studies are needed to further assess this SVR in these groups.

Real-world virological efficacy and safety of elbasvir and grazoprevir in patients with chronic hepatitis C virus genotype 1 infection in Japan.

PubMed

Toyoda, Hidenori; Atsukawa, Masanori; Takaguchi, Koichi; Senoh, Tomonori; Michitaka, Kojiro; Hiraoka, Atsushi; Fujioka, Shinichi; Kondo, Chisa; Okubo, Tomomi; Uojima, Haruki; Tada, Toshifumi; Yoneyama, Hirohito; Watanabe, Tsunamasa; Asano, Toru; Ishikawa, Toru; Tamai, Hideyuki; Abe, Hiroshi; Kato, Keizo; Tsuji, Kunihiko; Ogawa, Chikara; Shimada, Noritomo; Iio, Etsuko; Deguchi, Akihiro; Itobayashi, Ei; Mikami, Shigeru; Moriya, Akio; Okubo, Hironao; Tani, Joji; Tsubota, Akihito; Tanaka, Yasuhito; Masaki, Tsutomu; Iwakiri, Katsuhiko; Kumada, Takashi

2018-05-08

The real-world virological efficacy and safety of an interferon (IFN)-free direct-acting antiviral (DAA) therapy with elbasvir (EBR) and grazoprevir (GZR) were evaluated in Japanese patients chronically infected with hepatitis C virus (HCV) genotype 1. The rate of sustained virologic response (SVR) and safety were analyzed in patients who started the EBR/GZR regimen between November 2016 and July 2017. SVR rates were compared based on patient baseline characteristics. Overall, 371 of 381 patients (97.4%) achieved SVR. Multivariate analysis identified a history of failure to IFN-free DAA therapy and the presence of double resistance-associated substitutions (RASs) in HCV non-structural protein 5A (NS5A) as factors significantly associated with failure to EBR/GZR treatment. The SVR rates of patients with a history of IFN-free DAA therapy and those with double RASs were 55.6 and 63.6%, respectively. In all other subpopulations, the SVR rates were more than 90%. There were no severe adverse events associated with the treatment. The EBR/GZR regimen yielded high virological efficacy with acceptable safety. Patients with a history of failure to IFN-free DAA therapy or with double RASs in HCV-NS5A remained difficult to treat with this regimen.

A design of energy detector for ArF excimer lasers

NASA Astrophysics Data System (ADS)

Feng, Zebin; Han, Xiaoquan; Zhou, Yi; Bai, Lujun

2017-08-01

ArF excimer lasers with short wavelength and high photon energy are widely applied in the field of integrated circuit lithography, material processing, laser medicine, and so on. Excimer laser single pulse energy is a very important parameter in the application. In order to detect the single pulse energy on-line, one energy detector based on photodiode was designed. The signal processing circuit connected to the photodiode was designed so that the signal obtained by the photodiode was amplified and the pulse width was broadened. The amplified signal was acquired by a data acquisition card and stored in the computer for subsequent data processing. The peak of the pulse signal is used to characterize the single pulse energy of ArF excimer laser. In every condition of deferent pulse energy value levels, a series of data about laser pulses energy were acquired synchronously using the Ophir energy meter and the energy detector. A data set about the relationship between laser pulse energy and the peak of the pulse signal was acquired. Then, by using the data acquired, a model characterizing the functional relationship between the energy value and the peak value of the pulse was trained based on an algorithm of machine learning, Support Vector Regression (SVR). By using the model, the energy value can be obtained directly from the energy detector designed in this project. The result shows that the relative error between the energy obtained by the energy detector and by the Ophir energy meter is less than 2%.

No-Reference Video Quality Assessment Based on Statistical Analysis in 3D-DCT Domain.

PubMed

Li, Xuelong; Guo, Qun; Lu, Xiaoqiang

2016-05-13

It is an important task to design models for universal no-reference video quality assessment (NR-VQA) in multiple video processing and computer vision applications. However, most existing NR-VQA metrics are designed for specific distortion types which are not often aware in practical applications. A further deficiency is that the spatial and temporal information of videos is hardly considered simultaneously. In this paper, we propose a new NR-VQA metric based on the spatiotemporal natural video statistics (NVS) in 3D discrete cosine transform (3D-DCT) domain. In the proposed method, a set of features are firstly extracted based on the statistical analysis of 3D-DCT coefficients to characterize the spatiotemporal statistics of videos in different views. These features are used to predict the perceived video quality via the efficient linear support vector regression (SVR) model afterwards. The contributions of this paper are: 1) we explore the spatiotemporal statistics of videos in 3DDCT domain which has the inherent spatiotemporal encoding advantage over other widely used 2D transformations; 2) we extract a small set of simple but effective statistical features for video visual quality prediction; 3) the proposed method is universal for multiple types of distortions and robust to different databases. The proposed method is tested on four widely used video databases. Extensive experimental results demonstrate that the proposed method is competitive with the state-of-art NR-VQA metrics and the top-performing FR-VQA and RR-VQA metrics.

Spatial relationships between above-ground biomass and bird species biodiversity in Palawan, Philippines.

PubMed

Singh, Minerva; Friess, Daniel A; Vilela, Bruno; Alban, Jose Don T De; Monzon, Angelica Kristina V; Veridiano, Rizza Karen A; Tumaneng, Roven D

2017-01-01

This study maps distribution and spatial congruence between Above-Ground Biomass (AGB) and species richness of IUCN listed conservation-dependent and endemic avian fauna in Palawan, Philippines. Grey Level Co-Occurrence Texture Matrices (GLCMs) extracted from Landsat and ALOS-PALSAR were used in conjunction with local field data to model and map local-scale field AGB using the Random Forest algorithm (r = 0.92 and RMSE = 31.33 Mg·ha-1). A support vector regression (SVR) model was used to identify the factors influencing variation in avian species richness at a 1km scale. AGB is one of the most important determinants of avian species richness for the study area. Topographic factors and anthropogenic factors such as distance from the roads were also found to strongly influence avian species richness. Hotspots of high AGB and high species richness concentration were mapped using hotspot analysis and the overlaps between areas of high AGB and avian species richness was calculated. Results show that the overlaps between areas of high AGB with high IUCN red listed avian species richness and endemic avian species richness were fairly limited at 13% and 8% at the 1-km scale. The overlap between 1) low AGB and low IUCN richness, and 2) low AGB and low endemic avian species richness was higher at 36% and 12% respectively. The enhanced capacity to spatially map the correlation between AGB and avian species richness distribution will further assist the conservation and protection of forest areas and threatened avian species.

Benefit of hepatitis C virus core antigen assay in prediction of therapeutic response to interferon and ribavirin combination therapy.

PubMed

Takahashi, Masahiko; Saito, Hidetsugu; Higashimoto, Makiko; Atsukawa, Kazuhiro; Ishii, Hiromasa

2005-01-01

A highly sensitive second-generation hepatitis C virus (HCV) core antigen assay has recently been developed. We compared viral disappearance and first-phase kinetics between commercially available core antigen (Ag) assays, Lumipulse Ortho HCV Ag (Lumipulse-Ag), and a quantitative HCV RNA PCR assay, Cobas Amplicor HCV Monitor test, version 2 (Amplicor M), to estimate the predictive benefit of a sustained viral response (SVR) and non-SVR in 44 genotype 1b patients treated with interferon (IFN) and ribavirin. HCV core Ag negativity could predict SVR on day 1 (sensitivity = 100%, specificity = 85.0%, accuracy = 86.4%), whereas RNA negativity could predict SVR on day 7 (sensitivity = 100%, specificity = 87.2%, accuracy = 88.6%). None of the patients who had detectable serum core Ag or RNA on day 14 achieved SVR (specificity = 100%). The predictive accuracy on day 14 was higher by RNA negativity (93.2%) than that by core Ag negativity (75.0%). The combined predictive criterion of both viral load decline during the first 24 h and basal viral load was also predictive for SVR; the sensitivities of Lumipulse-Ag and Amplicor-M were 45.5 and 47.6%, respectively, and the specificity was 100%. Amplicor-M had better predictive accuracy than Lumipulse-Ag in 2-week disappearance tests because it had better sensitivity. On the other hand, estimates of kinetic parameters were similar regardless of the detection method. Although the correlations between Lumipulse-Ag and Amplicor-M were good both before and 24 h after IFN administration, HCV core Ag seemed to be relatively lower 24 h after IFN administration than before administration. Lumipulse-Ag seems to be useful for detecting the HCV concentration during IFN therapy; however, we still need to understand the characteristics of the assay.

Directly observed therapy of sofosbuvir/ribavirin +/- peginterferon with minimal monitoring for the treatment of chronic hepatitis C in people with a history of drug use in Chennai, India (C-DOT).

PubMed

Solomon, S S; Sulkowski, M S; Amrose, P; Srikrishnan, A K; McFall, A M; Ramasamy, B; Kumar, M S; Anand, S; Thomas, D L; Mehta, S H

2018-01-01

We assessed the feasibility of field-based directly observed therapy (DOT) with minimal monitoring to deliver HCV treatment to people with a history of drug use in Chennai, India. Fifty participants were randomized 1:1 to sofosbuvir+peginterferon alfa 2a+ribavirin (SOF+PR) for 12 weeks (Arm 1) vs sofosbuvir+ribavirin (SOF+R) for 24 weeks (Arm 2). SOF+R was delivered daily at participant chosen venues and weekly peginterferon injections at the study clinic. HCV RNA testing was performed to confirm active HCV infection and sustained virologic response 12 weeks after treatment completion (SVR12). No baseline genotyping or on-treatment viral loads were performed. Median age was 46 years. All were male and 20% had significant fibrosis/cirrhosis. All self-reported history of injection drug use, 18% recent noninjection drug use and 38% alcohol dependence. Six discontinued treatment (88% completed treatment in each arm). Of 22 who completed SOF+PR, all achieved SVR12 (22/25=88%); 15 of 22 who completed SOF+R achieved SVR12 (15/25=60%; P=.05). Among those completing SOF+R, SVR12 was significantly less common in participants reporting ongoing substance use (36% vs 100%) and missed doses. Active substance use and missed doses did not impact SVR with SOF+PR. Field-based DOT of HCV therapy without real-time HCV RNA monitoring was feasible; however, achieving 100% adherence was challenging. SOF+PR appeared superior to SOF+R in achieving SVR12, even when doses were missed with no discontinuations due to side effects. Further exploration of short duration treatment with peginterferon plus direct-acting antivirals is warranted. © 2017 John Wiley & Sons Ltd.

Real-World Study on Sofosbuvir-based Therapies in Asian Americans With Chronic Hepatitis C.

PubMed

Pan, Calvin Q; Tiongson, Benjamin C; Hu, Ke-Qin; Han, Steven-Huy B; Tong, Myron; Chu, Danny; Park, James; Lee, Tai Ping; Bhamidimarri, Kalyan Ram; Ma, Xiaoli; Xiao, Pei Ying; Mohanty, Smruti R; Wang, Dan

2018-06-16

Limited data exist with regard to treatment outcomes in Asian Americans with chronic hepatitis C (CHC). We evaluated sofosbuvir (SOF)-based regimens in a national cohort of Asian Americans. Eligible Asian Americans patients with CHC who had posttreatment follow-up of 24 weeks for SOF -based therapies from December 2013 to June 2017 were enrolled from 11 sites across the United States. The primary endpoint was sustained virologic response (SVR) rates at posttreatment weeks 12 and 24. Secondary endpoints were to evaluate safety by tolerability and adverse events (AEs). Among 231 patients screened, 186 were enrolled. At baseline, 31% (57/186) patients were cirrhotic, 34% (63/186) were treatment experienced. Most of the subjects (42%, 79/186) received ledispavir/SOF therapy. The overall SVR12 was 95%, ranging from 86% in genotype (GT) 1b on SOF+ribavirin to 100% in GT 1b patients on ledipasvir/SOF at subgroup analyses. SVR12 was significantly lower in cirrhotic than in noncirrhotic patients [88% (50/57) vs. 98% (126/129), P<0.01]. Stratified by GT, SVR12 were: 96% (43/45) in GT 1a; 93% (67/72) in GT 1b; 100% (23/23) in GT 2; 90% (19/21) in GT 3; 100% (1/1) in GT 4; 83% (5/6) in GT 5; and 100% (16/16) in GT 6. Cirrhotic patients with treatment failure were primarily GT 1, (GT 1a, n=2; GT 1b, n=4) with 1 GT 5 (n=1). Patients tolerated the treatment without serious AEs. Late relapse occurred in 1 patient after achieving SVR12. In Asian Americans with CHC, SOF-based regimens were well tolerated without serious AEs and could achieve high SVR12 regardless of hepatitis C viral infection GT.

Investigation of NS3 Protease Resistance-Associated Variants and Phenotypes for the Prediction of Treatment Response to HCV Triple Therapy.

PubMed

Dietz, Julia; Rupp, Daniel; Susser, Simone; Vermehren, Johannes; Peiffer, Kai-Henrik; Filmann, Natalie; Bon, Dimitra; Kuntzen, Thomas; Mauss, Stefan; Grammatikos, Georgios; Perner, Dany; Berkowski, Caterina; Herrmann, Eva; Zeuzem, Stefan; Bartenschlager, Ralf; Sarrazin, Christoph

2016-01-01

Triple therapy of chronic hepatitis C virus (HCV) infection with boceprevir (BOC) or telaprevir (TVR) leads to virologic failure in many patients which is often associated with the selection of resistance-associated variants (RAVs). These resistance profiles are of importance for the selection of potential rescue treatment options. In this study, we sequenced baseline NS3 RAVs population-based and investigated the sensitivity of NS3 phenotypes in an HCV replicon assay together with clinical factors for a prediction of treatment response in a cohort of 165 German and Swiss patients treated with a BOC or TVR-based triple therapy. Overall, the prevalence of baseline RAVs was low, although the frequency of RAVs was higher in patients with virologic failure compared to those who achieved a sustained virologic response (SVR) (7% versus 1%, P = 0.06). The occurrence of RAVs was associated with a resistant NS3 quasispecies phenotype (P<0.001), but the sensitivity of phenotypes was not associated with treatment outcome (P = 0.2). The majority of single viral and host predictors of SVR was only weakly associated with treatment response. In multivariate analyses, low AST levels, female sex and an IFNL4 CC genotype were independently associated with SVR. However, a combined analysis of negative predictors revealed a significantly lower overall number of negative predictors in patients with SVR in comparison to individuals with virologic failure (P<0.0001) and the presence of 2 or less negative predictors was indicative for SVR. These results demonstrate that most single baseline viral and host parameters have a weak influence on the response to triple therapy, whereas the overall number of negative predictors has a high predictive value for SVR.

Significance of left ventricular diastolic function on outcomes after surgical ventricular restoration.

PubMed

Marui, Akira; Nishina, Takeshi; Saji, Yoshiaki; Yamazaki, Kazuhiro; Shimamoto, Takeshi; Ikeda, Tadashi; Sakata, Ryuzo

2010-05-01

Surgical ventricular restoration (SVR) has been introduced to restore the dilated left ventricular (LV) chamber and improve LV systolic function; however, SVR has also been reported to detrimentally affect LV diastolic properties. We sought to investigate the impact of preoperative LV diastolic function on outcomes after SVR in patients with heart failure. Sixty-seven patients (60 +/- 14 years) with LV systolic dysfunction (LV ejection fraction, 0.27 +/- 0.10) underwent SVR. They were evaluated by echocardiography preoperatively, and early (

Direct-Acting Antivirals in Chronic Hepatitis C Genotype 4 Infection in Community Care Setting

PubMed Central

Gayam, Vijay; Khalid, Mazin; Mandal, Amrendra Kumar; Hussain, Muhammad Rajib; Mukhtar, Osama; Gill, Arshpal; Garlapati, Pavani; Shrestha, Binav; Guss, Debra; Sherigar, Jagannath; Mansour, Mohammed; Mohanty, Smruti

2018-01-01

Background Limited data exists comparing the safety, tolerability, and efficacy of direct-acting antivirals (DAAs) in patients with chronic hepatitis C genotype 4 (HCV GT-4) in the community practice setting. We aim to evaluate the treatment response of DAAs in these patients. Methods All the HCV GT-4 patients treated with DAAs between January 2014 and October 2017 in a community clinic setting were retrospectively analyzed. Pretreatment baseline patient characteristics, treatment efficacy with sustained virologic response (SVR) at 12 weeks post treatment (SVR12), and adverse reactions were assessed. Results Fifty-two patients of Middle Eastern (primarily Egyptian) descent were included in the study. Thirty-two patients were treated with ledipasvir/sofosbuvir (Harvoni®) ± ribavirin, 12 patients were treated with ombitasvir/paritaprevir/ritonavir/dasabuvir (ViekiraPak®) ± ribavirin, and eight patients were treated with sofosbuvir/Velpatasvir (Epclusa®). Ten patients (19.2%) had compensated cirrhosis. Overall, SVR at 12 weeks was achieved in 94% in patients who received one of the three DAA regimens (93.8% in Harvoni® group, 91.7 % in ViekiraPak® group and 100% in Epclusa® group). Prior treatment status and type of regimen used in the presence of compensated cirrhosis had no statistical significance on overall SVR achievement (P value = 0.442 and P value = 0.091, respectively). The most common adverse effect was fatigue (27%). Conclusions In the real-world setting, DAAs are effective and well tolerated in patients with chronic HCV GT-4 infection with a high overall SVR rate of 94%. Large-scale studies are needed to further assess this SVR in these groups. PMID:29707080

Ledipasvir plus sofosbuvir as salvage therapy for HCV genotype 1 failures to prior NS5A inhibitors regimens.

PubMed

Akuta, Norio; Sezaki, Hitomi; Suzuki, Fumitaka; Fujiyama, Shunichiro; Kawamura, Yusuke; Hosaka, Tetsuya; Kobayashi, Masahiro; Kobayashi, Mariko; Saitoh, Satoshi; Suzuki, Yoshiyuki; Arase, Yasuji; Ikeda, Kenji; Kumada, Hiromitsu

2017-07-01

There is little information on retreatment efficacy and predictors of the combination of ledipasvir and sofosbuvir (ledipasvir/sofosbuvir) for patients who fail to respond to NS5A inhibitors. NS5A resistance variants are known to persist for long periods after such treatment. Here, we evaluated 54 patients with chronic HCV genotype 1b infection, free of decompensated cirrhosis, and hepatocellular carcinoma, for sustained virological response after 12 weeks (SVR12) of once-daily treatment with 90 mg ledipasvir and 400 mg sofosbuvir. Intention-to-treat analysis showed SVR12 of 70%. Using ultra-deep sequencing, non-responder to ledipasvir/sofosbuvir showed no change in the rates of detection of NS5A and NS5B resistant-variants at re-elevation of viral loads, relative to baseline. According to response to prior treatment, SVR12 rates were 18, 69, 94, and 100% in non response, viral breakthrough, relapse, and discontinuation due to adverse events, respectively. SVR12 rates in non response were significantly lower than those of the others. Multivariate analysis identified response to previous treatment (failure except for non response) and FIB4 index (<3.25) as significant determinants of SVR12. The SVR12 rates were significantly lower in patients with FIB4 index of ≥3.25 and had not responded to prior treatment, relative to others. The specificity, and positive- and negative-predictive values were high for prediction of poor response based on the combination of two predictors. In conclusion, our study indicated that ledipasvir/sofosbuvir is a potentially useful salvage treatment for patients who fail prior NS5A inhibitors-based therapy. Response to prior treatment was an important predictor of retreatment efficacy. © 2017 Wiley Periodicals, Inc.

Uncertainty Quantification of Equilibrium Climate Sensitivity in CCSM4

NASA Astrophysics Data System (ADS)

Covey, C. C.; Lucas, D. D.; Tannahill, J.; Klein, R.

2013-12-01

Uncertainty in the global mean equilibrium surface warming due to doubled atmospheric CO2, as computed by a "slab ocean" configuration of the Community Climate System Model version 4 (CCSM4), is quantified using 1,039 perturbed-input-parameter simulations. The slab ocean configuration reduces the model's e-folding time when approaching an equilibrium state to ~5 years. This time is much less than for the full ocean configuration, consistent with the shallow depth of the upper well-mixed layer of the ocean represented by the "slab." Adoption of the slab ocean configuration requires the assumption of preset values for the convergence of ocean heat transport beneath the upper well-mixed layer. A standard procedure for choosing these values maximizes agreement with the full ocean version's simulation of the present-day climate when input parameters assume their default values. For each new set of input parameter values, we computed the change in ocean heat transport implied by a "Phase 1" model run in which sea surface temperatures and sea ice concentrations were set equal to present-day values. The resulting total ocean heat transport (= standard value + change implied by Phase 1 run) was then input into "Phase 2" slab ocean runs with varying values of atmospheric CO2. Our uncertainty estimate is based on Latin Hypercube sampling over expert-provided uncertainty ranges of N = 36 adjustable parameters in the atmosphere (CAM4) and sea ice (CICE4) components of CCSM4. Two-dimensional projections of our sampling distribution for the N(N-1)/2 possible pairs of input parameters indicate full coverage of the N-dimensional parameter space, including edges. We used a machine learning-based support vector regression (SVR) statistical model to estimate the probability density function (PDF) of equilibrium warming. This fitting procedure produces a PDF that is qualitatively consistent with the raw histogram of our CCSM4 results. Most of the values from the SVR statistical model are within ~0.1 K of the raw results, well below the inter-decile range inferred below. Independent validation of the fit indicates residual errors that are distributed about zero with a standard deviation of 0.17 K. Analysis of variance shows that the equilibrium warming in CCSM4 is mainly linear in parameter changes. Thus, in accord with the Central Limit Theorem of statistics, the PDF of the warming is approximately Gaussian, i.e. symmetric about its mean value (3.0 K). Since SVR allows for highly nonlinear fits, the symmetry is not an artifact of the fitting procedure. The 10-90 percentile range of the PDF is 2.6-3.4 K, consistent with earlier estimates from CCSM4 but narrower than estimates from other models, which sometimes produce a high-temperature asymmetric tail in the PDF. This work was performed under auspices of the US Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, and was funded by LLNL's Uncertainty Quantification Strategic Initiative (Laboratory Directed Research and Development Project 10-SI-013).

Randomized controlled trial of the NS5A inhibitor daclatasvir plus pegylated interferon and ribavirin for HCV genotype-4 (COMMAND-4).

PubMed

Hézode, Christophe; Alric, Laurent; Brown, Ashley; Hassanein, Tarek; Rizzetto, Mario; Buti, Maria; Bourlière, Marc; Thabut, Dominique; Molina, Esther; Rustgi, Vinod; Samuel, Didier; McPhee, Fiona; Liu, Zhaohui; Yin, Philip D; Hughes, Eric; Treitel, Michelle

2015-08-27

Treatment options for HCV genotype-4 (GT4) are limited. This Phase III study (COMMAND-4; AI444-042) evaluated the efficacy and safety of daclatasvir (DCV), a pan-genotypic HCV NS5A inhibitor, with pegylated interferon-α2a/ribavirin (PEG-IFN/RBV) in treatment-naive patients with HCV GT4 infection. Patients were randomly assigned (2:1; blinded) to treatment with DCV 60 mg (n=82) or placebo (n=42) once daily plus PEG-IFN 180 µg weekly and RBV 1,000-1,200 mg/day (weight-based) twice daily. DCV-treated patients with undetectable HCV RNA at weeks 4 and 12 (eRVR) received 24 weeks of DCV plus PEG-IFN/RBV; those without eRVR received an additional 24 weeks of PEG-IFN/RBV. All placebo-treated patients received 48 weeks of PEG-IFN/RBV. The primary end point was sustained virological response (SVR) at post-treatment week 12 (SVR12). Patients were 75% IL28B non-CC and 11% had cirrhosis. SVR rates (HCV RNA < lower limit of quantitation [LLOQ]) at post-treatment week 12 or later (imputed to include patients missing SVR12 assessments but had SVR after post-treatment week 12) were 82% (67/82) with DCV plus PEG-IFN/RBV versus 43% (18/42) with PEG-IFN/RBV (P<0.0001). In DCV recipients, SVR12 rates were comparable across subgroups. The safety and tolerability profile of DCV plus PEG-IFN/RBV was comparable to that of PEG-IFN/RBV. Discontinuations due to adverse events occurred in 4.9% of patients receiving DCV plus PEG-IFN/RBV and 7.1% of patients receiving PEG-IFN/RBV. In treatment-naive patients with HCV GT4 infection, DCV plus PEG-IFN/RBV achieved higher SVR12 rates than PEG-IFN/RBV alone. These data support DCV-based regimens for treatment of HCV GT4 infection, including all-oral combinations with other direct-acting antivirals (AI444-042; ClinicalTrials.gov NCT01448044).

A Synoptic Climatology of Combined Severe/Weather/Flash Flood Events

NASA Astrophysics Data System (ADS)

Pallozzi, Kyle J.

Classical forms of severe weather such as tornadoes, damaging convective wind gusts, and large hail, as well as flash flooding events, all have potentially large societal impacts. This impact is further magnified when these hazards occur simultaneously in time and space. A major challenge for operational forecasters is how to accurately predict the occurrence of combined storm hazards, and how to communicate the associated multiple threat hazards to the public. A seven-year climatology (2009-2015) of combined severe weather/flash flooding (SVR/FF) events across the contiguous United States was developed in attempt to study the combined SVR/FF event hazards further. A total of 211 total cases were identified and sub-divided into seven subcategories based on their convective morphology and meteorological characteristics. Heatmaps of event report frequency were created to extract spatial, seasonal and interannual patterns in SVR/FF event activity. Diurnal trends were examined from time series plots of tornado, hail, wind and flash flood/flood reports. Event-centered composites of environmental variables were created for each subcategory from 13 km RUC/RAP analyses. Representative cases studies were conducted for each subcategory. A "ring of fire" with the highest levels of SVR/FF event activity was noted across the central United States. SVR/FF events were least common in the Southeast, High Plains, and Northern Plains. Enhanced SVR/FF activity reflected contributions from synoptic events during the cool and shoulder seasons over the Lower Mississippi, Arkansas and Tennessee Valleys, and MCS activity during the warm season over the lower Great Plains, and the Upper Mississippi, Missouri and Ohio River Valleys. Results from the composite analyses indicated that relatively high values of CAPE, surface-500 hPa shear and precipitable water were observed for all subcategories. Case studies show that many high-end SVR/FF events featured slow-moving, or quasi-stationary fronts/outflow boundaries, a moist troposphere and front-paralleling 850-300 hPa mean winds. In this environment, individual convective cells can be advected downstream along the initiating boundary, resulting in flood-producing training echoes. A relatively moist troposphere leads to efficient precipitation production, limits cold-pool formation/off-boundary propagation, and further increases the likelihood of flash flooding.

Effectiveness of All-Oral Antiviral Regimens in 996 Human Immunodeficiency Virus/Hepatitis C Virus Genotype 1-Coinfected Patients Treated in Routine Practice.

PubMed

Bhattacharya, Debika; Belperio, Pamela S; Shahoumian, Troy A; Loomis, Timothy P; Goetz, Matthew B; Mole, Larry A; Backus, Lisa I

2017-06-15

Large cohorts are needed to assess human immunodeficiency virus (HIV)/hepatitis C virus (HCV) real-world treatment outcomes. We examined the effectiveness of ledipasvir/sofosbuvir with or without ribavirin (LDV/SOF ± RBV) and ombitasvir/ paritaprevir/ritonavir plus dasabuvir (OPrD) ± RBV in HIV/HCV genotype 1 (GT1)-coinfected patients initiating HCV therapy in clinical practice. Observational intent-to-treat cohort analysis using the Veterans Affairs Clinical Case Registry to identify HIV/HCV GT1-coinfected veterans initiating 12 weeks of LDV/SOF ± RBV or OPrD ± RBV. Multivariate models of sustained virologic response (SVR) included age, race, cirrhosis, proton pump inhibitor (PPI) prescription, prior HCV treatment, body mass index, genotype subtype, and HCV treatment regimen. Nine hundred ninety-six HIV/HCV GT1-coinfected veterans initiated therapy: 757 LDV/SOF, 138 LDV/SOF + RBV, 28 OPrD, and 73 OPrD + RBV. Overall SVR was 90.9% (823/905); LDV/SOF 92.1% (631/685), LDV/SOF + RBV 86.3% (113/131), OPrD 88.9% (24/27), and OPrD + RBV 88.7% (55/62). SVR was 85.9% (176/205) and 92.4% (647/700) in those with and without cirrhosis (P = .006). SVR was similar between African Americans (90.5% [546/603]) and all others (91.7% [277/302]). PPI use with LDV/SOF ± RBV did not affect SVR (89.7% [131/146] with PPI and 91.5% [613/670] without PPI). Cirrhosis was predictive of reduced SVR (0.51 [95% confidence interval {CI}, .31-.87]; P = .01). Median creatinine change did not differ among patients receiving LDV/SOF and tenofovir disoproxil fumarate (TDF) without a protease inhibitor (PI) (0.18 [interquartile range {IQR}, 0.08-0.30]; n = 372), LDV/SOF and TDF/PI (0.17 [IQR, 0.04-0.30]; n = 100), and LDV/SOF without TDF (0.15 [IQR, 0.00-0.30]; n = 423). SVR rates in HIV/HCV GT1-coinfected patients were high. African American race or PPI use with LDV/SOF ± RBV was not associated with lower SVR rates, but cirrhosis was. Renal function did not worsen on LDV/SOF regimens with TDF. Published by Oxford University Press for the Infectious Diseases Society of America 2017. This work is written by (a) US Government employee(s) and is in the public domain in the US.

The hemodynamic effects of methylene blue when administered at the onset of cardiopulmonary bypass.

PubMed

Maslow, Andrew D; Stearns, Gary; Butala, Parag; Batula, Parag; Schwartz, Carl S; Gough, Jeffrey; Singh, Arun K

2006-07-01

Hypotension occurs during cardiopulmonary bypass (CPB), in part because of induction of the inflammatory response, for which nitric oxide and guanylate cyclase play a central role. In this study we examined the hemodynamic effects of methylene blue (MB), an inhibitor of guanylate cyclase, administered during cardiopulmonary bypass (CPB) to patients taking angiotensin-converting enzyme inhibitors. Thirty patients undergoing cardiac surgery were randomized to receive either MB (3 mg/kg) or saline (S) after institution of CPB and cardioplegic arrest. CPB was managed similarly for all study patients. Hemodynamic data were assessed before, during, and after CPB. The use of vasopressors was recorded. All study patients experienced a similar reduction in mean arterial blood pressure (MAP) and systemic vascular resistance (SVR) with the onset of CPB and cardioplegic arrest. MB increased MAP and SVR and this effect lasted for 40 minutes. The saline group demonstrated a persistently reduced MAP and SVR throughout CPB. The saline group received phenylephrine more frequently during CPB, and more norepinephrine after CPB to maintain a desirable MAP. The MB group recorded significantly lower serum lactate levels despite equal or greater MAP and SVR. In conclusion, administration of MB after institution of CPB for patients taking angiotensin-converting enzyme inhibitors increased MAP and SVR and reduced the need for vasopressors. Furthermore, serum lactate levels were lower in MB patients, suggesting more favorable tissue perfusion.

Angiotensin II and CRF receptors in the central nucleus of the amygdala mediate hemodynamic response variability to cocaine in conscious rats.

PubMed

Watanabe, Mari A; Kucenas, Sarah; Bowman, Tamara A; Ruhlman, Melissa; Knuepfer, Mark M

2010-01-14

Stress or cocaine evokes either a large increase in systemic vascular resistance (SVR) or a smaller increase in SVR accompanied by an increase in cardiac output (designated vascular and mixed responders, respectively) in Sprague-Dawley rats. We hypothesized that the central nucleus of the amygdala (CeA) mediates this variability. Conscious, freely-moving rats, instrumented for measurement of arterial pressure and cardiac output and for drug delivery into the CeA, were given cocaine (5 mg/kg, iv, 4-6 times) and characterized as vascular (n=15) or mixed responders (n=10). Subsequently, we administered cocaine after bilateral microinjections (100 nl) of saline or selective agents in the CeA. Muscimol (80 pmol), a GABA(A) agonist, or losartan (43.4 pmol), an AT(1) receptor antagonist, attenuated the cocaine-induced increase in SVR in vascular responders, selectively, such that vascular responders were no longer different from mixed responders. The corticotropin releasing factor (CRF) antagonist, alpha-helical CRF(9-41) (15.7 pmol), abolished the difference between cardiac output and SVR in mixed and vascular responders. We conclude that greater increases in SVR observed in vascular responders are dependent on AT(1) receptor activation and, to a lesser extent on CRF receptors. Therefore, AT(1) and CRF receptors in the CeA contribute to hemodynamic response variability to intravenous cocaine.

Angiotensin II and CRF Receptors in the Central Nucleus of the Amygdala Mediate Hemodynamic Response Variability to Cocaine in Conscious Rats

PubMed Central

Watanabe, Mari A.; Kucenas, Sarah; Bowman, Tamara A.; Ruhlman, Melissa; Knuepfer, Mark M.

2009-01-01

Stress or cocaine evokes either a large increase in systemic vascular resistance (SVR) or a smaller increase in SVR accompanied by an increase in cardiac output (designated vascular and mixed responders, respectively) in Sprague-Dawley rats. We hypothesized that the central nucleus of the amygdala (CeA) mediates this variability. Conscious, freely-moving rats, instrumented for measurement of arterial pressure and cardiac output and for drug delivery into the CeA, were given cocaine (5 mg/kg, iv, 4-6 times) and characterized as vascular (n=15) or mixed responders (n=10). Subsequently, we administered cocaine after bilateral microinjections (100 nl) of saline or selective agents in the CeA. Muscimol (80 pmol), a GABAA agonist, or losartan (43.4 pmol), an AT1 receptor antagonist, attenuated the cocaine-induced increase in SVR in vascular responders, selectively, such that vascular responders were no longer different from mixed responders. The corticotropin releasing factor (CRF) antagonist, α-helical CRF9-41 (15.7 pmol), abolished the difference between cardiac output and SVR in mixed and vascular responders. We conclude that greater increases in SVR observed in vascular responders are dependent on AT1 receptor activation and, to a lesser extent on CRF receptors. Therefore, AT1 and CRF receptors in the CeA contribute to hemodynamic response variability to intravenous cocaine. PMID:19879859

Field assessment of noncontact stream gauging using portable surface velocity radars (SVR)

NASA Astrophysics Data System (ADS)

Welber, Matilde; Le Coz, Jérôme; Laronne, Jonathan B.; Zolezzi, Guido; Zamler, Daniel; Dramais, Guillaume; Hauet, Alexandre; Salvaro, Martino

2016-02-01

The applicability of a portable, commercially available surface velocity radar (SVR) for noncontact stream gauging was evaluated through a series of field-scale experiments carried out in a variety of sites and deployment conditions. Comparisons with various concurrent techniques showed acceptable agreement with velocity profiles, with larger uncertainties close to the banks. In addition to discharge error sources shared with intrusive velocity-area techniques, SVR discharge estimates are affected by flood-induced changes in the bed profile and by the selection of a depth-averaged to surface velocity ratio, or velocity coefficient (α). Cross-sectional averaged velocity coefficients showed smaller fluctuations and closer agreement with theoretical values than those computed on individual verticals, especially in channels with high relative roughness. Our findings confirm that α = 0.85 is a valid default value, with a preferred site-specific calibration to avoid underestimation of discharge in very smooth channels (relative roughness ˜ 0.001) and overestimation in very rough channels (relative roughness > 0.05). Theoretically derived and site-calibrated values of α also give accurate SVR-based discharge estimates (within 10%) for low and intermediate roughness flows (relative roughness 0.001 to 0.05). Moreover, discharge uncertainty does not exceed 10% even for a limited number of SVR positions along the cross section (particularly advantageous to gauge unsteady flood flows and very large floods), thereby extending the range of validity of rating curves.

Paradoxical low-flow aortic stenosis is defined by increased ventricular hydraulic load and reduced longitudinal strain.

PubMed

Holmes, Anthony A; Taub, Cynthia C; Garcia, Mario J; Shan, Jian; Slovut, David P

2017-02-01

Patients with paradoxical low-flow severe aortic stenosis (PLF-AS) reportedly have higher left ventricular hydraulic load and more systolic strain dysfunction than patients with normal-flow aortic stenosis. This study investigates the relationship of systolic loading and strain to PLF-AS to further define its pathophysiology. One hundred and twenty patients (age 79 ± 12 years, 37% men) with an indexed aortic valve area (AVAi) of 0.6 cm/m or less and an ejection fraction of 50% or higher were divided into two groups based on indexed stroke volume (SVi): PLF-AS, SVi ≤ 35 ml/m, N = 46; normal-flow aortic stenosis, SVi > 35 ml/m, N = 74). Valvular and arterial load were assessed using multiple measurements, and strain was assessed using speckle-tracking echocardiography. Patients with PLF-AS were found to have more valvular load (lower AVAi, P = 0.028; lower energy loss coefficient, P = 0.001), more arterial load [decreased arterial compliance and increased systemic vascular resistance (SVR), both P < 0.001] and more total hydraulic load [increased valvuloarterial impedance (Zva), P < 0.001]. Transvalvular gradients and arterial pressures were similar. Longitudinal strain was lower in PLF-AS (P < 0.001), but circumferential and rotation strains were similar. On adjusted regression, AVAi, SVR and longitudinal strain were associated with PLF-AS [odds ratio (OR) = 1.34, P = 0.043; OR = 1.31, P = 0.004; OR = 1.34, P = 0.011, respectively]. When SVR and AVAi were replaced with Zva, longitudinal strain and Zva (OR = 1.38, P = 0.015; OR = 1.33, P < 0.001 for both, respectively) were associated with PLF-AS. Increased hydraulic load, from more severe valvular stenosis and increased vascular resistance, and longitudinal strain impairment are associated with PLF-AS and their interplay is likely fundamental to its pathophysiology.

Liver-related factors associated with low vitamin D levels in HIV and HIV/HCV coinfected patients and comparison to general population.

PubMed

Milazzo, Laura; Mazzali, Cristina; Bestetti, Giovanna; Longhi, Erika; Foschi, Antonella; Viola, Anita; Vago, Tarcisio; Galli, Massimo; Parravicini, Carlo; Antinori, Spinello

2011-04-01

Low 25-Hydroxyvitamin D (25[OH]D) was associated with severe fibrosis and low sustained virological response (SVR) after interferon (IFN)-based therapy in chronic hepatitis C. Furthermore, hypovitaminosis D was reported in HIV-infected individuals, but its role in liver disease progression in HIV/HCV coinfection is unknown. 25(OH)D was retrospectively measured in 237 HIV-infected patients (93 with HCV coinfection) and 76 healthy controls. Multivariate analysis included season, immuno-virological data, combined antiretroviral therapy (cART) and, in a subgroup of 51 HIV/HCV-genotype 1 coinfected patients, factors influencing SVR to pegylated-IFN and ribavirin. In a group of 20 patients, liver expression of cytochrome (CY)-P27A1 and CYP2R1, 25-hydroxylating enzymes, was assessed by immunohistochemistry. Median 25(OH)D levels were 23.4 (interquartile range 16.7-33.7) ng/mL in the HIV-infected population and 24 ng/mL (18.3-29.5) in healthy controls (p=0.9). At multiple regression analysis, only winter/spring measurements correlated with lower 25(OH)D levels. No correlation with HCV coinfection, nor with cART regimens was found. Low 25(OH)D was independently associated with advanced fibrosis in HIV/HCV coinfected patients (p=0.023), whereas no association emerged with SVR to IFN-based therapy. CYP27A1 and CYP2R1 expression was associated neither with 25(OH)D serum levels nor with HCV-infection, liver histology, or cART. In our experience, despite the high prevalence of 25(OH)D insufficiency, HIV and HCV-infection did not seem to influence vitamin D status. The role of HIV, HCV and cART on hypovitaminosis D needs further validation in larger cohorts that account for the vitamin levels in general populations and for seasonal and regional variability.

Flatbed scanners as a source of imaging. Brightness assessment and additives determination in a nickel electroplating bath.

PubMed

Vidal, M; Amigo, J M; Bro, R; Ostra, M; Ubide, C; Zuriarrain, J

2011-05-23

Desktop flatbed scanners are very well-known devices that can provide digitized information of flat surfaces. They are practically present in most laboratories as a part of the computer support. Several quality levels can be found in the market, but all of them can be considered as tools with a high performance and low cost. The present paper shows how the information obtained with a scanner, from a flat surface, can be used with fine results for exploratory and quantitative purposes through image analysis. It provides cheap analytical measurements for assessment of quality parameters of coated metallic surfaces and monitoring of electrochemical coating bath lives. The samples used were steel sheets nickel-plated in an electrodeposition bath. The quality of the final deposit depends on the bath conditions and, especially, on the concentration of the additives in the bath. Some additives become degraded with the bath life and so is the quality of the plate finish. Analysis of the scanner images can be used to follow the evolution of the metal deposit and the concentration of additives in the bath. Principal component analysis (PCA) is applied to find significant differences in the coating of sheets, to find directions of maximum variability and to identify odd samples. The results found are favorably compared with those obtained by means of specular reflectance (SR), which is here used as a reference technique. Also the concentration of additives SPB and SA-1 along a nickel bath life can be followed using image data handled with algorithms such as partial least squares (PLS) regression and support vector regression (SVR). The quantitative results obtained with these and other algorithms are compared. All this opens new qualitative and quantitative possibilities to flatbed scanners. Copyright © 2011 Elsevier B.V. All rights reserved.

Value of subjective visual reduction in patients with acute-onset floaters and/or flashes.

PubMed

Hurst, Jonathan; Johnson, Davin; Law, Christine; Schweitzer, Kelly; Sharma, Sanjay

2015-08-01

To quantify the association between subjective visual reduction (SVR) and retinal pathology in patients with acute-onset monocular floaters or flashes, or both. Prospective cohorts study involving all new patients referred for acute-onset floaters or flashes, or both, to a tertiary care emergency eye clinic in Kingston, Ontario, between July 1, 2011, and June 29, 2012 (n = 333). All patients were evaluated for the presence of SVR in a standardized fashion, as well as other known risk factors for retina pathology including a family history of retinal tear or retinal detachment, a personal history of retinal tear or detachment, high myopia, and ocular trauma. Our major outcome was urgent retinal pathology, defined as retina pathology requiring a same-day referral to a retina specialist for evaluation, management, or both. SVR was strongly associated with retinal pathology (likelihood ratio 7.9, 95% CI 5.2-12.1). Patients with SVR are at increased risk for urgent retinal pathology and should be triaged for urgent ophthalmologic examination. Copyright © 2015 Canadian Ophthalmological Society. Published by Elsevier Inc. All rights reserved.

Interferon-free treatment for patients with chronic hepatitis C and autoimmune liver disease: higher SVR rates with special precautions for deterioration of autoimmune hepatitis.

PubMed

Kanda, Tatsuo; Yasui, Shin; Nakamura, Masato; Nakamoto, Shingo; Takahashi, Koji; Wu, Shuang; Sasaki, Reina; Haga, Yuki; Ogasawara, Sadahisa; Saito, Tomoko; Kobayashi, Kazufumi; Kiyono, Soichiro; Ooka, Yoshihiko; Suzuki, Eiichiro; Chiba, Tetsuhiro; Maruyama, Hitoshi; Imazeki, Fumio; Moriyama, Mitsuhiko; Kato, Naoya

2018-02-20

Interferon-free treatment can achieve higher sustained virological response (SVR) rates, even in patients in whom hepatitis C virus (HCV) could not be eradicated in the interferon treatment era. Immune restoration in the liver is occasionally associated with HCV infection. We examined the safety and effects of interferon-free regimens on HCV patients with autoimmune liver diseases. All 7 HCV patients with autoimmune hepatitis (AIH) completed treatment and achieved SVR. Three patients took prednisolone (PSL) at baseline, and 3 did not take PSL during interferon-free treatment. In one HCV patient with AIH and cirrhosis, PSL were not administered at baseline, but she needed to take 40 mg/day PSL at week 8 for liver dysfunction. She also complained back pain and was diagnosed with vasospastic angina by coronary angiography at week 11. However, she completed interferon-free treatment. All 5 HCV patients with primary biliary cholangitis (PBC) completed treatment and achieved SVR. Three of these HCV patients with PBC were treated with UDCA during interferon-free treatment. Interferon-free regimens could result in higher SVR rates in HCV patients with autoimmune liver diseases. As interferon-free treatment for HCV may have an effect on hepatic immunity and activity of the autoimmune liver diseases, careful attention should be paid to unexpected adverse events in their treatments. Total 12 patients with HCV and autoimmune liver diseases [7 AIH and PBC], who were treated with interferon-free regimens, were retrospectively analyzed.

Hepatitis C virus infection in Argentina: Burden of chronic disease

PubMed Central

Ridruejo, Ezequiel; Bessone, Fernando; Daruich, Jorge R; Estes, Chris; Gadano, Adrián C; Razavi, Homie; Villamil, Federico G; Silva, Marcelo O

2016-01-01

AIM: To estimate the progression of the hepatitis C virus (HCV) epidemic and measure the burden of HCV-related morbidity and mortality. METHODS: Age- and gender-defined cohorts were used to follow the viremic population in Argentina and estimate HCV incidence, prevalence, hepatic complications, and mortality. The relative impact of two scenarios on HCV-related outcomes was assessed: (1) increased sustained virologic response (SVR); and (2) increased SVR and treatment. RESULTS: Under scenario 1, SVR raised to 85%-95% in 2016. Compared to the base case scenario, there was a 0.3% reduction in prevalent cases and liver-related deaths by 2030. Given low treatment rates, cases of hepatocellular carcinoma and decompensated cirrhosis decreased < 1%, in contrast to the base case in 2030. Under scenario 2, the same increases in SVR were modeled, with gradual increases in the annual diagnosed and treated populations. This scenario decreased prevalent infections 45%, liver-related deaths 55%, liver cancer cases 60%, and decompensated cirrhosis 55%, as compared to the base case by 2030. CONCLUSION: In Argentina, cases of end stage liver disease and liver-related deaths due to HCV are still growing, while its prevalence is decreasing. Increasing in SVR rates is not enough, and increasing in the number of patients diagnosed and candidates for treatment is needed to reduce the HCV disease burden. Based on this scenario, strategies to increase diagnosis and treatment uptake must be developed to reduce HCV burden in Argentina. PMID:27239258

Evaluation of a Hepatitis C Patient Management Program at a University Specialty Pharmacy.

PubMed

Zaepfel, Michelle; Cristofaro, Lisa; Trawinski, Allison; McCarthy, Katharine; Rightmier, Elizabeth; Khadem, Tina

2017-04-01

The University of Rochester (UR) Specialty Pharmacy hepatitis C patient management program offers a unique advantage of being integrated within the same health system as the University of Rochester Medical Center (URMC) Gastroenterology and Hepatology division. The primary purpose of this study was to assess treatment success through the incidence of achieving a sustained virological response (SVR) in patients served by the UR Specialty Pharmacy versus other nonintegrated pharmacies. This was a single-center retrospective cohort study in adult patients of URMC Gastroenterology and Hepatology prescribed hepatitis C treatment between January 1, 2014, and July 15, 2015. The incidence of SVR, adherence, delay in therapy initiation, early treatment discontinuation, rate of attainment of viral load measurement post-therapy completion, and predictors associated with treatment outcome were assessed. A total of 414 patients were prescribed hepatitis C virus treatment during the study period; 137 did not initiate therapy. The rate of SVR was 93% among patients at the UR Specialty Pharmacy and 89% at nonintegrated pharmacies ( P = 0.357). Adherence to therapy was 100% and 97% at the UR Specialty Pharmacy and nonintegrated pharmacies, respectively ( P = 0.046). The UR Specialty Pharmacy was associated with a 93% SVR rate and significantly greater adherence compared with nonintegrated pharmacies. Larger studies are needed to determine if a significant difference in SVR exists between integrated and nonintegrated pharmacies. This study provides a framework for other institutions to justify developing integrated hepatitis C specialty pharmacy services and evaluate their success.

IL28B But Not ITPA Polymorphism Is Predictive of Response to Pegylated Interferon, Ribavirin, and Telaprevir Triple Therapy in Patients With Genotype 1 Hepatitis C

PubMed Central

Hayes, C. Nelson; Abe, Hiromi; Miki, Daiki; Ochi, Hidenori; Karino, Yoshiyasu; Toyota, Joji; Nakamura, Yusuke; Kamatani, Naoyuki; Sezaki, Hitomi; Kobayashi, Mariko; Akuta, Norio; Suzuki, Fumitaka; Kumada, Hiromitsu

2011-01-01

Background. Pegylated interferon, ribavirin, and telaprevir triple therapy is a new strategy expected to eradicate the hepatitis C virus (HCV) even in patients infected with difficult-to-treat genotype 1 strains, although adverse effects, such as anemia and rash, are frequent. Methods. We assessed efficacy and predictive factors for sustained virological response (SVR) for triple therapy in 94 Japanese patients with HCV genotype 1. We included recently identified predictive factors, such as IL28B and ITPA polymorphism, and substitutions in the HCV core and NS5A proteins. Results. Patients treated with triple therapy achieved comparatively high SVR rates (73%), especially among treatment-naive patients (80%). Of note, however, patients who experienced relapse during prior pegylated interferon plus ribavirin combination therapy were highly likely to achieve SVR while receiving triple therapy (93%); conversely, prior nonresponders were much less likely to respond to triple therapy (32%). In addition to prior treatment response, IL28B SNP genotype and rapid viral response were significant independent predictors for SVR. Patients with the anemia-susceptible ITPA SNP rs1127354 genotype typically required ribavirin dose reduction earlier than did patients with other genotypes. Conclusions. Analysis of predictive factors identified IL28B SNP, rapid viral response, and transient response to previous therapy as significant independent predictors of SVR after triple therapy. PMID:21628662

Influence of baseline MELD score in the efficacy of treatment of hepatitis C with simeprevir and sofosbuvir.

PubMed

Moreno-Planas, José María; Larrubia-Marfil, Juan Ramón; Sánchez-Ruano, Juan José; Morillas-Ariño, Julia; Patón-Arenas, Roberto; Sáiz-Chumillas, Rosa María; Tébar-Romero, Emilia; Lucendo-Villarín, Alfredo; Gancedo-Bringas, Pilar; Solera-Muñoz, Mario; Vicente-Gutiérrez, María Del Mar; Martínez-Alfaro, Elisa

2018-05-01

There are few published studies on predictors of response to treatment with sofosbuvir and simeprevir in HCV patients. The objective of the study was to analyse possible predictors of response to simeprevir (SMV) and sofosbuvir (SOF) in patients infected with hepatitis C genotypes 1 or 4. Prospective observational cohort study in 12 hospitals. The primary efficacy endpoint was SVR rate 12 weeks after end of treatment (SVR12). 204 patients (62.3% male, mean age 55 years) were included: 186 (91.2%) genotype 1 (60.3% 1b 25% 1a) and 18 (8.8%) genotype 4. 132 (64.7%) cirrhotic (87.9% Child A), 33 (16.2%) F3, 31 (15.2%) F2, 8 (3.9%) F0-1. 80.8% MELD<10. 93 (45.6%) naive. Ribavirin was added in 68 (33.3%). Mean baseline viral load 2,151,549 IU/ml (SD: 2,391,840). Treatment duration 12 weeks in 93.1%. 4 discontinued therapy: suicide, psychotic attack, hyperbilirubinaemia and liver cancer recurrence. 190 (93.1%) achieved SVR12. There were no differences in SVR12 depending on the genotype, treatment duration, ribavirin use, prior therapy, viral load (VL) or baseline platelets. In univariate analysis, undetectable VL at 4 weeks (p=0.042), absence of cirrhosis (p=0.021), baseline albumin ≥ 4g/dl (p=0.001) and MELD<10 (p<0.0001) were associated with higher SVR12. In multivariate analysis, only baseline MELD score <10 patients had higher SVR12 (p<0.001). The combination of simeprevir and sofosbuvir in patients infected with genotype 1 and 4 hepatitis C is highly effective. It is a safe therapy, especially in patients without ribavirin. This combination was more effective in patients with a MELD score below 10. Copyright © 2017 Elsevier España, S.L.U. and Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica. All rights reserved.

Benefit of Hepatitis C Virus Core Antigen Assay in Prediction of Therapeutic Response to Interferon and Ribavirin Combination Therapy

PubMed Central

Takahashi, Masahiko; Saito, Hidetsugu; Higashimoto, Makiko; Atsukawa, Kazuhiro; Ishii, Hiromasa

2005-01-01

A highly sensitive second-generation hepatitis C virus (HCV) core antigen assay has recently been developed. We compared viral disappearance and first-phase kinetics between commercially available core antigen (Ag) assays, Lumipulse Ortho HCV Ag (Lumipulse-Ag), and a quantitative HCV RNA PCR assay, Cobas Amplicor HCV Monitor test, version 2 (Amplicor M), to estimate the predictive benefit of a sustained viral response (SVR) and non-SVR in 44 genotype 1b patients treated with interferon (IFN) and ribavirin. HCV core Ag negativity could predict SVR on day 1 (sensitivity = 100%, specificity = 85.0%, accuracy = 86.4%), whereas RNA negativity could predict SVR on day 7 (sensitivity = 100%, specificity = 87.2%, accuracy = 88.6%). None of the patients who had detectable serum core Ag or RNA on day 14 achieved SVR (specificity = 100%). The predictive accuracy on day 14 was higher by RNA negativity (93.2%) than that by core Ag negativity (75.0%). The combined predictive criterion of both viral load decline during the first 24 h and basal viral load was also predictive for SVR; the sensitivities of Lumipulse-Ag and Amplicor-M were 45.5 and 47.6%, respectively, and the specificity was 100%. Amplicor-M had better predictive accuracy than Lumipulse-Ag in 2-week disappearance tests because it had better sensitivity. On the other hand, estimates of kinetic parameters were similar regardless of the detection method. Although the correlations between Lumipulse-Ag and Amplicor-M were good both before and 24 h after IFN administration, HCV core Ag seemed to be relatively lower 24 h after IFN administration than before administration. Lumipulse-Ag seems to be useful for detecting the HCV concentration during IFN therapy; however, we still need to understand the characteristics of the assay. PMID:15634970

Genotyping of Campylobacter coli strains isolated in Brazil suggests possible contamination amongst environmental, human, animal and food sources.

PubMed

Gomes, Carolina N; Souza, Roberto A; Passaglia, Jaqueline; Duque, Sheila S; Medeiros, Marta I C; Falcão, Juliana P

2016-01-01

Campylobacter coli and Campylobacter jejuni are two of the most common causative agents of food-borne gastroenteritis in numerous countries worldwide. In Brazil, campylobacteriosis is under diagnosed and under-reported, and few studies have molecularly characterized Campylobacter spp. in this country. The current study genotyped 63 C. coli strains isolated from humans (n512), animals (n521), food (n510) and the environment (n520) between 1995 and 2011 in Brazil. The strains were genotyped using pulsed-field gel electrophoresis (PFGE), sequencing the short variable region (SVR) of the flaA gene ( flaA-SVR) and high-resolution melting analysis (HRMA) of the clustered regularly interspaced short palindromic repeat (CRISPR) locus to better understand C. coli genotypic diversity and compare the suitability of these three methods for genotyping this species. Additionally, the discrimination index (DI) of each of these methods was assessed. Some C. coli strains isolated from clinical and non-clinical origins presented ≥80 % genotypic similarity by PFGE and flaA-SVR sequencing. HRMA of the CRISPR locus revealed only four different melting profiles. In total, 22 different flaA-SVR alleles were detected. Of these, seven alleles, comprising gt1647–gt1653, were classified as novel. The most frequent genotypes were gt30 and gt1647. This distribution reveals the diversity of selected Brazilian isolates in comparison with the alleles described in the PubMLST database. The DIs for PFGE, flaA–SVR sequencing and CRISPR-HRMA were 0.986, 0.916 and 0.550, respectively. PFGE and flaA-SVR sequencing were suitable for subtyping C. coli strains, in contrast to CRISPR-HRMA. The high genomic similarity amongst some C. coli strains confirms the hypothesis that environmental and food sources potentially lead to human and animal contamination in Brazil.

Serum microRNA-122 level correlates with virologic responses to pegylated interferon therapy in chronic hepatitis C.

PubMed

Su, Tung-Hung; Liu, Chen-Hua; Liu, Chun-Jen; Chen, Chi-Ling; Ting, Te-Tien; Tseng, Tai-Chung; Chen, Pei-Jer; Kao, Jia-Horng; Chen, Ding-Shinn

2013-05-07

MicroRNA-122 (miR-122) facilitates hepatitis C virus replication in vitro. Serum miR-122 has been implicated as a biomarker for various liver diseases; however, its role in chronic hepatitis C remains unclear. To address this issue, 126 patients with chronic hepatitis C who completed pegylated IFN plus ribavirin therapy with sustained virologic response (SVR) or nonresponse (NR) were retrospectively included, and their pretreatment clinical profiles and treatment responses were collected. Serum miR-122 was quantified before and during treatment. Another 51 patients in SVR and NR groups were prospectively enrolled for validation. Serum miR-122 was found to be a surrogate for hepatic miR-122 and positively correlated with hepatic necroinflammation. Patients who showed complete early virologic response and SVR had significantly higher pretreatment serum miR-122 levels than those with NR (P = 0.001 and P = 0.008, respectively), especially in subgroups of patients with hepatitis C virus genotype 2 and IL-28B rs8099917 TT genotype. Patients with IL-28B TT genotype had significantly better treatment responses and higher pretreatment serum miR-122 level than those with GT or GG genotypes. Univariate analysis showed that pretreatment body mass index, γ-glutamyl transpeptidase, triglyceride, IL-28B TT genotype, and serum miR-122 are predictors for SVR. Multivariate analysis specifically in IL-28B TT genotype demonstrated that pretreatment serum miR-122 independently predicted SVR. The validation cohort confirmed a significantly greater pretreatment serum miR-122 level in patients with SVR compared with NR (P = 0.025). In conclusion, serum miR-122 may serve as a surrogate of hepatic miR-122, and a higher pretreatment serum miR-122 level can help predict virologic responses to pegylated IFN plus ribavirin therapy.

Fatty liver in hepatitis C patients post-sustained virological response with direct-acting antivirals

PubMed Central

Noureddin, Mazen; Wong, Micaela M; Todo, Tsuyoshi; Lu, Shelly C; Sanyal, Arun J; Mena, Edward A

2018-01-01

AIM To determine steatosis and fibrosis prevalence in hepatitis C patients after a sustained virological response achieved with direct-acting antivirals. METHODS Transient elastography with controlled attenuation parameter (CAP) was used to assess hepatic steatosis post-sustained virological response (SVR); the CAP technology was not available in the United States at study initiation. Liver stiffness/fibrosis was measured before and 47 wk after treatment completion. Patients with genotype 3 and patients with cirrhosis were excluded. RESULTS One hundred and one patients were included in the study. Post-SVR there were decreases from baseline in alanine aminotransferase (ALT) (63.1 to 17.8 U/L), aspartate aminotransferase (51.8 to 21.5 U/L) and fibrosis score (7.4 to 6.1 kPa) (P < 0.05). Post-SVR, 48 patients (47.5%) had steatosis on CAP; of these, 6.25% had advanced fibrosis. Patients with steatosis had higher body mass index (29.0 vs 26.1 kg/m2), glucose (107.8 vs 96.6 mg/dL), ALT (20.4 vs 15.3 mg/dL), CAP score (296.3 vs 212.4 dB/m) and fibrosis score (7.0 vs 5.3 kPa); P < 0.05. Interestingly, compared to baseline, both patients with and without steatosis had change in fibrosis score post-SVR (7.7 kPa vs 7.0 kPa and 7.0 kPa vs 5.3 kPa); alternatively, (P < 0.05) and therefore patients with steatosis continued to have clinically significant stiffness (≥ 7 kPa). CONCLUSION Fatty liver is very common in hepatitis C virus (HCV) patients post-SVR. These patients continue to have elevated mean fibrosis score (≥ 7 kPa) compared to those without fatty liver; some have advanced fibrosis. Long term follow up is needed to assess steatosis and fibrosis in HCV patients post-SVR. PMID:29568207

Glecaprevir/Pibrentasvir Treatment in Liver or Kidney Transplant Patients With Hepatitis C Virus Infection.

PubMed

Reau, Nancy; Kwo, Paul Y; Rhee, Susan; Brown, Robert S; Agarwal, Kosh; Angus, Peter; Gane, Edward; Kao, Jia-Horng; Mantry, Parvez S; Mutimer, David; Reddy, K Rajender; Tran, Tram T; Hu, Yiran B; Gulati, Abhishek; Krishnan, Preethi; Dumas, Emily O; Porcalla, Ariel; Shulman, Nancy S; Liu, Wei; Samanta, Suvajit; Trinh, Roger; Forns, Xavier

2018-04-19

Well-tolerated, ribavirin-free, pangenotypic hepatitis C virus (HCV) treatments for transplant recipients remain a high priority. Once-daily glecaprevir/pibrentasvir demonstrates high rates of sustained virologic response for 12 weeks post-treatment (SVR12) across all major HCV genotypes (GT). This trial evaluated the safety and efficacy of glecaprevir/pibrentasvir for patients with chronic HCV GT1-6 infection who had received a liver or kidney transplant. MAGELLAN-2 was a phase 3, open-label trial conducted in patients who were ≥3 months post-transplant. Patients without cirrhosis who were HCV treatment-naïve (GT1-6) or treatment-experienced (GT1, 2, 4-6; with interferon-based therapy with or without sofosbuvir, or sofosbuvir plus ribavirin) received glecaprevir/pibrentasvir (300/120 mg) once daily for 12 weeks. The primary endpoint compared the percentage of patients receiving glecaprevir/pibrentasvir with SVR12 to a historic SVR12 rate based on the standard of care. Safety of glecaprevir/pibrentasvir was assessed. In total, 80 liver and 20 kidney transplant patients participated in the trial. Most patients had no or minimal fibrosis (80% had fibrosis scores F0-F1) and were infected with HCV GT1 (57%) or GT3 (24%). The overall SVR12 was 98% (n/N=98/100; 95% confidence interval, 95.3%-100%), which exceeded the pre-specified historic standard of care SVR12 threshold of 94%. One patient experienced virologic failure. One patient discontinued because of an adverse event considered to be unrelated to treatment; this patient achieved SVR12. Adverse events were mostly mild in severity and laboratory abnormalities were infrequent. Once-daily glecaprevir/pibrentasvir for 12 weeks is a well-tolerated and efficacious, ribavirin-free treatment for patients with chronic HCV GT1-6 infection who had received a liver or kidney transplant. ClinicalTrials.gov NCT02692703. This article is protected by copyright. All rights reserved. © 2018 by the American Association for the Study of Liver Diseases.

High HCV cure rates for people who use drugs treated with direct acting antiviral therapy at an urban primary care clinic.

PubMed

Norton, Brianna L; Fleming, Julia; Bachhuber, Marcus A; Steinman, Meredith; DeLuca, Joseph; Cunningham, Chinazo O; Johnson, Nirah; Laraque, Fabienne; Litwin, Alain H

2017-09-01

Though direct acting antivirals (DAAs) promise high cure rates, many providers and payers remain concerned about successful treatment for people who use drugs (PWUD), even among those engaged in opioid agonist treatment (OAT). The efficacy of DAAs among PWUD in real-world settings is unclear. We conducted a cohort study of patients initiating HCV treatment between January 2014 and August 2015 (n=89) at a primary care clinic in the Bronx, NY. Onsite HCV treatment with DAAs was performed by an HCV specialist, with support from a care coordinator funded by the NYC Department of Health. We identified four categories of drug use and drug treatment: (1) no active drug use/not receiving OAT (defined as non-PWUD); (2) no active drug use/receiving OAT; (3) active drug use/not receiving OAT; and (4) active drug use/receiving OAT. The primary outcome was SVR at 12 weeks post-treatment. Overall SVR rates were 95% (n=41/43) for non-PWUD and 96% (n=44/46) for patients actively using drugs and/or receiving OAT [p=0.95]. There were no differences in SVR rates by drug use or drug treatment category. Compared to non-PWUD, those with no active drug use/receiving OAT had 100% SVR (n=15/15; p=1.0), those actively using drugs/not receiving OAT had 90% SVR (n=9/10; p=0.47), and those actively using drugs/receiving OAT had 95% SVR (20/21; p=1.0). Regardless of active drug use or OAT, patients who received DAA therapy at an urban primary care clinic achieved high HCV cure rates. We found no clinical evidence to justify restricting access to HCV treatment for patients actively using drugs and/or receiving OAT. Copyright © 2017 Elsevier B.V. All rights reserved.

High HCV cure rates for people who use drugs treated with direct acting antiviral therapy at an urban primary care clinic

PubMed Central

Norton, Brianna L.; Fleming, Julia; Bachhuber, Marcus A.; Steinman, Meredith; DeLuca, Joseph; Cunningham, Chinazo O.; Johnson, Nirah; Laraque, Fabienne; Litwin, Alain H.

2018-01-01

Background Though direct acting antivirals (DAAs) promise high cure rates, many providers and payers remain concerned about successful treatment for people who use drugs (PWUD), even among those engaged in opioid agonist treatment (OAT). The efficacy of DAAs among PWUD in real-world settings is unclear. Methods We conducted a cohort study of patients initiating HCV treatment between January 2014 and August 2015 (n = 89) at a primary care clinic in the Bronx, NY. Onsite HCV treatment with DAAs was performed by an HCV specialist, with support from a care coordinator funded by the NYC Department of Health. We identified four categories of drug use and drug treatment: (1) no active drug use/not receiving OAT (defined as non-PWUD); (2) no active drug use/receiving OAT; (3) active drug use/not receiving OAT; and (4) active drug use/receiving OAT. The primary outcome was SVR at 12 weeks post-treatment. Results Overall SVR rates were 95% (n = 41/43) for non-PWUD and 96% (n = 44/46) for patients actively using drugs and/or receiving OAT [p = 0.95]. There were no differences in SVR rates by drug use or drug treatment category. Compared to non-PWUD, those with no active drug use/receiving OAT had 100% SVR (n = 15/15; p = 1.0), those actively using drugs/not receiving OAT had 90% SVR (n = 9/10; p = 0.47), and those actively using drugs/receiving OAT had 95% SVR (20/21; p = 1.0). Conclusion Regardless of active drug use or OAT, patients who received DAA therapy at an urban primary care clinic achieved high HCV cure rates. We found no clinical evidence to justify restricting access to HCV treatment for patients actively using drugs and/or receiving OAT. PMID:28811158

Interferon-γ-inducible protein-10 in chronic hepatitis C: Correlations with insulin resistance, histological features & sustained virological response.

PubMed

Crisan, Dana; Grigorescu, Mircea Dan; Radu, Corina; Suciu, Alina; Grigorescu, Mircea

2017-04-01

One of the multiple factors contributing to virological response in chronic hepatitis C (CHC) is interferon-gamma-inducible protein-10 (IP-10). Its level reflects the status of interferon-stimulated genes, which in turn is associated with virological response to antiviral therapy. The aim of this study was to evaluate the role of serum IP-10 levels on sustained virological response (SVR) and the association of this parameter with insulin resistance (IR) and liver histology. Two hundred and three consecutive biopsy proven CHC patients were included in the study. Serum levels of IP-10 were determined using ELISA method. IR was evaluated by homeostasis model assessment-IR (HOMA-IR). Histological features were assessed invasively by liver biopsy and noninvasively using FibroTest, ActiTest and SteatoTest. Predictive factors for SVR and their interrelations were assessed. A cut-off value for IP-10 of 392 pg/ml was obtained to discriminate between responders and non-responders. SVR was obtained in 107 patients (52.70%). Area under the receiver operating characteristic curve for SVR was 0.875 with a sensitivity of 91.6 per cent, specificity 74.7 per cent, positive predictive value 80.3 per cent and negative predictive value 88.7 per cent. Higher values of IP-10 were associated with increasing stages of fibrosis (P<0.01) and higher grades of inflammation (P=0.02, P=0.07) assessed morphologically and noninvasively through FibroTest and ActiTest. Significant steatosis and IR were also associated with increased levels of IP-10 (P=0.01 and P=0.02). In multivariate analysis, IP-10 levels and fibrosis stages were independently associated with SVR. Our findings showed that the assessment of serum IP-10 level could be a predictive factor for SVR and it was associated with fibrosis, necroinflammatory activity, significant steatosis and IR in patients with chronic HCV infection.

Cirrhosis and Rapid Virological Response to Peginterferon Plus Ribavirin Determine Treatment Outcome in HCV-1 IL28B rs12979860 CC Patients

PubMed Central

Aghemo, Alessio; Degasperi, Elisabetta; Rumi, Maria Grazia; Galmozzi, Enrico; Valenti, Luca; De Francesco, Raffaele; De Nicola, Stella; Cheroni, Cristina; Grassi, Eleonora; Colombo, Massimo

2013-01-01

Background. The rs12979860 CC genotype of the interleukin 28B (IL28B) polymorphism is associated with high rates of sustained virological response (SVR) to peginterferon (PegIFN) and ribavirin (Rbv) in hepatitis C virus genotype-1 (HCV-1) patients. The impact of baseline predictors of treatment outcome and their interplay with viral kinetics in HCV-1 CC patients has not been fully evaluated. Aim. To identify baseline and on-therapy predictors of treatment failure in HCV-1 IL28B CC patients. Methods. Treatment-naïve HCV-1 patients, compliant to PegIFN and Rbv who did not discontinue treatment for nonvirological reasons, were analyzed. Results. 109 HCV-1 IL28B CC were studied. Sixty were males, 39 with BMI >25, 69 with >600,000 IU/mL HCV RNA, 15 with HCV1a, and 30 with cirrhosis. Overall, 75 (69%) achieved an SVR; cirrhosis was the only baseline predictor of treatment failure (OR: 2.58, 95% CI: 1.07–6.21) as SVR rates were 53% in cirrhotics versus 75% in noncirrhotics (P = 0.03). HCV RNA undetectability (<50 IU/mL) at week 4 (RVR) was achieved by 58 patients (53%). The SVR rates were independent of RVR in noncirrhotics, 76% (34/45) RVR (+) and 74% (25/34) RVR (−) (P = 0.9). In cirrhotic patients, SVR rates were significantly higher in RVR (+) compared to RVR (−) (10/13 (77%) versus 6/17 (35%) P = 0.03). Conclusions. In HCV-1 IL28B CC patients, cirrhosis is the only clinical baseline predictor of PegIFN and Rbv treatment failure. However, in IL28B CC cirrhotics, the achievement of RVR identifies those patients who still have high rates of SVR to Peg-IFN/Rbv therapy. PMID:23936821

High efficacy of Sofosbuvir plus Simeprevir in a large cohort of Spanish cirrhotic patients infected with genotypes 1 and 4.

PubMed

Mariño, Zoe; Pascasio-Acevedo, Juan M; Gallego, Adolfo; Diago, Moisés; Baliellas, Carme; Morillas, Rosa; Prieto, Martín; Moreno, José M; Sánchez-Antolín, Gloria; Vergara, Mercedes; Forné, Montserrat; Fernández, Inmaculada; Castro, María A; Pascual, Sonia; Gómez, Alexandra; Castells, Lluis; Montero, José L; Crespo, Javier; Calleja, José L; García-Samaniego, Javier; Carrión, Jose A; Arencibia, Ana C; Blasco, Alejandro; López-Núñez, Carmen; Sánchez-Ruano, Juan J; Gea-Rodríguez, Francisco; Giráldez, Álvaro; Cabezas, Joaquín; Hontangas, Vanessa; Torras, Xavier; Castellote, Jose; Romero-Gómez, Manuel; Turnes, Juan; de Artaza, Tomás; Narváez, Isidoro; Cuervas-Mons, Valentín; Forns, Xavier

2017-12-01

Hepatitis C (HCV) therapy with Sofosbuvir (SOF)/Simeprevir (SMV) in clinical trials and real-world clinical practice, showed high rates of sustained virological response (SVR) in non-cirrhotic genotype (GT)-1 and GT-4 patients. These results were slightly lower in cirrhotic patients. We investigated real-life effectiveness and safety of SOF/SMV with or without ribavirin (RBV) in a large cohort of cirrhotic patients. This collaborative multicentre study included data from 968 patients with cirrhosis infected with HCV-GT1 or 4, treated with SOF/SMV±RBV in 30 centres across Spain between January-2014 and December-2015. Demographic, clinical, virological and safety data were analysed. Overall SVR was 92.3%; the majority of patients were treated with RBV (62%) for 12 weeks (92.4%). No significant differences in SVR were observed between genotypes (GT1a:94.3%; GT1b:91.7%; GT4:91.1%). Those patients with more advanced liver disease (Child B/C, MELD≥10) or portal hypertension (platelet count≤100×10 9 /L, transient elastography≥21 Kpa) showed significantly lower SVR rates (84.4%-91.9%) than patients with less advanced liver disease (93.8%-95.9%, P<.01 in all cases). In the multivariate analysis, the use of RBV, female gender, baseline albumin≥35 g/L, MELD<10 and lack of exposure to a triple therapy regimen were independent predictors of SVR (P<.05). Serious adverse events (SAEs) and SAE-associated discontinuation events occurred in 5.9% and 2.6%. In this large cohort of cirrhotic patients managed in the real-world setting in Spain, SOF/SMV±RBV yielded to excellent SVR rates, especially in patients with compensated liver cirrhosis. In addition, this combination showed to be safe, with low rates of SAEs and early discontinuations. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Anti-E1E2 antibodies status prior therapy favors direct-acting antiviral treatment efficacy.

PubMed

Virlogeux, Victor; Berthillon, Pascale; Bordes, Isabelle; Larrat, Sylvie; Crouy, Stéphanie; Scholtès, Caroline; Pradat, Pierre; Maynard, Marianne; Zoulim, Fabien; Leroy, Vincent; Chemin, Isabelle; Trépo, Christian; Petit, Marie-Anne

2018-03-15

Presence of anti-E1E2 antibodies was previously associated with spontaneous cure of hepatitis C virus (HCV) and predictive before treatment of a sustained virological response (SVR) to bi- or tri-therapy in naïve or experienced patients, regardless of HCV genotype. We investigated the impact of anti-E1E2 seroprevalence at baseline on treatment response in patients receiving direct-acting antiviral (DAA) therapy. We screened anti-E1E2 antibodies by ELISA in serum samples collected at treatment initiation for two groups of patients: 59 with SVR at the end of DAA treatment and 44 relapsers after DAA treatment. Nineteen patients received a combination of ribavirin (RBV) or PEG-interferon/ribavirin with sofosbuvir or daclatasvir and others received interferon-free treatment with DAA±RBV. HCV viral load was measured at different time points during treatment in a subgroup of patients. A significant association was observed between presence of anti-E1E2 and HCV viral load<6log10 prior treatment. Among patients with anti-E1E2 at baseline, 70% achieved SVR whereas among patients without anti-E1E2, only 45% achieved SVR. Conversely, 66% of patients experiencing DAA-failure were anti-E1E2 negative at baseline. In the multivariate analysis, presence of anti-E1E2 was significantly associated with SVR after adjustment on potential cofounders such as age, sex, fibrosis stage, prior HCV treatment and alanine aminotransferase (ALT) level. The presence of anti-E1E2 at treatment initiation is a predictive factor of SVR among patients treated with DAA and more likely among patients with low initial HCV viral load (<6log10). Absence of anti-E1E2 at baseline could predict DAA-treatment failure. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

Fatty liver in hepatitis C patients post-sustained virological response with direct-acting antivirals.

PubMed

Noureddin, Mazen; Wong, Micaela M; Todo, Tsuyoshi; Lu, Shelly C; Sanyal, Arun J; Mena, Edward A

2018-03-21

To determine steatosis and fibrosis prevalence in hepatitis C patients after a sustained virological response achieved with direct-acting antivirals. Transient elastography with controlled attenuation parameter (CAP) was used to assess hepatic steatosis post-sustained virological response (SVR); the CAP technology was not available in the United States at study initiation. Liver stiffness/fibrosis was measured before and 47 wk after treatment completion. Patients with genotype 3 and patients with cirrhosis were excluded. One hundred and one patients were included in the study. Post-SVR there were decreases from baseline in alanine aminotransferase (ALT) (63.1 to 17.8 U/L), aspartate aminotransferase (51.8 to 21.5 U/L) and fibrosis score (7.4 to 6.1 kPa) ( P < 0.05). Post-SVR, 48 patients (47.5%) had steatosis on CAP; of these, 6.25% had advanced fibrosis. Patients with steatosis had higher body mass index (29.0 vs 26.1 kg/m 2 ), glucose (107.8 vs 96.6 mg/dL), ALT (20.4 vs 15.3 mg/dL), CAP score (296.3 vs 212.4 dB/m) and fibrosis score (7.0 vs 5.3 kPa); P < 0.05. Interestingly, compared to baseline, both patients with and without steatosis had change in fibrosis score post-SVR (7.7 kPa vs 7.0 kPa and 7.0 kPa vs 5.3 kPa); alternatively, ( P < 0.05) and therefore patients with steatosis continued to have clinically significant stiffness (≥ 7 kPa). Fatty liver is very common in hepatitis C virus (HCV) patients post-SVR. These patients continue to have elevated mean fibrosis score (≥ 7 kPa) compared to those without fatty liver; some have advanced fibrosis. Long term follow up is needed to assess steatosis and fibrosis in HCV patients post-SVR.

Efficacy of HCV treatment in Poland at the turn of the interferon era - the EpiTer study.

PubMed

Flisiak, Robert; Pogorzelska, Joanna; Berak, Hanna; Horban, Andrzej; Orłowska, Iwona; Simon, Krzysztof; Tuchendler, Ewelina; Madej, Grzegorz; Piekarska, Anna; Jabłkowski, Maciej; Deroń, Zbigniew; Mazur, Włodzimierz; Kaczmarczyk, Marcin; Janczewska, Ewa; Pisula, Arkadiusz; Smykał, Jacek; Nowak, Krzysztof; Matukiewicz, Marek; Halota, Waldemar; Wernik, Joanna; Sikorska, Katarzyna; Mozer-Lisewska, Iwona; Rozpłochowski, Błażej; Garlicki, Aleksander; Tomasiewicz, Krzysztof; Krzowska-Firych, Joanna; Baka-Ćwierz, Barbara; Kryczka, Wiesław; Zarębska-Michaluk, Dorota; Olszok, Iwona; Boroń-Kaczmarska, Anna; Sobala-Szczygieł, Barbara; Szlauer, Bronisława; Korcz-Ondrzejek, Bogumiła; Sieklucki, Jerzy; Pleśniak, Robert; Ruszała, Agata; Postawa-Kłosińska, Barbara; Citko, Jolanta; Lachowicz-Wawrzyniak, Anna; Musialik, Joanna; Jezierska, Edyta; Dobracki, Witold; Dobracka, Beata; Hałubiec, Jan; Krygier, Rafał; Strokowska, Anna; Chomczyk, Wojciech; Witczak-Malinowska, Krystyna

2016-12-01

Was to analyze the efficacy achieved with regimens available for chronic hepatitis C (CHC) in Poland between 2013 and 2016. Data were collected from 29 centers and included 6786 patients with available sustained virologic response (SVR) data between 1 January 2013 and 31 March 2016. The sustained virologic response rate for genotypes (G) 1a, 1b, 2, 3 and 4 was 62%, 56%, 92%, 67% and 56% respectively; 71% patients ( n = 4832) were treated with pegylated interferon α (Peg-IFNα) and ribavirin (RBV), with SVR rates of 58%, 49%, 92%, 67% and 55% respectively. The sustained virologic response among 5646 G1 infected patients was the lowest with natural interferon α (7%, n = 70) or PegIFN (50%, n = 3779) with RBV, and improved in those receiving triple regimens of Peg-IFN + RBV combined with boceprevir (47%, n = 485), telaprevir (64%, n = 805), simeprevir (73%, n = 132) or sofosbuvir (70%, n = 23). The sustained virologic response with interferon-free regimens of sofosbuvir and RBV ( n = 7), sofosbuvir and simeprevir ( n = 53), and ledipasvir and sofosbuvir ( n = 64) achieved 86%, 89% and 94% respectively. The highest SVR of 98% was observed with ombitasvir/paritaprevir combined with dasabuvir ( n = 227). Patients infected with G3 ( n = 896) and G4 ( n = 220) received mostly Peg-IFN + RBV with SVR of 67% and 56% respectively. Interferon-free regimens were administered in 18 G3/G4 patients and all achieved an SVR. Sofosbuvir combined with Peg-IFN and RBV was administered to 33 patients with an SVR rate of 94%, and a similar rate was achieved among 13 G2 patients treated with interferon and RBV. We observed significant differences in efficacy of HCV regimens available in Poland at the turn of the interferon era. The data will be useful as a comparison for therapeutic options expected in the next few years.

Efficacy of HCV treatment in Poland at the turn of the interferon era – the EpiTer study

PubMed Central

Pogorzelska, Joanna; Berak, Hanna; Horban, Andrzej; Orłowska, Iwona; Simon, Krzysztof; Tuchendler, Ewelina; Madej, Grzegorz; Piekarska, Anna; Jabłkowski, Maciej; Deroń, Zbigniew; Mazur, Włodzimierz; Kaczmarczyk, Marcin; Janczewska, Ewa; Pisula, Arkadiusz; Smykał, Jacek; Nowak, Krzysztof; Matukiewicz, Marek; Halota, Waldemar; Wernik, Joanna; Sikorska, Katarzyna; Mozer-Lisewska, Iwona; Rozpłochowski, Błażej; Garlicki, Aleksander; Tomasiewicz, Krzysztof; Krzowska-Firych, Joanna; Baka-Ćwierz, Barbara; Kryczka, Wiesław; Zarębska-Michaluk, Dorota; Olszok, Iwona; Boroń-Kaczmarska, Anna; Sobala-Szczygieł, Barbara; Szlauer, Bronisława; Korcz-Ondrzejek, Bogumiła; Sieklucki, Jerzy; Pleśniak, Robert; Ruszała, Agata; Postawa-Kłosińska, Barbara; Citko, Jolanta; Lachowicz-Wawrzyniak, Anna; Musialik, Joanna; Jezierska, Edyta; Dobracki, Witold; Dobracka, Beata; Hałubiec, Jan; Krygier, Rafał; Strokowska, Anna; Chomczyk, Wojciech; Witczak-Malinowska, Krystyna

2016-01-01

The aim of the study Was to analyze the efficacy achieved with regimens available for chronic hepatitis C (CHC) in Poland between 2013 and 2016. Material and methods Data were collected from 29 centers and included 6786 patients with available sustained virologic response (SVR) data between 1 January 2013 and 31 March 2016. Results The sustained virologic response rate for genotypes (G) 1a, 1b, 2, 3 and 4 was 62%, 56%, 92%, 67% and 56% respectively; 71% patients (n = 4832) were treated with pegylated interferon α (Peg-IFNα) and ribavirin (RBV), with SVR rates of 58%, 49%, 92%, 67% and 55% respectively. The sustained virologic response among 5646 G1 infected patients was the lowest with natural interferon α (7%, n = 70) or PegIFN (50%, n = 3779) with RBV, and improved in those receiving triple regimens of Peg-IFN + RBV combined with boceprevir (47%, n = 485), telaprevir (64%, n = 805), simeprevir (73%, n = 132) or sofosbuvir (70%, n = 23). The sustained virologic response with interferon-free regimens of sofosbuvir and RBV (n = 7), sofosbuvir and simeprevir (n = 53), and ledipasvir and sofosbuvir (n = 64) achieved 86%, 89% and 94% respectively. The highest SVR of 98% was observed with ombitasvir/paritaprevir combined with dasabuvir (n = 227). Patients infected with G3 (n = 896) and G4 (n = 220) received mostly Peg-IFN + RBV with SVR of 67% and 56% respectively. Interferon-free regimens were administered in 18 G3/G4 patients and all achieved an SVR. Sofosbuvir combined with Peg-IFN and RBV was administered to 33 patients with an SVR rate of 94%, and a similar rate was achieved among 13 G2 patients treated with interferon and RBV. Conclusions We observed significant differences in efficacy of HCV regimens available in Poland at the turn of the interferon era. The data will be useful as a comparison for therapeutic options expected in the next few years. PMID:28856278

Vitamin D level and sustained virologic response to interferon-based antiviral therapy in chronic hepatitis C: a systematic review and meta-analysis.

PubMed

Kitson, Matthew T; Sarrazin, Christoph; Toniutto, Pierluigi; Eslick, Guy D; Roberts, Stuart K

2014-12-01

The baseline 25-hydroxyvitamin D (25[OH]D) level has recently been reported to be an independent predictor of sustained virologic response (SVR) to treatment with pegylated interferon (PEG-IFN) plus ribavirin (RBV) for chronic hepatitis C virus (HCV) infection. However, studies have yielded inconsistent results. Thus, we conducted a systematic review and meta-analysis to clarify any association between baseline 25(OH)D level and SVR in HCV therapy. Two reviewers searched four electronic databases (Medline, Embase, PubMed, and Cochrane trials register) and relevant international conference proceedings up to March 2014 for studies treating chronic HCV infection with PEG-IFN plus RBV where baseline 25(OH)D level was tested. Studies involving patients with HIV co-infection, previous liver transplantation or those receiving vitamin D supplementation were excluded. The mean baseline 25(OH)D level was compared between those who achieved and those who failed to achieve SVR. Pooled standard difference in mean 25(OH)D level, odds ratios (OR) and 95% confidence intervals (CI) were calculated with the Comprehensive Meta-Analysis software (version 2.0) using a random effects model. 11 studies comprising 2605 patients were included in the meta-analysis. There was no significant association between the baseline mean 25(OH)D level and SVR (OR 1.44, 95% CI 0.92-2.26; p=0.11), either in patients infected with genotypes 1/4/5 (OR 1.48, 95% CI 0.94-2.34; p=0.09) or genotypes 2/3 (OR 1.51, 95% CI 0.26-8.87; p=0.65). The baseline 25(OH)D level is not associated with SVR to PEG-IFN plus RBV therapy in chronic HCV infection, regardless of genotype. Any effect of vitamin D supplementation on SVR is yet to be definitively determined. Copyright © 2014 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.

Mericitabine and Either Boceprevir or Telaprevir in Combination with Peginterferon Alfa-2a plus Ribavirin for Patients with Chronic Hepatitis C Genotype 1 Infection and Prior Null Response: The Randomized DYNAMO 1 and DYNAMO 2 Studies.

PubMed

Wedemeyer, Heiner; Forns, Xavier; Hézode, Christophe; Lee, Samuel S; Scalori, Astrid; Voulgari, Athina; Le Pogam, Sophie; Nájera, Isabel; Thommes, James A

2016-01-01

Most patients with chronic hepatitis C virus (HCV) genotype 1 infection who have had a previous null response (<2-log10 reduction in HCV RNA by treatment week 12) to peginterferon/ribavirin (PegIFN/RBV) do not achieve a sustained virological response (SVR) when re-treated with a first-generation HCV protease inhibitor (PI) administered in combination with PegIFN/RBV. We studied the incremental benefits associated with adding mericitabine (nucleoside analog inhibitor of HCV polymerase) to PI plus PegIFN alfa-2a/RBV-based therapy in two double-blind randomized multicenter phase 2 trials (with boceprevir in DYNAMO 1, and with telaprevir in DYNAMO 2). The primary endpoint in both trials was SVR, defined as HCV RNA <25 IU/mL 12 weeks after the end of treatment (SVR12). Overall, the addition of mericitabine to PI plus PegIFN alfa-2a/RBV therapy resulted in SVR12 rates of 60-70% in DYNAMO 1 and of 71-96% in DYNAMO 2. SVR12 rates were similar in patients infected with HCV genotype 1a and 1b in both trials. The placebo control arms in both studies were stopped because of high rates of virological failure. Numerically lower relapse rates were associated with longer treatment with mericitabine (24 versus 12 weeks), telaprevir-containing regimens, and regimens that included 48 weeks of PegIFN alfa-2a/RBV therapy. No mericitabine resistance mutations were identified in any patient in either trial. The addition of mericitabine did not add to the safety burden associated with either telaprevir or boceprevir-based regimens. These studies demonstrate increased SVR rates and reduced relapse rates in difficult-to-treat patients when a nucleoside polymerase inhibitor with intermediate antiviral potency is added to regimens containing a first-generation PI. ClinicalTrials.gov NCT01482403 and ClinicalTrials.gov NCT01482390.

Efficacy of the retreatment of hepatitis C virus infections after liver transplantation: role of an aggressive approach.

PubMed

Berenguer, Marina; Roche, Bruno; Aguilera, Victoria; Duclos-Vallée, Jean-Charles; Navarro, Laia; Rubín, Angel; Pons, Jose-Antonio; de la Mata, Manuel; Prieto, Martín; Samuel, Didier

2013-01-01

A sustained virological response (SVR) is achieved by 30% of naive liver transplantation (LT) recipients treated with pegylated interferon (PEG-IFN) and ribavirin (RBV). Almost no data are available about retreatment. The aim of this study was to assess the efficacy, tolerability, and SVR predictors of retreatment. Data were collected from 4 centers on the retreatment of prior nonresponders to standard therapy or PEG-IFN (with or without RBV) and relapsers. Seventy-nine of 301 treatment-experienced LT patients (26%), who had a median age of 59 years (range = 35-77 years) and were mostly male (72%) and infected with genotype 1 (87%), were retreated with PEG-IFN and RBV at a median of 6.9 years after LT. During the first course of therapy, 35% were treated with interferon, 49% received tacrolimus, 52% received steroids, and 49.5% were relapsers. Retreatment was started at a median of 1.9 years (range = 45 days to 8.2 years) after the end of the first course. The proportion of patients with cirrhosis increased from 10% to 37% (P < 0.001). In addition, in retreated patients, full initial RBV doses (P = 0.03), growth factors [erythropoietin (P < 0.001) and granulocyte colony-stimulating factor (P = 0.048)], and transfusions (P = 0.03) were used more frequently, and the treatment duration was longer (P = 0.03). An end-of-treatment response was achieved in 61%, whereas SVR, which was associated with improved survival, occurred in 28 (35%). The variables predicting SVR were age (P = 0.04), disease severity [fibrosis (50% with F0-F2 versus 26% with F3-4), P = 0.03; bilirubin, P = 0.006; platelet count, P = 0.03], adherence, and viral kinetics. None of the patients without an early virological response achieved SVR. There was a trend of prior relapsers achieving higher SVR rates than prior nonresponders. In conclusion, SVR, which was achieved by approximately one-third of the retreated patients, can be predicted with the same variables used for naive LT recipients (age, disease severity, adherence, and viral kinetics) and is associated with enhanced survival. Copyright © 2012 American Association for the Study of Liver Diseases.

But Why Is Everything so Hard to Do? Exploring Learning and the Complexity Factor in Social Virtual Reality

ERIC Educational Resources Information Center

Honebein, Peter C.; Goldsworthy, Richard

2012-01-01

Virtual classrooms and virtual activities have waxed and waned, with most focusing on fostering learning in the cognitive domain and, realistically, most becoming rapidly discontinued. But social virtual realities (SVR) are uniquely "social," so what about interpersonal skills? This article describes the authors' experiences exploring SVR as a…

Vocabulary Does Not Complicate the Simple View of Reading

ERIC Educational Resources Information Center

Braze, David; Katz, Leonard; Magnuson, James S.; Mencl, W. Einar; Tabor, Whitney; Van Dyke, Julie A.; Gong, Tao; Johns, Clinton L.; Shankweiler, Donald P.

2016-01-01

Gough and Tunmer's (1986) simple view of reading (SVR) proposed that reading comprehension (RC) is a function of language comprehension (LC) and word recognition/decoding. Braze et al. (2007) presented data suggesting an extension of the SVR in which knowledge of vocabulary (V) affected RC over and above the effects of LC. Tunmer and Chapman…

Examining the Simple View of Reading in a Transparent Orthography: A Longitudinal Study from Kindergarten to Grade 3

ERIC Educational Resources Information Center

Torppa, Minna; Georgiou, George K.; Lerkkanen, Marja-Kristiina; Niemi, Pekka; Poikkeus, Anna-Maija

2016-01-01

This study examined the dynamic relationships among the components of the Simple View of Reading (SVR) in a transparent orthography (Finnish) and the predictive value of cognitive skills (phonological awareness, letter knowledge, rapid naming, and vocabulary) on the SVR components. Altogether, 1,815 Finnish children were followed from kindergarten…

A comparison of large-scale climate signals and the North American Multi-Model Ensemble (NMME) for drought prediction in China

NASA Astrophysics Data System (ADS)

Xu, Lei; Chen, Nengcheng; Zhang, Xiang

2018-02-01

Drought is an extreme natural disaster that can lead to huge socioeconomic losses. Drought prediction ahead of months is helpful for early drought warning and preparations. In this study, we developed a statistical model, two weighted dynamic models and a statistical-dynamic (hybrid) model for 1-6 month lead drought prediction in China. Specifically, statistical component refers to climate signals weighting by support vector regression (SVR), dynamic components consist of the ensemble mean (EM) and Bayesian model averaging (BMA) of the North American Multi-Model Ensemble (NMME) climatic models, and the hybrid part denotes a combination of statistical and dynamic components by assigning weights based on their historical performances. The results indicate that the statistical and hybrid models show better rainfall predictions than NMME-EM and NMME-BMA models, which have good predictability only in southern China. In the 2011 China winter-spring drought event, the statistical model well predicted the spatial extent and severity of drought nationwide, although the severity was underestimated in the mid-lower reaches of Yangtze River (MLRYR) region. The NMME-EM and NMME-BMA models largely overestimated rainfall in northern and western China in 2011 drought. In the 2013 China summer drought, the NMME-EM model forecasted the drought extent and severity in eastern China well, while the statistical and hybrid models falsely detected negative precipitation anomaly (NPA) in some areas. Model ensembles such as multiple statistical approaches, multiple dynamic models or multiple hybrid models for drought predictions were highlighted. These conclusions may be helpful for drought prediction and early drought warnings in China.

Multispectral analysis of biological agents to implement a quick tool for stand-off biological detection

NASA Astrophysics Data System (ADS)

Carestia, M.; Pizzoferrato, R.; Lungaroni, M.; Gabriele, J.; Ludovici, G. M.; Cenciarelli, O.; Gelfusa, M.; Murari, A.; Malizia, A.; Gaudio, P.

2015-10-01

With the aim of identifying an approach to exploit the differences in the fluorescence signatures of biological agents BAs, we have investigated the response of some BAs simulants to a set of different excitation wavelengths in the UV spectral range (i.e. 266, 273, 280, 300, 340, 355 nm). Our preliminary results on bacterial spores and vegetative forms, dispersed in water, showed that the differences in the fluorescence spectra can be enhanced, and more easily revealed, by using different excitation wavelengths. Specifically, the photo luminescence (PL) spectra coming from different species of Bacillus, in the form of spores (used as simulants of Bacillus anthracis), show significant differences under excitation at all the wavelengths, with slightly larger differences at 300, 340, 355 nm. On the other hand, the vegetative forms of two Bacillus species, did not show any appreciable difference, i.e. the PL spectra are virtually identical, for the excitation wavelengths of 266, 273, 280 nm. Conversely, small yet appreciable difference appear at 300, 340, 355 nm. Finally, large difference appear between the spore and the vegetative form of each species at all the wavelengths, with slightly larger variations at 300, 340, 355 nm. Together, these preliminary results support the hypothesis that a multi-wavelength approach could be used to improve the sensitivity and specificity of UV-LIF based BAs detection systems. The second step of this work concerns the application of a Support Vector Regression (SVR) method, as evaluated in our previous work to define a methodology for the setup of a multispectral database for the stand-off detection of BAs.

Prediction and Analysis of CO2 Emission in Chongqing for the Protection of Environment and Public Health

PubMed Central

Yang, Shuai; Wang, Yu; Ao, Wengang; Bai, Yun; Li, Chuan

2018-01-01

Based on the consumption of fossil energy, the CO2 emissions of Chongqing are calculated and analyzed from 1997 to 2015 in this paper. Based on the calculation results, the consumption of fossil fuels and the corresponding CO2 emissions of Chongqing in 2020 are predicted, and the supporting data and corresponding policies are provided for the government of Chongqing to reach its goal as the economic unit of low-carbon emission in the ‘13th Five-Year Plan’. The results of the analysis show that there is a rapid decreasing trend of CO2 emissions in Chongqing during the ‘12th Five-Year Plan’, which are caused by the adjustment policy of the energy structure in Chongqing. Therefore, the analysis and prediction are primarily based on the adjustment of Chongqing’s coal energy consumption in this paper. At the initial stage, support vector regression (SVR) method is applied to predict the other fossil energy consumption and the corresponding CO2 emissions of Chongqing in 2020. Then, with the energy intensity of 2015 and the official target of CO2 intensity in 2020, the total fossil energy consumption and CO2 emissions of Chongqing in 2020 are predicted respectively. By the above results of calculation, the coal consumption and its corresponding CO2 emissions of Chongqing in 2020 are determined. To achieve the goal of CO2 emissions of Chongqing in 2020, the coal consumption level and energy intensity of Chongqing are calculated, and the adjustment strategies for energy consumption structure in Chongqing are proposed. PMID:29547505

Spatial relationships between above-ground biomass and bird species biodiversity in Palawan, Philippines

PubMed Central

Singh, Minerva; Friess, Daniel A.; Vilela, Bruno; Alban, Jose Don T. De; Monzon, Angelica Kristina V.; Veridiano, Rizza Karen A.; Tumaneng, Roven D.

2017-01-01

This study maps distribution and spatial congruence between Above-Ground Biomass (AGB) and species richness of IUCN listed conservation-dependent and endemic avian fauna in Palawan, Philippines. Grey Level Co-Occurrence Texture Matrices (GLCMs) extracted from Landsat and ALOS-PALSAR were used in conjunction with local field data to model and map local-scale field AGB using the Random Forest algorithm (r = 0.92 and RMSE = 31.33 Mg·ha-1). A support vector regression (SVR) model was used to identify the factors influencing variation in avian species richness at a 1km scale. AGB is one of the most important determinants of avian species richness for the study area. Topographic factors and anthropogenic factors such as distance from the roads were also found to strongly influence avian species richness. Hotspots of high AGB and high species richness concentration were mapped using hotspot analysis and the overlaps between areas of high AGB and avian species richness was calculated. Results show that the overlaps between areas of high AGB with high IUCN red listed avian species richness and endemic avian species richness were fairly limited at 13% and 8% at the 1-km scale. The overlap between 1) low AGB and low IUCN richness, and 2) low AGB and low endemic avian species richness was higher at 36% and 12% respectively. The enhanced capacity to spatially map the correlation between AGB and avian species richness distribution will further assist the conservation and protection of forest areas and threatened avian species. PMID:29206228

High-throughput NIR spectroscopic (NIRS) detection of microplastics in soil.

PubMed

Paul, Andrea; Wander, Lukas; Becker, Roland; Goedecke, Caroline; Braun, Ulrike

2018-05-12

The increasing pollution of terrestrial and aquatic ecosystems with plastic debris leads to the accumulation of microscopic plastic particles of still unknown amount. To monitor the degree of contamination, analytical methods are urgently needed, which help to quantify microplastics (MP). Currently, time-costly purified materials enriched on filters are investigated both by micro-infrared spectroscopy and/or micro-Raman. Although yielding precise results, these techniques are time consuming, and are restricted to the analysis of a small part of the sample in the order of few micrograms. To overcome these problems, we tested a macroscopic dimensioned near-infrared (NIR) process-spectroscopic method in combination with chemometrics. For calibration, artificial MP/ soil mixtures containing defined ratios of polyethylene, polyethylene terephthalate, polypropylene, and polystyrene with diameters < 125 μm were prepared and measured by a process FT-NIR spectrometer equipped with a fiber-optic reflection probe. The resulting spectra were processed by chemometric models including support vector machine regression (SVR), and partial least squares discriminant analysis (PLS-DA). Validation of models by MP mixtures, MP-free soils, and real-world samples, e.g., fermenter residue, suggests a reliable detection and a possible classification of MP at levels above 0.5 to 1.0 mass% depending on the polymer. The benefit of the combined NIRS chemometric approach lies in the rapid assessment whether soil contains MP, without any chemical pretreatment. The method can be used with larger sample volumes and even allows for an online prediction and thus meets the demand of a high-throughput method.

Towards cheminformatics-based estimation of drug therapeutic index: Predicting the protective index of anticonvulsants using a new quantitative structure-index relationship approach.

PubMed

Chen, Shangying; Zhang, Peng; Liu, Xin; Qin, Chu; Tao, Lin; Zhang, Cheng; Yang, Sheng Yong; Chen, Yu Zong; Chui, Wai Keung

2016-06-01

The overall efficacy and safety profile of a new drug is partially evaluated by the therapeutic index in clinical studies and by the protective index (PI) in preclinical studies. In-silico predictive methods may facilitate the assessment of these indicators. Although QSAR and QSTR models can be used for predicting PI, their predictive capability has not been evaluated. To test this capability, we developed QSAR and QSTR models for predicting the activity and toxicity of anticonvulsants at accuracy levels above the literature-reported threshold (LT) of good QSAR models as tested by both the internal 5-fold cross validation and external validation method. These models showed significantly compromised PI predictive capability due to the cumulative errors of the QSAR and QSTR models. Therefore, in this investigation a new quantitative structure-index relationship (QSIR) model was devised and it showed improved PI predictive capability that superseded the LT of good QSAR models. The QSAR, QSTR and QSIR models were developed using support vector regression (SVR) method with the parameters optimized by using the greedy search method. The molecular descriptors relevant to the prediction of anticonvulsant activities, toxicities and PIs were analyzed by a recursive feature elimination method. The selected molecular descriptors are primarily associated with the drug-like, pharmacological and toxicological features and those used in the published anticonvulsant QSAR and QSTR models. This study suggested that QSIR is useful for estimating the therapeutic index of drug candidates. Copyright © 2016. Published by Elsevier Inc.

[Effectiveness of Self-efficacy Promoting Vestibular Rehabilitation Program for Patients with Vestibular Hypofunction].

PubMed

Lee, Hyun Jung; Choi-Kwon, Smi

2016-10-01

In this study an examination was done of the effect of self-efficacy promoting vestibular rehabilitation (S-VR) on dizziness, exercise selfefficacy, adherence to vestibular rehabilitation (VR), subjective and objective vestibular function, vestibular compensation and the recurrence of dizziness in patients with vestibular hypofunction. This was a randomized controlled study. Data were collected 3 times at baseline, 4 and 8 weeks after beginning the intervention. Outcome measures were level of dizziness, exercise self-efficacy, and level of adherence to VR. Subjective and objective vestibular function, vestibular compensation and the recurrence of dizziness were also obtained. Data were analyzed using Windows SPSS 21.0 program. After 4 weeks of S-VR, there was no difference between the groups for dizziness, subjective and objective vestibular functions. However, exercise self-efficacy and adherence to VR were higher in the experimental group than in the control group. After 8 weeks of S-VR, dizziness (p=.018) exercise self-efficacy (p<.001), adherence to VR (p<.001), total-dizziness handicap inventory (DHI) (p=.012), vision analysis ratio (p=.046) in the experimental group differ significantly from that of the control group. The number of patients with recurring dizziness were higher in the control group than in the experimental group (p<.001). The results indicate that continuous 8 weeks of S-VR is effective in reducing dizziness, and improving exercise self-efficacy, subjective vestibular function and adherence to VR. Objective vestibular function and vestibular compensation were also improved in the experimental group at the end of 8 weeks of S-VR.

Interferon-free treatment for patients with chronic hepatitis C and autoimmune liver disease: higher SVR rates with special precautions for deterioration of autoimmune hepatitis

PubMed Central

Kanda, Tatsuo; Yasui, Shin; Nakamura, Masato; Nakamoto, Shingo; Takahashi, Koji; Wu, Shuang; Sasaki, Reina; Haga, Yuki; Ogasawara, Sadahisa; Saito, Tomoko; Kobayashi, Kazufumi; Kiyono, Soichiro; Ooka, Yoshihiko; Suzuki, Eiichiro; Chiba, Tetsuhiro; Maruyama, Hitoshi; Imazeki, Fumio; Moriyama, Mitsuhiko; Kato, Naoya

2018-01-01

Background Interferon-free treatment can achieve higher sustained virological response (SVR) rates, even in patients in whom hepatitis C virus (HCV) could not be eradicated in the interferon treatment era. Immune restoration in the liver is occasionally associated with HCV infection. We examined the safety and effects of interferon-free regimens on HCV patients with autoimmune liver diseases. Results All 7 HCV patients with autoimmune hepatitis (AIH) completed treatment and achieved SVR. Three patients took prednisolone (PSL) at baseline, and 3 did not take PSL during interferon-free treatment. In one HCV patient with AIH and cirrhosis, PSL were not administered at baseline, but she needed to take 40 mg/day PSL at week 8 for liver dysfunction. She also complained back pain and was diagnosed with vasospastic angina by coronary angiography at week 11. However, she completed interferon-free treatment. All 5 HCV patients with primary biliary cholangitis (PBC) completed treatment and achieved SVR. Three of these HCV patients with PBC were treated with UDCA during interferon-free treatment. Conclusions Interferon-free regimens could result in higher SVR rates in HCV patients with autoimmune liver diseases. As interferon-free treatment for HCV may have an effect on hepatic immunity and activity of the autoimmune liver diseases, careful attention should be paid to unexpected adverse events in their treatments. Methods Total 12 patients with HCV and autoimmune liver diseases [7 AIH and PBC], who were treated with interferon-free regimens, were retrospectively analyzed. PMID:29545925

Examining the Simple View of Reading among Subgroups of Spanish-Speaking English Language Learners

ERIC Educational Resources Information Center

Grimm, Ryan Ponce

2015-01-01

The Simple View of Reading (SVR; Gough & Tunmer, 1986; Hoover & Gough, 1990) has a longstanding history as a model of reading comprehension, but it has mostly been applied to native English speakers. The SVR posits reading comprehension is a function of the interaction between word-level reading skills and oral language skills. It has been…

For US Students, L2 Reading Comprehension Is Hard Because L2 Listening Comprehension Is Hard, Too

ERIC Educational Resources Information Center

Sparks, Richard; Patton, Jon; Luebbers, Julie

2018-01-01

The Simple View of Reading (SVR) model posits that reading is the product of word decoding and language comprehension and that oral language (listening) comprehension is the best predictor of reading comprehension once word-decoding skill has been established. The SVR model also proposes that there are good readers and three types of poor…

Comparison of outliers and novelty detection to identify ionospheric TEC irregularities during geomagnetic storm and substorm

NASA Astrophysics Data System (ADS)

Pattisahusiwa, Asis; Houw Liong, The; Purqon, Acep

2016-08-01

In this study, we compare two learning mechanisms: outliers and novelty detection in order to detect ionospheric TEC disturbance by November 2004 geomagnetic storm and January 2005 substorm. The mechanisms are applied by using v-SVR learning algorithm which is a regression version of SVM. Our results show that both mechanisms are quiet accurate in learning TEC data. However, novelty detection is more accurate than outliers detection in extracting anomalies related to geomagnetic events. The detected anomalies by outliers detection are mostly related to trend of data, while novelty detection are associated to geomagnetic events. Novelty detection also shows evidence of LSTID during geomagnetic events.

Effect of Aldehyde Dehydrogenase 2 Gene Polymorphism on Hemodynamics After Nitroglycerin Intervention in Northern Chinese Han Population

PubMed Central

Xia, Jia-Qi; Song, Jie; Zhang, Yi; An, Ni-Na; Ding, Lei; Zhang, Zheng

2015-01-01

Background: Nitroglycerin (NTG) is one of the few immediate treatments for acute angina. Aldehyde dehydrogenase 2 (ALDH2) is a key enzyme in the human body that facilitates the biological metabolism of NTG. The biological mechanism of NTG serves an important function in NTG efficacy. Some reports still contradict the results that the correlation between ALDH2 gene polymorphisms and NTG and its clinical efficacy is different. However, data on NTG measurement by pain relief are subjective. This study aimed to investigate the influence of ALDH2 gene polymorphism on intervention with sublingual NTG using noninvasive hemodynamic parameters of cardiac output (CO) and systemic vascular resistance (SVR) in Northern Chinese Han population. Methods: This study selected 559 patients from the Affiliated Hospital of Qingdao University. A total of 203 patients presented with coronary heart disease (CHD) and 356 had non-CHD (NCHD) cases. All patient ALDH2 genotypes (G504A) were detected and divided into two types: Wild (GG) and mutant (GA/AA). Among the CHD group, 103 were wild-type cases, and 100 were mutant-type cases. Moreover, 196 cases were wild-type, and 160 cases were mutant type among the NCHD volunteers. A noninvasive hemodynamic detector was used to monitor the CO and the SVR at the 0, 5, and 15 minute time points after medication with 0.5 mg sublingual NTG. Two CO and SVR indicators were used for a comparative analysis of all case genotypes. Results: Both CO and SVR indicators significantly differed between the wild and mutant genotypes at various time points after intervention with sublingual NTG at 5 and 15 minutes in the NCHD (F = 16.460, 15.003, P = 0.000, 0.000) and CHD groups (F = 194.482, 60.582, P = 0.000, 0.000). All CO values in the wild-type case of both NCHD and CHD groups increased, whereas those in the mutant type decreased. The CO and ΔCO differences were statistically significant (P < 0.05; P < 0.05). The SVR and ΔSVR changed between the wild- and mutant-type cases at all-time points in both NCHD and CHD groups had statistically significant differences (P < 0.05; P < 0.05). Conclusion: ALDH2 (G504A) gene polymorphism is associated with changes in noninvasive hemodynamic parameters (i.e. CO and SVR) after intervention with sublingual NTG. This gene polymorphism may influence the effect of NTG intervention on Northern Chinese Han population. PMID:25591559

Boceprevir: a protease inhibitor for the treatment of hepatitis C.

PubMed

Chang, Mei H; Gordon, Lori A; Fung, Horatio B

2012-10-01

Boceprevir is a protease inhibitor indicated for the treatment of chronic hepatitis C virus (HCV) genotype 1 infection in combination with peginterferon and ribavirin for treatment-naive patients and those who previously failed to improve with interferon and ribavirin treatment. This article provides an overview of the mechanism of action, pharmacologic and pharmacokinetic properties, clinical efficacy, and tolerability of boceprevir. Relevant information was identified through a search of PubMed (1990-July 2012), EMBASE (1990-July 2012), International Pharmaceutical Abstracts (1970-July 2012), and Google Scholar using the key words boceprevir, SCH 503034, non-structural protein 3 (NS3) serine protease inhibitor, and direct-acting antiviral agent (DAA). Additional information was obtained from the US Food and Drug Administration's Web site, review of the reference lists of identified articles, and posters and abstracts from scientific meetings. Clinical efficacy of boceprevir was assessed in 2 Phase III trials, Serine Protease Inhibitor Therapy-2 (SPRINT-2) for treatment-naive patients and Retreatment with HCV Serine Protease Inhibitor Boceprevir and PegIntron/Rebetol 2 (RESPOND-2) for treatment-experienced patients. In SPRINT-2, patients were randomized to receive peginterferon + ribavirin (PR) or peginterferon + ribavirin + boceprevir (PRB); duration of boceprevir therapy varied from 24, 32, to 44 weeks on the basis of HCV RNA results. The primary endpoint was achievement of sustained virologic response (SVR; lower limit of detection, 9.3 IU/mL). The addition of boceprevir was shown to be superior, with overall SVR rates ranging from 63% to 66% compared with 38% with PR (P < 0.001). Results of SVR in SPRINT-2 were also reorganized to monitor SVRs in black and non-black patients. Treatment-experienced patients were assessed in RESPOND-2; however, null responders were excluded. Patients were again randomized to PR or PRB; duration of boceprevir therapy varied from 32 to 44 weeks on the basis of HCV RNA results. SVR was significantly higher in patients receiving boceprevir (59%-66% vs 21% with PR; P < 0.001). This benefit was seen in both previous nonresponders (SVR, 40%-52% vs 7% with PR), as well as previous relapsers (SVR, 69%-75% vs 29% with PR). Importantly, SVR could be attained with a shortened course of therapy in almost one half of all treated patients in SPRINT-2 (44%) and RESPOND-2 (46%). Boceprevir was well tolerated in clinical trials and a welcomed addition to our HCV armamentarium. Published by EM Inc USA.

Evidence for the Early Emergence of the Simple View of Reading in a Transparent Orthography

ERIC Educational Resources Information Center

Kendeou, Panayiota; Papadopoulos, Timothy C.; Kotzapoulou, Marianna

2013-01-01

The main aim of the present study was to empirically test the emergence of the Simple View of Reading (SVR) in a transparent orthography, and specifically in Greek. To do so, we examined whether the constituent components of the SVR could be identified in young, Greek-speaking children even before the beginning of formal reading instruction. Our…

English Word-Level Decoding and Oral Language Factors as Predictors of Third and Fifth Grade English Language Learners' Reading Comprehension Performance

ERIC Educational Resources Information Center

Landon, Laura L.

2017-01-01

This study examines the application of the Simple View of Reading (SVR), a reading comprehension theory focusing on word recognition and linguistic comprehension, to English Language Learners' (ELLs') English reading development. This study examines the concurrent and predictive validity of two components of the SVR, oral language and word-level…

Identification of groups with poor cost-effectiveness of peginterferon plus ribavirin for naïve hepatitis C patients with a real-world cohort and database.

PubMed

Tsai, Pei-Chien; Liu, Ta-Wei; Tsai, Yi-Shan; Ko, Yu-Min; Chen, Kuan-Yu; Lin, Ching-Chih; Huang, Ching-I; Liang, Po-Cheng; Lin, Yi-Hung; Hsieh, Ming-Yen; Hou, Nai-Jen; Huang, Chung-Feng; Yeh, Ming-Lun; Lin, Zu-Yau; Chen, Shinn-Cherng; Dai, Chia-Yen; Chuang, Wan-Long; Huang, Jee-Fu; Yu, Ming-Lung

2017-06-01

For decades, peginterferon and ribavirin (PegIFN/RBV) have been the standard-of-care for chronic hepatitis C virus (CHC) infection. However, the actual cost-effectiveness of this therapy remains unclear. We purposed to explore the real-world cost effectiveness for subgroups of treatment-naïve CHC patients with PegIFN/RBV therapy in a large real-world cohort using a whole population database. A total of 1809 treatment-naïve chronic hepatitis C virus (HCV) patients (829 HCV genotype 1 [G1] and 980 HCV G2) treated with PegIFN/RBV therapies were linked to the National Health Insurance Research Database, covering the entire population of Taiwan from 1998 to 2013 to collect the total medical-care expenses of outpatient (antiviral agents, nonantiviral agents, laboratory, and consultation costs) and inpatient (medication, logistic, laboratory, and intervention costs) visits. The costs per treatment and the cost per sustained virological response (SVR) achieved were calculated. The average medical-care cost was USD $4823 (±$2984) per treatment and $6105 (±$3778) per SVR achieved. With SVR rates of 68.6% and 87.8%, the cost/SVR was significantly higher in G1 than those in G2 patients, respectively ($8285 vs $4663, P < .001). Treatment-naïve G1 patients of old ages, those with advanced fibrosis, high viral loads, or interleukin-28B unfavorable genotypes, or those without a rapid virological response (RVR: undetectable HCV RNA at week 4), or those with complete early virological response (cEVR: undetectable HCV RNA at week 12). Treatment-naïve G2 patients with high viral loads or without RVR or cEVR incurred significantly higher costs per SVR than their counterparts. The cost/SVR was extremely high among patients without RVR and in patients without cEVR. We investigated the real-world cost effectiveness data for different subgroups of treatment-naïve HCV patients with PegIFN/RBV therapies, which could provide useful, informative evidence for making decisions regarding future therapeutic strategies comprising costly direct-acting antivirals.

Ledipasvir + Sofosbuvir for Liver Transplant Recipients With Recurrent Hepatitis C: A Systematic Review and Meta-analysis.

PubMed

Liao, H-T; Tan, P; Huang, J-W; Yuan, K-F

2017-10-01

Studies focusing on the efficacy and safety of ledipasvir (LDV) + sofosbuvir (SOF) therapy in liver transplant (LT) recipients with hepatitis C virus (HCV) recurrence are still limited. Therefore, the aim of our work was to perform a systematic review and meta-analysis to evaluate outcome data of LDV + SOF therapy in LT recipients. Multiple databases were systematically searched for eligible studies. We included studies reporting sustained virological response 12 weeks after treatment (SVR12) and treatment-related adverse events (AEs) in LT recipients treated with LDV + SOF ± ribavirin (RBV) for HCV recurrence. All statistical analyses were conducted by using R version 3.3.1 (The R Foundation for Statistical Computing, Vienna, Austria). Twelve studies with a total of 994 LT recipients were included, most of which were diagnosed with HCV genotype 1 infection. The overall SVR12 reached 96.3% (95% confidence interval [CI]: 94.9%-97.5%) and no significant heterogeneity was observed (Q statistic = 10.63, P = .47; I 2  = 0%). No difference was found in SVR12 between treatments for 12 weeks and 24 weeks (P = .18). Patients treated with LDV + SOF + RBV (n = 525) exhibited an SVR12 rate of 95.1% (95% CI 92.8%-96.6%), which showed no difference from the findings in the LDV + SOF treatment group (n = 314) with an SVR12 reaching 94.9% (95% CI 91.5%-97.0%; P = .92). There was a tendency for a higher SVR12 in patients without cirrhosis than those with cirrhosis (P < .05). The most common AEs were listed as following: anemia 41.9% (n = 203 of 484), fatigue 39.1% (n = 207 of 530), headache 24.2% (n = 128 of 530), nausea 21.9% (n = 106 of 484), and diarrhea 19.0% (n = 92 of 484). LDV + SOF-based treatment is highly effective and well tolerated in LT recipients with HCV reinfection. Copyright © 2017 Elsevier Inc. All rights reserved.

HCV mono-infected and HIV/HCV co-infected individuals treated with direct-acting antivirals: to what extent do they differ?

PubMed

Bruno, Giuseppe; Saracino, Annalisa; Scudeller, Luigia; Fabrizio, Claudia; Dell'Acqua, Raffaele; Milano, Eugenio; Milella, Michele; Ladisa, Nicoletta; Monno, Laura; Angarano, Gioacchino

2017-09-01

Direct-acting antiviral (DAA)-based treatment of hepatitis C virus (HCV) has been associated with high sustained virological response (SVR) rates and good tolerability in randomized clinical trials. This study was performed to assess the safety and effectiveness of DAAs in both HCV mono-infected and HIV/HCV co-infected patients. All consecutive HCV-infected patients, including HIV/HCV co-infected patients, receiving DAA-based treatment from February 2015 to September 2016 at the study clinic were included. Clinical, virological, and biochemical data were retrieved. The primary end-point was the SVR12 (HCV RNA undetectable 12 weeks after the end of treatment) is commonly used worldwide. The secondary end-point was the safety profile of DAAs during the treatment period. A total of 382 patients were included; 62 were HIV/HCV co-infected. Cirrhosis was found in 256 patients (67.4%). SVR12 was achieved in 365/382 (95.5%) individuals (58/62 HIV/HCV co-infected, 93.5%) in the intention-to-treat (ITT) analysis. A platelet count <90×10 9 /l (odds ratio (OR) 4.12, 95% confidence interval (CI) 1.5-11.3, p=0.006), HCV genotype 3 infection (OR 5.49, 95% CI 1.9-15.7, p=0.002), liver stiffness >20kPa (OR 3.05, 95% CI 1.03-8.96, p=0.04), and Model for End-Stage Liver Disease (MELD) score >10 (OR 5.27, 95% CI 1.16-23.8, p=0.03) were associated with lower SVR rates. On multivariate analysis, only genotype 3 infection remained a negative predictor of SVR (OR 21.6, 95% CI 3.81-123, p=0.001). Treatment discontinuation was observed in 10 subjects. Severe adverse events (SAEs) occurred in 17 patients (4.5%). High SVR12 rates were observed in both HCV mono-infected and HIV/HCV co-infected individuals. Overall, DAA-based treatment was safe and there were no differences in terms of SAEs and treatment discontinuation between the two groups. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Ledipasvir/sofosbuvir for treatment of hepatitis C virus in sofosbuvir-experienced, NS5A treatment-naïve patients: Findings from two randomized trials.

PubMed

Tam, Edward; Luetkemeyer, Anne F; Mantry, Parvez S; Satapathy, Sanjaya K; Ghali, Peter; Kang, Minhee; Haubrich, Richard; Shen, Xianlin; Ni, Liyun; Camus, Gregory; Copans, Amanda; Rossaro, Lorenzo; Guyer, Bill; Brown, Robert S

2018-06-01

We report data from two similarly designed studies that evaluated the efficacy, safety, and optimal duration of ledipasvir/sofosbuvir (LDV/SOF) ± ribavirin (RBV) for retreatment of chronic hepatitis C virus (HCV) in individuals who failed to achieve sustained virological response (SVR) with prior SOF-based, non-NS5A inhibitor-containing regimens. The RESCUE study enrolled HCV mono-infected adults with genotype (GT) 1 or 4. Non-cirrhotic participants were randomized to 12 weeks of LDV/SOF or LDV/SOF + RBV. Compensated cirrhotic participants were randomized to LDV/SOF + RBV (12 weeks) or LDV/SOF (24 weeks). The AIDS Clinical Trials Group A5348 study randomized genotype 1 adults with HCV/HIV co-infection to LDV/SOF + RBV (12 weeks) or LDV/SOF (24 weeks). Both studies used SVR at 12 weeks post-treatment (SVR12) as the primary endpoint. In the RESCUE study, 82 participants were randomized and treated, and all completed treatment. Overall, SVR12 was 88% (72/82); 81-100% in non-cirrhotic participants treated with LDV/SOF or LDV/SOF + RBV for 12 weeks and 80-92% in cirrhotic participants treated with LDV/SOF + RBV for 12 weeks or LDV/SOF for 24 weeks. Adverse events (AEs), mostly mild-to-moderate in severity, were experienced by 78% of participants, with headache and fatigue most frequently reported. One serious AE, not related to treatment, was observed. No premature discontinuations of study drug, or deaths occurred. In the A5348 study, seven participants were randomized (cirrhotic n = 1; GT1a n = 5) and all attained SVR12, with no serious AEs or premature discontinuations. In this SOF-experienced, NS5A inhibitor-naïve population, which included participants with cirrhosis or HCV/HIV co-infection, high SVR12 rates were achieved. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Mericitabine and Either Boceprevir or Telaprevir in Combination with Peginterferon Alfa-2a plus Ribavirin for Patients with Chronic Hepatitis C Genotype 1 Infection and Prior Null Response: The Randomized DYNAMO 1 and DYNAMO 2 Studies

PubMed Central

Wedemeyer, Heiner; Forns, Xavier; Hézode, Christophe; Lee, Samuel S.; Scalori, Astrid; Voulgari, Athina; Le Pogam, Sophie; Nájera, Isabel; Thommes, James A.

2016-01-01

Most patients with chronic hepatitis C virus (HCV) genotype 1 infection who have had a previous null response (<2-log10 reduction in HCV RNA by treatment week 12) to peginterferon/ribavirin (PegIFN/RBV) do not achieve a sustained virological response (SVR) when re-treated with a first-generation HCV protease inhibitor (PI) administered in combination with PegIFN/RBV. We studied the incremental benefits associated with adding mericitabine (nucleoside analog inhibitor of HCV polymerase) to PI plus PegIFN alfa-2a/RBV-based therapy in two double-blind randomized multicenter phase 2 trials (with boceprevir in DYNAMO 1, and with telaprevir in DYNAMO 2). The primary endpoint in both trials was SVR, defined as HCV RNA <25 IU/mL 12 weeks after the end of treatment (SVR12). Overall, the addition of mericitabine to PI plus PegIFN alfa-2a/RBV therapy resulted in SVR12 rates of 60–70% in DYNAMO 1 and of 71–96% in DYNAMO 2. SVR12 rates were similar in patients infected with HCV genotype 1a and 1b in both trials. The placebo control arms in both studies were stopped because of high rates of virological failure. Numerically lower relapse rates were associated with longer treatment with mericitabine (24 versus 12 weeks), telaprevir-containing regimens, and regimens that included 48 weeks of PegIFN alfa-2a/RBV therapy. No mericitabine resistance mutations were identified in any patient in either trial. The addition of mericitabine did not add to the safety burden associated with either telaprevir or boceprevir-based regimens. These studies demonstrate increased SVR rates and reduced relapse rates in difficult-to-treat patients when a nucleoside polymerase inhibitor with intermediate antiviral potency is added to regimens containing a first-generation PI. Trial Registration: ClinicalTrials.gov NCT01482403 and ClinicalTrials.gov NCT01482390 PMID:26752189

Identification of sequence motifs significantly associated with antisense activity.

PubMed

McQuisten, Kyle A; Peek, Andrew S

2007-06-07

Predicting the suppression activity of antisense oligonucleotide sequences is the main goal of the rational design of nucleic acids. To create an effective predictive model, it is important to know what properties of an oligonucleotide sequence associate significantly with antisense activity. Also, for the model to be efficient we must know what properties do not associate significantly and can be omitted from the model. This paper will discuss the results of a randomization procedure to find motifs that associate significantly with either high or low antisense suppression activity, analysis of their properties, as well as the results of support vector machine modelling using these significant motifs as features. We discovered 155 motifs that associate significantly with high antisense suppression activity and 202 motifs that associate significantly with low suppression activity. The motifs range in length from 2 to 5 bases, contain several motifs that have been previously discovered as associating highly with antisense activity, and have thermodynamic properties consistent with previous work associating thermodynamic properties of sequences with their antisense activity. Statistical analysis revealed no correlation between a motif's position within an antisense sequence and that sequences antisense activity. Also, many significant motifs existed as subwords of other significant motifs. Support vector regression experiments indicated that the feature set of significant motifs increased correlation compared to all possible motifs as well as several subsets of the significant motifs. The thermodynamic properties of the significantly associated motifs support existing data correlating the thermodynamic properties of the antisense oligonucleotide with antisense efficiency, reinforcing our hypothesis that antisense suppression is strongly associated with probe/target thermodynamics, as there are no enzymatic mediators to speed the process along like the RNA Induced Silencing Complex (RISC) in RNAi. The independence of motif position and antisense activity also allows us to bypass consideration of this feature in the modelling process, promoting model efficiency and reducing the chance of overfitting when predicting antisense activity. The increase in SVR correlation with significant features compared to nearest-neighbour features indicates that thermodynamics alone is likely not the only factor in determining antisense efficiency.

Hepatitis C virus treatment rates and outcomes in HIV/hepatitis C virus co-infected individuals at an urban HIV clinic.

PubMed

Murray, Melanie C M; Barrios, Rolando; Zhang, Wendy; Hull, Mark; Montessori, Valentina; Hogg, Robert S; Montaner, Julio S G

2011-01-01

The factors associated with hepatitis C virus (HCV) treatment uptake and responses were assessed among HCV/HIV co-infected individuals referred for HCV therapy at an urban HIV clinic. Retrospective review of HIV/HCV patients enrolled in the HCV treatment program at the John Ruedy Immunodeficiency Clinic in Vancouver. The factors associated with treatment uptake were assessed using multivariate analysis. A total of 134 HCV/HIV co-infected individuals were recalled for assessment for HCV therapy. Overall 64 (48%) initiated treatment, and of those treated 49 (76.6%) attained end treatment response, whereas 35 (57.8%) achieved sustained virological response (SVR). When evaluated by genotype, 53% (17/32) of those with genotype 1, and 65% (20/31) of those with genotype 2 or 3 infections attained SVR. In treated individuals, alanine aminotransferase dropped significantly after treatment (P<0.001). During treatment, CD4 counts dropped significantly (P<0.001) in all patients. The counts recovered to baseline in patients who achieved SVR, but remained lower in patients who failed the therapy (P=0.015). On multivariate analysis, history of injection drug use (odds ratio: 3.48; 95% confidence interval: 1.37-8.79; P=0.009) and low hemoglobin levels (odds ratio: 4.23; 95% confidence interval: 1.36-13.10; P=0.013) were associated with those who did not enter the treatment. Only half of treatment-eligible co-infected patients referred for the therapy initiated treatment. Of those referred for the therapy, history of injection drug use was associated with lower rates of treatment uptake. Treated HIV/HCV co-infected individuals benefitted from both decreased alanine aminotransferase (independent of SVR), and rates of SVR similar to those described in HCV monoinfected patients.

Effectiveness of direct-acting antiviral therapy for hepatitis C in difficult-to-treat patients in a safety-net health system: a retrospective cohort study.

PubMed

Yek, Christina; de la Flor, Carolina; Marshall, John; Zoellner, Cindy; Thompson, Grace; Quirk, Lisa; Mayorga, Christian; Turner, Barbara J; Singal, Amit G; Jain, Mamta K

2017-11-20

Direct-acting antivirals (DAAs) have revolutionized chronic hepatitis C (HCV) treatment, but real-world effectiveness among vulnerable populations, including uninsured patients, is lacking. This study was conducted to characterize the effectiveness of DAAs in a socioeconomically disadvantaged and underinsured patient cohort. This retrospective observational study included all patients undergoing HCV treatment with DAA-based therapy between April 2014 and June 2016 at a large urban safety-net health system (Parkland Health and Hospital System, Dallas, TX, USA). The primary outcome was sustained virologic response (SVR), with secondary outcomes including treatment discontinuation, treatment relapse, and loss to follow-up. DAA-based therapy was initiated in 512 patients. The cohort was socioeconomically disadvantaged (56% uninsured and 13% Medicaid), with high historic rates of alcohol (41%) and substance (50%) use, and mental health disorders (38%). SVR was achieved in 90% of patients (n = 459); 26 patients (5%) were lost to follow-up. SVR was significantly lower in patients with decompensated cirrhosis (82% SVR; OR 0.37, 95% CI 0.16-0.85) but did not differ by insurance status (P = 0.98) or alcohol/substance use (P = 0.34). Reasons for treatment failure included loss to follow-up (n = 26, 5%), viral relapse (n = 16, 3%), non-treatment-related death (n = 7, 1%), and treatment discontinuation (n = 4, 1%). Of patients with viral relapse, 6 reported non-compliance and have not been retreated, 5 have been retreated and achieved SVR, 4 have undergone resistance testing but not yet initiated retreatment, and 1 was lost to follow-up. Effective outcomes with DAA-based therapy can be achieved in difficult-to-treat underinsured populations followed in resource-constrained safety-net health systems.

Impact of Direct Acting Antiviral Therapy for Treatment of Hepatitis C Genotypes 1, 3 and 4: A Real Life Experience from India.

PubMed

Mehta, Varun; Mahajan, Ramit; Midha, Vandana; Narang, Vikram; Kaur, Kirandeep; Singh, Arshdeep; Malhotra, Anand; Parvez, Aslam; Sood, Ajit

2018-03-01

To assess impact of Direct Acting Antiviral (DAA) therapies for treatment of Hepatitis C Virus (HCV) genotypes 1, 3 and 4 in a real-world cohort from India. Adults with chronic HCV infection treated with Sofosbuvir (SOF) and Ledipasvir (LDV) (genotypes 1 and 4) or SOF and Daclatasvir (DCV) (genotype 3), with or without Ribavirin (RBV) between December 2015 and December 2016 were included. The primary endpoint was Sustained Virological Response at Post-treatment Week 12 (SVR12). Of the 648 patients, 181 received SOF/LDV (65 with RBV) and 467 received SOF/DCV (135 with RBV). Most patients were males (65.4%), aged 41-60 years (49.4%) and treatment-naïve (92.6%). Genotype 3 (72.1%) was most common, followed by genotypes 1 (22.4%) and 4 (5.6%). Forty two percent patients ( n  = 271) had cirrhosis (112 patients were decompensated). SVR12 (modified intention-to-treat) was achieved by 98.1% of patients (512/522) (100% in genotypes 1 and 4, and 97.3% (362/372) in genotype 3). On intention to treat analysis, SVR12 was 88.1% (512/581) [genotype 1-96.8% (121/125), genotype 3-85.2%, genotype 4-93.5% (29/31)]. Seventy patients had treatment failure (non response in 6, virological breakthrough in 2, 10 patients relapsed, 2 died and 50 were lost to follow up). High SVR was observed regardless of HCV genotype, presence of cirrhosis or past history of treatment. No major adverse events warranting discontinuation of treatment were noted. DAA therapy for HCV genotypes 1, 3 and 4 achieves high SVR rates in all patients, including those with cirrhosis and previous non-responders.

Anti-E1E2 antibodies do predict response to triple therapy in treatment-experienced Hepatitis C Virus-cirrhosis cases.

PubMed

Petit, Marie-Anne; Berthillon, Pascale; Pradat, Pierre; Arnaud, Clémence; Bordes, Isabelle; Virlogeux, Victor; Maynard, Marianne; Bailly, François; Zoulim, Fabien; Chemin, Isabelle; Trépo, Christian

2015-12-01

We previously showed that pre-treatment serum anti-E1E2 predicted hepatitis C virus (HCV) RNA viral kinetics (VKs) and treatment outcome in patients with chronic hepatitis C receiving pegylated interferon/ribavirin (Peg-IFN/RBV) double therapy. Here, we determined whether baseline anti-E1E2 was correlated with the on-treatment VK and could predict virological outcome in treatment-experienced HCV-infected cirrhotic patients receiving protease inhibitor-based triple therapy. Sera from 19 patients with HCV genotype 1 infection and compensated cirrhosis who failed to respond to a prior course of Peg-IFN/RBV were selected at time 0 before starting triple therapy with boceprevir or telaprevir. We assessed patients with sustained viral response 12 weeks after the end of triple therapy (SVR12) by analyzing VKs at weeks 4, 12, 24, 36, 48 (end of treatment) and 60. Patients baseline characteristics were similar to the well-defined CUPIC cohort (age, HCV subtype, baseline viremia, and treatment history). Among the 19 patients, 11 achieved an SVR12. Fifteen patients were positive for pre-treatment anti-E1E2 and all of them achieved SVR12. Moreover, anti-E1E2 and SVR12 correlated with prior response to IFN/RBV therapy (relapse, partial or null response). Baseline anti-E1E2 could be considered as a new biomarker to predict SVR12 after triple therapy in this most difficult-to-treat population. These results warrant further validation on larger cohorts including patients receiving highly effective direct-acting antivirals to explore whether this test could help in better defining treatment duration for these very costly molecules. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

A systematic review of the efficacy and limitations of venous intervention in stasis ulceration.

PubMed

Montminy, Myriam L; Jayaraj, Arjun; Raju, Seshadri

2018-05-01

Surgical techniques to address various components of chronic venous disease are rapidly evolving. Their efficacy and generally good results in treating superficial venous reflux (SVR) have been documented and compared in patients presenting with pain and swelling. A growing amount of literature is now available suggesting their efficacy in patients with venous leg ulcer (VLU). This review attempts to summarize the efficacy and limitations of commonly used venous interventions in the treatment of SVR and incompetent perforator veins (IPVs) in patients with VLU. A systematic review of the published literature was performed. Two different searches were conducted in MEDLINE, Embase, and EBSCOhost to identify studies that examined the efficacy of SVR ablation and IPV ablation on healing rate and recurrence rate of VLU. In the whole review, 1940 articles were screened. Of those, 45 were included in the SVR ablation review and 4 in the IPV ablation review. Data were too heterogeneous to perform an adequate meta-analysis. The quality of evidence assessed by the Grading of Recommendations Assessment, Development, and Evaluation for the two outcomes varied from very low to moderate. Ulcer healing rate and recurrence rate were between 70% and 100% and 0% and 49% in the SVR ablation review and between 59% and 93% and 4% and 33% in the IPV ablation review, respectively. To explain those variable results, limitations such as inadequate diagnostic techniques, saphenous size, concomitant calf pump dysfunction, and associated deep venous reflux are discussed. Currently available minimally invasive techniques correct most venous pathologic processes in chronic venous disease with a good sustainable healing rate. There are still specific diagnostic and efficacy limitations that mandate proper match of individual patients with the planned approach. Copyright © 2017 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Sustained virological response with intravenous silibinin: individualized IFN-free therapy via real-time modelling of HCV kinetics.

PubMed

Dahari, Harel; Shteingart, Shimon; Gafanovich, Inna; Cotler, Scott J; D'Amato, Massimo; Pohl, Ralf T; Weiss, Gali; Ashkenazi, Yaakov J; Tichler, Thomas; Goldin, Eran; Lurie, Yoav

2015-02-01

Intravenous silibinin (SIL) is a potent antiviral agent against hepatitis C virus (HCV) genotype-1. In this proof of concept case-study we tested: (i) whether interferon-alfa (IFN)-free treatment with SIL plus ribavirin (RBV) can achieve sustained virological response (SVR); (ii) whether SIL is safe and feasible for prolonged duration of treatment and (iii) whether mathematical modelling of early on-treatment HCV kinetics can guide duration of therapy to achieve SVR. A 44 year-old female HCV-(genotype-1)-infected patient who developed severe psychiatric adverse events to a previous course of pegIFN+RBV, initiated combination treatment with 1200 mg/day of SIL, 1200 mg/day of RBV and 6000 u/day vitamin D. Blood samples were collected frequently till week 4, thereafter every 1-12 weeks until the end of therapy. The standard biphasic mathematical model with time-varying SIL effectiveness was used to predict the duration of therapy to achieve SVR. Based on modelling the observed viral kinetics during the first 3 weeks of treatment, SVR was predicted to be achieved within 34 weeks of therapy. Provided with this information, the patient agreed to complete 34 weeks of treatment. IFN-free treatment with SIL+RBV was feasible, safe and achieved SVR (week-33). We report, for the first time, the use of real-time mathematical modelling of HCV kinetics to individualize duration of IFN-free therapy and to empower a patient to participate in shared decision making regarding length of treatment. SIL-based individualized therapy provides a treatment option for patients who do not respond to or cannot receive other HCV agents and should be further validated. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Sustained virological response with intravenous silibinin: individualized IFN-free therapy via real-time modeling of HCV kinetics

PubMed Central

Dahari, Harel; Shteingart, Shimon; Gafanovich, Inna; Cotler, Scott J.; D'Amato, Massimo; Pohl, Ralf T.; Weiss, Gali; Ashkenazi, Yaakov Jack; Tichler, Thomas; Goldin, Eran; Lurie, Yoav

2014-01-01

Background & Aims Intravenous silibinin (SIL) is a potent antiviral agent against hepatitis C virus (HCV) genotype-1. In this proof of concept case-study we tested: (i) whether interferon-alfa (IFN)-free treatment with SIL plus ribavirin (RBV) can achieve sustained virological response (SVR), (ii) whether SIL is safe and feasible for prolonged duration of treatment, and (iii) whether mathematical modeling of early on-treatment HCV kinetics can guide duration of therapy to achieve SVR. Methods A 44 year-old female HCV-(genotype-1)-infected patient who developed severe psychiatric adverse events to a previous course of pegIFN+RBV, initiated combination treatment with 1200 mg/day of SIL, 1200 mg/day of RBV and 6000 u/day vitamin D. Blood samples were collected frequently till week 4, thereafter every 1 to 12 weeks until the end of therapy. The standard-biphasic-mathematical model was used to predict the duration of therapy to achieve SVR. Results Based on modeling the observed viral kinetics during the first 3 weeks of treatment, SVR was predicted to be achieved within 34 weeks of therapy. Provided with this information, the patient agreed to complete 34 weeks of treatment. IFN-free treatment with SIL+RBV was feasible, safe, and achieved SVR (week-33). Conclusions We report, for the first time, the use of real-time mathematical modeling of HCV kinetics to individualize duration of IFN-free therapy and to empower a patient to participate in shared decision making regarding length of treatment. SIL-based individualized therapy provides a treatment option for patients who do not respond to or cannot receive other HCV agents and should be further validated. PMID:25251042

Optimization of direct anti-viral agent treatment schedule: Focus on HCV genotype 3.

PubMed

Morisco, Filomena; Granata, Rocco; Camera, Silvia; Ippolito, Antonio; Milella, Michele; Conti, Fabio; Masetti, Chiara; Smedile, Antonella; Tundo, Paolo; Santantonio, Teresa; Valvano, Maria Rosa; Termite, Antonio; Gatti, Pietro; Messina, Vincenzo; Iacobellis, Angelo; Librandi, Marta; Caporaso, Nicola; Andriulli, Angelo

2018-03-01

Direct antiviral agents (DAAs) have led to high sustained virological responses (SVR) in hepatitis C virus (HCV) patients. However, genotype 3 patients respond to treatment in a suboptimal way. This study aims to identify which of the several treatment schedules recommended for genotype 3 would constitute the best option. Twenty-four Italian centers were involved in this real-life study of HCV genotype 3 patients treated with DAAs. To expand the number of cases, we conducted a systematic review of the literature on the outcome of genotype 3 patients treated with DAAs. A total of 233 patients with HCV genotype 3 were enrolled. Cirrhotic patients accounted for 83.7%. Overall, the SVR12 rate was achieved by 205 patients (88.0%); the SVR rates were 78.8% after sofosbuvir/ribavirin, 92.5% after sofosbuvir/daclatasvir ± ribavirin, and 100% after sofosbuvir/ledipasvir (seven patients). No difference in rate of SVR was observed in cirrhotic and non-cirrhotic patients (92.2 vs 94.4) using a combination regimen of NS5A and NS5B inhibitors.The systematic review of the literature provided data of 3311 patients: The mean weighted SVR12 rate was 84.4% (CI: 80.4-87.8); the rates varied from 79.0% (CI: 70.9-85.3) with sofosbuvir/ribavirin, to 83.7% (CI: 66.2-93.1) with sofosbuvir/ledispavir, and to 88.2% (CI: 83.3-91.7) with sofosbuvir/daclatasvir. Our results reinforce the concept that patients with HCV genotype 3 should no longer be considered difficult-to-treat individuals. The optimal therapeutic regimen for these patients appears to be the combination sofosbuvir/daclatasvir, administered for 12 weeks without the use of RBV in non-cirrhotic patients. In cirrhotics the meta-analytic approach suggests extending therapy to 24 weeks.

Effect of viral suppression on hepatic venous pressure gradient in hepatitis C with cirrhosis and portal hypertension.

PubMed

Afdhal, N; Everson, G T; Calleja, J L; McCaughan, G W; Bosch, J; Brainard, D M; McHutchison, J G; De-Oertel, S; An, D; Charlton, M; Reddy, K R; Asselah, T; Gane, E; Curry, M P; Forns, X

2017-10-01

Portal hypertension is a predictor of liver-related clinical events and mortality in patients with hepatitis C and cirrhosis. The effect of interferon-free hepatitis C treatment on portal pressure is unknown. Fifty patients with Child-Pugh-Turcotte (CPT) A and B cirrhosis and portal hypertension (hepatic venous pressure gradient [HVPG] >6 mm Hg) were randomized to receive 48 weeks of open-label sofosbuvir plus ribavirin at Day 1 or after a 24-week observation period. The primary endpoint was sustained virologic response 12 weeks after therapy (SVR12) in patients who received ≥1 dose of treatment. Secondary endpoints included changes in HVPG, laboratory parameters, and MELD and CPT scores. A subset of patients was followed 48 weeks posttreatment to determine late changes in HVPG. SVR12 occurred in 72% of patients (33/46). In the 37 patients with paired HVPG measurements at baseline and the end of treatment, mean HVPG decreased by -1.0 (SD 3.97) mm Hg. Nine patients (24%) had ≥20% decreases in HVPG during treatment. Among 39 patients with pretreatment HVPG ≥12 mm Hg, 27 (69%) achieved SVR12. Four of the 33 (12%) patients with baseline HVPG ≥12 mm Hg had HVPG <12 mm Hg at the end of treatment. Of nine patients with pretreatment HVPG ≥12 mm Hg who achieved SVR12 and completed 48 weeks of follow-up, eight (89%) had a ≥20% reduction in HVPG, and three reduced their pressure to <12 mm Hg. Patients with chronic HCV and compensated or decompensated cirrhosis who achieve SVR can have clinically meaningful reductions in HVPG at long-term follow-up. (EudraCT 2012-002457-29). © 2017 John Wiley & Sons Ltd.

SEMIPARAMETRIC QUANTILE REGRESSION WITH HIGH-DIMENSIONAL COVARIATES

PubMed Central

Zhu, Liping; Huang, Mian; Li, Runze

2012-01-01

This paper is concerned with quantile regression for a semiparametric regression model, in which both the conditional mean and conditional variance function of the response given the covariates admit a single-index structure. This semiparametric regression model enables us to reduce the dimension of the covariates and simultaneously retains the flexibility of nonparametric regression. Under mild conditions, we show that the simple linear quantile regression offers a consistent estimate of the index parameter vector. This is a surprising and interesting result because the single-index model is possibly misspecified under the linear quantile regression. With a root-n consistent estimate of the index vector, one may employ a local polynomial regression technique to estimate the conditional quantile function. This procedure is computationally efficient, which is very appealing in high-dimensional data analysis. We show that the resulting estimator of the quantile function performs asymptotically as efficiently as if the true value of the index vector were known. The methodologies are demonstrated through comprehensive simulation studies and an application to a real dataset. PMID:24501536

Rank-Optimized Logistic Matrix Regression toward Improved Matrix Data Classification.

PubMed

Zhang, Jianguang; Jiang, Jianmin

2018-02-01

While existing logistic regression suffers from overfitting and often fails in considering structural information, we propose a novel matrix-based logistic regression to overcome the weakness. In the proposed method, 2D matrices are directly used to learn two groups of parameter vectors along each dimension without vectorization, which allows the proposed method to fully exploit the underlying structural information embedded inside the 2D matrices. Further, we add a joint [Formula: see text]-norm on two parameter matrices, which are organized by aligning each group of parameter vectors in columns. This added co-regularization term has two roles-enhancing the effect of regularization and optimizing the rank during the learning process. With our proposed fast iterative solution, we carried out extensive experiments. The results show that in comparison to both the traditional tensor-based methods and the vector-based regression methods, our proposed solution achieves better performance for matrix data classifications.

ℓ(p)-Norm multikernel learning approach for stock market price forecasting.

PubMed

Shao, Xigao; Wu, Kun; Liao, Bifeng

2012-01-01

Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ(1)-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ(p)-norm multiple kernel support vector regression (1 ≤ p < ∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ(1)-norm multiple support vector regression model.

Eradication of hepatitis C virus and non-liver-related non-acquired immune deficiency syndrome-related events in human immunodeficiency virus/hepatitis C virus coinfection.

PubMed

Berenguer, Juan; Rodríguez-Castellano, Elena; Carrero, Ana; Von Wichmann, Miguel A; Montero, Marta; Galindo, María J; Mallolas, Josep; Crespo, Manuel; Téllez, María J; Quereda, Carmen; Sanz, José; Barros, Carlos; Tural, Cristina; Santos, Ignacio; Pulido, Federico; Guardiola, Josep M; Rubio, Rafael; Ortega, Enrique; Montes, María L; Jusdado, Juan J; Gaspar, Gabriel; Esteban, Herminia; Bellón, José M; González-García, Juan

2017-08-01

We assessed non-liver-related non-acquired immunodeficiency syndrome (AIDS)-related (NLR-NAR) events and mortality in a cohort of human immunodeficiency virus (HIV)/hepatitis C virus (HCV)-coinfected patients treated with interferon (IFN) and ribavirin (RBV), between 2000 and 2008. The censoring date was May 31, 2014. Cox regression analysis was performed to assess the adjusted hazard rate (HR) of overall death in responders and nonresponders. Fine and Gray regression analysis was conducted to determine the adjusted subhazard rate (sHR) of NLR deaths and NLR-NAR events considering death as the competing risk. The NLR-NAR events analyzed included diabetes mellitus, chronic renal failure, cardiovascular events, NLR-NAR cancer, bone events, and non-AIDS-related infections. The variables for adjustment were age, sex, past AIDS, HIV transmission category, nadir CD4 + T-cell count, antiretroviral therapy, HIV RNA, liver fibrosis, HCV genotype, and exposure to specific anti-HIV drugs. Of the 1,625 patients included, 592 (36%) had a sustained viral response (SVR). After a median 5-year follow-up, SVR was found to be associated with a significant decrease in the hazard of diabetes mellitus (sHR, 0.57; 95% confidence interval [CI], 0.35-0.93; P = 0.024) and decline in the hazard of chronic renal failure close to the threshold of significance (sHR, 0.43; 95% CI, 0.17-1.09; P = 0.075). Our data suggest that eradication of HCV in coinfected patients is associated not only with a reduction in the frequency of death, HIV progression, and liver-related events, but also with a reduced hazard of diabetes mellitus and possibly of chronic renal failure. These findings argue for the prescription of HCV therapy in coinfected patients regardless of fibrosis stage. (Hepatology 2017;66:344-356). © 2017 The Authors. Hepatology published by Wiley Periodicals, Inc., on behalf of the American Association for the Study of Liver Diseases.

The macrophage activation marker CD163 is associated with IL28B genotype and hepatic inflammation in chronic hepatitis C virus infected patients.

PubMed

Dultz, G; Gerber, L; Zeuzem, S; Sarrazin, C; Waidmann, O

2016-04-01

Recent data highlighted the association of the macrophage activation marker CD163 with histological inflammation and fibrosis in chronic hepatitis C virus (HCV) infection. The aim of this study was to investigate the influence of successful antiviral treatment and IL28B genotypes on macrophage activation reflected by CD163 levels in HCV infected patients. In a retrospective cohort study, serum sCD163 levels were correlated with results of liver histopathology, IL28B genotyping and clinical parameters in 329 patients with HCV infection, 15 healthy controls and in 161 patients who achieved a sustained virologic response after antiviral treatment. sCD163 levels were significantly higher in patients with chronic HCV infection in comparison to healthy controls (5202 vs 896 ng/mL, P < 0.001). In the multivariate logistic regression analyses, sCD163 was independently associated with histologically determined inflammation (P = 0.043) but not with fibrosis (P = 0.091). sCD163 dropped significantly after successful antiviral treatment in comparison to baseline values (5202 vs 3093 ng/mL, P < 0.001). In the univariate analyses, sCD163 was significantly associated with IL28B genotype (C/C vs C/T+T/T) with higher values in the C/C group (6098 vs 4812 ng/mL, P = 0.003). In the multivariate logistic regression model, sCD163 levels were significantly associated with IL28B genotype (P = 0.003) and sustained virologic response (SVR) (P < 0.001). Our data support the association of activated liver macrophages with hepatic necroinflammation in chronic HCV infection as sCD163 levels drop rapidly after SVR. The irresponsiveness of IL28B minor genotypes to interferon might be related to a lower level of macrophage activation in these patients. © 2015 John Wiley & Sons Ltd.

The Simple View of Reading as a Framework for National Literacy Initiatives: A Hierarchical Model of Pupil-Level and Classroom-Level Factors

ERIC Educational Resources Information Center

Savage, Robert; Burgos, Giovani; Wood, Eileen; Piquette, Noella

2015-01-01

The Simple View of Reading (SVR) describes Reading Comprehension as the product of distinct child-level variance in decoding (D) and linguistic comprehension (LC) component abilities. When used as a model for educational policy, distinct classroom-level influences of each of the components of the SVR model have been assumed, but have not yet been…

Outcomes of Hepatitis C Treatment by Primary Care Providers

PubMed Central

Arora, Sanjeev; Thornton, Karla; Murata, Glen; Deming, Paulina; Kalishman, Summers; Dion, Denise; Parish, Brooke; Burke, Thomas; Pak, Wesley; Dunkelberg, Jeffrey; Kistin, Martin; Brown, John; Jenkusky, Steven; Komaromy, Miriam; Qualls, Clifford

2013-01-01

Background The Extension for Community Healthcare Outcomes (ECHO) model was developed to improve access to care for complex health problems such as hepatitis C virus (HCV) infection for underserved populations. Using videoconferencing technology, ECHO trains primary care providers to treat complex diseases. Methods A prospective cohort study compared treatment of HCV at the University of New Mexico (UNM) HCV clinic to treatment by primary care clinicians at 21 ECHO sites in rural areas and prisons in New Mexico. A total of 407 treatment naive patients with chronic HCV were enrolled. The primary end point was a sustained viral response (SVR). Results The rate of SVR was 57.5% (84/146) for patients treated at UNM and 58.2% (152 /261) at ECHO sites (P=0.89); difference between SVR rates 0.7% (95% CI -9.2%, 10.7%). In genotype 1 infection the SVR rate was 45.8% (38 /83) at UNM and 49.7% (73 /147) at ECHO sites (P=0.57). Serious adverse events occurred in 13.7% of the UNM HCV clinic cohort and 6.9% of the ECHO cohort. Conclusions This study demonstrates that the ECHO model is an effective way to treat HCV in underserved communities. Implementation of this model would allow other states and nations to treat more patients with HCV. PMID:21631316

The Geometry of Enhancement in Multiple Regression

ERIC Educational Resources Information Center

Waller, Niels G.

2011-01-01

In linear multiple regression, "enhancement" is said to occur when R[superscript 2] = b[prime]r greater than r[prime]r, where b is a p x 1 vector of standardized regression coefficients and r is a p x 1 vector of correlations between a criterion y and a set of standardized regressors, x. When p = 1 then b [is congruent to] r and…

Examining the clinical course of genotype 1 chronic hepatitis C patients treated with the cosmos regimen: including patients with advanced liver disease and East Asian ancestry

PubMed Central

Roytman, Marina; Ramkissoon, Resham; Wu, Christina; Hong, Leena; Trujillo, Ruby; Huddleston, Leslie; Poerzgen, Peter; Seto, Todd; Wong, Linda; Tsai, Naoky

2017-01-01

Background/objectives The COSMOS study was a phase 2a clinical trial that showed high cure rates of genotype 1 chronic hepatitis C (CHC) and a favorable side effect profile using a 12-week regimen of simeprevir + sofosbuvir (SIM + SOF). Given the small number of patients treated with the SIM + SOF regimen in the COSMOS trial, there is uncertainty regarding the efficacy and safety of this combination therapy. We now report our experience with the COSMOS regimen in the multiethnic population of Hawaii, including patients of East Asian ancestry and with decompensated cirrhosis. Methods This study is a retrospective review of 138 patients treated with a fixed dose regimen of SIM 150 mg and SOF 400 mg daily at a single referral center. We collected data on demographics, side effects, laboratory studies and sustained virological response (SVR). Statistical analysis was performed with Stata v8.2 software. Results Baseline characteristics of the 138 patients initiated with SIM + SOF therapy were: 68.8 % cirrhotic (22.1 % of those Child-Pugh Class B), 37 % Asian, 11.6 % Pacific Islander, 63 % male, mean age 61.3 ± 7.8 years, mean BMI 27.8 ± 6.1 kg/m2, 26.8 % diabetic, 63.8 % genotype 1a, 44.9 % previously treatment experienced. A total of 100 % of patients that completed therapy (n = 137) had undetectable viral loads at end of treatment (EOT). Twelve patients relapsed post-treatment resulting in an overall 12 week SVR (SVR12) rate of 89.1 %. 95 % of decompensated cirrhotic patients achieved SVR12, compared to 85.3 % of compensated cirrhotic patients and 93 % of non-cirrhotic patients. 92 % of Asian patients achieved SVR12 compared to 87.5 % in non-Asian patients. There were no statistically significant differences in SVR12 between treatment naive and treatment experienced patients (86.8 vs 91.9 %). 87.5 % of post-transplant patients achieved SVR12. The main side effects were headache 16.2 %, fatigue 24.2 %, pruritis 14.1 %; none were >grade 2 in severity. There were no differences in side effect profiles of patients with decompensated cirrhosis. Pruritis only was statistically significant between Asians and non-Asians (22 vs 5.7 %). Trends toward improvement in platelet counts and total bilirubin were noted at 12-weeks post treatment, while improvement in albumin in cirrhotic patients reached statistical significance (3.77–4.01 mg/dL, p = 0.0108). Conclusions The 12-week fixed dose course of SIM + SOF was well tolerated in a multiethnic population of primarily cirrhotic patients, including those with decompensated disease. This real world trial achieved SVR12 rates comparable to the COSMOS data. Higher incidence of adverse side effects was not observed with an exception of higher rate of pruritis in Asians. The increase in albumin in cirrhotic patients was statistically significant and suggested early improvement in synthetic function following viral eradication. Higher BMI (≥30 kg/m2) was the only factor that correlated with post-treatment relapse by multivariate analysis. PMID:27026431

ℓ p-Norm Multikernel Learning Approach for Stock Market Price Forecasting

PubMed Central

Shao, Xigao; Wu, Kun; Liao, Bifeng

2012-01-01

Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ 1-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ p-norm multiple kernel support vector regression (1 ≤ p < ∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ 1-norm multiple support vector regression model. PMID:23365561

Interferon-free treatment for HCV-infected patients with decompensated cirrhosis.

PubMed

Kanda, Tatsuo

2017-01-01

Progress in interferon-free treatment against hepatitis C virus (HCV) has remained a challenge in patients with decompensated cirrhosis due to a paucity of information on efficacy and safety profiles. This review illustrates that interferon-free treatment could result in greater than 85 % sustained virological response (SVR) rates in patients with HCV genotype 1 and decompensated cirrhosis. The combination of pangenotypic HCV NS5A inhibitor velpatasvir and HCV NS5B inhibitor sofosbuvir has demonstrated high SVR rates in patients with HCV genotypes 1, 2, 3, 4 or 6 and decompensated cirrhosis. Certain patients discontinued treatment due to adverse events, death or having liver transplantation. Taken together, interferon-free treatment could produce higher SVR rates in decompensated hepatic cirrhosis. However, as adverse events were occasionally observed, liver transplantation should always be considered as well. Further improvements in treatment are called for in patients with decompensated cirrhosis.

Financial impact of two different ways of evaluating early virological response to peginterferon-alpha-2b plus ribavirin therapy in treatment-naive patients with chronic hepatitis C virus genotype 1.

PubMed

Buti, Maria; Casado, Miguel A; Fosbrook, Leslie; Esteban, Rafael

2005-01-01

Patients infected with chronic hepatitis C virus (HCV) genotype 1 are the least responsive to peginterferon (pegIFN) and ribavirin therapy. The monitoring of early virological response (EVR) is therefore an important tool for quickly identifying non-responders, permitting therapy discontinuation and avoiding adverse effects and costs. To analyse the financial impact, in treatment-naive patients infected with HCV genotype 1, of two different measurement techniques for evaluating the EVR during pegIFN-alpha-2b plus ribavirin therapy, and to compare the results of a 48-week standard course of therapy with pegIFN-alpha-2b plus ribavirin without measuring EVR. A budget impact model was constructed using a decision-tree analysis. EVR was defined as a >2 log decline in HCV RNA levels at week 12 either tested with two quantitative HCV RNA tests or undetectable HCV core antigen (HCV core Ag) protein levels at week 12 (one HCV core Ag test). Clinical data were taken from multicentre trials and costs from the published literature (euro, 2003 values). The analysis was carried out from the perspective of the Spanish healthcare system and therefore only direct costs were considered. The base-case scenario assumed that a potential study population of 18,504 people in Spain with chronic HCV genotype 1 would be eligible for treatment with pegIFN-alpha-2b plus ribavirin. In the base case, the most effective strategy was testing EVR by HCV core Ag. This resulted in 12,745 patients reaching a sustained virological response (SVR) at an overall cost of 243.98 million euro (19,142 euro per SVR). Conversely, quantitative HCV RNA testing resulted in 11,776 patients with an SVR at a cost of 232.73 million euro ( 19,763 euro per SVR). The incremental cost per successfully treated patient with HCV core Ag testing versus quantitative HCV RNA testing was 11,597 euro. One-way sensitivity analyses demonstrated that changes in the study parameters did not modify the outcomes, except when increasing the EVR or SVR of strategy 2 or when decreasing the EVR or SVR of strategy 3. This model suggests, with its underlying assumptions and data, that the assessment of EVR at week 12 by HCV core Ag testing in chronic HCV patients infected with genotype 1 permits identification of those patients expected to achieve an SVR with pegIFN-alpha-2b and ribavirin, resulting in a lower overall cost to the Spanish healthcare system than HCV RNA testing or no testing at all.

An Analytical Investigation of the Robustness and Power of ANCOVA with the Presence of Heterogeneous Regression Slopes.

ERIC Educational Resources Information Center

Hollingsworth, Holly H.

This study shows that the test statistic for Analysis of Covariance (ANCOVA) has a noncentral F-districution with noncentrality parameter equal to zero if and only if the regression planes are homogeneous and/or the vector of overall covariate means is the null vector. The effect of heterogeneous regression slope parameters is to either increase…

Ensemble of surrogates-based optimization for identifying an optimal surfactant-enhanced aquifer remediation strategy at heterogeneous DNAPL-contaminated sites

NASA Astrophysics Data System (ADS)

Jiang, Xue; Lu, Wenxi; Hou, Zeyu; Zhao, Haiqing; Na, Jin

2015-11-01

The purpose of this study was to identify an optimal surfactant-enhanced aquifer remediation (SEAR) strategy for aquifers contaminated by dense non-aqueous phase liquid (DNAPL) based on an ensemble of surrogates-based optimization technique. A saturated heterogeneous medium contaminated by nitrobenzene was selected as case study. A new kind of surrogate-based SEAR optimization employing an ensemble surrogate (ES) model together with a genetic algorithm (GA) is presented. Four methods, namely radial basis function artificial neural network (RBFANN), kriging (KRG), support vector regression (SVR), and kernel extreme learning machines (KELM), were used to create four individual surrogate models, which were then compared. The comparison enabled us to select the two most accurate models (KELM and KRG) to establish an ES model of the SEAR simulation model, and the developed ES model as well as these four stand-alone surrogate models was compared. The results showed that the average relative error of the average nitrobenzene removal rates between the ES model and the simulation model for 20 test samples was 0.8%, which is a high approximation accuracy, and which indicates that the ES model provides more accurate predictions than the stand-alone surrogate models. Then, a nonlinear optimization model was formulated for the minimum cost, and the developed ES model was embedded into this optimization model as a constrained condition. Besides, GA was used to solve the optimization model to provide the optimal SEAR strategy. The developed ensemble surrogate-optimization approach was effective in seeking a cost-effective SEAR strategy for heterogeneous DNAPL-contaminated sites. This research is expected to enrich and develop the theoretical and technical implications for the analysis of remediation strategy optimization of DNAPL-contaminated aquifers.

Ensemble of Surrogates-based Optimization for Identifying an Optimal Surfactant-enhanced Aquifer Remediation Strategy at Heterogeneous DNAPL-contaminated Sites

NASA Astrophysics Data System (ADS)

Lu, W., Sr.; Xin, X.; Luo, J.; Jiang, X.; Zhang, Y.; Zhao, Y.; Chen, M.; Hou, Z.; Ouyang, Q.

2015-12-01

The purpose of this study was to identify an optimal surfactant-enhanced aquifer remediation (SEAR) strategy for aquifers contaminated by dense non-aqueous phase liquid (DNAPL) based on an ensemble of surrogates-based optimization technique. A saturated heterogeneous medium contaminated by nitrobenzene was selected as case study. A new kind of surrogate-based SEAR optimization employing an ensemble surrogate (ES) model together with a genetic algorithm (GA) is presented. Four methods, namely radial basis function artificial neural network (RBFANN), kriging (KRG), support vector regression (SVR), and kernel extreme learning machines (KELM), were used to create four individual surrogate models, which were then compared. The comparison enabled us to select the two most accurate models (KELM and KRG) to establish an ES model of the SEAR simulation model, and the developed ES model as well as these four stand-alone surrogate models was compared. The results showed that the average relative error of the average nitrobenzene removal rates between the ES model and the simulation model for 20 test samples was 0.8%, which is a high approximation accuracy, and which indicates that the ES model provides more accurate predictions than the stand-alone surrogate models. Then, a nonlinear optimization model was formulated for the minimum cost, and the developed ES model was embedded into this optimization model as a constrained condition. Besides, GA was used to solve the optimization model to provide the optimal SEAR strategy. The developed ensemble surrogate-optimization approach was effective in seeking a cost-effective SEAR strategy for heterogeneous DNAPL-contaminated sites. This research is expected to enrich and develop the theoretical and technical implications for the analysis of remediation strategy optimization of DNAPL-contaminated aquifers.

Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation.

PubMed

Li, Xiang; Peng, Ling; Yao, Xiaojing; Cui, Shaolong; Hu, Yuan; You, Chengzeng; Chi, Tianhe

2017-12-01

Air pollutant concentration forecasting is an effective method of protecting public health by providing an early warning against harmful air pollutants. However, existing methods of air pollutant concentration prediction fail to effectively model long-term dependencies, and most neglect spatial correlations. In this paper, a novel long short-term memory neural network extended (LSTME) model that inherently considers spatiotemporal correlations is proposed for air pollutant concentration prediction. Long short-term memory (LSTM) layers were used to automatically extract inherent useful features from historical air pollutant data, and auxiliary data, including meteorological data and time stamp data, were merged into the proposed model to enhance the performance. Hourly PM 2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm) concentration data collected at 12 air quality monitoring stations in Beijing City from Jan/01/2014 to May/28/2016 were used to validate the effectiveness of the proposed LSTME model. Experiments were performed using the spatiotemporal deep learning (STDL) model, the time delay neural network (TDNN) model, the autoregressive moving average (ARMA) model, the support vector regression (SVR) model, and the traditional LSTM NN model, and a comparison of the results demonstrated that the LSTME model is superior to the other statistics-based models. Additionally, the use of auxiliary data improved model performance. For the one-hour prediction tasks, the proposed model performed well and exhibited a mean absolute percentage error (MAPE) of 11.93%. In addition, we conducted multiscale predictions over different time spans and achieved satisfactory performance, even for 13-24 h prediction tasks (MAPE = 31.47%). Copyright © 2017 Elsevier Ltd. All rights reserved.

Inferring deep-brain activity from cortical activity using functional near-infrared spectroscopy

PubMed Central

Liu, Ning; Cui, Xu; Bryant, Daniel M.; Glover, Gary H.; Reiss, Allan L.

2015-01-01

Functional near-infrared spectroscopy (fNIRS) is an increasingly popular technology for studying brain function because it is non-invasive, non-irradiating and relatively inexpensive. Further, fNIRS potentially allows measurement of hemodynamic activity with high temporal resolution (milliseconds) and in naturalistic settings. However, in comparison with other imaging modalities, namely fMRI, fNIRS has a significant drawback: limited sensitivity to hemodynamic changes in deep-brain regions. To overcome this limitation, we developed a computational method to infer deep-brain activity using fNIRS measurements of cortical activity. Using simultaneous fNIRS and fMRI, we measured brain activity in 17 participants as they completed three cognitive tasks. A support vector regression (SVR) learning algorithm was used to predict activity in twelve deep-brain regions using information from surface fNIRS measurements. We compared these predictions against actual fMRI-measured activity using Pearson’s correlation to quantify prediction performance. To provide a benchmark for comparison, we also used fMRI measurements of cortical activity to infer deep-brain activity. When using fMRI-measured activity from the entire cortex, we were able to predict deep-brain activity in the fusiform cortex with an average correlation coefficient of 0.80 and in all deep-brain regions with an average correlation coefficient of 0.67. The top 15% of predictions using fNIRS signal achieved an accuracy of 0.7. To our knowledge, this study is the first to investigate the feasibility of using cortical activity to infer deep-brain activity. This new method has the potential to extend fNIRS applications in cognitive and clinical neuroscience research. PMID:25798327

Uncertainty Quantification and Regional Sensitivity Analysis of Snow-related Parameters in the Canadian LAnd Surface Scheme (CLASS)

NASA Astrophysics Data System (ADS)

Badawy, B.; Fletcher, C. G.

2017-12-01

The parameterization of snow processes in land surface models is an important source of uncertainty in climate simulations. Quantifying the importance of snow-related parameters, and their uncertainties, may therefore lead to better understanding and quantification of uncertainty within integrated earth system models. However, quantifying the uncertainty arising from parameterized snow processes is challenging due to the high-dimensional parameter space, poor observational constraints, and parameter interaction. In this study, we investigate the sensitivity of the land simulation to uncertainty in snow microphysical parameters in the Canadian LAnd Surface Scheme (CLASS) using an uncertainty quantification (UQ) approach. A set of training cases (n=400) from CLASS is used to sample each parameter across its full range of empirical uncertainty, as determined from available observations and expert elicitation. A statistical learning model using support vector regression (SVR) is then constructed from the training data (CLASS output variables) to efficiently emulate the dynamical CLASS simulations over a much larger (n=220) set of cases. This approach is used to constrain the plausible range for each parameter using a skill score, and to identify the parameters with largest influence on the land simulation in CLASS at global and regional scales, using a random forest (RF) permutation importance algorithm. Preliminary sensitivity tests indicate that snow albedo refreshment threshold and the limiting snow depth, below which bare patches begin to appear, have the highest impact on snow output variables. The results also show a considerable reduction of the plausible ranges of the parameters values and hence reducing their uncertainty ranges, which can lead to a significant reduction of the model uncertainty. The implementation and results of this study will be presented and discussed in details.

The Italian compassionate use of sofosbuvir in HCV patients waitlisted for liver transplantation: A national real-life experience.

PubMed

Martini, Silvia; Donato, Maria Francesca; Mazzarelli, Chiara; Rendina, Maria; Visco-Comandini, Ubaldo; Filì, Daniela; Gianstefani, Alice; Fagiuoli, Stefano; Melazzini, Mario; Montilla, Simona; Pani, Luca; Petraglia, Sandra; Russo, Pierluigi; Trotta, Maria Paola; Carrai, Paola; Caraceni, Paolo

2018-04-01

This study aimed to assess the real-life clinical and virological outcomes of HCV waitlisted patients for liver transplantation (LT) who received sofosbuvir/ribavirin (SOF/R) within the Italian compassionate use program. Clinical and virological data were collected in 224 patients with decompensated cirrhosis and/or hepatocellular carcinoma (HCC) receiving daily SOF/R until LT or up a maximum of 48 weeks. Of 100 transplanted patients, 51 were HCV-RNA negative for >4 weeks before LT (SVR12: 88%) and 49 negative for <4 weeks or still viraemic at transplant: 34 patients continued treatment after LT (bridging therapy) (SVR12: 88%), while 15 stopped treatment (SVR12: 53%). 98 patients completed SOF/R without LT (SVR12: 73%). In patients with advanced decompensated cirrhosis (basal MELD ≥15 and/or C-P ≥B8), a marked improvement of the scores occurred in about 50% of cases and almost 20% of decompensated patients without HCC reached a condition suitable for inactivation and delisting. These real-life data indicate that in waitlisted patients: (i) bridging antiviral therapy can be an option for patients still viraemic or negative <4 weeks at LT; and (ii) clinical improvement to a condition suitable for delisting can occur even in patients with advanced decompensated cirrhosis. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Retreatment of patients with treatment failure of direct-acting antivirals: Focus on hepatitis C virus genotype 1b.

PubMed

Kanda, Tatsuo; Nirei, Kazushige; Matsumoto, Naoki; Higuchi, Teruhisa; Nakamura, Hitomi; Yamagami, Hiroaki; Matsuoka, Shunichi; Moriyama, Mitsuhiko

2017-12-14

The recent development of direct-acting antiviral agents (DAAs) against hepatitis C virus (HCV) infection could lead to higher sustained virological response (SVR) rates, with shorter treatment durations and fewer adverse events compared with regimens that include interferon. However, a relatively small proportion of patients cannot achieve SVR in the first treatment, including DAAs with or without peginterferon and/or ribavirin. Although retreatment with a combination of DAAs should be conducted for these patients, it is more difficult to achieve SVR when retreating these patients because of resistance-associated substitutions (RASs) or treatment-emergent substitutions. In Japan, HCV genotype 1b (GT1b) is founded in 70% of HCV-infected individuals. In this minireview, we summarize the retreatment regimens and their SVR rates for HCV GT1b. It is important to avoid drugs that target the regions targeted by initial drugs, but next-generation combinations of DAAs, such as sofosbuvir/velpatasvir/voxilaprevir for 12 wk or glecaprevir/pibrentasvir for 12 wk, are proposed to be potential solution for the HCV GT1b-infected patients with treatment failure, mainly on a basis of targeting distinctive regions. Clinicians should follow the new information and resources for DAAs and select the proper combination of DAAs for the retreatment of HCV GT1b-infected patients with treatment failure.

Spur-of-the-Moment Modification in National Treatment Policies Leads to a Surprising HCV Viral Suppression in All Treated Patients: Real-Life Egyptian Experience.

PubMed

El Kassas, Mohamed; Omran, Dalia; Elsaeed, Kadry; Alboraie, Mohamed; Elakel, Wafaa; El Tahan, Adel; Abd El Latif, Yasmeen; Nabeel, Mohamed Mahmoud; Korany, Mohamed; Ezzat, Sameera; El-Serafy, Magdy; ElShazly, Yehia; Doss, Wahid; Esmat, Gamal

2018-02-01

The aim of this study was to retrospectively analyze the outcome of an unscheduled change in national Egyptian policies for the treatment of hepatitis C virus (HCV), which was transpired as a result of a reduction in interferon supplies, and to manage patients who already started interferon-based therapy. After completing a priming 4-weeks course of sofosbuvir/pegylated interferon/ribavirin (SOF/PEG IFN/RBV), a 12-weeks course of sofosbuvir/daclatasvir (SOF/DCV) combination was initiated. We evaluated the sustained virologic response at 12 weeks posttreatment (SVR12) for 2 groups of patients; Group 1, which included patients who had the previous regimen with IFN priming, and group 2, which included the first consecutive group of patients who received SOF/DCV for 12 weeks from the start without IFN priming. All group 1 patients (1,214 patients) achieved SVR12 (100%) and this was statistically significant when compared with the overall SVR12 in group 2 [8,869 patients with sustained virologic response [SVR] of 98.9%] (P value <0.001). No serious adverse events were reported in both groups. In this real-life treatment experience, interferon-based directly acting antiviral treatment with SOF/PEG IFN/RBV as a priming for 4 weeks, followed by SOF/DCV combination for 12 weeks, led to HCV viral suppression in all treated patients.

Potential Liver Transplant Recipients with Hepatitis C: Should They Be Treated Before or After Transplantation?

PubMed

Anand, Anil C

2017-03-01

Treatment of hepatitis C virus (HCV) with newer directly acting antivirals (DAAs) and lead to sustained viral response (SVR) in majority of patients and SVR has been documented to be associated with reversal of liver cirrhosis. The improved SVR rates and safety profiles of DAAs have led to the treatment of patients with decompensated cirrhosis awaiting liver transplantation (LT). Several clinical trials of DAAs in decompensated HCV patients have recently demonstrated SVR rates above 80%, which have been associated with significant improvements, in the Child-Pugh-Turcotte scores/or model for end-stage liver disease scores in a proportion of patients. Moreover, it has been shown that HCV RNA becomes negative after 2-4 weeks of treatment, and those who are transplanted after becoming HCV RNA negative will be have very low the risk of HCV recurrence after transplantation. Some of the patients may have reached the "point of no return" and may proceed to worsening of decomposition over time. To avoid the risk of worsening, there is an additional option of treating these patients after LT should they develop recurrent HCV infection. Currently there are no guidelines as to select patients who would benefit from treatment prior to LT as opposed to those who will be better off being treated after the transplant surgery. The article discusses a possible approach for such selection.

Real-World Single-Center Experience with Sofosbuvir-Based Regimens for the Treatment of Chronic Hepatitis C Genotype 1 Patients

PubMed Central

Shin, Hyun Phil; Burman, Blaire; Kozarek, Richard A.; Zeigler, Amy; Wang, Chia; Lee, Houghton; Zehr, Troy; Edwards, Alicia M.; Siddique, Asma

2017-01-01

Background/Aims The approval of sofosbuvir (SOF), a direct-acting antiviral, has revolutionized the treatment of chronic hepatitis C virus (HCV). Methods We assessed the sustained virological response (SVR) of SOF-based regimens in a real-world single-center setting for the treatment of chronic HCV genotype 1 (G1) patients. This was a retrospective review of chronic HCV G1 adult patients treated with a SOF-based regimen at Virginia Mason Medical Center between December 2013 and August 2015. Results The cohort comprised 343 patients. Patients received SOF+ledipasvir (LDV) (n=155), SOF+simeprevir (SIM) (n=154), or SOF+peginterferon (PEG)+ribavirin (RBV) (n=34). Of the patients, 50.1% (n=172) had cirrhosis. The SVR rate was 92.2% for SOF/LDV, 87.0% for SOF/SIM, and 82.4% for SOF/PEG/RBV. Compared with the cirrhotic patients, the patients without cirrhosis had a higher SVR (96.8% vs 85.5%, p=0.01, SOF/LDV; 98.2% vs 80.6%, p=0.002, SOF/SIM; 86.4% vs 75.0%, p=0.41, SOF/PEG/RBV). In this study, prior treatment experience adversely affected the response rate in subjects treated with SOF/PEG/RBV. Conclusions In this single-center, real-world setting, the treatment of chronic HCV G1 resulted in a high rate of SVR, especially in patients without cirrhosis. PMID:28651301

Real-World Single-Center Experience with Sofosbuvir-Based Regimens for the Treatment of Chronic Hepatitis C Genotype 1 Patients.

PubMed

Shin, Hyun Phil; Burman, Blaire; Kozarek, Richard A; Zeigler, Amy; Wang, Chia; Lee, Houghton; Zehr, Troy; Edwards, Alicia M; Siddique, Asma

2017-09-15

The approval of sofosbuvir (SOF), a direct-acting antiviral, has revolutionized the treatment of chronic hepatitis C virus (HCV). We assessed the sustained virological response (SVR) of SOF-based regimens in a real-world single-center setting for the treatment of chronic HCV genotype 1 (G1) patients. This was a retrospective review of chronic HCV G1 adult patients treated with a SOF-based regimen at Virginia Mason Medical Center between December 2013 and August 2015. The cohort comprised 343 patients. Patients received SOF+ledipasvir (LDV) (n=155), SOF+simeprevir (SIM) (n=154), or SOF+peginterferon (PEG)+ribavirin (RBV) (n=34). Of the patients, 50.1% (n=172) had cirrhosis. The SVR rate was 92.2% for SOF/LDV, 87.0% for SOF/SIM, and 82.4% for SOF/PEG/RBV. Compared with the cirrhotic patients, the patients without cirrhosis had a higher SVR (96.8% vs 85.5%, p=0.01, SOF/LDV; 98.2% vs 80.6%, p=0.002, SOF/SIM; 86.4% vs 75.0%, p=0.41, SOF/PEG/RBV). In this study, prior treatment experience adversely affected the response rate in subjects treated with SOF/PEG/RBV. In this single-center, real-world setting, the treatment of chronic HCV G1 resulted in a high rate of SVR, especially in patients without cirrhosis.

Prevalence and impact of baseline resistance-associated substitutions on the efficacy of ledipasvir/sofosbuvir or simeprevir/sofosbuvir against GT1 HCV infection.

PubMed

Wang, Gary P; Terrault, Norah; Reeves, Jacqueline D; Liu, Lin; Li, Eric; Zhao, Lisa; Lim, Joseph K; Morelli, Giuseppe; Kuo, Alexander; Levitsky, Josh; Sherman, Kenneth E; Frazier, Lynn M; Ramani, Ananthakrishnan; Peter, Joy; Akuskevich, Lucy; Fried, Michael W; Nelson, David R

2018-02-16

Baseline resistance-associated substitutions (RASs) have variable impacts in clinical trials but their prevalence and impact in real-world patients remains unclear. We performed baseline resistance testing using a commercial assay (10% cutoff) for 486 patients treated with LDV/SOF or SMV/SOF, with or without ribavirin, in the multi-center, observational HCV-TARGET cohort. Linkage of RASs was evaluated in selected samples using a novel quantitative single variant sequencing assay. Our results showed that the prevalence of NS3, NS5A, NS5B RASs was 45%, 13%, and 8%, respectively, and 10% of patients harbored RASs in 2 or more drug classes. Baseline LDV RASs in GT1a, TE, and cirrhosis LDV/SOF subgroup was associated with 2-4% lower SVR12 rates. SMV RASs was associated with lower SVR12 rates in GT1a, treatment-experienced, cirrhotics SMV/SOF subgroup. Pooled analysis of all patients with baseline RASs revealed that SVR12 was 100% (19/19) in patients treated for longer than 98 days but was 87% (81/93) in patients treated for shorter than 98 days. These results demonstrate that RASs prevalence and their impact in real world practice are in general agreement with registration trials, and suggest that longer treatment duration may overcome the negative impact of baseline RASs on SVR12 rates in clinical practice.

Examining the clinical course of genotype 1 chronic hepatitis C patients treated with the cosmos regimen: including patients with advanced liver disease and East Asian ancestry.

PubMed

Roytman, Marina; Ramkissoon, Resham; Wu, Christina; Hong, Leena; Trujillo, Ruby; Huddleston, Leslie; Poerzgen, Peter; Seto, Todd; Wong, Linda; Tsai, Naoky

2016-07-01

The COSMOS study was a phase 2a clinical trial that showed high cure rates of genotype 1 chronic hepatitis C (CHC) and a favorable side effect profile using a 12-week regimen of simeprevir + sofosbuvir (SIM + SOF). Given the small number of patients treated with the SIM + SOF regimen in the COSMOS trial, there is uncertainty regarding the efficacy and safety of this combination therapy. We now report our experience with the COSMOS regimen in the multiethnic population of Hawaii, including patients of East Asian ancestry and with decompensated cirrhosis. This study is a retrospective review of 138 patients treated with a fixed dose regimen of SIM 150 mg and SOF 400 mg daily at a single referral center. We collected data on demographics, side effects, laboratory studies and sustained virological response (SVR). Statistical analysis was performed with Stata v8.2 software. Baseline characteristics of the 138 patients initiated with SIM + SOF therapy were: 68.8 % cirrhotic (22.1 % of those Child-Pugh Class B), 37 % Asian, 11.6 % Pacific Islander, 63 % male, mean age 61.3 ± 7.8 years, mean BMI 27.8 ± 6.1 kg/m(2), 26.8 % diabetic, 63.8 % genotype 1a, 44.9 % previously treatment experienced. A total of 100 % of patients that completed therapy (n = 137) had undetectable viral loads at end of treatment (EOT). Twelve patients relapsed post-treatment resulting in an overall 12 week SVR (SVR12) rate of 89.1 %. 95 % of decompensated cirrhotic patients achieved SVR12, compared to 85.3 % of compensated cirrhotic patients and 93 % of non-cirrhotic patients. 92 % of Asian patients achieved SVR12 compared to 87.5 % in non-Asian patients. There were no statistically significant differences in SVR12 between treatment naive and treatment experienced patients (86.8 vs 91.9 %). 87.5 % of post-transplant patients achieved SVR12. The main side effects were headache 16.2 %, fatigue 24.2 %, pruritis 14.1 %; none were >grade 2 in severity. There were no differences in side effect profiles of patients with decompensated cirrhosis. Pruritis only was statistically significant between Asians and non-Asians (22 vs 5.7 %). Trends toward improvement in platelet counts and total bilirubin were noted at 12-weeks post treatment, while improvement in albumin in cirrhotic patients reached statistical significance (3.77-4.01 mg/dL, p = 0.0108). The 12-week fixed dose course of SIM + SOF was well tolerated in a multiethnic population of primarily cirrhotic patients, including those with decompensated disease. This real world trial achieved SVR12 rates comparable to the COSMOS data. Higher incidence of adverse side effects was not observed with an exception of higher rate of pruritis in Asians. The increase in albumin in cirrhotic patients was statistically significant and suggested early improvement in synthetic function following viral eradication. Higher BMI (≥30 kg/m(2)) was the only factor that correlated with post-treatment relapse by multivariate analysis.

Moderate Sustained Virologic Response Rates With 6-Week Combination Directly Acting Anti-Hepatitis C Virus Therapy in Patients With Advanced Liver Disease.

PubMed

Kattakuzhy, Sarah; Wilson, Eleanor; Sidharthan, Sreetha; Sims, Zayani; McLaughlin, Mary; Price, Angie; Silk, Rachel; Gross, Chloe; Akoth, Elizabeth; McManus, Maryellen; Emmanuel, Benjamin; Shrivastava, Shikha; Tang, Lydia; Nelson, Amy; Teferi, Gebeyehu; Chavez, Jose; Lam, Brian; Mo, Hongmei; Osinusi, Anuoluwapo; Polis, Michael A; Masur, Henry; Kohli, Anita; Kottilil, Shyamasundaran

2016-02-15

Treatment of genotype 1 hepatitis C virus (HCV) infection with combination directly acting antivirals (DAA) for 8-24 weeks is associated with high rates of sustained virologic response (SVR). We previously demonstrated that adding a third DAA to ledipasvir and sofosbuvir (LDV/SOF) can result in high SVR rates in patients without cirrhosis. In this study, we investigated whether a similar regimen would yield equivalent rates of cure in patients with advanced liver fibrosis. Fifty patients were enrolled at the Clinical Research Center of the National Institutes of Health and associated healthcare centers. Enrollment and follow-up data from April 2014 to June 2015 are reported here. Eligible participants were aged ≥18 years, had chronic HCV genotype 1 infection (serum HCV RNA ≥2000 IU/mL), and stage 3-4 liver fibrosis. HCV RNA was measured using a reverse-transcription polymerase chain reaction assay. Of patients treated with LDV, SOF, and the NS3/4A protease inhibitor GS-9451 for 6 weeks, 76% (38 of 50; 95% confidence interval, 60%-85%) had SVR achieved 12 weeks after the end of treatment. There was no statistically significant difference in treatment efficacy between treatment-naive patients (72%, 18 of 25) and those with treatment experience (80%; 20 of 25) (P = .51). Overall, 11 patients (22%) experienced virologic relapse, and 1 (2%) was lost to follow-up at 4 weeks after treatment. No serious adverse events, discontinuations, or deaths were associated with this regimen. Adding a third DAA to LDV/SOF may result in a moderate SVR rate, lower than that observed in patients without cirrhosis. Significant liver fibrosis remains an impediment to achieving SVR with short-duration DAA therapy. CT01805882. Published by Oxford University Press for the Infectious Diseases Society of America 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Functional pathway analysis of genes associated with response to treatment for chronic hepatitis C.

PubMed

Birerdinc, A; Afendy, A; Stepanova, M; Younossi, I; Manyam, G; Baranova, A; Younossi, Z M

2010-10-01

Chronic hepatitis C (CH-C) is among the most common causes of chronic liver disease. Approximately 50% of patients with CH-C treated with pegylated interferon-α and ribavirin (PEG-IFN-α + RBV) achieve a sustained virological response (SVR). Several factors such as genotype 1, African American (AA) race, obesity and the absence of an early virological response (EVR) are associated with low SVR. This study elucidates molecular pathways deregulated in patients with CH-C with negative predictors of response to antiviral therapy. Sixty-eight patients with CH-C who underwent a full course of treatment with PEG-IFN-α + RBV were included in the study. Pretreatment blood samples were collected in PAXgene™ RNA tubes. EVR, complete EVR (cEVR), and SVR rates were 76%, 57% and 41%, respectively. Total RNA was extracted from pretreatment peripheral blood mononuclear cells, quantified and used for one-step RT-PCR to profile 154 mRNAs. The expression of mRNAs was normalized with six 'housekeeping' genes. Differentially expressed genes were separated into up and downregulated gene lists according to the presence or absence of a risk factor and subjected to KEGG Pathway Painter which allows high-throughput visualization of the pathway-specific changes in expression profiles. The genes were consolidated into the networks associated with known predictors of response. Before treatment, various genes associated with core components of the JAK/STAT pathway were activated in the cohorts least likely to achieve SVR. Genes related to focal adhesion and TGF-β pathways were activated in some patients with negative predictors of response. Pathway-centred analysis of gene expression profiles from treated patients with CH-C points to the Janus kinase-signal transducers and activators of transcription signalling cascade as the major pathogenetic component responsible for not achieving SVR. In addition, focal adhesion and TGF-β pathways are associated with some predictors of response. © 2009 Blackwell Publishing Ltd.

Week 4 response predicts sustained virological response to all-oral direct-acting antiviral-based therapy in cirrhotic patients with hepatitis C virus genotype 3 infection.

PubMed

Pineda, J A; Morano-Amado, L E; Granados, R; Macías, J; Téllez, F; García-Deltoro, M; Ríos, M J; Collado, A; Delgado-Fernández, M; Suárez-Santamaría, M; Serrano, M; Miralles-Álvarez, C; Neukam, K

2017-06-01

The aim of this study was to determine the predictive capacity of response at treatment week (TW) 4 for the achievement of sustained virological response 12 weeks after the scheduled end of therapy date (SVR 12 ) to treatment against hepatitis C virus (HCV) genotype 3 (GT3) infection with all-oral direct-acting antiviral (DAA) -based regimens. From a prospective multicohort study, HCV GT3-infected patients who completed a course of currently recommended DAA-based therapy at 33 Spanish hospitals and who had reached the SVR 12 evaluation time-point were selected. TW4 HCV-RNA levels were categorized as target-not-detected (TND), below the lower limit of quantification (LLOQ TD ) and ≥LLOQ. A total of 123 patients were included, 86 (70%) received sofosbuvir/ daclatasvir±ribavirin, 27 (22%) received sofosbuvir/ ledipasvir/ ribavirin and 10 (8.1%) received sofosbuvir/ ribavirin, respectively. In all, 114 (92.7%) of the 123 patients presented SVR 12 in an on-treatment approach, but nine (7.3%) patients relapsed, all of them had presented cirrhosis at baseline. In those who achieved TND, LLOQ TD and ≥LLOQ, SVR 12 was observed in 81/83 (98%; 95% CI 91.5%-99.7%), 24/28 (85.7%; 95% CI 67.3%-96%) and 9/12 (75%; 95% CI 42.8%-94.5%), respectively; p (linear association) 0.001. Corresponding numbers for subjects with cirrhosis were: 52/54 (96.3%; 95% CI 87.3%-95.5%), 14/18 (77.8%; 95% CI 52.4%-93.6%) and 7/10 (70%; 95% CI 34.8%-93.3%); p 0.004. TW4-response indicates the probability of achieving SVR 12 to currently used DAA-based therapy in HCV genotype 3-infected individuals with cirrhosis. This finding may be useful to tailor treatment strategy in this setting. Copyright © 2017 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

Association between Leptin and Complement in Hepatitis C Patients with Viral Clearance: Homeostasis of Metabolism and Immunity.

PubMed

Chang, Ming-Ling; Kuo, Chia-Jung; Huang, Hsin-Chih; Chu, Yin-Yi; Chiu, Cheng-Tang

2016-01-01

The association between leptin and complement in hepatitis C virus (HCV) infection remains unknown. A prospective study was conducted including 474 (250 genotype 1, 224 genotype 2) consecutive chronic hepatitis C (CHC) patients who had completed an anti-HCV therapy course and undergone pre-therapy and 24-week post-therapy assessments of interferon λ3-rs12979860 and HCV RNA/genotypes, anthropometric measurements, metabolic and liver profiles, and complement component 3 (C3), C4, and leptin levels. Of the 474 patients, 395 had a sustained virological response (SVR). Pre-therapy leptin levels did not differ between patients with and without an SVR. Univariate and multivariate analyses showed that sex (pre- and post-therapy, p<0.001), body mass index (BMI) (pre- and post-therapy, p<0.001), and C3 levels (pre-therapy, p = 0.027; post-therapy, p = 0.02) were independently associated with leptin levels with or without HCV infection. Pre-therapy BMI, total cholesterol (TC), C4 levels, and the rs12979860 genotype were independently associated with pre-therapy C3 levels in all patients. Post-therapy BMI, alanine aminotransferase, TC, C4 levels, white blood cell counts, and hepatic steatosis were independently associated with the post-therapy C3 levels of SVR patients. Compared with pre-therapy levels, SVR patients showed higher 24-week post-therapy C4 (20.32+/-7.30 vs. 21.55+/-7.07 mg/dL, p<0.001) and TC (171.68+/-32.67 vs. 186.97+/-36.09 mg/dL, p<0.001) levels; however, leptin and C3 levels remained unchanged after therapy in patients with and without an SVR. Leptin and C3 may maintain immune and metabolic homeostasis through association with C4 and TC. Positive alterations in C4 and TC levels reflect viral clearance after therapy in CHC patients.

Real-World Effectiveness of Elbasvir/Grazoprevir in HCV-Infected Patients in the US Veterans Affairs Healthcare System.

PubMed

Kramer, Jennifer R; Puenpatom, Amy; Erickson, Kevin; Cao, Yumei; Smith, Donna; El-Serag, Hashem; Kanwal, Fasiha

2018-05-31

Elbasvir/grazoprevir (EBR/GZR) is an all-oral direct-acting antiviral agent (DAA) with high sustained virologic response (SVR) in clinical trials. This study's primary objective was to evaluate effectiveness of EBR/GZR among HCV-infected patients in a real-world clinical setting. We conducted a nationwide retrospective observational cohort study of HCV-infected patients in the US Department of Veterans Affairs (VA) using the VA Corporate Data Warehouse. The study population included patients with positive HCV RNA who initiated EBR/GZR from February 1 to August 1, 2016. We calculated the 95% confidence interval for binomial proportions for SVR overall and by demographic subgroups. Clinical and demographic characteristics were also evaluated. We included 2,436 patients in the study cohort. Most were male (96.5%), African-American (57.5%), with mean age of 63.5 (SD=5.9), and 95.4% infected with genotype (GT) 1 [GT1a (34.7%), GT1b (58.6%)]. Other comorbidities included diabetes (53.2%), depression (57.2%), and HIV (3.0%). More than 50% had history of drug or alcohol abuse (53.9% and 60.5%, respectively). 33.2% of the cohort had cirrhosis. A total of 95.6% (2,328/2,436; 95% CI: 94.7%-96.4%) achieved SVR. The SVR rates by subgroups were: male, 95.5% (2245/2350); female, 96.5% (83/86); GT1a, 93.4%, GT1b, 96.6%, GT4, 96.9%, African-American, 95.9% (1,342/1,400); treatment-experienced, 96.3% (310/322); cirrhosis, 95.6% (732/766); stage 4-5 CKD, 96.3% (392/407); and HIV, 98.6% (73/74). SVR rates were high overall and across patient subgroups regardless of gender, race/ethnicity, cirrhosis, renal impairment, or HIV. This study provided important data regarding the effectiveness of EBR/GZR in a large clinical setting. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Estimating the clinical and economic benefit associated with incremental improvements in sustained virologic response in chronic hepatitis C.

PubMed

McEwan, Phil; Ward, Thomas; Bennett, Hayley; Kalsekar, Anupama; Webster, Samantha; Brenner, Michael; Yuan, Yong

2015-01-01

Hepatitis C virus (HCV) infection is one of the principle causes of chronic liver disease. Successful treatment significantly decreases the risk of hepatic morbidity and mortality. Current standard of care achieves sustained virologic response (SVR) rates of 40-80%; however, the HCV therapy landscape is rapidly evolving. The objective of this study was to quantify the clinical and economic benefit associated with increasing levels of SVR. A published Markov model (MONARCH) that simulates the natural history of hepatitis C over a lifetime horizon was used. Discounted and non-discounted life-years (LYs), quality-adjusted life-years (QALYs) and cost of complication management were estimated for various plausible SVR rates. To demonstrate the robustness of projections obtained, the model was validated to ten UK-specific HCV studies. QALY estimates ranged from 18.0 years for those treated successfully in fibrosis stage F0 to 7.5 years (discounted) for patients in fibrosis stage F4 who remain untreated. Predicted QALY gains per 10% improvement in SVR ranged from 0.23 (F0) to 0.64 (F4) and 0.58 (F0) to 1.35 (F4) in 40 year old patients (discounted and non-discounted results respectively). In those aged 40, projected discounted HCV-related costs are minimised with successful treatment in F0/F1 (at approximately £ 300), increasing to £ 49,300 in F4 patients who remain untreated. Validation of the model to published UK cost-effectiveness studies produce R2 goodness of fit statistics of 0.988, 0.978 and of 0.973 for total costs, QALYs and incremental cost effectiveness ratios, respectively. Projecting the long-term clinical and economic consequences associated with chronic hepatitis C is a necessary requirement for the evaluation of new treatments. The principle analysis demonstrates the significant impact on expected costs, LYs and QALYs associated with increasing SVR. A validation analysis demonstrated the robustness of the results reported.

Sustained virological response with intravenous silibinin: individualized IFN-free therapy via real-time modelling of HCV kinetics

DOE PAGES

Dahari, Harel; Shteingart, Shimon; Gafanovich, Inna; ...

2014-10-10

Providing here our aims and project background, we note that intravenous silibinin (SIL) is a potent antiviral agent against hepatitis C virus (HCV) genotype-1. In this proof of concept case-study we tested: (i) whether interferon-alfa (IFN)-free treatment with SIL plus ribavirin (RBV) can achieve sustained virological response (SVR); (ii) whether SIL is safe and feasible for prolonged duration of treatment and (iii) whether mathematical modelling of early on-treatment HCV kinetics can guide duration of therapy to achieve SVR. With our method, a 44 year-old female HCV-(genotype-1)-infected patient who developed severe psychiatric adverse events to a previous course of pegIFN+RBV, initiatedmore » combination treatment with 1200 mg/day of SIL, 1200 mg/day of RBV and 6000 u/day vitamin D. Blood samples were collected frequently till week 4, thereafter every 1-12 weeks until the end of therapy. The standard biphasic mathematical model with time-varying SIL effectiveness was used to predict the duration of therapy to achieve SVR. Our results show that, based on modelling the observed viral kinetics during the first 3 weeks of treatment, SVR was predicted to be achieved within 34 weeks of therapy. Provided with this information, the patient agreed to complete 34 weeks of treatment. IFN-free treatment with SIL+RBV was feasible, safe and achieved SVR (week-33). In conclusion, we report, for the first time, the use of real-time mathematical modelling of HCV kinetics to individualize duration of IFN-free therapy and to empower a patient to participate in shared decision making regarding length of treatment. SIL-based individualized therapy provides a treatment option for patients who do not respond to or cannot receive other HCV agents and should be further validated.« less

Sustained virological response with intravenous silibinin: individualized IFN-free therapy via real-time modelling of HCV kinetics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dahari, Harel; Shteingart, Shimon; Gafanovich, Inna

Providing here our aims and project background, we note that intravenous silibinin (SIL) is a potent antiviral agent against hepatitis C virus (HCV) genotype-1. In this proof of concept case-study we tested: (i) whether interferon-alfa (IFN)-free treatment with SIL plus ribavirin (RBV) can achieve sustained virological response (SVR); (ii) whether SIL is safe and feasible for prolonged duration of treatment and (iii) whether mathematical modelling of early on-treatment HCV kinetics can guide duration of therapy to achieve SVR. With our method, a 44 year-old female HCV-(genotype-1)-infected patient who developed severe psychiatric adverse events to a previous course of pegIFN+RBV, initiatedmore » combination treatment with 1200 mg/day of SIL, 1200 mg/day of RBV and 6000 u/day vitamin D. Blood samples were collected frequently till week 4, thereafter every 1-12 weeks until the end of therapy. The standard biphasic mathematical model with time-varying SIL effectiveness was used to predict the duration of therapy to achieve SVR. Our results show that, based on modelling the observed viral kinetics during the first 3 weeks of treatment, SVR was predicted to be achieved within 34 weeks of therapy. Provided with this information, the patient agreed to complete 34 weeks of treatment. IFN-free treatment with SIL+RBV was feasible, safe and achieved SVR (week-33). In conclusion, we report, for the first time, the use of real-time mathematical modelling of HCV kinetics to individualize duration of IFN-free therapy and to empower a patient to participate in shared decision making regarding length of treatment. SIL-based individualized therapy provides a treatment option for patients who do not respond to or cannot receive other HCV agents and should be further validated.« less

Sofosbuvir and ribavirin for genotype 2 HCV infected patients with cirrhosis: A real life experience.

PubMed

Mangia, Alessandra; Susser, Simone; Piazzolla, Valeria; Agostinacchio, Ernesto; De Stefano, Giulio; Palmieri, Vincenzo; Spinzi, Giancarlo; Carraturo, Immacolata; Potenza, Domenico; Losappio, Ruggero; Arleo, Andrea; Miscio, Maria; Santoro, Rosanna; Sarrazin, Christoph; Copetti, Massimiliano

2017-04-01

Sofosbuvir (SOF) and weight-based ribarivin (RBV) represented until recently the standard of care in hepatitis C virus (HCV) genotype (GT)2 patients. In registration studies 12-16weeks duration were associated with a 90% sustained virological response at 12weeks (SVR12). Real life cohorts showed lower SVR12 rates. SVR12 rates attained in an Italian real life cohort and possible benefits of a duration extended up to 20weeks was investigated in HCV GT2 patients with cirrhosis. The role of 2k/1b chimeras as potential predictor of treatment failure was also analysed. Overall, 291 HCV GT2 infected patients with bridging fibrosis or cirrhosis were evaluated. Median age was 68years (18-87); 163 were treatment naïve. Of 168 cirrhotic patients, 149 had Child-Pugh score A and 19 B, 50 platelets count <100,000/mm 3 and 62 albumin <3.5g/dl. SVR12 were 95.53% overall, with 99.15% in non-cirrhotic patients and 93.06% in cirrhotic patients. In patients who completed treatment, SVR rates for cirrhotic patients resulted in 94.51%, and 94.94% after 16 or 20weeks respectively. Predictors of SVR were low platelet count and esophageal varices (OR 7.2; 95% CI 1.67-31.25; p=0.0022 and OR 0.1; 95% CI 0.01-0.72; p=0.0079, respectively). Anemia was mild in 12.4%, moderate in 3.4%, and severe in 2.4% of cases. Anemia was slightly more frequent among longer duration but not associated with treatment discontinuations. No 2k/1b strains or genotypes different from those at baseline were identified at relapse. In GT2 cirrhotic patients, SOF/RBV for 16 or 20weeks is associated with real life SVR12 rates of 95%. A duration of treatment of 16-20weeks was recommended for treatment of HCV GT2 patients using the combination of sofosbuvir and ribavirin. Real life experiences, where patients received 12weeks of treatment regardless of the severity of liver disease, suggested that response rates are lower than expected, in particular in patients with liver cirrhosis. A misleading genotyping of a 2k/1b strain as GT2 was also hypothesized as a further explanation for less effectiveness. We demonstrated that using the recommended extended duration in patients with more severe disease 95% of patients with severe liver disease including cirrhosis can be cured and that 2k/1b strain plays only a secondary role in specific countries like Germany. Although this combination has been recently replaced by sofosbuvir and velpatasvir fixed dose combination as the standard of care for treating HCV GT2 patients, our findings may inform physicians from countries where the new regimen is not yet available. Copyright © 2016 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.

Acute Cardiovascular Response during Resistance Exercise with Whole-body Vibration in Sedentary Subjects: A Randomized Cross-over Trial.

PubMed

Dias, Thaisa; Polito, Marcos

2015-01-01

This study aimed to compare the acute cardiovascular responses during and after resistance exercise with and without whole-body vibration. Nineteen sedentary adults randomly performed one session of isometric squats without vibration and the same exercise with vibration. Systolic (SBP) and diastolic blood pressure (DBP), heart rate (HR), stroke volume (SV), cardiac output (CO) and systemic vascular resistance (SVR) were measured. SBP, DBP and HR were also measured for 20 min after the sessions. The exercise with vibration demonstrated significant values ​​(P < 0.05) for SBP (second to sixth sets), DBP (third to sixth sets) and SVR (second to sixth sets) compared with the exercise without vibration. After the sessions, the values ​​of SBP for both exercises were significantly lower than the respective resting values; with no difference between the sessions. In conclusion, exercise with vibration caused increases in SBP, DBP and SVR compared with exercise with no vibration in sedentary adults.

Hepatitis C treatment outcomes using interferon- and ribavirin-based therapy in Kigali, Rwanda.

PubMed

Riedel, David J; Taylor, Simone; Simango, Raulina; Kiromera, Athanase; Sebeza, Jackson; Baribwira, Cyprien; Musabeyezu, Emmanuel

2016-08-01

Hepatitis C virus (HCV) treatment data in sub-Saharan Africa are limited. This study was to determine HCV sustained virologic response(SVR) at 24 weeks in patients undergoing HCV therapy in Kigali, Rwanda. The paper presents data for all patients treated for HCV with ribavirin/interferon at King Faisal Hospital in Kigali, Rwanda, from 1 January 2007 to 31 December 2014. There were 69 evaluable patients. HCV genotype 4(61%, 42/69) predominated. 24-week SVR was 70%(26/37) by per-protocol and 32%(26/69) by intention-to-treat analysis. HCV treatment in Rwanda is feasible. SVR with interferon/ribavirin was acceptable in the per-protocol analysis. Transition to newer direct acting antivirals is urgently needed in Rwanda and sub-Saharan Africa more generally to improve treatment outcomes. © The Author 2016. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Sofosbuvir and ABT-450: terminator of hepatitis C virus?

PubMed

Zeng, Qing-Lei; Zhang, Ji-Yuan; Zhang, Zheng; Wang, Li-Feng; Wang, Fu-Sheng

2013-06-07

Combination therapy with peginterferon (pegIFN)-α and ribavirin (RBV) has been the standard of care (SOC) for chronic hepatitis C. Unfortunately, not all patients can achieve a sustained virologic response (SVR) with this regimen. SVR rates are approximately 80% in patients with hepatitis C virus (HCV) genotype 2, 3, 5 and 6 and 40%-50% in patients with genotype 1 and 4. Therefore, strategies to improve SVR rates have been an important issue for clinical physicians. Several direct acting antiviral agents (DAAs) have significantly higher SVR rates when combined with pegIFN-α and RBV than pegIFN-α and RBV alone. Treatments containing DAAs have several advantages over the previous SOC, including higher specificity and efficacy, shorter treatment durations, fewer side effects, and oral administration. Based on these advantages, treatment with pegIFN-α and RBV plus telaprevir or boceprevir has become the current SOC for patients with genotype 1 HCV infection. However, many patients are either not eligible for therapy or decline treatment due to coexisting relative or absolute contraindications as well as an inability to tolerate the hematological side effects and adverse events caused by the new SOC. These factors have contributed to the advent of pegIFN-α-free regimens. The newest therapeutic regimens containing sofosbuvir and ABT-450 have shown promising results. In this review, we summarize the development of anti-HCV agents and the clinical efficacy of sofosbuvir and ABT-450-based therapies as well as the potential for future HCV studies.

Safety and Efficacy of Pegylated Interferon Lambda, Ribavirin, and Daclatasvir in HCV and HIV-Coinfected Patients.

PubMed

Nelson, Mark; Rubio, Rafael; Lazzarin, Adriano; Romanova, Svetlana; Luetkemeyer, Annie; Conway, Brian; Molina, Jean-Michel; Xu, Dong; Srinivasan, Subasree; Portsmouth, Simon

2017-03-01

To evaluate the efficacy and safety of pegylated interferon-lambda-1a (Lambda)/ribavirin (RBV)/daclatasvir (DCV) for treatment of patients coinfected with chronic hepatitis C virus (HCV) and human immunodeficiency virus (HIV). Treatment-naive patients were assigned to cohort A [HCV genotype (GT)-2 or -3] or cohort B [HCV GT-1(a or b) or -4]. All patients received Lambda/RBV/DCV for the first 12 weeks; cohort A received Lambda/RBV for an additional 12 weeks, followed by 24 weeks of follow-up, and cohort B received response-guided therapy. The primary endpoint was the proportion of patients who achieved a sustained virologic response at post-treatment week 12 (SVR12). In cohort A (n = 104), 84.6% achieved SVR12 (95.0% in GT-2; 83.1% in GT-3). In cohort B (n = 196), 76.0% achieved SVR12 (71.7% in GT-1a; 86.0% in GT-1b; 70.7% in GT-4). Rates of discontinuation due to adverse events (AEs) (3.8% and 6.1%) and serious AEs (5.8% and 6.1%) were low in cohorts A and B, respectively. In addition, treatment with Lambda/RBV/DCV had little impact on CD4 counts. SVR12 rates with Lambda/RBV/DCV in an HCV/HIV-coinfected population ranged from 71.7% to 95.0%. Treatment was generally well tolerated, with a low proportion of patients discontinuing due to AEs. Clinical trial registration NCT01866930.

Effect of discounting on estimation of benefits determined by hepatitis C treatment.

PubMed

Messori, Andrea; Fadda, Valeria; Maratea, Dario; Trippoli, Sabrina

2012-06-21

The combination of either boceprevir or telaprevir with ribavirin and interferon (triple therapy) has been shown to be more effective than ribavirin+interferon (dual therapy) for the treatment of genotype 1 hepatitis C. Since the benefit of these treatments takes place after years, simulation models are needed to predict long-term outcomes. In simulation models, the choice of different values of yearly discount rates (e.g., 6%, 3.5%, 2%, 1.5% or 0%) influences the results, but no studies have specifically addressed this issue. We examined this point by determining the long-term benefits under different conditions on the basis of standard modelling and using quality-adjusted life years (QALYs) to quantify the benefits. In our base case scenario, we compared the long-term benefit between patients given a treatment with a 40% sustained virologic response (SVR) (dual therapy) and patients given a treatment with a 70% SVR (triple therapy), and we then examined how these specific yearly discount rates influenced the incremental benefit. The gain between a 70% SVR and a 40% SVR decreased from 0.45 QALYs with a 0% discount rate to 0.22 QALYs with a 6% discount rate (ratio between the two values = 2.04). Testing the other discounting assumptions confirmed that the discount rate has a marked impact on the magnitude of the model-estimated incremental benefit. In conclusion, the results of our analysis can be helpful to better interpret cost-effectiveness studies evaluating new treatment for hepatitis C.

A 2015 roadmap for the management of hepatitis C virus infections in Asia.

PubMed

Lim, Seng Gee; Dan, Yock Young

2015-07-01

The prevalence of hepatitis C virus (HCV) in Asia is 0.5% to 4.7%, with three different genotypes predominating, depending on the geographic region: genotype 1b in East Asia, genotype 3 in South and Southeast Asia, and genotype 6 in Indochina. Official approval for direct-acting antiviral agents (DAAs) in Asia lags significantly behind that in the West, such that in most countries the mainstay of therapy is still pegylated interferon and ribavirin (PR). Because the interleukin-28B genetic variant, associated with a high sustained virologic response (SVR), is common in Asians, this treatment is still acceptable in Asian patients with HCV infections. A roadmap for HCV therapy that starts with PR and takes into account those DAAs already approved in some Asian countries can provide guidance as to the best strategies for management, particularly of genotype 1 and 3 infections, based on SVR rates. Sofosbuvir and PR are likely to be the initial therapies for genotype 1 and 3 disease, although in the former these drugs may be suboptimal in patients with cirrhosis (62% SVR) and the extension of treatment to 24 weeks may be required. For difficult to treat genotype 3 infections in treatment-experienced patients with cirrhosis, a combination of sofosbuvir and PR result in an 83% SVR and is, therefore, currently the optimal treatment regimen. Treatment failure is best avoided since data on rescue therapies for DAA failure are still incomplete.

Association study of IL28B: rs12979860 and rs8099917 polymorphisms with SVR in patients infected with chronic HCV genotype 1 to PEG-INF/RBV therapy using systematic meta-analysis.

PubMed

Luo, Yueqiu; Jin, Caixia; Ling, Zongxin; Mou, Xiaozhou; Zhang, Qiong; Xiang, Charlie

2013-01-25

Recently, genome-wide associated studies (GWAS) have identified that host genetics IL28B SNPs rs12979860 and rs8099917 were significantly associated with SVR in patients infected with chronic HCV genotype 1 to PEG-INF/RBV therapy. Results from these studies remain conflicting. We conducted this meta-analysis to estimate the overall association of SVR with rs12979860 and rs8099917. We searched the PubMed, Embase, Scholar Google, ISI Web of Knowledge, and Chinese National Knowledge Infrastructure (CNKI) databases for all articles before July 30, 2012. The odds ratio (OR) corresponding to the 95% confidence interval (CI) was used to assess the association. The statistical heterogeneity among studies was assessed with the I(2) statistics. Begg's test and Egger's test were performed to evaluate the publication bias. Eventually, twenty studies were selected for the meta-analysis. The IL-28B SNPs rs12979860 genotype CC and rs8099917 genotype TT significantly positive associated with SVR in patients infected chronic HCV genotype 1 to PEG-INF/RBV therapy (OR=4.473, 95% CI=3.814-5.246, OR=5.171, 95% CI=4.372-6.117 respectively). The results suggested that rs12979860 genotype CC and rs8099917 genotype TT could be used as independent predictors of the HCV-1 infected patients. Copyright © 2012 Elsevier B.V. All rights reserved.

Effects of a 24-week course of interferon-α therapy after curative treatment of hepatitis C virus-associated hepatocellular carcinoma

PubMed Central

Jeong, Soo Cheol; Aikata, Hiroshi; Katamura, Yoshio; Azakami, Takahiro; Kawaoka, Tomokazu; Saneto, Hiromi; Uka, Kiminori; Mori, Nami; Takaki, Shintaro; Kodama, Hideaki; Waki, Koji; Imamura, Michio; Shirakawa, Hiroo; Kawakami, Yoshiiku; Takahashi, Shoichi; Chayama, Kazuaki

2007-01-01

AIM: To assess whether a 24-wk course of interferon (IFN) could prevent hepatocellular carcinoma (HCC) recurrence and worsening of liver function in patients with hepatitis C virus (HCV)-infected patients after receiving curative treatment for primary HCC. METHODS: Outcomes in 42 patients with HCV infection treated with IFN-α, after curative treatment for primary HCC (IFN group), were compared with 42 matched curatively treated historical controls not given IFN (non-IFN group). RESULTS: Although the rate of initial recurrence did not differ significantly between IFN group and non-IFN group (0%, 44%, 61%, and 67% vs 4.8%, 53%, 81%, and 87% at 1, 3, 5, and 7 years, P = 0.153, respectively), IFN group showed a lower rate than the non-IFN group for second recurrence (0%, 10.4%, 28%, and 35% vs 0%, 30%, 59%, and 66% at 1, 3, 5 and 7 years, P = 0.022, respectively). Among the IFN group, patients with sustained virologic response (SVR) were less likely to have a second HCC recurrence than IFN patients without an SVR, or non-IFN patients. Multivariate analysis identified the lack of SVR as the only independent risk factor for a second recurrence, while SVR and Child-Pugh class A independently favored overall survival. CONCLUSION: Most intrahepatic recurrences of HCV-related HCC occurred during persistent viral infection. Eradication of HCV is essential for the prevention of HCC recurrence and improvement of survival. PMID:17879404

Omega-3 fatty acid supplementation enhances stroke volume and cardiac output during dynamic exercise.

PubMed

Walser, Buddy; Stebbins, Charles L

2008-10-01

Docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) have beneficial effects on cardiovascular function. We tested the hypotheses that dietary supplementation with DHA (2 g/day) + EPA (3 g/day) enhances increases in stroke volume (SV) and cardiac output (CO) and decreases in systemic vascular resistance (SVR) during dynamic exercise. Healthy subjects received DHA + EPA (eight men, four women) or safflower oil (six men, three women) for 6 weeks. Both groups performed 20 min of bicycle exercise (10 min each at a low and moderate work intensity) before and after DHA + EPA or safflower oil treatment. Mean arterial pressure (MAP), heart rate (HR), SV, CO, and SVR were assessed before exercise and during both workloads. HR was unaffected by DHA + EPA and MAP was reduced, but only at rest (88 +/- 5 vs. 83 +/- 4 mm Hg). DHA + EPA augmented increases in SV (14.1 +/- 6.3 vs. 32.3 +/- 8.7 ml) and CO (8.5 +/- 1.0 vs. 10.3 +/- 1.2 L/min) and tended to attenuate decreases in SVR (-7.0 +/- 0.6 vs. -10.1 +/- 1.6 mm Hg L(-1) min(-1)) during the moderate workload. Safflower oil treatment had no effects on MAP, HR, SV, CO or SVR at rest or during exercise. DHA + EPA-induced increases in SV and CO imply that dietary supplementation with these fatty acids can increase oxygen delivery during exercise, which may have beneficial clinical implications for individuals with cardiovascular disease and reduced exercise tolerance.

Direct Acting Antiviral Agents in Korean Patients with Chronic Hepatitis C and Hemophilia Who Are Treatment-Naïve or Treatment-Experienced.

PubMed

Lee, Hyun Woong; Yoo, Ki Young; Won, Joung Won; Kim, Hyung Joon

2017-09-15

Chronic hepatitis C (CHC) is a major comorbidity in patients with hemophilia. Patients (n=30) were enrolled between September 2015 and April 2016. Twenty-six patients were genotype 1 (1b, n=21; 1a, n=5) and four patients were genotype 2a/2b. Among 21 patients with genotype 1b, Y93H resistance-associated variants (RAVs) were detected in three patients (14.3%). We evaluated sustained virologic response (SVRs) at 12 weeks, as well as relapse and safety. Five patients with genotype 1a and three patients with genotype 1b (RAV positive) received ledipasvir/sofosbuvir for 12 weeks. SVR12 rate was 100% (8/8). Eleven patients with genotype 1b were treatment-naïve and received daclatasvir plus asunaprevir for 24 weeks. SVR12 rate was 91% (10/11). One patient experienced viral breakthrough without RAV at 12 weeks. Seven treatment-experienced patients with genotype 1b received daclatasvir plus asunaprevir for 24 weeks. SVR12 rate was 85.7% (6/7). One patient experienced viral breakthrough with RAV (L31M, Y93H) at 12 weeks. Four patients with genotype 2a/2b received sofosbuvir plus ribavirin for 12 weeks. SVR12 rate was 100% (4/4). No serious adverse event-related discontinuations were noted. New direct acting antiviral treatment achieved high SVRs rates at 12 weeks in CHC patients with hemophilia without serious adverse events.

Real-World Experiences with the Combination Treatment of Ledipasvir plus Sofosbuvir for 12 Weeks in HCV Genotype 1-Infected Japanese Patients: Achievement of a Sustained Virological Response in Previous Users of Peginterferon plus Ribavirin with HCV NS3/4A Inhibitors.

PubMed

Kanda, Tatsuo; Yasui, Shin; Nakamura, Masato; Suzuki, Eiichiro; Arai, Makoto; Ooka, Yoshihiko; Ogasawara, Sadahisa; Chiba, Tetsuhiro; Saito, Tomoko; Haga, Yuki; Takahashi, Koji; Sasaki, Reina; Wu, Shuang; Nakamoto, Shingo; Tawada, Akinobu; Maruyama, Hitoshi; Imazeki, Fumio; Kato, Naoya; Yokosuka, Osamu

2017-04-25

The aim of this study was to characterize the treatment response and serious adverse events of ledipasvir plus sofosbuvir therapies in Japanese patients infected with hepatitis C virus (HCV) genotype 1 (GT1). This retrospective study analyzed 240 Japanese HCV GT1 patients treated for 12 weeks with 90 mg of ledipasvir plus 400 mg of sofosbuvir daily. Sustained virological response at 12 weeks post-treatment (SVR12) was achieved in 236 of 240 (98.3%) patients. Among treatment-naïve patients, SVR12 was achieved in 136 of 138 (98.6%) patients, and among treatment-experienced patients, SVR12 was achieved in 100 of 102 (98.0%) patients. In patients previously treated with peginterferon plus ribavirin with various HCV NS3/4A inhibitors, 100% SVR rates (25/25) were achieved. Two relapsers had HCV NS5A resistance-associated variants (RAVs), but no HCV NS5B-S282 was observed after they relapsed. We experienced two patients with cardiac events during treatment. In conclusion, combination of ledipasvir plus sofosbuvir for 12 weeks is a potential therapy for HCV GT1 patients. Caution is needed for HCV NS5A RAVs, which were selected by HCV NS5A inhibitors and cardiac adverse events.

Photoplethysmographic venous refilling times following ultrasound guided foam sclerotherapy for symptomatic superficial venous reflux: relationship with clinical outcomes.

PubMed

Darvall, K A L; Sam, R C; Bate, G R; Adam, D J; Silverman, S H; Bradbury, A W

2010-08-01

Digital photoplethysmography (PPG) provides an inexpensive, reproducible, quantitative, non-invasive assessment of lower limb venous function. To examine the relationship between venous refilling time (VRT) and severity of venous disease, and also between changes in VRT and symptomatic improvement after ultrasound guided foam sclerotherapy (UGFS) for symptomatic superficial venous reflux (SVR). Prior to and 6 months after UGFS, 246 patients (317 limbs) completed a symptom questionnaire, underwent duplex ultrasonography and clinical assessment, and VRT measurement by digital PPG. Health related quality of life (HRQL) questionnaires were also completed. Median VRT improved from 11 to 31 s (P < 0.0005, Wilcoxon Signed Ranks). Abnormal VRT (<20 s) correlated well with the presence of SVR on duplex (sensitivity 75%, specificity 94%). Pre-treatment there was a significant relationship between reducing VRT and increasing CEAP clinical grade (P < 0.0005, chi(2)), extent of SVR on duplex (P < 0.0005) and a non-significant relationship with overall increasing symptom severity (P = 0.097). Relief of all symptoms was more likely when there was normalisation of VRT after treatment (80% vs. 65%, P < 0.0005, chi(2)). Pre-treatment VRT correlated with both generic physical (r = 0.428, P = 0.002) and disease-specific (r = -0.413, P = 0.003, Spearman's rank) HRQL. UGFS for SVR improves VRT measured by digital PPG and that improvement correlates with symptom relief. Copyright (c) 2010 European Society for Vascular Surgery. Published by Elsevier Ltd. All rights reserved.

Experimental and computational prediction of glass transition temperature of drugs.

PubMed

Alzghoul, Ahmad; Alhalaweh, Amjad; Mahlin, Denny; Bergström, Christel A S

2014-12-22

Glass transition temperature (Tg) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between Tg and melting temperature (Tm) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of Tg were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on Tm predicted Tg with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict Tg of drug-like molecules with high accuracy were developed. If Tm is available, a simple linear regression can be used to predict Tg. However, the results also suggest that support vector regression and calculated molecular descriptors can predict Tg with equal accuracy, already before compound synthesis.

Closing the Gap: The Challenges of Treating Hepatitis C Virus Genotype 3 Infection.

PubMed

Martin, Michelle T; Deming, Paulina

2017-06-01

The efficacy of hepatitis C virus (HCV) treatment has increased over the last 5 years to nearly 100% for many patient groups. Patients with genotype (GT) 3 HCV infection, however, and specifically cirrhotic or treatment-experienced patients, have lower sustained virologic response (SVR) rates than patients with other GTs. Because GT 3 presents more clinical challenges than other GTs, this review focuses on the evolution and efficacy of direct-acting antiviral (DAA) treatment options for HCV GT 3 infection after the historical standard of care with pegylated interferon and ribavirin. Our objective was to review the SVR rates with available and late-pipeline DAAs for HCV GT 3 infection and discuss challenges with successful GT 3 treatment. Authors performed a literature search of the PubMed/MEDLINE database (inception to March 27, 2017) and narrowed the field to clinical trials published in English. Trials that evaluated alternative treatments, non-DAA historical treatment, and DAAs not currently indicated for HCV were excluded. Trials only involving patients with human immunodeficiency virus/HCV coinfection were also excluded. Additional trials were identified from a review of the ClinicalTrials.gov database. Authors further identified references from a review of literature citations and reviewed annual meeting abstracts from the American Association for the Study of Liver Diseases and the European Association for the Study of the Liver for pipeline and real-world GT 3 data. Phase III trial data were not available to support all GT 3 treatment recommendations found in the guidelines. The SVR rates were lower in treatment-experienced and cirrhotic patients with GT 3 than other HCV populations. Treatment failure was associated with resistance to current treatment regimens. Clinical studies included patients with various levels of advanced liver disease, but few patients with decompensated cirrhosis were represented. Recent advances in pharmacologic treatment with DAAs have greatly increased SVR rates in patients with all HCV GTs, but SVR rates for treatment-experienced cirrhotic patients with GT 3 are lower than for other GTs. Given the limited data and observed SVR rates in this patient population, the optimal therapy for patients with decompensated cirrhotic GT 3 HCV infection is not yet established. Newer agents and recommendations regarding baseline resistance are likely to evolve treatment strategies in the near future. © 2017 The Authors. Pharmacotherapy: The Journal of Human Pharmacology and Drug Therapy published by Wiley Periodicals, Inc. on behalf of Pharmacotherapy Publications, Inc.

No Difference in Effectiveness of 8 vs 12 Weeks of Ledipasvir and Sofosbuvir for Treatment of Hepatitis C in Black Patients.

PubMed

Marcus, Julia L; Hurley, Leo B; Chamberland, Scott; Champsi, Jamila H; Gittleman, Laura C; Korn, Daniel G; Lai, Jennifer B; Lam, Jennifer O; Pauly, Mary Patricia; Quesenberry, Charles P; Ready, Joanna; Saxena, Varun; Seo, Suk I; Witt, David J; Silverberg, Michael J

2018-06-01

Treatment with the combination of ledipasvir and sofosbuvir for 12 weeks has been approved by the Food and Drug Administration for patients with genotype 1 hepatitis C virus (HCV) infection; some patients can be treated with an 8-week course. Guidelines recommend a 12-week treatment course for black patients, but studies have not compared the effectiveness of 8 vs 12 weeks in black patients who are otherwise eligible for an 8-week treatment regimen. We conducted an observational study of Kaiser Permanente Northern California members with HCV genotype 1 infection who were eligible for 8 weeks of treatment with ledipasvir and sofosbuvir (treatment-naïve, no cirrhosis, no HIV infection, level of HCV RNA <6 million IU/mL) and were treated for 8 or 12 weeks from October 2014 through December 2016. We used χ 2 analyses to compare sustained virologic response 12 weeks after the end of treatment (SVR12) among patients treated for 8 vs 12 weeks, and adjusted Poisson models to identify factors associated with receipt of 12 weeks of therapy among patients eligible for 8 weeks. Of 2653 patients eligible for 8 weeks of treatment with ledipasvir and sofosbuvir, 1958 (73.8%) received 8 weeks of treatment and 695 (26.2%) received 12 weeks; the proportions of patients with SVR12 were 96.3% and 96.3%, respectively (P = .94). Among 435 black patients eligible for the 8-week treatment regimen, there was no difference in the proportions who achieved an SVR12 following 8 vs 12 weeks' treatment (95.6% vs 95.8%; P = .90). Male sex, higher transient elastography or FIB-4 scores, higher INR and level of bilirubin, lower level of albumin, obesity, diabetes, and ≥15 alcohol drinks consumed/week were independently associated with receiving 12 weeks of treatment among patients eligible for the 8-week treatment regimen, but were not associated with reduced SVR12 after 8 weeks of treatment. In an observational study of patients who received ledipasvir and sofosbuvir treatment for HCV genotype 1 infection, we found that contrary to guidelines, 8-week and 12-week treatment regimens do not result in statistically significant differences in SVR12 in black patients. Patient characteristics were associated with receipt of 12-week regimens among patients eligible for 8 weeks, but were not associated with reduced SVR12 after 8 weeks. Shorter treatment courses might therefore be more widely used without compromising treatment effectiveness. Copyright © 2018 AGA Institute. Published by Elsevier Inc. All rights reserved.

Efficacy of sofosbuvir plus ribavirin with or without peginterferon-alfa in patients with hepatitis C virus genotype 3 infection and treatment-experienced patients with cirrhosis and hepatitis C virus genotype 2 infection.

PubMed

Foster, Graham R; Pianko, Stephen; Brown, Ashley; Forton, Daniel; Nahass, Ronald G; George, Jacob; Barnes, Eleanor; Brainard, Diana M; Massetto, Benedetta; Lin, Ming; Han, Bin; McHutchison, John G; Subramanian, G Mani; Cooper, Curtis; Agarwal, Kosh

2015-11-01

We conducted an open-label, randomized, phase 3 trial to determine the efficacy and safety of sofosbuvir and ribavirin, with and without peginterferon-alfa, in treatment-experienced patients with cirrhosis and hepatitis C virus (HCV) genotype 2 infection and treatment-naïve or treatment-experienced patients with HCV genotype 3 infection. The study was conducted at 80 sites in Europe, North America, Australia, and New Zealand Patients were randomly assigned (1:1:1) to groups given sofosbuvir and ribavirin for 16 weeks (n = 196); sofosbuvir and ribavirin for 24 weeks (n = 199); or sofosbuvir, peginterferon-alfa, and ribavirin for 12 weeks (n = 197). The primary end point was the percentage of patients with HCV RNA <15 IU/mL 12 weeks after stopping therapy (sustained virologic response [SVR12]). From October 2013 until April 2014, we enrolled and treated 592 patients-48 with genotype 2 HCV and compensated cirrhosis who had not achieved SVR with previous treatments and 544 with genotype 3 HCV (279 treatment-naïve and 265 previously treated). Overall, 219 patients (37%) had compensated cirrhosis. The last post-treatment week 12 patient visit was in January 2015. Rates of SVR12 among patients with genotype 2 HCV were 87% and 100%, for those receiving 16 and 24 weeks of sofosbuvir and ribavirin, respectively, and 94% for those receiving sofosbuvir, peginterferon, and ribavirin for 12 weeks. Rates of SVR12 among patients with genotype 3 HCV were 71% and 84% in those receiving 16 and 24 weeks of sofosbuvir and ribavirin, respectively, and 93% in those receiving sofosbuvir, peginterferon, and ribavirin. On-treatment virologic failure occurred in 3 patients with HCV genotype 3a receiving sofosbuvir and ribavirin for 24 weeks. The most common adverse events were fatigue, headache, insomnia, and nausea. Overall, 1% of patients discontinued treatment due to adverse events. Among patients with genotype 3 HCV infection, including a large proportion of treatment-experienced patients with cirrhosis, the combination of sofosbuvir, peginterferon, and ribavirin for 12 weeks produces high rates of SVR. Treatment-experienced patients with cirrhosis and genotype 2 HCV infection had high rates of SVR in all groups. EudraCT ID 2013-002641-11. Copyright © 2015 AGA Institute. Published by Elsevier Inc. All rights reserved.

The impact of IL28B genotype on the gene expression profile of patients with chronic hepatitis C treated with pegylated interferon alpha and ribavirin

PubMed Central

2012-01-01

Background Recent studies of CH-C patients have demonstrated a strong association between IL28B CC genotype and sustained virologic response (SVR) after PEG-IFN/RBV treatment. We aimed to assess whether IL28B alleles rs12979860 genotype influences gene expression in response to PEG-IFN/RBV in CH-C patients. Methods Clinical data and gene expression data were available for 56 patients treated with PEG-IFN/RBV. Whole blood was used to determine IL28B genotypes. Differential expression of 153 human genes was assessed for each treatment time point (Days: 0, 1, 7, 28, 56) and was correlated with IL28B genotype (IL28B C/C or non-C/C) over the course of the PEG-IFN/RBV treatment. Genes with statistically significant changes in their expression at each time point were used as an input for pathway analysis using KEGG Pathway Painter (KPP). Pathways were ranked based on number of gene involved separately per each study cohort. Results The most striking difference between the response patterns of patients with IL28B C/C and T* genotypes during treatment, across all pathways, is a sustained pattern of treatment-induced gene expression in patients carrying IL28B C/C. In the case of IL28B T* genotype, pre-activation of genes, the lack of sustained pattern of gene expression or a combination of both were observed. This observation could potentially provide an explanation for the lower rate of SVR observed in these patients. Additionally, when the lists of IL28B genotype-specific genes which were differentially expressed in patients without SVR were compared at their baseline, IRF2 and SOCS1 genes were down-regulated regardless of patients' IL28B genotype. Furthermore, our data suggest that CH-C patients who do not have the SOCS1 gene silenced have a better chance of achieving SVR. Our observations suggest that the action of SOCS1 is independent of IL28B genotype. Conclusions IL28B CC genotype patients with CH-C show a sustained treatment-induced gene expression profile which is not seen in non-CC genotype patients. Silencing of SOCS1 is a negative and independent predictor of SVR. These data may provide some mechanistic explanation for higher rate of SVR in IL28B CC patients who are treated with PEG-IFN/RBV. PMID:22313623

The impact of IL28B genotype on the gene expression profile of patients with chronic hepatitis C treated with pegylated interferon alpha and ribavirin.

PubMed

Younossi, Zobair M; Birerdinc, Aybike; Estep, Mike; Stepanova, Maria; Afendy, Arian; Baranova, Ancha

2012-02-07

Recent studies of CH-C patients have demonstrated a strong association between IL28B CC genotype and sustained virologic response (SVR) after PEG-IFN/RBV treatment. We aimed to assess whether IL28B alleles rs12979860 genotype influences gene expression in response to PEG-IFN/RBV in CH-C patients. Clinical data and gene expression data were available for 56 patients treated with PEG-IFN/RBV. Whole blood was used to determine IL28B genotypes. Differential expression of 153 human genes was assessed for each treatment time point (Days: 0, 1, 7, 28, 56) and was correlated with IL28B genotype (IL28B C/C or non-C/C) over the course of the PEG-IFN/RBV treatment. Genes with statistically significant changes in their expression at each time point were used as an input for pathway analysis using KEGG Pathway Painter (KPP). Pathways were ranked based on number of gene involved separately per each study cohort. The most striking difference between the response patterns of patients with IL28B C/C and T* genotypes during treatment, across all pathways, is a sustained pattern of treatment-induced gene expression in patients carrying IL28B C/C. In the case of IL28B T* genotype, pre-activation of genes, the lack of sustained pattern of gene expression or a combination of both were observed. This observation could potentially provide an explanation for the lower rate of SVR observed in these patients. Additionally, when the lists of IL28B genotype-specific genes which were differentially expressed in patients without SVR were compared at their baseline, IRF2 and SOCS1 genes were down-regulated regardless of patients' IL28B genotype. Furthermore, our data suggest that CH-C patients who do not have the SOCS1 gene silenced have a better chance of achieving SVR. Our observations suggest that the action of SOCS1 is independent of IL28B genotype. IL28B CC genotype patients with CH-C show a sustained treatment-induced gene expression profile which is not seen in non-CC genotype patients. Silencing of SOCS1 is a negative and independent predictor of SVR. These data may provide some mechanistic explanation for higher rate of SVR in IL28B CC patients who are treated with PEG-IFN/RBV.

A Lead-In with Silibinin Prior to Triple-Therapy Translates into Favorable Treatment Outcomes in Difficult-To-Treat HIV/Hepatitis C Coinfected Patients.

PubMed

Braun, Dominique L; Rauch, Andri; Aouri, Manel; Durisch, Nina; Eberhard, Nadia; Anagnostopoulos, Alexia; Ledergerber, Bruno; Müllhaupt, Beat; Metzner, Karin J; Decosterd, Laurent; Böni, Jürg; Weber, Rainer; Fehr, Jan

2015-01-01

The efficacy of first-generation protease inhibitor based triple-therapy against hepatitis C virus (HCV) infection is limited in HIV/HCV-coinfected patients with advanced liver fibrosis and non-response to previous peginterferon-ribavirin. These patients have a low chance of achieving a sustained virologic response (SVR) using first generation triple-therapy, with a success rate of only 20%. We investigated the efficacy and safety of lead-in therapy with intravenous silibinin followed by triple-therapy in this difficult-to-treat patient group. Inclusion criteria were HIV/HCV coinfection with advanced liver fibrosis and documented previous treatment failure on peginterferon-ribavirin. The intervention was a lead-in therapy with intravenous silibinin 20 mg/kg/day for 14 days, followed by triple-therapy (peginterferon-ribavirin and telaprevir) for 12 weeks, and peginterferon-ribavirin alone for 36 weeks. Outcome measurements were HCV-RNA after silibinin lead-in and during triple-therapy, SVR data at week 12, and safety and tolerability of silibinin. We examined sixteen HIV/HCV-coinfected patients with previous peginterferon-ribavirin failure, of whom 14 had a fibrosis grade METAVIR ≥F3. All were on successful antiretroviral therapy. Median (IQR) HCV-RNA decline after silibinin therapy was 2.65 (2.1-2.8) log10 copies/mL. Fifteen of sixteen patients (94%) had undetectable HCV RNA at weeks 4 and 12, eleven patients (69%) showed end-of-treatment response (i.e., undetectable HCV-RNA at week 48), and ten patients (63%) reached SVR at week 12 (SVR 12). Six of the sixteen patients (37%) did not reach SVR 12: One patient had rapid virologic response (RVR) (i.e., undetectable HCV-RNA at week 4) but stopped treatment at week 8 due to major depression. Five patients had RVR, but experienced viral breakthroughs at week 21, 22, 25, or 32, or a relapse at week 52. The HIV RNA remained below the limit of detection in all patients during the complete treatment period. No serious adverse events and no significant drug-drug interactions were associated with silibinin. A lead-in with silibinin before triple-therapy was safe and highly effective in difficult-to-treat HIV/HCV coinfected patients, with a pronounced HCV-RNA decline during the lead-in phase, which translates into 63% SVR. An add-on of intravenous silibinin to standard of care HCV treatment is worth further exploration in selected difficult-to-treat patients. ClinicalTrials.gov NCT01816490.

Baseline Polymorphisms and Emergence of Drug Resistance in the NS3/4A Protease of Hepatitis C Virus Genotype 1 following Treatment with Faldaprevir and Pegylated Interferon Alpha 2a/Ribavirin in Phase 2 and Phase 3 Studies.

PubMed

Berger, K L; Scherer, J; Ranga, M; Sha, N; Stern, J O; Quinson, A-M; Kukolj, G

2015-10-01

Analysis of data pooled from multiple phase 2 (SILEN-C1 to 3) and phase 3 studies (STARTVerso1 to 4) of the hepatitis C virus (HCV) nonstructural protein 3/4A (NS3/4A) protease inhibitor faldaprevir plus pegylated interferon alpha/ribavirin (PR) provides a comprehensive evaluation of baseline and treatment-emergent NS3/4A amino acid variants among HCV genotype-1 (GT-1)-infected patients. Pooled analyses of GT-1a and GT-1b NS3 population-based pretreatment sequences (n = 3,124) showed that faldaprevir resistance-associated variants (RAVs) at NS3 R155 and D168 were rare (<1%). No single, noncanonical NS3 protease or NS4A cofactor baseline polymorphism was associated with a reduced sustained virologic response (SVR) to faldaprevir plus PR, including Q80K. The GT-1b NS3 helicase polymorphism T344I was associated with reduced SVR to faldaprevir plus PR (P < 0.0001) but was not faldaprevir specific, as reduced SVR was also observed with placebo plus PR. Among patients who did not achieve SVR and had available NS3 population sequences (n = 507 GT-1a; n = 349 GT-1b), 94% of GT-1a and 83% of GT-1b encoded faldaprevir treatment-emergent RAVs. The predominant GT-1a RAV was R155K (88%), whereas GT-1b encoded D168 substitutions (78%) in which D168V was predominant (67%). The novel GT-1b NS3 S61L substitution emerged in 7% of virologic failures as a covariant with D168V, most often among the faldaprevir breakthroughs; S61L in combination with D168V had a minimal impact on faldaprevir susceptibility compared with that for D168V alone (1.5-fold difference in vitro). The median time to loss of D168 RAVs among GT-1b-infected patients who did not have a sustained virologic response at 12 weeks posttreatment (non-SVR12) after virologic failure was 5 months, which was shorter than the 14 months for R155 RAVs among GT-1a-infected non-SVR12 patients, suggesting that D168V is less fit than R155K in the absence of faldaprevir selective pressure. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Simple score to predict risk of hepatocellular carcinoma in chronic hepatitis C patients with advanced fibrosis after pegylated interferon and ribavirin therapy.

PubMed

Hu, Ching-Chih; Weng, Cheng-Hao; Chang, Liang-Che; Lin, Chih-Lang; Chen, Yen-Ting; Hu, Ching-Fang; Hua, Man-Chin; Chen, Li-Wei; Chien, Rong-Nan

2018-01-01

Eradication of chronic hepatitis C virus (HCV) after interferon-based therapy and its association with the reduction of risk of hepatocellular carcinoma (HCC) in HCV-infected patients with advanced fibrosis is controversial. The study is aimed to develop a simple scoring model for HCC prediction among advanced fibrotic chronic hepatitis C (CHC) patients after pegylated interferon (pegIFN) and ribavirin (RBV) therapy. We enrolled 271 biopsy-proven CHC patients with advanced fibrosis between 2003 and 2016, and divided them into non-HCC (n=211) and HCC (n=60) groups. The median observation duration was 6.0 years (range: 0.9-12.6 years). The HCC prevalence after pegIFN and RBV therapy in CHC patients with sustained virologic response (SVR) and without SVR was 14.7% and 32.2%, respectively. Multivariate Cox regression showed age ≥59.5 years old at initiation of therapy (HR: 2.542, 95% CI: 1.390-4.650, P =0.002), pretreatment total bilirubin ≥1.1 mg/dL (HR: 2.630, 95% CI: 1.420-4.871, P =0.002), pretreatment platelet counts <146.5 × 10 3 /μL (HR: 2.751, 95% CI: 1.373-5.511, P =0.004), no achievement of SVR (HR: 2.331, 95% CI: 1.277-4.253, P =0.006), and no diabetes at treatment initiation (HR: 3.085, 95% CI: 1.283-7.418, P =0.012) were significant predictors of HCC development. The scoring model consisted of the five categorical predictors and had an optimal cutoff point of 2.5. The area under receiver operating characteristic (AUROC) of the scoring model was 0.774±0.035 ( P <0.001). The sensitivity and specificity of the cutoff value to detect HCC were 81.3% and 57.5%. The 5-year and 10-year cumulative incidence of HCC was 4.9% and 10.0% in patients with simple score ≤2; and 25.9% and 44.6% in patients with simple score ≥3 ( P <0.001). The simple clinical-guided score has high discriminatory power for HCC prediction in advanced fibrotic CHC patients after pegIFN and RBV therapy.

Subpixel urban land cover estimation: comparing cubist, random forests, and support vector regression

Treesearch

Jeffrey T. Walton

2008-01-01

Three machine learning subpixel estimation methods (Cubist, Random Forests, and support vector regression) were applied to estimate urban cover. Urban forest canopy cover and impervious surface cover were estimated from Landsat-7 ETM+ imagery using a higher resolution cover map resampled to 30 m as training and reference data. Three different band combinations (...

Effectiveness of Sofosbuvir, Ledipasvir/Sofosbuvir, or Paritaprevir/Ritonavir/Ombitasvir and Dasabuvir Regimens for Treatment of Patients With Hepatitis C in the Veterans Affairs National Health Care System.

PubMed

Ioannou, George N; Beste, Lauren A; Chang, Michael F; Green, Pamela K; Lowy, Elliott; Tsui, Judith I; Su, Feng; Berry, Kristin

2016-09-01

We investigated the real-world effectiveness of sofosbuvir, ledipasvir/sofosbuvir, and paritaprevir/ritonavir/ombitasvir and dasabuvir (PrOD) in treatment of different subgroups of patients infected with hepatitis C virus (HCV) genotypes 1, 2, 3, or 4. We performed a retrospective analysis of data from 17,487 patients with HCV infection (13,974 with HCV genotype 1; 2131 with genotype 2; 1237 with genotype 3; and 135 with genotype 4) who began treatment with sofosbuvir (n = 2986), ledipasvir/sofosbuvir (n = 11,327), or PrOD (n = 3174), with or without ribavirin, from January 1, 2014 through June 20, 2015 in the Veterans Affairs health care system. Data through April 15, 2016 were analyzed to assess completion of treatments and sustained virologic response 12 weeks after treatment (SVR12). Mean age of patients was 61 ± 7 years, 97% were male, 52% were non-Hispanic white, 29% were non-Hispanic black, 32% had a diagnosis of cirrhosis (9.9% with decompensated cirrhosis), 36% had a Fibrosis-4 index score >3.25 (indicator of cirrhosis), and 29% had received prior antiviral treatment. An SVR12 was achieved by 92.8% (95% confidence interval [CI], 92.3%-93.2%) of subjects with HCV genotype 1 infection (no significant difference between ledipasvir/sofosbuvir and PrOD regimens), 86.2% (95% CI, 84.6%-87.7%) of those with genotype 2 infection (treated with sofosbuvir and ribavirin), 74.8% (95% CI, 72.2%-77.3%) of those with genotype 3 infection (77.9% in patients given ledipasvir/sofosbuvir plus ribavirin, 87.0% in patients given sofosbuvir and pegylated-interferon plus ribavirin, and 70.6% of patients given sofosbuvir plus ribavirin), and 89.6% (95% CI 82.8%-93.9%) of those with genotype 4 infection. Among patients with cirrhosis, 90.6% of patients with HCV genotype 1, 77.3% with HCV genotype 2, 65.7% with HCV genotype 3, and 83.9% with HCV genotype 4 achieved an SVR12. Among previously treated patients, 92.6% with genotype 1; 80.2% with genotype 2; 69.2% with genotype 3; and 93.5% with genotype 4 achieved SVR12. Among treatment-naive patients, 92.8% with genotype 1; 88.0% with genotype 2; 77.5% with genotype 3; and 88.3% with genotype 4 achieved SVR12. Eight-week regimens of ledipasvir/sofosbuvir produced an SVR12 in 94.3% of eligible patients with HCV genotype 1 infection; this regimen was underused. High proportions of patients with HCV infections genotypes 1-4 (ranging from 75% to 93%) in the Veterans Affairs national health care system achieved SVR12, approaching the results reported in clinical trials, especially in patients with genotype 1 infection. An 8-week regimen of ledipasvir/sofosbuvir is effective for eligible patients with HCV genotype 1 infection and could reduce costs. There is substantial room for improvement in SVRs among persons with cirrhosis and genotype 2 or 3 infections. Copyright © 2016 AGA Institute. Published by Elsevier Inc. All rights reserved.

Effectiveness of Sofosbuvir, Ledipasvir/Sofosbuvir, or Paritaprevir/Ritonavir/Ombitasvir and Dasabuvir Regimens for Treatment of Patients With Hepatitis C in the Veterans Affairs National Health Care System

PubMed Central

Ioannou, George N.; Beste, Lauren A.; Chang, Michael F.; Green, Pamela K.; Lowy, Elliott; Tsui, Judith I.; Su, Feng; Berry, Kristin

2017-01-01

BACKGROUND & AIMS We investigated the real-world effectiveness of sofosbuvir, ledipasvir/sofosbuvir, and paritaprevir/ ritonavir/ombitasvir and dasabuvir (PrOD) in treatment of different subgroups of patients infected with hepatitis C virus (HCV) genotypes 1, 2, 3, or 4. METHODS We performed a retrospective analysis of data from 17,487 patients with HCV infection (13,974 with HCV genotype 1; 2131 with genotype 2; 1237 with genotype 3; and 135 with genotype 4) who began treatment with sofosbuvir (n = 2986), ledipasvir/sofosbuvir (n = 11,327), or PrOD (n = 3174), with or without ribavirin, from January 1, 2014 through June 20, 2015 in the Veterans Affairs health care system. Data through April 15, 2016 were analyzed to assess completion of treatments and sustained virologic response 12 weeks after treatment (SVR12). Mean age of patients was 61 ± 7 years, 97% were male, 52% were non-Hispanic white, 29% were non-Hispanic black, 32% had a diagnosis of cirrhosis (9.9% with decompensated cirrhosis), 36% had a Fibrosis-4 index score >3.25 (indicator of cirrhosis), and 29% had received prior antiviral treatment. RESULTS An SVR12 was achieved by 92.8% (95% confidence interval [CI], 92.3%–93.2%) of subjects with HCV genotype 1 infection (no significant difference between ledipasvir/sofosbuvir and PrOD regimens), 86.2% (95% CI, 84.6%–87.7%) of those with genotype 2 infection (treated with sofosbuvir and ribavirin), 74.8% (95% CI, 72.2%–77.3%) of those with genotype 3 infection (77.9% in patients given ledipasvir/sofosbuvir plus ribavirin, 87.0% in patients given sofosbuvir and pegylated-interferon plus ribavirin, and 70.6% of patients given sofosbuvir plus ribavirin), and 89.6% (95% CI 82.8%–93.9%) of those with genotype 4 infection. Among patients with cirrhosis, 90.6% of patients with HCV genotype 1, 77.3% with HCV genotype 2, 65.7% with HCV genotype 3, and 83.9% with HCV genotype 4 achieved an SVR12. Among previously treated patients, 92.6% with genotype 1; 80.2% with genotype 2; 69.2% with genotype 3; and 93.5% with genotype 4 achieved SVR12. Among treatment-naive patients, 92.8% with genotype 1; 88.0% with genotype 2; 77.5% with genotype 3; and 88.3% with genotype 4 achieved SVR12. Eight-week regimens of ledipasvir/sofosbuvir produced an SVR12 in 94.3% of eligible patients with HCV genotype 1 infection; this regimen was underused. CONCLUSIONS High proportions of patients with HCV infections genotypes 1–4 (ranging from 75% to 93%) in the Veterans Affairs national health care system achieved SVR12, approaching the results reported in clinical trials, especially in patients with genotype 1 infection. An 8-week regimen of ledipasvir/sofosbuvir is effective for eligible patients with HCV genotype 1 infection and could reduce costs. There is substantial room for improvement in SVRs among persons with cirrhosis and genotype 2 or 3 infections. PMID:27267053

Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data.

PubMed

Held, Elizabeth; Cape, Joshua; Tintle, Nathan

2016-01-01

Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few best practices exist for the application of these methods. We extend a recently proposed supervised machine learning approach for predicting disease risk by genotypes to be able to incorporate gene expression data and rare variants. We then apply 2 different versions of the approach (radial and linear support vector machines) to simulated data from Genetic Analysis Workshop 19 and compare performance to logistic regression. Method performance was not radically different across the 3 methods, although the linear support vector machine tended to show small gains in predictive ability relative to a radial support vector machine and logistic regression. Importantly, as the number of genes in the models was increased, even when those genes contained causal rare variants, model predictive ability showed a statistically significant decrease in performance for both the radial support vector machine and logistic regression. The linear support vector machine showed more robust performance to the inclusion of additional genes. Further work is needed to evaluate machine learning approaches on larger samples and to evaluate the relative improvement in model prediction from the incorporation of gene expression data.

The effectiveness of retreatment with peginterferon alfa and ribavirin in patients with chronic viral hepatitis C genotype 2 and 3: a prospective cohort study in Brazil.

PubMed

Artico, Simara; Amaral, Karine Medeiros; Gonçalves, Candice Beatriz Treter; Picon, Paulo Dornelles

2012-12-27

More than 50% of patients infected with chronic hepatitis C virus (HCV) do not respond to treatment with conventional interferon (IFN) combined with ribavirin (RBV). The aim of our study was to evaluate the effectiveness of retreatment with peginterferon alfa-2a or 2b (PEG-IFN 2a or 2b) concomitantly with RBV in patients with HCV genotype 2 and 3, which were non-responders or relapsers to initial treatment with IFN / RBV and to identify possible predictors of sustained virological response (SVR). From September 2003 to March 2009 a cohort of 216 patients who had previously failed therapy with a regimen of standard interferon and ribavirin, were followed in a specialized service implemented in the Brazilian Unified Health System, Rio Grande do Sul. All patients were retreated with PEG-IFN 2a or 2b per week, associated with RBV, through oral route, with doses determined according to weight (1,000 mg if weight ≤ 75 Kg and 1,250 mg if ≥ 75 Kg) per day for 48 weeks. The HCV-RNA was tested by Polymerase Chain Reaction (PCR). Virological Response (VR) within 48 weeks and SVR in the 72 weeks was considered for evaluation of treatment efficacy. Analyses were performed in patients who received at least one dose of PEG-IFN. The SVR rate for non-responders to previous treatment was 34.4% and for relapsers was 50% (p = 0.031). As predictive factors that contribute to improve SVR, were identified the age (p = 0.005), to be relapsers to previous treatment (p = 0.023) and present liver biopsy examination Metavir F0-F2 (p = 0.004). In assessing the safety profile, 51 patients (23.6%) discontinued treatment prematurely. This alternative retreatment for patients who have failed prior therapies for anti-HCV, has demonstrated promising SVR rate, provided that it includes a careful selection of patients with predictors of response and adverse events monitored.

The effectiveness of retreatment with peginterferon alfa and ribavirin in patients with chronic viral hepatitis C genotype 2 and 3: a prospective cohort study in Brazil

PubMed Central

2012-01-01

Background More than 50% of patients infected with chronic hepatitis C virus (HCV) do not respond to treatment with conventional interferon (IFN) combined with ribavirin (RBV). The aim of our study was to evaluate the effectiveness of retreatment with peginterferon alfa-2a or 2b (PEG-IFN 2a or 2b) concomitantly with RBV in patients with HCV genotype 2 and 3, which were non-responders or relapsers to initial treatment with IFN / RBV and to identify possible predictors of sustained virological response (SVR). Methods From September 2003 to March 2009 a cohort of 216 patients who had previously failed therapy with a regimen of standard interferon and ribavirin, were followed in a specialized service implemented in the Brazilian Unified Health System, Rio Grande do Sul. All patients were retreated with PEG-IFN 2a or 2b per week, associated with RBV, through oral route, with doses determined according to weight (1,000 mg if weight ≤ 75 Kg and 1,250 mg if ≥ 75 Kg) per day for 48 weeks. The HCV-RNA was tested by Polymerase Chain Reaction (PCR). Virological Response (VR) within 48 weeks and SVR in the 72 weeks was considered for evaluation of treatment efficacy. Analyses were performed in patients who received at least one dose of PEG-IFN. Results The SVR rate for non-responders to previous treatment was 34.4% and for relapsers was 50% (p = 0.031). As predictive factors that contribute to improve SVR, were identified the age (p = 0.005), to be relapsers to previous treatment (p = 0.023) and present liver biopsy examination Metavir F0-F2 (p = 0.004). In assessing the safety profile, 51 patients (23.6%) discontinued treatment prematurely. Conclusions This alternative retreatment for patients who have failed prior therapies for anti-HCV, has demonstrated promising SVR rate, provided that it includes a careful selection of patients with predictors of response and adverse events monitored. PMID:23270376

Acute hepatitis C: a 24-week course of pegylated interferon α-2b versus a 12-week course of pegylated interferon α-2b alone or with ribavirin.

PubMed

Santantonio, Teresa; Fasano, Massimo; Sagnelli, Evangelista; Tundo, Paolo; Babudieri, Sergio; Fabris, Paolo; Toti, Mario; Di Perri, Giovanni; Marino, Nicoletta; Pizzigallo, Eligio; Angarano, Gioacchino

2014-06-01

Therapy of acute hepatitis C (AHC) has not yet been standardized and several issues are still unresolved. This open, randomized, multicenter trial aimed to assess the efficacy and safety of a 24-week course of pegylated IFN (Peg-IFN) alpha-2b versus a 12-week course of Peg-IFN alpha-2b alone or with ribavirin (RBV) in AHC patients. One hundred and thirty HCV acutely infected patients who did not spontaneously resolve by week 12 after onset were consecutively enrolled and randomized to receive Peg-IFN alpha-2b monotherapy (1.5 μg/kg/week) for 24 or 12 weeks (arm 1, n = 44 and arm 2, n = 43, respectively) or in combination with RBV (10.6 mg/kg/day) for 12 weeks (arm 3, n = 43). The primary endpoint was undetectable HCV RNA at 6-month posttreatment follow-up (sustained virological response; SVR). All patients were followed for 48 weeks after therapy cessation. HCV RNA levels were determined by real-time polymerase chain reaction (limit of detection: 15 IU/mL) at the central laboratory at baseline, week 4, end of treatment, and 6 and 12 months posttreatment. Using an intent-to-treat analysis, overall SVR rate was 71.5%. In particular, an SVR was achieved in 31 of 44 (70.5%), 31 of 43 (72.1%), and 31 of 43 (72.1%) patients in arms 1, 2, and 3, respectively (P = 0.898). Sixteen patients (12.3%) prematurely discontinued therapy or were lost to follow-up; thus, sustained response rates with per-protocol analysis were 81.6%, 81.6%, and 81.6% for patients in arms 1, 2, and 3 respectively. With multivariate analysis, virologic response at week 4 of treatment was an independent predictor of SVR. Peg-IFN alpha-2b was well tolerated. Peg-IFN alpha-2b induces a high SVR in chronically evolving AHC patients. Response rates were not influenced by combination therapy or treatment duration. © 2014 by the American Association for the Study of Liver Diseases.

Elbasvir/grazoprevir and sofosbuvir for hepatitis C virus genotype 3 infection with compensated cirrhosis: A randomized trial.

PubMed

Foster, Graham R; Agarwal, Kosh; Cramp, Matthew E; Moreea, Sulleman; Barclay, Stephen; Collier, Jane; Brown, Ashley S; Ryder, Stephen D; Ustianowski, Andrew; Forton, Daniel M; Fox, Ray; Gordon, Fiona; Rosenberg, William M; Mutimer, David J; Du, Jiejun; Gilbert, Christopher L; Asante-Appiah, Ernest; Wahl, Janice; Robertson, Michael N; Barr, Eliav; Haber, Barbara

2018-06-01

Many direct-acting antiviral regimens have reduced activity in people with hepatitis C virus (HCV) genotype (GT) 3 infection and cirrhosis. The C-ISLE study assessed the efficacy and safety of elbasvir/grazoprevir (EBR/GZR) plus sofosbuvir (SOF) with and without ribavirin (RBV) in compensated cirrhotic participants with GT3 infection. This was a phase 2, randomized, open-label study. Treatment-naive participants received EBR/GZR + SOF + RBV for 8 weeks or EBR/GZR + SOF for 12 weeks, and peginterferon/RBV treatment-experienced participants received EBR/GZR + SOF ± RBV for 12 weeks or EBR/GZR + SOF for 16 weeks. The primary endpoint was HCV RNA <15 IU/mL 12 weeks after the end of treatment (sustained virologic response at 12 weeks [SVR12]). Among treatment-naive participants, SVR12 was 91% (21/23) in those treated with RBV for 8 weeks and 96% (23/24) in those treated for 12 weeks. Among treatment-experienced participants, SVR12 was 94% (17/18) and 100% (17/17) in the 12-week arm, with and without RBV, respectively, and 94% (17/18) in the 16-week arm. Five participants failed to achieve SVR: 2 relapsed (both in the 8-week arm), 1 discontinued due to vomiting/cellulitis (16-week arm), and 2 discontinued (consent withdrawn/lost to follow-up). SVR12 was not affected by the presence of resistance-associated substitutions (RASs). There was no consistent change in insulin resistance, and 5 participants reported serious adverse events (pneumonia, chest pain, opiate overdose, cellulitis, decreased creatinine). High efficacy was demonstrated in participants with HCV GT3 infection and cirrhosis. Treatment beyond 12 weeks was not required, and efficacy was maintained regardless of baseline RASs. Data from this study support the use of EBR/GZR plus SOF for 12 weeks without RBV for treatment-naive and peginterferon/RBV-experienced people with GT3 infection and cirrhosis (ClinicalTrials.gov NCT02601573). (Hepatology 2018;67:2113-2126). © 2018 by the American Association for the Study of Liver Diseases.

Development of precursors recognition methods in vector signals

NASA Astrophysics Data System (ADS)

Kapralov, V. G.; Elagin, V. V.; Kaveeva, E. G.; Stankevich, L. A.; Dremin, M. M.; Krylov, S. V.; Borovov, A. E.; Harfush, H. A.; Sedov, K. S.

2017-10-01

Precursor recognition methods in vector signals of plasma diagnostics are presented. Their requirements and possible options for their development are considered. In particular, the variants of using symbolic regression for building a plasma disruption prediction system are discussed. The initial data preparation using correlation analysis and symbolic regression is discussed. Special attention is paid to the possibility of using algorithms in real time.

IFNL4 ss469415590 Variant Is Associated with Treatment Response in Japanese HCV Genotype 1 Infected Individuals Treated with IFN-Including Regimens

PubMed Central

Miyamura, Tatsuo; Kanda, Tatsuo; Nakamoto, Shingo; Arai, Makoto; Nakamura, Masato; Wu, Shuang; Jiang, Xia; Sasaki, Reina; Yasui, Shin; Ooka, Yoshihiko; Imazeki, Fumio; Mikami, Shigeru; Yokosuka, Osamu

2014-01-01

Aim. Eradication of hepatitis C virus (HCV) is still challenging even if interferon- (IFN-) free regimens with direct-acting antiviral agents (DAAs) for HCV-infected individuals are available in clinical practice. IFNL4 is a newly described protein, associated with human antiviral defenses. We investigated whether IFNL4 ss469415590 variant has an effect on the prediction of treatment response in HCV-infected patients treated with IFN-including regimens. Patients and Methods. In all, 185 patients infected with HCV genotype 1 treated with peg-IFN plus ribavirin, with or without telaprevir, were genotyped for IFNL4 ss469415590. We retrospectively investigated whether the role of IFNL4 ss469415590 variant and other factors could predict sustained virological response (SVR) in Japanese patients infected with HCV genotype 1. Results. There were 65.7%, 31.5%, and 2.8% patients in the IFNL4 ss469415590 TT/TT, TT/-G, and -G/-G groups, respectively. SVR rates were 82.1% or 49.3% in patients treated with peg-IFN plus ribavirin with or without telaprevir, respectively. IFNL4 ss469415590 variant and HCV viral loads or IFNL4 ss469415590 variant and early virological response were better predictors of SVR in patients treated with peg-IFN plus ribavirin with or without telaprevir, respectively. Conclusion. In the era of DAAs, measurement of IFNL4 ss469415590 variant could help the prediction of SVR in Japanese HCV genotype 1 infected individuals treated with IFN-including regimens. PMID:25548683

A functional simian virus 40 origin of replication is required for the generation of a super T antigen with a molecular weight of 100,000 in transformed mouse cells.

PubMed Central

Chen, S; Grass, D S; Blanck, G; Hoganson, N; Manley, J L; Pollack, R E

1983-01-01

We used two recombinant plasmids, one containing wild-type simian virus 40 DNA (pSVR1) and the other containing a simian virus 40 genome with a defective origin of replication (pSVR1-origin-minus) to transfect NIH3T3 cells. Quantitation of T-antigen synthesis by indirect immunofluorescence at 48 h after transfection with either DNA revealed the same percentage of T-positive nuclei. The transformation frequencies observed were also similar with both plasmids. Immunoprecipitation of [35S]methionine-labeled cell extracts showed the expected 94,000-dalton (94K) T and 17K t antigens in all clones examined. In pSVR1-generated transformants, a 100K super T antigen was also detected. Transformants isolated from pSVR1-origin-minus transfection, however, never expressed this 100K super T antigen, and some of these clones originally also showed greatly reduced levels of 94K T antigen. However, after growth in culture for several generations, the levels of 94K T antigen synthesis in these underproducer clones were dramatically increased. A direct correlation between the amounts of T antigen synthesized and the ability to grow independently of anchorage was observed. The mechanism which brings about increasing levels of T-antigen synthesis in some of the clones is not clear, but it appears not to be due to changes in either the copy number or the methylation pattern of the integrated simian virus 40 DNA. Images PMID:6312105

Direct Acting Antiviral Agents in Korean Patients with Chronic Hepatitis C and Hemophilia Who Are Treatment-Naïve or Treatment-Experienced

PubMed Central

Lee, Hyun Woong; Yoo, Ki Young; Won, Joung Won; Kim, Hyung Joon

2017-01-01

Background/Aims Chronic hepatitis C (CHC) is a major comorbidity in patients with hemophilia. Methods Patients (n=30) were enrolled between September 2015 and April 2016. Twenty-six patients were genotype 1 (1b, n=21; 1a, n=5) and four patients were genotype 2a/2b. Among 21 patients with genotype 1b, Y93H resistance-associated variants (RAVs) were detected in three patients (14.3%). We evaluated sustained virologic response (SVRs) at 12 weeks, as well as relapse and safety. Results Five patients with genotype 1a and three patients with genotype 1b (RAV positive) received ledipasvir/sofosbuvir for 12 weeks. SVR12 rate was 100% (8/8). Eleven patients with genotype 1b were treatment-naïve and received daclatasvir plus asunaprevir for 24 weeks. SVR12 rate was 91% (10/11). One patient experienced viral breakthrough without RAV at 12 weeks. Seven treatment-experienced patients with genotype 1b received daclatasvir plus asunaprevir for 24 weeks. SVR12 rate was 85.7% (6/7). One patient experienced viral breakthrough with RAV (L31M, Y93H) at 12 weeks. Four patients with genotype 2a/2b received sofosbuvir plus ribavirin for 12 weeks. SVR12 rate was 100% (4/4). No serious adverse event-related discontinuations were noted. Conclusions New direct acting antiviral treatment achieved high SVRs rates at 12 weeks in CHC patients with hemophilia without serious adverse events. PMID:28874040

Heart rate variability (HRV) during virtual reality immersion

PubMed Central

Malińska, Marzena; Zużewicz, Krystyna; Bugajska, Joanna; Grabowski, Andrzej

2015-01-01

The goal of the study was assessment of the hour-long training involving handling virtual environment (sVR) and watching a stereoscopic 3D movie on the mechanisms of autonomic heart rate (HR) regulation among the subjects who were not predisposed to motion sickness. In order to exclude predispositions to motion sickness, all the participants (n=19) underwent a Coriolis test. During an exposure to 3D and sVR the ECG signal was continuously recorded using the Holter method. For the twelve consecutive 5-min epochs of ECG signal, the analysis of heart rate variability (HRV) in time and frequency domains was conducted. After 30 min from the beginning of the training in handling the virtual workstation a significant increase in LF spectral power was noted. The values of the sympathovagal LF/HF index while sVR indicated a significant increase in sympathetic predominance in four time intervals, namely between the 5th and the 10th minute, between the 15th and the 20th minute, between the 35th and 40th minute and between the 55th and the 60th minute of exposure. PMID:26327262

The predictive value of IL28B rs12979860, rs11881222 and rs8099917 polymorphisms and IP-10 in the therapeutic response of Egyptian genotype 4 patients.

PubMed

Derbala, Moutaz; Rizk, Nasser M; Al-Kaabi, Saad; John, Anil; Sharma, Manik; El-dweik, Nazeeh; Yakoob, Rafie; Pasic, Fuad; Almohanadi, Muneera; Alejji, Khalid; Abdelmola, Abdulatif; Butt, Mohamed

2013-09-01

Interleukin-28B (IL28B) polymorphisms have previously been reported to be strongly associated with spontaneous and treatment-induced HCV viral clearance. To assess the impact of four different IL28B polymorphisms and their haplotype combination and interferon-c inducible protein 10 (IP-10) in response to treatment in Egyptian genotype 4 patients. 159 HCV-genotype 4 patients were included. All patients were treated with Peginterferon alph2a/Ribavirin for 48 wk. The following polymorphisms rs12979860, rs11881222, rs8103142 and rs8099917 and rs80803142 of Il-28 were known to be associated with the sustained virological response. They were genotyped using the TaqMan assay. IP-10 was assessed by Eliza. The data indicated that all SNPs are within the Hardy-Weinberg Equilibrium (HWE) except for rs8103142 (p=6.255(-9)), therefore it was excluded from the study since it deviates from HWE-P. The CC, AA and TT genotypes of rs12979860, rs11881222 and rs8099917 were the more frequent genotypes among the responders at RVR, EVR, ETR and SVR, respectively. The frequency of CC, CT, and TT genotype was 46.4%, 38.1% and 15.5% among responders of RVR, and was 46.9%, 45.9% and 7.2 among responders of SVR for rs12979860, respectively. The relapse rate was 18.0% and 16.0 % during EVR and ETR, while the response rate was 52.8%, 58.5%, 59.7% and 61.6% after 4, 12, 48 and 72 weeks of treatment. The transient virological response (TVR) was 6.9% among HCV patients. The results showed that the odds ratio and 95% CI of HCV genotype 4 patients to have a better sustained response to treatment (SVR) was 2.92, (1.83-4.68, p=2.01(-5)), 2.89 (1.79-4.70, p=2.53(-5)), and 2.73 (0.21-0.65, p=0.0007) for those with the major allele "C" of rs12979860, the "A" allele of rs11881222, and the "T" allele of rs8099917, respectively. Furthermore, the positive predictive value (PPV) of the major homozygous alleles for SVR with better response to therapy was in the following order: 78.69%, 68.42%, and 32.14% with a positive likelihood ratio of 1.95, 1.25, and 0.86 for rs12979860, rs11881222 and rs8099917, respectively. The haplotype formed between the 3 studied SNPs (rs12979860, rs11881222 and rs8099917) showed that two haplotypes (TGG and TGT) increased the probability of a poor response to therapy, but the CAT haplotype had the opposite effect. Multinomial logistic regression analysis revealed that the viral load and rs12979860 are the only significant actors involved in the efficacy of the treatment response among the cohort study. In addition, patients with SVR had significantly lower values of IP-10 than non-responder patients (NR), with a P-value<=0.001. In genotype 4 cases, the IL28B SNPs rs12979860 rs8099917, and rs11881222 are the strongest predictors of a response, while IP-10 is a strong negative biomarker of a response. Accounting for this factor is important in the individualization of treatment and enhances the degree of predictiveness of the IL28 polymorphism in the final treatment outcome. The frequent distribution of C, A and T alleles of IL28 polymorphism are higher among TVR, which may reflect sensitivity to prolonged course. Copyright © 2013. Published by Elsevier Inc.

Magnitude and Kinetics of Decrease in Liver Stiffness After Antiviral Therapy in Patients With Chronic Hepatitis C: A Systematic Review and Meta-analysis.

PubMed

Singh, Siddharth; Facciorusso, Antonio; Loomba, Rohit; Falck-Ytter, Yngve T

2018-01-01

We performed a systematic review and meta-analysis to estimate the decrease in liver stiffness, measured by vibration-controlled transient elastrography (VCTE), in patients with hepatitis C virus infection who achieved a sustained virologic response (SVR). We searched the literature through October 2016 for observational studies or randomized controlled trials of adults with hepatitis C virus infection who received antiviral therapy (either direct-acting antiviral agents or interferon-based therapies), underwent liver stiffness measurement using VCTE before starting therapy, and had at least 1 follow-up VCTE after completion of therapy; studies also provided data on mean or median liver stiffness measurements for patients who did and did not achieve an SVR. We identified 24 studies, and estimated weighted mean difference (and 95% confidence interval) in liver stiffness in patients with versus without SVR using random-effects meta-analysis. In patients who achieved SVR, liver stiffness decreased by 2.4 kPa at the end of therapy (95% CI, -1.7 to -3.0), by 3.1 kPa 1-6 months after therapy (95% CI, -1.6 to -4.7), by 3.2 kPa 6-12 months after therapy (90% CI, -2.6 to -3.9), and 4.1 kPa 12 months or more after therapy (95% CI, -3.3 to -4.9) (median decrease, 28.2%; interquartile range, 21.8-34.8). In contrast, there was no significant change in liver stiffness in patients who did not achieve an SVR (at 6-12 months after therapy, decrease of 0.6 kPa; 95% CI, -1.7 to 0.5). Decreases in liver stiffness were significantly greater in patients treated with direct-acting antiviral agents than with interferon-based therapy (decrease of 4.5 kPa vs decrease of 2.6 kPa; P = .03), cirrhosis at baseline (decrease of 5.1 kPa vs decrease of 2.8 kPa in patients with no cirrhosis; P = .02), or high pretreatment levels of alanine aminotransferase (P < .01). Among patients with baseline liver stiffness >9.5 kPa, 47% (95% CI, 27%-68%) achieved posttreatment liver stiffness of <9.5 kPa. In a systematic review and meta-analysis, we associated eradication of hepatitis C virus infection (SVR) with significant decreases in liver stiffness, particularly in patients with high baseline level of inflammation or patients who received direct-acting antiviral agents. Almost half the patients considered to have advanced fibrosis, based on VCTE, before therapy achieved posttreatment liver stiffness levels <9.5 kPa. Clinical Trial Registration no: CRD42016051034. Copyright © 2018 AGA Institute. Published by Elsevier Inc. All rights reserved.

Effectiveness and safety of first-generation protease inhibitors in clinical practice: Hepatitis C virus patients with advanced fibrosis

PubMed Central

Salmerón, Javier; Vinaixa, Carmen; Berenguer, Rubén; Pascasio, Juan Manuel; Sánchez Ruano, Juan José; Serra, Miguel Ángel; Gila, Ana; Diago, Moisés; Romero-Gómez, Manuel; Navarro, José María; Testillano, Milagros; Fernández, Conrado; Espinosa, Dolores; Carmona, Isabel; Pons, José Antonio; Jorquera, Francisco; Rodriguez, Francisco Javier; Pérez, Ramón; Montero, José Luis; Granados, Rafael; Fernández, Miguel; Martín, Ana Belén; Muñoz de Rueda, Paloma; Quiles, Rosa

2015-01-01

AIM: To evaluates the effectiveness and safety of the first generation, NS3/4A protease inhibitors (PIs) in clinical practice against chronic C virus, especially in patients with advanced fibrosis. METHODS: Prospective study and non-experimental analysis of a multicentre cohort of 38 Spanish hospitals that includes patients with chronic hepatitis C genotype 1, treatment-naïve (TN) or treatment-experienced (TE), who underwent triple therapy with the first generation NS3/4A protease inhibitors, boceprevir (BOC) and telaprevir (TVR), in combination with pegylated interferon and ribavirin. The patients were treatment in routine practice settings. Data on the study population and on adverse clinical and virologic effects were compiled during the treatment period and during follow up. RESULTS: One thousand and fifty seven patients were included, 405 (38%) were treated with BOC and 652 (62%) with TVR. Of this total, 30% (n = 319) were TN and the remaining were TE: 28% (n = 298) relapsers, 12% (n = 123) partial responders (PR), 25% (n = 260) null-responders (NR) and for 5% (n = 57) with prior response unknown. The rate of sustained virologic response (SVR) by intention-to-treatment (ITT) was greater in those treated with TVR (65%) than in those treated with BOC (52%) (P < 0.0001), whereas by modified intention-to-treatment (mITT) no were found significant differences. By degree of fibrosis, 56% of patients were F4 and the highest SVR rates were recorded in the non-F4 patients, both TN and TE. In the analysis by groups, the TN patients treated with TVR by ITT showed a higher SVR (P = 0.005). However, by mITT there were no significant differences between BOC and TVR. In the multivariate analysis by mITT, the significant SVR factors were relapsers, IL28B CC and non-F4; the type of treatment (BOC or TVR) was not significant. The lowest SVR values were presented by the F4-NR patients, treated with BOC (46%) or with TVR (45%). 28% of the patients interrupted the treatment, mainly by non-viral response (51%): this outcome was more frequent in the TE than in the TN patients (57% vs 40%, P = 0.01). With respect to severe haematological disorders, neutropaenia was more likely to affect the patients treated with BOC (33% vs 20%, P ≤ 0.0001), and thrombocytopaenia and anaemia, the F4 patients (P = 0.000, P = 0.025, respectively). CONCLUSION: In a real clinical practice setting with a high proportion of patients with advanced fibrosis, effectiveness of first-generation PIs was high except for NR patients, with similar SVR rates being achieved by BOC and TVR. PMID:26290644

CyberShake-derived ground-motion prediction models for the Los Angeles region with application to earthquake early warning

USGS Publications Warehouse

Bose, Maren; Graves, Robert; Gill, David; Callaghan, Scott; Maechling, Phillip J.

2014-01-01

Real-time applications such as earthquake early warning (EEW) typically use empirical ground-motion prediction equations (GMPEs) along with event magnitude and source-to-site distances to estimate expected shaking levels. In this simplified approach, effects due to finite-fault geometry, directivity and site and basin response are often generalized, which may lead to a significant under- or overestimation of shaking from large earthquakes (M > 6.5) in some locations. For enhanced site-specific ground-motion predictions considering 3-D wave-propagation effects, we develop support vector regression (SVR) models from the SCEC CyberShake low-frequency (<0.5 Hz) and broad-band (0–10 Hz) data sets. CyberShake encompasses 3-D wave-propagation simulations of >415 000 finite-fault rupture scenarios (6.5 ≤ M ≤ 8.5) for southern California defined in UCERF 2.0. We use CyberShake to demonstrate the application of synthetic waveform data to EEW as a ‘proof of concept’, being aware that these simulations are not yet fully validated and might not appropriately sample the range of rupture uncertainty. Our regression models predict the maximum and the temporal evolution of instrumental intensity (MMI) at 71 selected test sites using only the hypocentre, magnitude and rupture ratio, which characterizes uni- and bilateral rupture propagation. Our regression approach is completely data-driven (where here the CyberShake simulations are considered data) and does not enforce pre-defined functional forms or dependencies among input parameters. The models were established from a subset (∼20 per cent) of CyberShake simulations, but can explain MMI values of all >400 k rupture scenarios with a standard deviation of about 0.4 intensity units. We apply our models to determine threshold magnitudes (and warning times) for various active faults in southern California that earthquakes need to exceed to cause at least ‘moderate’, ‘strong’ or ‘very strong’ shaking in the Los Angeles (LA) basin. These thresholds are used to construct a simple and robust EEW algorithm: to declare a warning, the algorithm only needs to locate the earthquake and to verify that the corresponding magnitude threshold is exceeded. The models predict that a relatively moderate M6.5–7 earthquake along the Palos Verdes, Newport-Inglewood/Rose Canyon, Elsinore or San Jacinto faults with a rupture propagating towards LA could cause ‘very strong’ to ‘severe’ shaking in the LA basin; however, warning times for these events could exceed 30 s.

Effects of Occupational Noise Exposure on 24-Hour Ambulatory Vascular Properties in Male Workers

PubMed Central

Chang, Ta-Yuan; Su, Ta-Chen; Lin, Shou-Yu; Jain, Ruei-Man; Chan, Chang-Chuan

2007-01-01

Background Epidemiologic studies have demonstrated that occupational noise exposure is associated with hypertension, but the related mechanism in vascular structural changes is unclear. Objective This panel study aimed to investigate effects of occupational noise exposure on ambulatory vascular structural properties in male workers. Methods We recruited 20 volunteers and divided them into a high-noise–exposure group of 15 and a low-noise–exposure group of 5 based on environmental noise measurement in an automobile manufacturing company. We determined individual noise exposure and measured personal ambulatory vascular property parameters simultaneously during 24 hr. Linear mixed-effects regression models were used to estimate transient and sustained effects of noise exposure on vascular parameters by adjusting some confounders collected from self-administrated questionnaires and health checkups. Results The high-noise–exposed (85 ± 8 dBA) workers had significantly higher systemic vascular resistance (SVR) than the low-noise–exposed workers (59 ± 4 dBA) during work and sleep periods. Contrarily, low-noise–exposed workers had significantly higher brachial artery compliance (BAC), brachial artery distensibility (BAD), and systemic vascular compliance (SVC; marginal, p = 0.07) than high-noise–exposed workers during off-duty periods. We also found that high-noise–exposed workers had significantly lower BAC (1.38 ± 0.55 %mL/mmHg) and BAD (1.29 ± 0.51 %/mmHg), as well as lower SVC (0.24 ± 0.10 mL/L/mmHg), but higher SVR (1.93 ± 0.67 mL/L/min) compared with low-noise–exposed workers over a 24-hr period. Conclusions Our findings suggest that in automobile workers, occupational noise exposure may have sustained, not transient, effects on vascular properties and also enhances the development of hypertension. PMID:18008000

The evolving relationship between adiponectin and insulin sensitivity in hepatitis C patients during viral clearance.

PubMed

Chang, Ming-Ling; Kuo, Chia-Jung; Pao, Li-Heng; Hsu, Chen-Ming; Chiu, Cheng-Tang

2017-10-03

The evolution of the relationship between adiponectin and insulin sensitivity in hepatitis C virus (HCV) patients during viral clearance is unclear and warrants investigation. A prospective study including 747 consecutive chronic hepatitis C (CHC) patients, of whom 546 had completed a course of anti-HCV therapy and underwent pre-, peri- and post-therapy surveys for anthropomorphic, viral, metabolic and hepatic profiles and adiponectin levels, was conducted in a tertiary care center. Multivariate analyses indicated associations of sex, triglyceride levels and hepatic steatosis with adiponectin levels and of triglyceride levels and interferon λ3 (IFNL3) genotype with homeostasis model assessment-estimated insulin resistance (HOMA-IR) levels before anti-HCV therapy. In patients with a sustained virological response (SVR; n = 455), at 24 weeks post-therapy, sex, BMI, aspartate aminotransferase to platelet ratio index (APRI), HOMA-IR and steatosis were associated with adiponectin levels, and IFNL3 genotype was associated with HOMA-IR levels. GEE analysis demonstrated that SVR affected longitudinal trends in adiponectin levels. Compared with pre-therapy levels, adiponectin and APRI levels decreased 24 weeks post-therapy in SVR patients, regardless of baseline insulin resistance (IR). However, HOMA-IR levels decreased in SVR patients with baseline IR but increased in those without baseline IR. Compared with controls, immunohistochemical studies showed that pre-therapy CHC patients had higher hepatic adiponectin expression associated with hepatic fibrosis. During HCV infection, adiponectin may affect insulin sensitivity through triglycerides. After viral clearance, adiponectin levels were directly associated with insulin sensitivity and decreased upon improved hepatic fibrosis; with a link to the IFNL3 genotype, insulin sensitivity improved only in patients with baseline IR.

Peginterferon alfa-2b and ribavirin: effective in patients with hepatitis C who failed interferon alfa/ribavirin therapy.

PubMed

Poynard, Thierry; Colombo, Massimo; Bruix, Jordi; Schiff, Eugene; Terg, Ruben; Flamm, Steven; Moreno-Otero, Ricardo; Carrilho, Flair; Schmidt, Warren; Berg, Thomas; McGarrity, Thomas; Heathcote, E Jenny; Gonçales, Fernando; Diago, Moises; Craxi, Antonio; Silva, Marcelo; Bedossa, Pierre; Mukhopadhyay, Pabak; Griffel, Louis; Burroughs, Margaret; Brass, Clifford; Albrecht, Janice

2009-05-01

Treatment with peginterferon alfa and ribavirin produces a sustained virologic response (SVR) in approximately 60% of hepatitis C virus (HCV)-infected patients. Alternate options are needed for patients who relapse or do not respond to therapy. This prospective, international, multicenter, open-label study evaluated efficacy and safety of peginterferon alfa-2b (1.5 microg/kg/wk) plus weight-based ribavirin (800-1400 mg/day) in 2333 chronic HCV-infected patients with significant fibrosis/cirrhosis whose previous interferon alfa/ribavirin therapy failed. Patients with undetectable HCV-RNA at treatment week (TW) 12 received 48 weeks of therapy; patients with detectable HCV-RNA at TW12 could enter maintenance studies at TW18; 188 patients with low/detectable HCV-RNA at TW12 continued therapy at the investigator's request. Overall, 22% of the patients attained SVR (56% with undetectable HCV-RNA and 12% with low/detectable HCV-RNA at TW12). SVR was better in relapsers (38%) than nonresponders (14%), regardless of previous treatment, and in patients previously treated with interferon-alfa/ribavirin (25%) than peginterferon alfa-ribavirin (17%). Predictors of response in patients with undetectable HCV-RNA at TW12 were genotype (2/3 vs 1, respectively; odds ratio [OR] 2.4; P < .0001), fibrosis score (F2 vs F4; OR, 2.2; F3 vs F4; OR, 1.7; P < .0001), and baseline viral load (< or =600,000 vs >600,000 IU/mL; OR, 1.4; P = .0223). These factors plus previous treatment and response were overall predictors of SVR. Safety was similar among fibrosis groups. Peginterferon alfa-2b plus weight-based ribavirin is effective and safe in patients who failed interferon alfa/ribavirin therapy. Genotype, baseline viral load, and fibrosis stage were predictors of response.

Peginterferon alfa-2a and ribavirin for 24 weeks in hepatitis C type 1 and 4 patients with rapid virological response.

PubMed

Ferenci, Peter; Laferl, Hermann; Scherzer, Thomas-Matthias; Gschwantler, Michael; Maieron, Andreas; Brunner, Harald; Stauber, Rudolf; Bischof, Martin; Bauer, Bernhard; Datz, Christian; Löschenberger, Karin; Formann, Elisabeth; Staufer, Katharina; Steindl-Munda, Petra

2008-08-01

This analysis reports the rate of sustained virological response (SVR) in patients infected with hepatitis C virus (HCV) genotype 1 or 4 who were assigned to 24 weeks of treatment with pegylated interferon (peginterferon) alfa-2a 180 mug/wk plus ribavirin 1000/1200 mg/day after achieving a rapid virological response (RVR; HCV RNA level <50 IU/mL) at week 4 in a prospective trial investigating response-guided therapy. Non-RVR patients with an early virological response were randomized to 48 or 72 weeks of therapy (this is a still-ongoing trial). A total of 150 of 516 patients (29%) had an RVR, 143 of whom completed 24 weeks of treatment. Younger patients, leaner patients, and those with an HCV RNA level

Randomized clinical trial comparing high versus standard dose of ribavirin plus peginterferon alfa-2a in hepatitis C genotype 3 and high viral load. Dargen-3 study.

PubMed

Fernández-Rodríguez, Conrado M; Morillas, Rosa María; Masnou, Helena; Navarro, José María; Bárcena, Rafael; González, José Manuel; Martín-Martín, Leticia; Poyato, Antonio; Miquel-Planas, Mireia; Jorquera, Francisco; Casanovas, Teresa; Salmerón, Javier; Calleja, José Luis; Solà, Ricard; Alonso, Sonia; Planas, Ramón; Romero-Gomez, Manuel

2014-01-01

Less than half of patients with chronic hepatitis C genotype 3 (G3) and high viral load (HVL) without a rapid virological response (RVR) achieve a sustained virological response (SVR) when treated with peginterferon plus ribavirin (RBV). To assess the impact of high doses of RBV on SVR in patients with G3 and HVL. Ninety-seven patients were randomized to receive peginterferon α-2a+RBV 800 mg/day (A; n=42) or peginterferon α-2a+RBV 1600 mg/day+epoetin β 400 IU/kg/week SC (B; n=55). Patients allocated to group B who achieved RVR continued on RBV (800mg/day) for a further 20 weeks (B1; n=42) while non-RVR patients received a higher dose of RBV (1600 mg/day)+epoetin β (B2; n=13). RVR was observed in 64.3% of patients in A and in 76.4% in B (p=0.259). Intention-to-treat (ITT) analysis showed SVR rates of 64.3% (A) and 61.8% (B), with a reduction of -2.5% (-21.8% to 16.9%) (p=0.835). The SVR rate was 61.9% in arm B1 and 61.5% in arm B2. No serious adverse events were reported, and the rate of moderate adverse events was < 5%. G3 patients with high viral load without RVR did not obtain a benefit from a higher dose of RBV. Higher doses of RBV plus epoetin β were safe and well tolerated (Clin Trials Gov NCT00830609). Copyright © 2013 Elsevier España, S.L. and AEEH y AEG. All rights reserved.

Changes in Liver Volume in Patients with Chronic Hepatitis C Undergoing Antiviral Therapy

PubMed Central

Fitzpatrick, Julie A.; Kim, Jin Un; Cobbold, Jeremy F.L.; McPhail, Mark J.W.; Crossey, Mary M.E.; Bak-Bol, Aluel A.; Zaky, Ashraf; Taylor-Robinson, Simon D.

2016-01-01

Aim Liver volumetric analysis has not been used to detect hepatic remodelling during antiviral therapy before. We measured liver volume (LV) changes on volumetric magnetic resonance imaging during hepatitis C antiviral therapy. Methods 22 biopsy-staged patients (median [range] age 4519–65 years; 9F, 13M) with chronic hepatitis C virus infection were studied. LV was measured at the beginning, end of treatment and 6 months post-treatment using 3D T1-weighted acquisition, normalised to patient weight. Liver outlines were drawn manually on 4 mm thick image slices and LV calculated. Inter-observer agreement was analysed. Patients were also assessed longitudinally using biochemical parameters and liver stiffness using Fibroscan™. Results Sustained viral response (SVR) was achieved in 13 patients with a mean baseline LV/kg of 0.022 (SD 0.004) L/kg. At the end of treatment, the mean LV/kg was 0.025 (SD 0.004, P = 0.024 cf baseline LV/kg) and 0.026 (SD 0.004, P = 0.008 cf baseline LV/kg) 6 months post-treatment (P = 0.030 cf baseline, P = 0.004). Body weight-corrected end of treatment LV change was significantly higher in patients with SVR compared to patients not attaining SVR (P = 0.050). End of treatment LV change was correlated to initial ALT (R2 = 0.479, P = 0.037), but not APRI, AST, viral load or liver stiffness measurements. There was a correlation of 0.89 between observers for measured slice thickness. Conclusions LV increased during anti-viral treatment, while the body weight-corrected LV increase persisted post-antiviral therapy and was larger in patients with SVR. PMID:27194891

Changes in Liver Volume in Patients with Chronic Hepatitis C Undergoing Antiviral Therapy.

PubMed

Fitzpatrick, Julie A; Kim, Jin Un; Cobbold, Jeremy F L; McPhail, Mark J W; Crossey, Mary M E; Bak-Bol, Aluel A; Zaky, Ashraf; Taylor-Robinson, Simon D

2016-03-01

Liver volumetric analysis has not been used to detect hepatic remodelling during antiviral therapy before. We measured liver volume (LV) changes on volumetric magnetic resonance imaging during hepatitis C antiviral therapy. 22 biopsy-staged patients (median [range] age 45(19-65) years; 9F, 13M) with chronic hepatitis C virus infection were studied. LV was measured at the beginning, end of treatment and 6 months post-treatment using 3D T1-weighted acquisition, normalised to patient weight. Liver outlines were drawn manually on 4 mm thick image slices and LV calculated. Inter-observer agreement was analysed. Patients were also assessed longitudinally using biochemical parameters and liver stiffness using Fibroscan™. Sustained viral response (SVR) was achieved in 13 patients with a mean baseline LV/kg of 0.022 (SD 0.004) L/kg. At the end of treatment, the mean LV/kg was 0.025 (SD 0.004, P = 0.024 cf baseline LV/kg) and 0.026 (SD 0.004, P = 0.008 cf baseline LV/kg) 6 months post-treatment (P = 0.030 cf baseline, P = 0.004). Body weight-corrected end of treatment LV change was significantly higher in patients with SVR compared to patients not attaining SVR (P = 0.050). End of treatment LV change was correlated to initial ALT (R (2) = 0.479, P = 0.037), but not APRI, AST, viral load or liver stiffness measurements. There was a correlation of 0.89 between observers for measured slice thickness. LV increased during anti-viral treatment, while the body weight-corrected LV increase persisted post-antiviral therapy and was larger in patients with SVR.

Treatment of Chronic HCV Infection with the New Direct Acting Antivirals (DAA): First Report of a Real World Experience in Southern Brazil.

PubMed

Cheinquer, Hugo; Sette, Hoel; Wolff, Fernando H; de Araujo, Alexandre; Coelho-Borges, Silvia; Soares, Silvia R P; Barros, Mauricio F A

2017-01-01

There is almost no data regarding the efficacy of direct acting antivirals (DAAs) therapy in Brazil. The aim of this historical cohort study is to describe the sustained virologic response (SVR) rate among real-world compensated chronic hepatitis C patients in three hepatology centers from Southern Brazil. Patients were included if they had at least 12 weeks follow-up after the end of therapy. Patients that were lost to follow-up or had treatment prematurely interrupted for any reason were considered treatment failure in this intention to treat analysis. 219 patients were analyzed. Mean age was 57.4 ± 10.9 years and 142/219 (64.8%) were male. Genotype 1 was present in 166 patients (75.8%; 1a 29.2%, 1b 46.6%); Genotypes 2, 3 and 4 in 8 (3.7%), 43 (19.6%) and 2 (0.9%), respectively. 96 (43.8%) were cirrhotic. 134 (59.5%) were treatment experienced. DAA therapies were: sofosbuvir (SOF) + ribavirin (RBV) in 10 patients; SOF + simeprevir (SMV) ± RBV in 73; SOF + pegylated interferon (PEG-IFN) + RBV in 6; SOF + daclatasvir (DCV) ± RBV in 51, SOF + ledipasvir (LDV) ± RBV in 61, and paritaprevir/ ritonavir + ombitasvir + dasabuvir (PTVr/OBV/DSV) ± RBV in 18 patients. SVR-12 was achieved in 208/219 (95%). Ten patients had virologic failure: 6 cirrhotic, 7 treatment experienced, and 6 either genotype 3 or 1a. No adverse event was attributed to the DAA therapy. Real world experience with DAA therapy in Southern Brazil showed a high rate of SVR and excellent tolerability. Failure to achieve SVR was mainly observed among patients with at least one negative predictor of response: cirrhosis and/or genotypes 1a or 3.

Data mining model using simple and readily available factors could identify patients at high risk for hepatocellular carcinoma in chronic hepatitis C.

PubMed

Kurosaki, Masayuki; Hiramatsu, Naoki; Sakamoto, Minoru; Suzuki, Yoshiyuki; Iwasaki, Manabu; Tamori, Akihiro; Matsuura, Kentaro; Kakinuma, Sei; Sugauchi, Fuminaka; Sakamoto, Naoya; Nakagawa, Mina; Izumi, Namiki

2012-03-01

Assessment of the risk of hepatocellular carcinoma (HCC) development is essential for formulating personalized surveillance or antiviral treatment plan for chronic hepatitis C. We aimed to build a simple model for the identification of patients at high risk of developing HCC. Chronic hepatitis C patients followed for at least 5 years (n=1003) were analyzed by data mining to build a predictive model for HCC development. The model was externally validated using a cohort of 1072 patients (472 with sustained virological response (SVR) and 600 with nonSVR to PEG-interferon plus ribavirin therapy). On the basis of factors such as age, platelet, albumin, and aspartate aminotransferase, the HCC risk prediction model identified subgroups with high-, intermediate-, and low-risk of HCC with a 5-year HCC development rate of 20.9%, 6.3-7.3%, and 0-1.5%, respectively. The reproducibility of the model was confirmed through external validation (r(2)=0.981). The 10-year HCC development rate was also significantly higher in the high-and intermediate-risk group than in the low-risk group (24.5% vs. 4.8%; p<0.0001). In the high-and intermediate-risk group, the incidence of HCC development was significantly reduced in patients with SVR compared to those with nonSVR (5-year rate, 9.5% vs. 4.5%; p=0.040). The HCC risk prediction model uses simple and readily available factors and identifies patients at a high risk of HCC development. The model allows physicians to identify patients requiring HCC surveillance and those who benefit from IFN therapy to prevent HCC. Copyright © 2011 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.

HCV treatment rates and sustained viral response among people who inject drugs in seven UK sites: real world results and modelling of treatment impact.

PubMed

Martin, N K; Foster, G R; Vilar, J; Ryder, S; Cramp, M E; Gordon, F; Dillon, J F; Craine, N; Busse, H; Clements, A; Hutchinson, S J; Ustianowski, A; Ramsay, M; Goldberg, D J; Irving, W; Hope, V; De Angelis, D; Lyons, M; Vickerman, P; Hickman, M

2015-04-01

Hepatitis C virus (HCV) antiviral treatment for people who inject drugs (PWID) could prevent onwards transmission and reduce chronic prevalence. We assessed current PWID treatment rates in seven UK settings and projected the potential impact of current and scaled-up treatment on HCV chronic prevalence. Data on number of PWID treated and sustained viral response rates (SVR) were collected from seven UK settings: Bristol (37-48% HCV chronic prevalence among PWID), East London (37-48%), Manchester (48-56%), Nottingham (37-44%), Plymouth (30-37%), Dundee (20-27%) and North Wales (27-33%). A model of HCV transmission among PWID projected the 10-year impact of (i) current treatment rates and SVR (ii) scale-up with interferon-free direct acting antivirals (IFN-free DAAs) with 90% SVR. Treatment rates varied from <5 to over 25 per 1000 PWID. Pooled intention-to-treat SVR for PWID were 45% genotypes 1/4 [95%CI 33-57%] and 61% genotypes 2/3 [95%CI 47-76%]. Projections of chronic HCV prevalence among PWID after 10 years of current levels of treatment overlapped substantially with current HCV prevalence estimates. Scaling-up treatment to 26/1000 PWID annually (achieved already in two sites) with IFN-free DAAs could achieve an observable absolute reduction in HCV chronic prevalence of at least 15% among PWID in all sites and greater than a halving in chronic HCV in Plymouth, Dundee and North Wales within a decade. Current treatment rates among PWID are unlikely to achieve observable reductions in HCV chronic prevalence over the next 10 years. Achievable scale-up, however, could lead to substantial reductions in HCV chronic prevalence. © 2014 The Authors Journal of Viral Hepatitis Published by John Wiley & Sons Ltd.

HCV treatment rates and sustained viral response among people who inject drugs in seven UK sites: real world results and modelling of treatment impact

PubMed Central

Martin, N K; Foster, G R; Vilar, J; Ryder, S; E Cramp, M; Gordon, F; Dillon, J F; Craine, N; Busse, H; Clements, A; Hutchinson, S J; Ustianowski, A; Ramsay, M; Goldberg, D J; Irving, W; Hope, V; De Angelis, D; Lyons, M; Vickerman, P; Hickman, M

2015-01-01

Hepatitis C virus (HCV) antiviral treatment for people who inject drugs (PWID) could prevent onwards transmission and reduce chronic prevalence. We assessed current PWID treatment rates in seven UK settings and projected the potential impact of current and scaled-up treatment on HCV chronic prevalence. Data on number of PWID treated and sustained viral response rates (SVR) were collected from seven UK settings: Bristol (37–48% HCV chronic prevalence among PWID), East London (37–48%), Manchester (48–56%), Nottingham (37–44%), Plymouth (30–37%), Dundee (20–27%) and North Wales (27–33%). A model of HCV transmission among PWID projected the 10-year impact of (i) current treatment rates and SVR (ii) scale-up with interferon-free direct acting antivirals (IFN-free DAAs) with 90% SVR. Treatment rates varied from <5 to over 25 per 1000 PWID. Pooled intention-to-treat SVR for PWID were 45% genotypes 1/4 [95%CI 33–57%] and 61% genotypes 2/3 [95%CI 47–76%]. Projections of chronic HCV prevalence among PWID after 10 years of current levels of treatment overlapped substantially with current HCV prevalence estimates. Scaling-up treatment to 26/1000 PWID annually (achieved already in two sites) with IFN-free DAAs could achieve an observable absolute reduction in HCV chronic prevalence of at least 15% among PWID in all sites and greater than a halving in chronic HCV in Plymouth, Dundee and North Wales within a decade. Current treatment rates among PWID are unlikely to achieve observable reductions in HCV chronic prevalence over the next 10 years. Achievable scale-up, however, could lead to substantial reductions in HCV chronic prevalence. PMID:25288193

A pilot single arm observational study of sofosbuvir/ledipasvir (200 + 45 mg) in 6- to 12- year old children.

PubMed

El-Shabrawi, M H F; Kamal, N M; El-Khayat, H R; Kamal, E M; AbdElgawad, M M A H; Yakoot, M

2018-04-25

No available data on the use of sofosbuvir/ledipasvir combination in treatment of hepatitis C virus (HCV) infection in children 6- to 12- year old. To assess the safety and efficacy of sofosbuvir plus ledipasvir in children 6- to 12- year old with chronic HCV genotype 4 infection. This is a pilot prospective single arm observational open-label multicentre study. A total of 20 consecutive eligible chronic HCV infected children, aged from 6- to 12- years were included in this study and treated with a fixed sofosbuvir/ledipasvir combination in half the adult dose (200/45 mg) once daily for 12 weeks. Laboratory tests including virological markers were measured at baseline, 2, 4, 8 and 12 weeks (end of treatment [EOT]), and 12 weeks after end of treatment for sustained virological response 12 (SVR12). The intention-to-treat (ITT) SVR12 rate was 19/20 (95%; 95% CI: 76.4%-99.1%). SVR12 was not assessed in one patient who was lost to follow-up after showing viral negativity at the EOT12. All the remaining 19 patients (100%, 95% CI: 83.18%-100%) who completed the full protocol and follow-up visits achieved SVR12 with normal liver, haematological, and renal function tests and no side effects or fatalities. This pilot study demonstrated that the fixed dose sofosbuvir/ledipasvir combination could be safe and effective treatment in children 6- to 12- years with chronic hepatitis C genotype 4 infection. Our pilot results might encourage larger and multicentre studies in this age group. © 2018 John Wiley & Sons Ltd.