Predicting metabolic syndrome using decision tree and support vector machine methods.

PubMed

Karimi-Alavijeh, Farzaneh; Jalili, Saeed; Sadeghi, Masoumeh

2016-05-01

Metabolic syndrome which underlies the increased prevalence of cardiovascular disease and Type 2 diabetes is considered as a group of metabolic abnormalities including central obesity, hypertriglyceridemia, glucose intolerance, hypertension, and dyslipidemia. Recently, artificial intelligence based health-care systems are highly regarded because of its success in diagnosis, prediction, and choice of treatment. This study employs machine learning technics for predict the metabolic syndrome. This study aims to employ decision tree and support vector machine (SVM) to predict the 7-year incidence of metabolic syndrome. This research is a practical one in which data from 2107 participants of Isfahan Cohort Study has been utilized. The subjects without metabolic syndrome according to the ATPIII criteria were selected. The features that have been used in this data set include: gender, age, weight, body mass index, waist circumference, waist-to-hip ratio, hip circumference, physical activity, smoking, hypertension, antihypertensive medication use, systolic blood pressure (BP), diastolic BP, fasting blood sugar, 2-hour blood glucose, triglycerides (TGs), total cholesterol, low-density lipoprotein, high density lipoprotein-cholesterol, mean corpuscular volume, and mean corpuscular hemoglobin. Metabolic syndrome was diagnosed based on ATPIII criteria and two methods of decision tree and SVM were selected to predict the metabolic syndrome. The criteria of sensitivity, specificity and accuracy were used for validation. SVM and decision tree methods were examined according to the criteria of sensitivity, specificity and accuracy. Sensitivity, specificity and accuracy were 0.774 (0.758), 0.74 (0.72) and 0.757 (0.739) in SVM (decision tree) method. The results show that SVM method sensitivity, specificity and accuracy is more efficient than decision tree. The results of decision tree method show that the TG is the most important feature in predicting metabolic syndrome. According to this study, in cases where only the final result of the decision is regarded significant, SVM method can be used with acceptable accuracy in decision making medical issues. This method has not been implemented in the previous research.

Robust Decision-making Applied to Model Selection

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

Hemez, Francois M.

2012-08-06

The scientific and engineering communities are relying more and more on numerical models to simulate ever-increasingly complex phenomena. Selecting a model, from among a family of models that meets the simulation requirements, presents a challenge to modern-day analysts. To address this concern, a framework is adopted anchored in info-gap decision theory. The framework proposes to select models by examining the trade-offs between prediction accuracy and sensitivity to epistemic uncertainty. The framework is demonstrated on two structural engineering applications by asking the following question: Which model, of several numerical models, approximates the behavior of a structure when parameters that define eachmore » of those models are unknown? One observation is that models that are nominally more accurate are not necessarily more robust, and their accuracy can deteriorate greatly depending upon the assumptions made. It is posited that, as reliance on numerical models increases, establishing robustness will become as important as demonstrating accuracy.« less

Improving the sensitivity and specificity of a bioanalytical assay for the measurement of certolizumab pegol.

PubMed

Smeraglia, John; Silva, John-Paul; Jones, Kieran

2017-08-01

In order to evaluate placental transfer of certolizumab pegol (CZP), a more sensitive and selective bioanalytical assay was required to accurately measure low CZP concentrations in infant and umbilical cord blood. Results & methodology: A new electrochemiluminescence immunoassay was developed to measure CZP levels in human plasma. Validation experiments demonstrated improved selectivity (no matrix interference observed) and a detection range of 0.032-5.0 μg/ml. Accuracy and precision met acceptance criteria (mean total error ≤20.8%). Dilution linearity and sample stability were acceptable and sufficient to support the method. The electrochemiluminescence immunoassay was validated for measuring low CZP concentrations in human plasma. The method demonstrated a more than tenfold increase in sensitivity compared with previous assays, and improved selectivity for intact CZP.

[Ways to improve measurement accuracy of blood glucose sensing by mid-infrared spectroscopy].

PubMed

Wang, Yan; Li, Ning; Xu, Kexin

2006-06-01

Mid-infrared (MIR) spectroscopy is applicable to blood glucose sensing without using any reagent, however, due to a result of inadequate accuracy, till now this method has not been used in clinical detection. The principle and key technologies of blood glucose sensing by MIR spectroscopy are presented in this paper. Along with our experimental results, the paper analyzes ways to enhance measurement accuracy and prediction accuracy by the following four methods: selection of optimized spectral region; application of spectra data processing method; elimination of the interference with other components in the blood, and promotion in system hardware. According to these four improving methods, we designed four experiments, i.e., strict determination of the region where glucose concentration changes most sensitively in MIR, application of genetic algorithm for wavelength selection, normalization of spectra for the purpose of enhancing measuring reproduction, and utilization of CO2 laser as light source. The results show that the measurement accuracy of blood glucose concentration is enhanced almost to a clinical detection level.

Characterization and Selection of Polymer Materials for Binary Munitions Storage. Part 3. Branch Content Determination.

DTIC Science & Technology

1987-09-01

accuracy. The data aquisition system combines a position- sensitive X-ray detector with a 65 kilobyte microcomputer capable of operating as a...The rapid X-ray diffraction system measures intensity versus 20 patterns by placing the detector with its sensitivity axis positioned parallel to the...plane of the diffractometer (see Figure 2). As shown in Figure 2, the detector sensitivity axis z is coplanar with both the incident beam and the

Bias in estimating accuracy of a binary screening test with differential disease verification

PubMed Central

Brinton, John T.; Ringham, Brandy M.; Glueck, Deborah H.

2011-01-01

SUMMARY Sensitivity, specificity, positive and negative predictive value are typically used to quantify the accuracy of a binary screening test. In some studies it may not be ethical or feasible to obtain definitive disease ascertainment for all subjects using a gold standard test. When a gold standard test cannot be used an imperfect reference test that is less than 100% sensitive and specific may be used instead. In breast cancer screening, for example, follow-up for cancer diagnosis is used as an imperfect reference test for women where it is not possible to obtain gold standard results. This incomplete ascertainment of true disease, or differential disease verification, can result in biased estimates of accuracy. In this paper, we derive the apparent accuracy values for studies subject to differential verification. We determine how the bias is affected by the accuracy of the imperfect reference test, the percent who receive the imperfect reference standard test not receiving the gold standard, the prevalence of the disease, and the correlation between the results for the screening test and the imperfect reference test. It is shown that designs with differential disease verification can yield biased estimates of accuracy. Estimates of sensitivity in cancer screening trials may be substantially biased. However, careful design decisions, including selection of the imperfect reference test, can help to minimize bias. A hypothetical breast cancer screening study is used to illustrate the problem. PMID:21495059

The Accuracy of Integrated [18F] Fluorodeoxyglucose-Positron Emission Tomography/Computed Tomography in Detection of Pelvic and Para-aortic Nodal Metastasis in Patients with High Risk Endometrial Cancer

PubMed Central

Gholkar, Nikhil Shirish; Saha, Subhas Chandra; Prasad, GRV; Bhattacharya, Anish; Srinivasan, Radhika; Suri, Vanita

2014-01-01

Lymph nodal (LN) metastasis is the most important prognostic factor in high-risk endometrial cancer. However, the benefit of routine lymphadenectomy in endometrial cancer is controversial. This study was conducted to assess the accuracy of [18F] fluorodeoxyglucose-positron emission tomography/computed tomography ([18F] FDG-PET/CT) in detection of pelvic and para-aortic nodal metastases in high-risk endometrial cancer. 20 patients with high-risk endometrial carcinoma underwent [18F] FDG-PET/CT followed by total abdominal hysterectomy, bilateral salpingo-oophorectomy and systematic pelvic lymphadenectomy with or without para-aortic lymphadenectomy. The findings on histopathology were compared with [18F] FDG-PET/CT findings to calculate the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of [18F] FDG-PET/CT. The pelvic nodal findings were analyzed on a patient and nodal chain based criteria. The para-aortic nodal findings were reported separately. Histopathology documented nodal involvement in two patients (10%). For detection of pelvic nodes, on a patient based analysis, [18F] FDG-PET/CT had a sensitivity of 100%, specificity of 61.11%, PPV of 22.22%, NPV of 100% and accuracy of 65% and on a nodal chain based analysis, [18F] FDG-PET/CT had a sensitivity of 100%, specificity of 80%, PPV of 20%, NPV of 100%, and accuracy of 80.95%. For detection of para-aortic nodes, [18F] FDG-PET/CT had sensitivity of 100%, specificity of 66.67%, PPV of 20%, NPV of 100%, and accuracy of 69.23%. Although [18F] FDG-PET/CT has high sensitivity for detection of LN metastasis in endometrial carcinoma, it had moderate accuracy and high false positivity. However, the high NPV is important in selecting patients in whom lymphadenectomy may be omitted. PMID:25538488

Fragment-based prediction of skin sensitization using recursive partitioning

NASA Astrophysics Data System (ADS)

Lu, Jing; Zheng, Mingyue; Wang, Yong; Shen, Qiancheng; Luo, Xiaomin; Jiang, Hualiang; Chen, Kaixian

2011-09-01

Skin sensitization is an important toxic endpoint in the risk assessment of chemicals. In this paper, structure-activity relationships analysis was performed on the skin sensitization potential of 357 compounds with local lymph node assay data. Structural fragments were extracted by GASTON (GrAph/Sequence/Tree extractiON) from the training set. Eight fragments with accuracy significantly higher than 0.73 ( p < 0.1) were retained to make up an indicator descriptor fragment. The fragment descriptor and eight other physicochemical descriptors closely related to the endpoint were calculated to construct the recursive partitioning tree (RP tree) for classification. The balanced accuracy of the training set, test set I, and test set II in the leave-one-out model were 0.846, 0.800, and 0.809, respectively. The results highlight that fragment-based RP tree is a preferable method for identifying skin sensitizers. Moreover, the selected fragments provide useful structural information for exploring sensitization mechanisms, and RP tree creates a graphic tree to identify the most important properties associated with skin sensitization. They can provide some guidance for designing of drugs with lower sensitization level.

Molecular tools for diagnosis of visceral leishmaniasis: systematic review and meta-analysis of diagnostic test accuracy.

PubMed

de Ruiter, C M; van der Veer, C; Leeflang, M M G; Deborggraeve, S; Lucas, C; Adams, E R

2014-09-01

Molecular methods have been proposed as highly sensitive tools for the detection of Leishmania parasites in visceral leishmaniasis (VL) patients. Here, we evaluate the diagnostic accuracy of these tools in a meta-analysis of the published literature. The selection criteria were original studies that evaluate the sensitivities and specificities of molecular tests for diagnosis of VL, adequate classification of study participants, and the absolute numbers of true positives and negatives derivable from the data presented. Forty studies met the selection criteria, including PCR, real-time PCR, nucleic acid sequence-based amplification (NASBA), and loop-mediated isothermal amplification (LAMP). The sensitivities of the individual studies ranged from 29 to 100%, and the specificities ranged from 25 to 100%. The pooled sensitivity of PCR in whole blood was 93.1% (95% confidence interval [CI], 90.0 to 95.2), and the specificity was 95.6% (95% CI, 87.0 to 98.6). The specificity was significantly lower in consecutive studies, at 63.3% (95% CI, 53.9 to 71.8), due either to true-positive patients not being identified by parasitological methods or to the number of asymptomatic carriers in areas of endemicity. PCR for patients with HIV-VL coinfection showed high diagnostic accuracy in buffy coat and bone marrow, ranging from 93.1 to 96.9%. Molecular tools are highly sensitive assays for Leishmania detection and may contribute as an additional test in the algorithm, together with a clear clinical case definition. We observed wide variety in reference standards and study designs and now recommend consecutively designed studies. Copyright © 2014, American Society for Microbiology. All Rights Reserved.

Reduced size first-order subsonic and supersonic aeroelastic modeling

NASA Technical Reports Server (NTRS)

Karpel, Mordechay

1990-01-01

Various aeroelastic, aeroservoelastic, dynamic-response, and sensitivity analyses are based on a time-domain first-order (state-space) formulation of the equations of motion. The formulation of this paper is based on the minimum-state (MS) aerodynamic approximation method, which yields a low number of aerodynamic augmenting states. Modifications of the MS and the physical weighting procedures make the modeling method even more attractive. The flexibility of constraint selection is increased without increasing the approximation problem size; the accuracy of dynamic residualization of high-frequency modes is improved; and the resulting model is less sensitive to parametric changes in subsequent analyses. Applications to subsonic and supersonic cases demonstrate the generality, flexibility, accuracy, and efficiency of the method.

Mammogram classification scheme using 2D-discrete wavelet and local binary pattern for detection of breast cancer

NASA Astrophysics Data System (ADS)

Adi Putra, Januar

2018-04-01

In this paper, we propose a new mammogram classification scheme to classify the breast tissues as normal or abnormal. Feature matrix is generated using Local Binary Pattern to all the detailed coefficients from 2D-DWT of the region of interest (ROI) of a mammogram. Feature selection is done by selecting the relevant features that affect the classification. Feature selection is used to reduce the dimensionality of data and features that are not relevant, in this paper the F-test and Ttest will be performed to the results of the feature extraction dataset to reduce and select the relevant feature. The best features are used in a Neural Network classifier for classification. In this research we use MIAS and DDSM database. In addition to the suggested scheme, the competent schemes are also simulated for comparative analysis. It is observed that the proposed scheme has a better say with respect to accuracy, specificity and sensitivity. Based on experiments, the performance of the proposed scheme can produce high accuracy that is 92.71%, while the lowest accuracy obtained is 77.08%.

Improved accuracy of co-morbidity coding over time after the introduction of ICD-10 administrative data

PubMed Central

2011-01-01

Background Co-morbidity information derived from administrative data needs to be validated to allow its regular use. We assessed evolution in the accuracy of coding for Charlson and Elixhauser co-morbidities at three time points over a 5-year period, following the introduction of the International Classification of Diseases, 10th Revision (ICD-10), coding of hospital discharges. Methods Cross-sectional time trend evaluation study of coding accuracy using hospital chart data of 3'499 randomly selected patients who were discharged in 1999, 2001 and 2003, from two teaching and one non-teaching hospital in Switzerland. We measured sensitivity, positive predictive and Kappa values for agreement between administrative data coded with ICD-10 and chart data as the 'reference standard' for recording 36 co-morbidities. Results For the 17 the Charlson co-morbidities, the sensitivity - median (min-max) - was 36.5% (17.4-64.1) in 1999, 42.5% (22.2-64.6) in 2001 and 42.8% (8.4-75.6) in 2003. For the 29 Elixhauser co-morbidities, the sensitivity was 34.2% (1.9-64.1) in 1999, 38.6% (10.5-66.5) in 2001 and 41.6% (5.1-76.5) in 2003. Between 1999 and 2003, sensitivity estimates increased for 30 co-morbidities and decreased for 6 co-morbidities. The increase in sensitivities was statistically significant for six conditions and the decrease significant for one. Kappa values were increased for 29 co-morbidities and decreased for seven. Conclusions Accuracy of administrative data in recording clinical conditions improved slightly between 1999 and 2003. These findings are of relevance to all jurisdictions introducing new coding systems, because they demonstrate a phenomenon of improved administrative data accuracy that may relate to a coding 'learning curve' with the new coding system. PMID:21849089

Improved accuracy of co-morbidity coding over time after the introduction of ICD-10 administrative data.

PubMed

Januel, Jean-Marie; Luthi, Jean-Christophe; Quan, Hude; Borst, François; Taffé, Patrick; Ghali, William A; Burnand, Bernard

2011-08-18

Co-morbidity information derived from administrative data needs to be validated to allow its regular use. We assessed evolution in the accuracy of coding for Charlson and Elixhauser co-morbidities at three time points over a 5-year period, following the introduction of the International Classification of Diseases, 10th Revision (ICD-10), coding of hospital discharges. Cross-sectional time trend evaluation study of coding accuracy using hospital chart data of 3'499 randomly selected patients who were discharged in 1999, 2001 and 2003, from two teaching and one non-teaching hospital in Switzerland. We measured sensitivity, positive predictive and Kappa values for agreement between administrative data coded with ICD-10 and chart data as the 'reference standard' for recording 36 co-morbidities. For the 17 the Charlson co-morbidities, the sensitivity - median (min-max) - was 36.5% (17.4-64.1) in 1999, 42.5% (22.2-64.6) in 2001 and 42.8% (8.4-75.6) in 2003. For the 29 Elixhauser co-morbidities, the sensitivity was 34.2% (1.9-64.1) in 1999, 38.6% (10.5-66.5) in 2001 and 41.6% (5.1-76.5) in 2003. Between 1999 and 2003, sensitivity estimates increased for 30 co-morbidities and decreased for 6 co-morbidities. The increase in sensitivities was statistically significant for six conditions and the decrease significant for one. Kappa values were increased for 29 co-morbidities and decreased for seven. Accuracy of administrative data in recording clinical conditions improved slightly between 1999 and 2003. These findings are of relevance to all jurisdictions introducing new coding systems, because they demonstrate a phenomenon of improved administrative data accuracy that may relate to a coding 'learning curve' with the new coding system.

[The diagnostic value of ultrasonic elastography and ultrasonography comprehensive score in cervical lesions].

PubMed

Lu, R; Xiao, Y

2017-07-18

Objective: To evaluate the clinical value of ultrasonic elastography and ultrasonography comprehensive scoring method in the diagnosis of cervical lesions. Methods: A total of 116 patients were selected from the Department of Gynecology of the first hospital affiliated with Central South University from March 2014 to September 2015.All of the lesions were preoperatively examined by Doppler Ultrasound and elastography.The elasticity score was determined by a 5-point scoring method. Calculation of the strain ratio was based on a comparison of the average strain measured in the lesion with the adjacent tissue of the same depth, size, and shape.All these ultrasonic parameters were quantified, added, and arrived at ultrasonography comprehensive scores.To use surgical pathology as the gold standard, the sensitivity, specificity, accuracy of Doppler Ultrasound, elasticity score and strain ratio methods and ultrasonography comprehensive scoring method were comparatively analyzed. Results: (1) The sensitivity, specificity, and accuracy of Doppler Ultrasound in diagnosing cervical lesions were 82.89% (63/76), 85.0% (34/40), and 83.62% (97/116), respectively.(2) The sensitivity, specificity, and accuracy of the elasticity score method were 77.63% (59/76), 82.5% (33/40), and 79.31% (92/116), respectively; the sensitivity, specificity, and accuracy of the strain ratio measure method were 84.21% (64/76), 87.5% (35/40), and 85.34% (99/116), respectively.(3) The sensitivity, specificity, and accuracy of ultrasonography comprehensive scoring method were 90.79% (69/76), 92.5% (37/40), and 91.38% (106/116), respectively. Conclusion: (1) It was obvious that ultrasonic elastography had certain diagnostic value in cervical lesions. Strain ratio measurement can be more objective than elasticity score method.(2) The combined application of ultrasonography comprehensive scoring method, ultrasonic elastography and conventional sonography was more accurate than single parameter.

Accuracy of software-assisted detection of tumour feeders in transcatheter hepatic chemoembolization using three target definition protocols.

PubMed

Iwazawa, J; Ohue, S; Hashimoto, N; Mitani, T

2014-02-01

To compare the accuracy of computer software analysis using three different target-definition protocols to detect tumour feeder vessels for transarterial chemoembolization of hepatocellular carcinoma. C-arm computed tomography (CT) data were analysed for 81 tumours from 57 patients who had undergone chemoembolization using software-assisted detection of tumour feeders. Small, medium, and large-sized targets were manually defined for each tumour. The tumour feeder was verified when the target tumour was enhanced on selective C-arm CT of the investigated vessel during chemoembolization. The sensitivity, specificity, and accuracy of the three protocols were evaluated and compared. One hundred and eight feeder vessels supplying 81 lesions were detected. The sensitivity of the small, medium, and large target protocols was 79.8%, 91.7%, and 96.3%, respectively; specificity was 95%, 88%, and 50%, respectively; and accuracy was 87.5%, 89.9%, and 74%, respectively. The sensitivity was significantly higher for the medium (p = 0.003) and large (p < 0.001) target protocols than for the small target protocol. The specificity and accuracy were higher for the small (p < 0.001 and p < 0.001, respectively) and medium (p < 0.001 and p < 0.001, respectively) target protocols than for the large target protocol. The overall accuracy of software-assisted automated feeder analysis in transarterial chemoembolization for hepatocellular carcinoma is affected by the target definition size. A large target definition increases sensitivity and decreases specificity in detecting tumour feeders. A target size equivalent to the tumour size most accurately predicts tumour feeders. Copyright © 2013 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Evaluation Methodology between Globalization and Localization Features Approaches for Skin Cancer Lesions Classification

NASA Astrophysics Data System (ADS)

Ahmed, H. M.; Al-azawi, R. J.; Abdulhameed, A. A.

2018-05-01

Huge efforts have been put in the developing of diagnostic methods to skin cancer disease. In this paper, two different approaches have been addressed for detection the skin cancer in dermoscopy images. The first approach uses a global method that uses global features for classifying skin lesions, whereas the second approach uses a local method that uses local features for classifying skin lesions. The aim of this paper is selecting the best approach for skin lesion classification. The dataset has been used in this paper consist of 200 dermoscopy images from Pedro Hispano Hospital (PH2). The achieved results are; sensitivity about 96%, specificity about 100%, precision about 100%, and accuracy about 97% for globalization approach while, sensitivity about 100%, specificity about 100%, precision about 100%, and accuracy about 100% for Localization Approach, these results showed that the localization approach achieved acceptable accuracy and better than globalization approach for skin cancer lesions classification.

Is early detection of abused children possible?: a systematic review of the diagnostic accuracy of the identification of abused children

PubMed Central

2013-01-01

Background Early detection of abused children could help decrease mortality and morbidity related to this major public health problem. Several authors have proposed tools to screen for child maltreatment. The aim of this systematic review was to examine the evidence on accuracy of tools proposed to identify abused children before their death and assess if any were adapted to screening. Methods We searched in PUBMED, PsycINFO, SCOPUS, FRANCIS and PASCAL for studies estimating diagnostic accuracy of tools identifying neglect, or physical, psychological or sexual abuse of children, published in English or French from 1961 to April 2012. We extracted selected information about study design, patient populations, assessment methods, and the accuracy parameters. Study quality was assessed using QUADAS criteria. Results A total of 2 280 articles were identified. Thirteen studies were selected, of which seven dealt with physical abuse, four with sexual abuse, one with emotional abuse, and one with any abuse and physical neglect. Study quality was low, even when not considering the lack of gold standard for detection of abused children. In 11 studies, instruments identified abused children only when they had clinical symptoms. Sensitivity of tests varied between 0.26 (95% confidence interval [0.17-0.36]) and 0.97 [0.84-1], and specificity between 0.51 [0.39-0.63] and 1 [0.95-1]. The sensitivity was greater than 90% only for three tests: the absence of scalp swelling to identify children victims of inflicted head injury; a decision tool to identify physically-abused children among those hospitalized in a Pediatric Intensive Care Unit; and a parental interview integrating twelve child symptoms to identify sexually-abused children. When the sensitivity was high, the specificity was always smaller than 90%. Conclusions In 2012, there is low-quality evidence on the accuracy of instruments for identifying abused children. Identified tools were not adapted to screening because of low sensitivity and late identification of abused children when they have already serious consequences of maltreatment. Development of valid screening instruments is a pre-requisite before considering screening programs. PMID:24314318

Selection of noisy measurement locations for error reduction in static parameter identification

NASA Astrophysics Data System (ADS)

Sanayei, Masoud; Onipede, Oladipo; Babu, Suresh R.

1992-09-01

An incomplete set of noisy static force and displacement measurements is used for parameter identification of structures at the element level. Measurement location and the level of accuracy in the measured data can drastically affect the accuracy of the identified parameters. A heuristic method is presented to select a limited number of degrees of freedom (DOF) to perform a successful parameter identification and to reduce the impact of measurement errors on the identified parameters. This pretest simulation uses an error sensitivity analysis to determine the effect of measurement errors on the parameter estimates. The selected DOF can be used for nondestructive testing and health monitoring of structures. Two numerical examples, one for a truss and one for a frame, are presented to demonstrate that using the measurements at the selected subset of DOF can limit the error in the parameter estimates.

A ratiometric strategy -based electrochemical sensing interface for the sensitive and reliable detection of imidacloprid.

PubMed

Li, Xueyan; Kan, Xianwen

2018-04-30

In this study, a ratiometric strategy-based electrochemical sensor was developed by electropolymerization of thionine (THI) and β-cyclodextrin (β-CD) composite films on a glassy carbon electrode surface for imidacloprid (IMI) detection. THI played the role of an inner reference element to provide a built-in correction. In addition, the modified β-CD showed good selective enrichment for IMI to improve the sensitivity and anti-interference ability of the sensor. The current ratio between IMI and THI was calculated as the detected signal for IMI sensing. Compared with common single-signal sensing, the proposed ratiometric strategy showed a higher linear range and a lower limit of detection of 4.0 × 10-8-1.0 × 10-5 mol L-1 and 1.7 × 10-8 mol L-1, respectively, for IMI detection. On the other hand, the ratiometric strategy endowed the sensor with good accuracy, reproducibility, and stability. The sensor was also used for IMI determination in real samples with satisfactory results. The simple, effective, and reliable way reported in this study can be further used to prepare ratiometric strategy-based electrochemical sensors for the selective and sensitive detection of other compounds with good accuracy and stability.

Dental History Predictors of Caries Related Dental Emergencies.

DTIC Science & Technology

1981-11-01

10+) 50% of those with U- lesions would be selected and only 4% of those without disease would be selected. The accuracy of such a system as well as...sufficient sensitivity, specificity, and diagnostic power to be useful as predictive tools. Dental health classification systems are typically only...predicted with some reliability given the intimacy of the relationship and the relatively long duration of the pre-emergency state. The incidence of

Strategies to Improve the Accuracy of Mars-GRAM Sensitivity Studies at Large Optical Depths

NASA Technical Reports Server (NTRS)

Justh, Hilary L.; Justus, Carl G.; Badger, Andrew M.

2010-01-01

The poster provides an overview of techniques to improve the Mars Global Reference Atmospheric Model (Mars-GRAM) sensitivity. It has been discovered during the Mars Science Laboratory (MSL) site selection process that the Mars Global Reference Atmospheric Model (Mars-GRAM) when used for sensitivity studies for TES MapYear = 0 and large optical depth values such as tau = 3 is less than realistic. A preliminary fix has been made to Mars-GRAM by adding a density factor value that was determined for tau = 0.3, 1 and 3.

[Quantitative surface analysis of Pt-Co, Cu-Au and Cu-Ag alloy films by XPS and AES].

PubMed

Li, Lian-Zhong; Zhuo, Shang-Jun; Shen, Ru-Xiang; Qian, Rong; Gao, Jie

2013-11-01

In order to improve the quantitative analysis accuracy of AES, We associated XPS with AES and studied the method to reduce the error of AES quantitative analysis, selected Pt-Co, Cu-Au and Cu-Ag binary alloy thin-films as the samples, used XPS to correct AES quantitative analysis results by changing the auger sensitivity factors to make their quantitative analysis results more similar. Then we verified the accuracy of the quantitative analysis of AES when using the revised sensitivity factors by other samples with different composition ratio, and the results showed that the corrected relative sensitivity factors can reduce the error in quantitative analysis of AES to less than 10%. Peak defining is difficult in the form of the integral spectrum of AES analysis since choosing the starting point and ending point when determining the characteristic auger peak intensity area with great uncertainty, and to make analysis easier, we also processed data in the form of the differential spectrum, made quantitative analysis on the basis of peak to peak height instead of peak area, corrected the relative sensitivity factors, and verified the accuracy of quantitative analysis by the other samples with different composition ratio. The result showed that the analytical error in quantitative analysis of AES reduced to less than 9%. It showed that the accuracy of AES quantitative analysis can be highly improved by the way of associating XPS with AES to correct the auger sensitivity factors since the matrix effects are taken into account. Good consistency was presented, proving the feasibility of this method.

Vertical or horizontal orientation of foot radiographs does not affect image interpretation

PubMed Central

Ferran, Nicholas Antonio; Ball, Luke; Maffulli, Nicola

2012-01-01

Summary This study determined whether the orientation of dorsoplantar and oblique foot radiographs has an effect on radiograph interpretation. A test set of 50 consecutive foot radiographs were selected (25 with fractures, and 25 normal), and duplicated in the horizontal orientation. The images were randomly arranged, numbered 1 through 100, and analysed by six image interpreters. Vertical and horizontal area under the ROC curve, accuracy, sensitivity and specificity were calculated for each image interpreter. There was no significant difference in the area under the ROC curve, accuracy, sensitivity or specificity of image interpretation between images viewed in the vertical or horizontal orientation. While conventions for display of radiographs may help to improve the development of an efficient visual search strategy in trainees, and allow for standardisation of publication of radiographic images, variation from the convention in clinical practice does not appear to affect the sensitivity or specificity of image interpretation. PMID:23738310

Field design factors affecting the precision of ryegrass forage yield estimation

USDA-ARS?s Scientific Manuscript database

Field-based agronomic and genetic research relies heavily on the data generated from field evaluations. Therefore, it is imperative to optimize the precision and accuracy of yield estimates in cultivar evaluation trials to make reliable selections. Experimental error in yield trials is sensitive to ...

The urine dipstick test useful to rule out infections. A meta-analysis of the accuracy

PubMed Central

Devillé, Walter LJM; Yzermans, Joris C; van Duijn, Nico P; Bezemer, P Dick; van der Windt, Daniëlle AWM; Bouter, Lex M

2004-01-01

Background Many studies have evaluated the accuracy of dipstick tests as rapid detectors of bacteriuria and urinary tract infections (UTI). The lack of an adequate explanation for the heterogeneity of the dipstick accuracy stimulates an ongoing debate. The objective of the present meta-analysis was to summarise the available evidence on the diagnostic accuracy of the urine dipstick test, taking into account various pre-defined potential sources of heterogeneity. Methods Literature from 1990 through 1999 was searched in Medline and Embase, and by reference tracking. Selected publications should be concerned with the diagnosis of bacteriuria or urinary tract infections, investigate the use of dipstick tests for nitrites and/or leukocyte esterase, and present empirical data. A checklist was used to assess methodological quality. Results 70 publications were included. Accuracy of nitrites was high in pregnant women (Diagnostic Odds Ratio = 165) and elderly people (DOR = 108). Positive predictive values were ≥80% in elderly and in family medicine. Accuracy of leukocyte-esterase was high in studies in urology patients (DOR = 276). Sensitivities were highest in family medicine (86%). Negative predictive values were high in both tests in all patient groups and settings, except for in family medicine. The combination of both test results showed an important increase in sensitivity. Accuracy was high in studies in urology patients (DOR = 52), in children (DOR = 46), and if clinical information was present (DOR = 28). Sensitivity was highest in studies carried out in family medicine (90%). Predictive values of combinations of positive test results were low in all other situations. Conclusions Overall, this review demonstrates that the urine dipstick test alone seems to be useful in all populations to exclude the presence of infection if the results of both nitrites and leukocyte-esterase are negative. Sensitivities of the combination of both tests vary between 68 and 88% in different patient groups, but positive test results have to be confirmed. Although the combination of positive test results is very sensitive in family practice, the usefulness of the dipstick test alone to rule in infection remains doubtful, even with high pre-test probabilities. PMID:15175113

Decoding facial expressions based on face-selective and motion-sensitive areas.

PubMed

Liang, Yin; Liu, Baolin; Xu, Junhai; Zhang, Gaoyan; Li, Xianglin; Wang, Peiyuan; Wang, Bin

2017-06-01

Humans can easily recognize others' facial expressions. Among the brain substrates that enable this ability, considerable attention has been paid to face-selective areas; in contrast, whether motion-sensitive areas, which clearly exhibit sensitivity to facial movements, are involved in facial expression recognition remained unclear. The present functional magnetic resonance imaging (fMRI) study used multi-voxel pattern analysis (MVPA) to explore facial expression decoding in both face-selective and motion-sensitive areas. In a block design experiment, participants viewed facial expressions of six basic emotions (anger, disgust, fear, joy, sadness, and surprise) in images, videos, and eyes-obscured videos. Due to the use of multiple stimulus types, the impacts of facial motion and eye-related information on facial expression decoding were also examined. It was found that motion-sensitive areas showed significant responses to emotional expressions and that dynamic expressions could be successfully decoded in both face-selective and motion-sensitive areas. Compared with static stimuli, dynamic expressions elicited consistently higher neural responses and decoding performance in all regions. A significant decrease in both activation and decoding accuracy due to the absence of eye-related information was also observed. Overall, the findings showed that emotional expressions are represented in motion-sensitive areas in addition to conventional face-selective areas, suggesting that motion-sensitive regions may also effectively contribute to facial expression recognition. The results also suggested that facial motion and eye-related information played important roles by carrying considerable expression information that could facilitate facial expression recognition. Hum Brain Mapp 38:3113-3125, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Office hysteroscopic-guided selective tubal chromopertubation: acceptability, feasibility and diagnostic accuracy of this new diagnostic non-invasive technique in infertile women.

PubMed

Carta, Gaspare; Palermo, Patrizia; Pasquale, Chiara; Conte, Valeria; Pulcinella, Ruggero; Necozione, Stefano; Cofini, Vincenza; Patacchiola, Felice

2018-06-01

The aim of this study was to evaluate accuracy, tolerability and side effects of office hysteroscopic-guided chromoperturbations in infertile women without anaesthesia. Forty-nine infertile women underwent the procedure to evaluate tubal patency and the uterine cavity. Women with unilateral or bilateral tubal stenosis at hysteroscopy with chromoperturbation, and women with bilateral tubal patency who did not conceive during the period of six months, underwent laparoscopy with chromoperturbation. The results obtained from hysteroscopy and laparoscopy in the assessment of tubal patency were compared. Sensitivity, specificity, accuracy, positive-predictive value and negative-predictive value were used to describe diagnostic performance. Pain and tolerance were assessed during procedure using a visual analogue scale (VAS). Side effects or late complications and pregnancy rate were also recorded three and six months after the procedure. The specificity was 87.8% (95% CI: 73.80-95.90), sensitivity was 85.7% (95% CI 57.20-98.20), positive and negative predictive values were 70.6% (95% CI: 44.00-89) and 94.7% (95% CI: 82.30-99.40), respectively. Pregnancy rate (PR) within six months after performance of hysteroscopy with chromoperturbation was 27%. Office hysteroscopy-guided selective chromoperturbation in infertile patients is a valid technique to evaluate tubal patency and uterine cavity.

Signal Detection Theory as a Tool for Successful Student Selection

ERIC Educational Resources Information Center

van Ooijen-van der Linden, Linda; van der Smagt, Maarten J.; Woertman, Liesbeth; te Pas, Susan F.

2017-01-01

Prediction accuracy of academic achievement for admission purposes requires adequate "sensitivity" and "specificity" of admission tools, yet the available information on the validity and predictive power of admission tools is largely based on studies using correlational and regression statistics. The goal of this study was to…

The Evaluation of Bivariate Mixed Models in Meta-analyses of Diagnostic Accuracy Studies with SAS, Stata and R.

PubMed

Vogelgesang, Felicitas; Schlattmann, Peter; Dewey, Marc

2018-05-01

Meta-analyses require a thoroughly planned procedure to obtain unbiased overall estimates. From a statistical point of view not only model selection but also model implementation in the software affects the results. The present simulation study investigates the accuracy of different implementations of general and generalized bivariate mixed models in SAS (using proc mixed, proc glimmix and proc nlmixed), Stata (using gllamm, xtmelogit and midas) and R (using reitsma from package mada and glmer from package lme4). Both models incorporate the relationship between sensitivity and specificity - the two outcomes of interest in meta-analyses of diagnostic accuracy studies - utilizing random effects. Model performance is compared in nine meta-analytic scenarios reflecting the combination of three sizes for meta-analyses (89, 30 and 10 studies) with three pairs of sensitivity/specificity values (97%/87%; 85%/75%; 90%/93%). The evaluation of accuracy in terms of bias, standard error and mean squared error reveals that all implementations of the generalized bivariate model calculate sensitivity and specificity estimates with deviations less than two percentage points. proc mixed which together with reitsma implements the general bivariate mixed model proposed by Reitsma rather shows convergence problems. The random effect parameters are in general underestimated. This study shows that flexibility and simplicity of model specification together with convergence robustness should influence implementation recommendations, as the accuracy in terms of bias was acceptable in all implementations using the generalized approach. Schattauer GmbH.

Automatic classification of small bowel mucosa alterations in celiac disease for confocal laser endomicroscopy

NASA Astrophysics Data System (ADS)

Boschetto, Davide; Di Claudio, Gianluca; Mirzaei, Hadis; Leong, Rupert; Grisan, Enrico

2016-03-01

Celiac disease (CD) is an immune-mediated enteropathy triggered by exposure to gluten and similar proteins, affecting genetically susceptible persons, increasing their risk of different complications. Small bowels mucosa damage due to CD involves various degrees of endoscopically relevant lesions, which are not easily recognized: their overall sensitivity and positive predictive values are poor even when zoom-endoscopy is used. Confocal Laser Endomicroscopy (CLE) allows skilled and trained experts to qualitative evaluate mucosa alteration such as a decrease in goblet cells density, presence of villous atrophy or crypt hypertrophy. We present a method for automatically classifying CLE images into three different classes: normal regions, villous atrophy and crypt hypertrophy. This classification is performed after a features selection process, in which four features are extracted from each image, through the application of homomorphic filtering and border identification through Canny and Sobel operators. Three different classifiers have been tested on a dataset of 67 different images labeled by experts in three classes (normal, VA and CH): linear approach, Naive-Bayes quadratic approach and a standard quadratic analysis, all validated with a ten-fold cross validation. Linear classification achieves 82.09% accuracy (class accuracies: 90.32% for normal villi, 82.35% for VA and 68.42% for CH, sensitivity: 0.68, specificity 1.00), Naive Bayes analysis returns 83.58% accuracy (90.32% for normal villi, 70.59% for VA and 84.21% for CH, sensitivity: 0.84 specificity: 0.92), while the quadratic analysis achieves a final accuracy of 94.03% (96.77% accuracy for normal villi, 94.12% for VA and 89.47% for CH, sensitivity: 0.89, specificity: 0.98).

Selective testing strategies for diagnosing group A streptococcal infection in children with pharyngitis: a systematic review and prospective multicentre external validation study

PubMed Central

Cohen, Jérémie F.; Cohen, Robert; Levy, Corinne; Thollot, Franck; Benani, Mohamed; Bidet, Philippe; Chalumeau, Martin

2015-01-01

Background: Several clinical prediction rules for diagnosing group A streptococcal infection in children with pharyngitis are available. We aimed to compare the diagnostic accuracy of rules-based selective testing strategies in a prospective cohort of children with pharyngitis. Methods: We identified clinical prediction rules through a systematic search of MEDLINE and Embase (1975–2014), which we then validated in a prospective cohort involving French children who presented with pharyngitis during a 1-year period (2010–2011). We diagnosed infection with group A streptococcus using two throat swabs: one obtained for a rapid antigen detection test (StreptAtest, Dectrapharm) and one obtained for culture (reference standard). We validated rules-based selective testing strategies as follows: low risk of group A streptococcal infection, no further testing or antibiotic therapy needed; intermediate risk of infection, rapid antigen detection for all patients and antibiotic therapy for those with a positive test result; and high risk of infection, empiric antibiotic treatment. Results: We identified 8 clinical prediction rules, 6 of which could be prospectively validated. Sensitivity and specificity of rules-based selective testing strategies ranged from 66% (95% confidence interval [CI] 61–72) to 94% (95% CI 92–97) and from 40% (95% CI 35–45) to 88% (95% CI 85–91), respectively. Use of rapid antigen detection testing following the clinical prediction rule ranged from 24% (95% CI 21–27) to 86% (95% CI 84–89). None of the rules-based selective testing strategies achieved our diagnostic accuracy target (sensitivity and specificity > 85%). Interpretation: Rules-based selective testing strategies did not show sufficient diagnostic accuracy in this study population. The relevance of clinical prediction rules for determining which children with pharyngitis should undergo a rapid antigen detection test remains questionable. PMID:25487666

Applications of random forest feature selection for fine-scale genetic population assignment.

PubMed

Sylvester, Emma V A; Bentzen, Paul; Bradbury, Ian R; Clément, Marie; Pearce, Jon; Horne, John; Beiko, Robert G

2018-02-01

Genetic population assignment used to inform wildlife management and conservation efforts requires panels of highly informative genetic markers and sensitive assignment tests. We explored the utility of machine-learning algorithms (random forest, regularized random forest and guided regularized random forest) compared with F ST ranking for selection of single nucleotide polymorphisms (SNP) for fine-scale population assignment. We applied these methods to an unpublished SNP data set for Atlantic salmon ( Salmo salar ) and a published SNP data set for Alaskan Chinook salmon ( Oncorhynchus tshawytscha ). In each species, we identified the minimum panel size required to obtain a self-assignment accuracy of at least 90% using each method to create panels of 50-700 markers Panels of SNPs identified using random forest-based methods performed up to 7.8 and 11.2 percentage points better than F ST -selected panels of similar size for the Atlantic salmon and Chinook salmon data, respectively. Self-assignment accuracy ≥90% was obtained with panels of 670 and 384 SNPs for each data set, respectively, a level of accuracy never reached for these species using F ST -selected panels. Our results demonstrate a role for machine-learning approaches in marker selection across large genomic data sets to improve assignment for management and conservation of exploited populations.

The performance of magnetic resonance imaging in the detection of triangular fibrocartilage complex injury: a meta-analysis.

PubMed

Wang, Z X; Chen, S L; Wang, Q Q; Liu, B; Zhu, J; Shen, J

2015-06-01

The aim of this study was to evaluate the accuracy of magnetic resonance imaging in the detection of triangular fibrocartilage complex injury through a meta-analysis. A comprehensive literature search was conducted before 1 April 2014. All studies comparing magnetic resonance imaging results with arthroscopy or open surgery findings were reviewed, and 25 studies that satisfied the eligibility criteria were included. Data were pooled to yield pooled sensitivity and specificity, which were respectively 0.83 and 0.82. In detection of central and peripheral tears, magnetic resonance imaging had respectively a pooled sensitivity of 0.90 and 0.88 and a pooled specificity of 0.97 and 0.97. Six high-quality studies using Ringler's recommended magnetic resonance imaging parameters were selected for analysis to determine whether optimal imaging protocols yielded better results. The pooled sensitivity and specificity of these six studies were 0.92 and 0.82, respectively. The overall accuracy of magnetic resonance imaging was acceptable. For peripheral tears, the pooled data showed a relatively high accuracy. Magnetic resonance imaging with appropriate parameters are an ideal method for diagnosing different types of triangular fibrocartilage complex tears. © The Author(s) 2015.

Accuracy of un-supervised versus provider-supervised self-administered HIV testing in Uganda: A randomized implementation trial.

PubMed

Asiimwe, Stephen; Oloya, James; Song, Xiao; Whalen, Christopher C

2014-12-01

Unsupervised HIV self-testing (HST) has potential to increase knowledge of HIV status; however, its accuracy is unknown. To estimate the accuracy of unsupervised HST in field settings in Uganda, we performed a non-blinded, randomized controlled, non-inferiority trial of unsupervised compared with supervised HST among selected high HIV risk fisherfolk (22.1 % HIV Prevalence) in three fishing villages in Uganda between July and September 2013. The study enrolled 246 participants and randomized them in a 1:1 ratio to unsupervised HST or provider-supervised HST. In an intent-to-treat analysis, the HST sensitivity was 90 % in the unsupervised arm and 100 % among the provider-supervised, yielding a difference 0f -10 % (90 % CI -21, 1 %); non-inferiority was not shown. In a per protocol analysis, the difference in sensitivity was -5.6 % (90 % CI -14.4, 3.3 %) and did show non-inferiority. We conclude that unsupervised HST is feasible in rural Africa and may be non-inferior to provider-supervised HST.

Diagnostic accuracy of Xpert MTB/RIF for tuberculosis detection in different regions with different endemic burden: A systematic review and meta-analysis.

PubMed

Li, Shiying; Liu, Bin; Peng, Mingli; Chen, Min; Yin, Wenwei; Tang, Hui; Luo, Yuxuan; Hu, Peng; Ren, Hong

2017-01-01

To estimate the diagnostic accuracy of Xpert MTB/RIF, a systematic review and meta-analysis were carried out. Up to June 20, 2015, multiple databases were screened for relevant studies. Accordingly, 106 studies included 52,410 samples were selected. Diagnostic accuracy of Xpert MTB/RIF for TB detection was validated against either culture or a composite reference standard (CRS). Additionally, selected studies were further subgrouped in four groups based on sample's type, subject's age, status of HIV co-infection and smear-positivity. The overall pooled sensitivity and specificity of Xpert MTB/RIF was 0.85 (95% confidence interval [CI] 0.82-0.88) and 0.98 (95% CI 0.96-0.98), respectively, compared to culture; while it was 0.59 (95% CI 0.44-0.72) and 0.99 (95% CI 0.97-1.00) compared to CRS. The overall sensitivity was lower in countries with high TB prevalence than countries with middle/low prevalence (0.84, 95% CI: 0.80-0.88 versus 0.89, 95% CI: 0.84-0.93). Furthermore, Xpert MTB/RIF has higher sensitivity in patients with positive smears (0.99, 95% CI 0.97-0.99), in patients with pulmonary TB samples (0.87, 95% CI 0.83-0.90), in adults (0.82, 95% CI 0.76-0.86) and in HIV-positive patients (0.81, 95% CI 0.73-0.87). Taken together, Xpert MTB/RIF is a quick and accurate diagnostic assay for TB which will significantly help the physicians to make their clinical decisions.

Combined ECG, Echocardiographic, and Biomarker Criteria for Diagnosing Acute Myocardial Infarction in Out-of-Hospital Cardiac Arrest Patients.

PubMed

Lee, Sang-Eun; Uhm, Jae-Sun; Kim, Jong-Youn; Pak, Hui-Nam; Lee, Moon-Hyoung; Joung, Boyoung

2015-07-01

Acute coronary lesions commonly trigger out-of-hospital cardiac arrest (OHCA). However, the prevalence of coronary artery disease (CAD) in Asian patients with OHCA and whether electrocardiogram (ECG) and other findings might predict acute myocardial infarction (AMI) have not been fully elucidated. Of 284 consecutive resuscitated OHCA patients seen between January 2006 and July 2013, we enrolled 135 patients who had undergone coronary evaluation. ECGs, echocardiography, and biomarkers were compared between patients with or without CAD. We included 135 consecutive patients aged 54 years (interquartile range 45-65) with sustained return of spontaneous circulation after OHCA between 2006 and 2012. Sixty six (45%) patients had CAD. The initial rhythm was shockable and non-shockable in 110 (81%) and 25 (19%) patients, respectively. ST-segment elevation predicted CAD with 42% sensitivity, 87% specificity, and 65% accuracy. ST elevation and/or regional wall motion abnormality (RWMA) showed 68% sensitivity, 52% specificity, and 70% accuracy in the prediction of CAD. Finally, a combination of ST elevation and/or RWMA and/or troponin T elevation predicted CAD with 94% sensitivity, 17% specificity, and 55% accuracy. In patients with OHCA without obvious non-cardiac causes, selection for coronary angiogram based on the combined criterion could detect 94% of CADs. However, compared with ECG only criteria, the combined criterion failed to improve diagnostic accuracy with a lower specificity.

Prediction of Skin Sensitization with a Particle Swarm Optimized Support Vector Machine

PubMed Central

Yuan, Hua; Huang, Jianping; Cao, Chenzhong

2009-01-01

Skin sensitization is the most commonly reported occupational illness, causing much suffering to a wide range of people. Identification and labeling of environmental allergens is urgently required to protect people from skin sensitization. The guinea pig maximization test (GPMT) and murine local lymph node assay (LLNA) are the two most important in vivo models for identification of skin sensitizers. In order to reduce the number of animal tests, quantitative structure-activity relationships (QSARs) are strongly encouraged in the assessment of skin sensitization of chemicals. This paper has investigated the skin sensitization potential of 162 compounds with LLNA results and 92 compounds with GPMT results using a support vector machine. A particle swarm optimization algorithm was implemented for feature selection from a large number of molecular descriptors calculated by Dragon. For the LLNA data set, the classification accuracies are 95.37% and 88.89% for the training and the test sets, respectively. For the GPMT data set, the classification accuracies are 91.80% and 90.32% for the training and the test sets, respectively. The classification performances were greatly improved compared to those reported in the literature, indicating that the support vector machine optimized by particle swarm in this paper is competent for the identification of skin sensitizers. PMID:19742136

Development of a tunable diode laser sensor for measurements of gas turbine exhaust temperature

NASA Astrophysics Data System (ADS)

Liu, X.; Jeffries, J. B.; Hanson, R. K.; Hinckley, K. M.; Woodmansee, M. A.

2006-03-01

A tunable diode laser (TDL) temperature sensor is designed, constructed, tested, and demonstrated in the exhaust of an industrial gas turbine. Temperature is determined from the ratio of the measured absorbance of two water vapor overtone transitions in the near infrared where telecommunication diode lasers are available. Design rules are developed to select the optimal pair of transitions for direct absorption measurements using spectral simulations by systematically examining the absorption strength, spectral isolation, and temperature sensitivity to maximize temperature accuracy in the core flow and minimize sensitivity to water vapor in the cold boundary layer. The contribution to temperature uncertainty from the spectroscopic database is evaluated and precise line-strength data are measured for the selected transitions. Gas-temperature measurements in a heated cell are used to verify the sensor accuracy (over the temperature range of 350 to 1000 K, ΔT˜2 K for the optimal line pair and ΔT˜5 K for an alternative line pair). Field measurements of exhaust-gas temperature in an industrial gas turbine demonstrate the practical utility of TDL sensing in harsh industrial environments.

Feature selection for elderly faller classification based on wearable sensors.

PubMed

Howcroft, Jennifer; Kofman, Jonathan; Lemaire, Edward D

2017-05-30

Wearable sensors can be used to derive numerous gait pattern features for elderly fall risk and faller classification; however, an appropriate feature set is required to avoid high computational costs and the inclusion of irrelevant features. The objectives of this study were to identify and evaluate smaller feature sets for faller classification from large feature sets derived from wearable accelerometer and pressure-sensing insole gait data. A convenience sample of 100 older adults (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, left and right shanks. Feature selection was performed using correlation-based feature selection (CFS), fast correlation based filter (FCBF), and Relief-F algorithms. Faller classification was performed using multi-layer perceptron neural network, naïve Bayesian, and support vector machine classifiers, with 75:25 single stratified holdout and repeated random sampling. The best performing model was a support vector machine with 78% accuracy, 26% sensitivity, 95% specificity, 0.36 F1 score, and 0.31 MCC and one posterior pelvis accelerometer input feature (left acceleration standard deviation). The second best model achieved better sensitivity (44%) and used a support vector machine with 74% accuracy, 83% specificity, 0.44 F1 score, and 0.29 MCC. This model had ten input features: maximum, mean and standard deviation posterior acceleration; maximum, mean and standard deviation anterior acceleration; mean superior acceleration; and three impulse features. The best multi-sensor model sensitivity (56%) was achieved using posterior pelvis and both shank accelerometers and a naïve Bayesian classifier. The best single-sensor model sensitivity (41%) was achieved using the posterior pelvis accelerometer and a naïve Bayesian classifier. Feature selection provided models with smaller feature sets and improved faller classification compared to faller classification without feature selection. CFS and FCBF provided the best feature subset (one posterior pelvis accelerometer feature) for faller classification. However, better sensitivity was achieved by the second best model based on a Relief-F feature subset with three pressure-sensing insole features and seven head accelerometer features. Feature selection should be considered as an important step in faller classification using wearable sensors.

Accuracy of magnetic resonance imaging in the pre-operative staging of rectal adenocarcinoma: Experience from a regional Australian cancer center.

PubMed

White, Rohen; Ung, Kim Ann; Mathlum, Maitham

2013-12-01

Selection of the optimal treatment pathway in patients with rectal adenocarcinoma relies on accurate locoregional staging. This study aims to assess the accuracy of staging with magnetic resonance imaging (MRI) and in particular, its accuracy in differentiating patients with early stage disease from those with more advanced disease who benefit from a different treatment approach. Patients who were staged with MRI and received surgery as the first line of treatment for biopsy-proven adenocarcinoma of the rectum were identified. Comparison was made between the clinical stage on MRI and the pathological stage of the surgical specimen. The sensitivity, specificity and overall accuracy of MRI was assessed. In all, 58 eligible patients were identified. In 31% of patients, the extent of disease was underrepresented on preoperative MRI. Sensitivity, specificity and overall accuracy of anorectal MRI in detecting stage II/III disease status in this cohort was 59, 71 and 62%, respectively. MRI underestimated the pathological stage in many patients in this series who may have benefited from the addition of neoadjuvant radiotherapy to their management. This study supports further refinement of preoperative staging and demonstrates that impressive results from highly controlled settings may be difficult to reproduce in community practice. © 2012 Wiley Publishing Asia Pty Ltd.

Real-data comparison of data mining methods in prediction of diabetes in iran.

PubMed

Tapak, Lily; Mahjub, Hossein; Hamidi, Omid; Poorolajal, Jalal

2013-09-01

Diabetes is one of the most common non-communicable diseases in developing countries. Early screening and diagnosis play an important role in effective prevention strategies. This study compared two traditional classification methods (logistic regression and Fisher linear discriminant analysis) and four machine-learning classifiers (neural networks, support vector machines, fuzzy c-mean, and random forests) to classify persons with and without diabetes. The data set used in this study included 6,500 subjects from the Iranian national non-communicable diseases risk factors surveillance obtained through a cross-sectional survey. The obtained sample was based on cluster sampling of the Iran population which was conducted in 2005-2009 to assess the prevalence of major non-communicable disease risk factors. Ten risk factors that are commonly associated with diabetes were selected to compare the performance of six classifiers in terms of sensitivity, specificity, total accuracy, and area under the receiver operating characteristic (ROC) curve criteria. Support vector machines showed the highest total accuracy (0.986) as well as area under the ROC (0.979). Also, this method showed high specificity (1.000) and sensitivity (0.820). All other methods produced total accuracy of more than 85%, but for all methods, the sensitivity values were very low (less than 0.350). The results of this study indicate that, in terms of sensitivity, specificity, and overall classification accuracy, the support vector machine model ranks first among all the classifiers tested in the prediction of diabetes. Therefore, this approach is a promising classifier for predicting diabetes, and it should be further investigated for the prediction of other diseases.

Feasibility and validity of the structured attention module among economically disadvantaged preschool-age children.

PubMed

Bush, Hillary H; Eisenhower, Abbey; Briggs-Gowan, Margaret; Carter, Alice S

2015-01-01

Rooted in the theory of attention put forth by Mirsky, Anthony, Duncan, Ahearn, and Kellam (1991), the Structured Attention Module (SAM) is a developmentally sensitive, computer-based performance task designed specifically to assess sustained selective attention among 3- to 6-year-old children. The current study addressed the feasibility and validity of the SAM among 64 economically disadvantaged preschool-age children (mean age = 58 months; 55% female); a population known to be at risk for attention problems and adverse math performance outcomes. Feasibility was demonstrated by high completion rates and strong associations between SAM performance and age. Principal Factor Analysis with rotation produced robust support for a three-factor model (Accuracy, Speed, and Endurance) of SAM performance, which largely corresponded with existing theorized models of selective and sustained attention. Construct validity was evidenced by positive correlations between SAM Composite scores and all three SAM factors and IQ, and between SAM Accuracy and sequential memory. Value-added predictive validity was not confirmed through main effects of SAM on math performance above and beyond age and IQ; however, significant interactions by child sex were observed: Accuracy and Endurance both interacted with child sex to predict math performance. In both cases, the SAM factors predicted math performance more strongly for girls than for boys. There were no overall sex differences in SAM performance. In sum, the current findings suggest that interindividual variation in sustained selective attention, and potentially other aspects of attention and executive function, among young, high-risk children can be captured validly with developmentally sensitive measures.

An EEG-based functional connectivity measure for automatic detection of alcohol use disorder.

PubMed

Mumtaz, Wajid; Saad, Mohamad Naufal B Mohamad; Kamel, Nidal; Ali, Syed Saad Azhar; Malik, Aamir Saeed

2018-01-01

The abnormal alcohol consumption could cause toxicity and could alter the human brain's structure and function, termed as alcohol used disorder (AUD). Unfortunately, the conventional screening methods for AUD patients are subjective and manual. Hence, to perform automatic screening of AUD patients, objective methods are needed. The electroencephalographic (EEG) data have been utilized to study the differences of brain signals between alcoholics and healthy controls that could further developed as an automatic screening tool for alcoholics. In this work, resting-state EEG-derived features were utilized as input data to the proposed feature selection and classification method. The aim was to perform automatic classification of AUD patients and healthy controls. The validation of the proposed method involved real-EEG data acquired from 30 AUD patients and 30 age-matched healthy controls. The resting-state EEG-derived features such as synchronization likelihood (SL) were computed involving 19 scalp locations resulted into 513 features. Furthermore, the features were rank-ordered to select the most discriminant features involving a rank-based feature selection method according to a criterion, i.e., receiver operating characteristics (ROC). Consequently, a reduced set of most discriminant features was identified and utilized further during classification of AUD patients and healthy controls. In this study, three different classification models such as Support Vector Machine (SVM), Naïve Bayesian (NB), and Logistic Regression (LR) were used. The study resulted into SVM classification accuracy=98%, sensitivity=99.9%, specificity=95%, and f-measure=0.97; LR classification accuracy=91.7%, sensitivity=86.66%, specificity=96.6%, and f-measure=0.90; NB classification accuracy=93.6%, sensitivity=100%, specificity=87.9%, and f-measure=0.95. The SL features could be utilized as objective markers to screen the AUD patients and healthy controls. Copyright © 2017 Elsevier B.V. All rights reserved.

The CERAD Neuropsychological Assessment Battery Is Sensitive to Alcohol-Related Cognitive Deficiencies in Elderly Patients: A Retrospective Matched Case-Control Study.

PubMed

Kaufmann, Liane; Huber, Stefan; Mayer, Daniel; Moeller, Korbinian; Marksteiner, Josef

2018-04-01

Adverse effects of heavy drinking on cognition have frequently been reported. In the present study, we systematically examined for the first time whether clinical neuropsychological assessments may be sensitive to alcohol abuse in elderly patients with suspected minor neurocognitive disorder. A total of 144 elderly with and without alcohol abuse (each group n=72; mean age 66.7 years) were selected from a patient pool of n=738 by applying propensity score matching (a statistical method allowing to match participants in experimental and control group by balancing various covariates to reduce selection bias). Accordingly, study groups were almost perfectly matched regarding age, education, gender, and Mini Mental State Examination score. Neuropsychological performance was measured using the CERAD (Consortium to Establish a Registry for Alzheimer's Disease). Classification analyses (i.e., decision tree and boosted trees models) were conducted to examine whether CERAD variables or total score contributed to group classification. Decision tree models disclosed that groups could be reliably classified based on the CERAD variables "Word List Discriminability" (tapping verbal recognition memory, 64% classification accuracy) and "Trail Making Test A" (measuring visuo-motor speed, 59% classification accuracy). Boosted tree analyses further indicated the sensitivity of "Word List Recall" (measuring free verbal recall) for discriminating elderly with versus without a history of alcohol abuse. This indicates that specific CERAD variables seem to be sensitive to alcohol-related cognitive dysfunctions in elderly patients with suspected minor neurocognitive disorder. (JINS, 2018, 24, 360-371).

TreePOD: Sensitivity-Aware Selection of Pareto-Optimal Decision Trees.

PubMed

Muhlbacher, Thomas; Linhardt, Lorenz; Moller, Torsten; Piringer, Harald

2018-01-01

Balancing accuracy gains with other objectives such as interpretability is a key challenge when building decision trees. However, this process is difficult to automate because it involves know-how about the domain as well as the purpose of the model. This paper presents TreePOD, a new approach for sensitivity-aware model selection along trade-offs. TreePOD is based on exploring a large set of candidate trees generated by sampling the parameters of tree construction algorithms. Based on this set, visualizations of quantitative and qualitative tree aspects provide a comprehensive overview of possible tree characteristics. Along trade-offs between two objectives, TreePOD provides efficient selection guidance by focusing on Pareto-optimal tree candidates. TreePOD also conveys the sensitivities of tree characteristics on variations of selected parameters by extending the tree generation process with a full-factorial sampling. We demonstrate how TreePOD supports a variety of tasks involved in decision tree selection and describe its integration in a holistic workflow for building and selecting decision trees. For evaluation, we illustrate a case study for predicting critical power grid states, and we report qualitative feedback from domain experts in the energy sector. This feedback suggests that TreePOD enables users with and without statistical background a confident and efficient identification of suitable decision trees.

Beyond Semantic Accuracy: Preschoolers Evaluate a Speaker's Reasons

ERIC Educational Resources Information Center

Koenig, Melissa A.

2012-01-01

Children's sensitivity to the quality of epistemic reasons and their selective trust in the more reasonable of 2 informants was investigated in 2 experiments. Three-, 4-, and 5-year-old children (N = 90) were presented with speakers who stated different kinds of evidence for what they believed. Experiment 1 showed that children of all age groups…

Determination of Mercury in Milk by Cold Vapor Atomic Fluorescence: A Green Analytical Chemistry Laboratory Experiment

ERIC Educational Resources Information Center

Armenta, Sergio; de la Guardia, Miguel

2011-01-01

Green analytical chemistry principles were introduced to undergraduate students in a laboratory experiment focused on determining the mercury concentration in cow and goat milk. In addition to traditional goals, such as accuracy, precision, sensitivity, and limits of detection in method selection and development, attention was paid to the…

Diagnostic accuracy of Xpert MTB/RIF for tuberculosis detection in different regions with different endemic burden: A systematic review and meta-analysis

PubMed Central

Li, Shiying; Liu, Bin; Peng, Mingli; Chen, Min; Yin, Wenwei; Tang, Hui; Luo, Yuxuan; Ren, Hong

2017-01-01

Purpose To estimate the diagnostic accuracy of Xpert MTB/RIF, a systematic review and meta-analysis were carried out. Methods Up to June 20, 2015, multiple databases were screened for relevant studies. Results Accordingly, 106 studies included 52,410 samples were selected. Diagnostic accuracy of Xpert MTB/RIF for TB detection was validated against either culture or a composite reference standard (CRS). Additionally, selected studies were further subgrouped in four groups based on sample’s type, subject’s age, status of HIV co-infection and smear-positivity. The overall pooled sensitivity and specificity of Xpert MTB/RIF was 0.85 (95% confidence interval [CI] 0.82–0.88) and 0.98 (95% CI 0.96–0.98), respectively, compared to culture; while it was 0.59 (95% CI 0.44–0.72) and 0.99 (95% CI 0.97–1.00) compared to CRS. The overall sensitivity was lower in countries with high TB prevalence than countries with middle/low prevalence (0.84, 95% CI: 0.80–0.88 versus 0.89, 95% CI: 0.84–0.93). Furthermore, Xpert MTB/RIF has higher sensitivity in patients with positive smears (0.99, 95% CI 0.97–0.99), in patients with pulmonary TB samples (0.87, 95% CI 0.83–0.90), in adults (0.82, 95% CI 0.76–0.86) and in HIV-positive patients (0.81, 95% CI 0.73–0.87). Conclusions Taken together, Xpert MTB/RIF is a quick and accurate diagnostic assay for TB which will significantly help the physicians to make their clinical decisions. PMID:28708844

Diagnostic Accuracy of Molecular Amplification Tests for Human African Trypanosomiasis—Systematic Review

PubMed Central

Boer, Kimberly R.; Dyserinck, Heleen C.; Büscher, Philippe; Schallig, Henk D. H. F.; Leeflang, Mariska M. G.

2012-01-01

Background A range of molecular amplification techniques have been developed for the diagnosis of Human African Trypanosomiasis (HAT); however, careful evaluation of these tests must precede implementation to ensure their high clinical accuracy. Here, we investigated the diagnostic accuracy of molecular amplification tests for HAT, the quality of articles and reasons for variation in accuracy. Methodology Data from studies assessing diagnostic molecular amplification tests were extracted and pooled to calculate accuracy. Articles were included if they reported sensitivity and specificity or data whereby values could be calculated. Study quality was assessed using QUADAS and selected studies were analysed using the bivariate random effects model. Results 16 articles evaluating molecular amplification tests fulfilled the inclusion criteria: PCR (n = 12), NASBA (n = 2), LAMP (n = 1) and a study comparing PCR and NASBA (n = 1). Fourteen articles, including 19 different studies were included in the meta-analysis. Summary sensitivity for PCR on blood was 99.0% (95% CI 92.8 to 99.9) and the specificity was 97.7% (95% CI 93.0 to 99.3). Differences in study design and readout method did not significantly change estimates although use of satellite DNA as a target significantly lowers specificity. Sensitivity and specificity of PCR on CSF for staging varied from 87.6% to 100%, and 55.6% to 82.9% respectively. Conclusion Here, PCR seems to have sufficient accuracy to replace microscopy where facilities allow, although this conclusion is based on multiple reference standards and a patient population that was not always representative. Future studies should, therefore, include patients for which PCR may become the test of choice and consider well designed diagnostic accuracy studies to provide extra evidence on the value of PCR in practice. Another use of PCR for control of disease could be to screen samples collected from rural areas and test in reference laboratories, to spot epidemics quickly and direct resources appropriately. PMID:22253934

Diagnostic accuracy of molecular amplification tests for human African trypanosomiasis--systematic review.

PubMed

Mugasa, Claire M; Adams, Emily R; Boer, Kimberly R; Dyserinck, Heleen C; Büscher, Philippe; Schallig, Henk D H F; Leeflang, Mariska M G

2012-01-01

A range of molecular amplification techniques have been developed for the diagnosis of Human African Trypanosomiasis (HAT); however, careful evaluation of these tests must precede implementation to ensure their high clinical accuracy. Here, we investigated the diagnostic accuracy of molecular amplification tests for HAT, the quality of articles and reasons for variation in accuracy. Data from studies assessing diagnostic molecular amplification tests were extracted and pooled to calculate accuracy. Articles were included if they reported sensitivity and specificity or data whereby values could be calculated. Study quality was assessed using QUADAS and selected studies were analysed using the bivariate random effects model. 16 articles evaluating molecular amplification tests fulfilled the inclusion criteria: PCR (n = 12), NASBA (n = 2), LAMP (n = 1) and a study comparing PCR and NASBA (n = 1). Fourteen articles, including 19 different studies were included in the meta-analysis. Summary sensitivity for PCR on blood was 99.0% (95% CI 92.8 to 99.9) and the specificity was 97.7% (95% CI 93.0 to 99.3). Differences in study design and readout method did not significantly change estimates although use of satellite DNA as a target significantly lowers specificity. Sensitivity and specificity of PCR on CSF for staging varied from 87.6% to 100%, and 55.6% to 82.9% respectively. Here, PCR seems to have sufficient accuracy to replace microscopy where facilities allow, although this conclusion is based on multiple reference standards and a patient population that was not always representative. Future studies should, therefore, include patients for which PCR may become the test of choice and consider well designed diagnostic accuracy studies to provide extra evidence on the value of PCR in practice. Another use of PCR for control of disease could be to screen samples collected from rural areas and test in reference laboratories, to spot epidemics quickly and direct resources appropriately.

The development of a stepped frequency microwave radiometer and its application to remote sensing of the Earth

NASA Technical Reports Server (NTRS)

Harrington, R. F.

1980-01-01

The design, development, application, and capabilities of a variable frequency microwave radiometer are described. This radiometer demonstrated the versatility, accuracy, and stability required to provide contributions to the geophysical understanding of ocean and ice processes. A closed-loop feedback method was used, whereby noise pulses were added to the received electromagnetic radiation to achieve a null balance in a Dicke switched radiometer. Stability was achieved through the use of a constant temperature enclosure around the low loss microwave front end. The Dicke reference temperature was maintained to an absolute accuracy of 0.1 K using a closed-loop proportional temperature controller. A microprocessor based digital controller operates the radiometer and records the data on computer compatible tapes. This radiometer exhibits an absolute accuracy of better than 0.5 K when the sensitivity is 0.1 K. The sensitivity varies between 0.0125 K and 1.25 K depending upon the bandwidth and integration time selected by the digital controller. Remote sensing experiments were conducted from an aircraft platform and the first radiometeric mapping of an ocean polar front; exploratory experiments to measure the thickness of lake ice; first discrimination between first year and multiyear ice below 10 GHz; and the first known measurements of frequency sensitive characteristics of sea ice.

Asymmetric bagging and feature selection for activities prediction of drug molecules.

PubMed

Li, Guo-Zheng; Meng, Hao-Hua; Lu, Wen-Cong; Yang, Jack Y; Yang, Mary Qu

2008-05-28

Activities of drug molecules can be predicted by QSAR (quantitative structure activity relationship) models, which overcomes the disadvantages of high cost and long cycle by employing the traditional experimental method. With the fact that the number of drug molecules with positive activity is rather fewer than that of negatives, it is important to predict molecular activities considering such an unbalanced situation. Here, asymmetric bagging and feature selection are introduced into the problem and asymmetric bagging of support vector machines (asBagging) is proposed on predicting drug activities to treat the unbalanced problem. At the same time, the features extracted from the structures of drug molecules affect prediction accuracy of QSAR models. Therefore, a novel algorithm named PRIFEAB is proposed, which applies an embedded feature selection method to remove redundant and irrelevant features for asBagging. Numerical experimental results on a data set of molecular activities show that asBagging improve the AUC and sensitivity values of molecular activities and PRIFEAB with feature selection further helps to improve the prediction ability. Asymmetric bagging can help to improve prediction accuracy of activities of drug molecules, which can be furthermore improved by performing feature selection to select relevant features from the drug molecules data sets.

CON4EI: Short Time Exposure (STE) test method for hazard identification and labelling of eye irritating chemicals.

PubMed

Adriaens, E; Willoughby, J A; Meyer, B R; Blakeman, L C; Alépée, N; Fochtman, P; Guest, R; Kandarova, H; Verstraelen, S; Van Rompay, A R

2018-06-01

Assessment of ocular irritancy is an international regulatory requirement in the safety evaluation of industrial and consumer products. Although many in vitro ocular irritation assays exist, alone they are incapable of fully categorizing chemicals. Therefore, the CEFIC-LRI-AIMT6-VITO CON4EI consortium was developed to assess the reliability of eight in vitro test methods and establish an optimal tiered-testing strategy. One assay selected was the Short Time Exposure (STE) assay. This assay measures the viability of SIRC rabbit corneal cells after 5min exposure to 5% and 0.05% solutions of test material, and is capable of categorizing of Category 1 and No Category chemicals. The accuracy of the STE test method to identify Cat 1 chemicals was 61.3% with 23.7% sensitivity and 95.2% specificity. If non-soluble chemicals and unqualified results were excluded, the performance to identify Cat 1 chemicals remained similar (accuracy 62.2% with 22.7% sensitivity and 100% specificity). The accuracy of the STE test method to identify No Cat chemicals was 72.5% with 66.2% sensitivity and 100% specificity. Excluding highly volatile chemicals, non-surfactant solids and non-qualified results resulted in an important improvement of the performance of the STE test method (accuracy 96.2% with 81.8% sensitivity and 100% specificity). Furthermore, it seems that solids are more difficult to test in the STE, 71.4% of the solids resulted in unqualified results (solubility issues and/or high variation between independent runs) whereas for liquids 13.2% of the results were not qualified, supporting the restriction of the test method regarding the testing of solids. Copyright © 2017. Published by Elsevier Ltd.

Ordered Monolayer Gold Nano-urchin Structures and Their Size Induced Control for High Gas Sensing Performance

PubMed Central

Sabri, Ylias M.; Kandjani, Ahmad Esmaielzadeh; Ippolito, Samuel J.; Bhargava, Suresh K.

2016-01-01

The synthesis of ordered monolayers of gold nano-urchin (Au-NU) nanostructures with controlled size, directly on thin films using a simple electrochemical method is reported in this study. In order to demonstrate one of the vast potential applications, the developed Au-NUs were formed on the electrodes of transducers (QCM) to selectively detect low concentrations of elemental mercury (Hg0) vapor. It was found that the sensitivity and selectivity of the sensor device is enhanced by increasing the size of the nanospikes on the Au-NUs. The Au-NU-12 min QCM (Au-NUs with nanospikes grown on it for a period of 12 min) had the best performance in terms of transducer based Hg0 vapor detection. The sensor had 98% accuracy, 92% recovery, 96% precision (repeatability) and significantly, showed the highest sensitivity reported to date, resulting in a limit of detection (LoD) of only 32 μg/m3 at 75 °C. When compared to the control counterpart, the accuracy and sensitivity of the Au-NU-12 min was enhanced by ~2 and ~5 times, respectively. The results demonstrate the excellent activity of the developed materials which can be applied to a range of applications due to their long range order, tunable size and ability to form directly on thin-films. PMID:27090570

Simultaneous determination of triamcinolone hexacetonide and triamcinolone acetonide in rabbit plasma using a highly sensitive and selective UPLC-MS/MS method.

PubMed

Sun, Wei; Ho, Stacy; Fang, Xiaojun Rick; O'Shea, Thomas; Liu, Hanlan

2018-05-10

An ultra-high pressure liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was successfully developed and qualified for the simultaneous determination of triamcinolone hexacetonide (TAH) and triamcinolone acetonide (TAA, the active metabolite of TAH) in rabbit plasma. To prevent the hydrolysis of TAH to TAA ex vivo during sample collection and processing, we evaluated the effectiveness of several esterase inhibitors to stabilize TAH in plasma. Phenylmethanesulfonyl fluoride (PMSF) at 2.0 mM was chosen to stabilize TAH in rabbit plasma. The developed method is highly sensitive with a lower limit of quantitation of 10.0 pg/mL for both TAA and TAH using a 300 μL plasma aliquot. The method demonstrated good linearity, accuracy, precision, sensitivity, selectivity, recovery, matrix effects, dilution integrity, carryover, and stability. Linearity was obtained over the range of 10-2500 pg/mL. Both intra- and inter-run coefficients of variation were less than 9.1% and accuracies across the assay range were all within 100 ± 8.4%. The run time is under 5 minutes. The method was successfully implemented to support a rabbit pharmacokinetic study of TAH and TAA following a single intra-articular administration of TAH (Aristospan ® ). Copyright © 2018 Elsevier B.V. All rights reserved.

Ordered Monolayer Gold Nano-urchin Structures and Their Size Induced Control for High Gas Sensing Performance

NASA Astrophysics Data System (ADS)

Sabri, Ylias M.; Kandjani, Ahmad Esmaielzadeh; Ippolito, Samuel J.; Bhargava, Suresh K.

2016-04-01

The synthesis of ordered monolayers of gold nano-urchin (Au-NU) nanostructures with controlled size, directly on thin films using a simple electrochemical method is reported in this study. In order to demonstrate one of the vast potential applications, the developed Au-NUs were formed on the electrodes of transducers (QCM) to selectively detect low concentrations of elemental mercury (Hg0) vapor. It was found that the sensitivity and selectivity of the sensor device is enhanced by increasing the size of the nanospikes on the Au-NUs. The Au-NU-12 min QCM (Au-NUs with nanospikes grown on it for a period of 12 min) had the best performance in terms of transducer based Hg0 vapor detection. The sensor had 98% accuracy, 92% recovery, 96% precision (repeatability) and significantly, showed the highest sensitivity reported to date, resulting in a limit of detection (LoD) of only 32 μg/m3 at 75 °C. When compared to the control counterpart, the accuracy and sensitivity of the Au-NU-12 min was enhanced by ~2 and ~5 times, respectively. The results demonstrate the excellent activity of the developed materials which can be applied to a range of applications due to their long range order, tunable size and ability to form directly on thin-films.

Computational simulation and aerodynamic sensitivity analysis of film-cooled turbines

NASA Astrophysics Data System (ADS)

Massa, Luca

A computational tool is developed for the time accurate sensitivity analysis of the stage performance of hot gas, unsteady turbine components. An existing turbomachinery internal flow solver is adapted to the high temperature environment typical of the hot section of jet engines. A real gas model and film cooling capabilities are successfully incorporated in the software. The modifications to the existing algorithm are described; both the theoretical model and the numerical implementation are validated. The accuracy of the code in evaluating turbine stage performance is tested using a turbine geometry typical of the last stage of aeronautical jet engines. The results of the performance analysis show that the predictions differ from the experimental data by less than 3%. A reliable grid generator, applicable to the domain discretization of the internal flow field of axial flow turbine is developed. A sensitivity analysis capability is added to the flow solver, by rendering it able to accurately evaluate the derivatives of the time varying output functions. The complex Taylor's series expansion (CTSE) technique is reviewed. Two of them are used to demonstrate the accuracy and time dependency of the differentiation process. The results are compared with finite differences (FD) approximations. The CTSE is more accurate than the FD, but less efficient. A "black box" differentiation of the source code, resulting from the automated application of the CTSE, generates high fidelity sensitivity algorithms, but with low computational efficiency and high memory requirements. New formulations of the CTSE are proposed and applied. Selective differentiation of the method for solving the non-linear implicit residual equation leads to sensitivity algorithms with the same accuracy but improved run time. The time dependent sensitivity derivatives are computed in run times comparable to the ones required by the FD approach.

New machine-learning algorithms for prediction of Parkinson's disease

NASA Astrophysics Data System (ADS)

Mandal, Indrajit; Sairam, N.

2014-03-01

This article presents an enhanced prediction accuracy of diagnosis of Parkinson's disease (PD) to prevent the delay and misdiagnosis of patients using the proposed robust inference system. New machine-learning methods are proposed and performance comparisons are based on specificity, sensitivity, accuracy and other measurable parameters. The robust methods of treating Parkinson's disease (PD) includes sparse multinomial logistic regression, rotation forest ensemble with support vector machines and principal components analysis, artificial neural networks, boosting methods. A new ensemble method comprising of the Bayesian network optimised by Tabu search algorithm as classifier and Haar wavelets as projection filter is used for relevant feature selection and ranking. The highest accuracy obtained by linear logistic regression and sparse multinomial logistic regression is 100% and sensitivity, specificity of 0.983 and 0.996, respectively. All the experiments are conducted over 95% and 99% confidence levels and establish the results with corrected t-tests. This work shows a high degree of advancement in software reliability and quality of the computer-aided diagnosis system and experimentally shows best results with supportive statistical inference.

Comparative Approach of MRI-Based Brain Tumor Segmentation and Classification Using Genetic Algorithm.

PubMed

Bahadure, Nilesh Bhaskarrao; Ray, Arun Kumar; Thethi, Har Pal

2018-01-17

The detection of a brain tumor and its classification from modern imaging modalities is a primary concern, but a time-consuming and tedious work was performed by radiologists or clinical supervisors. The accuracy of detection and classification of tumor stages performed by radiologists is depended on their experience only, so the computer-aided technology is very important to aid with the diagnosis accuracy. In this study, to improve the performance of tumor detection, we investigated comparative approach of different segmentation techniques and selected the best one by comparing their segmentation score. Further, to improve the classification accuracy, the genetic algorithm is employed for the automatic classification of tumor stage. The decision of classification stage is supported by extracting relevant features and area calculation. The experimental results of proposed technique are evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on segmentation score, accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 92.03% accuracy, 91.42% specificity, 92.36% sensitivity, and an average segmentation score between 0.82 and 0.93 demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 93.79% dice similarity index coefficient, which indicates better overlap between the automated extracted tumor regions with manually extracted tumor region by radiologists.

Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

PubMed

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-07-30

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm.

RAIRS2 a new expert system for diagnosing tuberculosis with real-world tournament selection mechanism inside artificial immune recognition system.

PubMed

Saybani, Mahmoud Reza; Shamshirband, Shahaboddin; Golzari, Shahram; Wah, Teh Ying; Saeed, Aghabozorgi; Mat Kiah, Miss Laiha; Balas, Valentina Emilia

2016-03-01

Tuberculosis is a major global health problem that has been ranked as the second leading cause of death from an infectious disease worldwide, after the human immunodeficiency virus. Diagnosis based on cultured specimens is the reference standard; however, results take weeks to obtain. Slow and insensitive diagnostic methods hampered the global control of tuberculosis, and scientists are looking for early detection strategies, which remain the foundation of tuberculosis control. Consequently, there is a need to develop an expert system that helps medical professionals to accurately diagnose the disease. The objective of this study is to diagnose tuberculosis using a machine learning method. Artificial immune recognition system (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. In order to increase the classification accuracy, this study introduces a new hybrid system that incorporates real tournament selection mechanism into the AIRS. This mechanism is used to control the population size of the model and to overcome the existing selection pressure. Patient epacris reports obtained from the Pasteur laboratory in northern Iran were used as the benchmark data set. The sample consisted of 175 records, from which 114 (65 %) were positive for TB, and the remaining 61 (35 %) were negative. The classification performance was measured through tenfold cross-validation, root-mean-square error, sensitivity, and specificity. With an accuracy of 100 %, RMSE of 0, sensitivity of 100 %, and specificity of 100 %, the proposed method was able to successfully classify tuberculosis cases. In addition, the proposed method is comparable with top classifiers used in this research.

Value of lower respiratory tract surveillance cultures to predict bacterial pathogens in ventilator-associated pneumonia: systematic review and diagnostic test accuracy meta-analysis.

PubMed

Brusselaers, Nele; Labeau, Sonia; Vogelaers, Dirk; Blot, Stijn

2013-03-01

In ventilator-associated pneumonia (VAP), early appropriate antimicrobial therapy may be hampered by involvement of multidrug-resistant (MDR) pathogens. A systematic review and diagnostic test accuracy meta-analysis were performed to analyse whether lower respiratory tract surveillance cultures accurately predict the causative pathogens of subsequent VAP in adult patients. Selection and assessment of eligibility were performed by three investigators by mutual consideration. Of the 525 studies retrieved, 14 were eligible for inclusion (all in English; published since 1994), accounting for 791 VAP episodes. The following data were collected: study and population characteristics; in- and exclusion criteria; diagnostic criteria for VAP; microbiological workup of surveillance and diagnostic VAP cultures. Sub-analyses were conducted for VAP caused by Staphylococcus aureus, Pseudomonas spp., and Acinetobacter spp., MDR microorganisms, frequency of sampling, and consideration of all versus the most recent surveillance cultures. The meta-analysis showed a high accuracy of surveillance cultures, with pooled sensitivities up to 0.75 and specificities up to 0.92 in culture-positive VAP. The area under the curve (AUC) of the hierarchical summary receiver-operating characteristic curve demonstrates moderate accuracy (AUC: 0.90) in predicting multidrug resistance. A sampling frequency of >2/week (sensitivity 0.79; specificity 0.96) and consideration of only the most recent surveillance culture (sensitivity 0.78; specificity 0.96) are associated with a higher accuracy of prediction. This study provides evidence for the benefit of surveillance cultures in predicting MDR bacterial pathogens in VAP. However, clinical and statistical heterogeneity, limited samples sizes, and bias remain important limitations of this meta-analysis.

Information Retrieval in Telemedicine: a Comparative Study on Bibliographic Databases

PubMed Central

Ahmadi, Maryam; Sarabi, Roghayeh Ershad; Orak, Roohangiz Jamshidi; Bahaadinbeigy, Kambiz

2015-01-01

Background and Aims: The first step in each systematic review is selection of the most valid database that can provide the highest number of relevant references. This study was carried out to determine the most suitable database for information retrieval in telemedicine field. Methods: Cinhal, PubMed, Web of Science and Scopus databases were searched for telemedicine matched with Education, cost benefit and patient satisfaction. After analysis of the obtained results, the accuracy coefficient, sensitivity, uniqueness and overlap of databases were calculated. Results: The studied databases differed in the number of retrieved articles. PubMed was identified as the most suitable database for retrieving information on the selected topics with the accuracy and sensitivity ratios of 50.7% and 61.4% respectively. The uniqueness percent of retrieved articles ranged from 38% for Pubmed to 3.0% for Cinhal. The highest overlap rate (18.6%) was found between PubMed and Web of Science. Less than 1% of articles have been indexed in all searched databases. Conclusion: PubMed is suggested as the most suitable database for starting search in telemedicine and after PubMed, Scopus and Web of Science can retrieve about 90% of the relevant articles. PMID:26236086

Information Retrieval in Telemedicine: a Comparative Study on Bibliographic Databases.

PubMed

Ahmadi, Maryam; Sarabi, Roghayeh Ershad; Orak, Roohangiz Jamshidi; Bahaadinbeigy, Kambiz

2015-06-01

The first step in each systematic review is selection of the most valid database that can provide the highest number of relevant references. This study was carried out to determine the most suitable database for information retrieval in telemedicine field. Cinhal, PubMed, Web of Science and Scopus databases were searched for telemedicine matched with Education, cost benefit and patient satisfaction. After analysis of the obtained results, the accuracy coefficient, sensitivity, uniqueness and overlap of databases were calculated. The studied databases differed in the number of retrieved articles. PubMed was identified as the most suitable database for retrieving information on the selected topics with the accuracy and sensitivity ratios of 50.7% and 61.4% respectively. The uniqueness percent of retrieved articles ranged from 38% for Pubmed to 3.0% for Cinhal. The highest overlap rate (18.6%) was found between PubMed and Web of Science. Less than 1% of articles have been indexed in all searched databases. PubMed is suggested as the most suitable database for starting search in telemedicine and after PubMed, Scopus and Web of Science can retrieve about 90% of the relevant articles.

Performance of vegetation indices from Landsat time series in deforestation monitoring

NASA Astrophysics Data System (ADS)

Schultz, Michael; Clevers, Jan G. P. W.; Carter, Sarah; Verbesselt, Jan; Avitabile, Valerio; Quang, Hien Vu; Herold, Martin

2016-10-01

The performance of Landsat time series (LTS) of eight vegetation indices (VIs) was assessed for monitoring deforestation across the tropics. Three sites were selected based on differing remote sensing observation frequencies, deforestation drivers and environmental factors. The LTS of each VI was analysed using the Breaks For Additive Season and Trend (BFAST) Monitor method to identify deforestation. A robust reference database was used to evaluate the performance regarding spatial accuracy, sensitivity to observation frequency and combined use of multiple VIs. The canopy cover sensitive Normalized Difference Fraction Index (NDFI) was the most accurate. Among those tested, wetness related VIs (Normalized Difference Moisture Index (NDMI) and the Tasselled Cap wetness (TCw)) were spatially more accurate than greenness related VIs (Normalized Difference Vegetation Index (NDVI) and Tasselled Cap greenness (TCg)). When VIs were fused on feature level, spatial accuracy was improved and overestimation of change reduced. NDVI and NDFI produced the most robust results when observation frequency varies.

Displaying chest X-ray by beamer or monitor: comparison of diagnostic accuracy for subtle abnormalities.

PubMed

Kuiper, L M; Thijs, A; Smulders, Y M

2012-01-01

The advent of beamer projection of radiological images raises the issue of whether such projection compromises diagnostic accuracy. The purpose of this study was to evaluate whether beamer projection of chest X-rays is inferior to monitor display. We selected 53 chest X-rays with subtle abnormalities and 15 normal X-rays. The images were independently judged by a senior radiologist and a senior pulmonologist with a state-of-art computer monitor. We used their unanimous or consensus judgment as the reference test. Subsequently, four observers (one senior pulmonologist, one senior radiologist and one resident from each speciality) judged these X-rays on a standard clinical computer monitor and with beamer projection. We compared the number of correct results for each method. Overall, the sensitivity and specificity did not differ between monitor and beamer projection. Separate analyses in senior and junior examiners suggested that senior examiners had a moderate loss of diagnostic accuracy (8% lower sensitivity, pp<0.05, and 6% lower specificity, p=ns) associated with the use of beamer projection, whereas juniors showed similar performance on both imaging modalities. These initial data suggest that beamer projection may be associated with a small loss of diagnostic accuracy in specific subgroups of physicians. This finding illustrates the need for more extensive studies.

Automated system for characterization and classification of malaria-infected stages using light microscopic images of thin blood smears.

PubMed

Das, D K; Maiti, A K; Chakraborty, C

2015-03-01

In this paper, we propose a comprehensive image characterization cum classification framework for malaria-infected stage detection using microscopic images of thin blood smears. The methodology mainly includes microscopic imaging of Leishman stained blood slides, noise reduction and illumination correction, erythrocyte segmentation, feature selection followed by machine classification. Amongst three-image segmentation algorithms (namely, rule-based, Chan-Vese-based and marker-controlled watershed methods), marker-controlled watershed technique provides better boundary detection of erythrocytes specially in overlapping situations. Microscopic features at intensity, texture and morphology levels are extracted to discriminate infected and noninfected erythrocytes. In order to achieve subgroup of potential features, feature selection techniques, namely, F-statistic and information gain criteria are considered here for ranking. Finally, five different classifiers, namely, Naive Bayes, multilayer perceptron neural network, logistic regression, classification and regression tree (CART), RBF neural network have been trained and tested by 888 erythrocytes (infected and noninfected) for each features' subset. Performance evaluation of the proposed methodology shows that multilayer perceptron network provides higher accuracy for malaria-infected erythrocytes recognition and infected stage classification. Results show that top 90 features ranked by F-statistic (specificity: 98.64%, sensitivity: 100%, PPV: 99.73% and overall accuracy: 96.84%) and top 60 features ranked by information gain provides better results (specificity: 97.29%, sensitivity: 100%, PPV: 99.46% and overall accuracy: 96.73%) for malaria-infected stage classification. © 2014 The Authors Journal of Microscopy © 2014 Royal Microscopical Society.

Artificial intelligence techniques applied to the development of a decision–support system for diagnosing celiac disease

PubMed Central

Tenório, Josceli Maria; Hummel, Anderson Diniz; Cohrs, Frederico Molina; Sdepanian, Vera Lucia; Pisa, Ivan Torres; de Fátima Marin, Heimar

2013-01-01

Background Celiac disease (CD) is a difficult-to-diagnose condition because of its multiple clinical presentations and symptoms shared with other diseases. Gold-standard diagnostic confirmation of suspected CD is achieved by biopsying the small intestine. Objective To develop a clinical decision–support system (CDSS) integrated with an automated classifier to recognize CD cases, by selecting from experimental models developed using intelligence artificial techniques. Methods A web-based system was designed for constructing a retrospective database that included 178 clinical cases for training. Tests were run on 270 automated classifiers available in Weka 3.6.1 using five artificial intelligence techniques, namely decision trees, Bayesian inference, k-nearest neighbor algorithm, support vector machines and artificial neural networks. The parameters evaluated were accuracy, sensitivity, specificity and area under the ROC curve (AUC). AUC was used as a criterion for selecting the CDSS algorithm. A testing database was constructed including 38 clinical CD cases for CDSS evaluation. The diagnoses suggested by CDSS were compared with those made by physicians during patient consultations. Results The most accurate method during the training phase was the averaged one-dependence estimator (AODE) algorithm (a Bayesian classifier), which showed accuracy 80.0%, sensitivity 0.78, specificity 0.80 and AUC 0.84. This classifier was integrated into the web-based decision–support system. The gold-standard validation of CDSS achieved accuracy of 84.2% and k = 0.68 (p < 0.0001) with good agreement. The same accuracy was achieved in the comparison between the physician’s diagnostic impression and the gold standard k = 0. 64 (p < 0.0001). There was moderate agreement between the physician’s diagnostic impression and CDSS k = 0.46 (p = 0.0008). Conclusions The study results suggest that CDSS could be used to help in diagnosing CD, since the algorithm tested achieved excellent accuracy in differentiating possible positive from negative CD diagnoses. This study may contribute towards developing of a computer-assisted environment to support CD diagnosis. PMID:21917512

Artificial intelligence techniques applied to the development of a decision-support system for diagnosing celiac disease.

PubMed

Tenório, Josceli Maria; Hummel, Anderson Diniz; Cohrs, Frederico Molina; Sdepanian, Vera Lucia; Pisa, Ivan Torres; de Fátima Marin, Heimar

2011-11-01

Celiac disease (CD) is a difficult-to-diagnose condition because of its multiple clinical presentations and symptoms shared with other diseases. Gold-standard diagnostic confirmation of suspected CD is achieved by biopsying the small intestine. To develop a clinical decision-support system (CDSS) integrated with an automated classifier to recognize CD cases, by selecting from experimental models developed using intelligence artificial techniques. A web-based system was designed for constructing a retrospective database that included 178 clinical cases for training. Tests were run on 270 automated classifiers available in Weka 3.6.1 using five artificial intelligence techniques, namely decision trees, Bayesian inference, k-nearest neighbor algorithm, support vector machines and artificial neural networks. The parameters evaluated were accuracy, sensitivity, specificity and area under the ROC curve (AUC). AUC was used as a criterion for selecting the CDSS algorithm. A testing database was constructed including 38 clinical CD cases for CDSS evaluation. The diagnoses suggested by CDSS were compared with those made by physicians during patient consultations. The most accurate method during the training phase was the averaged one-dependence estimator (AODE) algorithm (a Bayesian classifier), which showed accuracy 80.0%, sensitivity 0.78, specificity 0.80 and AUC 0.84. This classifier was integrated into the web-based decision-support system. The gold-standard validation of CDSS achieved accuracy of 84.2% and k=0.68 (p<0.0001) with good agreement. The same accuracy was achieved in the comparison between the physician's diagnostic impression and the gold standard k=0. 64 (p<0.0001). There was moderate agreement between the physician's diagnostic impression and CDSS k=0.46 (p=0.0008). The study results suggest that CDSS could be used to help in diagnosing CD, since the algorithm tested achieved excellent accuracy in differentiating possible positive from negative CD diagnoses. This study may contribute towards developing of a computer-assisted environment to support CD diagnosis. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Prediction of brittleness based on anisotropic rock physics model for kerogen-rich shale

NASA Astrophysics Data System (ADS)

Qian, Ke-Ran; He, Zhi-Liang; Chen, Ye-Quan; Liu, Xi-Wu; Li, Xiang-Yang

2017-12-01

The construction of a shale rock physics model and the selection of an appropriate brittleness index ( BI) are two significant steps that can influence the accuracy of brittleness prediction. On one hand, the existing models of kerogen-rich shale are controversial, so a reasonable rock physics model needs to be built. On the other hand, several types of equations already exist for predicting the BI whose feasibility needs to be carefully considered. This study constructed a kerogen-rich rock physics model by performing the selfconsistent approximation and the differential effective medium theory to model intercoupled clay and kerogen mixtures. The feasibility of our model was confirmed by comparison with classical models, showing better accuracy. Templates were constructed based on our model to link physical properties and the BI. Different equations for the BI had different sensitivities, making them suitable for different types of formations. Equations based on Young's Modulus were sensitive to variations in lithology, while those using Lame's Coefficients were sensitive to porosity and pore fluids. Physical information must be considered to improve brittleness prediction.

Design sensitivity analysis with Applicon IFAD using the adjoint variable method

NASA Technical Reports Server (NTRS)

Frederick, Marjorie C.; Choi, Kyung K.

1984-01-01

A numerical method is presented to implement structural design sensitivity analysis using the versatility and convenience of existing finite element structural analysis program and the theoretical foundation in structural design sensitivity analysis. Conventional design variables, such as thickness and cross-sectional areas, are considered. Structural performance functionals considered include compliance, displacement, and stress. It is shown that calculations can be carried out outside existing finite element codes, using postprocessing data only. That is, design sensitivity analysis software does not have to be imbedded in an existing finite element code. The finite element structural analysis program used in the implementation presented is IFAD. Feasibility of the method is shown through analysis of several problems, including built-up structures. Accurate design sensitivity results are obtained without the uncertainty of numerical accuracy associated with selection of a finite difference perturbation.

An index with improved diagnostic accuracy for the diagnosis of Crohn's disease derived from the Lennard-Jones criteria.

PubMed

Reinisch, S; Schweiger, K; Pablik, E; Collet-Fenetrier, B; Peyrin-Biroulet, L; Alfaro, I; Panés, J; Moayyedi, P; Reinisch, W

2016-09-01

The Lennard-Jones criteria are considered the gold standard for diagnosing Crohn's disease (CD) and include the items granuloma, macroscopic discontinuity, transmural inflammation, fibrosis, lymphoid aggregates and discontinuous inflammation on histology. The criteria have never been subjected to a formal validation process. To develop a validated and improved diagnostic index based on the items of Lennard-Jones criteria. Included were 328 adult patients with long-standing CD (median disease duration 10 years) from three centres and classified as 'established', 'probable' or 'non-CD' by Lennard-Jones criteria at time of diagnosis. Controls were patients with ulcerative colitis (n = 170). The performance of each of the six diagnostic items of Lennard-Jones criteria was modelled by logistic regression and a new index based on stepwise backward selection and cut-offs was developed. The diagnostic value of the new index was analysed by comparing sensitivity, specificity and accuracy vs. Lennard-Jones criteria. By Lennard-Jones criteria 49% (n = 162) of CD patients would have been diagnosed as 'non-CD' at time of diagnosis (sensitivity/specificity/accuracy, 'established' CD: 0.34/0.99/0.67; 'probable' CD: 0.51/0.95/0.73). A new index was derived from granuloma, fibrosis, transmural inflammation and macroscopic discontinuity, but excluded lymphoid aggregates and discontinuous inflammation on histology. Our index provided improved diagnostic accuracy for 'established' and 'probable' CD (sensitivity/specificity/accuracy, 'established' CD: 0.45/1/0.72; 'probable' CD: 0.8/0.85/0.82), including the subgroup isolated colonic CD ('probable' CD, new index: 0.73/0.85/0.79; Lennard-Jones criteria: 0.43/0.95/0.69). We developed an index based on items of Lennard-Jones criteria providing improved diagnostic accuracy for the differential diagnosis between CD and UC. © 2016 John Wiley & Sons Ltd.

Accuracy of fluorodeoxyglucose-positron emission tomography within the clinical practice of the American College of Surgeons Oncology Group Z4031 trial to diagnose clinical stage I non-small cell lung cancer.

PubMed

Grogan, Eric L; Deppen, Stephen A; Ballman, Karla V; Andrade, Gabriela M; Verdial, Francys C; Aldrich, Melinda C; Chen, Chiu L; Decker, Paul A; Harpole, David H; Cerfolio, Robert J; Keenan, Robert J; Jones, David R; D'Amico, Thomas A; Shrager, Joseph B; Meyers, Bryan F; Putnam, Joe B

2014-04-01

Fluorodeoxyglucose-positron emission tomography (FDG-PET) is recommended for diagnosis and staging of non-small cell lung cancer (NSCLC). Meta-analyses of FDG-PET diagnostic accuracy demonstrated sensitivity of 96% and specificity of 78% but were performed in select centers, introducing potential bias. This study evaluates the accuracy of FDG-PET to diagnose NSCLC and examines differences across enrolling sites in the national American College of Surgeons Oncology Group (ACOSOG) Z4031 trial. Between 2004 and 2006, 959 eligible patients with clinical stage I (cT1-2 N0 M0) known or suspected NSCLC were enrolled in the Z4031 trial, and with a baseline FDG-PET available for 682. Final diagnosis was determined by pathologic examination. FDG-PET avidity was categorized into avid or not avid by radiologist description or reported maximum standard uptake value. FDG-PET diagnostic accuracy was calculated for the entire cohort. Accuracy differences based on preoperative size and by enrolling site were examined. Preoperative FDG-PET results were available for 682 participants enrolled at 51 sites in 39 cities. Lung cancer prevalence was 83%. FDG-PET sensitivity was 82% (95% confidence interval, 79 to 85) and specificity was 31% (95% confidence interval, 23% to 40%). Positive and negative predictive values were 85% and 26%, respectively. Accuracy improved with lesion size. Of 80 false-positive scans, 69% were granulomas. False-negative scans occurred in 101 patients, with adenocarcinoma being the most frequent (64%), and 11 were 10 mm or less. The sensitivity varied from 68% to 91% (p=0.03), and the specificity ranged from 15% to 44% (p=0.72) across cities with more than 25 participants. In a national surgical population with clinical stage I NSCLC, FDG-PET to diagnose lung cancer performed poorly compared with published studies. Copyright © 2014 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

Predicting the probability of mortality of gastric cancer patients using decision tree.

PubMed

Mohammadzadeh, F; Noorkojuri, H; Pourhoseingholi, M A; Saadat, S; Baghestani, A R

2015-06-01

Gastric cancer is the fourth most common cancer worldwide. This reason motivated us to investigate and introduce gastric cancer risk factors utilizing statistical methods. The aim of this study was to identify the most important factors influencing the mortality of patients who suffer from gastric cancer disease and to introduce a classification approach according to decision tree model for predicting the probability of mortality from this disease. Data on 216 patients with gastric cancer, who were registered in Taleghani hospital in Tehran,Iran, were analyzed. At first, patients were divided into two groups: the dead and alive. Then, to fit decision tree model to our data, we randomly selected 20% of dataset to the test sample and remaining dataset considered as the training sample. Finally, the validity of the model examined with sensitivity, specificity, diagnosis accuracy and the area under the receiver operating characteristic curve. The CART version 6.0 and SPSS version 19.0 softwares were used for the analysis of the data. Diabetes, ethnicity, tobacco, tumor size, surgery, pathologic stage, age at diagnosis, exposure to chemical weapons and alcohol consumption were determined as effective factors on mortality of gastric cancer. The sensitivity, specificity and accuracy of decision tree were 0.72, 0.75 and 0.74 respectively. The indices of sensitivity, specificity and accuracy represented that the decision tree model has acceptable accuracy to prediction the probability of mortality in gastric cancer patients. So a simple decision tree consisted of factors affecting on mortality of gastric cancer may help clinicians as a reliable and practical tool to predict the probability of mortality in these patients.

Prediction of high incidence of dengue in the Philippines.

PubMed

Buczak, Anna L; Baugher, Benjamin; Babin, Steven M; Ramac-Thomas, Liane C; Guven, Erhan; Elbert, Yevgeniy; Koshute, Phillip T; Velasco, John Mark S; Roque, Vito G; Tayag, Enrique A; Yoon, In-Kyu; Lewis, Sheri H

2014-04-01

Accurate prediction of dengue incidence levels weeks in advance of an outbreak may reduce the morbidity and mortality associated with this neglected disease. Therefore, models were developed to predict high and low dengue incidence in order to provide timely forewarnings in the Philippines. Model inputs were chosen based on studies indicating variables that may impact dengue incidence. The method first uses Fuzzy Association Rule Mining techniques to extract association rules from these historical epidemiological, environmental, and socio-economic data, as well as climate data indicating future weather patterns. Selection criteria were used to choose a subset of these rules for a classifier, thereby generating a Prediction Model. The models predicted high or low incidence of dengue in a Philippines province four weeks in advance. The threshold between high and low was determined relative to historical incidence data. Model accuracy is described by Positive Predictive Value (PPV), Negative Predictive Value (NPV), Sensitivity, and Specificity computed on test data not previously used to develop the model. Selecting a model using the F0.5 measure, which gives PPV more importance than Sensitivity, gave these results: PPV = 0.780, NPV = 0.938, Sensitivity = 0.547, Specificity = 0.978. Using the F3 measure, which gives Sensitivity more importance than PPV, the selected model had PPV = 0.778, NPV = 0.948, Sensitivity = 0.627, Specificity = 0.974. The decision as to which model has greater utility depends on how the predictions will be used in a particular situation. This method builds prediction models for future dengue incidence in the Philippines and is capable of being modified for use in different situations; for diseases other than dengue; and for regions beyond the Philippines. The Philippines dengue prediction models predicted high or low incidence of dengue four weeks in advance of an outbreak with high accuracy, as measured by PPV, NPV, Sensitivity, and Specificity.

Prediction of High Incidence of Dengue in the Philippines

PubMed Central

Buczak, Anna L.; Baugher, Benjamin; Babin, Steven M.; Ramac-Thomas, Liane C.; Guven, Erhan; Elbert, Yevgeniy; Koshute, Phillip T.; Velasco, John Mark S.; Roque, Vito G.; Tayag, Enrique A.; Yoon, In-Kyu; Lewis, Sheri H.

2014-01-01

Background Accurate prediction of dengue incidence levels weeks in advance of an outbreak may reduce the morbidity and mortality associated with this neglected disease. Therefore, models were developed to predict high and low dengue incidence in order to provide timely forewarnings in the Philippines. Methods Model inputs were chosen based on studies indicating variables that may impact dengue incidence. The method first uses Fuzzy Association Rule Mining techniques to extract association rules from these historical epidemiological, environmental, and socio-economic data, as well as climate data indicating future weather patterns. Selection criteria were used to choose a subset of these rules for a classifier, thereby generating a Prediction Model. The models predicted high or low incidence of dengue in a Philippines province four weeks in advance. The threshold between high and low was determined relative to historical incidence data. Principal Findings Model accuracy is described by Positive Predictive Value (PPV), Negative Predictive Value (NPV), Sensitivity, and Specificity computed on test data not previously used to develop the model. Selecting a model using the F0.5 measure, which gives PPV more importance than Sensitivity, gave these results: PPV = 0.780, NPV = 0.938, Sensitivity = 0.547, Specificity = 0.978. Using the F3 measure, which gives Sensitivity more importance than PPV, the selected model had PPV = 0.778, NPV = 0.948, Sensitivity = 0.627, Specificity = 0.974. The decision as to which model has greater utility depends on how the predictions will be used in a particular situation. Conclusions This method builds prediction models for future dengue incidence in the Philippines and is capable of being modified for use in different situations; for diseases other than dengue; and for regions beyond the Philippines. The Philippines dengue prediction models predicted high or low incidence of dengue four weeks in advance of an outbreak with high accuracy, as measured by PPV, NPV, Sensitivity, and Specificity. PMID:24722434

An evaluation of selected in silico models for the assessment ...

EPA Pesticide Factsheets

Skin sensitization remains an important endpoint for consumers, manufacturers and regulators. Although the development of alternative approaches to assess skin sensitization potential has been extremely active over many years, the implication of regulations such as REACH and the Cosmetics Directive in EU has provided a much stronger impetus to actualize this research into practical tools for decision making. Thus there has been considerable focus on the development, evaluation, and integration of alternative approaches for skin sensitization hazard and risk assessment. This includes in silico approaches such as (Q)SARs and expert systems. This study aimed to evaluate the predictive performance of a selection of in silico models and then to explore whether combining those models led to an improvement in accuracy. A dataset of 473 substances that had been tested in the local lymph node assay (LLNA) was compiled. This comprised 295 sensitizers and 178 non-sensitizers. Four freely available models were identified - 2 statistical models VEGA and MultiCASE model A33 for skin sensitization (MCASE A33) from the Danish National Food Institute and two mechanistic models Toxtree’s Skin sensitization Reaction domains (Toxtree SS Rxn domains) and the OASIS v1.3 protein binding alerts for skin sensitization from the OECD Toolbox (OASIS). VEGA and MCASE A33 aim to predict sensitization as a binary score whereas the mechanistic models identified reaction domains or structura

Diagnosis of knee injuries: comparison of the physical examination and magnetic resonance imaging with the findings from arthroscopy☆

PubMed Central

Orlando Júnior, Nilton; de Souza Leão, Marcos George; de Oliveira, Nelson Henrique Carvalho

2015-01-01

Objectives To ascertain the sensitivity, specificity, accuracy and concordance of the physical examination (PE) and magnetic resonance imaging (MRI) in comparison with arthroscopy, in diagnosing knee injuries. Methods Prospective study on 72 patients, with evaluation and comparison of PE, MRI and arthroscopic findings, to determine the concordance, accuracy, sensitivity and specificity. Results PE showed sensitivity of 75.00%, specificity of 62.50% and accuracy of 69.44% for medial meniscal (MM) lesions, while it showed sensitivity of 47.82%, specificity of 93.87% and accuracy of 79.16% for lateral meniscal (LM) lesions. For anterior cruciate ligament (ACL) injuries, PE showed sensitivity of 88.67%, specificity of 94.73% and accuracy of 90.27%. For MM lesions, MRI showed sensitivity of 92.50%, specificity of 62.50% and accuracy of 69.44%, while for LM injuries, it showed sensitivity of 65.00%, specificity of 88.46% and accuracy of 81.94%. For ACL injuries, MRI showed sensitivity of 86.79%, specificity of 73.68% and accuracy of 83.33%. For ACL injuries, the best concordance was with PE, while for MM and LM lesions, it was with MRI (p < 0.001). Conclusions Meniscal and ligament injuries can be diagnosed through careful physical examination, while requests for MRI are reserved for complex or doubtful cases. PE and MRI used together have high sensitivity for ACL and MM lesions, while for LM lesions the specificity is higher. Level of evidence II – Development of diagnostic criteria on consecutive patients (with universally applied reference “gold” standard). PMID:27218085

Test Operations Procedure (TOP) 1-2-612 Nuclear Environment Survivability

DTIC Science & Technology

2008-10-24

measurements. The area equal to the area of gamma dose sensitive electronics will be mapped using CaF2 (Mn) TLDs . The selection of each STT...October 2008 8 2.3.3 HEMP / SREMP Instrumentation / Dosimetry . Measurement Parameter Preferred Device Measurement Accuracy Current...Calcium Fluoride Manganese CaF2 (Mn) Thermoluminescent Dosimeter ( TLDs ) and Compton diodes, respectively. The measured gamma dose values will be

Test battery with the human cell line activation test, direct peptide reactivity assay and DEREK based on a 139 chemical data set for predicting skin sensitizing potential and potency of chemicals.

PubMed

Takenouchi, Osamu; Fukui, Shiho; Okamoto, Kenji; Kurotani, Satoru; Imai, Noriyasu; Fujishiro, Miyuki; Kyotani, Daiki; Kato, Yoshinao; Kasahara, Toshihiko; Fujita, Masaharu; Toyoda, Akemi; Sekiya, Daisuke; Watanabe, Shinichi; Seto, Hirokazu; Hirota, Morihiko; Ashikaga, Takao; Miyazawa, Masaaki

2015-11-01

To develop a testing strategy incorporating the human cell line activation test (h-CLAT), direct peptide reactivity assay (DPRA) and DEREK, we created an expanded data set of 139 chemicals (102 sensitizers and 37 non-sensitizers) by combining the existing data set of 101 chemicals through the collaborative projects of Japan Cosmetic Industry Association. Of the additional 38 chemicals, 15 chemicals with relatively low water solubility (log Kow > 3.5) were selected to clarify the limitation of testing strategies regarding the lipophilic chemicals. Predictivities of the h-CLAT, DPRA and DEREK, and the combinations thereof were evaluated by comparison to results of the local lymph node assay. When evaluating 139 chemicals using combinations of three methods based on integrated testing strategy (ITS) concept (ITS-based test battery) and a sequential testing strategy (STS) weighing the predictive performance of the h-CLAT and DPRA, overall similar predictivities were found as before on the 101 chemical data set. An analysis of false negative chemicals suggested a major limitation of our strategies was the testing of low water-soluble chemicals. When excluded the negative results for chemicals with log Kow > 3.5, the sensitivity and accuracy of ITS improved to 97% (91 of 94 chemicals) and 89% (114 of 128). Likewise, the sensitivity and accuracy of STS to 98% (92 of 94) and 85% (111 of 129). Moreover, the ITS and STS also showed good correlation with local lymph node assay on three potency classifications, yielding accuracies of 74% (ITS) and 73% (STS). Thus, the inclusion of log Kow in analysis could give both strategies a higher predictive performance. Copyright © 2015 John Wiley & Sons, Ltd.

A hybrid Bayesian hierarchical model combining cohort and case-control studies for meta-analysis of diagnostic tests: Accounting for partial verification bias.

PubMed

Ma, Xiaoye; Chen, Yong; Cole, Stephen R; Chu, Haitao

2016-12-01

To account for between-study heterogeneity in meta-analysis of diagnostic accuracy studies, bivariate random effects models have been recommended to jointly model the sensitivities and specificities. As study design and population vary, the definition of disease status or severity could differ across studies. Consequently, sensitivity and specificity may be correlated with disease prevalence. To account for this dependence, a trivariate random effects model had been proposed. However, the proposed approach can only include cohort studies with information estimating study-specific disease prevalence. In addition, some diagnostic accuracy studies only select a subset of samples to be verified by the reference test. It is known that ignoring unverified subjects may lead to partial verification bias in the estimation of prevalence, sensitivities, and specificities in a single study. However, the impact of this bias on a meta-analysis has not been investigated. In this paper, we propose a novel hybrid Bayesian hierarchical model combining cohort and case-control studies and correcting partial verification bias at the same time. We investigate the performance of the proposed methods through a set of simulation studies. Two case studies on assessing the diagnostic accuracy of gadolinium-enhanced magnetic resonance imaging in detecting lymph node metastases and of adrenal fluorine-18 fluorodeoxyglucose positron emission tomography in characterizing adrenal masses are presented. © The Author(s) 2014.

A Hybrid Bayesian Hierarchical Model Combining Cohort and Case-control Studies for Meta-analysis of Diagnostic Tests: Accounting for Partial Verification Bias

PubMed Central

Ma, Xiaoye; Chen, Yong; Cole, Stephen R.; Chu, Haitao

2014-01-01

To account for between-study heterogeneity in meta-analysis of diagnostic accuracy studies, bivariate random effects models have been recommended to jointly model the sensitivities and specificities. As study design and population vary, the definition of disease status or severity could differ across studies. Consequently, sensitivity and specificity may be correlated with disease prevalence. To account for this dependence, a trivariate random effects model had been proposed. However, the proposed approach can only include cohort studies with information estimating study-specific disease prevalence. In addition, some diagnostic accuracy studies only select a subset of samples to be verified by the reference test. It is known that ignoring unverified subjects may lead to partial verification bias in the estimation of prevalence, sensitivities and specificities in a single study. However, the impact of this bias on a meta-analysis has not been investigated. In this paper, we propose a novel hybrid Bayesian hierarchical model combining cohort and case-control studies and correcting partial verification bias at the same time. We investigate the performance of the proposed methods through a set of simulation studies. Two case studies on assessing the diagnostic accuracy of gadolinium-enhanced magnetic resonance imaging in detecting lymph node metastases and of adrenal fluorine-18 fluorodeoxyglucose positron emission tomography in characterizing adrenal masses are presented. PMID:24862512

The limb movement analysis of rehabilitation exercises using wearable inertial sensors.

PubMed

Bingquan Huang; Giggins, Oonagh; Kechadi, Tahar; Caulfield, Brian

2016-08-01

Due to no supervision of a therapist in home based exercise programs, inertial sensor based feedback systems which can accurately assess movement repetitions are urgently required. The synchronicity and the degrees of freedom both show that one movement might resemble another movement signal which is mixed in with another not precisely defined movement. Therefore, the data and feature selections are important for movement analysis. This paper explores the data and feature selection for the limb movement analysis of rehabilitation exercises. The results highlight that the classification accuracy is very sensitive to the mount location of the sensors. The results show that the use of 2 or 3 sensor units, the combination of acceleration and gyroscope data, and the feature sets combined by the statistical feature set with another type of feature, can significantly improve the classification accuracy rates. The results illustrate that acceleration data is more effective than gyroscope data for most of the movement analysis.

The Generalized Anxiety Disorder Screener (GAD-7) and the anxiety module of the Hospital and Depression Scale (HADS-A) as screening tools for generalized anxiety disorder among cancer patients.

PubMed

Esser, Peter; Hartung, Tim J; Friedrich, Michael; Johansen, Christoffer; Wittchen, Hans-Ulrich; Faller, Hermann; Koch, Uwe; Härter, Martin; Keller, Monika; Schulz, Holger; Wegscheider, Karl; Weis, Joachim; Mehnert, Anja

2018-06-01

Anxiety in cancer patients may represent a normal psychological reaction. To detect patients with pathological levels, appropriate screeners with established cut-offs are needed. Given that previous research is sparse, we investigated the diagnostic accuracy of 2 frequently used screening tools in detecting generalized anxiety disorder (GAD). We used data of a multicenter study including 2141 cancer patients. Diagnostic accuracy was investigated for the Generalized Anxiety Disorder Screener (GAD-7) and the anxiety module of the Hospital Anxiety and Depression Scale (HADS-A). GAD, assessed with the Composite International Diagnostic Interview for Oncology, served as a reference standard. Overall accuracy was measured with the area under the receiver operating characteristics curve (AUC). The AUC of the 2 screeners were statistically compared. We also calculated accuracy measures for selected cut-offs. Diagnostic accuracy could be interpreted as adequate for both screeners, with an identical AUC of .81 (95% CI: .79-.82). Consequently, the 2 screeners did not differ in their performance (P = .86). The best balance between sensitivity and specificity was found for cut-offs ≥7 (GAD-7) and ≥8 (HADS-A). The officially recommended thresholds for the GAD-7 (≥ 10) and the HADS-A (≥11) showed low sensitivities of 55% and 48%, respectively. The GAD-7 and HADS-A showed AUC of adequate diagnostic accuracy and hence are applicable for GAD screening in cancer patients. Nevertheless, the choice of optimal cut-offs should be carefully evaluated. Copyright © 2018 John Wiley & Sons, Ltd.

Diagnostic accuracy of ultrasonography, MRI and MR arthrography in the characterisation of rotator cuff disorders: a systematic review and meta-analysis

PubMed Central

Roy, Jean-Sébastien; Braën, Caroline; Leblond, Jean; Desmeules, François; Dionne, Clermont E; MacDermid, Joy C; Bureau, Nathalie J; Frémont, Pierre

2015-01-01

Background Different diagnostic imaging modalities, such as ultrasonography (US), MRI, MR arthrography (MRA) are commonly used for the characterisation of rotator cuff (RC) disorders. Since the most recent systematic reviews on medical imaging, multiple diagnostic studies have been published, most using more advanced technological characteristics. The first objective was to perform a meta-analysis on the diagnostic accuracy of medical imaging for characterisation of RC disorders. Since US is used at the point of care in environments such as sports medicine, a secondary analysis assessed accuracy by radiologists and non-radiologists. Methods A systematic search in three databases was conducted. Two raters performed data extraction and evaluation of risk of bias independently, and agreement was achieved by consensus. Hierarchical summary receiver-operating characteristic package was used to calculate pooled estimates of included diagnostic studies. Results Diagnostic accuracy of US, MRI and MRA in the characterisation of full-thickness RC tears was high with overall estimates of sensitivity and specificity over 0.90. As for partial RC tears and tendinopathy, overall estimates of specificity were also high (>0.90), while sensitivity was lower (0.67–0.83). Diagnostic accuracy of US was similar whether a trained radiologist, sonographer or orthopaedist performed it. Conclusions Our results show the diagnostic accuracy of US, MRI and MRA in the characterisation of full-thickness RC tears. Since full thickness tear constitutes a key consideration for surgical repair, this is an important characteristic when selecting an imaging modality for RC disorder. When considering accuracy, cost, and safety, US is the best option. PMID:25677796

Diagnostic evaluation of sentinel lymph node biopsy using indocyanine green and infrared or fluorescent imaging in gastric cancer: a systematic review and meta-analysis.

PubMed

Skubleny, Daniel; Dang, Jerry T; Skulsky, Samuel; Switzer, Noah; Tian, Chunhong; Shi, Xinzhe; de Gara, Christopher; Birch, Daniel W; Karmali, Shahzeer

2018-06-01

Sentinel node navigation surgery (SNNS) for gastric cancer using infrared visualization of indocyanine green (ICG) is intriguing because it may limit operative morbidity. We are the first to systematically review and perform meta-analysis on the diagnostic utility of ICG and infrared electronic endoscopy (IREE) or near infrared fluorescent imaging (NIFI) for SNNS exclusively in gastric cancer. A search of electronic databases MEDLINE, EMBASE, SCOPUS, Web of Science, and the Cochrane Library using search terms "gastric/stomach" AND "tumor/carcinoma/cancer/neoplasm/adenocarcinoma/malignancy" AND "indocyanine green" was completed in May 2017. Articles were selected by two independent reviewers based on the following major inclusion criteria: (1) diagnostic accuracy study design; (2) indocyanine green was injected at tumor site; (3) IREE or NIFI was used for intraoperative visualization. 327 titles or abstracts were screened. The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2. Ten full text studies were selected. 643 patients were identified with the majority of patients possessing T1 tumors (79.8%). Pooled identification rate, diagnostic odds ratio, sensitivity, and specificity were 0.99 (0.97-1.0), 380.0 (68.71-2101), 0.87 (0.80-0.93), and 1.00 (0.99-1.00), respectively. The summary receiver operator characteristic for ICG + IREE/NIFI demonstrated a test accuracy of 98.3%. Subgroup analysis found improved test performance for studies with low-risk QUADAS-2 scores, studies published after 2010 and submucosal ICG injection. IREE had improved diagnostic odds ratio, sensitivity, and identification rate compared to NIFI. Heterogeneity among studies ranged from low (I 2  < 25%) to high (I 2  > 75%). We found encouraging results regarding the accuracy, diagnostic odds ratio, and specificity of the test. The sensitivity was not optimal but may be improved by a strict protocol to augment the technique. Given the number and heterogeneity of studies, our results must be viewed with caution.

Comparison of two data mining techniques in labeling diagnosis to Iranian pharmacy claim dataset: artificial neural network (ANN) versus decision tree model.

PubMed

Rezaei-Darzi, Ehsan; Farzadfar, Farshad; Hashemi-Meshkini, Amir; Navidi, Iman; Mahmoudi, Mahmoud; Varmaghani, Mehdi; Mehdipour, Parinaz; Soudi Alamdari, Mahsa; Tayefi, Batool; Naderimagham, Shohreh; Soleymani, Fatemeh; Mesdaghinia, Alireza; Delavari, Alireza; Mohammad, Kazem

2014-12-01

This study aimed to evaluate and compare the prediction accuracy of two data mining techniques, including decision tree and neural network models in labeling diagnosis to gastrointestinal prescriptions in Iran. This study was conducted in three phases: data preparation, training phase, and testing phase. A sample from a database consisting of 23 million pharmacy insurance claim records, from 2004 to 2011 was used, in which a total of 330 prescriptions were assessed and used to train and test the models simultaneously. In the training phase, the selected prescriptions were assessed by both a physician and a pharmacist separately and assigned a diagnosis. To test the performance of each model, a k-fold stratified cross validation was conducted in addition to measuring their sensitivity and specificity. Generally, two methods had very similar accuracies. Considering the weighted average of true positive rate (sensitivity) and true negative rate (specificity), the decision tree had slightly higher accuracy in its ability for correct classification (83.3% and 96% versus 80.3% and 95.1%, respectively). However, when the weighted average of ROC area (AUC between each class and all other classes) was measured, the ANN displayed higher accuracies in predicting the diagnosis (93.8% compared with 90.6%). According to the result of this study, artificial neural network and decision tree model represent similar accuracy in labeling diagnosis to GI prescription.

Genetic Particle Swarm Optimization–Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection

PubMed Central

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-01-01

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm. PMID:27483285

Diagnostic value of computed tomography in dogs with chronic nasal disease.

PubMed

Saunders, Jimmy H; van Bree, Henri; Gielen, Ingrid; de Rooster, Hilde

2003-01-01

Computed tomographic (CT) studies of 80 dogs with chronic nasal disease (nasal neoplasia (n = 19), nasal aspergillosis (n = 46), nonspecific rhinitis (n = 11), and foreign body rhinitis (n = 4)) were reviewed retrospectively by two independent observers. Each observer filled out a custom-designed list to record his or her interpretation of the CT signs and selected a diagnosis. Accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for the diagnosis of each disease. The agreement between observers was evaluated. The CT signs corresponded to those previously described in the literature. CT had an accuracy greater than 90% for each observer in all disease processes. The sensitivity, specificity, PPV, and NPV were greater than 80% in all dogs with the exception of the PPV of foreign body rhinitis (80% for observer A and 44% for observer B). There was a substantial, to almost perfect, agreement between the two observers regarding the CT signs and diagnosis. This study indicates a high accuracy of CT for diagnosis of dogs with chronic nasal disease. The differentiation between nasal aspergillosis restricted to the nasal passages and foreign body rhinitis may be difficult when the foreign body is not visible.

Performance of search strategies to retrieve systematic reviews of diagnostic test accuracy from the Cochrane Library.

PubMed

Huang, Yuansheng; Yang, Zhirong; Wang, Jing; Zhuo, Lin; Li, Zhixia; Zhan, Siyan

2016-05-06

To compare the performance of search strategies to retrieve systematic reviews of diagnostic test accuracy from The Cochrane Library. Databases of CDSR and DARE in the Cochrane Library were searched for systematic reviews of diagnostic test accuracy published between 2008 and 2012 through nine search strategies. Each strategy consists of one group or combination of groups of searching filters about diagnostic test accuracy. Four groups of diagnostic filters were used. The Strategy combing all the filters was used as the reference to determine the sensitivity, precision, and the sensitivity x precision product for another eight Strategies. The reference Strategy retrieved 8029 records, of which 832 were eligible. The strategy only composed of MeSH terms about "accuracy measures" achieved the highest values in both precision (69.71%) and product (52.45%) with a moderate sensitivity (75.24%). The combination of MeSH terms and free text words about "accuracy measures" contributed little to increasing the sensitivity. Strategies composed of filters about "diagnosis" had similar sensitivity but lower precision and product to those composed of filters about "accuracy measures". MeSH term "exp'diagnosis' " achieved the lowest precision (9.78%) and product (7.91%), while its hyponym retrieved only half the number of records at the expense of missing 53 target articles. The precision was negatively correlated with sensitivities among the nine strategies. Compared to the filters about "diagnosis", the filters about "accuracy measures" achieved similar sensitivities but higher precision. When combining both terms, sensitivity of the strategy was enhanced obviously. The combination of MeSH terms and free text words about the same concept seemed to be meaningless for enhancing sensitivity. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Highly sensitive measurement of whole blood chromium by inductively coupled plasma mass spectrometry.

PubMed

Cieslak, Wendy; Pap, Kathleen; Bunch, Dustin R; Reineks, Edmunds; Jackson, Raymond; Steinle, Roxanne; Wang, Sihe

2013-02-01

Chromium (Cr), a trace metal element, is implicated in diabetes and cardiovascular disease. A hypochromic state has been associated with poor blood glucose control and unfavorable lipid metabolism. Sensitive and accurate measurement of blood chromium is very important to assess the chromium nutritional status. However, interferents in biological matrices and contamination make the sensitive analysis challenging. The primary goal of this study was to develop a highly sensitive method for quantification of total Cr in whole blood by inductively coupled plasma mass spectrometry (ICP-MS) and to validate the reference interval in a local healthy population. This method was developed on an ICP-MS with a collision/reaction cell. Interference was minimized using both kinetic energy discrimination between the quadrupole and hexapole and a selective collision gas (helium). Reference interval was validated in whole blood samples (n=51) collected in trace element free EDTA tubes from healthy adults (12 males, 39 females), aged 19-64 years (38.8±12.6), after a minimum of 8 h fasting. Blood samples were aliquoted into cryogenic vials and stored at -70 °C until analysis. The assay linearity was 3.42 to 1446.59 nmol/L with an accuracy of 87.7 to 99.8%. The high sensitivity was achieved by minimization of interference through selective kinetic energy discrimination and selective collision using helium. The reference interval for total Cr using a non-parametric method was verified to be 3.92 to 7.48 nmol/L. This validated ICP-MS methodology is highly sensitive and selective for measuring total Cr in whole blood. Copyright © 2012 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved. Published by Elsevier Inc. All rights reserved.

[Non-invasive fibrosis indexes in predicting acute liver function deterioration after transcatheter arterial chemoembolization].

PubMed

Song, Y P; Zhao, Q Y; Li, S; Wang, H; Wu, P H

2016-03-08

To investigate the ability of two non-invasive fibrosis indexes-APRI, i. e. aspartate transaminase (AST) to platelet (PLT) ratio index, and fibrosis index based on the 4 factors (FIB-4)score in predicting ALFD in patients with unresectable primary HCC and underwent TACE. Clinical data of those patients treated with TACE in Department of Interventional Radiology of the Center from Jan 2010 to Aug 2014 were investigated retrospectively. A total of 366 cases were enrolled after randomized selection, 62 (18.5%) of which developed ALFD after TACE. Child-Pugh score, APRI and FIB-4 score in every case were calculated, receiver operating characteristic (ROC) curve of each model were performed and the predictive abilities of them were assessed by area under the curve (AUC), positive predictive value (PPV), negative predictive value (NPV), sensitivity and specificity. The AUC of Child-Pugh score, APRI and FIB-4 score were 0.783, 0.752 and 0.758 respectively, while the difference had no significance in statistics, indicating that predictive accuracies of them were similar. APRI≤1.15 and FIB-4≤3.08 had better NPV (90.6% and 93.6%) and sensitivity (65.6% and 80.0%) than Child-Pugh score>6 (NPV=85.8%, sensitivity=27.4%), PPV and specificity of them are 35.7%, 32.9%, 89.5% and 73.7%, 64.2%, 99.3% respectively. Comparing to Child-Pugh score, APRI and FIB-4 score have similar accuracy but better NPV and sensitivity in predicting post-TACE ALFD. Thereafter they are good for selection of low-risk patients for TACE treatment. Candidates with an APRI≤1.15 or a FIB-4≤3.08 or in Child-Pugh a stage are unlikely to develop ALFD thus could receive TACE safely.

Sensitive and selective liquid chromatography-tandem mass spectrometry method for the quantification of aniracetam in human plasma.

PubMed

Zhang, Jingjing; Liang, Jiabi; Tian, Yuan; Zhang, Zunjian; Chen, Yun

2007-10-15

A rapid, sensitive and selective LC-MS/MS method was developed and validated for the quantification of aniracetam in human plasma using estazolam as internal standard (IS). Following liquid-liquid extraction, the analytes were separated using a mobile phase of methanol-water (60:40, v/v) on a reverse phase C18 column and analyzed by a triple-quadrupole mass spectrometer in the selected reaction monitoring (SRM) mode using the respective [M+H]+ ions, m/z 220-->135 for aniracetam and m/z 295-->205 for the IS. The assay exhibited a linear dynamic range of 0.2-100 ng/mL for aniracetam in human plasma. The lower limit of quantification (LLOQ) was 0.2 ng/mL with a relative standard deviation of less than 15%. Acceptable precision and accuracy were obtained for concentrations over the standard curve range. The validated LC-MS/MS method has been successfully applied to study the pharmacokinetics of aniracetam in healthy male Chinese volunteers.

Sensitivity analysis and approximation methods for general eigenvalue problems

NASA Technical Reports Server (NTRS)

Murthy, D. V.; Haftka, R. T.

1986-01-01

Optimization of dynamic systems involving complex non-hermitian matrices is often computationally expensive. Major contributors to the computational expense are the sensitivity analysis and reanalysis of a modified design. The present work seeks to alleviate this computational burden by identifying efficient sensitivity analysis and approximate reanalysis methods. For the algebraic eigenvalue problem involving non-hermitian matrices, algorithms for sensitivity analysis and approximate reanalysis are classified, compared and evaluated for efficiency and accuracy. Proper eigenvector normalization is discussed. An improved method for calculating derivatives of eigenvectors is proposed based on a more rational normalization condition and taking advantage of matrix sparsity. Important numerical aspects of this method are also discussed. To alleviate the problem of reanalysis, various approximation methods for eigenvalues are proposed and evaluated. Linear and quadratic approximations are based directly on the Taylor series. Several approximation methods are developed based on the generalized Rayleigh quotient for the eigenvalue problem. Approximation methods based on trace theorem give high accuracy without needing any derivatives. Operation counts for the computation of the approximations are given. General recommendations are made for the selection of appropriate approximation technique as a function of the matrix size, number of design variables, number of eigenvalues of interest and the number of design points at which approximation is sought.

Diagnostic features of Alzheimer's disease extracted from PET sinograms

NASA Astrophysics Data System (ADS)

Sayeed, A.; Petrou, M.; Spyrou, N.; Kadyrov, A.; Spinks, T.

2002-01-01

Texture analysis of positron emission tomography (PET) images of the brain is a very difficult task, due to the poor signal to noise ratio. As a consequence, very few techniques can be implemented successfully. We use a new global analysis technique known as the Trace transform triple features. This technique can be applied directly to the raw sinograms to distinguish patients with Alzheimer's disease (AD) from normal volunteers. FDG-PET images of 18 AD and 10 normal controls obtained from the same CTI ECAT-953 scanner were used in this study. The Trace transform triple feature technique was used to extract features that were invariant to scaling, translation and rotation, referred to as invariant features, as well as features that were sensitive to rotation but invariant to scaling and translation, referred to as sensitive features in this study. The features were used to classify the groups using discriminant function analysis. Cross-validation tests using stepwise discriminant function analysis showed that combining both sensitive and invariant features produced the best results, when compared with the clinical diagnosis. Selecting the five best features produces an overall accuracy of 93% with sensitivity of 94% and specificity of 90%. This is comparable with the classification accuracy achieved by Kippenhan et al (1992), using regional metabolic activity.

Value of contrast-enhanced ultrasound in differential diagnosis of solid lesions of pancreas (SLP): A systematic review and a meta-analysis.

PubMed

Ran, Li; Zhao, Wenli; Zhao, Ye; Bu, Huaien

2017-07-01

Contrast-enhanced ultrasound (CEUS) is considered a novel method for diagnosing pancreatic cancer, but currently, there is no conclusive evidence of its accuracy. Using CEUS in discriminating between pancreatic carcinoma and other pancreatic lesions, we aimed to evaluate the diagnostic accuracy of CEUS in predicting pancreatic tumours. Relevant studies were selected from the PubMed, Cochrane Library, Elsevier, CNKI, VIP, and WANFANG databases dating from January 2006 to May 2017. The following terms were used as keywords: "pancreatic cancer" OR "pancreatic carcinoma," "contrast-enhanced ultrasonography" OR "contrast-enhanced ultrasound" OR "CEUS," and "diagnosis." The selection criteria are as follows: pancreatic carcinomas diagnosed by CEUS while the main reference standard was surgical pathology or biopsy (if it involved a clinical diagnosis, particular criteria emphasized); SonoVue or Levovist was the contrast agent; true positive, false positive, false negative, and true negative rates were obtained or calculated to construct the 2 × 2 contingency table; English or Chinese articles; at least 20 patients were enrolled in each group. The Quality Assessment for Studies of Diagnostic Accuracy was employed to evaluate the quality of articles. Pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, summary receiver-operating characteristic curves, and the area under curve were evaluated to estimate the overall diagnostic efficiency. Pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio with 95% confidence intervals (CIs) were calculated with fixed-effect models. Eight of 184 records were eligible for a meta-analysis after independent scrutinization by 2 reviewers. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratios were 0.86 (95% CI 0.81-0.90), 0.75 (95% CI 0.68-0.82), 3.56 (95% CI 2.64-4.78), 0.19 (95% CI 0.13-0.27), and 22.260 (95% CI 8.980-55.177), respectively. The area under the SROC curve was 0.9088. CEUS has a satisfying pooled sensitivity and specificity for discriminating pancreatic cancer from other pancreatic lesions.

Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection.

PubMed

Dong, Zuoli; Zhang, Naiqian; Li, Chun; Wang, Haiyun; Fang, Yun; Wang, Jun; Zheng, Xiaoqi

2015-06-30

An enduring challenge in personalized medicine is to select right drug for individual patients. Testing drugs on patients in large clinical trials is one way to assess their efficacy and toxicity, but it is impractical to test hundreds of drugs currently under development. Therefore the preclinical prediction model is highly expected as it enables prediction of drug response to hundreds of cell lines in parallel. Recently, two large-scale pharmacogenomic studies screened multiple anticancer drugs on over 1000 cell lines in an effort to elucidate the response mechanism of anticancer drugs. To this aim, we here used gene expression features and drug sensitivity data in Cancer Cell Line Encyclopedia (CCLE) to build a predictor based on Support Vector Machine (SVM) and a recursive feature selection tool. Robustness of our model was validated by cross-validation and an independent dataset, the Cancer Genome Project (CGP). Our model achieved good cross validation performance for most drugs in the Cancer Cell Line Encyclopedia (≥80% accuracy for 10 drugs, ≥75% accuracy for 19 drugs). Independent tests on eleven common drugs between CCLE and CGP achieved satisfactory performance for three of them, i.e., AZD6244, Erlotinib and PD-0325901, using expression levels of only twelve, six and seven genes, respectively. These results suggest that drug response could be effectively predicted from genomic features. Our model could be applied to predict drug response for some certain drugs and potentially play a complementary role in personalized medicine.

Rapid Damage Assessment. Volume I. Methodology for Selecting Repair Area of Ordnance-Damaged Pavements.

DTIC Science & Technology

1980-09-01

this system be given no further consideration. 14AGNETOMETER TECHNIQUES Four types of magnetometers are commonly in use today: fluxgate , proton...that are cumbersome to operate and less accurate than fluxgate and proton mag- netometers. The proton magnetometer is also gradually replacing the... fluxgate magnetometer because of its greater sensitivity (I gamma or better), absolute accuracy, nonmoving parts, and its ability Lo measure absolute

Bowel Ultrasonography in the Management of Crohn's Disease. A Review with Recommendations of an International Panel of Experts.

PubMed

Calabrese, Emma; Maaser, Christian; Zorzi, Francesca; Kannengiesser, Klaus; Hanauer, Stephen B; Bruining, David H; Iacucci, Marietta; Maconi, Giovanni; Novak, Kerri L; Panaccione, Remo; Strobel, Deike; Wilson, Stephanie R; Watanabe, Mamoru; Pallone, Francesco; Ghosh, Subrata

2016-05-01

Bowel ultrasonography (US) is considered a useful technique for assessing mural inflammation and complications in Crohn's disease (CD). The aim of this review is to appraise the evidence on the accuracy of bowel US for CD. In addition, we aim to provide recommendations for its optimal use. Publications were identified by literature search from 1992 to 2014 and selected based on predefined criteria: 15 or more patients; bowel US for diagnosing CD, complications, postoperative recurrence, activity; adequate reference standards; prospective study design; data reported to allow calculation of sensitivity, specificity, agreement, or correlation values; articles published in English. The search yielded 655 articles, of which 63 were found to be eligible and retrieved as full-text articles for analysis. Bowel US showed 79.7% sensitivity and 96.7% specificity for the diagnosis of suspected CD, and 89% sensitivity and 94.3% specificity for initial assessment in established patients with CD. Bowel US identified ileal CD with 92.7% sensitivity, 88.2% specificity, and colon CD with 81.8% sensitivity, 95.3% specificity, with lower accuracy for detecting proximal lesions. The oral contrast agent improves the sensitivity and specificity in determining CD lesions and in assessing sites and extent. Bowel US is a tool for evaluation of CD lesions in terms of complications, postoperative recurrence, and monitoring response to medical therapy; it reliably detects postoperative recurrence and complications, as well as offers the possibility of monitoring disease progression.

A sensitive and selective liquid chromatography/tandem mass spectrometry method for quantitative analysis of efavirenz in human plasma.

PubMed

Srivastava, Praveen; Moorthy, Ganesh S; Gross, Robert; Barrett, Jeffrey S

2013-01-01

A selective and a highly sensitive method for the determination of the non-nucleoside reverse transcriptase inhibitor (NNRTI), efavirenz, in human plasma has been developed and fully validated based on high performance liquid chromatography tandem mass spectrometry (LC-MS/MS). Sample preparation involved protein precipitation followed by one to one dilution with water. The analyte, efavirenz was separated by high performance liquid chromatography and detected with tandem mass spectrometry in negative ionization mode with multiple reaction monitoring. Efavirenz and ¹³C₆-efavirenz (Internal Standard), respectively, were detected via the following MRM transitions: m/z 314.20243.90 and m/z 320.20249.90. A gradient program was used to elute the analytes using 0.1% formic acid in water and 0.1% formic acid in acetonitrile as mobile phase solvents, at a flow-rate of 0.3 mL/min. The total run time was 5 min and the retention times for the internal standard (¹³C₆-efavirenz) and efavirenz was approximately 2.6 min. The calibration curves showed linearity (coefficient of regression, r>0.99) over the concentration range of 1.0-2,500 ng/mL. The intraday precision based on the standard deviation of replicates of lower limit of quantification (LLOQ) was 9.24% and for quality control (QC) samples ranged from 2.41% to 6.42% and with accuracy from 112% and 100-111% for LLOQ and QC samples. The inter day precision was 12.3% and 3.03-9.18% for LLOQ and quality controls samples, and the accuracy was 108% and 95.2-108% for LLOQ and QC samples. Stability studies showed that efavirenz was stable during the expected conditions for sample preparation and storage. The lower limit of quantification for efavirenz was 1 ng/mL. The analytical method showed excellent sensitivity, precision, and accuracy. This method is robust and is being successfully applied for therapeutic drug monitoring and pharmacokinetic studies in HIV-infected patients.

Validating malignant melanoma ICD-9-CM codes in Umbria, ASL Napoli 3 Sud and Friuli Venezia Giulia administrative healthcare databases: a diagnostic accuracy study.

PubMed

Orso, Massimiliano; Serraino, Diego; Abraha, Iosief; Fusco, Mario; Giovannini, Gianni; Casucci, Paola; Cozzolino, Francesco; Granata, Annalisa; Gobbato, Michele; Stracci, Fabrizio; Ciullo, Valerio; Vitale, Maria Francesca; Eusebi, Paolo; Orlandi, Walter; Montedori, Alessandro; Bidoli, Ettore

2018-04-20

To assess the accuracy of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes in identifying subjects with melanoma. A diagnostic accuracy study comparing melanoma ICD-9-CM codes (index test) with medical chart (reference standard). Case ascertainment was based on neoplastic lesion of the skin and a histological diagnosis from a primary or metastatic site positive for melanoma. Administrative databases from Umbria Region, Azienda Sanitaria Locale (ASL) Napoli 3 Sud (NA) and Friuli Venezia Giulia (FVG) Region. 112, 130 and 130 cases (subjects with melanoma) were randomly selected from Umbria, NA and FVG, respectively; 94 non-cases (subjects without melanoma) were randomly selected from each unit. Sensitivity and specificity for ICD-9-CM code 172.x located in primary position. The most common melanoma subtype was malignant melanoma of skin of trunk, except scrotum (ICD-9-CM code: 172.5), followed by malignant melanoma of skin of lower limb, including hip (ICD-9-CM code: 172.7). The mean age of the patients ranged from 60 to 61 years. Most of the diagnoses were performed in surgical departments.The sensitivities were 100% (95% CI 96% to 100%) for Umbria, 99% (95% CI 94% to 100%) for NA and 98% (95% CI 93% to 100%) for FVG. The specificities were 88% (95% CI 80% to 93%) for Umbria, 77% (95% CI 69% to 85%) for NA and 79% (95% CI 71% to 86%) for FVG. The case definition for melanoma based on clinical or instrumental diagnosis, confirmed by histological examination, showed excellent sensitivities and good specificities in the three operative units. Administrative databases from the three operative units can be used for epidemiological and outcome research of melanoma. © 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.

Validating malignant melanoma ICD-9-CM codes in Umbria, ASL Napoli 3 Sud and Friuli Venezia Giulia administrative healthcare databases: a diagnostic accuracy study

PubMed Central

Orso, Massimiliano; Serraino, Diego; Fusco, Mario; Giovannini, Gianni; Casucci, Paola; Cozzolino, Francesco; Granata, Annalisa; Gobbato, Michele; Stracci, Fabrizio; Ciullo, Valerio; Vitale, Maria Francesca; Orlandi, Walter; Montedori, Alessandro; Bidoli, Ettore

2018-01-01

Objectives To assess the accuracy of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes in identifying subjects with melanoma. Design A diagnostic accuracy study comparing melanoma ICD-9-CM codes (index test) with medical chart (reference standard). Case ascertainment was based on neoplastic lesion of the skin and a histological diagnosis from a primary or metastatic site positive for melanoma. Setting Administrative databases from Umbria Region, Azienda Sanitaria Locale (ASL) Napoli 3 Sud (NA) and Friuli Venezia Giulia (FVG) Region. Participants 112, 130 and 130 cases (subjects with melanoma) were randomly selected from Umbria, NA and FVG, respectively; 94 non-cases (subjects without melanoma) were randomly selected from each unit. Outcome measures Sensitivity and specificity for ICD-9-CM code 172.x located in primary position. Results The most common melanoma subtype was malignant melanoma of skin of trunk, except scrotum (ICD-9-CM code: 172.5), followed by malignant melanoma of skin of lower limb, including hip (ICD-9-CM code: 172.7). The mean age of the patients ranged from 60 to 61 years. Most of the diagnoses were performed in surgical departments. The sensitivities were 100% (95% CI 96% to 100%) for Umbria, 99% (95% CI 94% to 100%) for NA and 98% (95% CI 93% to 100%) for FVG. The specificities were 88% (95% CI 80% to 93%) for Umbria, 77% (95% CI 69% to 85%) for NA and 79% (95% CI 71% to 86%) for FVG. Conclusions The case definition for melanoma based on clinical or instrumental diagnosis, confirmed by histological examination, showed excellent sensitivities and good specificities in the three operative units. Administrative databases from the three operative units can be used for epidemiological and outcome research of melanoma. PMID:29678984

Utility of 18F-fluoro-deoxyglucose emission tomography/computed tomography fusion imaging (18F-FDG PET/CT) in combination with ultrasonography for axillary staging in primary breast cancer.

PubMed

Ueda, Shigeto; Tsuda, Hitoshi; Asakawa, Hideki; Omata, Jiro; Fukatsu, Kazuhiko; Kondo, Nobuo; Kondo, Tadaharu; Hama, Yukihiro; Tamura, Katsumi; Ishida, Jiro; Abe, Yoshiyuki; Mochizuki, Hidetaka

2008-06-09

Accurate evaluation of axillary lymph node (ALN) involvement is mandatory before treatment of primary breast cancer. The aim of this study is to compare preoperative diagnostic accuracy between positron emission tomography/computed tomography with 18F-fluorodeoxyglucose (18F-FDG PET/CT) and axillary ultrasonography (AUS) for detecting ALN metastasis in patients having operable breast cancer, and to assess the clinical management of axillary 18F-FDG PET/CT for therapeutic indication of sentinel node biopsy (SNB) and preoperative systemic chemotherapy (PSC). One hundred eighty-three patients with primary operable breast cancer were recruited. All patients underwent 18F-FDG PET/CT and AUS followed by SNB and/or ALN dissection (ALND). Using 18F-FDG PET/CT, we studied both a visual assessment of 18F-FDG uptake and standardized uptake value (SUV) for axillary staging. In a visual assessment of 18F-FDG PET/CT, the diagnostic accuracy of ALN metastasis was 83% with 58% in sensitivity and 95% in specificity, and when cut-off point of SUV was set at 1.8, sensitivity, specificity, and accuracy were 36, 100, and 79%, respectively. On the other hand, the diagnostic accuracy of AUS was 85% with 54% in sensitivity and 99% in specificity. By the combination of 18F-FDG PET/CT and AUS to the axilla, the sensitivity, specificity, and accuracy were 64, 94, and 85%, respectively. If either 18F-FDG PET uptake or AUS was positive in allixa, the probability of axillary metastasis was high; 50% (6 of 12) in 18F-FDG PET uptake only, 80% (4 of 5) in AUS positive only, and 100% (28 of 28) in dual positive. By the combination of AUS and 18F-FDG PET/CT, candidates of SNB were more appropriately selected. The axillary 18F-FDG uptake was correlated with the maximum size and nuclear grade of metastatic foci (p = 0.006 and p = 0.03). The diagnostic accuracy of 18F-FDG PET/CT was shown to be nearly equal to ultrasound, and considering their limited sensitivities, the high radiation exposure by 18F-FDG PET/CT and also costs of the examination, it is likely that AUS will be more cost-effective in detecting massive axillary tumor burden. However, when we cannot judge the axillary staging using AUS alone, metabolic approach of 18F-FDG PET/CT for axillary staging would enable us a much more confident diagnosis.

TAxonomy of Self-reported Sedentary behaviour Tools (TASST) framework for development, comparison and evaluation of self-report tools: content analysis and systematic review

PubMed Central

Dall, PM; Coulter, EH; Fitzsimons, CF; Skelton, DA; Chastin, SFM

2017-01-01

Objective Sedentary behaviour (SB) has distinct deleterious health outcomes, yet there is no consensus on best practice for measurement. This study aimed to identify the optimal self-report tool for population surveillance of SB, using a systematic framework. Design A framework, TAxonomy of Self-reported Sedentary behaviour Tools (TASST), consisting of four domains (type of assessment, recall period, temporal unit and assessment period), was developed based on a systematic inventory of existing tools. The inventory was achieved through a systematic review of studies reporting SB and tracing back to the original description. A systematic review of the accuracy and sensitivity to change of these tools was then mapped against TASST domains. Data sources Systematic searches were conducted via EBSCO, reference lists and expert opinion. Eligibility criteria for selecting studies The inventory included tools measuring SB in adults that could be self-completed at one sitting, and excluded tools measuring SB in specific populations or contexts. The systematic review included studies reporting on the accuracy against an objective measure of SB and/or sensitivity to change of a tool in the inventory. Results The systematic review initially identified 32 distinct tools (141 questions), which were used to develop the TASST framework. Twenty-two studies evaluated accuracy and/or sensitivity to change representing only eight taxa. Assessing SB as a sum of behaviours and using a previous day recall were the most promising features of existing tools. Accuracy was poor for all existing tools, with underestimation and overestimation of SB. There was a lack of evidence about sensitivity to change. Conclusions Despite the limited evidence, mapping existing SB tools onto the TASST framework has enabled informed recommendations to be made about the most promising features for a surveillance tool, identified aspects on which future research and development of SB surveillance tools should focus. Trial registration number International prospective register of systematic reviews (PROPSPERO)/CRD42014009851. PMID:28391233

The efficacy of the reverse contrast mode in digital radiography for the detection of proximal dentinal caries

PubMed Central

Miri, Shimasadat; Mehralizadeh, Sandra; Sadri, Donya; Motamedi, Mahmood Reza Kalantar

2015-01-01

Purpose This study evaluated the diagnostic accuracy of the reverse contrast mode in intraoral digital radiography for the detection of proximal dentinal caries, in comparison with the original digital radiographs. Materials and Methods Eighty extracted premolars with no clinically apparent caries were selected, and digital radiographs of them were taken separately in standard conditions. Four observers examined the original radiographs and the same radiographs in the reverse contrast mode with the goal of identifying proximal dentinal caries. Microscopic sections 5 µm in thickness were prepared from the teeth in the mesiodistal direction. Four slides prepared from each sample used as the diagnostic gold standard. The data were analyzed using SPSS (α=0.05). Results Our results showed that the original radiographs in order to identify proximal dentinal caries had the following values for sensitivity, specificity, positive predictive value, negative predictive value, and accuracy, respectively: 72.5%, 90%, 87.2%, 76.5%, and 80.9%. For the reverse contrast mode, however, the corresponding values were 63.1%, 89.4%, 87.1%, 73.5%, and 78.8%, respectively. The sensitivity of original digital radiograph for detecting proximal dentinal caries was significantly higher than that of reverse contrast mode (p<0.05). However, no statistically significant differences were found regarding specificity, positive predictive value, negative predictive value, or accuracy (p>0.05). Conclusion The sensitivity of the original digital radiograph for detecting proximal dentinal caries was significantly higher than that of the reversed contrast images. However, no statistically significant differences were found between these techniques regarding specificity, positive predictive value, negative predictive value, or accuracy. PMID:26389055

Systematic Review of Health Economic Evaluations of Diagnostic Tests in Brazil: How accurate are the results?

PubMed

Oliveira, Maria Regina Fernandes; Leandro, Roseli; Decimoni, Tassia Cristina; Rozman, Luciana Martins; Novaes, Hillegonda Maria Dutilh; De Soárez, Patrícia Coelho

2017-08-01

The aim of this study is to identify and characterize the health economic evaluations (HEEs) of diagnostic tests conducted in Brazil, in terms of their adherence to international guidelines for reporting economic studies and specific questions in test accuracy reports. We systematically searched multiple databases, selecting partial and full HEEs of diagnostic tests, published between 1980 and 2013. Two independent reviewers screened articles for relevance and extracted the data. We performed a qualitative narrative synthesis. Forty-three articles were reviewed. The most frequently studied diagnostic tests were laboratory tests (37.2%) and imaging tests (32.6%). Most were non-invasive tests (51.2%) and were performed in the adult population (48.8%). The intended purposes of the technologies evaluated were mostly diagnostic (69.8%), but diagnosis and treatment and screening, diagnosis, and treatment accounted for 25.6% and 4.7%, respectively. Of the reviewed studies, 12.5% described the methods used to estimate the quantities of resources, 33.3% reported the discount rate applied, and 29.2% listed the type of sensitivity analysis performed. Among the 12 cost-effectiveness analyses, only two studies (17%) referred to the application of formal methods to check the quality of the accuracy studies that provided support for the economic model. The existing Brazilian literature on the HEEs of diagnostic tests exhibited reasonably good performance. However, the following points still require improvement: 1) the methods used to estimate resource quantities and unit costs, 2) the discount rate, 3) descriptions of sensitivity analysis methods, 4) reporting of conflicts of interest, 5) evaluations of the quality of the accuracy studies considered in the cost-effectiveness models, and 6) the incorporation of accuracy measures into sensitivity analyses.

Early Detection of Ureteropelvic Junction Obstruction Using Signal Analysis and Machine Learning: A Dynamic Solution to a Dynamic Problem.

PubMed

Blum, Emily S; Porras, Antonio R; Biggs, Elijah; Tabrizi, Pooneh R; Sussman, Rachael D; Sprague, Bruce M; Shalaby-Rana, Eglal; Majd, Massoud; Pohl, Hans G; Linguraru, Marius George

2017-10-21

We sought to define features that describe the dynamic information in diuresis renograms for the early detection of clinically significant hydronephrosis caused by ureteropelvic junction obstruction. We studied the diuresis renogram of 55 patients with a mean ± SD age of 75 ± 66 days who had congenital hydronephrosis at initial presentation. Five patients had bilaterally affected kidneys for a total of 60 diuresis renograms. Surgery was performed on 35 kidneys. We extracted 45 features based on curve shape and wavelet analysis from the drainage curves recorded after furosemide administration. The optimal features were selected as the combination that maximized the ROC AUC obtained from a linear support vector machine classifier trained to classify patients as with or without obstruction. Using these optimal features we performed leave 1 out cross validation to estimate the accuracy, sensitivity and specificity of our framework. Results were compared to those obtained using post-diuresis drainage half-time and the percent of clearance after 30 minutes. Our framework had 93% accuracy, including 91% sensitivity and 96% specificity, to predict surgical cases. This was a significant improvement over the same accuracy of 82%, including 71% sensitivity and 96% specificity obtained from half-time and 30-minute clearance using the optimal thresholds of 24.57 minutes and 55.77%, respectively. Our machine learning framework significantly improved the diagnostic accuracy of clinically significant hydronephrosis compared to half-time and 30-minute clearance. This aids in the clinical decision making process by offering a tool for earlier detection of severe cases and it has the potential to reduce the number of diuresis renograms required for diagnosis. Copyright © 2018 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Colorimetric and Fluorescent Bimodal Ratiometric Probes for pH Sensing of Living Cells.

PubMed

Liu, Yuan-Yuan; Wu, Ming; Zhu, Li-Na; Feng, Xi-Zeng; Kong, De-Ming

2015-06-01

pH measurement is widely used in many fields. Ratiometric pH sensing is an important way to improve the detection accuracy. Herein, five water-soluble cationic porphyrin derivatives were synthesized and their optical property changes with pH value were investigated. Their pH-dependent assembly/disassembly behaviors caused significant changes in both absorption and fluorescence spectra, thus making them promising bimodal ratiometric probes for both colorimetric and fluorescent pH sensing. Different substituent identity and position confer these probes with different sensitive pH-sensing ranges, and the substituent position gives a larger effect. By selecting different porphyrins, different signal intensity ratios and different fluorescence excitation wavelengths, sensitive pH sensing can be achieved in the range of 2.1-8.0. Having demonstrated the excellent reversibility, good accuracy and low cytotoxicity of the probes, they were successfully applied in pH sensing inside living cells. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Assessment of Myometrial Invasion in Premenopausal Grade 1 Endometrial Carcinoma: Is Magnetic Resonance Imaging a Reliable Tool in Selecting Patients for Fertility-Preserving Therapy?

PubMed

Sakane, Makoto; Hori, Masatoshi; Onishi, Hiromitsu; Tsuboyama, Takahiro; Ota, Takashi; Tatsumi, Mitsuaki; Ueda, Yutaka; Kimura, Toshihiro; Kimura, Tadashi; Tomiyama, Noriyuki

The aim of this study was to evaluate the diagnostic ability of magnetic resonance imaging (MRI) in premenopausal women with G1 endometrial carcinoma. Twenty-six patients underwent T2W, diffusion weighted, and dynamic contrast-enhanced 3-T MRI. The degree of myometrial invasion was pathologically classified into no invasion, shallow (3 mm or less), and more. Two radiologists assessed myometrial invasion on MRI. Diagnostic accuracy, sensitivity, specificity, positive and negative predictive values, AUC, and interobserver agreement were analyzed. For assessing myometrial invasion, mean accuracy, sensitivity, specificity, positive predictive values, negative predictive values, and AUC, respectively, were as follows: 63%, 42%, 85%, 79%, 47%, and 0.75. Mean interobserver agreement was fair (k = 0.36). Shallow invasions were underestimated as no invasion on MRI in all 6 cases. Magnetic resonance imaging produced false-negative result on half of patients. The misjudgments tended to happen in patients with shallow invasion.

A metabolic fingerprinting approach based on selected ion flow tube mass spectrometry (SIFT-MS) and chemometrics: A reliable tool for Mediterranean origin-labeled olive oils authentication.

PubMed

Bajoub, Aadil; Medina-Rodríguez, Santiago; Ajal, El Amine; Cuadros-Rodríguez, Luis; Monasterio, Romina Paula; Vercammen, Joeri; Fernández-Gutiérrez, Alberto; Carrasco-Pancorbo, Alegría

2018-04-01

Selected Ion flow tube mass spectrometry (SIFT-MS) in combination with chemometrics was used to authenticate the geographical origin of Mediterranean virgin olive oils (VOOs) produced under geographical origin labels. In particular, 130 oil samples from six different Mediterranean regions (Kalamata (Greece); Toscana (Italy); Meknès and Tyout (Morocco); and Priego de Córdoba and Baena (Spain)) were considered. The headspace volatile fingerprints were measured by SIFT-MS in full scan with H 3 O + , NO + and O 2 + as precursor ions and the results were subjected to chemometric treatments. Principal Component Analysis (PCA) was used for preliminary multivariate data analysis and Partial Least Squares-Discriminant Analysis (PLS-DA) was applied to build different models (considering the three reagent ions) to classify samples according to the country of origin and regions (within the same country). The multi-class PLS-DA models showed very good performance in terms of fitting accuracy (98.90-100%) and prediction accuracy (96.70-100% accuracy for cross validation and 97.30-100% accuracy for external validation (test set)). Considering the two-class PLS-DA models, the one for the Spanish samples showed 100% sensitivity, specificity and accuracy in calibration, cross validation and external validation; the model for Moroccan oils also showed very satisfactory results (with perfect scores for almost every parameter in all the cases). Copyright © 2017 Elsevier Ltd. All rights reserved.

Freezing of Gait Detection in Parkinson's Disease: A Subject-Independent Detector Using Anomaly Scores.

PubMed

Pham, Thuy T; Moore, Steven T; Lewis, Simon John Geoffrey; Nguyen, Diep N; Dutkiewicz, Eryk; Fuglevand, Andrew J; McEwan, Alistair L; Leong, Philip H W

2017-11-01

Freezing of gait (FoG) is common in Parkinsonian gait and strongly relates to falls. Current clinical FoG assessments are patients' self-report diaries and experts' manual video analysis. Both are subjective and yield moderate reliability. Existing detection algorithms have been predominantly designed in subject-dependent settings. In this paper, we aim to develop an automated FoG detector for subject independent. After extracting highly relevant features, we apply anomaly detection techniques to detect FoG events. Specifically, feature selection is performed using correlation and clusterability metrics. From a list of 244 feature candidates, 36 candidates were selected using saliency and robustness criteria. We develop an anomaly score detector with adaptive thresholding to identify FoG events. Then, using accuracy metrics, we reduce the feature list to seven candidates. Our novel multichannel freezing index was the most selective across all window sizes, achieving sensitivity (specificity) of (). On the other hand, freezing index from the vertical axis was the best choice for a single input, achieving sensitivity (specificity) of () for ankle and () for back sensors. Our subject-independent method is not only significantly more accurate than those previously reported, but also uses a much smaller window (e.g., versus ) and/or lower tolerance (e.g., versus ).Freezing of gait (FoG) is common in Parkinsonian gait and strongly relates to falls. Current clinical FoG assessments are patients' self-report diaries and experts' manual video analysis. Both are subjective and yield moderate reliability. Existing detection algorithms have been predominantly designed in subject-dependent settings. In this paper, we aim to develop an automated FoG detector for subject independent. After extracting highly relevant features, we apply anomaly detection techniques to detect FoG events. Specifically, feature selection is performed using correlation and clusterability metrics. From a list of 244 feature candidates, 36 candidates were selected using saliency and robustness criteria. We develop an anomaly score detector with adaptive thresholding to identify FoG events. Then, using accuracy metrics, we reduce the feature list to seven candidates. Our novel multichannel freezing index was the most selective across all window sizes, achieving sensitivity (specificity) of (). On the other hand, freezing index from the vertical axis was the best choice for a single input, achieving sensitivity (specificity) of () for ankle and () for back sensors. Our subject-independent method is not only significantly more accurate than those previously reported, but also uses a much smaller window (e.g., versus ) and/or lower tolerance (e.g., versus ).

Diagnostic accuracy of central venous catheter confirmation by bedside ultrasound versus chest radiography in critically ill patients: A systematic review and meta-analysis

PubMed Central

Ablordeppey, Enyo A.; Drewry, Anne M.; Beyer, Alexander B.; Theodoro, Daniel L.; Fowler, Susan A.; Fuller, Brian M.; Carpenter, Christopher R.

2016-01-01

Objective We performed a systematic review and meta-analysis to examine the accuracy of bedside ultrasound for confirmation of central venous catheter position and exclusion of pneumothorax compared to chest radiography. Data Sources PubMed, EMBASE, Cochrane Central Register of Controlled Trials, reference lists, conference proceedings and ClinicalTrials.gov Study Selection Articles and abstracts describing the diagnostic accuracy of bedside ultrasound compared with chest radiography for confirmation of central venous catheters in sufficient detail to reconstruct 2×2 contingency tables were reviewed. Primary outcomes included the accuracy of confirming catheter positioning and detecting a pneumothorax. Secondary outcomes included feasibility, inter-rater reliability, and efficiency to complete bedside ultrasound confirmation of central venous catheter position. Data Extraction Investigators abstracted study details including research design and sonographic imaging technique to detect catheter malposition and procedure-related pneumothorax. Diagnostic accuracy measures included pooled sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio. Data Synthesis 15 studies with 1553 central venous catheter placements were identified with a pooled sensitivity and specificity of catheter malposition by ultrasound of 0.82 [0.77, 0.86] and 0.98 [0.97, 0.99] respectively. The pooled positive and negative likelihood ratios of catheter malposition by ultrasound were 31.12 [14.72, 65.78] and 0.25 [0.13, 0.47]. The sensitivity and specificity of ultrasound for pneumothorax detection was nearly 100% in the participating studies. Bedside ultrasound reduced mean central venous catheter confirmation time by 58.3 minutes. Risk of bias and clinical heterogeneity in the studies were high. Conclusions Bedside ultrasound is faster than radiography at identifying pneumothorax after central venous catheter insertion. When a central venous catheter malposition exists, bedside ultrasound will identify four out of every five earlier than chest radiography. PMID:27922877

Sensitivity, accuracy, and precision issues in opto-electronic holography based on fiber optics and high-spatial- and high-digitial-resolution cameras

NASA Astrophysics Data System (ADS)

Furlong, Cosme; Yokum, Jeffrey S.; Pryputniewicz, Ryszard J.

2002-06-01

Sensitivity, accuracy, and precision characteristics in quantitative optical metrology techniques, and specifically in optoelectronic holography based on fiber optics and high-spatial and high-digital resolution cameras, are discussed in this paper. It is shown that sensitivity, accuracy, and precision dependent on both, the effective determination of optical phase and the effective characterization of the illumination-observation conditions. Sensitivity, accuracy, and precision are investigated with the aid of National Institute of Standards and Technology (NIST) traceable gages, demonstrating the applicability of quantitative optical metrology techniques to satisfy constantly increasing needs for the study and development of emerging technologies.

Three-dimensional texture analysis of contrast enhanced CT images for treatment response assessment in Hodgkin lymphoma: Comparison with F-18-FDG PET

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

Knogler, Thomas; El-Rabadi, Karem; Weber, Michael

2014-12-15

Purpose: To determine the diagnostic performance of three-dimensional (3D) texture analysis (TA) of contrast-enhanced computed tomography (CE-CT) images for treatment response assessment in patients with Hodgkin lymphoma (HL), compared with F-18-fludeoxyglucose (FDG) positron emission tomography/CT. Methods: 3D TA of 48 lymph nodes in 29 patients was performed on venous-phase CE-CT images before and after chemotherapy. All lymph nodes showed pathologically elevated FDG uptake at baseline. A stepwise logistic regression with forward selection was performed to identify classic CT parameters and texture features (TF) that enable the separation of complete response (CR) and persistent disease. Results: The TF fraction of imagemore » in runs, calculated for the 45° direction, was able to correctly identify CR with an accuracy of 75%, a sensitivity of 79.3%, and a specificity of 68.4%. Classical CT features achieved an accuracy of 75%, a sensitivity of 86.2%, and a specificity of 57.9%, whereas the combination of TF and CT imaging achieved an accuracy of 83.3%, a sensitivity of 86.2%, and a specificity of 78.9%. Conclusions: 3D TA of CE-CT images is potentially useful to identify nodal residual disease in HL, with a performance comparable to that of classical CT parameters. Best results are achieved when TA and classical CT features are combined.« less

Derivation and validation of different machine-learning models in mortality prediction of trauma in motorcycle riders: a cross-sectional retrospective study in southern Taiwan

PubMed Central

Kuo, Pao-Jen; Wu, Shao-Chun; Chien, Peng-Chen; Rau, Cheng-Shyuan; Chen, Yi-Chun; Hsieh, Hsiao-Yun; Hsieh, Ching-Hua

2018-01-01

Objectives This study aimed to build and test the models of machine learning (ML) to predict the mortality of hospitalised motorcycle riders. Setting The study was conducted in a level-1 trauma centre in southern Taiwan. Participants Motorcycle riders who were hospitalised between January 2009 and December 2015 were classified into a training set (n=6306) and test set (n=946). Using the demographic information, injury characteristics and laboratory data of patients, logistic regression (LR), support vector machine (SVM) and decision tree (DT) analyses were performed to determine the mortality of individual motorcycle riders, under different conditions, using all samples or reduced samples, as well as all variables or selected features in the algorithm. Primary and secondary outcome measures The predictive performance of the model was evaluated based on accuracy, sensitivity, specificity and geometric mean, and an analysis of the area under the receiver operating characteristic curves of the two different models was carried out. Results In the training set, both LR and SVM had a significantly higher area under the receiver operating characteristic curve (AUC) than DT. No significant difference was observed in the AUC of LR and SVM, regardless of whether all samples or reduced samples and whether all variables or selected features were used. In the test set, the performance of the SVM model for all samples with selected features was better than that of all other models, with an accuracy of 98.73%, sensitivity of 86.96%, specificity of 99.02%, geometric mean of 92.79% and AUC of 0.9517, in mortality prediction. Conclusion ML can provide a feasible level of accuracy in predicting the mortality of motorcycle riders. Integration of the ML model, particularly the SVM algorithm in the trauma system, may help identify high-risk patients and, therefore, guide appropriate interventions by the clinical staff. PMID:29306885

Accuracy of MDCT in the preoperative definition of Peritoneal Cancer Index (PCI) in patients with advanced ovarian cancer who underwent peritonectomy and hyperthermic intraperitoneal chemotherapy (HIPEC).

PubMed

Mazzei, Maria Antonietta; Khader, Leila; Cirigliano, Alfredo; Cioffi Squitieri, Nevada; Guerrini, Susanna; Forzoni, Beatrice; Marrelli, Daniele; Roviello, Franco; Mazzei, Francesco Giuseppe; Volterrani, Luca

2013-12-01

To evaluate the accuracy of MDCT in the preoperative definition of Peritoneal Cancer Index (PCI) in patients with advanced ovarian cancer who underwent a peritonectomy and hyperthermic intraperitoneal chemotherapy (HIPEC) after neoadjuvant chemotherapy to obtain a pre-surgery prognostic evaluation and a prediction of optimal cytoreduction surgery. Pre-HIPEC CT examinations of 43 patients with advanced ovarian cancer after neoadjuvant chemotherapy were analyzed by two radiologists. The PCI was scored according to the Sugarbaker classification, based on lesion size and distribution. The results were compared with macroscopic and histologic data after peritonectomy and HIPEC. To evaluate the accuracy of MDCT to detect and localize peritoneal carcinomatosis, both patient-level and regional-level analyses were conducted. A correlation between PCI CT and histologic values for each patient was searched according to the PCI grading. Considering the patient-level analysis, CT shows a sensitivity, specificity, PPV, NPV, and an accuracy in detecting the peritoneal carcinomatosis of 100 %, 40 %, 93 % 100 %, and 93 %, respectively. Considering the regional level analysis, a sensitivity, specificity, PPV, NPV, and diagnostic accuracy of 72 %, 80 %, 66 %, 84 %, and 77 %, respectively were obtained for the correlation between CT and histology. Our results encourage the use of MDCT as the only technique sufficient to select patients with peritoneal carcinomatosis for cytoreductive surgery and HIPEC on the condition that a CT examination will be performed using a dedicated protocol optimized to detect minimal peritoneal disease and CT images will be analyzed by an experienced reader.

Integrated Droplet-Based Microextraction with ESI-MS for Removal of Matrix Interference in Single-Cell Analysis.

PubMed

Zhang, Xiao-Chao; Wei, Zhen-Wei; Gong, Xiao-Yun; Si, Xing-Yu; Zhao, Yao-Yao; Yang, Cheng-Dui; Zhang, Si-Chun; Zhang, Xin-Rong

2016-04-29

Integrating droplet-based microfluidics with mass spectrometry is essential to high-throughput and multiple analysis of single cells. Nevertheless, matrix effects such as the interference of culture medium and intracellular components influence the sensitivity and the accuracy of results in single-cell analysis. To resolve this problem, we developed a method that integrated droplet-based microextraction with single-cell mass spectrometry. Specific extraction solvent was used to selectively obtain intracellular components of interest and remove interference of other components. Using this method, UDP-Glc-NAc, GSH, GSSG, AMP, ADP and ATP were successfully detected in single MCF-7 cells. We also applied the method to study the change of unicellular metabolites in the biological process of dysfunctional oxidative phosphorylation. The method could not only realize matrix-free, selective and sensitive detection of metabolites in single cells, but also have the capability for reliable and high-throughput single-cell analysis.

Prediction of redox-sensitive cysteines using sequential distance and other sequence-based features.

PubMed

Sun, Ming-An; Zhang, Qing; Wang, Yejun; Ge, Wei; Guo, Dianjing

2016-08-24

Reactive oxygen species can modify the structure and function of proteins and may also act as important signaling molecules in various cellular processes. Cysteine thiol groups of proteins are particularly susceptible to oxidation. Meanwhile, their reversible oxidation is of critical roles for redox regulation and signaling. Recently, several computational tools have been developed for predicting redox-sensitive cysteines; however, those methods either only focus on catalytic redox-sensitive cysteines in thiol oxidoreductases, or heavily depend on protein structural data, thus cannot be widely used. In this study, we analyzed various sequence-based features potentially related to cysteine redox-sensitivity, and identified three types of features for efficient computational prediction of redox-sensitive cysteines. These features are: sequential distance to the nearby cysteines, PSSM profile and predicted secondary structure of flanking residues. After further feature selection using SVM-RFE, we developed Redox-Sensitive Cysteine Predictor (RSCP), a SVM based classifier for redox-sensitive cysteine prediction using primary sequence only. Using 10-fold cross-validation on RSC758 dataset, the accuracy, sensitivity, specificity, MCC and AUC were estimated as 0.679, 0.602, 0.756, 0.362 and 0.727, respectively. When evaluated using 10-fold cross-validation with BALOSCTdb dataset which has structure information, the model achieved performance comparable to current structure-based method. Further validation using an independent dataset indicates it is robust and of relatively better accuracy for predicting redox-sensitive cysteines from non-enzyme proteins. In this study, we developed a sequence-based classifier for predicting redox-sensitive cysteines. The major advantage of this method is that it does not rely on protein structure data, which ensures more extensive application compared to other current implementations. Accurate prediction of redox-sensitive cysteines not only enhances our understanding about the redox sensitivity of cysteine, it may also complement the proteomics approach and facilitate further experimental investigation of important redox-sensitive cysteines.

Source localization of rhythmic ictal EEG activity: a study of diagnostic accuracy following STARD criteria.

PubMed

Beniczky, Sándor; Lantz, Göran; Rosenzweig, Ivana; Åkeson, Per; Pedersen, Birthe; Pinborg, Lars H; Ziebell, Morten; Jespersen, Bo; Fuglsang-Frederiksen, Anders

2013-10-01

Although precise identification of the seizure-onset zone is an essential element of presurgical evaluation, source localization of ictal electroencephalography (EEG) signals has received little attention. The aim of our study was to estimate the accuracy of source localization of rhythmic ictal EEG activity using a distributed source model. Source localization of rhythmic ictal scalp EEG activity was performed in 42 consecutive cases fulfilling inclusion criteria. The study was designed according to recommendations for studies on diagnostic accuracy (STARD). The initial ictal EEG signals were selected using a standardized method, based on frequency analysis and voltage distribution of the ictal activity. A distributed source model-local autoregressive average (LAURA)-was used for the source localization. Sensitivity, specificity, and measurement of agreement (kappa) were determined based on the reference standard-the consensus conclusion of the multidisciplinary epilepsy surgery team. Predictive values were calculated from the surgical outcome of the operated patients. To estimate the clinical value of the ictal source analysis, we compared the likelihood ratios of concordant and discordant results. Source localization was performed blinded to the clinical data, and before the surgical decision. Reference standard was available for 33 patients. The ictal source localization had a sensitivity of 70% and a specificity of 76%. The mean measurement of agreement (kappa) was 0.61, corresponding to substantial agreement (95% confidence interval (CI) 0.38-0.84). Twenty patients underwent resective surgery. The positive predictive value (PPV) for seizure freedom was 92% and the negative predictive value (NPV) was 43%. The likelihood ratio was nine times higher for the concordant results, as compared with the discordant ones. Source localization of rhythmic ictal activity using a distributed source model (LAURA) for the ictal EEG signals selected with a standardized method is feasible in clinical practice and has a good diagnostic accuracy. Our findings encourage clinical neurophysiologists assessing ictal EEGs to include this method in their armamentarium. Wiley Periodicals, Inc. © 2013 International League Against Epilepsy.

ACCURACY OF PLASMA FREE METANEPHRINES IN THE DIAGNOSIS OF PHEOCHROMOCYTOMA AND PARAGANGLIOMA: A SYSTEMATIC REVIEW AND META-ANALYSIS.

PubMed

Chen, Yan; Xiao, Huangmeng; Zhou, Xieda; Huang, Xiaoyu; Li, Yanbing; Xiao, Haipeng; Cao, Xiaopei

2017-10-01

Various studies have validated plasma free metanephrines (MNs) as biomarkers for pheochromocytoma and paraganglioma (PPGL). This meta-analysis aimed to estimate the overall diagnostic accuracy of this biochemical test for PPGL. We searched the PubMed, the Cochrane Library, Web of Science, Embase, Scopus, OvidSP, and ProQuest Dissertations & Theses databases from January 1, 1995 to December 2, 2016 and selected studies written in English that assessed plasma free MNs in the diagnosis of PPGL. Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) was used to evaluate the quality of the included studies. We calculated pooled sensitivities, specificities, positive and negative likelihood ratios, diagnostic odds ratios (DORs) and areas under curve (AUCs) with their 95% confidence intervals (95% CIs). Heterogeneity was assessed by I 2 . To identify the source of heterogeneity, we evaluated the threshold effect and performed a meta-regression. Deeks' funnel plot was selected for investigating any potential publication bias. Although the combination of metanephrine (MN) and normetanephrine (NMN) carried lower specificity (0.94, 95% CI 0.90-0.97) than NMN (0.97, 95% CI 0.92-0.99), NMN was generally more accurate than individual tests, with the highest AUC (0.99, 95% CI 0.97-0.99), DOR (443.35, 95% CI 216.9-906.23), and pooled sensitivity (0.97, 95% CI 0.94-0.98) values. Threshold effect and meta-regression analyses showed that different cut-offs, blood sampling positions, study types and test methods contributed to heterogeneity. This meta-analysis suggested an effective value for combined plasma free MNs for the diagnosis of PPGL, but testing for MNs requires more standardization using tightly regulated studies. AUC = area under curve; CI = confidence interval; DOR = diagnostic odds ratio; EIA = enzyme immunoassay; LC-ECD = liquid chromatography-electrochemical detection; LC-MS/MS = liquid chromatography-tandem mass spectrometry; MN = metanephrine; NMN = normetaneprhine; PPGL = pheochromocytoma and paraganglioma; QUADAS-2 = Quality Assessment of Diagnostic Accuracy Studies 2.

Towards practical application of sensors for monitoring animal health; design and validation of a model to detect ketosis.

PubMed

Steensels, Machteld; Maltz, Ephraim; Bahr, Claudia; Berckmans, Daniel; Antler, Aharon; Halachmi, Ilan

2017-05-01

The objective of this study was to design and validate a mathematical model to detect post-calving ketosis. The validation was conducted in four commercial dairy farms in Israel, on a total of 706 multiparous Holstein dairy cows: 203 cows clinically diagnosed with ketosis and 503 healthy cows. A logistic binary regression model was developed, where the dependent variable is categorical (healthy/diseased) and a set of explanatory variables were measured with existing commercial sensors: rumination duration, activity and milk yield of each individual cow. In a first validation step (within-farm), the model was calibrated on the database of each farm separately. Two thirds of the sick cows and an equal number of healthy cows were randomly selected for model validation. The remaining one third of the cows, which did not participate in the model validation, were used for model calibration. In order to overcome the random selection effect, this procedure was repeated 100 times. In a second (between-farms) validation step, the model was calibrated on one farm and validated on another farm. Within-farm accuracy, ranging from 74 to 79%, was higher than between-farm accuracy, ranging from 49 to 72%, in all farms. The within-farm sensitivities ranged from 78 to 90%, and specificities ranged from 71 to 74%. The between-farms sensitivities ranged from 65 to 95%. The developed model can be improved in future research, by employing other variables that can be added; or by exploring other models to achieve greater sensitivity and specificity.

Diagnostic accuracy and reproducibility of the Ottawa Knee Rule vs the Pittsburgh Decision Rule.

PubMed

Cheung, Tung C; Tank, Yeliz; Breederveld, Roelf S; Tuinebreijer, Wim E; de Lange-de Klerk, Elly S M; Derksen, Robert J

2013-04-01

The aim of this present study was to compare the diagnostic accuracy and reproducibility of 2 clinical decision rules (the Ottawa Knee Rules [OKR] and Pittsburgh Decision Rules [PDR]) developed for selective use of x-rays in the evaluation of isolated knee trauma. Application of a decision rule leads to a more efficient evaluation of knee injuries and a reduction in health care costs. The diagnostic accuracy and reproducibility are compared in this study. A cross-sectional interobserver study was conducted in the emergency department of an urban teaching hospital from October 2008 to July 2009. Two observer groups collected data on standardized case-report forms: emergency medicine residents and surgical residents. Standard knee radiographs were performed in each patient. Participants were patients 18 years and older with isolated knee injuries. Pooled sensitivity and specificity were compared using χ(2) statistics, and interobserver agreement was calculated by using κ statistics. Ninety injuries were assessed. Seven injuries concerned fractures (7.8%). For the OKR, the pooled sensitivity and specificity were 0.86 (95% confidence interval [CI], 0.57-0.96) and 0.27 (95% CI, 0.21-0.35), respectively. The PDR had a pooled sensitivity and specificity of 0.86 (95% CI, 0.57-0.96) and 0.51 (95% CI, 0.44-0.59). The PDR was significantly (P = .002) more specific. The κ values for the OKR and PDR were 0.51 (95% CI, 0.32-0.71) and 0.71 (95% CI, 0.57-0.86), respectively. The PDR was found to be more specific than the OKR, with equal sensitivity. Interobserver agreement was moderate for the OKR and substantial for the PDR. Copyright © 2013 Elsevier Inc. All rights reserved.

Narrative review: should teaching of the respiratory physical examination be restricted only to signs with proven reliability and validity?

PubMed

Benbassat, Jochanan; Baumal, Reuben

2010-08-01

To review the reported reliability (reproducibility, inter-examiner agreement) and validity (sensitivity, specificity and likelihood ratios) of respiratory physical examination (PE) signs, and suggest an approach to teaching these signs to medical students. Review of the literature. We searched Paper Chase between 1966 and June 2009 to identify and evaluate published studies on the diagnostic accuracy of respiratory PE signs. Most studies have reported low to fair reliability and sensitivity values. However, some studies have found high specificites for selected PE signs. None of the studies that we reviewed adhered to all of the STARD criteria for reporting diagnostic accuracy. Possible flaws in study designs may have led to underestimates of the observed diagnostic accuracy of respiratory PE signs. The reported poor reliabilities may have been due to differences in the PE skills of the participating examiners, while the sensitivities may have been confounded by variations in the severity of the diseases of the participating patients. IMPLICATION FOR PRACTICE AND MEDICAL EDUCATION: Pending the results of properly controlled studies, the reported poor reliability and sensitivity of most respiratory PE signs do not necessarily detract from their clinical utility. Therefore, we believe that a meticulously performed respiratory PE, which aims to explore a diagnostic hypothesis, as opposed to a PE that aims to detect a disease in an asymptomatic person, remains a cornerstone of clinical practice. We propose teaching the respiratory PE signs according to their importance, beginning with signs of life-threatening conditions and those that have been reported to have a high specificity, and ending with signs that are "nice to know," but are no longer employed because of the availability of more easily performed tests.

Narrative Review: Should Teaching of the Respiratory Physical Examination Be Restricted Only to Signs with Proven Reliability and Validity?

PubMed Central

Baumal, Reuben

2010-01-01

OBJECTIVE To review the reported reliability (reproducibility, inter-examiner agreement) and validity (sensitivity, specificity and likelihood ratios) of respiratory physical examination (PE) signs, and suggest an approach to teaching these signs to medical students. METHODS Review of the literature. We searched Paper Chase between 1966 and June 2009 to identify and evaluate published studies on the diagnostic accuracy of respiratory PE signs. RESULTS Most studies have reported low to fair reliability and sensitivity values. However, some studies have found high specificites for selected PE signs. None of the studies that we reviewed adhered to all of the STARD criteria for reporting diagnostic accuracy. CONCLUSIONS Possible flaws in study designs may have led to underestimates of the observed diagnostic accuracy of respiratory PE signs. The reported poor reliabilities may have been due to differences in the PE skills of the participating examiners, while the sensitivities may have been confounded by variations in the severity of the diseases of the participating patients. IMPLICATION FOR PRACTICE AND MEDICAL EDUCATION Pending the results of properly controlled studies, the reported poor reliability and sensitivity of most respiratory PE signs do not necessarily detract from their clinical utility. Therefore, we believe that a meticulously performed respiratory PE, which aims to explore a diagnostic hypothesis, as opposed to a PE that aims to detect a disease in an asymptomatic person, remains a cornerstone of clinical practice. We propose teaching the respiratory PE signs according to their importance, beginning with signs of life-threatening conditions and those that have been reported to have a high specificity, and ending with signs that are "nice to know," but are no longer employed because of the availability of more easily performed tests. PMID:20349154

[Sensitivity analysis of AnnAGNPS model's hydrology and water quality parameters based on the perturbation analysis method].

PubMed

Xi, Qing; Li, Zhao-Fu; Luo, Chuan

2014-05-01

Sensitivity analysis of hydrology and water quality parameters has a great significance for integrated model's construction and application. Based on AnnAGNPS model's mechanism, terrain, hydrology and meteorology, field management, soil and other four major categories of 31 parameters were selected for the sensitivity analysis in Zhongtian river watershed which is a typical small watershed of hilly region in the Taihu Lake, and then used the perturbation method to evaluate the sensitivity of the parameters to the model's simulation results. The results showed that: in the 11 terrain parameters, LS was sensitive to all the model results, RMN, RS and RVC were generally sensitive and less sensitive to the output of sediment but insensitive to the remaining results. For hydrometeorological parameters, CN was more sensitive to runoff and sediment and relatively sensitive for the rest results. In field management, fertilizer and vegetation parameters, CCC, CRM and RR were less sensitive to sediment and particulate pollutants, the six fertilizer parameters (FR, FD, FID, FOD, FIP, FOP) were particularly sensitive for nitrogen and phosphorus nutrients. For soil parameters, K is quite sensitive to all the results except the runoff, the four parameters of the soil's nitrogen and phosphorus ratio (SONR, SINR, SOPR, SIPR) were less sensitive to the corresponding results. The simulation and verification results of runoff in Zhongtian watershed show a good accuracy with the deviation less than 10% during 2005- 2010. Research results have a direct reference value on AnnAGNPS model's parameter selection and calibration adjustment. The runoff simulation results of the study area also proved that the sensitivity analysis was practicable to the parameter's adjustment and showed the adaptability to the hydrology simulation in the Taihu Lake basin's hilly region and provide reference for the model's promotion in China.

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.

The predictive value of magnetic resonance imaging of retinoblastoma for the likelihood of high-risk pathologic features.

PubMed

Hiasat, Jamila G; Saleh, Alaa; Al-Hussaini, Maysa; Al Nawaiseh, Ibrahim; Mehyar, Mustafa; Qandeel, Monther; Mohammad, Mona; Deebajah, Rasha; Sultan, Iyad; Jaradat, Imad; Mansour, Asem; Yousef, Yacoub A

2018-06-01

To evaluate the predictive value of magnetic resonance imaging in retinoblastoma for the likelihood of high-risk pathologic features. A retrospective study of 64 eyes enucleated from 60 retinoblastoma patients. Contrast-enhanced magnetic resonance imaging was performed before enucleation. Main outcome measures included demographics, laterality, accuracy, sensitivity, and specificity of magnetic resonance imaging in detecting high-risk pathologic features. Optic nerve invasion and choroidal invasion were seen microscopically in 34 (53%) and 28 (44%) eyes, respectively, while they were detected in magnetic resonance imaging in 22 (34%) and 15 (23%) eyes, respectively. The accuracy of magnetic resonance imaging in detecting prelaminar invasion was 77% (sensitivity 89%, specificity 98%), 56% for laminar invasion (sensitivity 27%, specificity 94%), 84% for postlaminar invasion (sensitivity 42%, specificity 98%), and 100% for optic cut edge invasion (sensitivity100%, specificity 100%). The accuracy of magnetic resonance imaging in detecting focal choroidal invasion was 48% (sensitivity 33%, specificity 97%), and 84% for massive choroidal invasion (sensitivity 53%, specificity 98%), and the accuracy in detecting extrascleral extension was 96% (sensitivity 67%, specificity 98%). Magnetic resonance imaging should not be the only method to stratify patients at high risk from those who are not, eventhough it can predict with high accuracy extensive postlaminar optic nerve invasion, massive choroidal invasion, and extrascleral tumor extension.

Sensitive spectrophotometric determination of aceclofenac following azo dye formation with 4-carboxyl-2,6-dinitrobenzene diazonium ion.

PubMed

Aderibigbe, Segun A; Adegoke, Olajire A; Idowu, Olakunle S; Olaleye, Sefiu O

2012-01-01

The study is a description of a sensitive spectrophotometric determination of aceclofenac following azo dye formation with 4-carboxyl-2,6-dinitrobenzenediazonium ion (CDNBD). Spot test and thin layer chromatography revealed the formation of a new compound distinct from CDNBD and aceclofenac. Optimization studies established a reaction time of 5 min at 30 degrees C after vortex mixing the drug/CDNBD for 10 s. An absorption maximum of 430 nm was selected as analytical wavelength. A linear response was observed over 1.2-4.8 μg/mL of aceclofenac with a correlation coefficient of 0.9983 and the drug combined with CDNBD at stoichiometric ratio of 2 : 1. The method has a limit of detection of 0.403 μg/mL, limit of quantitation of 1.22 μg/mL and is reproducible over a three day assessment. The method gave Sandell's sensitivity of 3.279 ng/cm2. Intra- and inter-day accuracies (in terms of errors) were less than 6% while precisions were of the order of 0.03-1.89% (RSD). The developed spectrophotometric method is of equivalent accuracy (p > 0.05) with British Pharmacopoeia, 2010 potentiometric method. It has the advantages of speed, simplicity, sensitivity and more affordable instrumentation and could found application as a rapid and sensitive analytical method of aceclofenac. It is the first described method by azo dye derivatization for the analysis of aceclofenac in bulk samples and dosage forms.

A GC-MS method for the detection and quantitation of ten major drugs of abuse in human hair samples.

PubMed

Orfanidis, A; Mastrogianni, O; Koukou, A; Psarros, G; Gika, H; Theodoridis, G; Raikos, N

2017-03-15

A sensitive analytical method has been developed in order to identify and quantify major drugs of abuse (DOA), namely morphine, codeine, 6-monoacetylmorphine, cocaine, ecgonine methyl ester, benzoylecgonine, amphetamine, methamphetamine, methylenedioxymethamphetamine and methylenedioxyamphetamine in human hair. Samples of hair were extracted with methanol under ultrasonication at 50°C after a three step rinsing process to remove external contamination and dirt hair. Derivatization with BSTFA was selected in order to increase detection sensitivity of GC/MS analysis. Optimization of derivatization parameters was based on experiments for the selection of derivatization time, temperature and volume of derivatising agent. Validation of the method included evaluation of linearity which ranged from 2 to 350ng/mg of hair mean concentration for all DOA, evaluation of sensitivity, accuracy, precision and repeatability. Limits of detection ranged from 0.05 to 0.46ng/mg of hair. The developed method was applied for the analysis of hair samples obtained from three human subjects and were found positive in cocaine, and opiates. Published by Elsevier B.V.

Commercial enzyme-linked immunosorbent assay versuspolymerase chain reaction for the diagnosis of chronic Chagas disease: a systematic review and meta-analysis.

PubMed

Brasil, Pedro Emmanuel Alvarenga Americano do; Castro, Rodolfo; Castro, Liane de

2016-01-01

Chronic Chagas disease diagnosis relies on laboratory tests due to its clinical characteristics. The aim of this research was to review commercial enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR) diagnostic test performance. Performance of commercial ELISA or PCR for the diagnosis of chronic Chagas disease were systematically searched in PubMed, Scopus, Embase, ISI Web, and LILACS through the bibliography from 1980-2014 and by contact with the manufacturers. The risk of bias was assessed with QUADAS-2. Heterogeneity was estimated with the I2 statistic. Accuracies provided by the manufacturers usually overestimate the accuracy provided by academia. The risk of bias is high in most tests and in most QUADAS dimensions. Heterogeneity is high in either sensitivity, specificity, or both. The evidence regarding commercial ELISA and ELISA-rec sensitivity and specificity indicates that there is overestimation. The current recommendation to use two simultaneous serological tests can be supported by the risk of bias analysis and the amount of heterogeneity but not by the observed accuracies. The usefulness of PCR tests are debatable and health care providers should not order them on a routine basis. PCR may be used in selected cases due to its potential to detect seronegative subjects.

Commercial enzyme-linked immunosorbent assay versus polymerase chain reaction for the diagnosis of chronic Chagas disease: a systematic review and meta-analysis

PubMed Central

do Brasil, Pedro Emmanuel Alvarenga Americano; Castro, Rodolfo; de Castro, Liane

2016-01-01

Chronic Chagas disease diagnosis relies on laboratory tests due to its clinical characteristics. The aim of this research was to review commercial enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR) diagnostic test performance. Performance of commercial ELISA or PCR for the diagnosis of chronic Chagas disease were systematically searched in PubMed, Scopus, Embase, ISI Web, and LILACS through the bibliography from 1980-2014 and by contact with the manufacturers. The risk of bias was assessed with QUADAS-2. Heterogeneity was estimated with the I2 statistic. Accuracies provided by the manufacturers usually overestimate the accuracy provided by academia. The risk of bias is high in most tests and in most QUADAS dimensions. Heterogeneity is high in either sensitivity, specificity, or both. The evidence regarding commercial ELISA and ELISA-rec sensitivity and specificity indicates that there is overestimation. The current recommendation to use two simultaneous serological tests can be supported by the risk of bias analysis and the amount of heterogeneity but not by the observed accuracies. The usefulness of PCR tests are debatable and health care providers should not order them on a routine basis. PCR may be used in selected cases due to its potential to detect seronegative subjects. PMID:26814640

Diagnostic potential of Raman spectroscopy in Barrett's esophagus

NASA Astrophysics Data System (ADS)

Wong Kee Song, Louis-Michel; Molckovsky, Andrea; Wang, Kenneth K.; Burgart, Lawrence J.; Dolenko, Brion; Somorjai, Rajmund L.; Wilson, Brian C.

2005-04-01

Patients with Barrett's esophagus (BE) undergo periodic endoscopic surveillance with random biopsies in an effort to detect dysplastic or early cancerous lesions. Surveillance may be enhanced by near-infrared Raman spectroscopy (NIRS), which has the potential to identify endoscopically-occult dysplastic lesions within the Barrett's segment and allow for targeted biopsies. The aim of this study was to assess the diagnostic performance of NIRS for identifying dysplastic lesions in BE in vivo. Raman spectra (Pexc=70 mW; t=5 s) were collected from Barrett's mucosa at endoscopy using a custom-built NIRS system (λexc=785 nm) equipped with a filtered fiber-optic probe. Each probed site was biopsied for matching histological diagnosis as assessed by an expert pathologist. Diagnostic algorithms were developed using genetic algorithm-based feature selection and linear discriminant analysis, and classification was performed on all spectra with a bootstrap-based cross-validation scheme. The analysis comprised 192 samples (112 non-dysplastic, 54 low-grade dysplasia and 26 high-grade dysplasia/early adenocarcinoma) from 65 patients. Compared with histology, NIRS differentiated dysplastic from non-dysplastic Barrett's samples with 86% sensitivity, 88% specificity and 87% accuracy. NIRS identified 'high-risk' lesions (high-grade dysplasia/early adenocarcinoma) with 88% sensitivity, 89% specificity and 89% accuracy. In the present study, NIRS classified Barrett's epithelia with high and clinically-useful diagnostic accuracy.

Evaluation of in silico tools to predict the skin sensitization potential of chemicals.

PubMed

Verheyen, G R; Braeken, E; Van Deun, K; Van Miert, S

2017-01-01

Public domain and commercial in silico tools were compared for their performance in predicting the skin sensitization potential of chemicals. The packages were either statistical based (Vega, CASE Ultra) or rule based (OECD Toolbox, Toxtree, Derek Nexus). In practice, several of these in silico tools are used in gap filling and read-across, but here their use was limited to make predictions based on presence/absence of structural features associated to sensitization. The top 400 ranking substances of the ATSDR 2011 Priority List of Hazardous Substances were selected as a starting point. Experimental information was identified for 160 chemically diverse substances (82 positive and 78 negative). The prediction for skin sensitization potential was compared with the experimental data. Rule-based tools perform slightly better, with accuracies ranging from 0.6 (OECD Toolbox) to 0.78 (Derek Nexus), compared with statistical tools that had accuracies ranging from 0.48 (Vega) to 0.73 (CASE Ultra - LLNA weak model). Combining models increased the performance, with positive and negative predictive values up to 80% and 84%, respectively. However, the number of substances that were predicted positive or negative for skin sensitization in both models was low. Adding more substances to the dataset will increase the confidence in the conclusions reached. The insights obtained in this evaluation are incorporated in a web database www.asopus.weebly.com that provides a potential end user context for the scope and performance of different in silico tools with respect to a common dataset of curated skin sensitization data.

Journal Club: Comparison of assessment of preoperative pulmonary vasculature in patients with non-small cell lung cancer by non-contrast- and 4D contrast-enhanced 3-T MR angiography and contrast-enhanced 64-MDCT.

PubMed

Ohno, Yoshiharu; Nishio, Mizuho; Koyama, Hisanobu; Yoshikawa, Takeshi; Matsumoto, Sumiaki; Seki, Shinichiro; Sugimura, Kazuro

2014-03-01

The purpose of this article is to prospectively and directly compare the capabilities of non-contrast-enhanced MR angiography (MRA), 4D contrast-enhanced MRA, and contrast-enhanced MDCT for assessing pulmonary vasculature in patients with non-small cell lung cancer (NSCLC) before surgical treatment. A total of 77 consecutive patients (41 men and 36 women; mean age, 71 years) with pathologically proven and clinically assessed stage I NSCLC underwent thin-section contrast-enhanced MDCT, non-contrast-enhanced and contrast-enhanced MRA, and surgical treatment. The capability for anomaly assessment of the three methods was independently evaluated by two reviewers using a 5-point visual scoring system, and final assessment for each patient was made by consensus of the two readers. Interobserver agreement for pulmonary arterial and venous assessment was evaluated with the kappa statistic. Then, sensitivity, specificity, and accuracy for the detection of anomalies were directly compared among the three methods by use of the McNemar test. Interobserver agreement for pulmonary artery and vein assessment was substantial or almost perfect (κ=0.72-0.86). For pulmonary arterial and venous variation assessment, there were no significant differences in sensitivity, specificity, and accuracy among non-contrast-enhanced MRA (pulmonary arteries: sensitivity, 77.1%; specificity, 97.4%; accuracy, 87.7%; pulmonary veins: sensitivity, 50%; specificity, 98.5%; accuracy, 93.2%), 4D contrast-enhanced MRA (pulmonary arteries: sensitivity, 77.1%; specificity, 97.4%; accuracy, 87.7%; pulmonary veins: sensitivity, 62.5%; specificity, 100.0%; accuracy, 95.9%), and thin-section contrast-enhanced MDCT (pulmonary arteries: sensitivity, 91.4%; specificity, 89.5%; accuracy, 90.4%; pulmonary veins: sensitivity, 50%; specificity, 100.0%; accuracy, 95.9%) (p>0.05). Pulmonary vascular assessment of patients with NSCLC before surgical resection by non-contrast-enhanced MRA can be considered equivalent to that by 4D contrast-enhanced MRA and contrast-enhanced MDCT.

Diagnostic Accuracy of Cone-beam Computed Tomography and Conventional Radiography on Apical Periodontitis: A Systematic Review and Meta-analysis.

PubMed

Leonardi Dutra, Kamile; Haas, Letícia; Porporatti, André Luís; Flores-Mir, Carlos; Nascimento Santos, Juliana; Mezzomo, Luis André; Corrêa, Márcio; De Luca Canto, Graziela

2016-03-01

Endodontic diagnosis depends on accurate radiographic examination. Assessment of the location and extent of apical periodontitis (AP) can influence treatment planning and subsequent treatment outcomes. Therefore, this systematic review and meta-analysis assessed the diagnostic accuracy of conventional radiography and cone-beam computed tomographic (CBCT) imaging on the discrimination of AP from no lesion. Eight electronic databases with no language or time limitations were searched. Articles in which the primary objective was to evaluate the accuracy (sensitivity and specificity) of any type of radiographic technique to assess AP in humans were selected. The gold standard was the histologic examination for actual AP (in vivo) or in situ visualization of bone defects for induced artificial AP (in vitro). Accuracy measurements described in the studies were transformed to construct receiver operating characteristic curves and forest plots with the aid of Review Manager v.5.2 (The Nordic Cochrane Centre, Copenhagen, Denmark) and MetaDisc v.1.4. software (Unit of Clinical Biostatistics Team of the Ramón y Cajal Hospital, Madrid, Spain). The methodology of the selected studies was evaluated using the Quality Assessment Tool for Diagnostic Accuracy Studies-2. Only 9 studies met the inclusion criteria and were subjected to a qualitative analysis. A meta-analysis was conducted on 6 of these articles. All of these articles studied artificial AP with induced bone defects. The accuracy values (area under the curve) were 0.96 for CBCT imaging, 0.73 for conventional periapical radiography, and 0.72 for digital periapical radiography. No evidence was found for panoramic radiography. Periapical radiographs (digital and conventional) reported good diagnostic accuracy on the discrimination of artificial AP from no lesions, whereas CBCT imaging showed excellent accuracy values. Copyright © 2016 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

Sensitive determination of cholesterol and its metabolic steroid hormones by UHPLC-MS/MS via derivatization coupled with dual ultrasonic-assisted dispersive liquid-liquid microextraction.

PubMed

Zhao, Xian-En; Yan, Ping; Wang, Renjun; Zhu, Shuyun; You, Jinmao; Bai, Yu; Liu, Huwei

2016-08-01

Quantitative analysis of cholesterol and its metabolic steroid hormones plays a vital role in diagnosing endocrine disorders and understanding disease progression, as well as in clinical medicine studies. Because of their extremely low abundance in body fluids, it remains a challenging task to develop a sensitive detection method. A hyphenated technique of dual ultrasonic-assisted dispersive liquid-liquid microextraction (dual-UADLLME) coupled with microwave-assisted derivatization (MAD) was proposed for cleansing, enrichment and sensitivity enhancement. 4'-Carboxy-substituted rosamine (CSR) was synthesized and used as derivatization reagent. An ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) method was developed for determination of cholesterol and its metabolic steroid hormones in the multiple reaction monitoring mode. Parameters of dual-UADLLME, MAD and UHPLC-MS/MS were all optimized. Satisfactory linearity, recovery, repeatability, accuracy and precision, absence of matrix effect and extremely low limits of detection (LODs, 0.08-0.15 pg mL(-1) ) were achieved. Through the combination of dual-UADLLME and MAD, a determination method for cholesterol and its metabolic steroid hormones in human plasma, serum and urine samples was developed and validated with high sensitivity, selectivity, accuracy and perfect matrix effect results. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Proposed Diagnostic Criteria for Smartphone Addiction

PubMed Central

Lin, Yu-Hsuan; Chiang, Chih-Lin; Lin, Po-Hsien; Chang, Li-Ren; Ko, Chih-Hung; Lee, Yang-Han

2016-01-01

Background Global smartphone penetration has led to unprecedented addictive behaviors. The aims of this study are to develop diagnostic criteria of smartphone addiction and to examine the discriminative ability and the validity of the diagnostic criteria. Methods We developed twelve candidate criteria for characteristic symptoms of smartphone addiction and four criteria for functional impairment caused by excessive smartphone use. The participants consisted of 281 college students. Each participant was systematically assessed for smartphone-using behaviors by psychiatrist’s structured diagnostic interview. The sensitivity, specificity, and diagnostic accuracy of the candidate symptom criteria were analyzed with reference to the psychiatrists’ clinical global impression. The optimal model selection with its cutoff point of the diagnostic criteria differentiating the smartphone addicted subjects from non-addicted subjects was then determined by the best diagnostic accuracy. Results Six symptom criteria model with optimal cutoff point were determined based on the maximal diagnostic accuracy. The proposed smartphone addiction diagnostic criteria consisted of (1) six symptom criteria, (2) four functional impairment criteria and (3) exclusion criteria. Setting three symptom criteria as the cutoff point resulted in the highest diagnostic accuracy (84.3%), while the sensitivity and specificity were 79.4% and 87.5%, respectively. We suggested determining the functional impairment by two or more of the four domains considering the high accessibility and penetration of smartphone use. Conclusion The diagnostic criteria of smartphone addiction demonstrated the core symptoms “impaired control” paralleled with substance related and addictive disorders. The functional impairment involved multiple domains provide a strict standard for clinical assessment. PMID:27846211

Proposed Diagnostic Criteria for Smartphone Addiction.

PubMed

Lin, Yu-Hsuan; Chiang, Chih-Lin; Lin, Po-Hsien; Chang, Li-Ren; Ko, Chih-Hung; Lee, Yang-Han; Lin, Sheng-Hsuan

2016-01-01

Global smartphone penetration has led to unprecedented addictive behaviors. The aims of this study are to develop diagnostic criteria of smartphone addiction and to examine the discriminative ability and the validity of the diagnostic criteria. We developed twelve candidate criteria for characteristic symptoms of smartphone addiction and four criteria for functional impairment caused by excessive smartphone use. The participants consisted of 281 college students. Each participant was systematically assessed for smartphone-using behaviors by psychiatrist's structured diagnostic interview. The sensitivity, specificity, and diagnostic accuracy of the candidate symptom criteria were analyzed with reference to the psychiatrists' clinical global impression. The optimal model selection with its cutoff point of the diagnostic criteria differentiating the smartphone addicted subjects from non-addicted subjects was then determined by the best diagnostic accuracy. Six symptom criteria model with optimal cutoff point were determined based on the maximal diagnostic accuracy. The proposed smartphone addiction diagnostic criteria consisted of (1) six symptom criteria, (2) four functional impairment criteria and (3) exclusion criteria. Setting three symptom criteria as the cutoff point resulted in the highest diagnostic accuracy (84.3%), while the sensitivity and specificity were 79.4% and 87.5%, respectively. We suggested determining the functional impairment by two or more of the four domains considering the high accessibility and penetration of smartphone use. The diagnostic criteria of smartphone addiction demonstrated the core symptoms "impaired control" paralleled with substance related and addictive disorders. The functional impairment involved multiple domains provide a strict standard for clinical assessment.

Deep Learning to Classify Radiology Free-Text Reports.

PubMed

Chen, Matthew C; Ball, Robyn L; Yang, Lingyao; Moradzadeh, Nathaniel; Chapman, Brian E; Larson, David B; Langlotz, Curtis P; Amrhein, Timothy J; Lungren, Matthew P

2018-03-01

Purpose To evaluate the performance of a deep learning convolutional neural network (CNN) model compared with a traditional natural language processing (NLP) model in extracting pulmonary embolism (PE) findings from thoracic computed tomography (CT) reports from two institutions. Materials and Methods Contrast material-enhanced CT examinations of the chest performed between January 1, 1998, and January 1, 2016, were selected. Annotations by two human radiologists were made for three categories: the presence, chronicity, and location of PE. Classification of performance of a CNN model with an unsupervised learning algorithm for obtaining vector representations of words was compared with the open-source application PeFinder. Sensitivity, specificity, accuracy, and F1 scores for both the CNN model and PeFinder in the internal and external validation sets were determined. Results The CNN model demonstrated an accuracy of 99% and an area under the curve value of 0.97. For internal validation report data, the CNN model had a statistically significant larger F1 score (0.938) than did PeFinder (0.867) when classifying findings as either PE positive or PE negative, but no significant difference in sensitivity, specificity, or accuracy was found. For external validation report data, no statistical difference between the performance of the CNN model and PeFinder was found. Conclusion A deep learning CNN model can classify radiology free-text reports with accuracy equivalent to or beyond that of an existing traditional NLP model. © RSNA, 2017 Online supplemental material is available for this article.

Highly sensitive spectrofluorimetric determination of lomefloxacin in spiked human plasma, urine and pharmaceutical preparations.

PubMed

Ulu, Sevgi Tatar

2009-09-01

A sensitive, simple and selective spectrofluorimetric method was developed for the determination of lomefloxacin in biological fluids and pharmaceutical preparations. The method is based on the reaction between the drug and 4-chloro-7-nitrobenzodioxazole in borate buffer of pH 8.5 to yield a highly fluorescent derivative that is measured at 533 nm after excitation at 433 nm. The calibration curves were linear over the concentration ranges of 12.5-625, 15-1500 and 20-2000 ng/mL for plasma, urine and standard solution, respectively. The limits of detection were 4.0 ng/mL in plasma, 5.0 ng/mL in urine and 7.0 ng/mL in standard solution. The intra-assay accuracy and precision in plasma ranged from 0.032 to 2.40% and 0.23 to 0.36%, respectively, while inter-assay accuracy and precision ranged from 0.45 to 2.10% and 0.25 to 0.38%, respectively. The intra-assay accuracy and precision estimated on spiked samples in urine ranged from 1.27 to 4.20% and 0.12 to 0.24%, respectively, while inter-assay accuracy and precision ranged from 1.60 to 4.00% and 0.14 to 0.25%, respectively. The mean recovery of lomefloxacin from plasma and urine was 98.34 and 98.43%, respectively. The method was successfully applied to the determination of lomefloxacin in pharmaceuticals and biological fluids.

Accuracy of non-invasive prenatal testing using cell-free DNA for detection of Down, Edwards and Patau syndromes: a systematic review and meta-analysis

PubMed Central

Taylor-Phillips, Sian; Freeman, Karoline; Geppert, Julia; Agbebiyi, Adeola; Uthman, Olalekan A; Madan, Jason; Clarke, Angus; Quenby, Siobhan; Clarke, Aileen

2016-01-01

Objective To measure test accuracy of non-invasive prenatal testing (NIPT) for Down, Edwards and Patau syndromes using cell-free fetal DNA and identify factors affecting accuracy. Design Systematic review and meta-analysis of published studies. Data sources PubMed, Ovid Medline, Ovid Embase and the Cochrane Library published from 1997 to 9 February 2015, followed by weekly autoalerts until 1 April 2015. Eligibility criteria for selecting studies English language journal articles describing case–control studies with ≥15 trisomy cases or cohort studies with ≥50 pregnant women who had been given NIPT and a reference standard. Results 41, 37 and 30 studies of 2012 publications retrieved were included in the review for Down, Edwards and Patau syndromes. Quality appraisal identified high risk of bias in included studies, funnel plots showed evidence of publication bias. Pooled sensitivity was 99.3% (95% CI 98.9% to 99.6%) for Down, 97.4% (95.8% to 98.4%) for Edwards, and 97.4% (86.1% to 99.6%) for Patau syndrome. The pooled specificity was 99.9% (99.9% to 100%) for all three trisomies. In 100 000 pregnancies in the general obstetric population we would expect 417, 89 and 40 cases of Downs, Edwards and Patau syndromes to be detected by NIPT, with 94, 154 and 42 false positive results. Sensitivity was lower in twin than singleton pregnancies, reduced by 9% for Down, 28% for Edwards and 22% for Patau syndrome. Pooled sensitivity was also lower in the first trimester of pregnancy, in studies in the general obstetric population, and in cohort studies with consecutive enrolment. Conclusions NIPT using cell-free fetal DNA has very high sensitivity and specificity for Down syndrome, with slightly lower sensitivity for Edwards and Patau syndrome. However, it is not 100% accurate and should not be used as a final diagnosis for positive cases. Trial registration number CRD42014014947. PMID:26781507

Measurement of volatile plant compounds in field ambient air by thermal desorption-gas chromatography-mass spectrometry.

PubMed

Cai, Xiao-Ming; Xu, Xiu-Xiu; Bian, Lei; Luo, Zong-Xiu; Chen, Zong-Mao

2015-12-01

Determination of volatile plant compounds in field ambient air is important to understand chemical communication between plants and insects and will aid the development of semiochemicals from plants for pest control. In this study, a thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS) method was developed to measure ultra-trace levels of volatile plant compounds in field ambient air. The desorption parameters of TD, including sorbent tube material, tube desorption temperature, desorption time, and cold trap temperature, were selected and optimized. In GC-MS analysis, the selected ion monitoring mode was used for enhanced sensitivity and selectivity. This method was sufficiently sensitive to detect part-per-trillion levels of volatile plant compounds in field ambient air. Laboratory and field evaluation revealed that the method presented high precision and accuracy. Field studies indicated that the background odor of tea plantations contained some common volatile plant compounds, such as (Z)-3-hexenol, methyl salicylate, and (E)-ocimene, at concentrations ranging from 1 to 3400 ng m(-3). In addition, the background odor in summer was more abundant in quality and quantity than in autumn. Relative to previous methods, the TD-GC-MS method is more sensitive, permitting accurate qualitative and quantitative measurements of volatile plant compounds in field ambient air.

Optimizing Experimental Design for Comparing Models of Brain Function

PubMed Central

Daunizeau, Jean; Preuschoff, Kerstin; Friston, Karl; Stephan, Klaas

2011-01-01

This article presents the first attempt to formalize the optimization of experimental design with the aim of comparing models of brain function based on neuroimaging data. We demonstrate our approach in the context of Dynamic Causal Modelling (DCM), which relates experimental manipulations to observed network dynamics (via hidden neuronal states) and provides an inference framework for selecting among candidate models. Here, we show how to optimize the sensitivity of model selection by choosing among experimental designs according to their respective model selection accuracy. Using Bayesian decision theory, we (i) derive the Laplace-Chernoff risk for model selection, (ii) disclose its relationship with classical design optimality criteria and (iii) assess its sensitivity to basic modelling assumptions. We then evaluate the approach when identifying brain networks using DCM. Monte-Carlo simulations and empirical analyses of fMRI data from a simple bimanual motor task in humans serve to demonstrate the relationship between network identification and the optimal experimental design. For example, we show that deciding whether there is a feedback connection requires shorter epoch durations, relative to asking whether there is experimentally induced change in a connection that is known to be present. Finally, we discuss limitations and potential extensions of this work. PMID:22125485

Computer-aided detection in musculoskeletal projection radiography: A systematic review.

PubMed

Gundry, M; Knapp, K; Meertens, R; Meakin, J R

2018-05-01

To investigated the accuracy of computer-aided detection (CAD) software in musculoskeletal projection radiography via a systematic review. Following selection screening, eligible studies were assessed for bias, and had their study characteristics extracted resulting in 22 studies being included. Of these 22 three studies had tested their CAD software in a clinical setting; the first study investigated vertebral fractures, reporting a sensitivity score of 69.3% with CAD, compared to 59.8% sensitivity without CAD. The second study tested dental caries diagnosis producing a sensitivity score of 68.8% and specificity of 94.1% with CAD, compared to sensitivity of 39.3% and specificity of 96.7% without CAD. The third indicated osteoporotic cases based on CAD, resulting in 100% sensitivity and 81.3% specificity. The current evidence reported shows a lack of development into the clinical testing phase; however the research does show future promise in the variation of different CAD systems. Copyright © 2017 The College of Radiographers. Published by Elsevier Ltd. All rights reserved.

Oxygen measurement by multimode diode lasers employing gas correlation spectroscopy.

PubMed

Lou, Xiutao; Somesfalean, Gabriel; Chen, Bin; Zhang, Zhiguo

2009-02-10

Multimode diode laser (MDL)-based correlation spectroscopy (COSPEC) was used to measure oxygen in ambient air, thereby employing a diode laser (DL) having an emission spectrum that overlaps the oxygen absorption lines of the A band. A sensitivity of 700 ppm m was achieved with good accuracy (2%) and linearity (R(2)=0.999). For comparison, measurements of ambient oxygen were also performed by tunable DL absorption spectroscopy (TDLAS) technique employing a vertical cavity surface emitting laser. We demonstrate that, despite slightly degraded sensitivity, the MDL-based COSPEC-based oxygen sensor has the advantages of high stability, low cost, ease-of-use, and relaxed requirements in component selection and instrument buildup compared with the TDLAS-based instrument.

Using artificial intelligence to predict the risk for posterior capsule opacification after phacoemulsification.

PubMed

Mohammadi, Seyed-Farzad; Sabbaghi, Mostafa; Z-Mehrjardi, Hadi; Hashemi, Hassan; Alizadeh, Somayeh; Majdi, Mercede; Taee, Farough

2012-03-01

To apply artificial intelligence models to predict the occurrence of posterior capsule opacification (PCO) after phacoemulsification. Farabi Eye Hospital, Tehran, Iran. Clinical-based cross-sectional study. The posterior capsule status of eyes operated on for age-related cataract and the need for laser capsulotomy were determined. After a literature review, data polishing, and expert consultation, 10 input variables were selected. The QUEST algorithm was used to develop a decision tree. Three back-propagation artificial neural networks were constructed with 4, 20, and 40 neurons in 2 hidden layers and trained with the same transfer functions (log-sigmoid and linear transfer) and training protocol with randomly selected eyes. They were then tested on the remaining eyes and the networks compared for their performance. Performance indices were used to compare resultant models with the results of logistic regression analysis. The models were trained using 282 randomly selected eyes and then tested using 70 eyes. Laser capsulotomy for clinically significant PCO was indicated or had been performed 2 years postoperatively in 40 eyes. A sample decision tree was produced with accuracy of 50% (likelihood ratio 0.8). The best artificial neural network, which showed 87% accuracy and a positive likelihood ratio of 8, was achieved with 40 neurons. The area under the receiver-operating-characteristic curve was 0.71. In comparison, logistic regression reached accuracy of 80%; however, the likelihood ratio was not measurable because the sensitivity was zero. A prototype artificial neural network was developed that predicted posterior capsule status (requiring capsulotomy) with reasonable accuracy. No author has a financial or proprietary interest in any material or method mentioned. Copyright © 2012 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

Sensitivity to Spatiotemporal Percepts Predicts the Perception of Emotion

PubMed Central

Castro, Vanessa L.; Boone, R. Thomas

2015-01-01

The present studies examined how sensitivity to spatiotemporal percepts such as rhythm, angularity, configuration, and force predicts accuracy in perceiving emotion. In Study 1, participants (N = 99) completed a nonverbal test battery consisting of three nonverbal emotion perception tests and two perceptual sensitivity tasks assessing rhythm sensitivity and angularity sensitivity. Study 2 (N = 101) extended the findings of Study 1 with the addition of a fourth nonverbal test, a third configural sensitivity task, and a fourth force sensitivity task. Regression analyses across both studies revealed partial support for the association between perceptual sensitivity to spatiotemporal percepts and greater emotion perception accuracy. Results indicate that accuracy in perceiving emotions may be predicted by sensitivity to specific percepts embedded within channel- and emotion-specific displays. The significance of such research lies in the understanding of how individuals acquire emotion perception skill and the processes by which distinct features of percepts are related to the perception of emotion. PMID:26339111

Wavenumber selection based analysis in Raman spectroscopy improves skin cancer diagnostic specificity at high sensitivity levels (Conference Presentation)

NASA Astrophysics Data System (ADS)

Zhao, Jianhua; Zeng, Haishan; Kalia, Sunil; Lui, Harvey

2017-02-01

Background: Raman spectroscopy is a non-invasive optical technique which can measure molecular vibrational modes within tissue. A large-scale clinical study (n = 518) has demonstrated that real-time Raman spectroscopy could distinguish malignant from benign skin lesions with good diagnostic accuracy; this was validated by a follow-up independent study (n = 127). Objective: Most of the previous diagnostic algorithms have typically been based on analyzing the full band of the Raman spectra, either in the fingerprint or high wavenumber regions. Our objective in this presentation is to explore wavenumber selection based analysis in Raman spectroscopy for skin cancer diagnosis. Methods: A wavenumber selection algorithm was implemented using variably-sized wavenumber windows, which were determined by the correlation coefficient between wavenumbers. Wavenumber windows were chosen based on accumulated frequency from leave-one-out cross-validated stepwise regression or least and shrinkage selection operator (LASSO). The diagnostic algorithms were then generated from the selected wavenumber windows using multivariate statistical analyses, including principal component and general discriminant analysis (PC-GDA) and partial least squares (PLS). A total cohort of 645 confirmed lesions from 573 patients encompassing skin cancers, precancers and benign skin lesions were included. Lesion measurements were divided into training cohort (n = 518) and testing cohort (n = 127) according to the measurement time. Result: The area under the receiver operating characteristic curve (ROC) improved from 0.861-0.891 to 0.891-0.911 and the diagnostic specificity for sensitivity levels of 0.99-0.90 increased respectively from 0.17-0.65 to 0.20-0.75 by selecting specific wavenumber windows for analysis. Conclusion: Wavenumber selection based analysis in Raman spectroscopy improves skin cancer diagnostic specificity at high sensitivity levels.

Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

PubMed Central

2011-01-01

Background Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results Press' Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most sensitivity was around or even lower than a median value of 0.5. Conclusions When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing. PMID:21849043

[Diagnostic value of cardiac magnetic resonance in patients with acute viral myocarditis].

PubMed

Ouyang, Haichun; Chen, Haixiong; Hu, Yunzhao; Wu, Yanxian; Li, Wensheng; Chen, Yuying; Cen, Yujian

2014-11-01

To assess the diagnostic value of cardiac magnetic resonance (CMR) in patients with acute viral myocarditis. Thirty patients with suspected acute viral myocarditis admitted in first people's hospital of Shunde from June 2011 to June 2013 were included in this prospective study. The diagnostic sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of acute viral myocarditis were evaluated by clinical diagnosis. Diagnostic value among different scan methods and Lake Louise criteria were compared. Acute viral myocarditis was diagnosed in 63.33% (19/30) patients.Values for sensitivity, specificity, PPV, NPV, and diagnostic accuracy within the overall cohort were 57.89%, 72.73%, 78.57%, 50.00%, 63.33%, respectively by edema imaging (ER).Values for sensitivity, specificity, PPV, NPV, and diagnostic accuracy within the overall cohort were 78.95%, 63.64%, 78.95%, 63.64%, 73.33%, respectively using global relative enhancement (gRE).Values for sensitivity, specificity, PPV, NPV, and diagnostic accuracy within the overall cohort were 78.95%, 54.55%, 75.00%, 60.00%, 70.00%, respectively using late gadolinium enhancement (LGE) criteria.Values for sensitivity, specificity, PPV, NPV, and diagnostic accuracy within the overall cohort were 84.21%, 81.82%, 88.89%, 75.00%, 83.33% using Lake Louise criteria. The sensitivity, specificity, PPV, NPV, and diagnostic accuracy using Lake Louise criteria were significantly higher than using ER, gRE, LGE alone(all P < 0.05).Specificity was higher using ER than using gRE and LGE (both P < 0.05). The sensitivity, NPV, and diagnostic accuracy were significantly higher using gRE than using ER (all P < 0.05) and was similar as using LGE (all P > 0.05). Cardiac magnetic resonance is an excellent imaging modality for the diagnosis of acute viral myocarditis.

Development of machine learning models for diagnosis of glaucoma.

PubMed

Kim, Seong Jae; Cho, Kyong Jin; Oh, Sejong

2017-01-01

The study aimed to develop machine learning models that have strong prediction power and interpretability for diagnosis of glaucoma based on retinal nerve fiber layer (RNFL) thickness and visual field (VF). We collected various candidate features from the examination of retinal nerve fiber layer (RNFL) thickness and visual field (VF). We also developed synthesized features from original features. We then selected the best features proper for classification (diagnosis) through feature evaluation. We used 100 cases of data as a test dataset and 399 cases of data as a training and validation dataset. To develop the glaucoma prediction model, we considered four machine learning algorithms: C5.0, random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). We repeatedly composed a learning model using the training dataset and evaluated it by using the validation dataset. Finally, we got the best learning model that produces the highest validation accuracy. We analyzed quality of the models using several measures. The random forest model shows best performance and C5.0, SVM, and KNN models show similar accuracy. In the random forest model, the classification accuracy is 0.98, sensitivity is 0.983, specificity is 0.975, and AUC is 0.979. The developed prediction models show high accuracy, sensitivity, specificity, and AUC in classifying among glaucoma and healthy eyes. It will be used for predicting glaucoma against unknown examination records. Clinicians may reference the prediction results and be able to make better decisions. We may combine multiple learning models to increase prediction accuracy. The C5.0 model includes decision rules for prediction. It can be used to explain the reasons for specific predictions.

Transbronchial Lung Cryobiopsy and Video-assisted Thoracoscopic Lung Biopsy in the Diagnosis of Diffuse Parenchymal Lung Disease. A Meta-analysis of Diagnostic Test Accuracy.

PubMed

Iftikhar, Imran H; Alghothani, Lana; Sardi, Alejandro; Berkowitz, David; Musani, Ali I

2017-07-01

Transbronchial lung cryobiopsy is increasingly being used for the assessment of diffuse parenchymal lung diseases. Several studies have shown larger biopsy samples and higher yields compared with conventional transbronchial biopsies. However, the higher risk of bleeding and other complications has raised concerns for widespread use of this modality. To study the diagnostic accuracy and safety profile of transbronchial lung cryobiopsy and compare with video-assisted thoracoscopic surgery (VATS) by reviewing available evidence from the literature. Medline and PubMed were searched from inception until December 2016. Data on diagnostic performance were abstracted by constructing two-by-two contingency tables for each study. Data on a priori selected safety outcomes were collected. Risk of bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies tool. Random effects meta-analyses were performed to obtain summary estimates of the diagnostic accuracy. The pooled diagnostic yield, pooled sensitivity, and pooled specificity of transbronchial lung cryobiopsy were 83.7% (76.9-88.8%), 87% (85-89%), and 57% (40-73%), respectively. The pooled diagnostic yield, pooled sensitivity, and pooled specificity of VATS were 92.7% (87.6-95.8%), 91.0% (89-92%), and 58% (31-81%), respectively. The incidence of grade 2 (moderate to severe) endobronchial bleeding after transbronchial lung cryobiopsy and of post-procedural pneumothorax was 4.9% (2.2-10.7%) and 9.5% (5.9-14.9%), respectively. Although the diagnostic test accuracy measures of transbronchial lung cryobiopsy lag behind those of VATS, with an acceptable safety profile and potential cost savings, the former could be considered as an alternative in the evaluation of patients with diffuse parenchymal lung diseases.

How much articular displacement can be detected using fluoroscopy for tibial plateau fractures?

PubMed

Haller, Justin M; O'Toole, Robert; Graves, Matthew; Barei, David; Gardner, Michael; Kubiak, Erik; Nascone, Jason; Nork, Sean; Presson, Angela P; Higgins, Thomas F

2015-11-01

While there is conflicting evidence regarding the importance of anatomic reduction for tibial plateau fractures, there are currently no studies that analyse our ability to grade reduction based on fluoroscopic imaging. The purpose of this study was to determine the accuracy of fluoroscopy in judging tibial plateau articular reduction. Ten embalmed human cadavers were selected. The lateral plateau was sagitally sectioned, and the joint was reduced under direct visualization. Lateral, anterior-posterior (AP), and joint line fluoroscopic views were obtained. The same fluoroscopic views were obtained with 2mm displacement and 5mm displacement. The images were randomised, and eight orthopaedic traumatologists were asked whether the plateau was reduced. Within each pair of conditions (view and displacement from 0mm to 5mm) sensitivity, specificity, and intraclass correlations (ICC) were evaluated. The AP-lateral view with 5mm displacement yielded the highest accuracy for detecting reduction at 90% (95% CI: 83-94%). For the other conditions, accuracy ranged from (37-83%). Sensitivity was highest for the reduced lateral view (79%, 95% CI: 57-91%). Specificity was highest in the AP-lateral view 98% (95% CI: 93-99%) for 5mm step-off. ICC was perfect for the AP-lateral view with 5mm displacement, but otherwise agreement ranged from poor to moderate at ICC=0.09-0.46. Finally, there was no additional benefit to including the joint-line view with the AP and lateral views. Using both AP and lateral views for 5mm displacement had the highest accuracy, specificity, and ICC. Outside of this scenario, agreement was poor to moderate and accuracy was low. Applying this clinically, direct visualization of the articular surface may be necessary to ensure malreduction less than 5mm. Copyright © 2015 Elsevier Ltd. All rights reserved.

SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

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

Zhou, Z; Folkert, M; Wang, J

2016-06-15

Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidentialmore » reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.« less

Screening protocol for dysphagia in adults: comparison with videofluoroscopic findings.

PubMed

Sassi, Fernanda C; Medeiros, Gisele C; Zilberstein, Bruno; Jayanthi, Shri Krishna; de Andrade, Claudia R F

2017-12-01

To compare the videofluoroscopic findings of patients with suspected oropharyngeal dysphagia with the results of a clinical screening protocol. A retrospective observational cohort study was conducted on all consecutive patients with suspected oropharyngeal dysphagia between March 2015 and February 2016 who were assigned to receive a videofluoroscopic assessment of swallowing. All patients were first submitted to videofluoroscopy and then to the clinical assessment of swallowing. The clinical assessment was performed within the first 24 hours after videofluoroscopy. The videofluoroscopy results were analyzed regarding penetration/aspiration using an 8-point multidimensional perceptual scale. The accuracy of the clinical protocol was analyzed using the sensitivity, specificity, likelihood ratios and predictive values. The selected sample consisted of 50 patients. The clinical protocol presented a sensitivity of 50% and specificity of 95%, with an accuracy of 88%. "Cough" and "wet-hoarse" vocal quality after/during swallowing were clinical indicators that appeared to correctly identify the presence of penetration/aspiration risk. The clinical protocol used in the present study is a simple, rapid and reliable clinical assessment. Despite the absence of a completely satisfactory result, especially in terms of the sensitivity and positive predictive values, we suggest that lower rates of pneumonia can be achieved using a formal dysphagia screening method.

Minimizing effects of methodological decisions on interpretation and prediction in species distribution studies: An example with background selection

USGS Publications Warehouse

Jarnevich, Catherine S.; Talbert, Marian; Morisette, Jeffrey T.; Aldridge, Cameron L.; Brown, Cynthia; Kumar, Sunil; Manier, Daniel; Talbert, Colin; Holcombe, Tracy R.

2017-01-01

Evaluating the conditions where a species can persist is an important question in ecology both to understand tolerances of organisms and to predict distributions across landscapes. Presence data combined with background or pseudo-absence locations are commonly used with species distribution modeling to develop these relationships. However, there is not a standard method to generate background or pseudo-absence locations, and method choice affects model outcomes. We evaluated combinations of both model algorithms (simple and complex generalized linear models, multivariate adaptive regression splines, Maxent, boosted regression trees, and random forest) and background methods (random, minimum convex polygon, and continuous and binary kernel density estimator (KDE)) to assess the sensitivity of model outcomes to choices made. We evaluated six questions related to model results, including five beyond the common comparison of model accuracy assessment metrics (biological interpretability of response curves, cross-validation robustness, independent data accuracy and robustness, and prediction consistency). For our case study with cheatgrass in the western US, random forest was least sensitive to background choice and the binary KDE method was least sensitive to model algorithm choice. While this outcome may not hold for other locations or species, the methods we used can be implemented to help determine appropriate methodologies for particular research questions.

Prediction of early and late preeclampsia by flow-mediated dilation of the brachial artery*

PubMed Central

Brandão, Augusto Henriques Fulgêncio; Evangelista, Aline Aarão; Martins, Raphaela Menin Franco; Leite, Henrique Vítor; Cabral, Antônio Carlos Vieira

2014-01-01

Objective To assess the accuracy in the prediction of both early and late preeclampsia by flow-mediated dilation of the brachial artery (FMD), a biophysical marker for endothelial dysfunction. Materials and Methods A total of 91 patients, considered at high risk for development of preeclampsia were submitted to brachial artery FMD between 24 and 28 weeks of gestation. Results Nineteen out of the selected patients developed preeclampsia, 8 in its early form and 11 in the late form. With a cut-off value of 6.5%, the FMD sensitivity for early preeclampsia prediction was 75.0%, with specificity of 73.3%, positive predictive value (PPV) of 32.4% and negative predictive value (NPV) of 91.9%. For the prediction of late preeclampsia, sensitivity = 83.3%, specificity = 73.2%, PPV = 34.4% and NPV = 96.2% were observed. And for the prediction of all associated forms of preeclampsia, sensitivity = 84.2%, specificity = 73.6%, PPV = 45.7% and NPV = 94.6% were observed. Conclusion FMD of the brachial artery is a test with good accuracy in the prediction of both early and late preeclampsia, which may represent a positive impact on the follow-up of pregnant women at high risk for developing this syndrome. PMID:25741086

Intra-Operative Frozen Sections for Ovarian Tumors – A Tertiary Center Experience

PubMed Central

Arshad, Nur Zaiti Md; Ng, Beng Kwang; Paiman, Noor Asmaliza Md; Mahdy, Zaleha Abdullah; Noor, Rushdan Mohd

2018-01-01

Background: Accuracy of diagnosis with intra-operative frozen sections is extremely important in the evaluation of ovarian tumors so that appropriate surgical procedures can be selected. Study design: All patients who with intra-operative frozen sections for ovarian masses in a tertiary center over nine years from June 2008 until April 2017 were reviewed. Frozen section diagnosis and final histopathological reports were compared. Main outcome measures: Sensitivity, specificity, positive and negative predictive values of intra-operative frozen section as compared to final histopathological results for ovarian tumors. Results: A total of 92 cases were recruited for final evaluation. The frozen section diagnoses were comparable with the final histopathological reports in 83.7% of cases. The sensitivity, specificity, positive predictive value and negative predictive value for benign and malignant ovarian tumors were 95.6%, 85.1%, 86.0% and 95.2% and 69.2%, 100%, 100% and 89.2% respectively. For borderline ovarian tumors, the sensitivity and specificity were 76.2% and 88.7%, respectively; the positive predictive value was 66.7% and the negative predictive value was 92.7%. Conclusion: The accuracy of intra-operative frozen section diagnoses for ovarian tumors is high and this approach remains a reliable option in assessing ovarian masses intra-operatively. PMID:29373916

Comprehensive genetic testing for female and male infertility using next-generation sequencing.

PubMed

Patel, Bonny; Parets, Sasha; Akana, Matthew; Kellogg, Gregory; Jansen, Michael; Chang, Chihyu; Cai, Ying; Fox, Rebecca; Niknazar, Mohammad; Shraga, Roman; Hunter, Colby; Pollock, Andrew; Wisotzkey, Robert; Jaremko, Malgorzata; Bisignano, Alex; Puig, Oscar

2018-05-19

To develop a comprehensive genetic test for female and male infertility in support of medical decisions during assisted reproductive technology (ART) protocols. We developed a next-generation sequencing (NGS) gene panel consisting of 87 genes including promoters, 5' and 3' untranslated regions, exons, and selected introns. In addition, sex chromosome aneuploidies and Y chromosome microdeletions were analyzed concomitantly using the same panel. The NGS panel was analytically validated by retrospective analysis of 118 genomic DNA samples with known variants in loci representative of female and male infertility. Our results showed analytical accuracy of > 99%, with > 98% sensitivity for single-nucleotide variants (SNVs) and > 91% sensitivity for insertions/deletions (indels). Clinical sensitivity was assessed with samples containing variants representative of male and female infertility, and it was 100% for SNVs/indels, CFTR IVS8-5T variants, sex chromosome aneuploidies, and copy number variants (CNVs) and > 93% for Y chromosome microdeletions. Cost analysis shows potential savings when comparing this single NGS assay with the standard approach, which includes multiple assays. A single, comprehensive, NGS panel can simplify the ordering process for healthcare providers, reduce turnaround time, and lower the overall cost of testing for genetic assessment of infertility in females and males, while maintaining accuracy.

Spectrophotometric studies of reactions between pseudo-ephedrine with different inorganic and organic reagents and its micro-determination in pure and in pharmaceutical preparations

NASA Astrophysics Data System (ADS)

Zayed, M. A.; El-Rasheedy, El-Gazy A.

2012-03-01

Two simple, sensitive, cheep and reliable spectrophotometric methods are suggested for micro-determination of pseudoephedrine in its pure form and in pharmaceutical preparation (Sinofree Tablets). The first one depends on the drug reaction with inorganic sensitive reagent like molybdate anion in aqueous media via formation of ion-pair mechanism. The second one depends on the drug reaction with π-acceptor reagent like DDQ in non-aqueous media via formation of charge transfer complex. These reactions were studied under various conditions and the optimum parameters were selected. Under proper conditions the suggested procedures were successfully applied for micro-determination of pseudoephedrine in pure and in Sinofree Tablets without interference from excepients. The values of SD, RSD, recovery %, LOD, LOQ and Sandell sensitivity refer to the high accuracy and precession of the applied procedures. The results obtained were compared with the data obtained by an official method, referring to confidence and agreement with DDQ procedure results; but it referred to the more accuracy of the molybdate data. Therefore, the suggested procedures are now successfully being applied in routine analysis of this drug in its pharmaceutical formulation (Sinofree) in Saudi Arabian Pharmaceutical Company (SPIMACO) in Boridah El-Qaseem, Saudi Arabia instead of imported kits had been previously used.

Clinical evaluation of near-infrared light transillumination in approximal dentin caries detection.

PubMed

Ozkan, Gokhan; Guzel, Kadriye Gorkem Ulu

2017-08-01

The objective of this clinical study was to compare conventional caries detection techniques, pen-type laser fluorescence device, and near-infrared light transillumination method in approximal dentin caries lesions. The study included 157 patients, aged 12-18, without any cavity in the posterior teeth. Two calibrated examiners carried out the assessments of selected approximal caries sites independently. After the assessments, the unopened sites were excluded and a total of 161 approximal sites were included in the study. When both the examiners arrived at a consensus regarding the presence of dentin caries, the detected lesions were opened with a conical diamond burr, the cavity extent was examined and validated (gold standard). Sensitivity, specificity, negative predictive value, positive predictive value, accuracy, and area under the ROC curve (Az) values among the caries detection methods were calculated. Bitewing radiography and near-infrared (NIR) light transillumination methods showed the highest sensitivity (0.83-0.82) and accuracy (0.82-0.80) among the methods. Visual inspection showed the lowest sensitivity (0.54). Laser fluorescence device and visual inspection showed nearly equal performance. Near-infrared light transillumination can be used as an alternative method to approximal dentin caries detection. Visual inspection and laser fluorescence device alone should not be used for approximal dentin caries.

The validity and accuracy of MRI arthrogram in the assessment of painful articular disorders of the hip.

PubMed

Rajeev, Aysha; Tuinebreijer, Wim; Mohamed, Abdalla; Newby, Mike

2018-01-01

The assessment of a patient with chronic hip pain can be challenging. The differential diagnosis of intra-articular pathology causing hip pain can be diverse. These includes conditions such as osteoarthritis, fracture, and avascular necrosis, synovitis, loose bodies, labral tears, articular pathology and, femoro-acetabular impingement. Magnetic resonance imaging (MRI) arthrography of the hip has been widely used now for diagnosis of articular pathology of the hip. A retrospective analysis of 113 patients who had MRI arthrogram and who underwent hip arthroscopy was included in the study. The MRI arthrogram was performed using gadolinium injection and reported by a single radiologist. The findings were then compared to that found on arthroscopy. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy and 95% confidence interval were calculated for each pathology. Labral tear-sensitivity 84% (74.3-90.5), specificity 64% (40.7-82.8), PPV 91% (82.1-95.8), NPV 48% (29.5-67.5), accuracy 80%. Delamination -sensitivity 7% (0.8-22.1), specificity 98% (91.6-99.7), PPV 50% (6.8-93.2), NPV 74% (65.1-82.2) and accuracy 39%. Chondral changes-sensitivity 25% (13.3-38.9), specificity 83% (71.3-91.1), PPV 52% (30.6-73.2), NPV 59% (48.0-69.2) and accuracy 58%. Femoro-acetabular impingement (CAM deformity)-sensitivity 34% (19.6-51.4), specificity 83% (72.2-90.4), PPV 50% (29.9-70.1), NPV 71% (60.6-80.5) and accuracy 66%. Synovitis-sensitivity 11% (2.3-28.2), specificity 99% (93.6-100), PPV 75% (19.4-99.4), NPV 77% (68.1-84.6) and accuracy 77%. Our study conclusions are MRI arthrogram is a useful investigation tool in detecting labral tears, it is also helpful in the diagnosis of femoro-acetabular impingement. However, when it comes to the diagnosis of chondral changes, defects and cartilage delamination, the sensitivity and accuracy are low.

Neurocognitive assessment of emotional context sensitivity.

PubMed

Myruski, Sarah; Bonanno, George A; Gulyayeva, Olga; Egan, Laura J; Dennis-Tiwary, Tracy A

2017-10-01

Sensitivity to emotional context is an emerging construct for characterizing adaptive or maladaptive emotion regulation, but few measurement approaches exist. The current study combined behavioral and neurocognitive measures to assess context sensitivity in relation to self-report measures of adaptive emotional flexibility and well-being. Sixty-six adults completed an emotional go/no-go task using happy, fearful, and neutral faces as go and no-go cues, while EEG was recorded to generate event-related potentials (ERPs) reflecting attentional selection and discrimination (N170) and cognitive control (N2). Context sensitivity was measured as the degree of emotional facilitation or disruption in the go/no-go task and magnitude of ERP response to emotion cues. Participants self-reported on emotional flexibility, anxiety, and depression. Overall participants evidenced emotional context sensitivity, such that when happy faces were go stimuli, accuracy improved (greater behavioral facilitation), whereas when fearful faces were no-go stimuli, errors increased (disrupted behavioral inhibition). These indices predicted emotional flexibility and well-being: Greater behavioral facilitation following happy cues was associated with lower depression and anxiety, whereas greater disruption in behavioral inhibition following fearful cues was associated with lower flexibility. ERP indices of context sensitivity revealed additional associations: Greater N2 to fear go cues was associated with less anxiety and depression, and greater N2 and N170 to happy and fear no-go cues, respectively, were associated with greater emotional flexibility and well-being. Results suggest that pleasant and unpleasant emotions selectively enhance and disrupt components of context sensitivity, and that behavioral and ERP indices of context sensitivity predict flexibility and well-being.

A novel quantitative analysis method of three-dimensional fluorescence spectra for vegetable oils contents in edible blend oil

NASA Astrophysics Data System (ADS)

Xu, Jing; Wang, Yu-Tian; Liu, Xiao-Fei

2015-04-01

Edible blend oil is a mixture of vegetable oils. Eligible blend oil can meet the daily need of two essential fatty acids for human to achieve the balanced nutrition. Each vegetable oil has its different composition, so vegetable oils contents in edible blend oil determine nutritional components in blend oil. A high-precision quantitative analysis method to detect the vegetable oils contents in blend oil is necessary to ensure balanced nutrition for human being. Three-dimensional fluorescence technique is high selectivity, high sensitivity, and high-efficiency. Efficiency extraction and full use of information in tree-dimensional fluorescence spectra will improve the accuracy of the measurement. A novel quantitative analysis is proposed based on Quasi-Monte-Carlo integral to improve the measurement sensitivity and reduce the random error. Partial least squares method is used to solve nonlinear equations to avoid the effect of multicollinearity. The recovery rates of blend oil mixed by peanut oil, soybean oil and sunflower are calculated to verify the accuracy of the method, which are increased, compared the linear method used commonly for component concentration measurement.

Automatic differentiation evaluated as a tool for rotorcraft design and optimization

NASA Technical Reports Server (NTRS)

Walsh, Joanne L.; Young, Katherine C.

1995-01-01

This paper investigates the use of automatic differentiation (AD) as a means for generating sensitivity analyses in rotorcraft design and optimization. This technique transforms an existing computer program into a new program that performs sensitivity analysis in addition to the original analysis. The original FORTRAN program calculates a set of dependent (output) variables from a set of independent (input) variables, the new FORTRAN program calculates the partial derivatives of the dependent variables with respect to the independent variables. The AD technique is a systematic implementation of the chain rule of differentiation, this method produces derivatives to machine accuracy at a cost that is comparable with that of finite-differencing methods. For this study, an analysis code that consists of the Langley-developed hover analysis HOVT, the comprehensive rotor analysis CAMRAD/JA, and associated preprocessors is processed through the AD preprocessor ADIFOR 2.0. The resulting derivatives are compared with derivatives obtained from finite-differencing techniques. The derivatives obtained with ADIFOR 2.0 are exact within machine accuracy and do not depend on the selection of step-size, as are the derivatives obtained with finite-differencing techniques.

An efficient adaptive sampling strategy for global surrogate modeling with applications in multiphase flow simulation

NASA Astrophysics Data System (ADS)

Mo, S.; Lu, D.; Shi, X.; Zhang, G.; Ye, M.; Wu, J.

2016-12-01

Surrogate models have shown remarkable computational efficiency in hydrological simulations involving design space exploration, sensitivity analysis, uncertainty quantification, etc. The central task of constructing a global surrogate models is to achieve a prescribed approximation accuracy with as few original model executions as possible, which requires a good design strategy to optimize the distribution of data points in the parameter domains and an effective stopping criterion to automatically terminate the design process when desired approximation accuracy is achieved. This study proposes a novel adaptive sampling strategy, which starts from a small number of initial samples and adaptively selects additional samples by balancing the collection in unexplored regions and refinement in interesting areas. We define an efficient and effective evaluation metric basing on Taylor expansion to select the most promising potential samples from candidate points, and propose a robust stopping criterion basing on the approximation accuracy at new points to guarantee the achievement of desired accuracy. The numerical results of several benchmark analytical functions indicate that the proposed approach is more computationally efficient and robust than the widely used maximin distance design and two other well-known adaptive sampling strategies. The application to two complicated multiphase flow problems further demonstrates the efficiency and effectiveness of our method in constructing global surrogate models for high-dimensional and highly nonlinear problems. Acknowledgements: This work was financially supported by the National Nature Science Foundation of China grants No. 41030746 and 41172206.

The Remote Detection of Alpha-Radioactive Nucleus Decay

NASA Astrophysics Data System (ADS)

Gurkovskiy, Boris; Miroshnichenko, Vladimir; Onishchenko, Evgeny; Simakov, Andrey; Streil, Thomas

Results of the new device design for the alpha-radiation remote detection are presented. Negative ions from the alpha particle tracks are detected by the discharge wire counter opened to air. Ion clusters being transferred from the particle tracks to the detector volume by an air flux. The detector works in a counting mode that provides sharp selectivity and accuracy of measurements. The basic parameters of the device are: detecting distance -0.5 m; measurement time -30 s; the square sensitivity -0.05 Bq/cm2.

Role of 18F-FDG PET/CT in diagnosing peritoneal carcinomatosis in the restaging of patient with ovarian cancer as compared to contrast enhanced CT and tumor marker Ca-125.

PubMed

Rubini, G; Altini, C; Notaristefano, A; Merenda, N; Rubini, D; Ianora, A A Stabile; Asabella, A Niccoli

2014-01-01

To investigate the role of whole-body fluorine-18-2-deoxy-2-fluoro-d-glucose positron emission tomography/computed tomography ((18)F-FDG PET/CT) in the identification of peritoneal carcinomatosis in patients with ovarian cancer (OC). Seventy-nine patients with histologically proven stages III-IV OC who underwent (18)F-FDG PET/CT were studied retrospectively. We considered group A as 51 patients who also underwent computed-tomography with contrast-enhancement (CECT), and group B as 35 patients who had also been tested for biomarker Ca-125. Sensitivity, specificity, accuracy, positive predictive values (PPV) and negative predictive values (NPV) of (18)F-FDG PET/CT as compared to CECT and to Ca-125 were evaluated. (18)F-FDG PET/CT' sensitivity, specificity, accuracy, PPV and NPV for all 79 patients were: 85%, 92.31%, 88.61%, 91.89% and 85.71%, respectively. (18)F-FDG PET/CT sensitivity in group A was 78.6%, while it was 53.6% for CECT. (18)F-FDG PET/CT specificity, calculated in the same group, was 91.3%, while that of CECT was 60.9% (statistically significant difference, McNemar 4, P=0.039). Accuracy was 84.3% and 56.9%, respectively. (18)F-FDG PET/CT' sensitivity in group B was 86.4%, while that of Ca-125 was 81.8% (no statistical difference, McNemar 0, P=1). (18)F-FDG PET/CT specificity in group B was 84.6% while that of Ca-125 was 38.5% (clear but not statistically significant difference, McNemar 3.12, P=0.070). Accuracy calculated in the same group was 85.7% for (18)F-FDG PET/CT and 65.7% for Ca-125. (18)F-FDG PET/CT is a useful diagnostic tool when peritoneal biopsy cannot be performed and it can better select those who are candidates for adjuvant chemotherapy. Copyright © 2013 Elsevier España, S.L. and SEMNIM. All rights reserved.

Accuracy of Carotid Duplex Criteria in Diagnosis of Significant Carotid Stenosis in Asian Patients.

PubMed

Dharmasaroja, Pornpatr A; Uransilp, Nattaphol; Watcharakorn, Arvemas; Piyabhan, Pritsana

2018-03-01

Extracranial carotid stenosis can be diagnosed by velocity criteria of carotid duplex. Whether they are accurately applied to define severity of internal carotid artery (ICA) stenosis in Asian patients needs to be proved. The purpose of this study was to evaluate the accuracy of 2 carotid duplex velocity criteria in defining significant carotid stenosis. Carotid duplex studies and magnetic resonance angiography were reviewed. Criteria 1 was recommended by the Society of Radiologists in Ultrasound; moderate stenosis (50%-69%): peak systolic velocity (PSV) 125-230 cm/s, diastolic velocity (DV) 40-100 cm/s; severe stenosis (>70%): PSV greater than 230 cm/s, DV greater than 100 cm/s. Criteria 2 used PSV greater than 140 cm/s, DV less than 110 cm/s to define moderate stenosis (50%-75%) and PSV greater than 140 cm/s, DV greater than 110 cm/s for severe stenosis (76%-95%). A total of 854 ICA segments were reviewed. There was moderate stenosis in 72 ICAs, severe stenosis in 50 ICAs, and occlusion in 78 ICAs. Criteria 2 had slightly lower sensitivity, whereas higher specificity and accuracy than criteria 1 were observed in detecting moderate stenosis (criteria 1: sensitivity 95%, specificity 83%, accuracy 84%; criteria 2: sensitivity 92%, specificity 92%, and accuracy 92%). However, in detection of severe ICA stenosis, no significant difference in sensitivity, specificity, and accuracy was found (criteria 1: sensitivity 82%, specificity 99.57%, accuracy 98%; criteria 2: sensitivity 86%, specificity 99.68%, and accuracy 99%). In the subgroup of moderate stenosis, the criteria using ICA PSV greater than 140 cm/s had higher specificity and accuracy than the criteria using ICA PSV 125-230 cm/s. However, there was no significant difference in detection of severe stenosis or occlusion of ICA. Copyright © 2018 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Magnetic resonance imaging in local staging of endometrial carcinoma: diagnostic performance, pitfalls, and literature review.

PubMed

Zandrino, Franco; La Paglia, Ernesto; Musante, Francesco

2010-01-01

To assess the diagnostic accuracy of magnetic resonance imaging in local staging of endometrial carcinoma, and to review the results and pitfalls described in the literature. Thirty women with a histological diagnosis of endometrial carcinoma underwent magnetic resonance imaging. Unenhanced T2-weighted and dynamic contrast-enhanced Ti-weighted sequences were obtained. Hysterectomy and salpingo-oophorectomy was performed in all patients. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated for the detection of deep myometrial and cervical infiltration. For deep myometrial infiltration T2-weighted sequences reached a sensitivity of 85%, specificity of 76%, PPV of 73%, NVP of 87%, and accuracy of 80%, while contrast-enhanced scans reached a sensitivity of 90%, specificity of 80%, PPV of 82%, NPV of 89%, and accuracy of 85%. For cervical infiltration T2-weighted sequences reached a sensitivity of 75%, specificity of 88%, PPV of 50%, NPV of 96%, and accuracy of 87%, while contrast-enhanced scans reached a sensitivity of 100%, specificity of 94%, PPV of 75%, NPV of 100%, and accuracy of 95%. Unenhanced and dynamic gadolinium-enhanced magnetic resonance allows accurate assessment of myometrial and cervical infiltration. Information provided by magnetic resonance imaging can define prognosis and management.

Gold nanospikes based microsensor as a highly accurate mercury emission monitoring system

NASA Astrophysics Data System (ADS)

Sabri, Ylias M.; Ippolito, Samuel J.; Tardio, James; Bansal, Vipul; O'Mullane, Anthony P.; Bhargava, Suresh K.

2014-10-01

Anthropogenic elemental mercury (Hg0) emission is a serious worldwide environmental problem due to the extreme toxicity of the heavy metal to humans, plants and wildlife. Development of an accurate and cheap microsensor based online monitoring system which can be integrated as part of Hg0 removal and control processes in industry is still a major challenge. Here, we demonstrate that forming Au nanospike structures directly onto the electrodes of a quartz crystal microbalance (QCM) using a novel electrochemical route results in a self-regenerating, highly robust, stable, sensitive and selective Hg0 vapor sensor. The data from a 127 day continuous test performed in the presence of volatile organic compounds and high humidity levels, showed that the sensor with an electrodeposted sensitive layer had 260% higher response magnitude, 3.4 times lower detection limit (~22 μg/m3 or ~2.46 ppbv) and higher accuracy (98% Vs 35%) over a Au control based QCM (unmodified) when exposed to a Hg0 vapor concentration of 10.55 mg/m3 at 101°C. Statistical analysis of the long term data showed that the nano-engineered Hg0 sorption sites on the developed Au nanospikes sensitive layer play a critical role in the enhanced sensitivity and selectivity of the developed sensor towards Hg0 vapor.

A Sensitive and Selective Liquid Chromatography/Tandem Mass Spectrometry Method for Quantitative Analysis of Efavirenz in Human Plasma

PubMed Central

Srivastava, Praveen; Moorthy, Ganesh S.; Gross, Robert; Barrett, Jeffrey S.

2013-01-01

A selective and a highly sensitive method for the determination of the non-nucleoside reverse transcriptase inhibitor (NNRTI), efavirenz, in human plasma has been developed and fully validated based on high performance liquid chromatography tandem mass spectrometry (LC–MS/MS). Sample preparation involved protein precipitation followed by one to one dilution with water. The analyte, efavirenz was separated by high performance liquid chromatography and detected with tandem mass spectrometry in negative ionization mode with multiple reaction monitoring. Efavirenz and 13C6-efavirenz (Internal Standard), respectively, were detected via the following MRM transitions: m/z 314.20243.90 and m/z 320.20249.90. A gradient program was used to elute the analytes using 0.1% formic acid in water and 0.1% formic acid in acetonitrile as mobile phase solvents, at a flow-rate of 0.3 mL/min. The total run time was 5 min and the retention times for the internal standard (13C6-efavirenz) and efavirenz was approximately 2.6 min. The calibration curves showed linearity (coefficient of regression, r>0.99) over the concentration range of 1.0–2,500 ng/mL. The intraday precision based on the standard deviation of replicates of lower limit of quantification (LLOQ) was 9.24% and for quality control (QC) samples ranged from 2.41% to 6.42% and with accuracy from 112% and 100–111% for LLOQ and QC samples. The inter day precision was 12.3% and 3.03–9.18% for LLOQ and quality controls samples, and the accuracy was 108% and 95.2–108% for LLOQ and QC samples. Stability studies showed that efavirenz was stable during the expected conditions for sample preparation and storage. The lower limit of quantification for efavirenz was 1 ng/mL. The analytical method showed excellent sensitivity, precision, and accuracy. This method is robust and is being successfully applied for therapeutic drug monitoring and pharmacokinetic studies in HIV-infected patients. PMID:23755102

The NPP and J1 CrIS Operational High-Resolution Channel Selection for the NUCAPS algorithm: A demonstration of global applicability to meet users needs

NASA Astrophysics Data System (ADS)

Smith, J.; Gambacorta, A.; Barnet, C.; Smith, N.; Goldberg, M.; Pierce, B.; Wolf, W.; King, T.

2016-12-01

This work presents an overview of the NPP and J1 CrIS high resolution operational channel selection. Our methodology focuses on the spectral sensitivity characteristics of the available channels in order to maximize information content and spectral purity. These aspects are key to ensure accuracy in the retrieval products, particularly for trace gases. We will provide a demonstration of its global optimality by analyzing different test cases that are of particular interests to our JPSS Proving Ground and Risk Reduction user applications. A focus will be on high resolution trace gas retrieval capability in the context of the Alaska fire initiatives.

Classification of epileptic EEG signals based on simple random sampling and sequential feature selection.

PubMed

Ghayab, Hadi Ratham Al; Li, Yan; Abdulla, Shahab; Diykh, Mohammed; Wan, Xiangkui

2016-06-01

Electroencephalogram (EEG) signals are used broadly in the medical fields. The main applications of EEG signals are the diagnosis and treatment of diseases such as epilepsy, Alzheimer, sleep problems and so on. This paper presents a new method which extracts and selects features from multi-channel EEG signals. This research focuses on three main points. Firstly, simple random sampling (SRS) technique is used to extract features from the time domain of EEG signals. Secondly, the sequential feature selection (SFS) algorithm is applied to select the key features and to reduce the dimensionality of the data. Finally, the selected features are forwarded to a least square support vector machine (LS_SVM) classifier to classify the EEG signals. The LS_SVM classifier classified the features which are extracted and selected from the SRS and the SFS. The experimental results show that the method achieves 99.90, 99.80 and 100 % for classification accuracy, sensitivity and specificity, respectively.

Functional Neuroimaging Distinguishes Posttraumatic Stress Disorder from Traumatic Brain Injury in Focused and Large Community Datasets

PubMed Central

Tarzwell, Robert; Newberg, Andrew; Henderson, Theodore A.

2015-01-01

Background Traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) are highly heterogeneous and often present with overlapping symptomology, providing challenges in reliable classification and treatment. Single photon emission computed tomography (SPECT) may be advantageous in the diagnostic separation of these disorders when comorbid or clinically indistinct. Methods Subjects were selected from a multisite database, where rest and on-task SPECT scans were obtained on a large group of neuropsychiatric patients. Two groups were analyzed: Group 1 with TBI (n=104), PTSD (n=104) or both (n=73) closely matched for demographics and comorbidity, compared to each other and healthy controls (N=116); Group 2 with TBI (n=7,505), PTSD (n=1,077) or both (n=1,017) compared to n=11,147 without either. ROIs and visual readings (VRs) were analyzed using a binary logistic regression model with predicted probabilities inputted into a Receiver Operating Characteristic analysis to identify sensitivity, specificity, and accuracy. One-way ANOVA identified the most diagnostically significant regions of increased perfusion in PTSD compared to TBI. Analysis included a 10-fold cross validation of the protocol in the larger community sample (Group 2). Results For Group 1, baseline and on-task ROIs and VRs showed a high level of accuracy in differentiating PTSD, TBI and PTSD+TBI conditions. This carefully matched group separated with 100% sensitivity, specificity and accuracy for the ROI analysis and at 89% or above for VRs. Group 2 had lower sensitivity, specificity and accuracy, but still in a clinically relevant range. Compared to subjects with TBI, PTSD showed increases in the limbic regions, cingulum, basal ganglia, insula, thalamus, prefrontal cortex and temporal lobes. Conclusions This study demonstrates the ability to separate PTSD and TBI from healthy controls, from each other, and detect their co-occurrence, even in highly comorbid samples, using SPECT. This modality may offer a clinical option for aiding diagnosis and treatment of these conditions. PMID:26132293

Functional Neuroimaging Distinguishes Posttraumatic Stress Disorder from Traumatic Brain Injury in Focused and Large Community Datasets.

PubMed

Amen, Daniel G; Raji, Cyrus A; Willeumier, Kristen; Taylor, Derek; Tarzwell, Robert; Newberg, Andrew; Henderson, Theodore A

2015-01-01

Traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) are highly heterogeneous and often present with overlapping symptomology, providing challenges in reliable classification and treatment. Single photon emission computed tomography (SPECT) may be advantageous in the diagnostic separation of these disorders when comorbid or clinically indistinct. Subjects were selected from a multisite database, where rest and on-task SPECT scans were obtained on a large group of neuropsychiatric patients. Two groups were analyzed: Group 1 with TBI (n=104), PTSD (n=104) or both (n=73) closely matched for demographics and comorbidity, compared to each other and healthy controls (N=116); Group 2 with TBI (n=7,505), PTSD (n=1,077) or both (n=1,017) compared to n=11,147 without either. ROIs and visual readings (VRs) were analyzed using a binary logistic regression model with predicted probabilities inputted into a Receiver Operating Characteristic analysis to identify sensitivity, specificity, and accuracy. One-way ANOVA identified the most diagnostically significant regions of increased perfusion in PTSD compared to TBI. Analysis included a 10-fold cross validation of the protocol in the larger community sample (Group 2). For Group 1, baseline and on-task ROIs and VRs showed a high level of accuracy in differentiating PTSD, TBI and PTSD+TBI conditions. This carefully matched group separated with 100% sensitivity, specificity and accuracy for the ROI analysis and at 89% or above for VRs. Group 2 had lower sensitivity, specificity and accuracy, but still in a clinically relevant range. Compared to subjects with TBI, PTSD showed increases in the limbic regions, cingulum, basal ganglia, insula, thalamus, prefrontal cortex and temporal lobes. This study demonstrates the ability to separate PTSD and TBI from healthy controls, from each other, and detect their co-occurrence, even in highly comorbid samples, using SPECT. This modality may offer a clinical option for aiding diagnosis and treatment of these conditions.

Accuracy of ICD-10 Coding System for Identifying Comorbidities and Infectious Conditions Using Data from a Thai University Hospital Administrative Database.

PubMed

Rattanaumpawan, Pinyo; Wongkamhla, Thanyarak; Thamlikitkul, Visanu

2016-04-01

To determine the accuracy of International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system in identifying comorbidities and infectious conditions using data from a Thai university hospital administrative database. A retrospective cross-sectional study was conducted among patients hospitalized in six general medicine wards at Siriraj Hospital. ICD-10 code data was identified and retrieved directly from the hospital administrative database. Patient comorbidities were captured using the ICD-10 coding algorithm for the Charlson comorbidity index. Infectious conditions were captured using the groups of ICD-10 diagnostic codes that were carefully prepared by two independent infectious disease specialists. Accuracy of ICD-10 codes combined with microbiological dataf or diagnosis of urinary tract infection (UTI) and bloodstream infection (BSI) was evaluated. Clinical data gathered from chart review was considered the gold standard in this study. Between February 1 and May 31, 2013, a chart review of 546 hospitalization records was conducted. The mean age of hospitalized patients was 62.8 ± 17.8 years and 65.9% of patients were female. Median length of stay [range] was 10.0 [1.0-353.0] days and hospital mortality was 21.8%. Conditions with ICD-10 codes that had good sensitivity (90% or higher) were diabetes mellitus and HIV infection. Conditions with ICD-10 codes that had good specificity (90% or higher) were cerebrovascular disease, chronic lung disease, diabetes mellitus, cancer HIV infection, and all infectious conditions. By combining ICD-10 codes with microbiological results, sensitivity increased from 49.5 to 66%for UTI and from 78.3 to 92.8%for BS. The ICD-10 coding algorithm is reliable only in some selected conditions, including underlying diabetes mellitus and HIV infection. Combining microbiological results with ICD-10 codes increased sensitivity of ICD-10 codes for identifying BSI. Future research is needed to improve the accuracy of hospital administrative coding system in Thailand.

Accuracy of lung cancer ICD-9-CM codes in Umbria, Napoli 3 Sud and Friuli Venezia Giulia administrative healthcare databases: a diagnostic accuracy study

PubMed Central

Montedori, Alessandro; Bidoli, Ettore; Serraino, Diego; Fusco, Mario; Giovannini, Gianni; Casucci, Paola; Franchini, David; Granata, Annalisa; Ciullo, Valerio; Vitale, Maria Francesca; Gobbato, Michele; Chiari, Rita; Cozzolino, Francesco; Orso, Massimiliano; Orlandi, Walter

2018-01-01

Objectives To assess the accuracy of International Classification of Diseases 9th Revision–Clinical Modification (ICD-9-CM) codes in identifying subjects with lung cancer. Design A cross-sectional diagnostic accuracy study comparing ICD-9-CM 162.x code (index test) in primary position with medical chart (reference standard). Case ascertainment was based on the presence of a primary nodular lesion in the lung and cytological or histological documentation of cancer from a primary or metastatic site. Setting Three operative units: administrative databases from Umbria Region (890 000 residents), ASL Napoli 3 Sud (NA) (1 170 000 residents) and Friuli Venezia Giulia (FVG) Region (1 227 000 residents). Participants Incident subjects with lung cancer (n=386) diagnosed in primary position between 2012 and 2014 and a population of non-cases (n=280). Outcome measures Sensitivity, specificity and positive predictive value (PPV) for 162.x code. Results 130 cases and 94 non-cases were randomly selected from each database and the corresponding medical charts were reviewed. Most of the diagnoses for lung cancer were performed in medical departments. True positive rates were high for all the three units. Sensitivity was 99% (95% CI 95% to 100%) for Umbria, 97% (95% CI 91% to 100%) for NA, and 99% (95% CI 95% to 100%) for FVG. The false positive rates were 24%, 37% and 23% for Umbria, NA and FVG, respectively. PPVs were 79% (73% to 83%)%) for Umbria, 58% (53% to 63%)%) for NA and 79% (73% to 84%)%) for FVG. Conclusions Case ascertainment for lung cancer based on imaging or endoscopy associated with histological examination yielded an excellent sensitivity in all the three administrative databases. PPV was moderate for Umbria and FVG but lower for NA. PMID:29773701

A Highly Predictive Model for Diagnosis of Colorectal Neoplasms Using Plasma MicroRNA: Improving Specificity and Sensitivity

PubMed Central

Carter, Jane V.; Roberts, Henry L.; Pan, Jianmin; Rice, Jonathan D.; Burton, James F.; Galbraith, Norman J.; Eichenberger, M. Robert; Jorden, Jeffery; Deveaux, Peter; Farmer, Russell; Williford, Anna; Kanaan, Ziad; Rai, Shesh N.; Galandiuk, Susan

2016-01-01

OBJECTIVE(S) Develop a plasma-based microRNA (miRNA) diagnostic assay specific for colorectal neoplasms, building upon our prior work. BACKGROUND Colorectal neoplasms (colorectal cancer [CRC] and colorectal advanced adenoma [CAA]) frequently develop in individuals at ages when other common cancers also occur. Current screening methods lack sensitivity, specificity, and have poor patient compliance. METHODS Plasma was screened for 380 miRNAs using microfluidic array technology from a “Training” cohort of 60 patients, (10 each) control, CRC, CAA, breast (BC), pancreatic (PC) and lung (LC) cancer. We identified uniquely dysregulated miRNAs specific for colorectal neoplasia (p<0.05, false discovery rate: 5%, adjusted α=0.0038). These miRNAs were evaluated using single assays in a “Test” cohort of 120 patients. A mathematical model was developed to predict blinded sample identity in a 150 patient “Validation” cohort using repeat-sub-sampling validation of the testing dataset with 1000 iterations each to assess model detection accuracy. RESULTS Seven miRNAs (miR-21, miR-29c, miR-122, miR-192, miR-346, miR-372, miR-374a) were selected based upon p-value, area-under-the-curve (AUC), fold-change, and biological plausibility. AUC (±95% CI) for “Test” cohort comparisons were 0.91 (0.85-0.96), 0.79 (0.70-0.88) and 0.98 (0.96-1.0), respectively. Our mathematical model predicted blinded sample identity with 69-77% accuracy between all neoplasia and controls, 67-76% accuracy between colorectal neoplasia and other cancers, and 86-90% accuracy between colorectal cancer and colorectal adenoma. CONCLUSIONS Our plasma miRNA assay and prediction model differentiates colorectal neoplasia from patients with other neoplasms and from controls with higher sensitivity and specificity compared to current clinical standards. PMID:27471839

Sequence Based Prediction of Antioxidant Proteins Using a Classifier Selection Strategy

PubMed Central

Zhang, Lina; Zhang, Chengjin; Gao, Rui; Yang, Runtao; Song, Qing

2016-01-01

Antioxidant proteins perform significant functions in maintaining oxidation/antioxidation balance and have potential therapies for some diseases. Accurate identification of antioxidant proteins could contribute to revealing physiological processes of oxidation/antioxidation balance and developing novel antioxidation-based drugs. In this study, an ensemble method is presented to predict antioxidant proteins with hybrid features, incorporating SSI (Secondary Structure Information), PSSM (Position Specific Scoring Matrix), RSA (Relative Solvent Accessibility), and CTD (Composition, Transition, Distribution). The prediction results of the ensemble predictor are determined by an average of prediction results of multiple base classifiers. Based on a classifier selection strategy, we obtain an optimal ensemble classifier composed of RF (Random Forest), SMO (Sequential Minimal Optimization), NNA (Nearest Neighbor Algorithm), and J48 with an accuracy of 0.925. A Relief combined with IFS (Incremental Feature Selection) method is adopted to obtain optimal features from hybrid features. With the optimal features, the ensemble method achieves improved performance with a sensitivity of 0.95, a specificity of 0.93, an accuracy of 0.94, and an MCC (Matthew’s Correlation Coefficient) of 0.880, far better than the existing method. To evaluate the prediction performance objectively, the proposed method is compared with existing methods on the same independent testing dataset. Encouragingly, our method performs better than previous studies. In addition, our method achieves more balanced performance with a sensitivity of 0.878 and a specificity of 0.860. These results suggest that the proposed ensemble method can be a potential candidate for antioxidant protein prediction. For public access, we develop a user-friendly web server for antioxidant protein identification that is freely accessible at http://antioxidant.weka.cc. PMID:27662651

Diagnostic methods for assessing maxillary skeletal and dental transverse deficiencies: A systematic review

PubMed Central

Sawchuk, Dena; Currie, Kris; Vich, Manuel Lagravere; Palomo, Juan Martin

2016-01-01

Objective To evaluate the accuracy and reliability of the diagnostic tools available for assessing maxillary transverse deficiencies. Methods An electronic search of three databases was performed from their date of establishment to April 2015, with manual searching of reference lists of relevant articles. Articles were considered for inclusion if they reported the accuracy or reliability of a diagnostic method or evaluation technique for maxillary transverse dimensions in mixed or permanent dentitions. Risk of bias was assessed in the included articles, using the Quality Assessment of Diagnostic Accuracy Studies tool-2. Results Nine articles were selected. The studies were heterogeneous, with moderate to low methodological quality, and all had a high risk of bias. Four suggested that the use of arch width prediction indices with dental cast measurements is unreliable for use in diagnosis. Frontal cephalograms derived from cone-beam computed tomography (CBCT) images were reportedly more reliable for assessing intermaxillary transverse discrepancies than posteroanterior cephalograms. Two studies proposed new three-dimensional transverse analyses with CBCT images that were reportedly reliable, but have not been validated for clinical sensitivity or specificity. No studies reported sensitivity, specificity, positive or negative predictive values or likelihood ratios, or ROC curves of the methods for the diagnosis of transverse deficiencies. Conclusions Current evidence does not enable solid conclusions to be drawn, owing to a lack of reliable high quality diagnostic studies evaluating maxillary transverse deficiencies. CBCT images are reportedly more reliable for diagnosis, but further validation is required to confirm CBCT's accuracy and diagnostic superiority. PMID:27668196

Switch-task performance in rats is disturbed by 12 h of sleep deprivation but not by 12 h of sleep fragmentation.

PubMed

Leenaars, Cathalijn H C; Joosten, Ruud N J M A; Zwart, Allard; Sandberg, Hans; Ruimschotel, Emma; Hanegraaf, Maaike A J; Dematteis, Maurice; Feenstra, Matthijs G P; van Someren, Eus J W

2012-02-01

Task-switching is an executive function involving the prefrontal cortex. Switching temporarily attenuates the speed and/or accuracy of performance, phenomena referred to as switch costs. In accordance with the idea that prefrontal function is particularly sensitive to sleep loss, switch-costs increase during prolonged waking in humans. It has been difficult to investigate the underlying neurobiological mechanisms because of the lack of a suitable animal model. Here, we introduce the first switch-task for rats and report the effects of sleep deprivation and inactivation of the medial prefrontal cortex. Rats were trained to repeatedly switch between 2 stimulus-response associations, indicated by the presentation of a visual or an auditory stimulus. These stimulus-response associations were offered in blocks, and performance was compared for the first and fifth trials of each block. Performance was tested after exposure to 12 h of total sleep deprivation, sleep fragmentation, and their respective movement control conditions. Finally, it was tested after pharmacological inactivation of the medial prefrontal cortex. Controlled laboratory settings. 15 male Wistar rats. Both accuracy and latency showed switch-costs at baseline. Twelve hours of total sleep deprivation, but not sleep fragmentation, impaired accuracy selectively on the switch-trials. Inactivation of the medial prefrontal cortex by local neuronal inactivation resulted in an overall decrease in accuracy. We developed and validated a switch-task that is sensitive to sleep deprivation. This introduces the possibility for in-depth investigations on the neurobiological mechanisms underlying executive impairments after sleep disturbance in a rat model.

Accuracy of dengue clinical diagnosis with and without NS1 antigen rapid test: Comparison between human and Bayesian network model decision.

PubMed

Sa-Ngamuang, Chaitawat; Haddawy, Peter; Luvira, Viravarn; Piyaphanee, Watcharapong; Iamsirithaworn, Sopon; Lawpoolsri, Saranath

2018-06-18

Differentiating dengue patients from other acute febrile illness patients is a great challenge among physicians. Several dengue diagnosis methods are recommended by WHO. The application of specific laboratory tests is still limited due to high cost, lack of equipment, and uncertain validity. Therefore, clinical diagnosis remains a common practice especially in resource limited settings. Bayesian networks have been shown to be a useful tool for diagnostic decision support. This study aimed to construct Bayesian network models using basic demographic, clinical, and laboratory profiles of acute febrile illness patients to diagnose dengue. Data of 397 acute undifferentiated febrile illness patients who visited the fever clinic of the Bangkok Hospital for Tropical Diseases, Thailand, were used for model construction and validation. The two best final models were selected: one with and one without NS1 rapid test result. The diagnostic accuracy of the models was compared with that of physicians on the same set of patients. The Bayesian network models provided good diagnostic accuracy of dengue infection, with ROC AUC of 0.80 and 0.75 for models with and without NS1 rapid test result, respectively. The models had approximately 80% specificity and 70% sensitivity, similar to the diagnostic accuracy of the hospital's fellows in infectious disease. Including information on NS1 rapid test improved the specificity, but reduced the sensitivity, both in model and physician diagnoses. The Bayesian network model developed in this study could be useful to assist physicians in diagnosing dengue, particularly in regions where experienced physicians and laboratory confirmation tests are limited.

Realisation Of Polarisation Sensitive And Frequency Selective Surfaces On Microwave Reflectors By Laser Evaporation

NASA Astrophysics Data System (ADS)

Halm, R.; Kupper, Th.; Fischer, A.

1987-01-01

Gridded reflectors are used on communication satellites antennas to provide frequency reuse in dual linear polarisation mode of operation. The polarisation sensitive surface consists of metallic strips, forming a grid with width and spacings of the order of 0.1 mm. The use of frequency-selective surface (FSS) subreflectors allows the simultaneous generation of different microwave beams with the same main reflector. Such a reflector will require a structure of conductive arrays of either dipoles, rings, squares or square loops with typical dimensions of the order of 3-6 mm. Optimisation of the electrical design leads to critical dimensioning of these structures. By direct ablation of an aluminium surface coating by means of laser evaporation, high accuracies can be achieved. The major requirements were to minimize thermal damage of the substrate material and to produce dimensionally accurate grids. Experiments were carried out using a pulsed TEA-CO2 laser and a Q-switched Alexandrite laser. Details of the experimental set-up and conditions are described.

How genome complexity can explain the difficulty of aligning reads to genomes.

PubMed

Phan, Vinhthuy; Gao, Shanshan; Tran, Quang; Vo, Nam S

2015-01-01

Although it is frequently observed that aligning short reads to genomes becomes harder if they contain complex repeat patterns, there has not been much effort to quantify the relationship between complexity of genomes and difficulty of short-read alignment. Existing measures of sequence complexity seem unsuitable for the understanding and quantification of this relationship. We investigated several measures of complexity and found that length-sensitive measures of complexity had the highest correlation to accuracy of alignment. In particular, the rate of distinct substrings of length k, where k is similar to the read length, correlated very highly to alignment performance in terms of precision and recall. We showed how to compute this measure efficiently in linear time, making it useful in practice to estimate quickly the difficulty of alignment for new genomes without having to align reads to them first. We showed how the length-sensitive measures could provide additional information for choosing aligners that would align consistently accurately on new genomes. We formally established a connection between genome complexity and the accuracy of short-read aligners. The relationship between genome complexity and alignment accuracy provides additional useful information for selecting suitable aligners for new genomes. Further, this work suggests that the complexity of genomes sometimes should be thought of in terms of specific computational problems, such as the alignment of short reads to genomes.

Predictive accuracy of changes in transvaginal sonographic cervical length over time for preterm birth: a systematic review and metaanalysis.

PubMed

Conde-Agudelo, Agustin; Romero, Roberto

2015-12-01

To determine the accuracy of changes in transvaginal sonographic cervical length over time in predicting preterm birth in women with singleton and twin gestations. PubMed, Embase, Cinahl, Lilacs, and Medion (all from inception to June 30, 2015), bibliographies, Google scholar, and conference proceedings. Cohort or cross-sectional studies reporting on the predictive accuracy for preterm birth of changes in cervical length over time. Two reviewers independently selected studies, assessed the risk of bias, and extracted the data. Summary receiver-operating characteristic curves, pooled sensitivities and specificities, and summary likelihood ratios were generated. Fourteen studies met the inclusion criteria, of which 7 provided data on singleton gestations (3374 women) and 8 on twin gestations (1024 women). Among women with singleton gestations, the shortening of cervical length over time had a low predictive accuracy for preterm birth at <37 and <35 weeks of gestation with pooled sensitivities and specificities, and summary positive and negative likelihood ratios ranging from 49% to 74%, 44% to 85%, 1.3 to 4.1, and 0.3 to 0.7, respectively. In women with twin gestations, the shortening of cervical length over time had a low to moderate predictive accuracy for preterm birth at <34, <32, <30, and <28 weeks of gestation with pooled sensitivities and specificities, and summary positive and negative likelihood ratios ranging from 47% to 73%, 84% to 89%, 3.8 to 5.3, and 0.3 to 0.6, respectively. There were no statistically significant differences between the predictive accuracies for preterm birth of cervical length shortening over time and the single initial and/or final cervical length measurement in 8 of 11 studies that provided data for making these comparisons. In the largest and highest-quality study, a single measurement of cervical length obtained at 24 or 28 weeks of gestation was significantly more predictive of preterm birth than any decrease in cervical length between these gestational ages. Change in transvaginal sonographic cervical length over time is not a clinically useful test to predict preterm birth in women with singleton or twin gestations. A single cervical length measurement obtained between 18 and 24 weeks of gestation appears to be a better test to predict preterm birth than changes in cervical length over time. Published by Elsevier Inc.

Fluorescent microplate assay method for high-throughput detection of lipase transesterification activity.

PubMed

Zheng, Jianyong; Wei, Wei; Lan, Xing; Zhang, Yinjun; Wang, Zhao

2018-05-15

This study describes a sensitive and fluorescent microplate assay method to detect lipase transesterification activity. Lipase-catalyzed transesterification between butyryl 4-methyl umbelliferone (Bu-4-Mu) and methanol in tert-butanol was selected as the model reaction. The release of 4-methylumbelliferone (4-Mu) in the reaction was determined by detecting the fluorescence intensity at λ ex 330 nm and λ em 390 nm. Several lipases were used to investigate the accuracy and efficiency of the proposed method. Apparent Michaelis constant (Km) was calculated for transesterification between Bu-4-Mu and methanol by the lipases. The main advantages of the assay method include high sensitivity, inexpensive reagents, and simple detection process. Copyright © 2018 Elsevier Inc. All rights reserved.

Accuracy criteria voltage electrocardiography left ventricular hypertrophy to distinguish types of left ventricular hypertrophy geometry

NASA Astrophysics Data System (ADS)

Tumbur, O.; Safri, Z.; Hassan, R.

2018-03-01

Different types of left ventricular hypertrophy geometry are associated with different risk of cardiovascular disease. The purpose of this study was to determine the role of various ECG voltages of LVH to distinguish the type of LVH geometry. A cross-sectional study from June to November 2015, 100 patients in Adam Malik Hospital Medan. The result of LVH ECG criteria of Sokolow-Lyon was not met then obtained normal left ventricular geometry with 60% sensitivity, 72.22% specificity, and 71% accuracy. The eccentric type of LVH is obtained when the Cornel Voltage is not met; the sensitivity is 25%, specificity 71.88%, and 55% accuracy. Concentric geometric hypertrophy when the RV6/V5> 1 ratio is satisfied, the sensitivity is 55.56%, specificity 56.36%, and 56% accuracy. The RV6/V5>1 ratio was not met, the concentric geometry type of hypertrophy remodeling was determined with a sensitivity of 55.56%, specificity 49.45%, and 50% accuracy. Conclusions, various LVHECG criteria distinguish the type of LVH geometry. Sokolow-Lyon and Cornel Voltage sensitivity and specificity are better than the RV6/V5 ratio.

Heuristic use of perceptual evidence leads to dissociation between performance and metacognitive sensitivity.

PubMed

Maniscalco, Brian; Peters, Megan A K; Lau, Hakwan

2016-04-01

Zylberberg et al. [Zylberberg, Barttfeld, & Sigman (Frontiers in Integrative Neuroscience, 6; 79, 2012), Frontiers in Integrative Neuroscience 6:79] found that confidence decisions, but not perceptual decisions, are insensitive to evidence against a selected perceptual choice. We present a signal detection theoretic model to formalize this insight, which gave rise to a counter-intuitive empirical prediction: that depending on the observer's perceptual choice, increasing task performance can be associated with decreasing metacognitive sensitivity (i.e., the trial-by-trial correspondence between confidence and accuracy). The model also provides an explanation as to why metacognitive sensitivity tends to be less than optimal in actual subjects. These predictions were confirmed robustly in a psychophysics experiment. In a second experiment we found that, in at least some subjects, the effects were replicated even under performance feedback designed to encourage optimal behavior. However, some subjects did show improvement under feedback, suggesting the tendency to ignore evidence against a selected perceptual choice may be a heuristic adopted by the perceptual decision-making system, rather than reflecting inherent biological limitations. We present a Bayesian modeling framework that explains why this heuristic strategy may be advantageous in real-world contexts.

Competence in Streptococcus pneumoniae is regulated by the rate of ribosomal decoding errors.

PubMed

Stevens, Kathleen E; Chang, Diana; Zwack, Erin E; Sebert, Michael E

2011-01-01

Competence for genetic transformation in Streptococcus pneumoniae develops in response to accumulation of a secreted peptide pheromone and was one of the initial examples of bacterial quorum sensing. Activation of this signaling system induces not only expression of the proteins required for transformation but also the production of cellular chaperones and proteases. We have shown here that activity of this pathway is sensitively responsive to changes in the accuracy of protein synthesis that are triggered by either mutations in ribosomal proteins or exposure to antibiotics. Increasing the error rate during ribosomal decoding promoted competence, while reducing the error rate below the baseline level repressed the development of both spontaneous and antibiotic-induced competence. This pattern of regulation was promoted by the bacterial HtrA serine protease. Analysis of strains with the htrA (S234A) catalytic site mutation showed that the proteolytic activity of HtrA selectively repressed competence when translational fidelity was high but not when accuracy was low. These findings redefine the pneumococcal competence pathway as a response to errors during protein synthesis. This response has the capacity to address the immediate challenge of misfolded proteins through production of chaperones and proteases and may also be able to address, through genetic exchange, upstream coding errors that cause intrinsic protein folding defects. The competence pathway may thereby represent a strategy for dealing with lesions that impair proper protein coding and for maintaining the coding integrity of the genome. The signaling pathway that governs competence in the human respiratory tract pathogen Streptococcus pneumoniae regulates both genetic transformation and the production of cellular chaperones and proteases. The current study shows that this pathway is sensitively controlled in response to changes in the accuracy of protein synthesis. Increasing the error rate during ribosomal decoding induced competence, while decreasing the error rate repressed competence. This pattern of regulation was promoted by the HtrA protease, which selectively repressed competence when translational fidelity was high but not when accuracy was low. Our findings demonstrate that this organism is able to monitor the accuracy of information used for protein biosynthesis and suggest that errors trigger a response addressing both the immediate challenge of misfolded proteins and, through genetic exchange, upstream coding errors that may underlie protein folding defects. This pathway may represent an evolutionary strategy for maintaining the coding integrity of the genome.

Actinic Flux Calculations: A Model Sensitivity Study

NASA Technical Reports Server (NTRS)

Krotkov, Nickolay A.; Flittner, D.; Ahmad, Z.; Herman, J. R.; Einaudi, Franco (Technical Monitor)

2000-01-01

calculate direct and diffuse surface irradiance and actinic flux (downwelling (2p) and total (4p)) for the reference model. Sensitivity analysis has shown that the accuracy of the radiative transfer flux calculations for a unit ETS (i.e. atmospheric transmittance) together with a numerical interpolation technique for the constituents' vertical profiles is better than 1% for SZA less than 70(sub o) and wavelengths longer than 310 nm. The differences increase for shorter wavelengths and larger SZA, due to the differences in pseudo-spherical correction techniques and vertical discretetization among the codes. Our sensitivity study includes variation of ozone cross-sections, ETS spectra and the effects of wavelength shifts between vacuum and air scales. We also investigate the effects of aerosols on the spectral flux components in the UV and visible spectral regions. The "aerosol correction factors" (ACFs) were calculated at discrete wavelengths and different SZAs for each flux component (direct, diffuse, reflected) and prescribed IPMMI aerosol parameters. Finally, the sensitivity study was extended to calculation of selected photolysis rates coefficients.

A sensitive LC-MS/MS method for simultaneous determination of amygdalin and paeoniflorin in human plasma and its application.

PubMed

Li, Xiaobing; Shi, Fuguo; Gu, Pan; Liu, Lingye; He, Hua; Ding, Li

2014-04-01

A simple and sensitive HPLC-MS/MS method was developed and fully validated for the simultaneous determination of amygdalin (AD) and paeoniflorin (PF) in human plasma. For both analytes, the method exhibited high sensitivity (LLOQs of 0.6ng/mL) by selecting the ammonium adduct ions ([M+NH4](+)) as the precursor ions and good linearity over the concentration range of 0.6-2000ng/mL with the correlation coefficients>0.9972. The intra- and inter-day precision was lower than 10% in relation to relative standard deviation, while accuracy was within ±2.3% in terms of relative error for both analytes. The developed method was successfully applied to a pilot pharmacokinetic study of AD and PF in healthy volunteers after intravenous infusion administration of Huoxue-Tongluo lyophilized powder for injection. Copyright © 2014 Elsevier B.V. All rights reserved.

Detection of Sub-fM DNA with Target Recycling and Self-Assembly Amplification on Graphene Field-Effect Biosensors

PubMed Central

2018-01-01

All-electronic DNA biosensors based on graphene field-effect transistors (GFETs) offer the prospect of simple and cost-effective diagnostics. For GFET sensors based on complementary probe DNA, the sensitivity is limited by the binding affinity of the target oligonucleotide, in the nM range for 20 mer targets. We report a ∼20 000× improvement in sensitivity through the use of engineered hairpin probe DNA that allows for target recycling and hybridization chain reaction. This enables detection of 21 mer target DNA at sub-fM concentration and provides superior specificity against single-base mismatched oligomers. The work is based on a scalable fabrication process for biosensor arrays that is suitable for multiplexed detection. This approach overcomes the binding-affinity-dependent sensitivity of nucleic acid biosensors and offers a pathway toward multiplexed and label-free nucleic acid testing with high accuracy and selectivity. PMID:29768011

Detection of Sub-fM DNA with Target Recycling and Self-Assembly Amplification on Graphene Field-Effect Biosensors.

PubMed

Gao, Zhaoli; Xia, Han; Zauberman, Jonathan; Tomaiuolo, Maurizio; Ping, Jinglei; Zhang, Qicheng; Ducos, Pedro; Ye, Huacheng; Wang, Sheng; Yang, Xinping; Lubna, Fahmida; Luo, Zhengtang; Ren, Li; Johnson, Alan T Charlie

2018-06-13

All-electronic DNA biosensors based on graphene field-effect transistors (GFETs) offer the prospect of simple and cost-effective diagnostics. For GFET sensors based on complementary probe DNA, the sensitivity is limited by the binding affinity of the target oligonucleotide, in the nM range for 20 mer targets. We report a ∼20 000× improvement in sensitivity through the use of engineered hairpin probe DNA that allows for target recycling and hybridization chain reaction. This enables detection of 21 mer target DNA at sub-fM concentration and provides superior specificity against single-base mismatched oligomers. The work is based on a scalable fabrication process for biosensor arrays that is suitable for multiplexed detection. This approach overcomes the binding-affinity-dependent sensitivity of nucleic acid biosensors and offers a pathway toward multiplexed and label-free nucleic acid testing with high accuracy and selectivity.

VIBRATIONAL SPECTROSCOPIC SENSORS Fundamentals, Instrumentation and Applications

NASA Astrophysics Data System (ADS)

Kraft, Martin

In textbook descriptions of chemical sensors, almost invariably a chemical sensor is described as a combination of a (dumb) transducer and a (smart) recognition layer. The reason for this is that most transducers, while (reasonably) sensitive, have limited analyte specificity. This is in particular true for non-optical, e.g. mass-sensitive or electrochemical systems, but also many optical transducers are as such incapable of distinguishing between different substances. Consequently, to build sensors operational in multicomponent environments, such transducers must be combined with physicochemical, chemical or biochemical recognition systems providing the required analyte specificity. Although advancements have been made in this field over the last years, selective layers are frequently not (yet) up to the demands set by industrial or environmental applications, in particular when operated over prolonged periods of time. Another significant obstacle are cross-sensitivities that may interfere with the analytical accuracy. Together, these limitations restrict the real-world applicability of many otherwise promising chemical sensors.

A cascaded QSAR model for efficient prediction of overall power conversion efficiency of all-organic dye-sensitized solar cells.

PubMed

Li, Hongzhi; Zhong, Ziyan; Li, Lin; Gao, Rui; Cui, Jingxia; Gao, Ting; Hu, Li Hong; Lu, Yinghua; Su, Zhong-Min; Li, Hui

2015-05-30

A cascaded model is proposed to establish the quantitative structure-activity relationship (QSAR) between the overall power conversion efficiency (PCE) and quantum chemical molecular descriptors of all-organic dye sensitizers. The cascaded model is a two-level network in which the outputs of the first level (JSC, VOC, and FF) are the inputs of the second level, and the ultimate end-point is the overall PCE of dye-sensitized solar cells (DSSCs). The model combines quantum chemical methods and machine learning methods, further including quantum chemical calculations, data division, feature selection, regression, and validation steps. To improve the efficiency of the model and reduce the redundancy and noise of the molecular descriptors, six feature selection methods (multiple linear regression, genetic algorithms, mean impact value, forward selection, backward elimination, and +n-m algorithm) are used with the support vector machine. The best established cascaded model predicts the PCE values of DSSCs with a MAE of 0.57 (%), which is about 10% of the mean value PCE (5.62%). The validation parameters according to the OECD principles are R(2) (0.75), Q(2) (0.77), and Qcv2 (0.76), which demonstrate the great goodness-of-fit, predictivity, and robustness of the model. Additionally, the applicability domain of the cascaded QSAR model is defined for further application. This study demonstrates that the established cascaded model is able to effectively predict the PCE for organic dye sensitizers with very low cost and relatively high accuracy, providing a useful tool for the design of dye sensitizers with high PCE. © 2015 Wiley Periodicals, Inc.

Breast cancer Ki67 expression preoperative discrimination by DCE-MRI radiomics features

NASA Astrophysics Data System (ADS)

Ma, Wenjuan; Ji, Yu; Qin, Zhuanping; Guo, Xinpeng; Jian, Xiqi; Liu, Peifang

2018-02-01

To investigate whether quantitative radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are associated with Ki67 expression of breast cancer. In this institutional review board approved retrospective study, we collected 377 cases Chinese women who were diagnosed with invasive breast cancer in 2015. This cohort included 53 low-Ki67 expression (Ki67 proliferation index less than 14%) and 324 cases with high-Ki67 expression (Ki67 proliferation index more than 14%). A binary-classification of low- vs. high- Ki67 expression was performed. A set of 52 quantitative radiomics features, including morphological, gray scale statistic, and texture features, were extracted from the segmented lesion area. Three most common machine learning classification methods, including Naive Bayes, k-Nearest Neighbor and support vector machine with Gaussian kernel, were employed for the classification and the least absolute shrink age and selection operator (LASSO) method was used to select most predictive features set for the classifiers. Classification performance was evaluated by the area under receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity. The model that used Naive Bayes classification method achieved the best performance than the other two methods, yielding 0.773 AUC value, 0.757 accuracy, 0.777 sensitivity and 0.769 specificity. Our study showed that quantitative radiomics imaging features of breast tumor extracted from DCE-MRI are associated with breast cancer Ki67 expression. Future larger studies are needed in order to further evaluate the findings.

Supervised Learning Applied to Air Traffic Trajectory Classification

NASA Technical Reports Server (NTRS)

Bosson, Christabelle S.; Nikoleris, Tasos

2018-01-01

Given the recent increase of interest in introducing new vehicle types and missions into the National Airspace System, a transition towards a more autonomous air traffic control system is required in order to enable and handle increased density and complexity. This paper presents an exploratory effort of the needed autonomous capabilities by exploring supervised learning techniques in the context of aircraft trajectories. In particular, it focuses on the application of machine learning algorithms and neural network models to a runway recognition trajectory-classification study. It investigates the applicability and effectiveness of various classifiers using datasets containing trajectory records for a month of air traffic. A feature importance and sensitivity analysis are conducted to challenge the chosen time-based datasets and the ten selected features. The study demonstrates that classification accuracy levels of 90% and above can be reached in less than 40 seconds of training for most machine learning classifiers when one track data point, described by the ten selected features at a particular time step, per trajectory is used as input. It also shows that neural network models can achieve similar accuracy levels but at higher training time costs.

Using a genetic algorithm as an optimal band selector in the mid and thermal infrared (2.5-14 μm) to discriminate vegetation species.

PubMed

Ullah, Saleem; Groen, Thomas A; Schlerf, Martin; Skidmore, Andrew K; Nieuwenhuis, Willem; Vaiphasa, Chaichoke

2012-01-01

Genetic variation between various plant species determines differences in their physio-chemical makeup and ultimately in their hyperspectral emissivity signatures. The hyperspectral emissivity signatures, on the one hand, account for the subtle physio-chemical changes in the vegetation, but on the other hand, highlight the problem of high dimensionality. The aim of this paper is to investigate the performance of genetic algorithms coupled with the spectral angle mapper (SAM) to identify a meaningful subset of wavebands sensitive enough to discriminate thirteen broadleaved vegetation species from the laboratory measured hyperspectral emissivities. The performance was evaluated using an overall classification accuracy and Jeffries Matusita distance. For the multiple plant species, the targeted bands based on genetic algorithms resulted in a high overall classification accuracy (90%). Concentrating on the pairwise comparison results, the selected wavebands based on genetic algorithms resulted in higher Jeffries Matusita (J-M) distances than randomly selected wavebands did. This study concludes that targeted wavebands from leaf emissivity spectra are able to discriminate vegetation species.

Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) processing speed scores as measures of noncredible responding: The third generation of embedded performance validity indicators.

PubMed

Erdodi, Laszlo A; Abeare, Christopher A; Lichtenstein, Jonathan D; Tyson, Bradley T; Kucharski, Brittany; Zuccato, Brandon G; Roth, Robert M

2017-02-01

Research suggests that select processing speed measures can also serve as embedded validity indicators (EVIs). The present study examined the diagnostic utility of Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV) subtests as EVIs in a mixed clinical sample of 205 patients medically referred for neuropsychological assessment (53.3% female, mean age = 45.1). Classification accuracy was calculated against 3 composite measures of performance validity as criterion variables. A PSI ≤79 produced a good combination of sensitivity (.23-.56) and specificity (.92-.98). A Coding scaled score ≤5 resulted in good specificity (.94-1.00), but low and variable sensitivity (.04-.28). A Symbol Search scaled score ≤6 achieved a good balance between sensitivity (.38-.64) and specificity (.88-.93). A Coding-Symbol Search scaled score difference ≥5 produced adequate specificity (.89-.91) but consistently low sensitivity (.08-.12). A 2-tailed cutoff on the Coding/Symbol Search raw score ratio (≤1.41 or ≥3.57) produced acceptable specificity (.87-.93), but low sensitivity (.15-.24). Failing ≥2 of these EVIs produced variable specificity (.81-.93) and sensitivity (.31-.59). Failing ≥3 of these EVIs stabilized specificity (.89-.94) at a small cost to sensitivity (.23-.53). Results suggest that processing speed based EVIs have the potential to provide a cost-effective and expedient method for evaluating the validity of cognitive data. Given their generally low and variable sensitivity, however, they should not be used in isolation to determine the credibility of a given response set. They also produced unacceptably high rates of false positive errors in patients with moderate-to-severe head injury. Combining evidence from multiple EVIs has the potential to improve overall classification accuracy. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Estimation of Handgrip Force from SEMG Based on Wavelet Scale Selection.

PubMed

Wang, Kai; Zhang, Xianmin; Ota, Jun; Huang, Yanjiang

2018-02-24

This paper proposes a nonlinear correlation-based wavelet scale selection technology to select the effective wavelet scales for the estimation of handgrip force from surface electromyograms (SEMG). The SEMG signal corresponding to gripping force was collected from extensor and flexor forearm muscles during the force-varying analysis task. We performed a computational sensitivity analysis on the initial nonlinear SEMG-handgrip force model. To explore the nonlinear correlation between ten wavelet scales and handgrip force, a large-scale iteration based on the Monte Carlo simulation was conducted. To choose a suitable combination of scales, we proposed a rule to combine wavelet scales based on the sensitivity of each scale and selected the appropriate combination of wavelet scales based on sequence combination analysis (SCA). The results of SCA indicated that the scale combination VI is suitable for estimating force from the extensors and the combination V is suitable for the flexors. The proposed method was compared to two former methods through prolonged static and force-varying contraction tasks. The experiment results showed that the root mean square errors derived by the proposed method for both static and force-varying contraction tasks were less than 20%. The accuracy and robustness of the handgrip force derived by the proposed method is better than that obtained by the former methods.

TIMSS 2011 Student and Teacher Predictors for Mathematics Achievement Explored and Identified via Elastic Net.

PubMed

Yoo, Jin Eun

2018-01-01

A substantial body of research has been conducted on variables relating to students' mathematics achievement with TIMSS. However, most studies have employed conventional statistical methods, and have focused on selected few indicators instead of utilizing hundreds of variables TIMSS provides. This study aimed to find a prediction model for students' mathematics achievement using as many TIMSS student and teacher variables as possible. Elastic net, the selected machine learning technique in this study, takes advantage of both LASSO and ridge in terms of variable selection and multicollinearity, respectively. A logistic regression model was also employed to predict TIMSS 2011 Korean 4th graders' mathematics achievement. Ten-fold cross-validation with mean squared error was employed to determine the elastic net regularization parameter. Among 162 TIMSS variables explored, 12 student and 5 teacher variables were selected in the elastic net model, and the prediction accuracy, sensitivity, and specificity were 76.06, 70.23, and 80.34%, respectively. This study showed that the elastic net method can be successfully applied to educational large-scale data by selecting a subset of variables with reasonable prediction accuracy and finding new variables to predict students' mathematics achievement. Newly found variables via machine learning can shed light on the existing theories from a totally different perspective, which in turn propagates creation of a new theory or complement of existing ones. This study also examined the current scale development convention from a machine learning perspective.

TIMSS 2011 Student and Teacher Predictors for Mathematics Achievement Explored and Identified via Elastic Net

PubMed Central

Yoo, Jin Eun

2018-01-01

A substantial body of research has been conducted on variables relating to students' mathematics achievement with TIMSS. However, most studies have employed conventional statistical methods, and have focused on selected few indicators instead of utilizing hundreds of variables TIMSS provides. This study aimed to find a prediction model for students' mathematics achievement using as many TIMSS student and teacher variables as possible. Elastic net, the selected machine learning technique in this study, takes advantage of both LASSO and ridge in terms of variable selection and multicollinearity, respectively. A logistic regression model was also employed to predict TIMSS 2011 Korean 4th graders' mathematics achievement. Ten-fold cross-validation with mean squared error was employed to determine the elastic net regularization parameter. Among 162 TIMSS variables explored, 12 student and 5 teacher variables were selected in the elastic net model, and the prediction accuracy, sensitivity, and specificity were 76.06, 70.23, and 80.34%, respectively. This study showed that the elastic net method can be successfully applied to educational large-scale data by selecting a subset of variables with reasonable prediction accuracy and finding new variables to predict students' mathematics achievement. Newly found variables via machine learning can shed light on the existing theories from a totally different perspective, which in turn propagates creation of a new theory or complement of existing ones. This study also examined the current scale development convention from a machine learning perspective. PMID:29599736

Accuracy analysis and design of A3 parallel spindle head

NASA Astrophysics Data System (ADS)

Ni, Yanbing; Zhang, Biao; Sun, Yupeng; Zhang, Yuan

2016-03-01

As functional components of machine tools, parallel mechanisms are widely used in high efficiency machining of aviation components, and accuracy is one of the critical technical indexes. Lots of researchers have focused on the accuracy problem of parallel mechanisms, but in terms of controlling the errors and improving the accuracy in the stage of design and manufacturing, further efforts are required. Aiming at the accuracy design of a 3-DOF parallel spindle head(A3 head), its error model, sensitivity analysis and tolerance allocation are investigated. Based on the inverse kinematic analysis, the error model of A3 head is established by using the first-order perturbation theory and vector chain method. According to the mapping property of motion and constraint Jacobian matrix, the compensatable and uncompensatable error sources which affect the accuracy in the end-effector are separated. Furthermore, sensitivity analysis is performed on the uncompensatable error sources. The sensitivity probabilistic model is established and the global sensitivity index is proposed to analyze the influence of the uncompensatable error sources on the accuracy in the end-effector of the mechanism. The results show that orientation error sources have bigger effect on the accuracy in the end-effector. Based upon the sensitivity analysis results, the tolerance design is converted into the issue of nonlinearly constrained optimization with the manufacturing cost minimum being the optimization objective. By utilizing the genetic algorithm, the allocation of the tolerances on each component is finally determined. According to the tolerance allocation results, the tolerance ranges of ten kinds of geometric error sources are obtained. These research achievements can provide fundamental guidelines for component manufacturing and assembly of this kind of parallel mechanisms.

Diagnostic accuracy of imaging devices in glaucoma: A meta-analysis.

PubMed

Fallon, Monica; Valero, Oliver; Pazos, Marta; Antón, Alfonso

Imaging devices such as the Heidelberg retinal tomograph-3 (HRT3), scanning laser polarimetry (GDx), and optical coherence tomography (OCT) play an important role in glaucoma diagnosis. A systematic search for evidence-based data was performed for prospective studies evaluating the diagnostic accuracy of HRT3, GDx, and OCT. The diagnostic odds ratio (DOR) was calculated. To compare the accuracy among instruments and parameters, a meta-analysis considering the hierarchical summary receiver-operating characteristic model was performed. The risk of bias was assessed using quality assessment of diagnostic accuracy studies, version 2. Studies in the context of screening programs were used for qualitative analysis. Eighty-six articles were included. The DOR values were 29.5 for OCT, 18.6 for GDx, and 13.9 for HRT. The heterogeneity analysis demonstrated statistically a significant influence of degree of damage and ethnicity. Studies analyzing patients with earlier glaucoma showed poorer results. The risk of bias was high for patient selection. Screening studies showed lower sensitivity values and similar specificity values when compared with those included in the meta-analysis. The classification capabilities of GDx, HRT, and OCT were high and similar across the 3 instruments. The highest estimated DOR was obtained with OCT. Diagnostic accuracy could be overestimated in studies including prediagnosed groups of subjects. Copyright © 2017 Elsevier Inc. All rights reserved.

Interobserver Variability and Accuracy of High-Definition Endoscopic Diagnosis for Gastric Intestinal Metaplasia among Experienced and Inexperienced Endoscopists

PubMed Central

Hyun, Yil Sik; Bae, Joong Ho; Park, Hye Sun; Eun, Chang Soo

2013-01-01

Accurate diagnosis of gastric intestinal metaplasia is important; however, conventional endoscopy is known to be an unreliable modality for diagnosing gastric intestinal metaplasia (IM). The aims of the study were to evaluate the interobserver variation in diagnosing IM by high-definition (HD) endoscopy and the diagnostic accuracy of this modality for IM among experienced and inexperienced endoscopists. Selected 50 cases, taken with HD endoscopy, were sent for a diagnostic inquiry of gastric IM through visual inspection to five experienced and five inexperienced endoscopists. The interobserver agreement between endoscopists was evaluated to verify the diagnostic reliability of HD endoscopy in diagnosing IM, and the diagnostic accuracy, sensitivity, and specificity were evaluated for validity of HD endoscopy in diagnosing IM. Interobserver agreement among the experienced endoscopists was "poor" (κ = 0.38) and it was also "poor" (κ = 0.33) among the inexperienced endoscopists. The diagnostic accuracy of the experienced endoscopists was superior to that of the inexperienced endoscopists (P = 0.003). Since diagnosis through visual inspection is unreliable in the diagnosis of IM, all suspicious areas for gastric IM should be considered to be biopsied. Furthermore, endoscopic experience and education are needed to raise the diagnostic accuracy of gastric IM. PMID:23678267

Interobserver variability and accuracy of high-definition endoscopic diagnosis for gastric intestinal metaplasia among experienced and inexperienced endoscopists.

PubMed

Hyun, Yil Sik; Han, Dong Soo; Bae, Joong Ho; Park, Hye Sun; Eun, Chang Soo

2013-05-01

Accurate diagnosis of gastric intestinal metaplasia is important; however, conventional endoscopy is known to be an unreliable modality for diagnosing gastric intestinal metaplasia (IM). The aims of the study were to evaluate the interobserver variation in diagnosing IM by high-definition (HD) endoscopy and the diagnostic accuracy of this modality for IM among experienced and inexperienced endoscopists. Selected 50 cases, taken with HD endoscopy, were sent for a diagnostic inquiry of gastric IM through visual inspection to five experienced and five inexperienced endoscopists. The interobserver agreement between endoscopists was evaluated to verify the diagnostic reliability of HD endoscopy in diagnosing IM, and the diagnostic accuracy, sensitivity, and specificity were evaluated for validity of HD endoscopy in diagnosing IM. Interobserver agreement among the experienced endoscopists was "poor" (κ = 0.38) and it was also "poor" (κ = 0.33) among the inexperienced endoscopists. The diagnostic accuracy of the experienced endoscopists was superior to that of the inexperienced endoscopists (P = 0.003). Since diagnosis through visual inspection is unreliable in the diagnosis of IM, all suspicious areas for gastric IM should be considered to be biopsied. Furthermore, endoscopic experience and education are needed to raise the diagnostic accuracy of gastric IM.

Validation of the 2010 American College of Rheumatology preliminary diagnostic criteria for fibromyalgia in an Iranian population.

PubMed

Bidari, Ali; Hassanzadeh, Morteza; Ghavidel Parsa, Banafsheh; Kianmehr, Nahid; Kabir, Ali; Pirhadi, Sara; Sayfi, Mohammad; Toutounchi, Mehrangiz; Fattahi, Fariba; Zandi Karimi, Fereidoon

2013-12-01

The aim of this study was to validate the 2010 American College of Rheumatology (ACR) preliminary criteria for fibromyalgia (FM) in an Iranian population. In this multicenter prospective study, we enrolled 168 FM patients and 110 controls. All participants underwent dolorimetry examination by study assessors and completed a questionnaire containing variables of both the ACR 2010 preliminary and ACR 1990 criteria. We compared the performance of the ACR 2010 criteria with the expert diagnosis as well as the ACR 1990 criteria. Receiver operator characteristic analyses and Youden index were used to evaluate the test characteristics of a set of different cutoff points for two subcomponents of ACR 2010 criteria including widespread pain index (WPI) and symptom severity (SS) scale. Considering expert diagnosis as the gold standard, the ACR 2010 criteria showed comparable specificity with ACR 1990 (92.8 vs. 88.3 %, P = 0.073), but lower sensitivity (58.9 vs. 71.4 %, P = 0.003) and a tendency for lower accuracy (72.4 vs. 78.4 %, P = 0.105). Applying the ACR 1990 criteria as the gold standard, we observed a trend toward an increase in overall accuracy (72.4 vs. 79.1 %, P = 0.064). Optimal test characteristics were achieved for WPI ≥6 and SS scale score ≥4 and improved sensitivity and accuracy of ACR 2010 criteria when compared to expert, 76.1 and 81.7, respectively. The preliminary ACR 2010 criteria performed less desirably in terms of sensitivity in our set of Iranian patients. Selecting lower cutoff points as WPI ≥6 and SS scale score ≥4 improved the diagnostic values of the criteria.

Comparing Approaches to Optimize Cut-off Scores for Short Cognitive Screening Instruments in Mild Cognitive Impairment and Dementia

PubMed Central

O’Caoimh, Rónán; Gao, Yang; Svendovski, Anton; Gallagher, Paul; Eustace, Joseph; Molloy, D. William

2017-01-01

Background: Although required to improve the usability of cognitive screening instruments (CSIs), the use of cut-off scores is controversial yet poorly researched. Objective: To explore cut-off scores for two short CSIs: the Standardized Mini-Mental State Examination (SMMSE) and Quick Mild Cognitive Impairment (Qmci) screen, describing adjustments in scores for diagnosis (MCI or dementia), age (≤, >75 years), and education (<, ≥12 years), comparing two methods: the maximal accuracy approach, derived from receiver operating characteristic curves, and Youden’s Index. Methods: Pooled analysis of assessments from patients attending memory clinics in Canada between 1999–2010 : 766 with mild cognitive impairment (MCI) and 1,746 with dementia, and 875 normal controls. Results: The Qmci was more accurate than the SMMSE in differentiating controls from MCI or cognitive impairment (MCI and dementia). Employing the maximal accuracy approach, the optimal SMMSE cut-off for cognitive impairment was <28/30 (AUC 0.86, sensitivity 74%, specificity 88%) versus <63/100 for the Qmci (AUC 0.93, sensitivity 85%, specificity 85%). Using Youden’s Index, the optimal SMMSE cut-off remained <28/30 but fell slightly to <62/100 for the Qmci (sensitivity 83%, specificity 87%). The optimal cut-off for MCI was <29/30 for the SMMSE and <67/100 for the Qmci, irrespective of technique. The maximal accuracy approach generally produced higher Qmci cut-offs than Youden’s Index, both requiring adjustment for age and education. There were no clinically meaningful differences in SMMSE cut-off scores by age and education or method employed. Conclusion: Caution should be exercised selecting cut-offs as these differ by age, education, and method of derivation, with the extent of adjustment varying between CSIs. PMID:28222528

Accuracy of microscopic urine analysis and chest radiography in patients with severe sepsis and septic shock.

PubMed

Capp, Roberta; Chang, Yuchiao; Brown, David F M

2012-01-01

Diagnosis of source of infection in patients with septic shock and severe sepsis needs to be done rapidly and accurately to guide appropriate antibiotic therapy. The purpose of this study is to evaluate the accuracy of two diagnostic studies used in the emergency department (ED) to guide diagnosis of source of infection in this patient population. This was a retrospective review of ED patients admitted to an intensive care unit with the diagnosis of severe sepsis or septic shock over a 12-month period. We evaluated accuracy of initial microscopic urine analysis testing and chest radiography in the diagnosis of urinary tract infections and pneumonia, respectively. Of the 1400 patients admitted to intensive care units, 170 patients met criteria for severe sepsis and septic shock. There were a total of 47 patients diagnosed with urinary tract infection, and their initial microscopic urine analysis with counts>10 white blood cells were 80% sensitive (95% confidence interval [CI] .66-.90) and 66% specific (95% CI .52-.77) for the positive final urine culture result. There were 85 patients with final diagnosis of pneumonia. The sensitivity and specificity of initial chest radiography were, respectively, 58% (95% CI .46-.68) and 91% (95% CI .81-.95) for the diagnosis of pneumonia. In patients with severe sepsis and septic shock, the chest radiograph has low sensitivity of 58%, whereas urine analysis has a low specificity of 66%. Given the importance of appropriate antibiotic selection and optimal but not perfect test characteristics, this population may benefit from broad-spectrum antibiotics, rather than antibiotics tailored toward a particular source of infection. Published by Elsevier Inc.

Guided wave mode selection for inhomogeneous elastic waveguides using frequency domain finite element approach.

PubMed

Chillara, Vamshi Krishna; Ren, Baiyang; Lissenden, Cliff J

2016-04-01

This article describes the use of the frequency domain finite element (FDFE) technique for guided wave mode selection in inhomogeneous waveguides. Problems with Rayleigh-Lamb and Shear-Horizontal mode excitation in isotropic homogeneous plates are first studied to demonstrate the application of the approach. Then, two specific cases of inhomogeneous waveguides are studied using FDFE. Finally, an example of guided wave mode selection for inspecting disbonds in composites is presented. Identification of sensitive and insensitive modes for defect inspection is demonstrated. As the discretization parameters affect the accuracy of the results obtained from FDFE, effect of spatial discretization and the length of the domain used for the spatial fast Fourier transform are studied. Some recommendations with regard to the choice of the above parameters are provided. Copyright © 2015 Elsevier B.V. All rights reserved.

Staging of colorectal liver metastases after preoperative chemotherapy. Diffusion-weighted imaging in combination with Gd-EOB-DTPA MRI sequences increases sensitivity and diagnostic accuracy.

PubMed

Macera, Annalisa; Lario, Chiara; Petracchini, Massimo; Gallo, Teresa; Regge, Daniele; Floriani, Irene; Ribero, Dario; Capussotti, Lorenzo; Cirillo, Stefano

2013-03-01

To compare the diagnostic accuracy and sensitivity of Gd-EOB-DTPA MRI and diffusion-weighted (DWI) imaging alone and in combination for detecting colorectal liver metastases in patients who had undergone preoperative chemotherapy. Thirty-two consecutive patients with a total of 166 liver lesions were retrospectively enrolled. Of the lesions, 144 (86.8 %) were metastatic at pathology. Three image sets (1, Gd-EOB-DTPA; 2, DWI; 3, combined Gd-EOB-DTPA and DWI) were independently reviewed by two observers. Statistical analysis was performed on a per-lesion basis. Evaluation of image set 1 correctly identified 127/166 lesions (accuracy 76.5 %; 95 % CI 69.3-82.7) and 106/144 metastases (sensitivity 73.6 %, 95 % CI 65.6-80.6). Evaluation of image set 2 correctly identified 108/166 (accuracy 65.1 %, 95 % CI 57.3-72.3) and 87/144 metastases (sensitivity of 60.4 %, 95 % CI 51.9-68.5). Evaluation of image set 3 correctly identified 148/166 (accuracy 89.2 %, 95 % CI 83.4-93.4) and 131/144 metastases (sensitivity 91 %, 95 % CI 85.1-95.1). Differences were statistically significant (P < 0.001). Notably, similar results were obtained analysing only small lesions (<1 cm). The combination of DWI with Gd-EOB-DTPA-enhanced MRI imaging significantly increases the diagnostic accuracy and sensitivity in patients with colorectal liver metastases treated with preoperative chemotherapy, and it is particularly effective in the detection of small lesions.

Estimating a test's accuracy using tailored meta-analysis-How setting-specific data may aid study selection.

PubMed

Willis, Brian H; Hyde, Christopher J

2014-05-01

To determine a plausible estimate for a test's performance in a specific setting using a new method for selecting studies. It is shown how routine data from practice may be used to define an "applicable region" for studies in receiver operating characteristic space. After qualitative appraisal, studies are selected based on the probability that their study accuracy estimates arose from parameters lying in this applicable region. Three methods for calculating these probabilities are developed and used to tailor the selection of studies for meta-analysis. The Pap test applied to the UK National Health Service (NHS) Cervical Screening Programme provides a case example. The meta-analysis for the Pap test included 68 studies, but at most 17 studies were considered applicable to the NHS. For conventional meta-analysis, the sensitivity and specificity (with 95% confidence intervals) were estimated to be 72.8% (65.8, 78.8) and 75.4% (68.1, 81.5) compared with 50.9% (35.8, 66.0) and 98.0% (95.4, 99.1) from tailored meta-analysis using a binomial method for selection. Thus, for a cervical intraepithelial neoplasia (CIN) 1 prevalence of 2.2%, the post-test probability for CIN 1 would increase from 6.2% to 36.6% between the two methods of meta-analysis. Tailored meta-analysis provides a method for augmenting study selection based on the study's applicability to a setting. As such, the summary estimate is more likely to be plausible for a setting and could improve diagnostic prediction in practice. Copyright © 2014 Elsevier Inc. All rights reserved.

Green material selection for sustainability: A hybrid MCDM approach.

PubMed

Zhang, Honghao; Peng, Yong; Tian, Guangdong; Wang, Danqi; Xie, Pengpeng

2017-01-01

Green material selection is a crucial step for the material industry to comprehensively improve material properties and promote sustainable development. However, because of the subjectivity and conflicting evaluation criteria in its process, green material selection, as a multi-criteria decision making (MCDM) problem, has been a widespread concern to the relevant experts. Thus, this study proposes a hybrid MCDM approach that combines decision making and evaluation laboratory (DEMATEL), analytical network process (ANP), grey relational analysis (GRA) and technique for order performance by similarity to ideal solution (TOPSIS) to select the optimal green material for sustainability based on the product's needs. A nonlinear programming model with constraints was proposed to obtain the integrated closeness index. Subsequently, an empirical application of rubbish bins was used to illustrate the proposed method. In addition, a sensitivity analysis and a comparison with existing methods were employed to validate the accuracy and stability of the obtained final results. We found that this method provides a more accurate and effective decision support tool for alternative evaluation or strategy selection.

Green material selection for sustainability: A hybrid MCDM approach

PubMed Central

Zhang, Honghao; Peng, Yong; Tian, Guangdong; Wang, Danqi; Xie, Pengpeng

2017-01-01

Green material selection is a crucial step for the material industry to comprehensively improve material properties and promote sustainable development. However, because of the subjectivity and conflicting evaluation criteria in its process, green material selection, as a multi-criteria decision making (MCDM) problem, has been a widespread concern to the relevant experts. Thus, this study proposes a hybrid MCDM approach that combines decision making and evaluation laboratory (DEMATEL), analytical network process (ANP), grey relational analysis (GRA) and technique for order performance by similarity to ideal solution (TOPSIS) to select the optimal green material for sustainability based on the product's needs. A nonlinear programming model with constraints was proposed to obtain the integrated closeness index. Subsequently, an empirical application of rubbish bins was used to illustrate the proposed method. In addition, a sensitivity analysis and a comparison with existing methods were employed to validate the accuracy and stability of the obtained final results. We found that this method provides a more accurate and effective decision support tool for alternative evaluation or strategy selection. PMID:28498864

Conclusions about children's reporting accuracy for energy and macronutrients over multiple interviews depend on the analytic approach for comparing reported information to reference information.

PubMed

Baxter, Suzanne Domel; Smith, Albert F; Hardin, James W; Nichols, Michele D

2007-04-01

Validation study data are used to illustrate that conclusions about children's reporting accuracy for energy and macronutrients over multiple interviews (ie, time) depend on the analytic approach for comparing reported and reference information-conventional, which disregards accuracy of reported items and amounts, or reporting-error-sensitive, which classifies reported items as matches (eaten) or intrusions (not eaten), and amounts as corresponding or overreported. Children were observed eating school meals on 1 day (n=12), or 2 (n=13) or 3 (n=79) nonconsecutive days separated by >or=25 days, and interviewed in the morning after each observation day about intake the previous day. Reference (observed) and reported information were transformed to energy and macronutrients (ie, protein, carbohydrate, and fat), and compared. For energy and each macronutrient: report rates (reported/reference), correspondence rates (genuine accuracy measures), and inflation ratios (error measures). Mixed-model analyses. Using the conventional approach for analyzing energy and macronutrients, report rates did not vary systematically over interviews (all four P values >0.61). Using the reporting-error-sensitive approach for analyzing energy and macronutrients, correspondence rates increased over interviews (all four P values <0.04), indicating that reporting accuracy improved over time; inflation ratios decreased, although not significantly, over interviews, also suggesting that reporting accuracy improved over time. Correspondence rates were lower than report rates, indicating that reporting accuracy was worse than implied by conventional measures. When analyzed using the reporting-error-sensitive approach, children's dietary reporting accuracy for energy and macronutrients improved over time, but the conventional approach masked improvements and overestimated accuracy. The reporting-error-sensitive approach is recommended when analyzing data from validation studies of dietary reporting accuracy for energy and macronutrients.

Conclusions about children’s reporting accuracy for energy and macronutrients over multiple interviews depend on the analytic approach for comparing reported information to reference information

PubMed Central

Baxter, Suzanne Domel; Smith, Albert F.; Hardin, James W.; Nichols, Michele D.

2008-01-01

Objective Validation-study data are used to illustrate that conclusions about children’s reporting accuracy for energy and macronutrients over multiple interviews (ie, time) depend on the analytic approach for comparing reported and reference information—conventional, which disregards accuracy of reported items and amounts, or reporting-error-sensitive, which classifies reported items as matches (eaten) or intrusions (not eaten), and amounts as corresponding or overreported. Subjects and design Children were observed eating school meals on one day (n = 12), or two (n = 13) or three (n = 79) nonconsecutive days separated by ≥25 days, and interviewed in the morning after each observation day about intake the previous day. Reference (observed) and reported information were transformed to energy and macronutrients (protein, carbohydrate, fat), and compared. Main outcome measures For energy and each macronutrient: report rates (reported/reference), correspondence rates (genuine accuracy measures), inflation ratios (error measures). Statistical analyses Mixed-model analyses. Results Using the conventional approach for analyzing energy and macronutrients, report rates did not vary systematically over interviews (Ps > .61). Using the reporting-error-sensitive approach for analyzing energy and macronutrients, correspondence rates increased over interviews (Ps < .04), indicating that reporting accuracy improved over time; inflation ratios decreased, although not significantly, over interviews, also suggesting that reporting accuracy improved over time. Correspondence rates were lower than report rates, indicating that reporting accuracy was worse than implied by conventional measures. Conclusions When analyzed using the reporting-error-sensitive approach, children’s dietary reporting accuracy for energy and macronutrients improved over time, but the conventional approach masked improvements and overestimated accuracy. Applications The reporting-error-sensitive approach is recommended when analyzing data from validation studies of dietary reporting accuracy for energy and macronutrients. PMID:17383265

[Comparative study of protein markers in serum and bronchoalveolar lavage fluid from patients with lung cancer by surface-enhanced laser desorption ionization time of flight mass spectrometry].

PubMed

Zhou, Ji-hong; Liu, Guang-nan; Huang, Si-ming; Zhong, Xiao-ning; Su, Hong; Zhou, Yi

2011-04-01

To detect the protein markers in serum and bronchoalveolar lavage fluid (BALF) of the patients with lung cancer by surface-enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF-MS) technology, and to explore if they can be used as markers for the diagnosis of lung cancer. SELDI-TOF-MS technology and protein chips weak cation exchange (WCX-2 chip) were used to detect the protein mass spectrum in serum and BALF of 35 patients with lung cancer and 18 cases of benign pulmonary diseases. The different protein markers were analyzed by Biomarker Pattern Software and the initial diagnosis models were set up. The diagnosis models were verified further by blind screen to confirm the efficacy of diagnosis. Five protein peaks in the sera of the patients with lung cancer were significantly higher (P < 0.05). The protein peak with a mass/charge ratio (M/Z) of 5639 was selected to establish the classification tree model. The sensitivity of diagnosis was 80% (28/35) and the specificity was 78% (14/18). The results verified by blind screen showed a sensitivity of 85% (17/20), a specificity of 90% (9/10), a crude accuracy (CA) of 87% (26/30) and Youden's index (γ) of 0.7. Eight protein peaks in the BALF of the patients with lung cancer were significantly higher (P < 0.05). The different protein peaks with M/Z of 7976 and 11 809 respectively were selected to establish the classification tree model. The sensitivity of diagnosis was 86% (30/35) and the specificity was 72% (13/18). The results verified by blind screen showed a sensitivity of 90% (18/20), a specificity of 90% (9/10), a CA of 90% (27/30) and γ of 0.8. There was a complementary role in combination of differential proteins in serum and BALF and the sensitivity, specificity and accuracy of diagnosis for lung cancer were 100% by parallel test. The SELDI-TOF-MS technology can screen out the differential protein markers in serum and BALF of the patients with lung cancer, which show high sensitivity and specificity as tumor markers. The differential proteins in the BALF may be more promising for clinical application.

Classification of focal liver lesions on ultrasound images by extracting hybrid textural features and using an artificial neural network.

PubMed

Hwang, Yoo Na; Lee, Ju Hwan; Kim, Ga Young; Jiang, Yuan Yuan; Kim, Sung Min

2015-01-01

This paper focuses on the improvement of the diagnostic accuracy of focal liver lesions by quantifying the key features of cysts, hemangiomas, and malignant lesions on ultrasound images. The focal liver lesions were divided into 29 cysts, 37 hemangiomas, and 33 malignancies. A total of 42 hybrid textural features that composed of 5 first order statistics, 18 gray level co-occurrence matrices, 18 Law's, and echogenicity were extracted. A total of 29 key features that were selected by principal component analysis were used as a set of inputs for a feed-forward neural network. For each lesion, the performance of the diagnosis was evaluated by using the positive predictive value, negative predictive value, sensitivity, specificity, and accuracy. The results of the experiment indicate that the proposed method exhibits great performance, a high diagnosis accuracy of over 96% among all focal liver lesion groups (cyst vs. hemangioma, cyst vs. malignant, and hemangioma vs. malignant) on ultrasound images. The accuracy was slightly increased when echogenicity was included in the optimal feature set. These results indicate that it is possible for the proposed method to be applied clinically.

Not all ultrasounds are created equal: general sonography versus musculoskeletal sonography in the detection of rotator cuff tears

PubMed Central

Cole, Brandi; Twibill, Kristen; Lam, Patrick; Hackett, Lisa

2016-01-01

Background This cross-sectional analytic diagnostic accuracy study was designed to compare the accuracy of ultrasound performed by general sonographers in local radiology practices with ultrasound performed by an experienced musculoskeletal sonographer for the detection of rotator cuff tears. Methods In total, 238 patients undergoing arthroscopy who had previously had an ultrasound performed by both a general sonographer and a specialist musculoskeletal sonographer made up the study cohort. Accuracy of diagnosis was compared with the findings at arthroscopy. Results When analyzed as all tears versus no tears, musculoskeletal sonography had an accuracy of 97%, a sensitivity of 97% and a specificity of 95%, whereas general sonography had an accuracy of 91%, a sensitivity of 91% and a specificity of 86%. When the partial tears were split with those ≥ 50% thickness in the tear group and those < 50% thickness in the no-tear group, musculoskeletal sonography had an accuracy of 97%, a sensitivity of 97% and a specificity of 100% and general sonography had an accuracy of 85%, a sensitivity of 84% and a specificity of 87%. Conclusions Ultrasound in the hands of an experienced musculoskeletal sonographer is highly accurate for the diagnosis of rotator cuff tears. General sonography has improved subsequent to earlier studies but remains inferior to an ultrasound performed by a musculoskeletal sonographer. PMID:27660657

Predicting distant failure in early stage NSCLC treated with SBRT using clinical parameters.

PubMed

Zhou, Zhiguo; Folkert, Michael; Cannon, Nathan; Iyengar, Puneeth; Westover, Kenneth; Zhang, Yuanyuan; Choy, Hak; Timmerman, Robert; Yan, Jingsheng; Xie, Xian-J; Jiang, Steve; Wang, Jing

2016-06-01

The aim of this study is to predict early distant failure in early stage non-small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy (SBRT) using clinical parameters by machine learning algorithms. The dataset used in this work includes 81 early stage NSCLC patients with at least 6months of follow-up who underwent SBRT between 2006 and 2012 at a single institution. The clinical parameters (n=18) for each patient include demographic parameters, tumor characteristics, treatment fraction schemes, and pretreatment medications. Three predictive models were constructed based on different machine learning algorithms: (1) artificial neural network (ANN), (2) logistic regression (LR) and (3) support vector machine (SVM). Furthermore, to select an optimal clinical parameter set for the model construction, three strategies were adopted: (1) clonal selection algorithm (CSA) based selection strategy; (2) sequential forward selection (SFS) method; and (3) statistical analysis (SA) based strategy. 5-cross-validation is used to validate the performance of each predictive model. The accuracy was assessed by area under the receiver operating characteristic (ROC) curve (AUC), sensitivity and specificity of the system was also evaluated. The AUCs for ANN, LR and SVM were 0.75, 0.73, and 0.80, respectively. The sensitivity values for ANN, LR and SVM were 71.2%, 72.9% and 83.1%, while the specificity values for ANN, LR and SVM were 59.1%, 63.6% and 63.6%, respectively. Meanwhile, the CSA based strategy outperformed SFS and SA in terms of AUC, sensitivity and specificity. Based on clinical parameters, the SVM with the CSA optimal parameter set selection strategy achieves better performance than other strategies for predicting distant failure in lung SBRT patients. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Spectrophotometric studies of reactions between pseudo-ephedrine with different inorganic and organic reagents and its micro-determination in pure and in pharmaceutical preparations.

PubMed

Zayed, M A; El-Rasheedy, El-Gazy A

2012-03-01

Two simple, sensitive, cheep and reliable spectrophotometric methods are suggested for micro-determination of pseudoephedrine in its pure form and in pharmaceutical preparation (Sinofree Tablets). The first one depends on the drug reaction with inorganic sensitive reagent like molybdate anion in aqueous media via formation of ion-pair mechanism. The second one depends on the drug reaction with π-acceptor reagent like DDQ in non-aqueous media via formation of charge transfer complex. These reactions were studied under various conditions and the optimum parameters were selected. Under proper conditions the suggested procedures were successfully applied for micro-determination of pseudoephedrine in pure and in Sinofree Tablets without interference from excepients. The values of SD, RSD, recovery %, LOD, LOQ and Sandell sensitivity refer to the high accuracy and precession of the applied procedures. The results obtained were compared with the data obtained by an official method, referring to confidence and agreement with DDQ procedure results; but it referred to the more accuracy of the molybdate data. Therefore, the suggested procedures are now successfully being applied in routine analysis of this drug in its pharmaceutical formulation (Sinofree) in Saudi Arabian Pharmaceutical Company (SPIMACO) in Boridah El-Qaseem, Saudi Arabia instead of imported kits had been previously used. Copyright © 2011 Elsevier B.V. All rights reserved.

Comparison of a Commercial Accelerometer with Polysomnography and Actigraphy in Children and Adolescents

PubMed Central

Meltzer, Lisa J.; Hiruma, Laura S.; Avis, Kristin; Montgomery-Downs, Hawley; Valentin, Judith

2015-01-01

Study Objectives: To evaluate the reliability and validity of the commercially available Fitbit Ultra (2012) accelerometer compared to polysomnography (PSG) and two different actigraphs in a pediatric sample. Design and Setting: All subjects wore the Fitbit Ultra while undergoing overnight clinical polysomnography in a sleep laboratory; a randomly selected subset of participants also wore either the Ambulatory Monitoring Inc. Motionlogger Sleep Watch (AMI) or Phillips-Respironics Mini-Mitter Spectrum (PRMM). Participants: 63 youth (32 females, 31 males), ages 3–17 years (mean 9.7 years, SD 4.6 years). Measurements: Both “Normal” and “Sensitive” sleep-recording Fitbit Ultra modes were examined. Outcome variables included total sleep time (TST), wake after sleep onset (WASO), and sleep efficiency (SE). Primary analyses examined the differences between Fitbit Ultra and PSG using repeated-measures ANCOVA, with epoch-by-epoch comparisons between Fitbit Ultra and PSG used to determine sensitivity, specificity, and accuracy. Intra-device reliability, differences between Fitbit Ultra and actigraphy, and differences by both developmental age group and sleep disordered breathing (SDB) status were also examined. Results: Compared to PSG, the Normal Fitbit Ultra mode demonstrated good sensitivity (0.86) and accuracy (0.84), but poor specificity (0.52); conversely, the Sensitive Fitbit Ultra mode demonstrated adequate specificity (0.79), but inadequate sensitivity (0.70) and accuracy (0.71). Compared to PSG, the Fitbit Ultra significantly overestimated TST (41 min) and SE (8%) in Normal mode, and underestimated TST (105 min) and SE (21%) in Sensitive mode. Similar differences were found between Fitbit Ultra (both modes) and both brands of actigraphs. Conclusions: Despite its low cost and ease of use for consumers, neither sleep-recording mode of the Fitbit Ultra accelerometer provided clinically comparable results to PSG. Further, pediatric sleep researchers and clinicians should be cautious about substituting these devices for validated actigraphs, with a significant risk of either overestimating or underestimating outcome data including total sleep time and sleep efficiency. Citation: Meltzer LJ, Hiruma LS, Avis K, Montgomery-Downs H, Valentin J. Comparison of a commercial accelerometer with polysomnography and actigraphy in children and adolescents. SLEEP 2015;38(8):1323–1330. PMID:26118555

A Feature Selection Algorithm to Compute Gene Centric Methylation from Probe Level Methylation Data.

PubMed

Baur, Brittany; Bozdag, Serdar

2016-01-01

DNA methylation is an important epigenetic event that effects gene expression during development and various diseases such as cancer. Understanding the mechanism of action of DNA methylation is important for downstream analysis. In the Illumina Infinium HumanMethylation 450K array, there are tens of probes associated with each gene. Given methylation intensities of all these probes, it is necessary to compute which of these probes are most representative of the gene centric methylation level. In this study, we developed a feature selection algorithm based on sequential forward selection that utilized different classification methods to compute gene centric DNA methylation using probe level DNA methylation data. We compared our algorithm to other feature selection algorithms such as support vector machines with recursive feature elimination, genetic algorithms and ReliefF. We evaluated all methods based on the predictive power of selected probes on their mRNA expression levels and found that a K-Nearest Neighbors classification using the sequential forward selection algorithm performed better than other algorithms based on all metrics. We also observed that transcriptional activities of certain genes were more sensitive to DNA methylation changes than transcriptional activities of other genes. Our algorithm was able to predict the expression of those genes with high accuracy using only DNA methylation data. Our results also showed that those DNA methylation-sensitive genes were enriched in Gene Ontology terms related to the regulation of various biological processes.

Methods for analysis of selected metals in water by atomic absorption

USGS Publications Warehouse

Fishman, Marvin J.; Downs, Sanford C.

1966-01-01

This manual describes atomic-absorption-spectroscopy methods for determining calcium, copper, lithium, magnesium, manganese, potassium, sodium, strontium and zinc in atmospheric precipitation, fresh waters, and brines. The procedures are intended to be used by water quality laboratories of the Water Resources Division of the U.S. Geological Survey. Detailed procedures, calculations, and methods for the preparation of reagents are given for each element along with data on accuracy, precision, and sensitivity. Other topics discussed briefly are the principle of atomic absorption, instrumentation used, and special analytical techniques.

Active damping of modal vibrations by force apportioning

NASA Technical Reports Server (NTRS)

Hallauer, W. L., Jr.

1980-01-01

Force apportioning, a method of active structural damping based on that used in modal vibration testing of isolating modes by multiple shaker excitation, was analyzed and numerically simulated. A distribution of as few forces as possible on the structure is chosen so as to maximally affect selected vibration modes while minimally exciting all other modes. The accuracy of numerical simulations of active damping, active damping of higher-frequency modes, and studies of imperfection sensitivity are discussed. The computer programs developed are described and possible refinements of the research are examined.

Gold nanospikes based microsensor as a highly accurate mercury emission monitoring system

PubMed Central

Sabri, Ylias M.; Ippolito, Samuel J.; Tardio, James; Bansal, Vipul; O'Mullane, Anthony P.; Bhargava, Suresh K.

2014-01-01

Anthropogenic elemental mercury (Hg0) emission is a serious worldwide environmental problem due to the extreme toxicity of the heavy metal to humans, plants and wildlife. Development of an accurate and cheap microsensor based online monitoring system which can be integrated as part of Hg0 removal and control processes in industry is still a major challenge. Here, we demonstrate that forming Au nanospike structures directly onto the electrodes of a quartz crystal microbalance (QCM) using a novel electrochemical route results in a self-regenerating, highly robust, stable, sensitive and selective Hg0 vapor sensor. The data from a 127 day continuous test performed in the presence of volatile organic compounds and high humidity levels, showed that the sensor with an electrodeposted sensitive layer had 260% higher response magnitude, 3.4 times lower detection limit (~22 μg/m3 or ~2.46 ppbv) and higher accuracy (98% Vs 35%) over a Au control based QCM (unmodified) when exposed to a Hg0 vapor concentration of 10.55 mg/m3 at 101°C. Statistical analysis of the long term data showed that the nano-engineered Hg0 sorption sites on the developed Au nanospikes sensitive layer play a critical role in the enhanced sensitivity and selectivity of the developed sensor towards Hg0 vapor. PMID:25338965

The Usefulness of Selected Physicochemical Indices, Cell Membrane Integrity and Sperm Chromatin Structure in Assessments of Boar Semen Sensitivity

PubMed Central

Wysokińska, A.; Kondracki, S.; Iwanina, M.

2015-01-01

The present work describes experiments undertaken to evaluate the usefulness of selected physicochemical indices of semen, cell membrane integrity and sperm chromatin structure for the assessment of boar semen sensitivity to processes connected with pre-insemination procedures. The experiments were carried out on 30 boars: including 15 regarded as providers of sensitive semen and 15 regarded as providers of semen that is little sensitive to laboratory processing. The selection of boars for both groups was based on sperm morphology analyses, assuming secondary morphological change incidence in spermatozoa as the criterion. Two ejaculates were manually collected from each boar at an interval of 3 to 4 months. The following analyses were carried out for each ejaculate: sperm motility assessment, sperm pH measurement, sperm morphology assessment, sperm chromatin structure evaluation and cell membrane integrity assessment. The analyses were performed three times. Semen storage did not cause an increase in the incidence of secondary morphological changes in the group of boars considered to provide sperm of low sensitivity. On the other hand, with continued storage there was a marked increase in the incidence of spermatozoa with secondary morphological changes in the group of boars regarded as producing more sensitive semen. Ejaculates of group I boars evaluated directly after collection had an approximately 6% smaller share of spermatozoa with undamaged cell membranes than the ejaculates of boars in group II (p≤0.05). In the process of time the percentage of spermatozoa with undamaged cell membranes decreased. The sperm of group I boars was characterised with a lower sperm motility than the semen of group II boars. After 1 hour of storing diluted semen, the sperm motility of boars producing highly sensitive semen was already 4% lower (p≤0.05), and after 24 hours of storage it was 6.33% lower than that of the boars that produced semen with a low sensitivity. Factors that confirm the accuracy of insemination male selection can include a low rate of sperm motility decrease during the storage of diluted semen, low and contained incidence of secondary morphological changes in spermatozoa during semen storage and a high frequency of spermatozoa with undamaged cell membranes. PMID:26580438

Evaluating the diagnostic accuracy of Arabic SNAP test for children with hypernasality.

PubMed

Abou-Elsaad, Tamer; Afsah, Omayma; Baz, Hemmat; Mansy, Alzahraa

2016-06-01

Nasometry is a method of measuring the acoustic correlates of resonance through a computer-based instrument called nasometer. High nasalance scores in comparison to normative data suggest hypernasality and/or other nasality disorders, while low scores suggest hyponasality. Normative values of nasalance for Egyptian Arabic speakers were established using the Arabic SNAP (Simplified Nasometric Assessment Procedures) test. to evaluate the diagnostic accuracy of Arabic SNAP test to allow for its use in the differentiation between normal and hypernasal speech in Egyptian Arabic-speaking children. Nasalance scores of normal children (n=92) on Arabic SNAP test were compared to those of 30 children with velopharyngeal insufficiency due to cleft palate. Receiver operating characteristic (ROC) curve was used to determine cutoff points with the highest sensitivity and specificity. Statistically significant differences were found between both groups for all items in nasometric evaluation (p<0.05) except for prolonged/m/sound (p>0.05). Cutoff points were determined and certain items were selected for routine nasometric evaluation. The Arabic SNAP test is a sensitive and specific tool for evaluation of children with hypernasality and can be used for both diagnosis and follow up of these cases. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

The Principle of the Micro-Electronic Neural Bridge and a Prototype System Design.

PubMed

Huang, Zong-Hao; Wang, Zhi-Gong; Lu, Xiao-Ying; Li, Wen-Yuan; Zhou, Yu-Xuan; Shen, Xiao-Yan; Zhao, Xin-Tai

2016-01-01

The micro-electronic neural bridge (MENB) aims to rebuild lost motor function of paralyzed humans by routing movement-related signals from the brain, around the damage part in the spinal cord, to the external effectors. This study focused on the prototype system design of the MENB, including the principle of the MENB, the neural signal detecting circuit and the functional electrical stimulation (FES) circuit design, and the spike detecting and sorting algorithm. In this study, we developed a novel improved amplitude threshold spike detecting method based on variable forward difference threshold for both training and bridging phase. The discrete wavelet transform (DWT), a new level feature coefficient selection method based on Lilliefors test, and the k-means clustering method based on Mahalanobis distance were used for spike sorting. A real-time online spike detecting and sorting algorithm based on DWT and Euclidean distance was also implemented for the bridging phase. Tested by the data sets available at Caltech, in the training phase, the average sensitivity, specificity, and clustering accuracies are 99.43%, 97.83%, and 95.45%, respectively. Validated by the three-fold cross-validation method, the average sensitivity, specificity, and classification accuracy are 99.43%, 97.70%, and 96.46%, respectively.

Spatial and temporal processing in healthy aging: implications for perceptions of driving skills.

PubMed

Conlon, Elizabeth; Herkes, Kathleen

2008-07-01

Sensitivity to the attributes of a stimulus (form or motion) and accuracy when detecting rapidly presented stimulus information were measured in older (N = 36) and younger (N = 37) groups. Before and after practice, the older group was significantly less sensitive to global motion (but not to form) and less accurate on a rapid sequencing task when detecting the individual elements presented in long but not short sequences. These effect sizes produced power for the different analyses that ranged between 0.5 and 1.00. The reduced sensitivity found among older individuals to temporal but not spatial stimuli, adds support to previous findings of a selective age-related deficit in temporal processing. Older women were significantly less sensitive than older men, younger men and younger women on the global motion task. Gender effects were evident when, in response to global motion stimuli, complex extraction and integration processes needed to be undertaken rapidly. Significant moderate correlations were found between age, global motion sensitivity and reports of perceptions of other vehicles and road signs when driving. These associations suggest that reduced motion sensitivity may produce functional difficulties for the older adults when judging speeds or estimating gaps in traffic while driving.

MultiMiTar: a novel multi objective optimization based miRNA-target prediction method.

PubMed

Mitra, Ramkrishna; Bandyopadhyay, Sanghamitra

2011-01-01

Machine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction accuracy in terms of both sensitivity and specificity due to lack of the gold standard of negative examples, miRNA-targeting site context specific relevant features and efficient feature selection process. Moreover, all the sequence, structure and machine learning based algorithms are unable to distribute the true positive predictions preferentially at the top of the ranked list; hence the algorithms become unreliable to the biologists. In addition, these algorithms fail to obtain considerable combination of precision and recall for the target transcripts that are translationally repressed at protein level. In the proposed article, we introduce an efficient miRNA-target prediction system MultiMiTar, a Support Vector Machine (SVM) based classifier integrated with a multiobjective metaheuristic based feature selection technique. The robust performance of the proposed method is mainly the result of using high quality negative examples and selection of biologically relevant miRNA-targeting site context specific features. The features are selected by using a novel feature selection technique AMOSA-SVM, that integrates the multi objective optimization technique Archived Multi-Objective Simulated Annealing (AMOSA) and SVM. MultiMiTar is found to achieve much higher Matthew's correlation coefficient (MCC) of 0.583 and average class-wise accuracy (ACA) of 0.8 compared to the others target prediction methods for a completely independent test data set. The obtained MCC and ACA values of these algorithms range from -0.269 to 0.155 and 0.321 to 0.582, respectively. Moreover, it shows a more balanced result in terms of precision and sensitivity (recall) for the translationally repressed data set as compared to all the other existing methods. An important aspect is that the true positive predictions are distributed preferentially at the top of the ranked list that makes MultiMiTar reliable for the biologists. MultiMiTar is now available as an online tool at www.isical.ac.in/~bioinfo_miu/multimitar.htm. MultiMiTar software can be downloaded from www.isical.ac.in/~bioinfo_miu/multimitar-download.htm.

Comparison of Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia.

PubMed

Tohka, Jussi; Moradi, Elaheh; Huttunen, Heikki

2016-07-01

We present a comparative split-half resampling analysis of various data driven feature selection and classification methods for the whole brain voxel-based classification analysis of anatomical magnetic resonance images. We compared support vector machines (SVMs), with or without filter based feature selection, several embedded feature selection methods and stability selection. While comparisons of the accuracy of various classification methods have been reported previously, the variability of the out-of-training sample classification accuracy and the set of selected features due to independent training and test sets have not been previously addressed in a brain imaging context. We studied two classification problems: 1) Alzheimer's disease (AD) vs. normal control (NC) and 2) mild cognitive impairment (MCI) vs. NC classification. In AD vs. NC classification, the variability in the test accuracy due to the subject sample did not vary between different methods and exceeded the variability due to different classifiers. In MCI vs. NC classification, particularly with a large training set, embedded feature selection methods outperformed SVM-based ones with the difference in the test accuracy exceeding the test accuracy variability due to the subject sample. The filter and embedded methods produced divergent feature patterns for MCI vs. NC classification that suggests the utility of the embedded feature selection for this problem when linked with the good generalization performance. The stability of the feature sets was strongly correlated with the number of features selected, weakly correlated with the stability of classification accuracy, and uncorrelated with the average classification accuracy.

Accuracy of fluorodeoxyglucose-PET imaging for differentiating benign from malignant pleural effusions: a meta-analysis.

PubMed

Porcel, José M; Hernández, Paula; Martínez-Alonso, Montserrat; Bielsa, Silvia; Salud, Antonieta

2015-02-01

The role of fluorodeoxyglucose (FDG)-PET imaging for diagnosing malignant pleural effusions is not well defined. The aim of this study was to summarize the evidence for its use in ruling in or out the malignant origin of a pleural effusion or thickening. A meta-analysis was conducted of diagnostic accuracy studies published in the Cochrane Library, PubMed, and Embase (inception to June 2013) without language restrictions. Two investigators selected studies that had evaluated the performance of FDG-PET imaging in patients with pleural effusions or thickening, using pleural cytopathology or histopathology as the reference standard for malignancy. Subgroup analyses were conducted according to FDG-PET imaging interpretation (qualitative or semiquantitative), PET imaging equipment (PET vs integrated PET-CT imaging), and/or target population (known lung cancer or malignant pleural mesothelioma). Study quality was assessed using Quality Assessment of Diagnostic Accuracy Studies-2. We used a bivariate random-effects model for the analysis and pooling of diagnostic performance measures across studies. Fourteen non-high risk of bias studies, comprising 407 patients with malignant and 232 with benign pleural conditions, met the inclusion criteria. Semiquantitative PET imaging readings had a significantly lower sensitivity for diagnosing malignant effusions than visual assessments (82% vs 91%; P = .026). The pooled test characteristics of integrated PET-CT imaging systems using semiquantitative interpretations for identifying malignant effusions were: sensitivity, 81%; specificity, 74%; positive likelihood ratio (LR), 3.22; negative LR, 0.26; and area under the curve, 0.838. Resultant data were heterogeneous, and spectrum bias should be considered when appraising FDG-PET imaging operating characteristics. The moderate accuracy of PET-CT imaging using semiquantitative readings precludes its routine recommendation for discriminating malignant from benign pleural effusions.

Diagnostic accuracy of 18F-FDG-PET and PET/CT in the differential diagnosis between malignant and benign pleural lesions: a systematic review and meta-analysis.

PubMed

Treglia, Giorgio; Sadeghi, Ramin; Annunziata, Salvatore; Lococo, Filippo; Cafarotti, Stefano; Bertagna, Francesco; Prior, John O; Ceriani, Luca; Giovanella, Luca

2014-01-01

To systematically review and meta-analyze published data about the diagnostic accuracy of fluorine-18-fluorodeoxyglucose ((18)F-FDG) positron emission tomography (PET) and PET/computed tomography (CT) in the differential diagnosis between malignant and benign pleural lesions. A comprehensive literature search of studies published through June 2013 regarding the diagnostic performance of (18)F-FDG-PET and PET/CT in the differential diagnosis of pleural lesions was carried out. All retrieved studies were reviewed and qualitatively analyzed. Pooled sensitivity, specificity, positive and negative likelihood ratio (LR+ and LR-) and diagnostic odds ratio (DOR) of (18)F-FDG-PET or PET/CT in the differential diagnosis of pleural lesions on a per-patient-based analysis were calculated. The area under the summary receiver operating characteristic curve (AUC) was calculated to measure the accuracy of these methods. Subanalyses considering device used (PET or PET/CT) were performed. Sixteen studies including 745 patients were included in the systematic review. The meta-analysis of 11 selected studies provided the following results: sensitivity 95% (95% confidence interval [95%CI]: 92-97%), specificity 82% (95%CI: 76-88%), LR+ 5.3 (95%CI: 2.4-11.8), LR- 0.09 (95%CI: 0.05-0.14), DOR 74 (95%CI: 34-161). The AUC was 0.95. No significant improvement of the diagnostic accuracy considering PET/CT studies only was found. (18)F-FDG-PET and PET/CT demonstrated to be accurate diagnostic imaging methods in the differential diagnosis between malignant and benign pleural lesions; nevertheless, possible sources of false-negative and false-positive results should be kept in mind. Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.

Micro-anatomical quantitative optical imaging: toward automated assessment of breast tissues.

PubMed

Dobbs, Jessica L; Mueller, Jenna L; Krishnamurthy, Savitri; Shin, Dongsuk; Kuerer, Henry; Yang, Wei; Ramanujam, Nirmala; Richards-Kortum, Rebecca

2015-08-20

Pathologists currently diagnose breast lesions through histologic assessment, which requires fixation and tissue preparation. The diagnostic criteria used to classify breast lesions are qualitative and subjective, and inter-observer discordance has been shown to be a significant challenge in the diagnosis of selected breast lesions, particularly for borderline proliferative lesions. Thus, there is an opportunity to develop tools to rapidly visualize and quantitatively interpret breast tissue morphology for a variety of clinical applications. Toward this end, we acquired images of freshly excised breast tissue specimens from a total of 34 patients using confocal fluorescence microscopy and proflavine as a topical stain. We developed computerized algorithms to segment and quantify nuclear and ductal parameters that characterize breast architectural features. A total of 33 parameters were evaluated and used as input to develop a decision tree model to classify benign and malignant breast tissue. Benign features were classified in tissue specimens acquired from 30 patients and malignant features were classified in specimens from 22 patients. The decision tree model that achieved the highest accuracy for distinguishing between benign and malignant breast features used the following parameters: standard deviation of inter-nuclear distance and number of duct lumens. The model achieved 81 % sensitivity and 93 % specificity, corresponding to an area under the curve of 0.93 and an overall accuracy of 90 %. The model classified IDC and DCIS with 92 % and 96 % accuracy, respectively. The cross-validated model achieved 75 % sensitivity and 93 % specificity and an overall accuracy of 88 %. These results suggest that proflavine staining and confocal fluorescence microscopy combined with image analysis strategies to segment morphological features could potentially be used to quantitatively diagnose freshly obtained breast tissue at the point of care without the need for tissue preparation.

Diagnostic accuracy, risk assessment, and cost-effectiveness of component-resolved diagnostics for food allergy: A systematic review.

PubMed

Flores Kim, J; McCleary, N; Nwaru, B I; Stoddart, A; Sheikh, A

2018-01-10

Component-resolved diagnostics (CRD) are promising tools for diagnosing food allergy, offering the potential to determine specific phenotypes and to develop patient-tailored risk profiles. Nevertheless, the diagnostic accuracy of these tests varies across studies; thus, their clinical utility remains unclear. Therefore, we synthesized the evidence from studies investigating the diagnostic accuracy, risk assessment ability, and cost-effectiveness of CRD for food allergy. We systematically searched 10 electronic databases and four clinical trial registries for studies published from January 2000 to February 2017. The quality of included studies was assessed using QUADAS-2. Due to heterogeneity, we narratively synthesized the evidence. Eleven studies met inclusion criteria, altogether recruiting 1098 participants. The food allergies investigated were cow's milk, hen's egg, peanut, hazelnut, and shrimp. The components with the highest diagnostic accuracy for each allergen, along with their sensitivity-specificity pairs, were as follows: Bos d 4 for cow's milk (62.0% and 87.5%), Gal d 1 for hen's egg (84.2% and 89.8% for heated egg, and 60.6% and 97.1% for raw egg), Ara h 6 for peanut (94.9% and 95.1%), Cor a 14 for hazelnut (100% and 93.8%), and Lit v 1 for shrimp (82.8% and 56.3%) allergy. Selected components of cow's milk, hen's egg, peanut, hazelnut, and shrimp allergen showed high specificity, but lower sensitivity. However, few studies exist for each component, and studies vary widely regarding the cutoff values used, making it challenging to synthesize findings across studies. Further research is needed to determine clinically appropriate cutoff values, risk assessment abilities, and cost-effectiveness of CRD approaches. © 2018 The Authors. Allergy Published by John Wiley & Sons Ltd.

Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment

PubMed Central

Zhang, Daoqiang; Wang, Yaping; Zhou, Luping; Yuan, Hong; Shen, Dinggang

2011-01-01

Effective and accurate diagnosis of Alzheimer’s disease (AD), as well as its prodromal stage (i.e., mild cognitive impairment (MCI)), has attracted more and more attentions recently. So far, multiple biomarkers have been shown sensitive to the diagnosis of AD and MCI, i.e., structural MR imaging (MRI) for brain atrophy measurement, functional imaging (e.g., FDG-PET) for hypometabolism quantification, and cerebrospinal fluid (CSF) for quantification of specific proteins. However, most existing research focuses on only a single modality of biomarkers for diagnosis of AD and MCI, although recent studies have shown that different biomarkers may provide complementary information for diagnosis of AD and MCI. In this paper, we propose to combine three modalities of biomarkers, i.e., MRI, FDG-PET, and CSF biomarkers, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method. Specifically, ADNI baseline MRI, FDG-PET, and CSF data from 51 AD patients, 99 MCI patients (including 43 MCI converters who had converted to AD within 18 months and 56 MCI non-converters who had not converted to AD within 18 months), and 52 healthy controls are used for development and validation of our proposed multimodal classification method. In particular, for each MR or FDG-PET image, 93 volumetric features are extracted from the 93 regions of interest (ROIs), automatically labeled by an atlas warping algorithm. For CSF biomarkers, their original values are directly used as features. Then, a linear support vector machine (SVM) is adopted to evaluate the classification accuracy, using a 10-fold cross-validation. As a result, for classifying AD from healthy controls, we achieve a classification accuracy of 93.2% (with a sensitivity of 93% and a specificity of 93.3%) when combining all three modalities of biomarkers, and only 86.5% when using even the best individual modality of biomarkers. Similarly, for classifying MCI from healthy controls, we achieve a classification accuracy of 76.4% (with a sensitivity of 81.8% and a specificity of 66%) for our combined method, and only 72% even using the best individual modality of biomarkers. Further analysis on MCI sensitivity of our combined method indicates that 91.5% of MCI converters and 73.4% of MCI non-converters are correctly classified. Moreover, we also evaluate the classification performance when employing a feature selection method to select the most discriminative MR and FDG-PET features. Again, our combined method shows considerably better performance, compared to the case of using an individual modality of biomarkers. PMID:21236349

Global gravity survey by an orbiting gravity gradiometer

NASA Technical Reports Server (NTRS)

Paik, Ho Jung; Leung, Jurn-Sun; Morgan, Samuel H.; Parker, Joseph

1988-01-01

The scientific aims, design, and mission profile of the Superconducting Gravity Gradiometer Mission (SGGM), a NASA spacecraft mission proposed for the late 1990s, are discussed and illustrated with drawings and diagrams. SGGM would complement the two other planned gravimetry missions, GRM and Aristoteles, and would provide gravitational-field measurements with accuracy 2-3 mGal in 55 x 55-km blocks. The principal instruments are a (1) three-axis superconducting gravity gradiometer with intrinsic sensitivity 100 microeotvos/sq rt Hz, (2) a six-axis superconducting accelerometer with sensitivity 100 fg(E)/sq rt Hz linear and 10 prad/sec squared sq rt Hz angular, and (3) a six-axis shaker for active control of the platform. Consideration is given to the error budget and platform requirements, the orbit selection criteria, and the spacecraft design.

Accuracy of genetic code translation and its orthogonal corruption by aminoglycosides and Mg2+ ions.

PubMed

Zhang, Jingji; Pavlov, Michael Y; Ehrenberg, Måns

2018-02-16

We studied the effects of aminoglycosides and changing Mg2+ ion concentration on the accuracy of initial codon selection by aminoacyl-tRNA in ternary complex with elongation factor Tu and GTP (T3) on mRNA programmed ribosomes. Aminoglycosides decrease the accuracy by changing the equilibrium constants of 'monitoring bases' A1492, A1493 and G530 in 16S rRNA in favor of their 'activated' state by large, aminoglycoside-specific factors, which are the same for cognate and near-cognate codons. Increasing Mg2+ concentration decreases the accuracy by slowing dissociation of T3 from its initial codon- and aminoglycoside-independent binding state on the ribosome. The distinct accuracy-corrupting mechanisms for aminoglycosides and Mg2+ ions prompted us to re-interpret previous biochemical experiments and functional implications of existing high resolution ribosome structures. We estimate the upper thermodynamic limit to the accuracy, the 'intrinsic selectivity' of the ribosome. We conclude that aminoglycosides do not alter the intrinsic selectivity but reduce the fraction of it that is expressed as the accuracy of initial selection. We suggest that induced fit increases the accuracy and speed of codon reading at unaltered intrinsic selectivity of the ribosome.

Symbol-string sensitivity and children's reading.

PubMed

Pammer, Kristen; Lavis, Ruth; Hansen, Peter; Cornelissen, Piers L

2004-06-01

In this study of primary school children, a novel 'symbol-string' task is used to assess sensitivity to the position of briefly presented non-alphabetic but letter-like symbols. The results demonstrate that sensitivity in the symbol-string task explains a unique proportion of the variability in children's contextual reading accuracy. Moreover, developmental dyslexic readers show reduced sensitivity in this task, compared to chronological age controls. The results suggest that limitations set by visuo-spatial processes and/or attentional iconic memory resources may constrain children's reading accuracy.

Diagnosis of human malignancies using laser-induced breakdown spectroscopy in combination with chemometric methods

NASA Astrophysics Data System (ADS)

Chen, Xue; Li, Xiaohui; Yu, Xin; Chen, Deying; Liu, Aichun

2018-01-01

Diagnosis of malignancies is a challenging clinical issue. In this work, we present quick and robust diagnosis and discrimination of lymphoma and multiple myeloma (MM) using laser-induced breakdown spectroscopy (LIBS) conducted on human serum samples, in combination with chemometric methods. The serum samples collected from lymphoma and MM cancer patients and healthy controls were deposited on filter papers and ablated with a pulsed 1064 nm Nd:YAG laser. 24 atomic lines of Ca, Na, K, H, O, and N were selected for malignancy diagnosis. Principal component analysis (PCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and k nearest neighbors (kNN) classification were applied to build the malignancy diagnosis and discrimination models. The performances of the models were evaluated using 10-fold cross validation. The discrimination accuracy, confusion matrix and receiver operating characteristic (ROC) curves were obtained. The values of area under the ROC curve (AUC), sensitivity and specificity at the cut-points were determined. The kNN model exhibits the best performances with overall discrimination accuracy of 96.0%. Distinct discrimination between malignancies and healthy controls has been achieved with AUC, sensitivity and specificity for healthy controls all approaching 1. For lymphoma, the best discrimination performance values are AUC = 0.990, sensitivity = 0.970 and specificity = 0.956. For MM, the corresponding values are AUC = 0.986, sensitivity = 0.892 and specificity = 0.994. The results show that the serum-LIBS technique can serve as a quick, less invasive and robust method for diagnosis and discrimination of human malignancies.

Development and evaluation of a novel LAMP assay for the diagnosis of Cutaneous and Visceral Leishmaniasis.

PubMed

Adams, Emily Rebecca; Schoone, Gerard; Versteeg, Inge; Gomez, Maria Adelaida; Diro, Ermias; Mori, Yasuyoshi; Perlee, Desiree; Downing, Tim; Saravia, Nancy; Assaye, Ashenafi; Hailu, Asrat; Albertini, Audrey; Ndung'u, Joseph Mathu; Schallig, Henk

2018-04-25

A novel Pan-Leishmania LAMP assay was developed for diagnosis of Cutaneous and Visceral Leishmaniasis (CL & VL) which can be used in near-patient settings. Primers were designed on the 18S rDNA and the conserved region of minicircle kDNA selected on the basis of high copy number. LAMP assays were evaluated for CL in a prospective cohort trial of 105 patients in South-West Colombia. Lesion swab samples from CL suspects were collected and tested using LAMP and compared to a composite reference of microscopy AND/OR culture to calculate diagnostic accuracy. LAMP assays were tested on 50 VL suspected patients from Ethiopia, including whole blood, peripheral blood mononuclear cells, and buffy coat. Diagnostic accuracy was calculated against a reference standard of microscopy of splenic or bone marrow aspirates. To calculate analytical specificity 100 clinical samples and isolates with fever causing pathogens including malaria, arboviruses and bacterial infections were tested. The LAMP assay had a sensitivity of 95% (95% CI: 87.2% - 98.5 %) and a specificity of 86% (95% CI: 67.3% -95.9 %) for the diagnosis of CL. On VL suspects the sensitivity was 92% (95% CI: 74.9 - 99.1%) and specificity of 100% (95% CI: 85.8-100%) in whole blood. For CL, LAMP is a sensitive tool for diagnosis and requires less equipment, time and expertise than alternative CL diagnostics. For VL, LAMP is sensitive using a minimally invasive sample as compared to the gold standard. The analytical specificity was 100%. Copyright © 2018 Adams et al.

Identification and quantification of VOCs by proton transfer reaction time of flight mass spectrometry: An experimental workflow for the optimization of specificity, sensitivity, and accuracy

PubMed Central

Hanna, George B.

2018-01-01

Abstract Proton transfer reaction time of flight mass spectrometry (PTR‐ToF‐MS) is a direct injection MS technique, allowing for the sensitive and real‐time detection, identification, and quantification of volatile organic compounds. When aiming to employ PTR‐ToF‐MS for targeted volatile organic compound analysis, some methodological questions must be addressed, such as the need to correctly identify product ions, or evaluating the quantitation accuracy. This work proposes a workflow for PTR‐ToF‐MS method development, addressing the main issues affecting the reliable identification and quantification of target compounds. We determined the fragmentation patterns of 13 selected compounds (aldehydes, fatty acids, phenols). Experiments were conducted under breath‐relevant conditions (100% humid air), and within an extended range of reduced electric field values (E/N = 48–144 Td), obtained by changing drift tube voltage. Reactivity was inspected using H3O+, NO+, and O2 + as primary ions. The results show that a relatively low (<90 Td) E/N often permits to reduce fragmentation enhancing sensitivity and identification capabilities, particularly in the case of aldehydes using NO+, where a 4‐fold increase in sensitivity is obtained by means of drift voltage reduction. We developed a novel calibration methodology, relying on diffusion tubes used as gravimetric standards. For each of the tested compounds, it was possible to define suitable conditions whereby experimental error, defined as difference between gravimetric measurements and calculated concentrations, was 8% or lower. PMID:29336521

Meta-epidemiologic study showed frequent time trends in summary estimates from meta-analyses of diagnostic accuracy studies.

PubMed

Cohen, Jérémie F; Korevaar, Daniël A; Wang, Junfeng; Leeflang, Mariska M; Bossuyt, Patrick M

2016-09-01

To evaluate changes over time in summary estimates from meta-analyses of diagnostic accuracy studies. We included 48 meta-analyses from 35 MEDLINE-indexed systematic reviews published between September 2011 and January 2012 (743 diagnostic accuracy studies; 344,015 participants). Within each meta-analysis, we ranked studies by publication date. We applied random-effects cumulative meta-analysis to follow how summary estimates of sensitivity and specificity evolved over time. Time trends were assessed by fitting a weighted linear regression model of the summary accuracy estimate against rank of publication. The median of the 48 slopes was -0.02 (-0.08 to 0.03) for sensitivity and -0.01 (-0.03 to 0.03) for specificity. Twelve of 96 (12.5%) time trends in sensitivity or specificity were statistically significant. We found a significant time trend in at least one accuracy measure for 11 of the 48 (23%) meta-analyses. Time trends in summary estimates are relatively frequent in meta-analyses of diagnostic accuracy studies. Results from early meta-analyses of diagnostic accuracy studies should be considered with caution. Copyright © 2016 Elsevier Inc. All rights reserved.

Osteoporosis prediction from the mandible using cone-beam computed tomography

PubMed Central

Al Haffar, Iyad; Khattab, Razan

2014-01-01

Purpose This study aimed to evaluate the use of dental cone-beam computed tomography (CBCT) in the diagnosis of osteoporosis among menopausal and postmenopausal women by using only a CBCT viewer program. Materials and Methods Thirty-eight menopausal and postmenopausal women who underwent dual-energy X-ray absorptiometry (DXA) examination for hip and lumbar vertebrae were scanned using CBCT (field of view: 13 cm×15 cm; voxel size: 0.25 mm). Slices from the body of the mandible as well as the ramus were selected and some CBCT-derived variables, such as radiographic density (RD) as gray values, were calculated as gray values. Pearson's correlation, one-way analysis of variance (ANOVA), and accuracy (sensitivity and specificity) evaluation based on linear and logistic regression were performed to choose the variable that best correlated with the lumbar and femoral neck T-scores. Results RD of the whole bone area of the mandible was the variable that best correlated with and predicted both the femoral neck and the lumbar vertebrae T-scores; further, Pearson's correlation coefficients were 0.5/0.6 (p value=0.037/0.009). The sensitivity, specificity, and accuracy based on the logistic regression were 50%, 88.9%, and 78.4%, respectively, for the femoral neck, and 46.2%, 91.3%, and 75%, respectively, for the lumbar vertebrae. Conclusion Lumbar vertebrae and femoral neck osteoporosis can be predicted with high accuracy from the RD value of the body of the mandible by using a CBCT viewer program. PMID:25473633

Assessment of electrocardiographic criteria of left atrial enlargement.

PubMed

Batra, Mahesh Kumar; Khan, Atif; Farooq, Fawad; Masood, Tariq; Karim, Musa

2018-05-01

Background Left atrial enlargement is considered to be a robust, strong, and widely acceptable indicator of cardiovascular outcomes. Echocardiography is the gold standard for measurement of left atrial size, but electrocardiography can be simple, cost-effective, and noninvasive in clinical practice. This study was undertaken to assess the diagnostic accuracy of an established electrocardiographic criterion for left atrial enlargement, taking 2-dimensional echocardiography as the gold-standard technique. Methods A cross-sectional study was conducted on 146 consecutively selected patients with the complaints of dyspnea and palpitation and with a murmur detected on clinical examination, from September 10, 2016 to February 10, 2017. Electrocardiography and echocardiography were performed in all patients. Patients with a negative P wave terminal force in lead V 1  > 40 ms·mm on electrocardiography or left atrial dimension > 40 mm on echocardiography were classified as having left atrial enlargement. Sensitivity and specificity were calculated to assess the diagnostic accuracy. Results Taking 2-dimensional echocardiography as the gold-standard technique, electrocardiography correctly diagnosed 68 patients as positive for left atrial enlargement and 12 as negative. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of electrocardiography for left atrial enlargement were 54.4%, 57.1%, 88.3%, 17.4%, and 54.8%, respectively. Conclusion The electrocardiogram appears to be a reasonable indicator of left atrial enlargement. In case of nonavailability of echocardiography, electrocardiography can be used for diagnosis of left atrial enlargement.

Comparison of direct 13C and indirect 1H-[13C] MR detection methods for the study of dynamic metabolic turnover in the human brain

NASA Astrophysics Data System (ADS)

Chen, Hao; De Feyter, Henk M.; Brown, Peter B.; Rothman, Douglas L.; Cai, Shuhui; de Graaf, Robin A.

2017-10-01

A wide range of direct 13C and indirect 1H-[13C] MR detection methods exist to probe dynamic metabolic pathways in the human brain. Choosing an optimal detection method is difficult as sequence-specific features regarding spatial localization, broadband decoupling, spectral resolution, power requirements and sensitivity complicate a straightforward comparison. Here we combine density matrix simulations with experimentally determined values for intrinsic 1H and 13C sensitivity, T1 and T2 relaxation and transmit efficiency to allow selection of an optimal 13C MR detection method for a given application and magnetic field. The indirect proton-observed, carbon-edited (POCE) detection method provides the highest accuracy at reasonable RF power deposition both at 4 T and 7 T. The various polarization transfer methods all have comparable performances, but may become infeasible at 7 T due to the high RF power deposition. 2D MR methods have limited value for the metabolites considered (primarily glutamate, glutamine and γ-amino butyric acid (GABA)), but may prove valuable when additional information can be extracted, such as isotopomers or lipid composition. While providing the lowest accuracy, the detection of non-protonated carbons is the simplest to implement with the lowest RF power deposition. The magnetic field homogeneity is one of the most important parameters affecting the detection accuracy for all metabolites and all acquisition methods.

Comparison of pelvic phased-array versus endorectal coil magnetic resonance imaging at 3 Tesla for local staging of prostate cancer.

PubMed

Kim, Bum Soo; Kim, Tae-Hwan; Kwon, Tae Gyun; Yoo, Eun Sang

2012-05-01

Several studies have demonstrated the superiority of endorectal coil magnetic resonance imaging (MRI) over pelvic phased-array coil MRI at 1.5 Tesla for local staging of prostate cancer. However, few have studied which evaluation is more accurate at 3 Tesla MRI. In this study, we compared the accuracy of local staging of prostate cancer using pelvic phased-array coil or endorectal coil MRI at 3 Tesla. Between January 2005 and May 2010, 151 patients underwent radical prostatectomy. All patients were evaluated with either pelvic phased-array coil or endorectal coil prostate MRI prior to surgery (63 endorectal coils and 88 pelvic phased-array coils). Tumor stage based on MRI was compared with pathologic stage. We calculated the specificity, sensitivity and accuracy of each group in the evaluation of extracapsular extension and seminal vesicle invasion. Both endorectal coil and pelvic phased-array coil MRI achieved high specificity, low sensitivity and moderate accuracy for the detection of extracapsular extension and seminal vesicle invasion. There were statistically no differences in specificity, sensitivity and accuracy between the two groups. Overall staging accuracy, sensitivity and specificity were not significantly different between endorectal coil and pelvic phased-array coil MRI.

Diagnostic Accuracy of the Neck Tornado Test as a New Screening Test in Cervical Radiculopathy.

PubMed

Park, Juyeon; Park, Woo Young; Hong, Seungbae; An, Jiwon; Koh, Jae Chul; Lee, Youn-Woo; Kim, Yong Chan; Choi, Jong Bum

2017-01-01

The Spurling test, although a highly specific provocative test of the cervical spine in cervical radiculopathy (CR), has low to moderate sensitivity. Thus, we introduced the neck tornado test (NTT) to examine the neck and the cervical spine in CR. The aim of this study was to introduce a new provocative test, the NTT, and compare the diagnostic accuracy with a widely accepted provocative test, the Spurling test. Retrospective study. Medical records of 135 subjects with neck pain (CR, n = 67; without CR, n = 68) who had undergone cervical spine magnetic resonance imaging and been referred to the pain clinic between September 2014 and August 2015 were reviewed. Both the Spurling test and NTT were performed in all patients by expert examiners. Sensitivity, specificity, and accuracy were compared for both the Spurling test and the NTT. The sensitivity of the Spurling test and the NTT was 55.22% and 85.07% ( P < 0.0001); specificity, 98.53% and 86.76% ( P = 0.0026); accuracy, 77.04% and 85.93% ( P = 0.0423), respectively. The NTT is more sensitive with superior diagnostic accuracy for CR diagnosed by magnetic resonance imaging than the Spurling test.

Diagnostic accuracy of functional, imaging and biochemical tests for patients presenting with chest pain to the emergency department: A systematic review and meta-analysis.

PubMed

Iannaccone, Mario; Gili, Sebastiano; De Filippo, Ovidio; D'Amico, Salvatore; Gagliardi, Marco; Bertaina, Maurizio; Mazzilli, Silvia; Rettegno, Sara; Bongiovanni, Federica; Gatti, Paolo; Ugo, Fabrizio; Boccuzzi, Giacomo G; Colangelo, Salvatore; Prato, Silvia; Moretti, Claudio; D'Amico, Maurizio; Noussan, Patrizia; Garbo, Roberto; Hildick-Smith, David; Gaita, Fiorenzo; D'Ascenzo, Fabrizio

2018-01-01

Non-invasive ischaemia tests and biomarkers are widely adopted to rule out acute coronary syndrome in the emergency department. Their diagnostic accuracy has yet to be precisely defined. Medline, Cochrane Library CENTRAL, EMBASE and Biomed Central were systematically screened (start date 1 September 2016, end date 1 December 2016). Prospective studies (observational or randomised controlled trial) comparing functional/imaging or biochemical tests for patients presenting with chest pain to the emergency department were included. Overall, 77 studies were included, for a total of 49,541 patients (mean age 59.9 years). Fast and six-hour highly sensitive troponin T protocols did not show significant differences in their ability to detect acute coronary syndromes, as they reported a sensitivity and specificity of 0.89 (95% confidence interval 0.79-0.94) and 0.84 (0.74-0.9) vs 0.89 (0.78-0.94) and 0.83 (0.70-0.92), respectively. The addition of copeptin to troponin increased sensitivity and reduced specificity, without improving diagnostic accuracy. The diagnostic value of non-invasive tests for patients without troponin increase was tested. Coronary computed tomography showed the highest level of diagnostic accuracy (sensitivity 0.93 (0.81-0.98) and specificity 0.90 (0.93-0.94)), along with myocardial perfusion scintigraphy (sensitivity 0.85 (0.77-0.91) and specificity 0.92 (0.83-0.96)). Stress echography was inferior to coronary computed tomography but non-inferior to myocardial perfusion scintigraphy, while exercise testing showed the lower level of diagnostic accuracy. Fast and six-hour highly sensitive troponin T protocols provide an overall similar level of diagnostic accuracy to detect acute coronary syndrome. Among the non-invasive ischaemia tests for patients without troponin increase, coronary computed tomography and myocardial perfusion scintigraphy showed the highest sensitivity and specificity.

Wavelength optimization for in vivo multispectral photoacoustic/ultrasound tomography of hemoglobin oxygenation in ovarian cancer: clinical studies

NASA Astrophysics Data System (ADS)

Salehi, Hassan S.; Li, Hai; Kumavor, Patrick D.; Merkulov, Aleksey; Sanders, Melinda; Brewer, Molly; Zhu, Quing

2015-03-01

In this paper, wavelength selection for multispectral photoacoustic/ultrasound tomography was optimized to obtain accurate images of hemoglobin oxygen saturation (sO2) in vivo. Although wavelengths can be selected by theoretical methods, in practice the accuracy of reconstructed images will be affected by wavelength-specific and system-specific factors such as laser source power and ultrasound transducer sensitivity. By performing photoacoustic spectroscopy of mouse tumor models using 14 different wavelengths between 710 and 840 nm, we were able to identify a wavelength set which most accurately reproduced the results obtained using all 14 wavelengths via selection criteria. In clinical studies, the optimal wavelength set was successfully used to image human ovaries in vivo and noninvasively. Although these results are specific to our co-registered photoacoustic/ultrasound imaging system, the approach we developed can be applied to other functional photoacoustic and optical imaging systems.

Improving the sensitivity and accuracy of gamma activation analysis for the rapid determination of gold in mineral ores.

PubMed

Tickner, James; Ganly, Brianna; Lovric, Bojan; O'Dwyer, Joel

2017-04-01

Mining companies rely on chemical analysis methods to determine concentrations of gold in mineral ore samples. As gold is often mined commercially at concentrations around 1 part-per-million, it is necessary for any analysis method to provide good sensitivity as well as high absolute accuracy. We describe work to improve both the sensitivity and accuracy of the gamma activation analysis (GAA) method for gold. We present analysis results for several suites of ore samples and discuss the design of a GAA facility designed to replace conventional chemical assay in industrial applications. Copyright © 2017. Published by Elsevier Ltd.

Prediction of skin sensitization potency using machine learning approaches.

PubMed

Zang, Qingda; Paris, Michael; Lehmann, David M; Bell, Shannon; Kleinstreuer, Nicole; Allen, David; Matheson, Joanna; Jacobs, Abigail; Casey, Warren; Strickland, Judy

2017-07-01

The replacement of animal use in testing for regulatory classification of skin sensitizers is a priority for US federal agencies that use data from such testing. Machine learning models that classify substances as sensitizers or non-sensitizers without using animal data have been developed and evaluated. Because some regulatory agencies require that sensitizers be further classified into potency categories, we developed statistical models to predict skin sensitization potency for murine local lymph node assay (LLNA) and human outcomes. Input variables for our models included six physicochemical properties and data from three non-animal test methods: direct peptide reactivity assay; human cell line activation test; and KeratinoSens™ assay. Models were built to predict three potency categories using four machine learning approaches and were validated using external test sets and leave-one-out cross-validation. A one-tiered strategy modeled all three categories of response together while a two-tiered strategy modeled sensitizer/non-sensitizer responses and then classified the sensitizers as strong or weak sensitizers. The two-tiered model using the support vector machine with all assay and physicochemical data inputs provided the best performance, yielding accuracy of 88% for prediction of LLNA outcomes (120 substances) and 81% for prediction of human test outcomes (87 substances). The best one-tiered model predicted LLNA outcomes with 78% accuracy and human outcomes with 75% accuracy. By comparison, the LLNA predicts human potency categories with 69% accuracy (60 of 87 substances correctly categorized). These results suggest that computational models using non-animal methods may provide valuable information for assessing skin sensitization potency. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

The power of timing: Adding a time-to-completion cutoff to the Word Choice Test and Recognition Memory Test improves classification accuracy.

PubMed

Erdodi, Laszlo A; Tyson, Bradley T; Shahein, Ayman G; Lichtenstein, Jonathan D; Abeare, Christopher A; Pelletier, Chantalle L; Zuccato, Brandon G; Kucharski, Brittany; Roth, Robert M

2017-05-01

The Recognition Memory Test (RMT) and Word Choice Test (WCT) are structurally similar, but psychometrically different. Previous research demonstrated that adding a time-to-completion cutoff improved the classification accuracy of the RMT. However, the contribution of WCT time-cutoffs to improve the detection of invalid responding has not been investigated. The present study was designed to evaluate the classification accuracy of time-to-completion on the WCT compared to the accuracy score and the RMT. Both tests were administered to 202 adults (M age  = 45.3 years, SD = 16.8; 54.5% female) clinically referred for neuropsychological assessment in counterbalanced order as part of a larger battery of cognitive tests. Participants obtained lower and more variable scores on the RMT (M = 44.1, SD = 7.6) than on the WCT (M = 46.9, SD = 5.7). Similarly, they took longer to complete the recognition trial on the RMT (M = 157.2 s,SD = 71.8) than the WCT (M = 137.2 s, SD = 75.7). The optimal cutoff on the RMT (≤43) produced .60 sensitivity at .87 specificity. The optimal cutoff on the WCT (≤47) produced .57 sensitivity at .87 specificity. Time-cutoffs produced comparable classification accuracies for both RMT (≥192 s; .48 sensitivity at .88 specificity) and WCT (≥171 s; .49 sensitivity at .91 specificity). They also identified an additional 6-10% of the invalid profiles missed by accuracy score cutoffs, while maintaining good specificity (.93-.95). Functional equivalence was reached at accuracy scores ≤43 (RMT) and ≤47 (WCT) or time-to-completion ≥192 s (RMT) and ≥171 s (WCT). Time-to-completion cutoffs are valuable additions to both tests. They can function as independent validity indicators or enhance the sensitivity of accuracy scores without requiring additional measures or extending standard administration time.

Water vapor δ(2) H, δ(18) O and δ(17) O measurements using an off-axis integrated cavity output spectrometer - sensitivity to water vapor concentration, delta value and averaging-time.

PubMed

Tian, Chao; Wang, Lixin; Novick, Kimberly A

2016-10-15

High-precision analysis of atmospheric water vapor isotope compositions, especially δ(17) O values, can be used to improve our understanding of multiple hydrological and meteorological processes (e.g., differentiate equilibrium or kinetic fractionation). This study focused on assessing, for the first time, how the accuracy and precision of vapor δ(17) O laser spectroscopy measurements depend on vapor concentration, delta range, and averaging-time. A Triple Water Vapor Isotope Analyzer (T-WVIA) was used to evaluate the accuracy and precision of δ(2) H, δ(18) O and δ(17) O measurements. The sensitivity of accuracy and precision to water vapor concentration was evaluated using two international standards (GISP and SLAP2). The sensitivity of precision to delta value was evaluated using four working standards spanning a large delta range. The sensitivity of precision to averaging-time was assessed by measuring one standard continuously for 24 hours. Overall, the accuracy and precision of the δ(2) H, δ(18) O and δ(17) O measurements were high. Across all vapor concentrations, the accuracy of δ(2) H, δ(18) O and δ(17) O observations ranged from 0.10‰ to 1.84‰, 0.08‰ to 0.86‰ and 0.06‰ to 0.62‰, respectively, and the precision ranged from 0.099‰ to 0.430‰, 0.009‰ to 0.080‰ and 0.022‰ to 0.054‰, respectively. The accuracy and precision of all isotope measurements were sensitive to concentration, with the higher accuracy and precision generally observed under moderate vapor concentrations (i.e., 10000-15000 ppm) for all isotopes. The precision was also sensitive to the range of delta values, although the effect was not as large compared with the sensitivity to concentration. The precision was much less sensitive to averaging-time than the concentration and delta range effects. The accuracy and precision performance of the T-WVIA depend on concentration but depend less on the delta value and averaging-time. The instrument can simultaneously and continuously measure δ(2) H, δ(18) O and δ(17) O values in water vapor, opening a new window to better understand ecological, hydrological and meteorological processes. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Comparative Analysis of Alternative Spectral Bands of CO2 and O2 for the Sensing of CO2 Mixing Ratios

NASA Technical Reports Server (NTRS)

Pliutau, Denis; Prasad, Narasimha S.

2013-01-01

We performed comparative studies to establish favorable spectral regions and measurement wavelength combinations in alternative bands of CO2 and O2, for the sensing of CO2 mixing ratios (XCO2) in missions such as ASCENDS. The analysis employed several simulation approaches including separate layers calculations based on pre-analyzed atmospheric data from the modern-era retrospective analysis for research and applications (MERRA), and the line-byline radiative transfer model (LBLRTM) to obtain achievable accuracy estimates as a function of altitude and for the total path over an annual span of variations in atmospheric parameters. Separate layer error estimates also allowed investigation of the uncertainties in the weighting functions at varying altitudes and atmospheric conditions. The parameters influencing the measurement accuracy were analyzed independently and included temperature sensitivity, water vapor interferences, selection of favorable weighting functions, excitations wavelength stabilities and other factors. The results were used to identify favorable spectral regions and combinations of on / off line wavelengths leading to reductions in interferences and the improved total accuracy.

Cervical vertebral maturation as a biologic indicator of skeletal maturity.

PubMed

Santiago, Rodrigo César; de Miranda Costa, Luiz Felipe; Vitral, Robert Willer Farinazzo; Fraga, Marcelo Reis; Bolognese, Ana Maria; Maia, Lucianne Cople

2012-11-01

To identify and review the literature regarding the reliability of cervical vertebrae maturation (CVM) staging to predict the pubertal spurt. The selection criteria included cross-sectional and longitudinal descriptive studies in humans that evaluated qualitatively or quantitatively the accuracy and reproducibility of the CVM method on lateral cephalometric radiographs, as well as the correlation with a standard method established by hand-wrist radiographs. The searches retrieved 343 unique citations. Twenty-three studies met the inclusion criteria. Six articles had moderate to high scores, while 17 of 23 had low scores. Analysis also showed a moderate to high statistically significant correlation between CVM and hand-wrist maturation methods. There was a moderate to high reproducibility of the CVM method, and only one specific study investigated the accuracy of the CVM index in detecting peak pubertal growth. This systematic review has shown that the studies on CVM method for radiographic assessment of skeletal maturation stages suffer from serious methodological failures. Better-designed studies with adequate accuracy, reproducibility, and correlation analysis, including studies with appropriate sensitivity-specificity analysis, should be performed.

American College of Rheumatology provisional criteria for defining clinical inactive disease in select categories of juvenile idiopathic arthritis.

PubMed

Wallace, Carol A; Giannini, Edward H; Huang, Bin; Itert, Lukasz; Ruperto, Nicolino

2011-07-01

To prospectively validate the preliminary criteria for clinical inactive disease (CID) in patients with select categories of juvenile idiopathic arthritis (JIA). We used the process for development of classification and response criteria recommended by the American College of Rheumatology Quality of Care Committee. Patient-visit profiles were extracted from the phase III randomized controlled trial of infliximab in polyarticular-course JIA (i.e., patients considered to resemble those with select categories of JIA) and sent to an international group of expert physician raters. Using the physician ratings as the gold standard, the sensitivity and specificity were calculated using the preliminary criteria. Modifications to the criteria were made, and these were sent to a larger group of pediatric rheumatologists to determine quantitative, face, and content validity. Variables weighted heaviest by physicians when making their judgment were the number of joints with active arthritis, erythrocyte sedimentation rate (ESR), physician's global assessment, and duration of morning stiffness. Three modifications were made: the definition of uveitis, the definition of abnormal ESR, and the addition of morning stiffness. These changes did not alter the accuracy of the preliminary set. The modified criteria, termed the "criteria for CID in select categories of JIA," have excellent feasibility and face, content, criterion, and discriminant validity to detect CID in select categories of JIA. The small changes made to the preliminary criteria set did not alter the area under the receiver operating characteristic curve (0.954) or accuracy (91%), but have increased face and content validity. Copyright © 2011 by the American College of Rheumatology.

Combined texture feature analysis of segmentation and classification of benign and malignant tumour CT slices.

PubMed

Padma, A; Sukanesh, R

2013-01-01

A computer software system is designed for the segmentation and classification of benign from malignant tumour slices in brain computed tomography (CT) images. This paper presents a method to find and select both the dominant run length and co-occurrence texture features of region of interest (ROI) of the tumour region of each slice to be segmented by Fuzzy c means clustering (FCM) and evaluate the performance of support vector machine (SVM)-based classifiers in classifying benign and malignant tumour slices. Two hundred and six tumour confirmed CT slices are considered in this study. A total of 17 texture features are extracted by a feature extraction procedure, and six features are selected using Principal Component Analysis (PCA). This study constructed the SVM-based classifier with the selected features and by comparing the segmentation results with the experienced radiologist labelled ground truth (target). Quantitative analysis between ground truth and segmented tumour is presented in terms of segmentation accuracy, segmentation error and overlap similarity measures such as the Jaccard index. The classification performance of the SVM-based classifier with the same selected features is also evaluated using a 10-fold cross-validation method. The proposed system provides some newly found texture features have an important contribution in classifying benign and malignant tumour slices efficiently and accurately with less computational time. The experimental results showed that the proposed system is able to achieve the highest segmentation and classification accuracy effectiveness as measured by jaccard index and sensitivity and specificity.

Development of a high sensitivity pinhole type gamma camera using semiconductors for low dose rate fields

NASA Astrophysics Data System (ADS)

Ueno, Yuichiro; Takahashi, Isao; Ishitsu, Takafumi; Tadokoro, Takahiro; Okada, Koichi; Nagumo, Yasushi; Fujishima, Yasutake; Yoshida, Akira; Umegaki, Kikuo

2018-06-01

We developed a pinhole type gamma camera, using a compact detector module of a pixelated CdTe semiconductor, which has suitable sensitivity and quantitative accuracy for low dose rate fields. In order to improve the sensitivity of the pinhole type semiconductor gamma camera, we adopted three methods: a signal processing method to set the discriminating level lower, a high sensitivity pinhole collimator and a smoothing image filter that improves the efficiency of the source identification. We tested basic performances of the developed gamma camera and carefully examined effects of the three methods. From the sensitivity test, we found that the effective sensitivity was about 21 times higher than that of the gamma camera for high dose rate fields which we had previously developed. We confirmed that the gamma camera had sufficient sensitivity and high quantitative accuracy; for example, a weak hot spot (0.9 μSv/h) around a tree root could be detected within 45 min in a low dose rate field test, and errors of measured dose rates with point sources were less than 7% in a dose rate accuracy test.

Sensitivity and specificity considerations for fMRI encoding, decoding, and mapping of auditory cortex at ultra-high field.

PubMed

Moerel, Michelle; De Martino, Federico; Kemper, Valentin G; Schmitter, Sebastian; Vu, An T; Uğurbil, Kâmil; Formisano, Elia; Yacoub, Essa

2018-01-01

Following rapid technological advances, ultra-high field functional MRI (fMRI) enables exploring correlates of neuronal population activity at an increasing spatial resolution. However, as the fMRI blood-oxygenation-level-dependent (BOLD) contrast is a vascular signal, the spatial specificity of fMRI data is ultimately determined by the characteristics of the underlying vasculature. At 7T, fMRI measurement parameters determine the relative contribution of the macro- and microvasculature to the acquired signal. Here we investigate how these parameters affect relevant high-end fMRI analyses such as encoding, decoding, and submillimeter mapping of voxel preferences in the human auditory cortex. Specifically, we compare a T 2 * weighted fMRI dataset, obtained with 2D gradient echo (GE) EPI, to a predominantly T 2 weighted dataset obtained with 3D GRASE. We first investigated the decoding accuracy based on two encoding models that represented different hypotheses about auditory cortical processing. This encoding/decoding analysis profited from the large spatial coverage and sensitivity of the T 2 * weighted acquisitions, as evidenced by a significantly higher prediction accuracy in the GE-EPI dataset compared to the 3D GRASE dataset for both encoding models. The main disadvantage of the T 2 * weighted GE-EPI dataset for encoding/decoding analyses was that the prediction accuracy exhibited cortical depth dependent vascular biases. However, we propose that the comparison of prediction accuracy across the different encoding models may be used as a post processing technique to salvage the spatial interpretability of the GE-EPI cortical depth-dependent prediction accuracy. Second, we explored the mapping of voxel preferences. Large-scale maps of frequency preference (i.e., tonotopy) were similar across datasets, yet the GE-EPI dataset was preferable due to its larger spatial coverage and sensitivity. However, submillimeter tonotopy maps revealed biases in assigned frequency preference and selectivity for the GE-EPI dataset, but not for the 3D GRASE dataset. Thus, a T 2 weighted acquisition is recommended if high specificity in tonotopic maps is required. In conclusion, different fMRI acquisitions were better suited for different analyses. It is therefore critical that any sequence parameter optimization considers the eventual intended fMRI analyses and the nature of the neuroscience questions being asked. Copyright © 2017 Elsevier Inc. All rights reserved.

Rapid antigen detection test for group A streptococcus in children with pharyngitis.

PubMed

Cohen, Jérémie F; Bertille, Nathalie; Cohen, Robert; Chalumeau, Martin

2016-07-04

Group A streptococcus (GAS) accounts for 20% to 40% of cases of pharyngitis in children; the remaining cases are caused by viruses. Compared with throat culture, rapid antigen detection tests (RADTs) offer diagnosis at the point of care (within five to 10 minutes). To determine the diagnostic accuracy of RADTs for diagnosing GAS in children with pharyngitis. To assess the relative diagnostic accuracy of the two major types of RADTs (enzyme immunoassays (EIA) and optical immunoassays (OIA)) by indirect and direct comparison. We searched CENTRAL, MEDLINE, EMBASE, Web of Science, CDSR, DARE, MEDION and TRIP (January 1980 to July 2015). We also conducted related citations tracking via PubMed, handsearched reference lists of included studies and relevant review articles, and screened all articles citing included studies via Google Scholar. We included studies that compared RADT for GAS pharyngitis with throat culture on a blood agar plate in a microbiology laboratory in children seen in ambulatory care. Two review authors independently screened titles and abstracts for relevance, assessed full texts for inclusion, and carried out data extraction and quality assessment using the QUADAS-2 tool. We used bivariate meta-analysis to estimate summary sensitivity and specificity, and to investigate heterogeneity across studies. We compared the accuracy of EIA and OIA tests using indirect and direct evidence. We included 98 unique studies in the review (116 test evaluations; 101,121 participants). The overall methodological quality of included studies was poor, mainly because many studies were at high risk of bias regarding patient selection and the reference standard used (in 73% and 43% of test evaluations, respectively). In studies in which all participants underwent both RADT and throat culture (105 test evaluations; 58,244 participants; median prevalence of participants with GAS was 29.5%), RADT had a summary sensitivity of 85.6%; 95% confidence interval (CI) 83.3 to 87.6 and a summary specificity of 95.4%; 95% CI 94.5 to 96.2. There was substantial heterogeneity in sensitivity across studies; specificity was more stable. There was no evidence of a trade-off between sensitivity and specificity. Heterogeneity in accuracy was not explained by study-level characteristics such as whether an enrichment broth was used before plating, mean age and clinical severity of participants, and GAS prevalence. The sensitivity of EIA and OIA tests was comparable (summary sensitivity 85.4% versus 86.2%). Sensitivity analyses showed that summary estimates of sensitivity and specificity were stable in low risk of bias studies. In a population of 1000 children with a GAS prevalence of 30%, 43 patients with GAS will be missed. Whether or not RADT can be used as a stand-alone test to rule out GAS will depend mainly on the epidemiological context. The sensitivity of EIA and OIA tests seems comparable. RADT specificity is sufficiently high to ensure against unnecessary use of antibiotics. Based on these results, we would expect that amongst 100 children with strep throat, 86 would be correctly detected with the rapid test while 14 would be missed and not receive antibiotic treatment.

An EEG-based machine learning method to screen alcohol use disorder.

PubMed

Mumtaz, Wajid; Vuong, Pham Lam; Xia, Likun; Malik, Aamir Saeed; Rashid, Rusdi Bin Abd

2017-04-01

Screening alcohol use disorder (AUD) patients has been challenging due to the subjectivity involved in the process. Hence, robust and objective methods are needed to automate the screening of AUD patients. In this paper, a machine learning method is proposed that utilized resting-state electroencephalography (EEG)-derived features as input data to classify the AUD patients and healthy controls and to perform automatic screening of AUD patients. In this context, the EEG data were recorded during 5 min of eyes closed and 5 min of eyes open conditions. For this purpose, 30 AUD patients and 15 aged-matched healthy controls were recruited. After preprocessing the EEG data, EEG features such as inter-hemispheric coherences and spectral power for EEG delta, theta, alpha, beta and gamma bands were computed involving 19 scalp locations. The selection of most discriminant features was performed with a rank-based feature selection method assigning a weight value to each feature according to a criterion, i.e., receiver operating characteristics curve. For example, a feature with large weight was considered more relevant to the target labels than a feature with less weight. Therefore, a reduced set of most discriminant features was identified and further be utilized during classification of AUD patients and healthy controls. As results, the inter-hemispheric coherences between the brain regions were found significantly different between the study groups and provided high classification efficiency ( Accuracy  = 80.8, sensitivity  = 82.5, and specificity  = 80, F - Measure  = 0.78). In addition, the power computed in different EEG bands were found significant and provided an overall classification efficiency as ( Accuracy  = 86.6, sensitivity  = 95, specificity  = 82.5, and F - Measure  = 0.88). Further, the integration of these EEG feature resulted into even higher results ( Accuracy  = 89.3 %, sensitivity  = 88.5 %, specificity  = 91 %, and F - Measure  = 0.90). Based on the results, it is concluded that the EEG data (integration of the theta, beta, and gamma power and inter-hemispheric coherence) could be utilized as objective markers to screen the AUD patients and healthy controls.

Use of Fetal Magnetic Resonance Image Analysis and Machine Learning to Predict the Need for Postnatal Cerebrospinal Fluid Diversion in Fetal Ventriculomegaly.

PubMed

Pisapia, Jared M; Akbari, Hamed; Rozycki, Martin; Goldstein, Hannah; Bakas, Spyridon; Rathore, Saima; Moldenhauer, Julie S; Storm, Phillip B; Zarnow, Deborah M; Anderson, Richard C E; Heuer, Gregory G; Davatzikos, Christos

2018-02-01

Which children with fetal ventriculomegaly, or enlargement of the cerebral ventricles in utero, will develop hydrocephalus requiring treatment after birth is unclear. To determine whether extraction of multiple imaging features from fetal magnetic resonance imaging (MRI) and integration using machine learning techniques can predict which patients require postnatal cerebrospinal fluid (CSF) diversion after birth. This retrospective case-control study used an institutional database of 253 patients with fetal ventriculomegaly from January 1, 2008, through December 31, 2014, to generate a predictive model. Data were analyzed from January 1, 2008, through December 31, 2015. All 25 patients who required postnatal CSF diversion were selected and matched by gestational age with 25 patients with fetal ventriculomegaly who did not require CSF diversion (discovery cohort). The model was applied to a sample of 24 consecutive patients with fetal ventriculomegaly who underwent evaluation at a separate institution (replication cohort) from January 1, 1998, through December 31, 2007. Data were analyzed from January 1, 1998, through December 31, 2009. To generate the model, linear measurements, area, volume, and morphologic features were extracted from the fetal MRI, and a machine learning algorithm analyzed multiple features simultaneously to find the combination that was most predictive of the need for postnatal CSF diversion. Accuracy, sensitivity, and specificity of the model in correctly classifying patients requiring postnatal CSF diversion. A total of 74 patients (41 girls [55%] and 33 boys [45%]; mean [SD] gestational age, 27.0 [5.6] months) were included from both cohorts. In the discovery cohort, median time to CSF diversion was 6 days (interquartile range [IQR], 2-51 days), and patients with fetal ventriculomegaly who did not develop symptoms were followed up for a median of 29 months (IQR, 9-46 months). The model correctly classified patients who required CSF diversion with 82% accuracy, 80% sensitivity, and 84% specificity. In the replication cohort, the model achieved 91% accuracy, 75% sensitivity, and 95% specificity. Image analysis and machine learning can be applied to fetal MRI findings to predict the need for postnatal CSF diversion. The model provides prognostic information that may guide clinical management and select candidates for potential fetal surgical intervention.

Determination of the biotin content of select foods using accurate and sensitive HPLC/avidin binding

PubMed Central

Staggs, C.G.; Sealey, W.M.; McCabe, B.J.; Teague, A.M.; Mock, D.M.

2006-01-01

Assessing dietary biotin content, biotin bioavailability, and resulting biotin status are crucial in determining whether biotin deficiency is teratogenic in humans. Accuracy in estimating dietary biotin is limited both by data gaps in food composition tables and by inaccuracies in published data. The present study applied sensitive and specific analytical techniques to determine values for biotin content in a select group of foods. Total biotin content of 87 foods was determined using acid hydrolysis and the HPLC/avidin-binding assay. These values are consistent with published values in that meat, fish, poultry, egg, dairy, and some vegetables are relatively rich sources of biotin. However, these biotin values disagreed substantially with published values for many foods. Assay values varied between 247 times greater than published values for a given food to as much as 36% less than the published biotin value. Among 51 foods assayed for which published values were available, only seven agreed within analytical variability (720%). We conclude that published values for biotin content of foods are likely to be inaccurate. PMID:16648879

Age estimation using cortical surface pattern combining thickness with curvatures

PubMed Central

Wang, Jieqiong; Li, Wenjing; Miao, Wen; Dai, Dai; Hua, Jing; He, Huiguang

2014-01-01

Brain development and healthy aging have been proved to follow a specific pattern, which, in turn, can be applied to help doctors diagnose mental diseases. In this paper, we design a cortical surface pattern (CSP) combining the cortical thickness with curvatures, which constructs an accurate human age estimation model with relevance vector regression. We test our model with two public databases. One is the IXI database (360 healthy subjects aging from 20 to 82 years old were selected), and the other is the INDI database (303 subjects aging from 7 to 22 years old were selected). The results show that our model can achieve as small as 4.57 years deviation in the IXI database and 1.38 years deviation in the INDI database. Furthermore, we employ this surface pattern to age groups classification, and get a remarkably high accuracy (97.77%) and a significantly high sensitivity/specificity (97.30%/98.10%). These results suggest that our designed CSP combining thickness with curvatures is stable and sensitive to brain development, and it is much more powerful than voxel-based morphometry used in previous methods for age estimation. PMID:24395657

Multisensor-based real-time quality monitoring by means of feature extraction, selection and modeling for Al alloy in arc welding

NASA Astrophysics Data System (ADS)

Zhang, Zhifen; Chen, Huabin; Xu, Yanling; Zhong, Jiyong; Lv, Na; Chen, Shanben

2015-08-01

Multisensory data fusion-based online welding quality monitoring has gained increasing attention in intelligent welding process. This paper mainly focuses on the automatic detection of typical welding defect for Al alloy in gas tungsten arc welding (GTAW) by means of analzing arc spectrum, sound and voltage signal. Based on the developed algorithms in time and frequency domain, 41 feature parameters were successively extracted from these signals to characterize the welding process and seam quality. Then, the proposed feature selection approach, i.e., hybrid fisher-based filter and wrapper was successfully utilized to evaluate the sensitivity of each feature and reduce the feature dimensions. Finally, the optimal feature subset with 19 features was selected to obtain the highest accuracy, i.e., 94.72% using established classification model. This study provides a guideline for feature extraction, selection and dynamic modeling based on heterogeneous multisensory data to achieve a reliable online defect detection system in arc welding.

Immunodiagnosis of Canine Visceral Leishmaniasis Using Mimotope Peptides Selected from Phage Displayed Combinatorial Libraries

PubMed Central

Toledo-Machado, Christina Monerat; Machado de Avila, Ricardo Andrez; NGuyen, Christophe; Granier, Claude; Bueno, Lilian Lacerda; Carneiro, Claudia Martins; Menezes-Souza, Daniel; Carneiro, Rubens Antonio; Chávez-Olórtegui, Carlos; Fujiwara, Ricardo Toshio

2015-01-01

ELISA and RIFI are currently used for serodiagnosis of canine visceral leishmaniasis (CVL). The accuracy of these tests is controversial in endemic areas where canine infections by Trypanosoma cruzi may occur. We evaluated the usefulness of synthetic peptides that were selected through phage display technique in the serodiagnosis of CVL. Peptides were chosen based on their ability to bind to IgGs purified from infected dogs pooled sera. We selected three phage clones that reacted only with those IgGs. Peptides were synthesized, polymerized with glutaraldehyde, and used as antigens in ELISA assays. Each individual peptide or a mix of them was reactive with infected dogs serum. The assay was highly sensitive and specific when compared to soluble Leishmania antigen that showed cross-reactivity with anti-T. cruzi IgGs. Our results demonstrate that phage display technique is useful for selection of peptides that may represent valuable synthetic antigens for an improved serodiagnosis of CVL. PMID:25710003

Multivariate Models for Prediction of Human Skin Sensitization Hazard

PubMed Central

Strickland, Judy; Zang, Qingda; Paris, Michael; Lehmann, David M.; Allen, David; Choksi, Neepa; Matheson, Joanna; Jacobs, Abigail; Casey, Warren; Kleinstreuer, Nicole

2016-01-01

One of ICCVAM’s top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary to produce skin sensitization suggests that no single alternative method will replace the currently accepted animal tests. ICCVAM is evaluating an integrated approach to testing and assessment based on the adverse outcome pathway for skin sensitization that uses machine learning approaches to predict human skin sensitization hazard. We combined data from three in chemico or in vitro assays—the direct peptide reactivity assay (DPRA), human cell line activation test (h-CLAT), and KeratinoSens™ assay—six physicochemical properties, and an in silico read-across prediction of skin sensitization hazard into 12 variable groups. The variable groups were evaluated using two machine learning approaches, logistic regression (LR) and support vector machine (SVM), to predict human skin sensitization hazard. Models were trained on 72 substances and tested on an external set of 24 substances. The six models (three LR and three SVM) with the highest accuracy (92%) used: (1) DPRA, h-CLAT, and read-across; (2) DPRA, h-CLAT, read-across, and KeratinoSens; or (3) DPRA, h-CLAT, read-across, KeratinoSens, and log P. The models performed better at predicting human skin sensitization hazard than the murine local lymph node assay (accuracy = 88%), any of the alternative methods alone (accuracy = 63–79%), or test batteries combining data from the individual methods (accuracy = 75%). These results suggest that computational methods are promising tools to effectively identify potential human skin sensitizers without animal testing. PMID:27480324

Systematic Development and Validation of a Thin-Layer Densitometric Bioanalytical Method for Estimation of Mangiferin Employing Analytical Quality by Design (AQbD) Approach

PubMed Central

Khurana, Rajneet Kaur; Rao, Satish; Beg, Sarwar; Katare, O.P.; Singh, Bhupinder

2016-01-01

The present work aims at the systematic development of a simple, rapid and highly sensitive densitometry-based thin-layer chromatographic method for the quantification of mangiferin in bioanalytical samples. Initially, the quality target method profile was defined and critical analytical attributes (CAAs) earmarked, namely, retardation factor (Rf), peak height, capacity factor, theoretical plates and separation number. Face-centered cubic design was selected for optimization of volume loaded and plate dimensions as the critical method parameters selected from screening studies employing D-optimal and Plackett–Burman design studies, followed by evaluating their effect on the CAAs. The mobile phase containing a mixture of ethyl acetate : acetic acid : formic acid : water in a 7 : 1 : 1 : 1 (v/v/v/v) ratio was finally selected as the optimized solvent for apt chromatographic separation of mangiferin at 262 nm with Rf 0.68 ± 0.02 and all other parameters within the acceptance limits. Method validation studies revealed high linearity in the concentration range of 50–800 ng/band for mangiferin. The developed method showed high accuracy, precision, ruggedness, robustness, specificity, sensitivity, selectivity and recovery. In a nutshell, the bioanalytical method for analysis of mangiferin in plasma revealed the presence of well-resolved peaks and high recovery of mangiferin. PMID:26912808

Sensitivity of grass and alfalfa reference evapotranspiration to weather station sensor accuracy

USDA-ARS?s Scientific Manuscript database

A sensitivity analysis was conducted to determine the relative effects of measurement errors in climate data input parameters on the accuracy of calculated reference crop evapotranspiration (ET) using the ASCE-EWRI Standardized Reference ET Equation. Data for the period of 1991 to 2008 from an autom...

Approximate techniques of structural reanalysis

NASA Technical Reports Server (NTRS)

Noor, A. K.; Lowder, H. E.

1974-01-01

A study is made of two approximate techniques for structural reanalysis. These include Taylor series expansions for response variables in terms of design variables and the reduced-basis method. In addition, modifications to these techniques are proposed to overcome some of their major drawbacks. The modifications include a rational approach to the selection of the reduced-basis vectors and the use of Taylor series approximation in an iterative process. For the reduced basis a normalized set of vectors is chosen which consists of the original analyzed design and the first-order sensitivity analysis vectors. The use of the Taylor series approximation as a first (initial) estimate in an iterative process, can lead to significant improvements in accuracy, even with one iteration cycle. Therefore, the range of applicability of the reanalysis technique can be extended. Numerical examples are presented which demonstrate the gain in accuracy obtained by using the proposed modification techniques, for a wide range of variations in the design variables.

Channels selection using independent component analysis and scalp map projection for EEG-based driver fatigue classification.

PubMed

Rifai Chai; Naik, Ganesh R; Sai Ho Ling; Tran, Yvonne; Craig, Ashley; Nguyen, Hung T

2017-07-01

This paper presents a classification of driver fatigue with electroencephalography (EEG) channels selection analysis. The system employs independent component analysis (ICA) with scalp map back projection to select the dominant of EEG channels. After channel selection, the features of the selected EEG channels were extracted based on power spectral density (PSD), and then classified using a Bayesian neural network. The results of the ICA decomposition with the back-projected scalp map and a threshold showed that the EEG channels can be reduced from 32 channels into 16 dominants channels involved in fatigue assessment as chosen channels, which included AF3, F3, FC1, FC5, T7, CP5, P3, O1, P4, P8, CP6, T8, FC2, F8, AF4, FP2. The result of fatigue vs. alert classification of the selected 16 channels yielded a sensitivity of 76.8%, specificity of 74.3% and an accuracy of 75.5%. Also, the classification results of the selected 16 channels are comparable to those using the original 32 channels. So, the selected 16 channels is preferable for ergonomics improvement of EEG-based fatigue classification system.

Variation of a test's sensitivity and specificity with disease prevalence.

PubMed

Leeflang, Mariska M G; Rutjes, Anne W S; Reitsma, Johannes B; Hooft, Lotty; Bossuyt, Patrick M M

2013-08-06

Anecdotal evidence suggests that the sensitivity and specificity of a diagnostic test may vary with disease prevalence. Our objective was to investigate the associations between disease prevalence and test sensitivity and specificity using studies of diagnostic accuracy. We used data from 23 meta-analyses, each of which included 10-39 studies (416 total). The median prevalence per review ranged from 1% to 77%. We evaluated the effects of prevalence on sensitivity and specificity using a bivariate random-effects model for each meta-analysis, with prevalence as a covariate. We estimated the overall effect of prevalence by pooling the effects using the inverse variance method. Within a given review, a change in prevalence from the lowest to highest value resulted in a corresponding change in sensitivity or specificity from 0 to 40 percentage points. This effect was statistically significant (p < 0.05) for either sensitivity or specificity in 8 meta-analyses (35%). Overall, specificity tended to be lower with higher disease prevalence; there was no such systematic effect for sensitivity. The sensitivity and specificity of a test often vary with disease prevalence; this effect is likely to be the result of mechanisms, such as patient spectrum, that affect prevalence, sensitivity and specificity. Because it may be difficult to identify such mechanisms, clinicians should use prevalence as a guide when selecting studies that most closely match their situation.

Variation of a test’s sensitivity and specificity with disease prevalence

PubMed Central

Leeflang, Mariska M.G.; Rutjes, Anne W.S.; Reitsma, Johannes B.; Hooft, Lotty; Bossuyt, Patrick M.M.

2013-01-01

Background: Anecdotal evidence suggests that the sensitivity and specificity of a diagnostic test may vary with disease prevalence. Our objective was to investigate the associations between disease prevalence and test sensitivity and specificity using studies of diagnostic accuracy. Methods: We used data from 23 meta-analyses, each of which included 10–39 studies (416 total). The median prevalence per review ranged from 1% to 77%. We evaluated the effects of prevalence on sensitivity and specificity using a bivariate random-effects model for each meta-analysis, with prevalence as a covariate. We estimated the overall effect of prevalence by pooling the effects using the inverse variance method. Results: Within a given review, a change in prevalence from the lowest to highest value resulted in a corresponding change in sensitivity or specificity from 0 to 40 percentage points. This effect was statistically significant (p < 0.05) for either sensitivity or specificity in 8 meta-analyses (35%). Overall, specificity tended to be lower with higher disease prevalence; there was no such systematic effect for sensitivity. Interpretation: The sensitivity and specificity of a test often vary with disease prevalence; this effect is likely to be the result of mechanisms, such as patient spectrum, that affect prevalence, sensitivity and specificity. Because it may be difficult to identify such mechanisms, clinicians should use prevalence as a guide when selecting studies that most closely match their situation. PMID:23798453

Prospective analysis of magnetic resonance imaging accuracy in diagnosing traumatic injuries of the posterior ligamentous complex of the thoracolumbar spine.

PubMed

Pizones, Javier; Sánchez-Mariscal, Felisa; Zúñiga, Lorenzo; Álvarez, Patricia; Izquierdo, Enrique

2013-04-20

Prospective cohort study. To study magnetic resonance imaging (MRI) accuracy in diagnosing posterior ligamentous complex (PLC) damage, when applying the new dichotomic instability criteria in a prospective cohort of patients with vertebral fracture. Recent studies dispute MRI accuracy to diagnose PLC injuries. They analyze the complex based on 3 categories (intact/indeterminate/rupture), including the indeterminate in the ruptured group (measurement bias) in the accuracy analysis. Moreover, fractures with conservative treatment (selection bias) are not included. Both facts reduce the specificity. A recent study has proposed new criteria where posterior instability is determined with supraspinous ligament (SSL) rupture. Prospective study of patients with acute thoracolumbar fracture, using radiography and MRI (FS-T2-w/short-tau inversion-recovery sequences). 1. The integrity (ruptured/unruptured) of each isolated component of the PLC (facet capsules, interspinous ligament, SSL, and ligamentum flavum) was assessed via MRI and surgical findings. 2. PLC integrity as a whole was assessed, adopting the new dichotomic stability criteria from previous studies. In the MR images, PLC is considered ruptured when the SSL is found discontinued, and intact when not (this excludes the "indeterminate" category). In surgically treated fractures, PLC stability as a whole was assessed dynamically (ruptured/unruptured). In conservative fractures, PLC stability was assessed according to change in vertebral kyphosis measured with the local kyphotic angle at 2-year follow-up (ruptured if difference is > 5°/unruptured if difference is < 5°).3. Comparative analysis among findings provided MRI accuracy in diagnosing PLC damage. Fifty-eight vertebral fractures were studied (38 surgical, 20 conservative), of which 50% were in males; average age, 40.4 years. MRI sensitivity for injury diagnosis of each isolated PLC component varied between 92.3% (interspinous ligament) and 100% (ligamentum flavum). Specificity varied between 52% (facet capsules) and 100% (SSL). PLC integrity sensitivity and specificity as a whole were 91% and 100%, respectively. Adopting the new stability criteria, MRI accuracy in PLC injury diagnosis increases. Specificity is increased (true positives) both in isolated component analysis and PLC as a whole.

Simultaneous determination of L-tetrahydropalmatine and cocaine in human plasma by simple UPLC-FLD method: application in clinical studies.

PubMed

Yu, Mingming; Hassan, Hazem E; Ibrahim, Ahmed; Bauer, Kenneth S; Kelly, Deanna L; Wang, Jia Bei

2014-08-15

Currently, there are no FDA approved medications for treatment of cocaine addiction underscoring the dire need to develop such a product. There is an accumulating body of evidence that l-tetrahydropalmatine (l-THP), a non-selective dopamine antagonist, can be used for the treatment of cocaine addiction. Indeed, the FDA recently approved its usage in a Phase I study in cocaine abusers and it was indispensable to develop a simple and sensitive method for the simultaneous determination of l-THP and cocaine in human plasma. We developed a UPLC-FLD method for quantitation of these molecules using an ACQUITY BEH C18 column (2.1 mm × 50mm, 1.7 μm) and a mobile phase that consisted of 10mM ammonium phosphate (pH=4.75), methanol, and acetonitrile (v:v:v, 78:16:6). Venlafaxine was used as the internal standard while hexane was used for the liquid-liquid extraction. The flow rate was 0.4 mL/min with fluorescence detection using an excitation wavelength of 230 nm and emission detection wavelength of 315 nm. This method was selective, linear and sensitive with a lower limit of quantification of 2.5 ng/mL for both cocaine and l-THP. The intra-day precision of cocaine and l-THP was <9.50% while the accuracy was <4.29%. The inter-day precision of cocaine and l-THP was <9.14%, and the accuracy was <12.49%. The recovery for cocaine and l-THP ranged from 43.95 to 50.02% and 54.65 to 58.31%, respectively. In comparison to forty reported cocaine quantitation methods this method is simple, sensitive and cost-effective and can be used for simultaneous quantitation of l-THP and cocaine. This method meets the FDA guidelines and can be used in current and future clinical studies. Copyright © 2014 Elsevier B.V. All rights reserved.

EZSCAN for undiagnosed type 2 diabetes mellitus: A systematic review and meta-analysis.

PubMed

Bernabe-Ortiz, Antonio; Ruiz-Alejos, Andrea; Miranda, J Jaime; Mathur, Rohini; Perel, Pablo; Smeeth, Liam

2017-01-01

The EZSCAN is a non-invasive device that, by evaluating sweat gland function, may detect subjects with type 2 diabetes mellitus (T2DM). The aim of the study was to conduct a systematic review and meta-analysis including studies assessing the performance of the EZSCAN for detecting cases of undiagnosed T2DM. We searched for observational studies including diagnostic accuracy and performance results assessing EZSCAN for detecting cases of undiagnosed T2DM. OVID (Medline, Embase, Global Health), CINAHL and SCOPUS databases, plus secondary resources, were searched until March 29, 2017. The following keywords were utilized for the systematic searching: type 2 diabetes mellitus, hyperglycemia, EZSCAN, SUDOSCAN, and sudomotor function. Two investigators extracted the information for meta-analysis and assessed the quality of the data using the Revised Version of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklist. Pooled estimates were obtained by fitting the logistic-normal random-effects model without covariates but random intercepts and using the Freeman-Tukey Arcsine Transformation to stabilize variances. Heterogeneity was also assessed using the I2 measure. Four studies (n = 7,720) were included, three of them used oral glucose tolerance test as the gold standard. Using Hierarchical Summary Receiver Operating Characteristic model, summary sensitivity was 72.0% (95%CI: 60.0%- 83.0%), whereas specificity was 56.0% (95%CI: 38.0%- 74.0%). Studies were very heterogeneous (I2 for sensitivity: 79.2% and for specificity: 99.1%) regarding the inclusion criteria and bias was present mainly due to participants selection. The sensitivity of EZSCAN for detecting cases of undiagnosed T2DM seems to be acceptable, but evidence of high heterogeneity and participant selection bias was detected in most of the studies included. More studies are needed to evaluate the performance of the EZSCAN for undiagnosed T2DM screening, especially at the population level.

A spirooxazine derivative as a highly sensitive cyanide sensor by means of UV-visible difference spectroscopy.

PubMed

Zhu, Shaoyin; Li, Minjie; Sheng, Lan; Chen, Peng; Zhang, Yumo; Zhang, Sean Xiao-An

2012-12-07

A spirooxazine derivative 2-nitro-5a-(2-(4-dimethylaminophenyl)-ethylene)-6,6-dimethyl-5a,6-dihydro-12H-indolo[2,1-b][1,3]benzooxazine (P1) was explored as a sensitive cyanide probe. Different from conventional spiropyrans, P1 avoided locating the 3H-indolium cation and the 4-nitrophenolate anion in the same conjugated structure, which enhanced the positive charge of 3H-indolium cation so that the sensitivity and reaction speed were improved highly. UV-visible difference spectroscopy using P1 detection solution as a timely reference improved the measurement accuracy, prevented the error caused by the inherent absorption change of P1 solution with time. This enabled the "positive-negative alternative absorption peaks" in difference spectrum to be used as a finger-print to distinguish whether the spectral change was caused by cyanide. Benefiting from the special design of the molecular structure and the strategy of difference spectroscopy, P1 showed high selectivity and sensitivity for CN(-). A detection limit of 0.4 μM and a rate constant of 1.1 s(-1) were achieved.

Predicting treatment response to cognitive behavioral therapy in panic disorder with agoraphobia by integrating local neural information.

PubMed

Hahn, Tim; Kircher, Tilo; Straube, Benjamin; Wittchen, Hans-Ulrich; Konrad, Carsten; Ströhle, Andreas; Wittmann, André; Pfleiderer, Bettina; Reif, Andreas; Arolt, Volker; Lueken, Ulrike

2015-01-01

Although neuroimaging research has made substantial progress in identifying the large-scale neural substrate of anxiety disorders, its value for clinical application lags behind expectations. Machine-learning approaches have predictive potential for individual-patient prognostic purposes and might thus aid translational efforts in psychiatric research. To predict treatment response to cognitive behavioral therapy (CBT) on an individual-patient level based on functional magnetic resonance imaging data in patients with panic disorder with agoraphobia (PD/AG). We included 49 patients free of medication for at least 4 weeks and with a primary diagnosis of PD/AG in a longitudinal study performed at 8 clinical research institutes and outpatient centers across Germany. The functional magnetic resonance imaging study was conducted between July 2007 and March 2010. Twelve CBT sessions conducted 2 times a week focusing on behavioral exposure. Treatment response was defined as exceeding a 50% reduction in Hamilton Anxiety Rating Scale scores. Blood oxygenation level-dependent signal was measured during a differential fear-conditioning task. Regional and whole-brain gaussian process classifiers using a nested leave-one-out cross-validation were used to predict the treatment response from data acquired before CBT. Although no single brain region was predictive of treatment response, integrating regional classifiers based on data from the acquisition and the extinction phases of the fear-conditioning task for the whole brain yielded good predictive performance (accuracy, 82%; sensitivity, 92%; specificity, 72%; P < .001). Data from the acquisition phase enabled 73% correct individual-patient classifications (sensitivity, 80%; specificity, 67%; P < .001), whereas data from the extinction phase led to an accuracy of 74% (sensitivity, 64%; specificity, 83%; P < .001). Conservative reanalyses under consideration of potential confounders yielded nominally lower but comparable accuracy rates (acquisition phase, 70%; extinction phase, 71%; combined, 79%). Predicting treatment response to CBT based on functional neuroimaging data in PD/AG is possible with high accuracy on an individual-patient level. This novel machine-learning approach brings personalized medicine within reach, directly supporting clinical decisions for the selection of treatment options, thus helping to improve response rates.

Computerized detection of leukocytes in microscopic leukorrhea images.

PubMed

Zhang, Jing; Zhong, Ya; Wang, Xiangzhou; Ni, Guangming; Du, Xiaohui; Liu, Juanxiu; Liu, Lin; Liu, Yong

2017-09-01

Detection of leukocytes is critical for the routine leukorrhea exam, which is widely used in gynecological examinations. An elevated vaginal leukocyte count in women with bacterial vaginosis is a strong predictor of vaginal or cervical infections. In the routine leukorrhea exam, the counting of leukocytes is primarily performed by manual techniques. However, the viewing and counting of leukocytes from multiple high-power viewing fields on a glass slide under a microscope leads to subjectivity, low efficiency, and low accuracy. To date, many biological cells in stool, blood, and breast cancer have been studied to realize computerized detection; however, the detection of leukocytes in microscopic leukorrhea images has not been studied. Thus, there is an increasing need for computerized detection of leukocytes. There are two key processes in the computerized detection of leukocytes in digital image processing. One is segmentation; the other is intelligent classification. In this paper, we propose a combined ensemble to detect leukocytes in the microscopic leukorrhea image. After image segmentation and selecting likely leukocyte subimages, we obtain the leukocyte candidates. Then, for intelligent classification, we adopt two methods: feature extraction and classification by a support vector machine (SVM); applying a modified convolutional neural network (CNN) to the larger subimages. If different methods classify a candidate in the same category, the process is finished. If not, the outputs of the methods are provided to a classifier to further classify the candidate. After acquiring leukocyte candidates, we attempted three methods to perform classification. The first approach using features and SVM achieved 88% sensitivity, 97% specificity, and 92.5% accuracy. The second method using CNN achieved 95% sensitivity, 84% specificity, and 89.5% accuracy. Then, in the combination approach, we achieved 92% sensitivity, 95% specificity, and 93.5% accuracy. Finally, the images with marked and counted leukocytes were obtained. A novel computerized detection system was developed for automated detection of leukocytes in microscopic images. Different methods resulted in comparable overall qualities by enabling computerized detection of leukocytes. The proposed approach further improved the performance. This preliminary study proves the feasibility of computerized detection of leukocytes in clinical use. © 2017 American Association of Physicists in Medicine.

CT colonography of colorectal polyps: a metaanalysis.

PubMed

Sosna, Jacob; Morrin, Martina M; Kruskal, Jonathan B; Lavin, Philip T; Rosen, Max P; Raptopoulos, Vassilios

2003-12-01

For proper evaluation of the accuracy of CT colonography, prospective multiinstitutional trials would be ideal. Until these trials are available, data can be collectively analyzed. The purpose of this study is to use metaanalysis to assess the reported accuracy of CT colonography compared with conventional colonoscopy for detecting colorectal polyps. Articles comparing CT colonography and conventional colonoscopy were identified, and a standardized form was used to extract relevant study data. Fisher's exact test and the Mantel-Haenszel test were used for pooling of data. A 95% confidence interval (CI) was selected to determine sensitivity and specificity, and the Kruskal-Wallis exact test was used to identify trends relating to polyp size. Meta-analysis methods were used to test strength of results. Comparisons were made for the percentage of polyps detected grouped by size (> or = 10 mm, 6-9 mm, < or = 5 mm) and the percentage of patients identified who had polyps of the same size. Fourteen studies fulfilled all the study inclusion criteria and gave a total of 1,324 patients and 1,411 polyps. The pooled per-patient sensitivity for polyps 10 mm or larger was (sensitivity [95% CI]) 0.88 (0.84-0.93), for polyps 6-9 mm it was 0.84 (0.80-0.89), and for polyps 5 mm or smaller it was 0.65 (0.57-0.73). The pooled per-polyp sensitivity for polyps 10 mm or larger was 0.81 (0.76-0.85), for polyps 6-9 mm it was 0.62 (0.58-0.67), and for polyps 5 mm or smaller it was 0.43 (0.39-0.47). Sensitivity for detection of polyps increased as the polyp size increased (p < 0.00005). The pooled overall specificity for detection of polyps larger than 10 mm was 0.95 (0.94-0.97). The specificity and sensitivity of CT colonography are high for polyps larger than 10 mm.

Bridging the gap between marker-assisted and genomic selection of heading time and plant height in hybrid wheat.

PubMed

Zhao, Y; Mette, M F; Gowda, M; Longin, C F H; Reif, J C

2014-06-01

Based on data from field trials with a large collection of 135 elite winter wheat inbred lines and 1604 F1 hybrids derived from them, we compared the accuracy of prediction of marker-assisted selection and current genomic selection approaches for the model traits heading time and plant height in a cross-validation approach. For heading time, the high accuracy seen with marker-assisted selection severely dropped with genomic selection approaches RR-BLUP (ridge regression best linear unbiased prediction) and BayesCπ, whereas for plant height, accuracy was low with marker-assisted selection as well as RR-BLUP and BayesCπ. Differences in the linkage disequilibrium structure of the functional and single-nucleotide polymorphism markers relevant for the two traits were identified in a simulation study as a likely explanation for the different trends in accuracies of prediction. A new genomic selection approach, weighted best linear unbiased prediction (W-BLUP), designed to treat the effects of known functional markers more appropriately, proved to increase the accuracy of prediction for both traits and thus closes the gap between marker-assisted and genomic selection.

Bridging the gap between marker-assisted and genomic selection of heading time and plant height in hybrid wheat

PubMed Central

Zhao, Y; Mette, M F; Gowda, M; Longin, C F H; Reif, J C

2014-01-01

Based on data from field trials with a large collection of 135 elite winter wheat inbred lines and 1604 F1 hybrids derived from them, we compared the accuracy of prediction of marker-assisted selection and current genomic selection approaches for the model traits heading time and plant height in a cross-validation approach. For heading time, the high accuracy seen with marker-assisted selection severely dropped with genomic selection approaches RR-BLUP (ridge regression best linear unbiased prediction) and BayesCπ, whereas for plant height, accuracy was low with marker-assisted selection as well as RR-BLUP and BayesCπ. Differences in the linkage disequilibrium structure of the functional and single-nucleotide polymorphism markers relevant for the two traits were identified in a simulation study as a likely explanation for the different trends in accuracies of prediction. A new genomic selection approach, weighted best linear unbiased prediction (W-BLUP), designed to treat the effects of known functional markers more appropriately, proved to increase the accuracy of prediction for both traits and thus closes the gap between marker-assisted and genomic selection. PMID:24518889

Final Report for "Non-Accelerator Physics – Research in High Energy Physics: Dark Energy Research on DES"

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

Ritz, Steve; Jeltema, Tesla

One of the greatest mysteries in modern cosmology is the fact that the expansion of the universe is observed to be accelerating. This acceleration may stem from dark energy, an additional energy component of the universe, or may indicate that the theory of general relativity is incomplete on cosmological scales. The growth rate of large-scale structure in the universe and particularly the largest collapsed structures, clusters of galaxies, is highly sensitive to the underlying cosmology. Clusters will provide one of the single most precise methods of constraining dark energy with the ongoing Dark Energy Survey (DES). The accuracy of themore » cosmological constraints derived from DES clusters necessarily depends on having an optimized and well-calibrated algorithm for selecting clusters as well as an optical richness estimator whose mean relation and scatter compared to cluster mass are precisely known. Calibrating the galaxy cluster richness-mass relation and its scatter was the focus of the funded work. Specifically, we employ X-ray observations and optical spectroscopy with the Keck telescopes of optically-selected clusters to calibrate the relationship between optical richness (the number of galaxies in a cluster) and underlying mass. This work also probes aspects of cluster selection like the accuracy of cluster centering which are critical to weak lensing cluster studies.« less

CombiROC: an interactive web tool for selecting accurate marker combinations of omics data.

PubMed

Mazzara, Saveria; Rossi, Riccardo L; Grifantini, Renata; Donizetti, Simone; Abrignani, Sergio; Bombaci, Mauro

2017-03-30

Diagnostic accuracy can be improved considerably by combining multiple markers, whose performance in identifying diseased subjects is usually assessed via receiver operating characteristic (ROC) curves. The selection of multimarker signatures is a complicated process that requires integration of data signatures with sophisticated statistical methods. We developed a user-friendly tool, called CombiROC, to help researchers accurately determine optimal markers combinations from diverse omics methods. With CombiROC data from different domains, such as proteomics and transcriptomics, can be analyzed using sensitivity/specificity filters: the number of candidate marker panels rising from combinatorial analysis is easily optimized bypassing limitations imposed by the nature of different experimental approaches. Leaving to the user full control on initial selection stringency, CombiROC computes sensitivity and specificity for all markers combinations, performances of best combinations and ROC curves for automatic comparisons, all visualized in a graphic interface. CombiROC was designed without hard-coded thresholds, allowing a custom fit to each specific data: this dramatically reduces the computational burden and lowers the false negative rates given by fixed thresholds. The application was validated with published data, confirming the marker combination already originally described or even finding new ones. CombiROC is a novel tool for the scientific community freely available at http://CombiROC.eu.

Object-Based Paddy Rice Mapping Using HJ-1A/B Data and Temporal Features Extracted from Time Series MODIS NDVI Data

PubMed Central

Singha, Mrinal; Wu, Bingfang; Zhang, Miao

2016-01-01

Accurate and timely mapping of paddy rice is vital for food security and environmental sustainability. This study evaluates the utility of temporal features extracted from coarse resolution data for object-based paddy rice classification of fine resolution data. The coarse resolution vegetation index data is first fused with the fine resolution data to generate the time series fine resolution data. Temporal features are extracted from the fused data and added with the multi-spectral data to improve the classification accuracy. Temporal features provided the crop growth information, while multi-spectral data provided the pattern variation of paddy rice. The achieved overall classification accuracy and kappa coefficient were 84.37% and 0.68, respectively. The results indicate that the use of temporal features improved the overall classification accuracy of a single-date multi-spectral image by 18.75% from 65.62% to 84.37%. The minimum sensitivity (MS) of the paddy rice classification has also been improved. The comparison showed that the mapped paddy area was analogous to the agricultural statistics at the district level. This work also highlighted the importance of feature selection to achieve higher classification accuracies. These results demonstrate the potential of the combined use of temporal and spectral features for accurate paddy rice classification. PMID:28025525

Research on the Error Characteristics of a 110 kV Optical Voltage Transformer under Three Conditions: In the Laboratory, Off-Line in the Field and During On-Line Operation

PubMed Central

Xiao, Xia; Hu, Haoliang; Xu, Yan; Lei, Min; Xiong, Qianzhu

2016-01-01

Optical voltage transformers (OVTs) have been applied in power systems. When performing accuracy performance tests of OVTs large differences exist between the electromagnetic environment and the temperature variation in the laboratory and on-site. Therefore, OVTs may display different error characteristics under different conditions. In this paper, OVT prototypes with typical structures were selected to be tested for the error characteristics with the same testing equipment and testing method. The basic accuracy, the additional error caused by temperature and the adjacent phase in the laboratory, the accuracy in the field off-line, and the real-time monitoring error during on-line operation were tested. The error characteristics under the three conditions—laboratory, in the field off-line and during on-site operation—were compared and analyzed. The results showed that the effect of the transportation process, electromagnetic environment and the adjacent phase on the accuracy of OVTs could be ignored for level 0.2, but the error characteristics of OVTs are dependent on the environmental temperature and are sensitive to the temperature gradient. The temperature characteristics during on-line operation were significantly superior to those observed in the laboratory. PMID:27537895

Research on the Error Characteristics of a 110 kV Optical Voltage Transformer under Three Conditions: In the Laboratory, Off-Line in the Field and During On-Line Operation.

PubMed

Xiao, Xia; Hu, Haoliang; Xu, Yan; Lei, Min; Xiong, Qianzhu

2016-08-16

Optical voltage transformers (OVTs) have been applied in power systems. When performing accuracy performance tests of OVTs large differences exist between the electromagnetic environment and the temperature variation in the laboratory and on-site. Therefore, OVTs may display different error characteristics under different conditions. In this paper, OVT prototypes with typical structures were selected to be tested for the error characteristics with the same testing equipment and testing method. The basic accuracy, the additional error caused by temperature and the adjacent phase in the laboratory, the accuracy in the field off-line, and the real-time monitoring error during on-line operation were tested. The error characteristics under the three conditions-laboratory, in the field off-line and during on-site operation-were compared and analyzed. The results showed that the effect of the transportation process, electromagnetic environment and the adjacent phase on the accuracy of OVTs could be ignored for level 0.2, but the error characteristics of OVTs are dependent on the environmental temperature and are sensitive to the temperature gradient. The temperature characteristics during on-line operation were significantly superior to those observed in the laboratory.

Object-Based Paddy Rice Mapping Using HJ-1A/B Data and Temporal Features Extracted from Time Series MODIS NDVI Data.

PubMed

Singha, Mrinal; Wu, Bingfang; Zhang, Miao

2016-12-22

Accurate and timely mapping of paddy rice is vital for food security and environmental sustainability. This study evaluates the utility of temporal features extracted from coarse resolution data for object-based paddy rice classification of fine resolution data. The coarse resolution vegetation index data is first fused with the fine resolution data to generate the time series fine resolution data. Temporal features are extracted from the fused data and added with the multi-spectral data to improve the classification accuracy. Temporal features provided the crop growth information, while multi-spectral data provided the pattern variation of paddy rice. The achieved overall classification accuracy and kappa coefficient were 84.37% and 0.68, respectively. The results indicate that the use of temporal features improved the overall classification accuracy of a single-date multi-spectral image by 18.75% from 65.62% to 84.37%. The minimum sensitivity (MS) of the paddy rice classification has also been improved. The comparison showed that the mapped paddy area was analogous to the agricultural statistics at the district level. This work also highlighted the importance of feature selection to achieve higher classification accuracies. These results demonstrate the potential of the combined use of temporal and spectral features for accurate paddy rice classification.

Exploring Discretization Error in Simulation-Based Aerodynamic Databases

NASA Technical Reports Server (NTRS)

Aftosmis, Michael J.; Nemec, Marian

2010-01-01

This work examines the level of discretization error in simulation-based aerodynamic databases and introduces strategies for error control. Simulations are performed using a parallel, multi-level Euler solver on embedded-boundary Cartesian meshes. Discretization errors in user-selected outputs are estimated using the method of adjoint-weighted residuals and we use adaptive mesh refinement to reduce these errors to specified tolerances. Using this framework, we examine the behavior of discretization error throughout a token database computed for a NACA 0012 airfoil consisting of 120 cases. We compare the cost and accuracy of two approaches for aerodynamic database generation. In the first approach, mesh adaptation is used to compute all cases in the database to a prescribed level of accuracy. The second approach conducts all simulations using the same computational mesh without adaptation. We quantitatively assess the error landscape and computational costs in both databases. This investigation highlights sensitivities of the database under a variety of conditions. The presence of transonic shocks or the stiffness in the governing equations near the incompressible limit are shown to dramatically increase discretization error requiring additional mesh resolution to control. Results show that such pathologies lead to error levels that vary by over factor of 40 when using a fixed mesh throughout the database. Alternatively, controlling this sensitivity through mesh adaptation leads to mesh sizes which span two orders of magnitude. We propose strategies to minimize simulation cost in sensitive regions and discuss the role of error-estimation in database quality.

Half-Unit Insulin Pens: Disease Management in Patients With Diabetes Who Are Sensitive to Insulin.

PubMed

Klonoff, David C; Nayberg, Irina; Stauder, Udo; Oualali, Hamid; Domenger, Catherine

2017-05-01

Insulin pens represent a significant technological advancement in diabetes management. While the vast majority have been designed with 1U-dosing increments, improved accuracy and precision facilitated by half-unit increments may be particularly significant in specific patients who are sensitive to insulin. These include patients with low insulin requirements and in those requiring more precise dose adjustments, such as the pediatric patient population. This review summarized functional characteristics of insulin half-unit pens (HUPs) and their effect on user experience. The literature search was restricted to articles published in English between January 1, 2000, and January 1, 2015. A total of 17 publications met the set criteria and were included in the review. Overall, studies outlined characteristics for 4 insulin HUPs. Based on their functionality, the pens were generally similar and all met the ISO 11608-1 criteria for accuracy. However, some had specific advantageous features in terms of size, weight, design, dialing torque, and injection force. Although limited, the currently available user preference studies in children and adolescents with diabetes and their carers suggest that the selection of an HUP is likely to be influenced by a combination of factors such as these, in addition to the prescribed insulin and dosing regimen. Insulin HUPs are likely to be a key diabetes management tool for patients who are sensitive to insulin; specific pen features may further advance diabetes management in these populations.

Development and validation of sensitive LC/MS/MS method for quantitative bioanalysis of levonorgestrel in rat plasma and application to pharmacokinetics study.

PubMed

Ananthula, Suryatheja; Janagam, Dileep R; Jamalapuram, Seshulatha; Johnson, James R; Mandrell, Timothy D; Lowe, Tao L

2015-10-15

Rapid, sensitive, selective and accurate LC/MS/MS method was developed for quantitative determination of levonorgestrel (LNG) in rat plasma and further validated for specificity, linearity, accuracy, precision, sensitivity, matrix effect, recovery efficiency and stability. Liquid-liquid extraction procedure using hexane:ethyl acetate mixture at 80:20 v:v ratio was employed to efficiently extract LNG from rat plasma. Reversed phase Luna column C18(2) (50×2.0mm i.d., 3μM) installed on a AB SCIEX Triple Quad™ 4500 LC/MS/MS system was used to perform chromatographic separation. LNG was identified within 2min with high specificity. Linear calibration curve was drawn within 0.5-50ng·mL(-1) concentration range. The developed method was validated for intra-day and inter-day accuracy and precision whose values fell in the acceptable limits. Matrix effect was found to be minimal. Recovery efficiency at three quality control (QC) concentrations 0.5 (low), 5 (medium) and 50 (high) ng·mL(-1) was found to be >90%. Stability of LNG at various stages of experiment including storage, extraction and analysis was evaluated using QC samples, and the results showed that LNG was stable at all the conditions. This validated method was successfully used to study the pharmacokinetics of LNG in rats after SubQ injection, providing its applicability in relevant preclinical studies. Copyright © 2015 Elsevier B.V. All rights reserved.

Beware the black box: investigating the sensitivity of FEA simulations to modelling factors in comparative biomechanics.

PubMed

Walmsley, Christopher W; McCurry, Matthew R; Clausen, Phillip D; McHenry, Colin R

2013-01-01

Finite element analysis (FEA) is a computational technique of growing popularity in the field of comparative biomechanics, and is an easily accessible platform for form-function analyses of biological structures. However, its rapid evolution in recent years from a novel approach to common practice demands some scrutiny in regards to the validity of results and the appropriateness of assumptions inherent in setting up simulations. Both validation and sensitivity analyses remain unexplored in many comparative analyses, and assumptions considered to be 'reasonable' are often assumed to have little influence on the results and their interpretation. HERE WE REPORT AN EXTENSIVE SENSITIVITY ANALYSIS WHERE HIGH RESOLUTION FINITE ELEMENT (FE) MODELS OF MANDIBLES FROM SEVEN SPECIES OF CROCODILE WERE ANALYSED UNDER LOADS TYPICAL FOR COMPARATIVE ANALYSIS: biting, shaking, and twisting. Simulations explored the effect on both the absolute response and the interspecies pattern of results to variations in commonly used input parameters. Our sensitivity analysis focuses on assumptions relating to the selection of material properties (heterogeneous or homogeneous), scaling (standardising volume, surface area, or length), tooth position (front, mid, or back tooth engagement), and linear load case (type of loading for each feeding type). Our findings show that in a comparative context, FE models are far less sensitive to the selection of material property values and scaling to either volume or surface area than they are to those assumptions relating to the functional aspects of the simulation, such as tooth position and linear load case. Results show a complex interaction between simulation assumptions, depending on the combination of assumptions and the overall shape of each specimen. Keeping assumptions consistent between models in an analysis does not ensure that results can be generalised beyond the specific set of assumptions used. Logically, different comparative datasets would also be sensitive to identical simulation assumptions; hence, modelling assumptions should undergo rigorous selection. The accuracy of input data is paramount, and simulations should focus on taking biological context into account. Ideally, validation of simulations should be addressed; however, where validation is impossible or unfeasible, sensitivity analyses should be performed to identify which assumptions have the greatest influence upon the results.

Beware the black box: investigating the sensitivity of FEA simulations to modelling factors in comparative biomechanics

PubMed Central

McCurry, Matthew R.; Clausen, Phillip D.; McHenry, Colin R.

2013-01-01

Finite element analysis (FEA) is a computational technique of growing popularity in the field of comparative biomechanics, and is an easily accessible platform for form-function analyses of biological structures. However, its rapid evolution in recent years from a novel approach to common practice demands some scrutiny in regards to the validity of results and the appropriateness of assumptions inherent in setting up simulations. Both validation and sensitivity analyses remain unexplored in many comparative analyses, and assumptions considered to be ‘reasonable’ are often assumed to have little influence on the results and their interpretation. Here we report an extensive sensitivity analysis where high resolution finite element (FE) models of mandibles from seven species of crocodile were analysed under loads typical for comparative analysis: biting, shaking, and twisting. Simulations explored the effect on both the absolute response and the interspecies pattern of results to variations in commonly used input parameters. Our sensitivity analysis focuses on assumptions relating to the selection of material properties (heterogeneous or homogeneous), scaling (standardising volume, surface area, or length), tooth position (front, mid, or back tooth engagement), and linear load case (type of loading for each feeding type). Our findings show that in a comparative context, FE models are far less sensitive to the selection of material property values and scaling to either volume or surface area than they are to those assumptions relating to the functional aspects of the simulation, such as tooth position and linear load case. Results show a complex interaction between simulation assumptions, depending on the combination of assumptions and the overall shape of each specimen. Keeping assumptions consistent between models in an analysis does not ensure that results can be generalised beyond the specific set of assumptions used. Logically, different comparative datasets would also be sensitive to identical simulation assumptions; hence, modelling assumptions should undergo rigorous selection. The accuracy of input data is paramount, and simulations should focus on taking biological context into account. Ideally, validation of simulations should be addressed; however, where validation is impossible or unfeasible, sensitivity analyses should be performed to identify which assumptions have the greatest influence upon the results. PMID:24255817

Accuracy of Home-Based Ultrasonographic Diagnosis of Obstetric Risk Factors by Primary-Level Health Workers in Rural Nepal

PubMed Central

Kozuki, Naoko; Mullany, Luke C.; Khatry, Subarna K.; Ghimire, Ram K.; Paudel, Sharma; Blakemore, Karin; Bird, Christine; Tielsch, James M.; LeClerq, Steven C.; Katz, Joanne

2016-01-01

Objective To assess the feasibility of task shifting by estimating the accuracy at which primary-level health care workers can perform community-based third trimester ultrasound diagnosis for selected obstetric risk factors in rural Nepal. Methods Three auxiliary nurse midwives received two one-week ultrasound trainings at Tribhuvan University Teaching Hospital in Kathmandu. In our study site in rural Nepal, women who were ≥32 weeks in gestational age were enrolled and received ultrasound examinations from the auxiliary nurse midwives during home visits. Each auxiliary nurse midwife screened for non-cephalic presentation, multiple gestation, and placenta previa. All de-identified images were stored and uploaded onto an online server, where certified sonologists and sonographers reviewed the images and made their own diagnoses for the three conditions. Accuracy of auxiliary nurse midwife diagnoses was then calculated. Results We enrolled 804 women in the study. Each auxiliary nurse midwife’s kappa statistic for diagnosis of non-cephalic presentation was above 0.90 compared with the sonogram reviewers. Sensitivity, specificity, positive and negative predictive values were between 90–100% for all auxiliary nurse midwives For multiple gestation, the auxiliary nurse midwives were in perfect agreement with both the sonogram reviewers and maternal postpartum self-report. Two placenta previa cases were detected, and the sonogram reviewers agreed with both. Conclusion With limited training, primary-level health care workers in rural Nepal can accurately diagnose selected third trimester obstetric risk factors using ultrasonography. PMID:27500343

Accuracy of different Xpert MTB/Rif implementation strategies in programmatic settings at the regional referral hospitals in Uganda: Evidence for country wide roll out.

PubMed

Muttamba, Winters; Ssengooba, Willy; Sekibira, Rogers; Kirenga, Bruce; Katamba, Achilles; Joloba, Moses

2018-01-01

Xpert MTB/RIF assay is a highly sensitive test for TB diagnosis, but still costly to most low-income countries. Several implementation strategies instead of frontline have been suggested; however with scarce data. We assessed accuracy of different Xpert MTB/RIF implementation strategies to inform national roll-out. This was a cross-sectional study of 1,924 adult presumptive TB patients in five regional referral hospitals of Uganda. Two sputum samples were collected, one for fluorescent microscopy (FM) and Xpert MTB/RIF examined at the study site laboratories. The second sample was sent to the Uganda Supra National TB reference laboratory for culture using both Lowenstein Jensen (LJ) and liquid culture (MGIT). We compared the sensitivities of FM, Xpert MTB/RIF and the incremental sensitivity of Xpert MTB/RIF among patients negative on FM using LJ and/or MGIT as a reference standard. A total 1924 patients were enrolled of which 1596 (83%) patients had at least one laboratory result and 1083 respondents had a complete set of all the laboratory results. A total of 328 (30%) were TB positive on LJ and /or MGIT culture. The sensitivity of FM was n (%; 95% confidence interval) 246 (63.5%; 57.9-68.7) overall compared to 52 (55.4%; 44.1-66.3) among HIV positive individuals, while the sensitivity of Xpert MTB/RIF was 300 (76.2%; 71.7-80.7) and 69 (71.6%; 60.5-81.1) overall and among HIV positive individuals respectively. Overall incremental sensitivity of Xpert MTB/RIF was 60 (36.5%; 27.7-46.0) and 20 (41.7%; 25.5-59.2) among HIV positive individuals. Xpert MTB/RIF has a higher sensitivity than FM both in general population and HIV positive population. Xpert MTB/RIF offers a significant increase in terms of diagnostic sensitivity even when it is deployed selectively i.e. among smear negative presumptive TB patients. Our results support frontline use of Xpert MTB/RIF assay in high HIV/TB prevalent countries. In settings with limited access, mechanisms to refer smear negative sputum samples to Xpert MTB/RIF hubs are recommended.

Quantification of trans-1,4-polyisoprene in Eucommia ulmoides by fourier transform infrared spectroscopy and pyrolysis-gas chromatography/mass spectrometry.

PubMed

Takeno, Shinya; Bamba, Takeshi; Nakazawa, Yoshihisa; Fukusaki, Eiichiro; Okazawa, Atsushi; Kobayashi, Akio

2008-04-01

Commercial development of trans-1,4-polyisoprene from Eucommia ulmoides Oliver (EU-rubber) requires specific knowledge on selection of high-rubber-content lines and establishment of agronomic cultivation methods for achieving maximum EU-rubber yield. The development can be facilitated by high-throughput and highly sensitive analytical techniques for EU-rubber extraction and quantification. In this paper, we described an efficient EU-rubber extraction method, and validated that the accuracy was equivalent to that of the conventional Soxhlet extraction method. We also described a highly sensitive quantification method for EU-rubber by Fourier transform infrared spectroscopy (FT-IR) and pyrolysis-gas chromatography/mass spectrometry (PyGC/MS). We successfully applied the extraction/quantification method for study of seasonal changes in EU-rubber content and molecular weight distribution.

Zwitterionic peptide anchored to conducting polymer PEDOT for the development of antifouling and ultrasensitive electrochemical DNA sensor.

PubMed

Wang, Guixiang; Han, Rui; Su, Xiaoli; Li, Yinan; Xu, Guiyun; Luo, Xiliang

2017-06-15

Zwitterionic peptides were anchored to a conducting polymer of citrate doped poly(3,4-ethylenedioxythiophene) (PEDOT) via the nickel cation coordination, and the obtained peptide modified PEDOT, with excellent antifouling ability and good conductivity, was further used for the immobilization of a DNA probe to construct an electrochemical biosensor for the breast cancer marker BRCA1. The DNA biosensor was highly sensitive (with detection limit of 0.03fM) and selective, and it was able to detect BRCA1 in 5% (v/v) human plasma with satisfying accuracy and low fouling. The marriage of antifouling and biocompatible peptides with conducting polymers opened a new avenue to construct electrochemical biosensors capable of assaying targets in complex biological media with high sensitivity and without biofouling. Copyright © 2016 Elsevier B.V. All rights reserved.

Optimized and validated flow-injection spectrophotometric analysis of topiramate, piracetam and levetiracetam in pharmaceutical formulations.

PubMed

Hadad, Ghada M; Abdel-Salam, Randa A; Emara, Samy

2011-12-01

Application of a sensitive and rapid flow injection analysis (FIA) method for determination of topiramate, piracetam, and levetiracetam in pharmaceutical formulations has been investigated. The method is based on the reaction with ortho-phtalaldehyde and 2-mercaptoethanol in a basic buffer and measurement of absorbance at 295 nm under flow conditions. Variables affecting the determination such as sample injection volume, pH, ionic strength, reagent concentrations, flow rate of reagent and other FIA parameters were optimized to produce the most sensitive and reproducible results using a quarter-fraction factorial design, for five factors at two levels. Also, the method has been optimized and fully validated in terms of linearity and range, limit of detection and quantitation, precision, selectivity and accuracy. The method was successfully applied to the analysis of pharmaceutical preparations.

MicroRNA based Pan-Cancer Diagnosis and Treatment Recommendation.

PubMed

Cheerla, Nikhil; Gevaert, Olivier

2017-01-13

The current state-of-the-art in cancer diagnosis and treatment is not ideal; diagnostic tests are accurate but invasive, and treatments are "one-size fits-all" instead of being personalized. Recently, miRNA's have garnered significant attention as cancer biomarkers, owing to their ease of access (circulating miRNA in the blood) and stability. There have been many studies showing the effectiveness of miRNA data in diagnosing specific cancer types, but few studies explore the role of miRNA in predicting treatment outcome. Here we go a step further, using tissue miRNA and clinical data across 21 cancers from the 'The Cancer Genome Atlas' (TCGA) database. We use machine learning techniques to create an accurate pan-cancer diagnosis system, and a prediction model for treatment outcomes. Finally, using these models, we create a web-based tool that diagnoses cancer and recommends the best treatment options. We achieved 97.2% accuracy for classification using a support vector machine classifier with radial basis. The accuracies improved to 99.9-100% when climbing up the embryonic tree and classifying cancers at different stages. We define the accuracy as the ratio of the total number of instances correctly classified to the total instances. The classifier also performed well, achieving greater than 80% sensitivity for many cancer types on independent validation datasets. Many miRNAs selected by our feature selection algorithm had strong previous associations to various cancers and tumor progression. Then, using miRNA, clinical and treatment data and encoding it in a machine-learning readable format, we built a prognosis predictor model to predict the outcome of treatment with 85% accuracy. We used this model to create a tool that recommends personalized treatment regimens. Both the diagnosis and prognosis model, incorporating semi-supervised learning techniques to improve their accuracies with repeated use, were uploaded online for easy access. Our research is a step towards the final goal of diagnosing cancer and predicting treatment recommendations using non-invasive blood tests.

Spatial variability in sensitivity of reference crop ET to accuracy of climate data in the Texas High Plains

USDA-ARS?s Scientific Manuscript database

A detailed sensitivity analysis was conducted to determine the relative effects of measurement errors in climate data input parameters on the accuracy of calculated reference crop evapotranspiration (ET) using the ASCE-EWRI Standardized Reference ET Equation. Data for the period of 1995 to 2008, fro...

Accuracy, Sensitivity and Specificity of Fine Needle Aspiration Biopsy for Salivary Gland Tumors: A Retrospective Study from 2006 to 2011

PubMed

Silva, William P P; Stramandinoli-Zanicotti, Roberta T; Schussel, Juliana L; Ramos, Gyl H A; Ioshi, Sergio O; Sassi, Laurindo M

2016-11-01

Objective: This article concerns evaluation of the sensitivity, specificity and accuracy of FNAB for pre-surgical diagnosis of benign and malignant lesions of major and minor salivary glands of patients treated in the Department of Head and Neck Surgery of Erasto Gartner Hospital. Methods: This retrospective study analyzed medical records from January 2006 to December 2011 from patients with salivary gland lesions who underwent preoperative FNAB and, after surgical excision of the lesion, histopathological examination. Results: The study had a cohort of 130 cases, but 34 cases (26.2%) were considered unsatisfactory regarding cytology analyses. Based on the data, sensitivity was 66.7% (6/9), specificity was 81.6% (71/87), accuracy was 80.2% (77/96), the positive predictive value was 66,7% (6/9) and the negative predictive value was 81.6% (71/87). Conclusion: Despite the high rate of inadequate samples obtained in the FNAB in this study the technique offers high specificity, accuracy and acceptable sensitivity. Creative Commons Attribution License

Diagnostic Accuracy of Memory Measures in Alzheimer’s Dementia and Mild Cognitive Impairment: a Systematic Review and Meta-Analysis

PubMed Central

Weissberger, Gali H.; Strong, Jessica V.; Stefanidis, Kayla B.; Summers, Mathew J.; Bondi, Mark W.; Stricker, Nikki H.

2018-01-01

With an increasing focus on biomarkers in dementia research, illustrating the role of neuropsychological assessment in detecting mild cognitive impairment (MCI) and Alzheimer’s dementia (AD) is important. This systematic review and meta-analysis, conducted in accordance with PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) standards, summarizes the sensitivity and specificity of memory measures in individuals with MCI and AD. Both meta-analytic and qualitative examination of AD versus healthy control (HC) studies (n = 47) revealed generally high sensitivity and specificity (≥ 80% for AD comparisons) for measures of immediate (sensitivity = 87%, specificity = 88%) and delayed memory (sensitivity = 89%, specificity = 89%), especially those involving word-list recall. Examination of MCI versus HC studies (n = 38) revealed generally lower diagnostic accuracy for both immediate (sensitivity = 72%, specificity = 81%) and delayed memory (sensitivity = 75%, specificity = 81%). Measures that differentiated AD from other conditions (n = 10 studies) yielded mixed results, with generally high sensitivity in the context of low or variable specificity. Results confirm that memory measures have high diagnostic accuracy for identification of AD, are promising but require further refinement for identification of MCI, and provide support for ongoing investigation of neuropsychological assessment as a cognitive biomarker of preclinical AD. Emphasizing diagnostic test accuracy statistics over null hypothesis testing in future studies will promote the ongoing use of neuropsychological tests as Alzheimer’s disease research and clinical criteria increasingly rely upon cerebrospinal fluid (CSF) and neuroimaging biomarkers. PMID:28940127

Spectroflurimetric estimation of the new antiviral agent ledipasvir in presence of sofosbuvir

NASA Astrophysics Data System (ADS)

Salama, Fathy M.; Attia, Khalid A.; Abouserie, Ahmed A.; El-Olemy, Ahmed; Abolmagd, Ebrahim

2018-02-01

A spectroflurimetric method has been developed and validated for the selective quantitative determination of ledipasvir in presence of sofosbuvir. In this method the native fluorescence of ledipasvir in ethanol at 405 nm was measured after excitation at 340 nm. The proposed method was validated according to ICH guidelines and show high sensitivity, accuracy and precision. Furthermore this method was successfully applied to the analysis of ledipasvir in pharmaceutical dosage form without interference from sofosbuvir and other additives and the results were statistically compared to a reported method and found no significant difference.

Diagnostic Accuracy of the Neck Tornado Test as a New Screening Test in Cervical Radiculopathy

PubMed Central

Park, Juyeon; Park, Woo Young; Hong, Seungbae; An, Jiwon; Koh, Jae Chul; Lee, Youn-Woo; Kim, Yong Chan; Choi, Jong Bum

2017-01-01

Background: The Spurling test, although a highly specific provocative test of the cervical spine in cervical radiculopathy (CR), has low to moderate sensitivity. Thus, we introduced the neck tornado test (NTT) to examine the neck and the cervical spine in CR. Objectives: The aim of this study was to introduce a new provocative test, the NTT, and compare the diagnostic accuracy with a widely accepted provocative test, the Spurling test. Design: Retrospective study. Methods: Medical records of 135 subjects with neck pain (CR, n = 67; without CR, n = 68) who had undergone cervical spine magnetic resonance imaging and been referred to the pain clinic between September 2014 and August 2015 were reviewed. Both the Spurling test and NTT were performed in all patients by expert examiners. Sensitivity, specificity, and accuracy were compared for both the Spurling test and the NTT. Results: The sensitivity of the Spurling test and the NTT was 55.22% and 85.07% (P < 0.0001); specificity, 98.53% and 86.76% (P = 0.0026); accuracy, 77.04% and 85.93% (P = 0.0423), respectively. Conclusions: The NTT is more sensitive with superior diagnostic accuracy for CR diagnosed by magnetic resonance imaging than the Spurling test. PMID:28824298

Comparison of Pelvic Phased-Array versus Endorectal Coil Magnetic Resonance Imaging at 3 Tesla for Local Staging of Prostate Cancer

PubMed Central

Kim, Bum Soo; Kim, Tae-Hwan; Kwon, Tae Gyun

2012-01-01

Purpose Several studies have demonstrated the superiority of endorectal coil magnetic resonance imaging (MRI) over pelvic phased-array coil MRI at 1.5 Tesla for local staging of prostate cancer. However, few have studied which evaluation is more accurate at 3 Tesla MRI. In this study, we compared the accuracy of local staging of prostate cancer using pelvic phased-array coil or endorectal coil MRI at 3 Tesla. Materials and Methods Between January 2005 and May 2010, 151 patients underwent radical prostatectomy. All patients were evaluated with either pelvic phased-array coil or endorectal coil prostate MRI prior to surgery (63 endorectal coils and 88 pelvic phased-array coils). Tumor stage based on MRI was compared with pathologic stage. We calculated the specificity, sensitivity and accuracy of each group in the evaluation of extracapsular extension and seminal vesicle invasion. Results Both endorectal coil and pelvic phased-array coil MRI achieved high specificity, low sensitivity and moderate accuracy for the detection of extracapsular extension and seminal vesicle invasion. There were statistically no differences in specificity, sensitivity and accuracy between the two groups. Conclusion Overall staging accuracy, sensitivity and specificity were not significantly different between endorectal coil and pelvic phased-array coil MRI. PMID:22476999

Efficient strategies to find diagnostic test accuracy studies in kidney journals.

PubMed

Rogerson, Thomas E; Ladhani, Maleeka; Mitchell, Ruth; Craig, Jonathan C; Webster, Angela C

2015-08-01

Nephrologists looking for quick answers to diagnostic clinical questions in MEDLINE can use a range of published search strategies or Clinical Query limits to improve the precision of their searches. We aimed to evaluate existing search strategies for finding diagnostic test accuracy studies in nephrology journals. We assessed the accuracy of 14 search strategies for retrieving diagnostic test accuracy studies from three nephrology journals indexed in MEDLINE. Two investigators hand searched the same journals to create a reference set of diagnostic test accuracy studies to compare search strategy results against. We identified 103 diagnostic test accuracy studies, accounting for 2.1% of all studies published. The most specific search strategy was the Narrow Clinical Queries limit (sensitivity: 0.20, 95% CI 0.13-0.29; specificity: 0.99, 95% CI 0.99-0.99). Using the Narrow Clinical Queries limit, a searcher would need to screen three (95% CI 2-6) articles to find one diagnostic study. The most sensitive search strategy was van der Weijden 1999 Extended (sensitivity: 0.95; 95% CI 0.89-0.98; specificity 0.55, 95% CI 0.53-0.56) but required a searcher to screen 24 (95% CI 23-26) articles to find one diagnostic study. Bachmann 2002 was the best balanced search strategy, which was sensitive (0.88, 95% CI 0.81-0.94), but also specific (0.74, 95% CI 0.73-0.75), with a number needed to screen of 15 (95% CI 14-17). Diagnostic studies are infrequently published in nephrology journals. The addition of a strategy for diagnostic studies to a subject search strategy in MEDLINE may reduce the records needed to screen while preserving adequate search sensitivity for routine clinical use. © 2015 Asian Pacific Society of Nephrology.

An economic evaluation of second-trimester genetic ultrasonography for prenatal detection of down syndrome.

PubMed

Vintzileos, A M; Ananth, C V; Fisher, A J; Smulian, J C; Day-Salvatore, D; Beazoglou, T; Knuppel, R A

1998-11-01

The objective of this study was to perform an economic evaluation of second-trimester genetic ultrasonography for prenatal detection of Down syndrome. More specifically, we sought to determine the following: (1) the diagnostic accuracy requirements (from the cost-benefit point of view) of genetic ultrasonography versus genetic amniocentesis for women at increased risk for fetal Down syndrome and (2) the possible economic impact of second-trimester genetic ultrasonography for the US population on the basis of the ultrasonographic accuracies reported in previously published studies. A cost-benefit equation was developed from the hypothesis that the cost of universal genetic amniocentesis of patients at increased risk for carrying a fetus with Down syndrome should be at least equal to the cost of universal genetic ultrasonography with amniocentesis used only for those with abnormal ultrasonographic results. The main components of the equation included the diagnostic accuracy of genetic ultrasonography (sensitivity and specificity for detecting Down syndrome), the costs of the amniocentesis package and genetic ultrasonography, and the lifetime cost of Down syndrome cases not detected by the genetic ultrasonography. After appropriate manipulation of the equation a graph was constructed, representing the balance between sensitivity and false-positive rate of genetic ultrasonography; this was used to examine the accuracy of previously published studies from the cost-benefit point of view. Sensitivity analyses included individual risks for Down syndrome ranging from 1:261 (risk of a 35-year-old at 18 weeks' gestation) to 1:44 (risk of a 44-year-old at 18 weeks' gestation). This economic evaluation was conducted from the societal perspective. Genetic ultrasonography was found to be economically beneficial only if the overall sensitivity for detecting Down syndrome was >74%. Even then, the cost-benefit ratio depended on the corresponding false-positive rate. Of the 7 published studies that used multiple ultrasonographic markers for genetic ultrasonography, 6 had accuracies compatible with benefits. The required ultrasonographic accuracy (sensitivity and false-positive rate) varied according to the prevalence of Down syndrome in the population tested. The cost-benefit ratio of second-trimester genetic ultrasonography depends on its diagnostic accuracy, and it is beneficial only when its overall sensitivity for Down syndrome is >74%.

Utility of Water Siphon Maneuver for Eliciting Gastroesophageal Reflux During Barium Esophagography: Correlation With Histologic Findings.

PubMed

Dane, B; Doshi, A; Khan, A; Megibow, A

2018-06-12

The objective of this study is to evaluate whether the water siphon maneuver improves detection of gastroesophageal (GE) reflux during barium esophagography compared with observation for spontaneous reflux only. Histopathologic analysis is the reference standard. This retrospective study assessed 87 outpatients who underwent both barium esophagography and upper endoscopy-guided biopsy within a 30-day interval. The water siphon maneuver was routinely performed when spontaneous GE reflux was not observed during the fluoroscopic study. Radiology reports were reviewed for mentions of the presence of reflux and the circumstances in which it was observed (as a spontaneous occurrence or as a result of the water siphon maneuver). Pathology reports from subsequent endoscopic biopsies were reviewed to identify histologic changes of reflux disease. The sensitivity, specificity, and accuracy of esophagography, observation for spontaneous reflux, and the water siphon maneuver were calculated and then compared using a McNemar test. Of the 87 patients, 57 (65.5%) had GE reflux diagnosed on the basis of histologic changes noted on endoscopy, and 30 (34.5%) did not. A total of 57 patients (65.5%) showed reflux during esophagography, 41 (71.9%) of whom had reflux diagnosed by the water siphon maneuver, and 16 (28.1%) had reflux diagnosed on the basis of observation of spontaneous reflux. Forty-four patients had reflux diagnosed on the basis of both a barium study and histologic findings; 13 patients had reflux noted on esophagography but had negative histologic findings. The overall sensitivity, specificity, and accuracy of esophagography for reflux were 77.2%, 56.7%, and 70.1%, respectively. Spontaneous reflux alone had a sensitivity, specificity, and accuracy of 21.1%, 86.7%, and 43.7%, respectively. The water siphon maneuver showed a sensitivity of 71.1%, a specificity of 65.4%, and accuracy of 69.0%. The differences in the sensitivity, specificity, and accuracy of the water siphon maneuver versus observation of spontaneous reflux were statistically significant (p ≤ 0.004). A properly performed and interpreted water siphon maneuver significantly increases the sensitivity and accuracy for GE reflux during esophagography, compared with observation for spontaneous reflux alone. The water siphon maneuver is a simple addition to barium esophagography that improves sensitivity and accuracy for the diagnosis of GE reflux compared with observation alone.

Predictive models of lameness in dairy cows achieve high sensitivity and specificity with force measurements in three dimensions.

PubMed

Dunthorn, Jason; Dyer, Robert M; Neerchal, Nagaraj K; McHenry, Jonathan S; Rajkondawar, Parimal G; Steingraber, Gary; Tasch, Uri

2015-11-01

Lameness remains a significant cause of production losses, a growing welfare concern and may be a greater economic burden than clinical mastitis . A growing need for accurate, continuous automated detection systems continues because US prevalence of lameness is 12.5% while individual herds may experience prevalence's of 27.8-50.8%. To that end the first force-plate system restricted to the vertical dimension identified lame cows with 85% specificity and 52% sensitivity. These results lead to the hypothesis that addition of transverse and longitudinal dimensions could improve sensitivity of lameness detection. To address the hypothesis we upgraded the original force plate system to measure ground reaction forces (GRFs) across three directions. GRFs and locomotion scores were generated from randomly selected cows and logistic regression was used to develop a model that characterised relationships of locomotion scores to the GRFs. This preliminary study showed 76 variables across 3 dimensions produced a model with greater than 90% sensitivity, specificity, and area under the receiver operating curve (AUC). The result was a marked improvement on the 52% sensitivity, and 85% specificity previously observed with the 1 dimensional model or the 45% sensitivities reported with visual observations. Validation of model accuracy continues with the goal to finalise accurate automated methods of lameness detection.

Panel-based Genetic Diagnostic Testing for Inherited Eye Diseases is Highly Accurate and Reproducible and More Sensitive for Variant Detection Than Exome Sequencing

PubMed Central

Bujakowska, Kinga M.; Sousa, Maria E.; Fonseca-Kelly, Zoë D.; Taub, Daniel G.; Janessian, Maria; Wang, Dan Yi; Au, Elizabeth D.; Sims, Katherine B.; Sweetser, David A.; Fulton, Anne B.; Liu, Qin; Wiggs, Janey L.; Gai, Xiaowu; Pierce, Eric A.

2015-01-01

Purpose Next-generation sequencing (NGS) based methods are being adopted broadly for genetic diagnostic testing, but the performance characteristics of these techniques have not been fully defined with regard to test accuracy and reproducibility. Methods We developed a targeted enrichment and NGS approach for genetic diagnostic testing of patients with inherited eye disorders, including inherited retinal degenerations, optic atrophy and glaucoma. In preparation for providing this Genetic Eye Disease (GEDi) test on a CLIA-certified basis, we performed experiments to measure the sensitivity, specificity, reproducibility as well as the clinical sensitivity of the test. Results The GEDi test is highly reproducible and accurate, with sensitivity and specificity for single nucleotide variant detection of 97.9% and 100%, respectively. The sensitivity for variant detection was notably better than the 88.3% achieved by whole exome sequencing (WES) using the same metrics, due to better coverage of targeted genes in the GEDi test compared to commercially available exome capture sets. Prospective testing of 192 patients with IRDs indicated that the clinical sensitivity of the GEDi test is high, with a diagnostic rate of 51%. Conclusion The data suggest that based on quantified performance metrics, selective targeted enrichment is preferable to WES for genetic diagnostic testing. PMID:25412400

SPECT/CT with 99mTc-MAA in radioembolization with 90Y microspheres in patients with hepatocellular cancer.

PubMed

Hamami, Monia E; Poeppel, Thorsten D; Müller, Stephan; Heusner, Till; Bockisch, Andreas; Hilgard, Philipp; Antoch, Gerald

2009-05-01

Radioembolization with (90)Y microspheres is a novel treatment for hepatic tumors. Generally, hepatic arteriography and (99m)Tc-macroaggregated albumin (MAA) scanning are performed before selective internal radiation therapy to detect extrahepatic shunting to the lung or the gastrointestinal tract. Whereas previous studies have used only planar or SPECT scans, the present study used (99m)Tc-MAA SPECT/CT scintigraphy (SPECT with integrated low-dose CT) to evaluate whether SPECT/CT and additional diagnostic contrast-enhanced CT before radioembolization with (90)Y microspheres are superior to SPECT or planar imaging alone for detection of gastrointestinal shunting. In a prospective study, we enrolled 58 patients (mean age, 66 y; SD, 12 y; 10 women and 48 men) with hepatocellular carcinoma who underwent hepatic arteriography and scintigraphy with (99m)Tc-MAA using planar imaging, SPECT, and SPECT with integrated low-dose CT of the upper abdomen (acquired with a hybrid SPECT/CT camera). The ability of the different imaging modalities to detect extrahepatic MAA shunting was compared. Patient follow-up of a mean of 180 d served as the standard of reference. Gastrointestinal shunting was revealed by planar imaging in 4, by SPECT in 9, and by SPECT/CT in 16 of the 68 examinations. For planar imaging, the sensitivity for detection of gastrointestinal shunting was 25%, the specificity 87%, and the accuracy 72%. For SPECT without CT, the sensitivity was 56%, the specificity 87%, and the accuracy 79%. SPECT with CT fusion had a sensitivity of 100%, a specificity of 94%, and an accuracy of 96%. In 3 patients, MAA deposits in the portal vein could accurately be attributed to tumor thrombus only with additional information from contrast-enhanced CT. The follow-up did not show any gastrointestinal complications. SPECT with integrated low-dose CT using (99m)Tc-MAA is beneficial in radioembolization with (90)Y microspheres because it increases the sensitivity and specificity of (99m)Tc-MAA SPECT when detecting extrahepatic arterial shunting. The overall low risk of gastrointestinal complications in radioembolization may therefore be further reduced by SPECT/CT.

Ultrasonography in diagnosing clinically occult groin hernia: systematic review and meta-analysis.

PubMed

Kwee, Robert M; Kwee, Thomas C

2018-05-14

To provide an updated systematic review on the performance of ultrasonography (US) in diagnosing clinically occult groin hernia. A systematic search was performed in MEDLINE and Embase. Methodological quality of included studies was assessed. Accuracy data of US in detecting clinically occult groin hernia were extracted. Positive predictive value (PPV) was pooled with a random effects model. For studies investigating the performance of US in hernia type classification (inguinal vs femoral), correctly classified proportion was assessed. Sixteen studies were included. In the two studies without verification bias, sensitivities were 29.4% [95% confidence interval (CI), 15.1-47.5%] and 90.9% (95% CI, 70.8-98.9%); specificities were 90.0% (95% CI, 80.5-95.9%) and 90.6% (95% CI, 83.0-95.6%). Verification bias or a variation of it (i.e. study limited to only subjects with definitive proof of disease status) was present in all other studies. Sensitivity, specificity, and negative predictive value (NPV) were not pooled. PPV ranged from 58.8 to 100%. Pooled PPV, based on data from ten studies with low risk of bias and no applicability concerns with respect to patient selection, was 85.6% (95% CI, 76.5-92.7%). Proportion of correctly classified hernias, based on data from four studies, ranged between 94.4% and 99.1%. Sensitivity, specificity and NPV of US in detecting clinically occult groin hernia cannot reliably be determined based on current evidence. Further studies are necessary. Accuracy may strongly depend on the examiner's skills. PPV is high. Inguinal and femoral hernias can reliably be differentiated by US. • Sensitivity, specificity and NPV of ultrasound in detecting clinically occult groin hernia cannot reliably be determined based on current evidence. • Accuracy may strongly depend on the examiner's skills. • PPV of US in detection of clinically occult groin hernia is high [pooled PPV of 85.6% (95% confidence interval, 76.5-92.7%)]. • US has very high performance in correctly differentiating between clinically occult inguinal and femoral hernia (correctness of 94.4- 99.1%).

A systematic review of the diagnostic accuracy of provocative tests of the neck for diagnosing cervical radiculopathy

PubMed Central

Pool, Jan J. M.; van Tulder, Maurits W.; Riphagen, Ingrid I.; de Vet, Henrica C. W.

2006-01-01

Clinical provocative tests of the neck, which position the neck and arm inorder to aggravate or relieve arm symptoms, are commonly used in clinical practice in patients with a suspected cervical radiculopathy. Their diagnostic accuracy, however, has never been examined in a systematic review. A comprehensive search was conducted in order to identify all possible studies fulfilling the inclusion criteria. A study was included if: (1) any provocative test of the neck for diagnosing cervical radiculopathy was identified; (2) any reference standard was used; (3) sensitivity and specificity were reported or could be (re-)calculated; and, (4) the publication was a full report. Two reviewers independently selected studies, and assessed methodological quality. Only six studies met the inclusion criteria, which evaluated five provocative tests. In general, Spurling’s test demonstrated low to moderate sensitivity and high specificity, as did traction/neck distraction, and Valsalva’s maneuver. The upper limb tension test (ULTT) demonstrated high sensitivity and low specificity, while the shoulder abduction test demonstrated low to moderate sensitivity and moderate to high specificity. Common methodological flaws included lack of an optimal reference standard, disease progression bias, spectrum bias, and review bias. Limitations include few primary studies, substantial heterogeneity, and numerous methodological flaws among the studies; therefore, a meta-analysis was not conducted. This review suggests that, when consistent with the history and other physical findings, a positive Spurling’s, traction/neck distraction, and Valsalva’s might be indicative of a cervical radiculopathy, while a negative ULTT might be used to rule it out. However, the lack of evidence precludes any firm conclusions regarding their diagnostic value, especially when used in primary care. More high quality studies are necessary in order to resolve this issue. PMID:17013656

Use of point-of-care ultrasound in long bone fractures: a systematic review and meta-analysis.

PubMed

Chartier, Lucas B; Bosco, Laura; Lapointe-Shaw, Lauren; Chenkin, Jordan

2017-03-01

Long bone fractures (LBFs) are among the most frequent traumatic injuries seen in emergency departments. Reduction and immobilization is the most common form of treatment for displaced fractures. Point-of-care ultrasound (PoCUS) is a promising technique for diagnosing LBFs and assessing the success of reduction attempts. This article offers a comprehensive review of the use of PoCUS for the diagnosis and reduction of LBFs. Data source MEDLINE and EMBASE databases were searched through July 19, 2015. Study selection We included prospective studies that assessed test characteristics of PoCUS in 1) the diagnosis or 2) the reduction of LBFs. The methodological quality of the included studies was evaluated using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Data extraction Thirty studies met inclusion criteria (n=3,506; overall fracture rate 48.0%). Test characteristics of PoCUS for the diagnosis of LBFs were as follows: sensitivity 64.7%-100%, specificity 79.2%-100%, positive likelihood ratio (LR) 3.11-infinity, and negative LR zero-0.45. Sensitivity and specificity for the adequate reduction of LBFs with PoCUS were 94%-100% and 56%-100%, respectively. PoCUS diagnosis of pediatric forearm fractures in 10 studies showed a pooled sensitivity of 93.1% (95% confidence interval [CI], 87.2%-96.4%) and specificity of 92.9% (95% CI, 86.6%-96.4%), and PoCUS diagnosis of adult ankle fractures in four studies showed a pooled sensitivity of 89.5% (95% CI, 77.0%-95.6%) and specificity of 94.2% (95% CI, 86.1%-97.7%). PoCUS demonstrates good diagnostic accuracy in all LBFs studied, especially in pooled results of diagnosis of pediatric forearm and adult ankle fractures. PoCUS is an appropriate adjunct to plain radiographs for LBFs.

The diagnostic accuracy of screening questionnaires for the identification of adults with epilepsy: a systematic review.

PubMed

Keezer, Mark R; Bouma, Hanni K; Wolfson, Christina

2014-11-01

To describe the diagnostic accuracy of screening questionnaires to identify epilepsy in adults, we performed a systematic review of diagnostic studies that assessed the sensitivity and specificity of such screening questionnaires as compared to a physician's clinical assessment. We searched Ovid MEDLINE (1946 to present) and Ovid EMBASE (1947 to present) for studies that estimated the sensitivity and specificity of nonphysician administered screening questionnaires for adults with epilepsy. Both telephone and in-person administered questionnaires were included, whether applied to population or hospital/clinic-based cohorts. The risk of bias was assessed using the Quality Assessment of Diagnostic Studies-2 (QUADAS-2) tool. Our initial search strategy resulted in 917 records. We found nine studies eligible for inclusion. The estimated sensitivity and specificity of the questionnaires used to identify persons with a lifetime history of epilepsy ranged from 81.5% to 100% and 65.6% to 99.2%, respectively. The sensitivity and specificity of these questionnaires in identifying persons with active epilepsy ranged from 48.6% to 100% and 73.9% to 99.9%, respectively. Overall we found a high risk of bias in patient selection and study flow in the majority of studies. We identified nine validation studies of epilepsy screening questionnaires, summarized their study characteristics, presented their results, and performed a rigorous quality assessment. This review serves as a basis for future studies by providing a systematic review of existing work. Future research addressing previous limitations will ultimately allow us to more accurately estimate the burden and risk of epilepsy in the general population. Wiley Periodicals, Inc. © 2014 International League Against Epilepsy.

Analysis of Publically Available Skin Sensitization Data from REACH Registrations 2008–2014

PubMed Central

Luechtefeld, Thomas; Maertens, Alexandra; Russo, Daniel P.; Rovida, Costanza; Zhu, Hao; Hartung, Thomas

2017-01-01

Summary The public data on skin sensitization from REACH registrations already included 19,111 studies on skin sensitization in December 2014, making it the largest repository of such data so far (1,470 substances with mouse LLNA, 2,787 with GPMT, 762 with both in vivo and in vitro and 139 with only in vitro data). 21% were classified as sensitizers. The extracted skin sensitization data was analyzed to identify relationships in skin sensitization guidelines, visualize structural relationships of sensitizers, and build models to predict sensitization. A chemical with molecular weight > 500 Da is generally considered non-sensitizing owing to low bioavailability, but 49 sensitizing chemicals with a molecular weight > 500 Da were found. A chemical similarity map was produced using PubChem’s 2D Tanimoto similarity metric and Gephi force layout visualization. Nine clusters of chemicals were identified by Blondel’s module recognition algorithm revealing wide module-dependent variation. Approximately 31% of mapped chemicals are Michael’s acceptors but alone this does not imply skin sensitization. A simple sensitization model using molecular weight and five ToxTree structural alerts showed a balanced accuracy of 65.8% (specificity 80.4%, sensitivity 51.4%), demonstrating that structural alerts have information value. A simple variant of k-nearest neighbors outperformed the ToxTree approach even at 75% similarity threshold (82% balanced accuracy at 0.95 threshold). At higher thresholds, the balanced accuracy increased. Lower similarity thresholds decrease sensitivity faster than specificity. This analysis scopes the landscape of chemical skin sensitization, demonstrating the value of large public datasets for health hazard prediction. PMID:26863411

Fourier analysis for hydrostatic pressure sensing in a polarization-maintaining photonic crystal fiber.

PubMed

Childs, Paul; Wong, Allan C L; Fu, H Y; Liao, Yanbiao; Tam, Hwayaw; Lu, Chao; Wai, P K A

2010-12-20

We measured the hydrostatic pressure dependence of the birefringence and birefringent dispersion of a Sagnac interferometric sensor incorporating a length of highly birefringent photonic crystal fiber using Fourier analysis. Sensitivity of both the phase and chirp spectra to hydrostatic pressure is demonstrated. Using this analysis, phase-based measurements showed a good linearity with an effective sensitivity of 9.45 nm/MPa and an accuracy of ±7.8 kPa using wavelength-encoded data and an effective sensitivity of -55.7 cm(-1)/MPa and an accuracy of ±4.4 kPa using wavenumber-encoded data. Chirp-based measurements, though nonlinear in response, showed an improvement in accuracy at certain pressure ranges with an accuracy of ±5.5 kPa for the full range of measured pressures using wavelength-encoded data and dropping to within ±2.5 kPa in the range of 0.17 to 0.4 MPa using wavenumber-encoded data. Improvements of the accuracy demonstrated the usefulness of implementing chirp-based analysis for sensing purposes.

Accuracy of computed tomographic features in differentiating intestinal tuberculosis from Crohn's disease: a systematic review with meta-analysis.

PubMed

Kedia, Saurabh; Sharma, Raju; Sreenivas, Vishnubhatla; Madhusudhan, Kumble Seetharama; Sharma, Vishal; Bopanna, Sawan; Pratap Mouli, Venigalla; Dhingra, Rajan; Yadav, Dawesh Prakash; Makharia, Govind; Ahuja, Vineet

2017-04-01

Abdominal computed tomography (CT) can noninvasively image the entire gastrointestinal tract and assess extraintestinal features that are important in differentiating Crohn's disease (CD) and intestinal tuberculosis (ITB). The present meta-analysis pooled the results of all studies on the role of CT abdomen in differentiating between CD and ITB. We searched PubMed and Embase for all publications in English that analyzed the features differentiating between CD and ITB on abdominal CT. The features included comb sign, necrotic lymph nodes, asymmetric bowel wall thickening, skip lesions, fibrofatty proliferation, mural stratification, ileocaecal area, long segment, and left colonic involvements. Sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio (DOR) were calculated for all the features. Symmetric receiver operating characteristic curve was plotted for features present in >3 studies. Heterogeneity and publication bias was assessed and sensitivity analysis was performed by excluding studies that compared features on conventional abdominal CT instead of CT enterography (CTE). We included 6 studies (4 CTE, 1 conventional abdominal CT, and 1 CTE+conventional abdominal CT) involving 417 and 195 patients with CD and ITB, respectively. Necrotic lymph nodes had the highest diagnostic accuracy (sensitivity, 23%; specificity, 100%; DOR, 30.2) for ITB diagnosis, and comb sign (sensitivity, 82%; specificity, 81%; DOR, 21.5) followed by skip lesions (sensitivity, 86%; specificity, 74%; DOR, 16.5) had the highest diagnostic accuracy for CD diagnosis. On sensitivity analysis, the diagnostic accuracy of other features excluding asymmetric bowel wall thickening remained similar. Necrotic lymph nodes and comb sign on abdominal CT had the best diagnostic accuracy in differentiating CD and ITB.

Administrative database code accuracy did not vary notably with changes in disease prevalence.

PubMed

van Walraven, Carl; English, Shane; Austin, Peter C

2016-11-01

Previous mathematical analyses of diagnostic tests based on the categorization of a continuous measure have found that test sensitivity and specificity varies significantly by disease prevalence. This study determined if the accuracy of diagnostic codes varied by disease prevalence. We used data from two previous studies in which the true status of renal disease and primary subarachnoid hemorrhage, respectively, had been determined. In multiple stratified random samples from the two previous studies having varying disease prevalence, we measured the accuracy of diagnostic codes for each disease using sensitivity, specificity, and positive and negative predictive value. Diagnostic code sensitivity and specificity did not change notably within clinically sensible disease prevalence. In contrast, positive and negative predictive values changed significantly with disease prevalence. Disease prevalence had no important influence on the sensitivity and specificity of diagnostic codes in administrative databases. Copyright © 2016 Elsevier Inc. All rights reserved.

Retina Image Vessel Segmentation Using a Hybrid CGLI Level Set Method

PubMed Central

Chen, Meizhu; Li, Jichun; Zhang, Encai

2017-01-01

As a nonintrusive method, the retina imaging provides us with a better way for the diagnosis of ophthalmologic diseases. Extracting the vessel profile automatically from the retina image is an important step in analyzing retina images. A novel hybrid active contour model is proposed to segment the fundus image automatically in this paper. It combines the signed pressure force function introduced by the Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) model with the local intensity property introduced by the Local Binary fitting (LBF) model to overcome the difficulty of the low contrast in segmentation process. It is more robust to the initial condition than the traditional methods and is easily implemented compared to the supervised vessel extraction methods. Proposed segmentation method was evaluated on two public datasets, DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (Structured Analysis of the Retina) (the average accuracy of 0.9390 with 0.7358 sensitivity and 0.9680 specificity on DRIVE datasets and average accuracy of 0.9409 with 0.7449 sensitivity and 0.9690 specificity on STARE datasets). The experimental results show that our method is effective and our method is also robust to some kinds of pathology images compared with the traditional level set methods. PMID:28840122

Highly sensitive quantification for human plasma-targeted metabolomics using an amine derivatization reagent.

PubMed

Arashida, Naoko; Nishimoto, Rumi; Harada, Masashi; Shimbo, Kazutaka; Yamada, Naoyuki

2017-02-15

Amino acids and their related metabolites play important roles in various physiological processes and have consequently become biomarkers for diseases. However, accurate quantification methods have only been established for major compounds, such as amino acids and a limited number of target metabolites. We previously reported a highly sensitive high-throughput method for the simultaneous quantification of amines using 3-aminopyridyl-N-succinimidyl carbamate as a derivatization reagent combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS). Herein, we report the successful development of a practical and accurate LC-MS/MS method to analyze low concentrations of 40 physiological amines in 19 min. Thirty-five of these amines showed good linearity, limits of quantification, accuracy, precision, and recovery characteristics in plasma, with scheduled selected reaction monitoring acquisitions. Plasma samples from 10 healthy volunteers were evaluated using our newly developed method. The results revealed that 27 amines were detected in one of the samples, and that 24 of these compounds could be quantified. Notably, this new method successfully quantified metabolites with high accuracy across three orders of magnitude, with lowest and highest averaged concentrations of 31.7 nM (for spermine) and 18.3 μM (for α-aminobutyric acid), respectively. Copyright © 2016 Elsevier B.V. All rights reserved.

Automated diagnosis of dry eye using infrared thermography images

NASA Astrophysics Data System (ADS)

Acharya, U. Rajendra; Tan, Jen Hong; Koh, Joel E. W.; Sudarshan, Vidya K.; Yeo, Sharon; Too, Cheah Loon; Chua, Chua Kuang; Ng, E. Y. K.; Tong, Louis

2015-07-01

Dry Eye (DE) is a condition of either decreased tear production or increased tear film evaporation. Prolonged DE damages the cornea causing the corneal scarring, thinning and perforation. There is no single uniform diagnosis test available to date; combinations of diagnostic tests are to be performed to diagnose DE. The current diagnostic methods available are subjective, uncomfortable and invasive. Hence in this paper, we have developed an efficient, fast and non-invasive technique for the automated identification of normal and DE classes using infrared thermography images. The features are extracted from nonlinear method called Higher Order Spectra (HOS). Features are ranked using t-test ranking strategy. These ranked features are fed to various classifiers namely, K-Nearest Neighbor (KNN), Nave Bayesian Classifier (NBC), Decision Tree (DT), Probabilistic Neural Network (PNN), and Support Vector Machine (SVM) to select the best classifier using minimum number of features. Our proposed system is able to identify the DE and normal classes automatically with classification accuracy of 99.8%, sensitivity of 99.8%, and specificity if 99.8% for left eye using PNN and KNN classifiers. And we have reported classification accuracy of 99.8%, sensitivity of 99.9%, and specificity if 99.4% for right eye using SVM classifier with polynomial order 2 kernel.

New support vector machine-based method for microRNA target prediction.

PubMed

Li, L; Gao, Q; Mao, X; Cao, Y

2014-06-09

MicroRNA (miRNA) plays important roles in cell differentiation, proliferation, growth, mobility, and apoptosis. An accurate list of precise target genes is necessary in order to fully understand the importance of miRNAs in animal development and disease. Several computational methods have been proposed for miRNA target-gene identification. However, these methods still have limitations with respect to their sensitivity and accuracy. Thus, we developed a new miRNA target-prediction method based on the support vector machine (SVM) model. The model supplies information of two binding sites (primary and secondary) for a radial basis function kernel as a similarity measure for SVM features. The information is categorized based on structural, thermodynamic, and sequence conservation. Using high-confidence datasets selected from public miRNA target databases, we obtained a human miRNA target SVM classifier model with high performance and provided an efficient tool for human miRNA target gene identification. Experiments have shown that our method is a reliable tool for miRNA target-gene prediction, and a successful application of an SVM classifier. Compared with other methods, the method proposed here improves the sensitivity and accuracy of miRNA prediction. Its performance can be further improved by providing more training examples.

Dual ultrasonic-assisted dispersive liquid-liquid microextraction coupled with microwave-assisted derivatization for simultaneous determination of 20(S)-protopanaxadiol and 20(S)-protopanaxatriol by ultra high performance liquid chromatography-tandem mass spectrometry.

PubMed

Zhao, Xian-En; Lv, Tao; Zhu, Shuyun; Qu, Fei; Chen, Guang; He, Yongrui; Wei, Na; Li, Guoliang; Xia, Lian; Sun, Zhiwei; Zhang, Shijuan; You, Jinmao; Liu, Shu; Liu, Zhiqiang; Sun, Jing; Liu, Shuying

2016-03-11

This paper, for the first time, reported a speedy hyphenated technique of low toxic dual ultrasonic-assisted dispersive liquid-liquid microextraction (dual-UADLLME) coupled with microwave-assisted derivatization (MAD) for the simultaneous determination of 20(S)-protopanaxadiol (PPD) and 20(S)-protopanaxatriol (PPT). The developed method was based on ultra high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) detection using multiple-reaction monitoring (MRM) mode. A mass spectrometry sensitizing reagent, 4'-carboxy-substituted rosamine (CSR) with high reaction activity and ionization efficiency was synthesized and firstly used as derivatization reagent. Parameters of dual-UADLLME, MAD and UHPLC-MS/MS conditions were all optimized in detail. Low toxic brominated solvents were used as extractant instead of traditional chlorinated solvents. Satisfactory linearity, recovery, repeatability, accuracy and precision, absence of matrix effect and extremely low limits of detection (LODs, 0.010 and 0.015ng/mL for PPD and PPT, respectively) were achieved. The main advantages were rapid, sensitive and environmentally friendly, and exhibited high selectivity, accuracy and good matrix effect results. The proposed method was successfully applied to pharmacokinetics of PPD and PPT in rat plasma. Copyright © 2016 Elsevier B.V. All rights reserved.

Identifying Galactic Cosmic Ray Origins With Super-TIGER

NASA Technical Reports Server (NTRS)

deNolfo, Georgia; Binns, W. R.; Israel, M. H.; Christian, E. R.; Mitchell, J. W.; Hams, T.; Link, J. T.; Sasaki, M.; Labrador, A. W.; Mewaldt, R. A.;

Can continuous scans in orthogonal planes improve diagnostic performance of shear wave elastography for breast lesions?

PubMed

Yang, Pan; Peng, Yulan; Zhao, Haina; Luo, Honghao; Jin, Ya; He, Yushuang

2015-01-01

Static shear wave elastography (SWE) is used to detect breast lesions, but slice and plane selections result in discrepancies. To evaluate the intraobserver reproducibility of continuous SWE, and whether quantitative elasticities in orthogonal planes perform better in the differential diagnosis of breast lesions. One hundred and twenty-two breast lesions scheduled for ultrasound-guided biopsy were recruited. Continuous SWE scans were conducted in orthogonal planes separately. Quantitative elasticities and histopathology results were collected. Reproducibility in the same plane and diagnostic performance in different planes were evaluated. The maximum and mean elasticities of the hardest portion, and standard deviation of whole lesion, had high inter-class correlation coefficients (0.87 to 0.95) and large areas under receiver operation characteristic curve (0.887 to 0.899). Without loss of accuracy, sensitivities had increased in orthogonal planes compared with single plane (from 73.17% up to 82.93% at most). Mean elasticity of whole lesion and lesion-to-parenchyma ratio were significantly less reproducible and less accurate. Continuous SWE is highly reproducible for the same observer. The maximum and mean elasticities of the hardest portion and standard deviation of whole lesion are most reliable. Furthermore, the sensitivities of the three parameters are improved in orthogonal planes without loss of accuracies.

Multi-exposure speckle imaging of cerebral blood flow: a pilot clinical study (Conference Presentation)

NASA Astrophysics Data System (ADS)

Richards, Lisa M.; Kazmi, S. M. S.; Olin, Katherine E.; Waldron, James S.; Fox, Douglas J.; Dunn, Andrew K.

2017-03-01

Monitoring cerebral blood flow (CBF) during neurosurgery is essential for detecting ischemia in a timely manner for a wide range of procedures. Multiple clinical studies have demonstrated that laser speckle contrast imaging (LSCI) has high potential to be a valuable, label-free CBF monitoring technique during neurosurgery. LSCI is an optical imaging method that provides blood flow maps with high spatiotemporal resolution requiring only a coherent light source, a lens system, and a camera. However, the quantitative accuracy and sensitivity of LSCI is limited and highly dependent on the exposure time. An extension to LSCI called multi-exposure speckle imaging (MESI) overcomes these limitations, and was evaluated intraoperatively in patients undergoing brain tumor resection. This clinical study (n = 7) recorded multiple exposure times from the same cortical tissue area, and demonstrates that shorter exposure times (≤1 ms) provide the highest dynamic range and sensitivity for sampling flow rates in human neurovasculature. This study also combined exposure times using the MESI model, demonstrating high correlation with proper image calibration and acquisition. The physiological accuracy of speckle-estimated flow was validated using conservation of flow analysis on vascular bifurcations. Flow estimates were highly conserved in MESI and 1 ms exposure LSCI, with percent errors at 6.4% ± 5.3% and 7.2% ± 7.2%, respectively, while 5 ms exposure LSCI had higher errors at 21% ± 10% (n = 14 bifurcations). Results from this study demonstrate the importance of exposure time selection for LSCI, and that intraoperative MESI can be performed with high quantitative accuracy.

Accuracy and adequacy of waist circumference cut-off points currently recommended in Brazilian adults.

PubMed

Vianna, Carolina Avila; da Silva Linhares, Rogério; Bielemann, Renata Moraes; Machado, Eduardo Coelho; González-Chica, David Alejandro; Matijasevich, Alicia Manitto; Gigante, Denise Petrucci; da Silva Dos Santos, Iná

2014-04-01

To evaluate the adequacy and accuracy of cut-off values currently recommended by the WHO for assessment of cardiovascular risk in southern Brazil. Population-based study aimed at determining the predictive ability of waist circumference for cardiovascular risk based on the use of previous medical diagnosis for hypertension, diabetes mellitus and/or dyslipidaemia. Descriptive analysis was used for the adequacy of current cut-off values of waist circumference, receiver operating characteristic curves were constructed and the most accurate criteria according to the Youden index and points of optimal sensitivity and specificity were identified. Pelotas, southern Brazil. Individuals (n 2112) aged ≥20 years living in the city were selected by multistage sampling, since these individuals did not report the presence of previous myocardial infarction, angina pectoris or stroke. The cut-off values currently recommended by WHO were more appropriate in men than women, with overestimation of cardiovascular risk in women. The area under the receiver operating characteristic curve showed moderate predictive ability of waist circumference in men (0.74, 95% CI 0.71, 0.76) and women (0.75, 95% CI 0.73, 0.77). The method of optimal sensitivity and specificity showed better performance in assessing the accuracy, identifying the values of 95 cm in men and 87 cm in women as the best cut-off values of waist circumference to assess cardiovascular risk. The cut-off values currently recommended for waist circumference are not suitable for women. Longitudinal studies should be conducted to evaluate the consistency of the findings.

Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images.

PubMed

Marchetti, Michael A; Codella, Noel C F; Dusza, Stephen W; Gutman, David A; Helba, Brian; Kalloo, Aadi; Mishra, Nabin; Carrera, Cristina; Celebi, M Emre; DeFazio, Jennifer L; Jaimes, Natalia; Marghoob, Ashfaq A; Quigley, Elizabeth; Scope, Alon; Yélamos, Oriol; Halpern, Allan C

2018-02-01

Computer vision may aid in melanoma detection. We sought to compare melanoma diagnostic accuracy of computer algorithms to dermatologists using dermoscopic images. We conducted a cross-sectional study using 100 randomly selected dermoscopic images (50 melanomas, 44 nevi, and 6 lentigines) from an international computer vision melanoma challenge dataset (n = 379), along with individual algorithm results from 25 teams. We used 5 methods (nonlearned and machine learning) to combine individual automated predictions into "fusion" algorithms. In a companion study, 8 dermatologists classified the lesions in the 100 images as either benign or malignant. The average sensitivity and specificity of dermatologists in classification was 82% and 59%. At 82% sensitivity, dermatologist specificity was similar to the top challenge algorithm (59% vs. 62%, P = .68) but lower than the best-performing fusion algorithm (59% vs. 76%, P = .02). Receiver operating characteristic area of the top fusion algorithm was greater than the mean receiver operating characteristic area of dermatologists (0.86 vs. 0.71, P = .001). The dataset lacked the full spectrum of skin lesions encountered in clinical practice, particularly banal lesions. Readers and algorithms were not provided clinical data (eg, age or lesion history/symptoms). Results obtained using our study design cannot be extrapolated to clinical practice. Deep learning computer vision systems classified melanoma dermoscopy images with accuracy that exceeded some but not all dermatologists. Copyright © 2017 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

Characterization and discrimination of human breast cancer and normal breast tissues using resonance Raman spectroscopy

NASA Astrophysics Data System (ADS)

Wu, Binlin; Smith, Jason; Zhang, Lin; Gao, Xin; Alfano, Robert R.

2018-02-01

Worldwide breast cancer incidence has increased by more than twenty percent in the past decade. It is also known that in that time, mortality due to the affliction has increased by fourteen percent. Using optical-based diagnostic techniques, such as Raman spectroscopy, has been explored in order to increase diagnostic accuracy in a more objective way along with significantly decreasing diagnostic wait-times. In this study, Raman spectroscopy with 532-nm excitation was used in order to incite resonance effects to enhance Stokes Raman scattering from unique biomolecular vibrational modes. Seventy-two Raman spectra (41 cancerous, 31 normal) were collected from nine breast tissue samples by performing a ten-spectra average using a 500-ms acquisition time at each acquisition location. The raw spectral data was subsequently prepared for analysis with background correction and normalization. The spectral data in the Raman Shift range of 750- 2000 cm-1 was used for analysis since the detector has highest sensitivity around in this range. The matrix decomposition technique nonnegative matrix factorization (NMF) was then performed on this processed data. The resulting leave-oneout cross-validation using two selective feature components resulted in sensitivity, specificity and accuracy of 92.6%, 100% and 96.0% respectively. The performance of NMF was also compared to that using principal component analysis (PCA), and NMF was shown be to be superior to PCA in this study. This study shows that coupling the resonance Raman spectroscopy technique with subsequent NMF decomposition method shows potential for high characterization accuracy in breast cancer detection.

Diminished Sensitivity to Sad Facial Expressions in High Functioning Autism Spectrum Disorders Is Associated with Symptomatology and Adaptive Functioning

ERIC Educational Resources Information Center

Wallace, Gregory L.; Case, Laura K.; Harms, Madeline B.; Silvers, Jennifer A.; Kenworthy, Lauren; Martin, Alex

2011-01-01

Prior studies implicate facial emotion recognition (FER) difficulties among individuals with autism spectrum disorders (ASD); however, many investigations focus on FER accuracy alone and few examine ecological validity through links with everyday functioning. We compared FER accuracy and perceptual sensitivity (from neutral to full expression)…

Potential and limits to unravel the genetic architecture and predict the variation of Fusarium head blight resistance in European winter wheat (Triticum aestivum L.).

PubMed

Jiang, Y; Zhao, Y; Rodemann, B; Plieske, J; Kollers, S; Korzun, V; Ebmeyer, E; Argillier, O; Hinze, M; Ling, J; Röder, M S; Ganal, M W; Mette, M F; Reif, J C

2015-03-01

Genome-wide mapping approaches in diverse populations are powerful tools to unravel the genetic architecture of complex traits. The main goals of our study were to investigate the potential and limits to unravel the genetic architecture and to identify the factors determining the accuracy of prediction of the genotypic variation of Fusarium head blight (FHB) resistance in wheat (Triticum aestivum L.) based on data collected with a diverse panel of 372 European varieties. The wheat lines were phenotyped in multi-location field trials for FHB resistance and genotyped with 782 simple sequence repeat (SSR) markers, and 9k and 90k single-nucleotide polymorphism (SNP) arrays. We applied genome-wide association mapping in combination with fivefold cross-validations and observed surprisingly high accuracies of prediction for marker-assisted selection based on the detected quantitative trait loci (QTLs). Using a random sample of markers not selected for marker-trait associations revealed only a slight decrease in prediction accuracy compared with marker-based selection exploiting the QTL information. The same picture was confirmed in a simulation study, suggesting that relatedness is a main driver of the accuracy of prediction in marker-assisted selection of FHB resistance. When the accuracy of prediction of three genomic selection models was contrasted for the three marker data sets, no significant differences in accuracies among marker platforms and genomic selection models were observed. Marker density impacted the accuracy of prediction only marginally. Consequently, genomic selection of FHB resistance can be implemented most cost-efficiently based on low- to medium-density SNP arrays.

Integration of genomic information into sport horse breeding programs for optimization of accuracy of selection.

PubMed

Haberland, A M; König von Borstel, U; Simianer, H; König, S

2012-09-01

Reliable selection criteria are required for young riding horses to increase genetic gain by increasing accuracy of selection and decreasing generation intervals. In this study, selection strategies incorporating genomic breeding values (GEBVs) were evaluated. Relevant stages of selection in sport horse breeding programs were analyzed by applying selection index theory. Results in terms of accuracies of indices (r(TI) ) and relative selection response indicated that information on single nucleotide polymorphism (SNP) genotypes considerably increases the accuracy of breeding values estimated for young horses without own or progeny performance. In a first scenario, the correlation between the breeding value estimated from the SNP genotype and the true breeding value (= accuracy of GEBV) was fixed to a relatively low value of r(mg) = 0.5. For a low heritability trait (h(2) = 0.15), and an index for a young horse based only on information from both parents, additional genomic information doubles r(TI) from 0.27 to 0.54. Including the conventional information source 'own performance' into the before mentioned index, additional SNP information increases r(TI) by 40%. Thus, particularly with regard to traits of low heritability, genomic information can provide a tool for well-founded selection decisions early in life. In a further approach, different sources of breeding values (e.g. GEBV and estimated breeding values (EBVs) from different countries) were combined into an overall index when altering accuracies of EBVs and correlations between traits. In summary, we showed that genomic selection strategies have the potential to contribute to a substantial reduction in generation intervals in horse breeding programs.

On the Exploitation of Sensitivity Derivatives for Improving Sampling Methods

NASA Technical Reports Server (NTRS)

Cao, Yanzhao; Hussaini, M. Yousuff; Zang, Thomas A.

2003-01-01

Many application codes, such as finite-element structural analyses and computational fluid dynamics codes, are capable of producing many sensitivity derivatives at a small fraction of the cost of the underlying analysis. This paper describes a simple variance reduction method that exploits such inexpensive sensitivity derivatives to increase the accuracy of sampling methods. Three examples, including a finite-element structural analysis of an aircraft wing, are provided that illustrate an order of magnitude improvement in accuracy for both Monte Carlo and stratified sampling schemes.

B cell increases and ex vivo IL-2 production as secondary endpoints for the detection of sensitizers in non-radioisotopic local lymph node assay using flow cytometry.

PubMed

Jung, Kyoung-Mi; Jang, Won-Hee; Lee, Yong-Kyoung; Yum, Young Na; Sohn, Soojung; Kim, Bae-Hwan; Chung, Jin-Ho; Park, Young-Ho; Lim, Kyung-Min

2012-03-25

Non-radioisotopic local lymph node assay (LLNA) using 5-bromo-2'-deoxyuridine (BrdU) with flow cytometry (FCM) is gaining attention since it is free from the regulatory issues in traditional LLNA (tLLNA) accompanying in vivo uses of radioisotope, (3)H-thymidine. However, there is also concern over compromised performance of non-radioisotopic LLNA, raising needs for additional endpoints to improve the accuracy. With the full 22 reference substances enlisted in OECD Test Guideline No. 429, we evaluated the performance of LLNA:BrdU-FCM along with the concomitant measurements of B/T cell ratio and ex vivo cytokine production from isolated lymph node cells (LNCs) to examine the utility of these markers as secondary endpoints. Mice (Balb/c, female) were topically treated with substances on both ears for 3 days and then, BrdU was intraperitoneally injected on day 5. After a day, lymph nodes were isolated and undergone FCM to determine BrdU incorporation and B/T cell sub-typing with B220+ and CD3e+. Ex vivo cytokine production by LNCs was measured such as IL-2, IL-4, IL-6, IL-12, IFN-γ, MCP-1, GM-CSF and TNFα. Mice treated with sensitizers showed preferential increases in B cell population and the selective production of IL-2, which matched well with the increases in BrdU incorporation. When compared with guinea pig or human data, BrdU incorporation, B cell increase and IL-2 production ex vivo could successfully identify sensitizers with the accuracy comparable to tLLNA, suggesting that these markers may be useful for improving the accuracy of LLNA:BrdU-FCM or as stand-alone non-radioisotopic endpoints. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images.

PubMed

Guo, Shengwen; Lai, Chunren; Wu, Congling; Cen, Guiyin

2017-01-01

Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI-cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI-NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI-NC comparison. The best performances obtained by the SVM classifier using the essential features were 5-40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease.

Prediction of Dementia in Primary Care Patients

PubMed Central

Jessen, Frank; Wiese, Birgitt; Bickel, Horst; Eiffländer-Gorfer, Sandra; Fuchs, Angela; Kaduszkiewicz, Hanna; Köhler, Mirjam; Luck, Tobias; Mösch, Edelgard; Pentzek, Michael; Riedel-Heller, Steffi G.; Wagner, Michael; Weyerer, Siegfried; Maier, Wolfgang; van den Bussche, Hendrik

2011-01-01

Background Current approaches for AD prediction are based on biomarkers, which are however of restricted availability in primary care. AD prediction tools for primary care are therefore needed. We present a prediction score based on information that can be obtained in the primary care setting. Methodology/Principal Findings We performed a longitudinal cohort study in 3.055 non-demented individuals above 75 years recruited via primary care chart registries (Study on Aging, Cognition and Dementia, AgeCoDe). After the baseline investigation we performed three follow-up investigations at 18 months intervals with incident dementia as the primary outcome. The best set of predictors was extracted from the baseline variables in one randomly selected half of the sample. This set included age, subjective memory impairment, performance on delayed verbal recall and verbal fluency, on the Mini-Mental-State-Examination, and on an instrumental activities of daily living scale. These variables were aggregated to a prediction score, which achieved a prediction accuracy of 0.84 for AD. The score was applied to the second half of the sample (test cohort). Here, the prediction accuracy was 0.79. With a cut-off of at least 80% sensitivity in the first cohort, 79.6% sensitivity, 66.4% specificity, 14.7% positive predictive value (PPV) and 97.8% negative predictive value of (NPV) for AD were achieved in the test cohort. At a cut-off for a high risk population (5% of individuals with the highest risk score in the first cohort) the PPV for AD was 39.1% (52% for any dementia) in the test cohort. Conclusions The prediction score has useful prediction accuracy. It can define individuals (1) sensitively for low cost-low risk interventions, or (2) more specific and with increased PPV for measures of prevention with greater costs or risks. As it is independent of technical aids, it may be used within large scale prevention programs. PMID:21364746

Evaluation of an On-Farm Culture System (Accumast) for Fast Identification of Milk Pathogens Associated with Clinical Mastitis in Dairy Cows.

PubMed

Ganda, Erika Korzune; Bisinotto, Rafael Sisconeto; Decter, Dean Harrison; Bicalho, Rodrigo Carvalho

2016-01-01

The present study aimed evaluate an on-farm culture system for identification of milk pathogens associated with clinical mastitis in dairy cows using two different gold standard approaches: standard laboratory culture in study 1 and 16S rRNA sequencing in study 2. In study 1, milk from mastitic quarters (i.e. presence of flakes, clots, or serous milk; n = 538) was cultured on-farm using a single plate containing three selective chromogenic media (Accumast-FERA Animal Health LCC, Ithaca, NY) and in a reference laboratory using standard culture methods, which was considered the gold standard. In study 2, mastitic milk was cultured on-farm and analyzed through 16S rRNA sequencing (n = 214). In both studies, plates were cultured aerobically at 37°C for 24 h and read by a single technician masked to gold standard results. Accuracy, sensitivity, specificity, positive (PPV) and negative predictive value (NPV) were calculated based on standard laboratory culture in study 1, and PPV was calculated based on sequencing results in study 2. Overall accuracy of Accumast was 84.9%. Likewise, accuracy for identification of Gram-negative bacteria, Staphylococcus sp., and Streptococcus sp. was 96.4%, 93.8%, and 91.5%, respectively. Sensitivity, specificity, PPV, and NPV were 75.0%, 97.9%, 79.6%, and 97.3% for identification of E. coli, 100.0%, 99.8%, 87.5%, and 100.0% for S. aureus, 70.0%, 95.0%, 45.7%, and 98.1% for other Staphylococcus sp., and 90.0%, 92.9%, 91.8%, and 91.2% for Streptococcus sp. In study 2, Accumast PPV was 96.7% for E. coli, 100.0% for Enterococcus sp., 100.0% for Other Gram-negatives, 88.2% for Staphylococcus sp., and 95.0% for Streptococcus sp., respectively. In conclusion, Accumast is a unique approach for on-farm identification pathogens associated with mastitis, presenting overall sensitivity and specificity of 82.3% and 89.9% respectively.

MP3: a software tool for the prediction of pathogenic proteins in genomic and metagenomic data.

PubMed

Gupta, Ankit; Kapil, Rohan; Dhakan, Darshan B; Sharma, Vineet K

2014-01-01

The identification of virulent proteins in any de-novo sequenced genome is useful in estimating its pathogenic ability and understanding the mechanism of pathogenesis. Similarly, the identification of such proteins could be valuable in comparing the metagenome of healthy and diseased individuals and estimating the proportion of pathogenic species. However, the common challenge in both the above tasks is the identification of virulent proteins since a significant proportion of genomic and metagenomic proteins are novel and yet unannotated. The currently available tools which carry out the identification of virulent proteins provide limited accuracy and cannot be used on large datasets. Therefore, we have developed an MP3 standalone tool and web server for the prediction of pathogenic proteins in both genomic and metagenomic datasets. MP3 is developed using an integrated Support Vector Machine (SVM) and Hidden Markov Model (HMM) approach to carry out highly fast, sensitive and accurate prediction of pathogenic proteins. It displayed Sensitivity, Specificity, MCC and accuracy values of 92%, 100%, 0.92 and 96%, respectively, on blind dataset constructed using complete proteins. On the two metagenomic blind datasets (Blind A: 51-100 amino acids and Blind B: 30-50 amino acids), it displayed Sensitivity, Specificity, MCC and accuracy values of 82.39%, 97.86%, 0.80 and 89.32% for Blind A and 71.60%, 94.48%, 0.67 and 81.86% for Blind B, respectively. In addition, the performance of MP3 was validated on selected bacterial genomic and real metagenomic datasets. To our knowledge, MP3 is the only program that specializes in fast and accurate identification of partial pathogenic proteins predicted from short (100-150 bp) metagenomic reads and also performs exceptionally well on complete protein sequences. MP3 is publicly available at http://metagenomics.iiserb.ac.in/mp3/index.php.

MP3: A Software Tool for the Prediction of Pathogenic Proteins in Genomic and Metagenomic Data

PubMed Central

Gupta, Ankit; Kapil, Rohan; Dhakan, Darshan B.; Sharma, Vineet K.

2014-01-01

The identification of virulent proteins in any de-novo sequenced genome is useful in estimating its pathogenic ability and understanding the mechanism of pathogenesis. Similarly, the identification of such proteins could be valuable in comparing the metagenome of healthy and diseased individuals and estimating the proportion of pathogenic species. However, the common challenge in both the above tasks is the identification of virulent proteins since a significant proportion of genomic and metagenomic proteins are novel and yet unannotated. The currently available tools which carry out the identification of virulent proteins provide limited accuracy and cannot be used on large datasets. Therefore, we have developed an MP3 standalone tool and web server for the prediction of pathogenic proteins in both genomic and metagenomic datasets. MP3 is developed using an integrated Support Vector Machine (SVM) and Hidden Markov Model (HMM) approach to carry out highly fast, sensitive and accurate prediction of pathogenic proteins. It displayed Sensitivity, Specificity, MCC and accuracy values of 92%, 100%, 0.92 and 96%, respectively, on blind dataset constructed using complete proteins. On the two metagenomic blind datasets (Blind A: 51–100 amino acids and Blind B: 30–50 amino acids), it displayed Sensitivity, Specificity, MCC and accuracy values of 82.39%, 97.86%, 0.80 and 89.32% for Blind A and 71.60%, 94.48%, 0.67 and 81.86% for Blind B, respectively. In addition, the performance of MP3 was validated on selected bacterial genomic and real metagenomic datasets. To our knowledge, MP3 is the only program that specializes in fast and accurate identification of partial pathogenic proteins predicted from short (100–150 bp) metagenomic reads and also performs exceptionally well on complete protein sequences. MP3 is publicly available at http://metagenomics.iiserb.ac.in/mp3/index.php. PMID:24736651

Evaluation of an On-Farm Culture System (Accumast) for Fast Identification of Milk Pathogens Associated with Clinical Mastitis in Dairy Cows

PubMed Central

Ganda, Erika Korzune; Bisinotto, Rafael Sisconeto; Decter, Dean Harrison; Bicalho, Rodrigo Carvalho

2016-01-01

The present study aimed evaluate an on-farm culture system for identification of milk pathogens associated with clinical mastitis in dairy cows using two different gold standard approaches: standard laboratory culture in study 1 and 16S rRNA sequencing in study 2. In study 1, milk from mastitic quarters (i.e. presence of flakes, clots, or serous milk; n = 538) was cultured on-farm using a single plate containing three selective chromogenic media (Accumast—FERA Animal Health LCC, Ithaca, NY) and in a reference laboratory using standard culture methods, which was considered the gold standard. In study 2, mastitic milk was cultured on-farm and analyzed through 16S rRNA sequencing (n = 214). In both studies, plates were cultured aerobically at 37°C for 24 h and read by a single technician masked to gold standard results. Accuracy, sensitivity, specificity, positive (PPV) and negative predictive value (NPV) were calculated based on standard laboratory culture in study 1, and PPV was calculated based on sequencing results in study 2. Overall accuracy of Accumast was 84.9%. Likewise, accuracy for identification of Gram-negative bacteria, Staphylococcus sp., and Streptococcus sp. was 96.4%, 93.8%, and 91.5%, respectively. Sensitivity, specificity, PPV, and NPV were 75.0%, 97.9%, 79.6%, and 97.3% for identification of E. coli, 100.0%, 99.8%, 87.5%, and 100.0% for S. aureus, 70.0%, 95.0%, 45.7%, and 98.1% for other Staphylococcus sp., and 90.0%, 92.9%, 91.8%, and 91.2% for Streptococcus sp. In study 2, Accumast PPV was 96.7% for E. coli, 100.0% for Enterococcus sp., 100.0% for Other Gram-negatives, 88.2% for Staphylococcus sp., and 95.0% for Streptococcus sp., respectively. In conclusion, Accumast is a unique approach for on-farm identification pathogens associated with mastitis, presenting overall sensitivity and specificity of 82.3% and 89.9% respectively. PMID:27176216

Prediction of dementia in primary care patients.

PubMed

Jessen, Frank; Wiese, Birgitt; Bickel, Horst; Eiffländer-Gorfer, Sandra; Fuchs, Angela; Kaduszkiewicz, Hanna; Köhler, Mirjam; Luck, Tobias; Mösch, Edelgard; Pentzek, Michael; Riedel-Heller, Steffi G; Wagner, Michael; Weyerer, Siegfried; Maier, Wolfgang; van den Bussche, Hendrik

2011-02-18

Current approaches for AD prediction are based on biomarkers, which are however of restricted availability in primary care. AD prediction tools for primary care are therefore needed. We present a prediction score based on information that can be obtained in the primary care setting. We performed a longitudinal cohort study in 3.055 non-demented individuals above 75 years recruited via primary care chart registries (Study on Aging, Cognition and Dementia, AgeCoDe). After the baseline investigation we performed three follow-up investigations at 18 months intervals with incident dementia as the primary outcome. The best set of predictors was extracted from the baseline variables in one randomly selected half of the sample. This set included age, subjective memory impairment, performance on delayed verbal recall and verbal fluency, on the Mini-Mental-State-Examination, and on an instrumental activities of daily living scale. These variables were aggregated to a prediction score, which achieved a prediction accuracy of 0.84 for AD. The score was applied to the second half of the sample (test cohort). Here, the prediction accuracy was 0.79. With a cut-off of at least 80% sensitivity in the first cohort, 79.6% sensitivity, 66.4% specificity, 14.7% positive predictive value (PPV) and 97.8% negative predictive value of (NPV) for AD were achieved in the test cohort. At a cut-off for a high risk population (5% of individuals with the highest risk score in the first cohort) the PPV for AD was 39.1% (52% for any dementia) in the test cohort. The prediction score has useful prediction accuracy. It can define individuals (1) sensitively for low cost-low risk interventions, or (2) more specific and with increased PPV for measures of prevention with greater costs or risks. As it is independent of technical aids, it may be used within large scale prevention programs.

Genomic prediction of piglet response to infection with one of two porcine reproductive and respiratory syndrome virus isolates.

PubMed

Waide, Emily H; Tuggle, Christopher K; Serão, Nick V L; Schroyen, Martine; Hess, Andrew; Rowland, Raymond R R; Lunney, Joan K; Plastow, Graham; Dekkers, Jack C M

2018-02-01

Genomic prediction of the pig's response to the porcine reproductive and respiratory syndrome (PRRS) virus (PRRSV) would be a useful tool in the swine industry. This study investigated the accuracy of genomic prediction based on porcine SNP60 Beadchip data using training and validation datasets from populations with different genetic backgrounds that were challenged with different PRRSV isolates. Genomic prediction accuracy averaged 0.34 for viral load (VL) and 0.23 for weight gain (WG) following experimental PRRSV challenge, which demonstrates that genomic selection could be used to improve response to PRRSV infection. Training on WG data during infection with a less virulent PRRSV, KS06, resulted in poor accuracy of prediction for WG during infection with a more virulent PRRSV, NVSL. Inclusion of single nucleotide polymorphisms (SNPs) that are in linkage disequilibrium with a major quantitative trait locus (QTL) on chromosome 4 was vital for accurate prediction of VL. Overall, SNPs that were significantly associated with either trait in single SNP genome-wide association analysis were unable to predict the phenotypes with an accuracy as high as that obtained by using all genotyped SNPs across the genome. Inclusion of data from close relatives into the training population increased whole genome prediction accuracy by 33% for VL and by 37% for WG but did not affect the accuracy of prediction when using only SNPs in the major QTL region. Results show that genomic prediction of response to PRRSV infection is moderately accurate and, when using all SNPs on the porcine SNP60 Beadchip, is not very sensitive to differences in virulence of the PRRSV in training and validation populations. Including close relatives in the training population increased prediction accuracy when using the whole genome or SNPs other than those near a major QTL.

Carbon nanotubes-based chemiresistive immunosensor for small molecules: detection of nitroaromatic explosives.

PubMed

Park, Miso; Cella, Lakshmi N; Chen, Wilfred; Myung, Nosang V; Mulchandani, Ashok

2010-12-15

In recent years, there has been a growing focus on use of one-dimensional (1-D) nanostructures, such as carbon nanotubes and nanowires, as transducer elements for label-free chemiresistive/field-effect transistor biosensors as they provide label-free and high sensitivity detection. While research to-date has elucidated the power of carbon nanotubes- and other 1-D nanostructure-based field effect transistors immunosensors for large charged macromolecules such as proteins and viruses, their application to small uncharged or charged molecules has not been demonstrated. In this paper we report a single-walled carbon nanotubes (SWNTs)-based chemiresistive immunosensor for label-free, rapid, sensitive and selective detection of 2,4,6-trinitrotoluene (TNT), a small molecule. The newly developed immunosensor employed a displacement mode/format in which SWNTs network forming conduction channel of the sensor was first modified with trinitrophenyl (TNP), an analog of TNT, and then ligated with the anti-TNP single chain antibody. Upon exposure to TNT or its derivatives the bound antibodies were displaced producing a large change, several folds higher than the noise, in the resistance/conductance of SWNTs giving excellent limit of detection, sensitivity and selectivity. The sensor detected between 0.5 ppb and 5000 ppb TNT with good selectivity to other nitroaromatic explosives and demonstrated good accuracy for monitoring TNT in untreated environmental water matrix. We believe this new displacement format can be easily generalized to other one-dimensional nanostructure-based chemiresistive immuno/affinity-sensors for detecting small and/or uncharged molecules of interest in environmental monitoring and health care. Copyright © 2010 Elsevier B.V. All rights reserved.

Social stress reactivity alters reward and punishment learning

PubMed Central

Frank, Michael J.; Allen, John J. B.

2011-01-01

To examine how stress affects cognitive functioning, individual differences in trait vulnerability (punishment sensitivity) and state reactivity (negative affect) to social evaluative threat were examined during concurrent reinforcement learning. Lower trait-level punishment sensitivity predicted better reward learning and poorer punishment learning; the opposite pattern was found in more punishment sensitive individuals. Increasing state-level negative affect was directly related to punishment learning accuracy in highly punishment sensitive individuals, but these measures were inversely related in less sensitive individuals. Combined electrophysiological measurement, performance accuracy and computational estimations of learning parameters suggest that trait and state vulnerability to stress alter cortico-striatal functioning during reinforcement learning, possibly mediated via medio-frontal cortical systems. PMID:20453038

Social stress reactivity alters reward and punishment learning.

PubMed

Cavanagh, James F; Frank, Michael J; Allen, John J B

2011-06-01

To examine how stress affects cognitive functioning, individual differences in trait vulnerability (punishment sensitivity) and state reactivity (negative affect) to social evaluative threat were examined during concurrent reinforcement learning. Lower trait-level punishment sensitivity predicted better reward learning and poorer punishment learning; the opposite pattern was found in more punishment sensitive individuals. Increasing state-level negative affect was directly related to punishment learning accuracy in highly punishment sensitive individuals, but these measures were inversely related in less sensitive individuals. Combined electrophysiological measurement, performance accuracy and computational estimations of learning parameters suggest that trait and state vulnerability to stress alter cortico-striatal functioning during reinforcement learning, possibly mediated via medio-frontal cortical systems.

Assessing the dependence of sensitivity and specificity on prevalence in meta-analysis

PubMed Central

Li, Jialiang; Fine, Jason P.

2011-01-01

We consider modeling the dependence of sensitivity and specificity on the disease prevalence in diagnostic accuracy studies. Many meta-analyses compare test accuracy across studies and fail to incorporate the possible connection between the accuracy measures and the prevalence. We propose a Pearson type correlation coefficient and an estimating equation–based regression framework to help understand such a practical dependence. The results we derive may then be used to better interpret the results from meta-analyses. In the biomedical examples analyzed in this paper, the diagnostic accuracy of biomarkers are shown to be associated with prevalence, providing insights into the utility of these biomarkers in low- and high-prevalence populations. PMID:21525421

Selection on signal–reward correlation: limits and opportunities to the evolution of deceit in Turnera ulmifolia L.

PubMed

Benitez-Vieyra, S; Ordano, M; Fornoni, J; Boege, K; Domínguez, C A

2010-12-01

Because pollinators are unable to directly assess the amount of rewards offered by flowers, they rely on the information provided by advertising floral traits. Thus, having a lower intra-individual correlation between signal and reward (signal accuracy) than other plants in the population provides the opportunity to reduce investment in rewards and cheat pollinators. However, pollinators' cognitive capacities can impose a limit to the evolution of this plant cheating strategy if they can punish those plants with low signal accuracy. In this study, we examined the opportunity for cheating in the perennial weed Turnera ulmifolia L. evaluating the selective value of signal accuracy, floral display and reward production in a natural population. We found that plant reproductive success was positively related to signal accuracy and floral display, but not to nectar production. The intensity of selection on floral display was more than three times higher than on signal accuracy. The pattern of selection indicated that pollinators can select for signal accuracy provided by plants and suggests that learning abilities of pollinators can limit the evolution of deceptive strategies in T. ulmifolia. © 2010 The Authors. Journal Compilation © 2010 European Society For Evolutionary Biology.

Methodological quality of diagnostic accuracy studies on non-invasive coronary CT angiography: influence of QUADAS (Quality Assessment of Diagnostic Accuracy Studies included in systematic reviews) items on sensitivity and specificity.

PubMed

Schueler, Sabine; Walther, Stefan; Schuetz, Georg M; Schlattmann, Peter; Dewey, Marc

2013-06-01

To evaluate the methodological quality of diagnostic accuracy studies on coronary computed tomography (CT) angiography using the QUADAS (Quality Assessment of Diagnostic Accuracy Studies included in systematic reviews) tool. Each QUADAS item was individually defined to adapt it to the special requirements of studies on coronary CT angiography. Two independent investigators analysed 118 studies using 12 QUADAS items. Meta-regression and pooled analyses were performed to identify possible effects of methodological quality items on estimates of diagnostic accuracy. The overall methodological quality of coronary CT studies was merely moderate. They fulfilled a median of 7.5 out of 12 items. Only 9 of the 118 studies fulfilled more than 75 % of possible QUADAS items. One QUADAS item ("Uninterpretable Results") showed a significant influence (P = 0.02) on estimates of diagnostic accuracy with "no fulfilment" increasing specificity from 86 to 90 %. Furthermore, pooled analysis revealed that each QUADAS item that is not fulfilled has the potential to change estimates of diagnostic accuracy. The methodological quality of studies investigating the diagnostic accuracy of non-invasive coronary CT is only moderate and was found to affect the sensitivity and specificity. An improvement is highly desirable because good methodology is crucial for adequately assessing imaging technologies. • Good methodological quality is a basic requirement in diagnostic accuracy studies. • Most coronary CT angiography studies have only been of moderate design quality. • Weak methodological quality will affect the sensitivity and specificity. • No improvement in methodological quality was observed over time. • Authors should consider the QUADAS checklist when undertaking accuracy studies.

Accuracy of estimation of genomic breeding values in pigs using low-density genotypes and imputation.

PubMed

Badke, Yvonne M; Bates, Ronald O; Ernst, Catherine W; Fix, Justin; Steibel, Juan P

2014-04-16

Genomic selection has the potential to increase genetic progress. Genotype imputation of high-density single-nucleotide polymorphism (SNP) genotypes can improve the cost efficiency of genomic breeding value (GEBV) prediction for pig breeding. Consequently, the objectives of this work were to: (1) estimate accuracy of genomic evaluation and GEBV for three traits in a Yorkshire population and (2) quantify the loss of accuracy of genomic evaluation and GEBV when genotypes were imputed under two scenarios: a high-cost, high-accuracy scenario in which only selection candidates were imputed from a low-density platform and a low-cost, low-accuracy scenario in which all animals were imputed using a small reference panel of haplotypes. Phenotypes and genotypes obtained with the PorcineSNP60 BeadChip were available for 983 Yorkshire boars. Genotypes of selection candidates were masked and imputed using tagSNP in the GeneSeek Genomic Profiler (10K). Imputation was performed with BEAGLE using 128 or 1800 haplotypes as reference panels. GEBV were obtained through an animal-centric ridge regression model using de-regressed breeding values as response variables. Accuracy of genomic evaluation was estimated as the correlation between estimated breeding values and GEBV in a 10-fold cross validation design. Accuracy of genomic evaluation using observed genotypes was high for all traits (0.65-0.68). Using genotypes imputed from a large reference panel (accuracy: R(2) = 0.95) for genomic evaluation did not significantly decrease accuracy, whereas a scenario with genotypes imputed from a small reference panel (R(2) = 0.88) did show a significant decrease in accuracy. Genomic evaluation based on imputed genotypes in selection candidates can be implemented at a fraction of the cost of a genomic evaluation using observed genotypes and still yield virtually the same accuracy. On the other side, using a very small reference panel of haplotypes to impute training animals and candidates for selection results in lower accuracy of genomic evaluation.

An improved strategy for skin lesion detection and classification using uniform segmentation and feature selection based approach.

PubMed

Nasir, Muhammad; Attique Khan, Muhammad; Sharif, Muhammad; Lali, Ikram Ullah; Saba, Tanzila; Iqbal, Tassawar

2018-02-21

Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for highly equipped environment. The recent advancements in computerized solutions for these diagnoses are highly promising with improved accuracy and efficiency. In this article, we proposed a method for the classification of melanoma and benign skin lesions. Our approach integrates preprocessing, lesion segmentation, features extraction, features selection, and classification. Preprocessing is executed in the context of hair removal by DullRazor, whereas lesion texture and color information are utilized to enhance the lesion contrast. In lesion segmentation, a hybrid technique has been implemented and results are fused using additive law of probability. Serial based method is applied subsequently that extracts and fuses the traits such as color, texture, and HOG (shape). The fused features are selected afterwards by implementing a novel Boltzman Entropy method. Finally, the selected features are classified by Support Vector Machine. The proposed method is evaluated on publically available data set PH2. Our approach has provided promising results of sensitivity 97.7%, specificity 96.7%, accuracy 97.5%, and F-score 97.5%, which are significantly better than the results of existing methods available on the same data set. The proposed method detects and classifies melanoma significantly good as compared to existing methods. © 2018 Wiley Periodicals, Inc.

A Natural Language Processing-based Model to Automate MRI Brain Protocol Selection and Prioritization.

PubMed

Brown, Andrew D; Marotta, Thomas R

2017-02-01

Incorrect imaging protocol selection can contribute to increased healthcare cost and waste. To help healthcare providers improve the quality and safety of medical imaging services, we developed and evaluated three natural language processing (NLP) models to determine whether NLP techniques could be employed to aid in clinical decision support for protocoling and prioritization of magnetic resonance imaging (MRI) brain examinations. To test the feasibility of using an NLP model to support clinical decision making for MRI brain examinations, we designed three different medical imaging prediction tasks, each with a unique outcome: selecting an examination protocol, evaluating the need for contrast administration, and determining priority. We created three models for each prediction task, each using a different classification algorithm-random forest, support vector machine, or k-nearest neighbor-to predict outcomes based on the narrative clinical indications and demographic data associated with 13,982 MRI brain examinations performed from January 1, 2013 to June 30, 2015. Test datasets were used to calculate the accuracy, sensitivity and specificity, predictive values, and the area under the curve. Our optimal results show an accuracy of 82.9%, 83.0%, and 88.2% for the protocol selection, contrast administration, and prioritization tasks, respectively, demonstrating that predictive algorithms can be used to aid in clinical decision support for examination protocoling. NLP models developed from the narrative clinical information provided by referring clinicians and demographic data are feasible methods to predict the protocol and priority of MRI brain examinations. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Evaluating model structure adequacy: The case of the Maggia Valley groundwater system, southern Switzerland

USGS Publications Warehouse

Hill, Mary C.; L. Foglia,; S. W. Mehl,; P. Burlando,

2013-01-01

Model adequacy is evaluated with alternative models rated using model selection criteria (AICc, BIC, and KIC) and three other statistics. Model selection criteria are tested with cross-validation experiments and insights for using alternative models to evaluate model structural adequacy are provided. The study is conducted using the computer codes UCODE_2005 and MMA (MultiModel Analysis). One recharge alternative is simulated using the TOPKAPI hydrological model. The predictions evaluated include eight heads and three flows located where ecological consequences and model precision are of concern. Cross-validation is used to obtain measures of prediction accuracy. Sixty-four models were designed deterministically and differ in representation of river, recharge, bedrock topography, and hydraulic conductivity. Results include: (1) What may seem like inconsequential choices in model construction may be important to predictions. Analysis of predictions from alternative models is advised. (2) None of the model selection criteria consistently identified models with more accurate predictions. This is a disturbing result that suggests to reconsider the utility of model selection criteria, and/or the cross-validation measures used in this work to measure model accuracy. (3) KIC displayed poor performance for the present regression problems; theoretical considerations suggest that difficulties are associated with wide variations in the sensitivity term of KIC resulting from the models being nonlinear and the problems being ill-posed due to parameter correlations and insensitivity. The other criteria performed somewhat better, and similarly to each other. (4) Quantities with high leverage are more difficult to predict. The results are expected to be generally applicable to models of environmental systems.

ANALYSIS OF SAMPLING TECHNIQUES FOR IMBALANCED DATA: AN N=648 ADNI STUDY

PubMed Central

Dubey, Rashmi; Zhou, Jiayu; Wang, Yalin; Thompson, Paul M.; Ye, Jieping

2013-01-01

Many neuroimaging applications deal with imbalanced imaging data. For example, in Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, the mild cognitive impairment (MCI) cases eligible for the study are nearly two times the Alzheimer’s disease (AD) patients for structural magnetic resonance imaging (MRI) modality and six times the control cases for proteomics modality. Constructing an accurate classifier from imbalanced data is a challenging task. Traditional classifiers that aim to maximize the overall prediction accuracy tend to classify all data into the majority class. In this paper, we study an ensemble system of feature selection and data sampling for the class imbalance problem. We systematically analyze various sampling techniques by examining the efficacy of different rates and types of undersampling, oversampling, and a combination of over and under sampling approaches. We thoroughly examine six widely used feature selection algorithms to identify significant biomarkers and thereby reduce the complexity of the data. The efficacy of the ensemble techniques is evaluated using two different classifiers including Random Forest and Support Vector Machines based on classification accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity measures. Our extensive experimental results show that for various problem settings in ADNI, (1). a balanced training set obtained with K-Medoids technique based undersampling gives the best overall performance among different data sampling techniques and no sampling approach; and (2). sparse logistic regression with stability selection achieves competitive performance among various feature selection algorithms. Comprehensive experiments with various settings show that our proposed ensemble model of multiple undersampled datasets yields stable and promising results. PMID:24176869

Analysis of sampling techniques for imbalanced data: An n = 648 ADNI study.

PubMed

Dubey, Rashmi; Zhou, Jiayu; Wang, Yalin; Thompson, Paul M; Ye, Jieping

2014-02-15

Many neuroimaging applications deal with imbalanced imaging data. For example, in Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the mild cognitive impairment (MCI) cases eligible for the study are nearly two times the Alzheimer's disease (AD) patients for structural magnetic resonance imaging (MRI) modality and six times the control cases for proteomics modality. Constructing an accurate classifier from imbalanced data is a challenging task. Traditional classifiers that aim to maximize the overall prediction accuracy tend to classify all data into the majority class. In this paper, we study an ensemble system of feature selection and data sampling for the class imbalance problem. We systematically analyze various sampling techniques by examining the efficacy of different rates and types of undersampling, oversampling, and a combination of over and undersampling approaches. We thoroughly examine six widely used feature selection algorithms to identify significant biomarkers and thereby reduce the complexity of the data. The efficacy of the ensemble techniques is evaluated using two different classifiers including Random Forest and Support Vector Machines based on classification accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity measures. Our extensive experimental results show that for various problem settings in ADNI, (1) a balanced training set obtained with K-Medoids technique based undersampling gives the best overall performance among different data sampling techniques and no sampling approach; and (2) sparse logistic regression with stability selection achieves competitive performance among various feature selection algorithms. Comprehensive experiments with various settings show that our proposed ensemble model of multiple undersampled datasets yields stable and promising results. © 2013 Elsevier Inc. All rights reserved.

Improving virtual screening predictive accuracy of Human kallikrein 5 inhibitors using machine learning models.

PubMed

Fang, Xingang; Bagui, Sikha; Bagui, Subhash

2017-08-01

The readily available high throughput screening (HTS) data from the PubChem database provides an opportunity for mining of small molecules in a variety of biological systems using machine learning techniques. From the thousands of available molecular descriptors developed to encode useful chemical information representing the characteristics of molecules, descriptor selection is an essential step in building an optimal quantitative structural-activity relationship (QSAR) model. For the development of a systematic descriptor selection strategy, we need the understanding of the relationship between: (i) the descriptor selection; (ii) the choice of the machine learning model; and (iii) the characteristics of the target bio-molecule. In this work, we employed the Signature descriptor to generate a dataset on the Human kallikrein 5 (hK 5) inhibition confirmatory assay data and compared multiple classification models including logistic regression, support vector machine, random forest and k-nearest neighbor. Under optimal conditions, the logistic regression model provided extremely high overall accuracy (98%) and precision (90%), with good sensitivity (65%) in the cross validation test. In testing the primary HTS screening data with more than 200K molecular structures, the logistic regression model exhibited the capability of eliminating more than 99.9% of the inactive structures. As part of our exploration of the descriptor-model-target relationship, the excellent predictive performance of the combination of the Signature descriptor and the logistic regression model on the assay data of the Human kallikrein 5 (hK 5) target suggested a feasible descriptor/model selection strategy on similar targets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Classification of Alzheimer's disease patients with hippocampal shape wrapper-based feature selection and support vector machine

NASA Astrophysics Data System (ADS)

Young, Jonathan; Ridgway, Gerard; Leung, Kelvin; Ourselin, Sebastien

2012-02-01

It is well known that hippocampal atrophy is a marker of the onset of Alzheimer's disease (AD) and as a result hippocampal volumetry has been used in a number of studies to provide early diagnosis of AD and predict conversion of mild cognitive impairment patients to AD. However, rates of atrophy are not uniform across the hippocampus making shape analysis a potentially more accurate biomarker. This study studies the hippocampi from 226 healthy controls, 148 AD patients and 330 MCI patients obtained from T1 weighted structural MRI images from the ADNI database. The hippocampi are anatomically segmented using the MAPS multi-atlas segmentation method, and the resulting binary images are then processed with SPHARM software to decompose their shapes as a weighted sum of spherical harmonic basis functions. The resulting parameterizations are then used as feature vectors in Support Vector Machine (SVM) classification. A wrapper based feature selection method was used as this considers the utility of features in discriminating classes in combination, fully exploiting the multivariate nature of the data and optimizing the selected set of features for the type of classifier that is used. The leave-one-out cross validated accuracy obtained on training data is 88.6% for classifying AD vs controls and 74% for classifying MCI-converters vs MCI-stable with very compact feature sets, showing that this is a highly promising method. There is currently a considerable fall in accuracy on unseen data indicating that the feature selection is sensitive to the data used, however feature ensemble methods may overcome this.

A comparative study of cultural methods for the detection of Salmonella in feed and feed ingredients

PubMed Central

Koyuncu, Sevinc; Haggblom, Per

2009-01-01

Background Animal feed as a source of infection to food producing animals is much debated. In order to increase our present knowledge about possible feed transmission it is important to know that the present isolation methods for Salmonella are reliable also for feed materials. In a comparative study the ability of the standard method used for isolation of Salmonella in feed in the Nordic countries, the NMKL71 method (Nordic Committee on Food Analysis) was compared to the Modified Semisolid Rappaport Vassiliadis method (MSRV) and the international standard method (EN ISO 6579:2002). Five different feed materials were investigated, namely wheat grain, soybean meal, rape seed meal, palm kernel meal, pellets of pig feed and also scrapings from a feed mill elevator. Four different levels of the Salmonella serotypes S. Typhimurium, S. Cubana and S. Yoruba were added to each feed material, respectively. For all methods pre-enrichment in Buffered Peptone Water (BPW) were carried out followed by enrichments in the different selective media and finally plating on selective agar media. Results The results obtained with all three methods showed no differences in detection levels, with an accuracy and sensitivity of 65% and 56%, respectively. However, Müller-Kauffmann tetrathionate-novobiocin broth (MKTTn), performed less well due to many false-negative results on Brilliant Green agar (BGA) plates. Compared to other feed materials palm kernel meal showed a higher detection level with all serotypes and methods tested. Conclusion The results of this study showed that the accuracy, sensitivity and specificity of the investigated cultural methods were equivalent. However, the detection levels for different feed and feed ingredients varied considerably. PMID:19192298

Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines.

PubMed

Lajnef, Tarek; Chaibi, Sahbi; Ruby, Perrine; Aguera, Pierre-Emmanuel; Eichenlaub, Jean-Baptiste; Samet, Mounir; Kachouri, Abdennaceur; Jerbi, Karim

2015-07-30

Sleep staging is a critical step in a range of electrophysiological signal processing pipelines used in clinical routine as well as in sleep research. Although the results currently achievable with automatic sleep staging methods are promising, there is need for improvement, especially given the time-consuming and tedious nature of visual sleep scoring. Here we propose a sleep staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. The performance of the method was evaluated using polysomnographic data from 15 subjects (electroencephalogram (EEG), electrooculogram (EOG) and electromyogram (EMG) recordings). The decision tree, or dendrogram, was obtained using a hierarchical clustering technique and a wide range of time and frequency-domain features were extracted. Feature selection was carried out using forward sequential selection and classification was evaluated using k-fold cross-validation. The dendrogram-based SVM (DSVM) achieved mean specificity, sensitivity and overall accuracy of 0.92, 0.74 and 0.88 respectively, compared to expert visual scoring. Restricting DSVM classification to data where both experts' scoring was consistent (76.73% of the data) led to a mean specificity, sensitivity and overall accuracy of 0.94, 0.82 and 0.92 respectively. The DSVM framework outperforms classification with more standard multi-class "one-against-all" SVM and linear-discriminant analysis. The promising results of the proposed methodology suggest that it may be a valuable alternative to existing automatic methods and that it could accelerate visual scoring by providing a robust starting hypnogram that can be further fine-tuned by expert inspection. Copyright © 2015 Elsevier B.V. All rights reserved.

Optimal classification for the diagnosis of duchenne muscular dystrophy images using support vector machines.

PubMed

Zhang, Ming-Huan; Ma, Jun-Shan; Shen, Ying; Chen, Ying

2016-09-01

This study aimed to investigate the optimal support vector machines (SVM)-based classifier of duchenne muscular dystrophy (DMD) magnetic resonance imaging (MRI) images. T1-weighted (T1W) and T2-weighted (T2W) images of the 15 boys with DMD and 15 normal controls were obtained. Textural features of the images were extracted and wavelet decomposed, and then, principal features were selected. Scale transform was then performed for MRI images. Afterward, SVM-based classifiers of MRI images were analyzed based on the radical basis function and decomposition levels. The cost (C) parameter and kernel parameter [Formula: see text] were used for classification. Then, the optimal SVM-based classifier, expressed as [Formula: see text]), was identified by performance evaluation (sensitivity, specificity and accuracy). Eight of 12 textural features were selected as principal features (eigenvalues [Formula: see text]). The 16 SVM-based classifiers were obtained using combination of (C, [Formula: see text]), and those with lower C and [Formula: see text] values showed higher performances, especially classifier of [Formula: see text]). The SVM-based classifiers of T1W images showed higher performance than T1W images at the same decomposition level. The T1W images in classifier of [Formula: see text]) at level 2 decomposition showed the highest performance of all, and its overall correct sensitivity, specificity, and accuracy reached 96.9, 97.3, and 97.1 %, respectively. The T1W images in SVM-based classifier [Formula: see text] at level 2 decomposition showed the highest performance of all, demonstrating that it was the optimal classification for the diagnosis of DMD.

Diagnostic Accuracy of Memory Measures in Alzheimer's Dementia and Mild Cognitive Impairment: a Systematic Review and Meta-Analysis.

PubMed

Weissberger, Gali H; Strong, Jessica V; Stefanidis, Kayla B; Summers, Mathew J; Bondi, Mark W; Stricker, Nikki H

2017-12-01

With an increasing focus on biomarkers in dementia research, illustrating the role of neuropsychological assessment in detecting mild cognitive impairment (MCI) and Alzheimer's dementia (AD) is important. This systematic review and meta-analysis, conducted in accordance with PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) standards, summarizes the sensitivity and specificity of memory measures in individuals with MCI and AD. Both meta-analytic and qualitative examination of AD versus healthy control (HC) studies (n = 47) revealed generally high sensitivity and specificity (≥ 80% for AD comparisons) for measures of immediate (sensitivity = 87%, specificity = 88%) and delayed memory (sensitivity = 89%, specificity = 89%), especially those involving word-list recall. Examination of MCI versus HC studies (n = 38) revealed generally lower diagnostic accuracy for both immediate (sensitivity = 72%, specificity = 81%) and delayed memory (sensitivity = 75%, specificity = 81%). Measures that differentiated AD from other conditions (n = 10 studies) yielded mixed results, with generally high sensitivity in the context of low or variable specificity. Results confirm that memory measures have high diagnostic accuracy for identification of AD, are promising but require further refinement for identification of MCI, and provide support for ongoing investigation of neuropsychological assessment as a cognitive biomarker of preclinical AD. Emphasizing diagnostic test accuracy statistics over null hypothesis testing in future studies will promote the ongoing use of neuropsychological tests as Alzheimer's disease research and clinical criteria increasingly rely upon cerebrospinal fluid (CSF) and neuroimaging biomarkers.

Differential gene expression responses distinguish contact and respiratory sensitizers and nonsensitizing irritants in the local lymph node assay.

PubMed

Adenuga, David; Woolhiser, Michael R; Gollapudi, B Bhaskar; Boverhof, Darrell R

2012-04-01

Genomic approaches have the potential to enhance the specificity and predictive accuracy of existing toxicology endpoints, including those for chemical sensitization. The present study was conducted to determine whether gene expression responses can distinguish contact sensitizers (1-chloro-2,4-dinitrobenzene [DNCB] and hexyl cinnamic aldehyde [HCA]), respiratory sensitizers (ortho-phthalaldehyde and trimellitic anhydride [TMA]), and nonsensitizing irritants (methyl salicylate [MS] and nonanoic acid [NA]) in the local lymph node assay (LLNA). Female Balb/c mice received doses of each chemical as per the standard LLNA dosing regimen on days 1, 2, and 3. Auricular lymph nodes were analyzed for tritiated thymidine ((3)HTdR) incorporation on day 6 and for gene expression responses on days 6 and 10. All chemicals induced dose-dependent increases in stimulation index, which correlated strongly with the number of differentially expressed genes. A majority of genes modulated by the irritants were similarly altered by the sensitizers, consistent with the irritating effects of the sensitizers. However, a select number of responses involved with immune-specific functions, such as dendritic cell activation, were unique to the sensitizers and may offer the ability to distinguish sensitizers from irritants. Genes for the mast cell proteases 1 and 8, Lgals7, Tim2, Aicda, Il4, and Akr1c18 were more strongly regulated by respiratory sensitizers compared with contact sensitizers and may represent potential biomarkers for discriminating between contact and respiratory sensitizers. Collectively, these data suggest that gene expression responses may serve as useful biomarkers to distinguish between respiratory and contact sensitizers and nonsensitizing irritants in the LLNA.

Optimal full motion video registration with rigorous error propagation

NASA Astrophysics Data System (ADS)

Dolloff, John; Hottel, Bryant; Doucette, Peter; Theiss, Henry; Jocher, Glenn

2014-06-01

Optimal full motion video (FMV) registration is a crucial need for the Geospatial community. It is required for subsequent and optimal geopositioning with simultaneous and reliable accuracy prediction. An overall approach being developed for such registration is presented that models relevant error sources in terms of the expected magnitude and correlation of sensor errors. The corresponding estimator is selected based on the level of accuracy of the a priori information of the sensor's trajectory and attitude (pointing) information, in order to best deal with non-linearity effects. Estimator choices include near real-time Kalman Filters and batch Weighted Least Squares. Registration solves for corrections to the sensor a priori information for each frame. It also computes and makes available a posteriori accuracy information, i.e., the expected magnitude and correlation of sensor registration errors. Both the registered sensor data and its a posteriori accuracy information are then made available to "down-stream" Multi-Image Geopositioning (MIG) processes. An object of interest is then measured on the registered frames and a multi-image optimal solution, including reliable predicted solution accuracy, is then performed for the object's 3D coordinates. This paper also describes a robust approach to registration when a priori information of sensor attitude is unavailable. It makes use of structure-from-motion principles, but does not use standard Computer Vision techniques, such as estimation of the Essential Matrix which can be very sensitive to noise. The approach used instead is a novel, robust, direct search-based technique.

Optimizing spectral wave estimates with adjoint-based sensitivity maps

NASA Astrophysics Data System (ADS)

Orzech, Mark; Veeramony, Jay; Flampouris, Stylianos

2014-04-01

A discrete numerical adjoint has recently been developed for the stochastic wave model SWAN. In the present study, this adjoint code is used to construct spectral sensitivity maps for two nearshore domains. The maps display the correlations of spectral energy levels throughout the domain with the observed energy levels at a selected location or region of interest (LOI/ROI), providing a full spectrum of values at all locations in the domain. We investigate the effectiveness of sensitivity maps based on significant wave height ( H s ) in determining alternate offshore instrument deployment sites when a chosen nearshore location or region is inaccessible. Wave and bathymetry datasets are employed from one shallower, small-scale domain (Duck, NC) and one deeper, larger-scale domain (San Diego, CA). The effects of seasonal changes in wave climate, errors in bathymetry, and multiple assimilation points on sensitivity map shapes and model performance are investigated. Model accuracy is evaluated by comparing spectral statistics as well as with an RMS skill score, which estimates a mean model-data error across all spectral bins. Results indicate that data assimilation from identified high-sensitivity alternate locations consistently improves model performance at nearshore LOIs, while assimilation from low-sensitivity locations results in lesser or no improvement. Use of sub-sampled or alongshore-averaged bathymetry has a domain-specific effect on model performance when assimilating from a high-sensitivity alternate location. When multiple alternate assimilation locations are used from areas of lower sensitivity, model performance may be worse than with a single, high-sensitivity assimilation point.

Accuracy of genetic code translation and its orthogonal corruption by aminoglycosides and Mg2+ ions

PubMed Central

Zhang, Jingji

2018-01-01

Abstract We studied the effects of aminoglycosides and changing Mg2+ ion concentration on the accuracy of initial codon selection by aminoacyl-tRNA in ternary complex with elongation factor Tu and GTP (T3) on mRNA programmed ribosomes. Aminoglycosides decrease the accuracy by changing the equilibrium constants of ‘monitoring bases’ A1492, A1493 and G530 in 16S rRNA in favor of their ‘activated’ state by large, aminoglycoside-specific factors, which are the same for cognate and near-cognate codons. Increasing Mg2+ concentration decreases the accuracy by slowing dissociation of T3 from its initial codon- and aminoglycoside-independent binding state on the ribosome. The distinct accuracy-corrupting mechanisms for aminoglycosides and Mg2+ ions prompted us to re-interpret previous biochemical experiments and functional implications of existing high resolution ribosome structures. We estimate the upper thermodynamic limit to the accuracy, the ‘intrinsic selectivity’ of the ribosome. We conclude that aminoglycosides do not alter the intrinsic selectivity but reduce the fraction of it that is expressed as the accuracy of initial selection. We suggest that induced fit increases the accuracy and speed of codon reading at unaltered intrinsic selectivity of the ribosome. PMID:29267976

Electrochemical Impedance Sensors for Monitoring Trace Amounts of NO3 in Selected Growing Media.

PubMed

Ghaffari, Seyed Alireza; Caron, William-O; Loubier, Mathilde; Normandeau, Charles-O; Viens, Jeff; Lamhamedi, Mohammed S; Gosselin, Benoit; Messaddeq, Younes

2015-07-21

With the advent of smart cities and big data, precision agriculture allows the feeding of sensor data into online databases for continuous crop monitoring, production optimization, and data storage. This paper describes a low-cost, compact, and scalable nitrate sensor based on electrochemical impedance spectroscopy for monitoring trace amounts of NO3- in selected growing media. The nitrate sensor can be integrated to conventional microelectronics to perform online nitrate sensing continuously over a wide concentration range from 0.1 ppm to 100 ppm, with a response time of about 1 min, and feed data into a database for storage and analysis. The paper describes the structural design, the Nyquist impedance response, the measurement sensitivity and accuracy, and the field testing of the nitrate sensor performed within tree nursery settings under ISO/IEC 17025 certifications.

Electrochemical Impedance Sensors for Monitoring Trace Amounts of NO3 in Selected Growing Media

PubMed Central

Ghaffari, Seyed Alireza; Caron, William-O.; Loubier, Mathilde; Normandeau, Charles-O.; Viens, Jeff; Lamhamedi, Mohammed S.; Gosselin, Benoit; Messaddeq, Younes

2015-01-01

With the advent of smart cities and big data, precision agriculture allows the feeding of sensor data into online databases for continuous crop monitoring, production optimization, and data storage. This paper describes a low-cost, compact, and scalable nitrate sensor based on electrochemical impedance spectroscopy for monitoring trace amounts of NO3− in selected growing media. The nitrate sensor can be integrated to conventional microelectronics to perform online nitrate sensing continuously over a wide concentration range from 0.1 ppm to 100 ppm, with a response time of about 1 min, and feed data into a database for storage and analysis. The paper describes the structural design, the Nyquist impedance response, the measurement sensitivity and accuracy, and the field testing of the nitrate sensor performed within tree nursery settings under ISO/IEC 17025 certifications. PMID:26197322

A fast RCS accuracy assessment method for passive radar calibrators

NASA Astrophysics Data System (ADS)

Zhou, Yongsheng; Li, Chuanrong; Tang, Lingli; Ma, Lingling; Liu, QI

2016-10-01

In microwave radar radiometric calibration, the corner reflector acts as the standard reference target but its structure is usually deformed during the transportation and installation, or deformed by wind and gravity while permanently installed outdoor, which will decrease the RCS accuracy and therefore the radiometric calibration accuracy. A fast RCS accuracy measurement method based on 3-D measuring instrument and RCS simulation was proposed in this paper for tracking the characteristic variation of the corner reflector. In the first step, RCS simulation algorithm was selected and its simulation accuracy was assessed. In the second step, the 3-D measuring instrument was selected and its measuring accuracy was evaluated. Once the accuracy of the selected RCS simulation algorithm and 3-D measuring instrument was satisfied for the RCS accuracy assessment, the 3-D structure of the corner reflector would be obtained by the 3-D measuring instrument, and then the RCSs of the obtained 3-D structure and corresponding ideal structure would be calculated respectively based on the selected RCS simulation algorithm. The final RCS accuracy was the absolute difference of the two RCS calculation results. The advantage of the proposed method was that it could be applied outdoor easily, avoiding the correlation among the plate edge length error, plate orthogonality error, plate curvature error. The accuracy of this method is higher than the method using distortion equation. In the end of the paper, a measurement example was presented in order to show the performance of the proposed method.

Systematic Development and Validation of a Thin-Layer Densitometric Bioanalytical Method for Estimation of Mangiferin Employing Analytical Quality by Design (AQbD) Approach.

PubMed

Khurana, Rajneet Kaur; Rao, Satish; Beg, Sarwar; Katare, O P; Singh, Bhupinder

2016-01-01

The present work aims at the systematic development of a simple, rapid and highly sensitive densitometry-based thin-layer chromatographic method for the quantification of mangiferin in bioanalytical samples. Initially, the quality target method profile was defined and critical analytical attributes (CAAs) earmarked, namely, retardation factor (Rf), peak height, capacity factor, theoretical plates and separation number. Face-centered cubic design was selected for optimization of volume loaded and plate dimensions as the critical method parameters selected from screening studies employing D-optimal and Plackett-Burman design studies, followed by evaluating their effect on the CAAs. The mobile phase containing a mixture of ethyl acetate : acetic acid : formic acid : water in a 7 : 1 : 1 : 1 (v/v/v/v) ratio was finally selected as the optimized solvent for apt chromatographic separation of mangiferin at 262 nm withRf 0.68 ± 0.02 and all other parameters within the acceptance limits. Method validation studies revealed high linearity in the concentration range of 50-800 ng/band for mangiferin. The developed method showed high accuracy, precision, ruggedness, robustness, specificity, sensitivity, selectivity and recovery. In a nutshell, the bioanalytical method for analysis of mangiferin in plasma revealed the presence of well-resolved peaks and high recovery of mangiferin. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Quantitative assessment of hematopoietic chimerism by quantitative real-time polymerase chain reaction of sequence polymorphism systems after hematopoietic stem cell transplantation.

PubMed

Qin, Xiao-ying; Li, Guo-xuan; Qin, Ya-zhen; Wang, Yu; Wang, Feng-rong; Liu, Dai-hong; Xu, Lan-ping; Chen, Huan; Han, Wei; Wang, Jing-zhi; Zhang, Xiao-hui; Li, Jin-lan; Li, Ling-di; Liu, Kai-yan; Huang, Xiao-jun

2011-08-01

Analysis of changes in recipient and donor hematopoietic cell origin is extremely useful to monitor the effect of hematopoietic stem cell transplantation (HSCT) and sequential adoptive immunotherapy by donor lymphocyte infusions. We developed a sensitive, reliable and rapid real-time PCR method based on sequence polymorphism systems to quantitatively assess the hematopoietic chimerism after HSCT. A panel of 29 selected sequence polymorphism (SP) markers was screened by real-time PCR in 101 HSCT patients with leukemia and other hematological diseases. The chimerism kinetics of bone marrow samples of 8 HSCT patients in remission and relapse situations were followed longitudinally. Recipient genotype discrimination was possible in 97.0% (98 of 101) with a mean number of 2.5 (1-7) informative markers per recipient/donor pair. Using serial dilutions of plasmids containing specific SP markers, the linear correlation (r) of 0.99, the slope between -3.2 and -3.7 and the sensitivity of 0.1% were proved reproducible. By this method, it was possible to very accurately detect autologous signals in the range from 0.1% to 30%. The accuracy of the method in the very important range of autologous signals below 5% was extraordinarily high (standard deviation <1.85%), which might significantly improve detection accuracy of changes in autologous signals early in the post-transplantation course of follow-up. The main advantage of the real-time PCR method over short tandem repeat PCR chimerism assays is the absence of PCR competition and plateau biases, with demonstrated greater sensitivity and linearity. Finally, we prospectively analyzed bone marrow samples of 8 patients who received allografts and presented the chimerism kinetics of remission and relapse situations that illustrated the sensitivity level and the promising clinical application of this method. This SP-based real-time PCR assay provides a rapid, sensitive, and accurate quantitative assessment of mixed chimerism that can be useful in predicting graft rejection and early relapse.

Comparison of the Diagnostic Accuracy of Three Rapid Tests for the Serodiagnosis of Hepatic Cystic Echinococcosis in Humans

PubMed Central

Tamarozzi, Francesca; Covini, Ilaria; Mariconti, Mara; Narra, Roberta; Tinelli, Carmine; De Silvestri, Annalisa; Manzoni, Federica; Casulli, Adriano; Ito, Akira; Neumayr, Andreas; Brunetti, Enrico

2016-01-01

Background The diagnosis of cystic echinococcosis (CE) is based primarily on imaging, in particular with ultrasound for abdominal CE, complemented by serology when imaging results are unclear. In rural endemic areas, where expertise in ultrasound may be scant and conventional serology techniques are unavailable due to lack of laboratory equipment, Rapid Diagnostic Tests (RDTs) are appealing. Methodology/Principal Findings We evaluated the diagnostic accuracy of 3 commercial RDTs for the diagnosis of hepatic CE. Sera from 59 patients with single hepatic CE cysts in well-defined ultrasound stages (gold standard) and 25 patients with non-parasitic cysts were analyzed by RDTs VIRapid HYDATIDOSIS (Vircell, Spain), Echinococcus DIGFA (Unibiotest, China), ADAMU-CE (ICST, Japan), and by RIDASCREEN Echinococcus IgG ELISA (R-Biopharm, Germany). Sensitivity, specificity and ROC curves were compared with McNemar and t-test. For VIRapid and DIGFA, correlation between semiquantitative results and ELISA OD values were evaluated by Spearman’s coefficient. Reproducibility was assessed on 16 randomly selected sera with Cohen’s Kappa coefficient. Sensitivity and Specificity of VIRapid (74%, 96%) and ADAMU-CE (57%, 100%) did not differ from ELISA (69%, 96%) while DIGFA (72%, 72%) did (p = 0.045). ADAMU-CE was significantly less sensitive in the diagnosis of active cysts (p = 0.019) while DIGFA was significantly less specific (p = 0.014) compared to ELISA. All tests were poorly sensitive in diagnosing inactive cysts (33.3% ELISA and ADAMU-CE, 42.8% DIGFA, 47.6% VIRapid). The reproducibility of all RDTs was good-very good. Band intensity of VIRapid and DIGFA correlated with ELISA OD values (r = 0.76 and r = 0.79 respectively, p<0.001). Conclusions/Significance RDTs may be useful in resource-poor settings to complement ultrasound diagnosis of CE in uncertain cases. VIRapid test appears to perform best among the examined kits, but all tests are poorly sensitive in the presence of inactive cysts, which may pose problems with accurate diagnosis. PMID:26871432

454 next generation-sequencing outperforms allele-specific PCR, Sanger sequencing, and pyrosequencing for routine KRAS mutation analysis of formalin-fixed, paraffin-embedded samples

PubMed Central

Altimari, Annalisa; de Biase, Dario; De Maglio, Giovanna; Gruppioni, Elisa; Capizzi, Elisa; Degiovanni, Alessio; D’Errico, Antonia; Pession, Annalisa; Pizzolitto, Stefano; Fiorentino, Michelangelo; Tallini, Giovanni

2013-01-01

Detection of KRAS mutations in archival pathology samples is critical for therapeutic appropriateness of anti-EGFR monoclonal antibodies in colorectal cancer. We compared the sensitivity, specificity, and accuracy of Sanger sequencing, ARMS-Scorpion (TheraScreen®) real-time polymerase chain reaction (PCR), pyrosequencing, chip array hybridization, and 454 next-generation sequencing to assess KRAS codon 12 and 13 mutations in 60 nonconsecutive selected cases of colorectal cancer. Twenty of the 60 cases were detected as wild-type KRAS by all methods with 100% specificity. Among the 40 mutated cases, 13 were discrepant with at least one method. The sensitivity was 85%, 90%, 93%, and 92%, and the accuracy was 90%, 93%, 95%, and 95% for Sanger sequencing, TheraScreen real-time PCR, pyrosequencing, and chip array hybridization, respectively. The main limitation of Sanger sequencing was its low analytical sensitivity, whereas TheraScreen real-time PCR, pyrosequencing, and chip array hybridization showed higher sensitivity but suffered from the limitations of predesigned assays. Concordance between the methods was k = 0.79 for Sanger sequencing and k > 0.85 for the other techniques. Tumor cell enrichment correlated significantly with the abundance of KRAS-mutated deoxyribonucleic acid (DNA), evaluated as ΔCt for TheraScreen real-time PCR (P = 0.03), percentage of mutation for pyrosequencing (P = 0.001), ratio for chip array hybridization (P = 0.003), and percentage of mutation for 454 next-generation sequencing (P = 0.004). Also, 454 next-generation sequencing showed the best cross correlation for quantification of mutation abundance compared with all the other methods (P < 0.001). Our comparison showed the superiority of next-generation sequencing over the other techniques in terms of sensitivity and specificity. Next-generation sequencing will replace Sanger sequencing as the reference technique for diagnostic detection of KRAS mutation in archival tumor tissues. PMID:23950653

Spectrophotometry or visual inspection to most reliably detect xanthochromia in subarachnoid hemorrhage: systematic review.

PubMed

Chu, Kevin; Hann, Angus; Greenslade, Jaimi; Williams, Julian; Brown, Anthony

2014-09-01

We assess the sensitivity and specificity of xanthochromia as adjudicated by visual inspection and spectrophotometry at predicting the presence of cerebral aneurysm in patients with suspected subarachnoid hemorrhage who have a normal computed tomography (CT) head scan result. A systematic review was performed. MEDLINE and EMBASE databases were searched. Relevant studies with clinical data on the diagnostic accuracy of visual inspection or spectrophotometry were considered. Patients who had a normal CT head scan result followed by a lumbar puncture were included in this review. Sensitivities, specificities, and heterogeneity (I(2)) were calculated. Subgroup analyses were performed to explore reasons for the heterogeneity. There were major methodological limitations in the studies found. Twenty-two relevant articles were heterogeneous in regard to time to lumbar puncture, spectrophotometry methods, and follow-up of patients not undergoing cerebral angiography. Twelve of the 22 studies selected patients on the basis of a cerebral aneurysm or subarachnoid hemorrhage on imaging, or a positive lumbar puncture result. These studies were excluded from our initial analysis, which included only patients with clinically suspected subarachnoid hemorrhage. In this initial analysis, pooled estimates of sensitivity and specificity for spectrophotometry were 87% (95% confidence interval [CI] 71% to 96%; I(2)=26%) and 86% (95% CI 84% to 88%; I(2)=96%), respectively. For visual inspection, pooled sensitivity and specificity were 83% (95% CI 59% to 96%; I(2)=52%) and 96% (95% CI 93% to 97%; I(2)=76%), respectively. Sensitivity estimates are difficult to interpret without knowing time to lumbar puncture. The heterogeneity in the underlying studies, combined with significant overlap in pooled confidence limits, makes it impossible to provide a definite conclusion about the diagnostic accuracy of spectrophotometry versus visual inspection. Copyright © 2014 American College of Emergency Physicians. Published by Mosby, Inc. All rights reserved.

Molecular Nodal Staging Using miRNA Expression in Lung Cancer Patients by Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration.

PubMed

Inage, Terunaga; Nakajima, Takahiro; Itoga, Sakae; Ishige, Takayuki; Fujiwara, Taiki; Sakairi, Yuichi; Wada, Hironobu; Suzuki, Hidemi; Iwata, Takekazu; Chiyo, Masako; Yoshida, Shigetoshi; Matsushita, Kazuyuki; Yasufuku, Kazuhiro; Yoshino, Ichiro

2018-06-13

The limited negative predictive value of endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) has often been discussed. The aim of this study was to identify a highly sensitive molecular biomarker for lymph node staging by EBUS-TBNA. Five microRNAs (miRNAs) (miR-200a, miR-200b, miR-200c, miR-141, and let-7e) were selected as biomarker candidates for the detection of nodal metastasis in a miRNA expression analysis. After having established a cutoff level of expression for each marker to differentiate malignant from benign lymph nodes among surgically dissected lymph nodes, the cutoff level was applied to snap-frozen EBUS-TBNA samples. Archived formalin-fixed paraffin- embedded (FFPE) samples rebiopsied by EBUS-TBNA after induction chemoradiotherapy were also analyzed. The expression of all candidate miRNAs was significantly higher in metastatic lymph nodes than in benign ones (p < 0.05) among the surgical samples. miR-200c showed the highest diagnostic yield, with a sensitivity of 95.4% and a specificity of 100%. When the cutoff value for miR-200c was applied to the snap-frozen EBUS-TBNA samples, the sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 97.4, 81.8, 95.0, 90.0, and 94.0%, respectively. For restaging FFPE EBUS- TBNA samples, the sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 100, 60.0, 80.0, 100, and 84.6%, respectively. Among the restaged samples, 4 malignant lymph nodes were false negative by EBUS-TBNA, but they were accurately identified by miR-200c. miR-200c can be used as a highly sensitive molecular staging biomarker that will enhance nodal staging of lung cancer. © 2018 S. Karger AG, Basel.

MKID digital readout tuning with deep learning

NASA Astrophysics Data System (ADS)

Dodkins, R.; Mahashabde, S.; O'Brien, K.; Thatte, N.; Fruitwala, N.; Walter, A. B.; Meeker, S. R.; Szypryt, P.; Mazin, B. A.

2018-04-01

Microwave Kinetic Inductance Detector (MKID) devices offer inherent spectral resolution, simultaneous read out of thousands of pixels, and photon-limited sensitivity at optical wavelengths. Before taking observations the readout power and frequency of each pixel must be individually tuned, and if the equilibrium state of the pixels change, then the readout must be retuned. This process has previously been performed through manual inspection, and typically takes one hour per 500 resonators (20 h for a ten-kilo-pixel array). We present an algorithm based on a deep convolution neural network (CNN) architecture to determine the optimal bias power for each resonator. The bias point classifications from this CNN model, and those from alternative automated methods, are compared to those from human decisions, and the accuracy of each method is assessed. On a test feed-line dataset, the CNN achieves an accuracy of 90% within 1 dB of the designated optimal value, which is equivalent accuracy to a randomly selected human operator, and superior to the highest scoring alternative automated method by 10%. On a full ten-kilopixel array, the CNN performs the characterization in a matter of minutes - paving the way for future mega-pixel MKID arrays.

Diagnostic accuracy and measurement sensitivity of digital models for orthodontic purposes: A systematic review.

PubMed

Rossini, Gabriele; Parrini, Simone; Castroflorio, Tommaso; Deregibus, Andrea; Debernardi, Cesare L

2016-02-01

Our objective was to assess the accuracy, validity, and reliability of measurements obtained from virtual dental study models compared with those obtained from plaster models. PubMed, PubMed Central, National Library of Medicine Medline, Embase, Cochrane Central Register of Controlled Clinical trials, Web of Knowledge, Scopus, Google Scholar, and LILACs were searched from January 2000 to November 2014. A grading system described by the Swedish Council on Technology Assessment in Health Care and the Cochrane tool for risk of bias assessment were used to rate the methodologic quality of the articles. Thirty-five relevant articles were selected. The methodologic quality was high. No significant differences were observed for most of the studies in all the measured parameters, with the exception of the American Board of Orthodontics Objective Grading System. Digital models are as reliable as traditional plaster models, with high accuracy, reliability, and reproducibility. Landmark identification, rather than the measuring device or the software, appears to be the greatest limitation. Furthermore, with their advantages in terms of cost, time, and space required, digital models could be considered the new gold standard in current practice. Copyright © 2016 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.

Potential of Contrast-Enhanced Ultrasound as a Bedside Monitoring Technique in Cerebral Perfusion: a Systematic Review.

PubMed

Vinke, Elisabeth J; Kortenbout, Anna J; Eyding, Jens; Slump, Cornelis H; van der Hoeven, Johannes G; de Korte, Chris L; Hoedemaekers, Cornelia W E

2017-12-01

Contrast-enhanced ultrasound (CEUS) has been suggested as a new method to measure cerebral perfusion in patients with acute brain injury. In this systematic review, the tolerability, repeatability, reproducibility and accuracy of different CEUS techniques for the quantification of cerebral perfusion were assessed. We selected studies published between January 1994 and March 2017 using CEUS to measure cerebral perfusion. We included 43 studies (bolus kinetics n = 31, refill kinetics n = 6, depletion kinetics n = 6) with a total of 861 patients. Tolerability was reported in 28 studies describing 12 patients with mild and transient side effects. Repeatability was assessed in 3 studies, reproducibility in 2 studies and accuracy in 19 studies. Repeatability was high for experienced sonographers and significantly lower for less experienced sonographers. Reproducibility of CEUS was not clear. The sensitivity and specificity of CEUS for the detection of cerebral ischemia ranged from 75% to 96% and from 60% to 100%. Limited data on repeatability, reproducibility and accuracy may suggest that this technique could be feasible for use in acute brain injury patients. Copyright © 2017 World Federation for Ultrasound in Medicine and Biology. Published by Elsevier Inc. All rights reserved.

Evaluation of the Chondromalacia Patella Using a Microscopy Coil: Comparison of the Two-Dimensional Fast Spin Echo Techniques and the Three-Dimensional Fast Field Echo Techniques

PubMed Central

Kim, Hyun-joo; Kang, Chang Ho; Ryu, Jeong Ah; Shin, Myung Jin; Cho, Kyung-Ja; Cho, Woo Shin

2011-01-01

Objective We wanted to compare the two-dimensional (2D) fast spin echo (FSE) techniques and the three-dimensional (3D) fast field echo techniques for the evaluation of the chondromalacia patella using a microscopy coil. Materials and Methods Twenty five patients who underwent total knee arthroplasty were included in this study. Preoperative MRI evaluation of the patella was performed using a microscopy coil (47 mm). The proton density-weighted fast spin echo images (PD), the fat-suppressed PD images (FS-PD), the intermediate weighted-fat suppressed fast spin echo images (iw-FS-FSE), the 3D balanced-fast field echo images (B-FFE), the 3D water selective cartilage scan (WATS-c) and the 3D water selective fluid scan (WATS-f) were obtained on a 1.5T MRI scanner. The patellar cartilage was evaluated in nine areas: the superior, middle and the inferior portions that were subdivided into the medial, central and lateral facets in a total of 215 areas. Employing the Noyes grading system, the MRI grade 0-I, II and III lesions were compared using the gross and microscopic findings. The sensitivity, specificity and accuracy were evaluated for each sequence. The significance of the differences for the individual sequences was calculated using the McNemar test. Results The gross and microscopic findings demonstrated 167 grade 0-I lesions, 40 grade II lesions and eight grade III lesions. Iw-FS-FSE had the highest accuracy (sensitivity/specificity/accuracy = 88%/98%/96%), followed by FS-PD (78%/98%/93%, respectively), PD (76%/98%/93%, respectively), B-FFE (71%/100%/93%, respectively), WATS-c (67%/100%/92%, respectively) and WATS-f (58%/99%/89%, respectively). There were statistically significant differences for the iw-FS-FSE and WATS-f and for the PD-FS and WATS-f (p < 0.01). Conclusion The iw-FS-FSE images obtained with a microscopy coil show best diagnostic performance among the 2D and 3D GRE images for evaluating the chondromalacia patella. PMID:21228943

Evaluation of the chondromalacia patella using a microscopy coil: comparison of the two-dimensional fast spin echo techniques and the three-dimensional fast field echo techniques.

PubMed

Kim, Hyun-joo; Lee, Sang Hoon; Kang, Chang Ho; Ryu, Jeong Ah; Shin, Myung Jin; Cho, Kyung-Ja; Cho, Woo Shin

2011-01-01

We wanted to compare the two-dimensional (2D) fast spin echo (FSE) techniques and the three-dimensional (3D) fast field echo techniques for the evaluation of the chondromalacia patella using a microscopy coil. Twenty five patients who underwent total knee arthroplasty were included in this study. Preoperative MRI evaluation of the patella was performed using a microscopy coil (47 mm). The proton density-weighted fast spin echo images (PD), the fat-suppressed PD images (FS-PD), the intermediate weighted-fat suppressed fast spin echo images (iw-FS-FSE), the 3D balanced-fast field echo images (B-FFE), the 3D water selective cartilage scan (WATS-c) and the 3D water selective fluid scan (WATS-f) were obtained on a 1.5T MRI scanner. The patellar cartilage was evaluated in nine areas: the superior, middle and the inferior portions that were subdivided into the medial, central and lateral facets in a total of 215 areas. Employing the Noyes grading system, the MRI grade 0-I, II and III lesions were compared using the gross and microscopic findings. The sensitivity, specificity and accuracy were evaluated for each sequence. The significance of the differences for the individual sequences was calculated using the McNemar test. The gross and microscopic findings demonstrated 167 grade 0-I lesions, 40 grade II lesions and eight grade III lesions. Iw-FS-FSE had the highest accuracy (sensitivity/specificity/accuracy = 88%/98%/96%), followed by FS-PD (78%/98%/93%, respectively), PD (76%/98%/93%, respectively), B-FFE (71%/100%/93%, respectively), WATS-c (67%/100%/92%, respectively) and WATS-f (58%/99%/89%, respectively). There were statistically significant differences for the iw-FS-FSE and WATS-f and for the PD-FS and WATS-f (p < 0.01). The iw-FS-FSE images obtained with a microscopy coil show best diagnostic performance among the 2D and 3D GRE images for evaluating the chondromalacia patella.

Surface enhanced Raman spectroscopy for urinary tract infection diagnosis and antibiogram

NASA Astrophysics Data System (ADS)

Kastanos, Evdokia; Hadjigeorgiou, Katerina; Kyriakides, Alexandros; Pitris, Constantinos

2010-02-01

Urinary tract infection diagnosis and antibiogram require a minimum of 48 hours using standard laboratory practice. This long waiting period contributes to an increase in recurrent infections, rising health care costs, and a growing number of bacterial strains developing resistance to antibiotics. In this work, Surface Enhanced Raman Spectroscopy (SERS) was used as a novel method for classifying bacteria and determining their antibiogram. Five species of bacteria were classified with > 90% accuracy using their SERS spectra and a classification algorithm involving novel feature extraction and discriminant analysis. Antibiotic resistance or sensitivity was determined after just a two-hour exposure of bacteria to ciprofloxacin (sensitive) and amoxicillin (resistant) and analysis of their SERS spectra. These results can become the basis for the development of a novel method that would provide same day diagnosis and selection of the most appropriate antibiotic for most effective treatment of a urinary tract infection.

Simultaneous Determination of Eight Hypotensive Drugs of Various Chemical Groups in Pharmaceutical Preparations by HPLC-DAD.

PubMed

Stolarczyk, Mariusz; Hubicka, Urszula; Żuromska-Witek, Barbara; Krzek, Jan

2015-01-01

A new sensitive, simple, rapid, and precise HPLC method with diode array detection has been developed for separation and simultaneous determination of hydrochlorothiazide, furosemide, torasemide, losartane, quinapril, valsartan, spironolactone, and canrenone in combined pharmaceutical dosage forms. The chromatographic analysis of the tested drugs was performed on an ACE C18, 100 Å, 250×4.6 mm, 5 μm particle size column with 0.0.05 M phosphate buffer (pH=3.00)-acetonitrile-methanol (30+20+50 v/v/v) mobile phase at a flow rate of 1.0 mL/min. The column was thermostatted at 25°C. UV detection was performed at 230 nm. Analysis time was 10 min. The elaborated method meets the acceptance criteria for specificity, linearity, sensitivity, accuracy, and precision. The proposed method was successfully applied for the determination of the studied drugs in the selected combined dosage forms.

Dielectric properties characterization of saline solutions by near-field microwave microscopy

NASA Astrophysics Data System (ADS)

Gu, Sijia; Lin, Tianjun; Lasri, Tuami

2017-01-01

Saline solutions are of a great interest when characterizations of biological fluids are targeted. In this work a near-field microwave microscope is proposed for the characterization of liquids. An interferometric technique is suggested to enhance measurement sensitivity and accuracy. The validation of the setup and the measurement technique is conducted through the characterization of a large range of saline concentrations (0-160 mg ml-1). Based on the measured resonance frequency shift and quality factor, the complex permittivity is successfully extracted as exhibited by the good agreement found when comparing the results to data obtained from Cole-Cole model. We demonstrate that the near field microwave microscope (NFMM) brings a great advantage by offering the possibility to select a resonance frequency and a quality factor for a given concentration level. This method provides a very effective way to largely enhance the measurement sensitivity in high loss materials.

Determination of amantadine and rimantadine using a sensitive fluorescent probe

NASA Astrophysics Data System (ADS)

Wang, Guang-Quan; Qin, Yan-Fang; Du, Li-Ming; Li, Jun-Fei; Jing, Xu; Chang, Yin-Xia; Wu, Hao

2012-12-01

Amantadine hydrochloride (AMA) and rimantadine hydrochloride (RIM) are non-fluorescent in aqueous solutions. This property makes their determination through direct fluorescent method difficult. The competing reactions and the supramolecular interaction mechanisms between the two drugs and coptisine (COP) as they fight for occupancy of the cucurbit[7]uril (CB[7]) cavity, were studied using spectrofluorimetry, 1H NMR, and molecular modeling calculations. Based on the significant quenching of the supramolecular complex fluorescence intensity, a fluorescent probe method of high sensitivity and selectivity was developed to determine AMA or RIM in their pharmaceutical dosage forms and in urine samples with good precision and accuracy. The linear range of the method was from 0.0040 to 1.0 μg mL-1 with a detection limit ranging from 0.0012 to 0.0013 μg mL-1. This shows that the proposed method has promising potential for therapeutic monitoring and pharmacokinetics and for clinical application.

Biological Matrix Effects in Quantitative Tandem Mass Spectrometry-Based Analytical Methods: Advancing Biomonitoring

PubMed Central

Panuwet, Parinya; Hunter, Ronald E.; D’Souza, Priya E.; Chen, Xianyu; Radford, Samantha A.; Cohen, Jordan R.; Marder, M. Elizabeth; Kartavenka, Kostya; Ryan, P. Barry; Barr, Dana Boyd

2015-01-01

The ability to quantify levels of target analytes in biological samples accurately and precisely, in biomonitoring, involves the use of highly sensitive and selective instrumentation such as tandem mass spectrometers and a thorough understanding of highly variable matrix effects. Typically, matrix effects are caused by co-eluting matrix components that alter the ionization of target analytes as well as the chromatographic response of target analytes, leading to reduced or increased sensitivity of the analysis. Thus, before the desired accuracy and precision standards of laboratory data are achieved, these effects must be characterized and controlled. Here we present our review and observations of matrix effects encountered during the validation and implementation of tandem mass spectrometry-based analytical methods. We also provide systematic, comprehensive laboratory strategies needed to control challenges posed by matrix effects in order to ensure delivery of the most accurate data for biomonitoring studies assessing exposure to environmental toxicants. PMID:25562585

Fabrication and Structural Design of Micro Pressure Sensors for Tire Pressure Measurement Systems (TPMS).

PubMed

Tian, Bian; Zhao, Yulong; Jiang, Zhuangde; Zhang, Ling; Liao, Nansheng; Liu, Yuanhao; Meng, Chao

2009-01-01

In this paper we describe the design and testing of a micro piezoresistive pressure sensor for a Tire Pressure Measurement System (TPMS) which has the advantages of a minimized structure, high sensitivity, linearity and accuracy. Through analysis of the stress distribution of the diaphragm using the ANSYS software, a model of the structure was established. The fabrication on a single silicon substrate utilizes the technologies of anisotropic chemical etching and packaging through glass anodic bonding. The performance of this type of piezoresistive sensor, including size, sensitivity, and long-term stability, were investigated. The results indicate that the accuracy is 0.5% FS, therefore this design meets the requirements for a TPMS, and not only has a smaller size and simplicity of preparation, but also has high sensitivity and accuracy.

A comparative study between three stability indicating spectrophotometric methods for the determination of diatrizoate sodium in presence of its cytotoxic degradation product based on two-wavelength selection

NASA Astrophysics Data System (ADS)

Riad, Safaa M.; El-Rahman, Mohamed K. Abd; Fawaz, Esraa M.; Shehata, Mostafa A.

2015-06-01

Three sensitive, selective, and precise stability indicating spectrophotometric methods for the determination of the X-ray contrast agent, diatrizoate sodium (DTA) in the presence of its acidic degradation product (highly cytotoxic 3,5-diamino metabolite) and in pharmaceutical formulation, were developed and validated. The first method is ratio difference, the second one is the bivariate method, and the third one is the dual wavelength method. The calibration curves for the three proposed methods are linear over a concentration range of 2-24 μg/mL. The selectivity of the proposed methods was tested using laboratory prepared mixtures. The proposed methods have been successfully applied to the analysis of DTA in pharmaceutical dosage forms without interference from other dosage form additives. The results were statistically compared with the official US pharmacopeial method. No significant difference for either accuracy or precision was observed.

A comparative study between three stability indicating spectrophotometric methods for the determination of diatrizoate sodium in presence of its cytotoxic degradation product based on two-wavelength selection.

PubMed

Riad, Safaa M; El-Rahman, Mohamed K Abd; Fawaz, Esraa M; Shehata, Mostafa A

2015-06-15

Three sensitive, selective, and precise stability indicating spectrophotometric methods for the determination of the X-ray contrast agent, diatrizoate sodium (DTA) in the presence of its acidic degradation product (highly cytotoxic 3,5-diamino metabolite) and in pharmaceutical formulation, were developed and validated. The first method is ratio difference, the second one is the bivariate method, and the third one is the dual wavelength method. The calibration curves for the three proposed methods are linear over a concentration range of 2-24 μg/mL. The selectivity of the proposed methods was tested using laboratory prepared mixtures. The proposed methods have been successfully applied to the analysis of DTA in pharmaceutical dosage forms without interference from other dosage form additives. The results were statistically compared with the official US pharmacopeial method. No significant difference for either accuracy or precision was observed. Copyright © 2015 Elsevier B.V. All rights reserved.

Examining the accuracy of the infinite order sudden approximation using sensitivity analysis

NASA Astrophysics Data System (ADS)

Eno, Larry; Rabitz, Herschel

1981-08-01

A method is developed for assessing the accuracy of scattering observables calculated within the framework of the infinite order sudden (IOS) approximation. In particular, we focus on the energy sudden assumption of the IOS method and our approach involves the determination of the sensitivity of the IOS scattering matrix SIOS with respect to a parameter which reintroduces the internal energy operator ?0 into the IOS Hamiltonian. This procedure is an example of sensitivity analysis of missing model components (?0 in this case) in the reference Hamiltonian. In contrast to simple first-order perturbation theory a finite result is obtained for the effect of ?0 on SIOS. As an illustration, our method of analysis is applied to integral state-to-state cross sections for the scattering of an atom and rigid rotor. Results are generated within the He+H2 system and a comparison is made between IOS and coupled states cross sections and the corresponding IOS sensitivities. It is found that the sensitivity coefficients are very useful indicators of the accuracy of the IOS results. Finally, further developments and applications are discussed.

Assessing genomic selection prediction accuracy in a dynamic barley breeding

USDA-ARS?s Scientific Manuscript database

Genomic selection is a method to improve quantitative traits in crops and livestock by estimating breeding values of selection candidates using phenotype and genome-wide marker data sets. Prediction accuracy has been evaluated through simulation and cross-validation, however validation based on prog...

A self-assembled fluorescent organic nanoprobe and its application for sulfite detection in food samples and living systems.

PubMed

Gao, Tang; Cao, Xiaozheng; Ge, Peng; Dong, Jie; Yang, Shuqi; Xu, Huan; Wu, Yong; Gao, Feng; Zeng, Wenbin

2017-05-23

Sulfur dioxide (SO 2 ) is a widely distributed air pollutant, and humans can easily be exposed to sulfite by inhaling SO 2 , thus inducing respiratory responses and diseases. Hence, to develop a rapid, sensitive and selective method for detection of sulfites is of great importance. Herein, we designed and synthesized a novel tetraphenyl imidazole compound TIBM with aggregation-induced emission enhancement (AIEE). TIBM can self-assemble into well-organized nanoparticles and is reported as an excellent probe for detection of sulfite with high selectivity and sensitivity. The nanoprobe performed very well for the detection of sulfite with an ultrafast detection time (15 s) and an ultralow detection limit (7.4 nM), which is superior to most of the reported probes. Moreover, the nanoprobe was successfully used to detect sulfite in food samples with a favorable accuracy. In addition, we developed paper-based devices for point-of-care detection of sulfite with naked eyes. Furthermore, due to its high water solubility, cell membrane permeability and good biocompatibility, the nanoproboe was further applied to detect sulfite in living systems. This study may offer some helpful insights for designing other AIE-based fluorescent nanosensors for various analytes.

PCR-based Methodologies Used to Detect and Differentiate the Burkholderia pseudomallei complex: B. pseudomallei, B. mallei, and B. thailandensis.

PubMed

Lowe, Woan; March, Jordon K; Bunnell, Annette J; O'Neill, Kim L; Robison, Richard A

2014-01-01

Methods for the rapid detection and differentiation of the Burkholderia pseudomallei complex comprising B. pseudomallei, B. mallei, and B. thailandensis, have been the topic of recent research due to the high degree of phenotypic and genotypic similarities of these species. B. pseudomallei and B. mallei are recognized by the CDC as tier 1 select agents. The high mortality rates of glanders and melioidosis, their potential use as bioweapons, and their low infectious dose, necessitate the need for rapid and accurate detection methods. Although B. thailandensis is generally avirulent in mammals, this species displays very similar phenotypic characteristics to that of B. pseudomallei. Optimal identification of these species remains problematic, due to the difficulty in developing a sensitive, selective, and accurate assay. The development of PCR technologies has revolutionized diagnostic testing and these detection methods have become popular due to their speed, sensitivity, and accuracy. The purpose of this review is to provide a comprehensive overview and evaluation of the advancements in PCR-based detection and differentiation methodologies for the B. pseudomallei complex, and examine their potential uses in diagnostic and environmental testing.

Simultaneous Proteomic Discovery and Targeted Monitoring using Liquid Chromatography, Ion Mobility Spectrometry, and Mass Spectrometry*

PubMed Central

Burnum-Johnson, Kristin E.; Nie, Song; Casey, Cameron P.; Monroe, Matthew E.; Orton, Daniel J.; Ibrahim, Yehia M.; Gritsenko, Marina A.; Clauss, Therese R. W.; Shukla, Anil K.; Moore, Ronald J.; Purvine, Samuel O.; Shi, Tujin; Qian, Weijun; Liu, Tao; Baker, Erin S.; Smith, Richard D.

2016-01-01

Current proteomic approaches include both broad discovery measurements and quantitative targeted analyses. In many cases, discovery measurements are initially used to identify potentially important proteins (e.g. candidate biomarkers) and then targeted studies are employed to quantify a limited number of selected proteins. Both approaches, however, suffer from limitations. Discovery measurements aim to sample the whole proteome but have lower sensitivity, accuracy, and quantitation precision than targeted approaches, whereas targeted measurements are significantly more sensitive but only sample a limited portion of the proteome. Herein, we describe a new approach that performs both discovery and targeted monitoring (DTM) in a single analysis by combining liquid chromatography, ion mobility spectrometry and mass spectrometry (LC-IMS-MS). In DTM, heavy labeled target peptides are spiked into tryptic digests and both the labeled and unlabeled peptides are detected using LC-IMS-MS instrumentation. Compared with the broad LC-MS discovery measurements, DTM yields greater peptide/protein coverage and detects lower abundance species. DTM also achieved detection limits similar to selected reaction monitoring (SRM) indicating its potential for combined high quality discovery and targeted analyses, which is a significant step toward the convergence of discovery and targeted approaches. PMID:27670688

Switch-Task Performance in Rats Is Disturbed by 12 h of Sleep Deprivation But Not by 12 h of Sleep Fragmentation

PubMed Central

Leenaars, Cathalijn H.C.; Joosten, Ruud N.J.M.A.; Zwart, Allard; Sandberg, Hans; Ruimschotel, Emma; Hanegraaf, Maaike A.J.; Dematteis, Maurice; Feenstra, Matthijs G.P.; van Someren, Eus J.W.

2012-01-01

Study Objectives: Task-switching is an executive function involving the prefrontal cortex. Switching temporarily attenuates the speed and/or accuracy of performance, phenomena referred to as switch costs. In accordance with the idea that prefrontal function is particularly sensitive to sleep loss, switch-costs increase during prolonged waking in humans. It has been difficult to investigate the underlying neurobiological mechanisms because of the lack of a suitable animal model. Here, we introduce the first switch-task for rats and report the effects of sleep deprivation and inactivation of the medial prefrontal cortex. Design: Rats were trained to repeatedly switch between 2 stimulus-response associations, indicated by the presentation of a visual or an auditory stimulus. These stimulus-response associations were offered in blocks, and performance was compared for the first and fifth trials of each block. Performance was tested after exposure to 12 h of total sleep deprivation, sleep fragmentation, and their respective movement control conditions. Finally, it was tested after pharmacological inactivation of the medial prefrontal cortex. Settings: Controlled laboratory settings. Participants: 15 male Wistar rats. Measurements & Results: Both accuracy and latency showed switch-costs at baseline. Twelve hours of total sleep deprivation, but not sleep fragmentation, impaired accuracy selectively on the switch-trials. Inactivation of the medial prefrontal cortex by local neuronal inactivation resulted in an overall decrease in accuracy. Conclusions: We developed and validated a switch-task that is sensitive to sleep deprivation. This introduces the possibility for in-depth investigations on the neurobiological mechanisms underlying executive impairments after sleep disturbance in a rat model. Citation: Leenaars CHC; Joosten RNJMA; Zwart A; Sandberg H; Ruimschotel E; Hanegraaf MAJ; Dematteis M; Feenstra MGP; van Someren EJW. Switch-task performance in rats is disturbed by 12 h of sleep deprivation but not by 12 h of sleep fragmentation. SLEEP 2012;35(2):211-221. PMID:22294811

Systematic review of patient history and physical examination to diagnose chronic low back pain originating from the facet joints.

PubMed

Maas, E T; Juch, J N S; Ostelo, R W J G; Groeneweg, J G; Kallewaard, J W; Koes, B W; Verhagen, A P; Huygen, F J P M; van Tulder, M W

2017-03-01

Patient history and physical examination are frequently used procedures to diagnose chronic low back pain (CLBP) originating from the facet joints, although the diagnostic accuracy is controversial. The aim of this systematic review is to determine the diagnostic accuracy of patient history and/or physical examination to identify CLBP originating from the facet joints using diagnostic blocks as reference standard. We searched MEDLINE, EMBASE, CINAHL, Web of Science and the Cochrane Collaboration database from inception until June 2016. Two review authors independently selected studies for inclusion, extracted data and assessed the risk of bias. We calculated sensitivity and specificity values, with 95% confidence intervals (95% CI). Twelve studies were included, in which 129 combinations of index tests and reference standards were presented. Most of these index tests have only been evaluated in single studies with a high risk of bias. Four studies evaluated the diagnostic accuracy of the Revel's criteria combination. Because of the clinical heterogeneity, results were not pooled. The published sensitivities ranged from 0.11 (95% CI 0.02-0.29) to 1.00 (95% CI 0.75-1.00), and the specificities ranged from 0.66 (95% CI 0.46-0.82) to 0.91 (95% CI 0.83-0.96). Due to clinical heterogeneity, the evidence for the diagnostic accuracy of patient history and/or physical examination to identify facet joint pain is inconclusive. Patient history and physical examination cannot be used to limit the need of a diagnostic block. The validity of the diagnostic facet joint block should be studied, and high quality studies are required to confirm the results of single studies. Patient history and physical examination cannot be used to limit the need of a diagnostic block. The validity of the diagnostic facet joint block should be studied, and high quality studies are required to confirm the results of single studies. © 2016 European Pain Federation - EFIC®.

Accuracy of LightCycler® SeptiFast for the detection and identification of pathogens in the blood of patients with suspected sepsis: a systematic review protocol

PubMed Central

Wilson, Claire; Blackwood, Bronagh; McAuley, Danny F; Perkins, Gavin D; McMullan, Ronan; Gates, Simon; Warhurst, Geoffrey

2012-01-01

Background There is growing interest in the potential utility of molecular diagnostics in improving the detection of life-threatening infection (sepsis). LightCycler® SeptiFast is a multipathogen probe-based real-time PCR system targeting DNA sequences of bacteria and fungi present in blood samples within a few hours. We report here the protocol of the first systematic review of published clinical diagnostic accuracy studies of this technology when compared with blood culture in the setting of suspected sepsis. Methods/design Data sources: the Cochrane Database of Systematic Reviews, the Database of Abstracts of Reviews of Effects (DARE), the Health Technology Assessment Database (HTA), the NHS Economic Evaluation Database (NHSEED), The Cochrane Library, MEDLINE, EMBASE, ISI Web of Science, BIOSIS Previews, MEDION and the Aggressive Research Intelligence Facility Database (ARIF). Study selection: diagnostic accuracy studies that compare the real-time PCR technology with standard culture results performed on a patient's blood sample during the management of sepsis. Data extraction: three reviewers, working independently, will determine the level of evidence, methodological quality and a standard data set relating to demographics and diagnostic accuracy metrics for each study. Statistical analysis/data synthesis: heterogeneity of studies will be investigated using a coupled forest plot of sensitivity and specificity and a scatter plot in Receiver Operator Characteristic (ROC) space. Bivariate model method will be used to estimate summary sensitivity and specificity. The authors will investigate reporting biases using funnel plots based on effective sample size and regression tests of asymmetry. Subgroup analyses are planned for adults, children and infection setting (hospital vs community) if sufficient data are uncovered. Dissemination Recommendations will be made to the Department of Health (as part of an open-access HTA report) as to whether the real-time PCR technology has sufficient clinical diagnostic accuracy potential to move forward to efficacy testing during the provision of routine clinical care. Registration PROSPERO—NIHR Prospective Register of Systematic Reviews (CRD42011001289). PMID:22240646

Evaluating the performance of selection scans to detect selective sweeps in domestic dogs

PubMed Central

Schlamp, Florencia; van der Made, Julian; Stambler, Rebecca; Chesebrough, Lewis; Boyko, Adam R.; Messer, Philipp W.

2015-01-01

Selective breeding of dogs has resulted in repeated artificial selection on breed-specific morphological phenotypes. A number of quantitative trait loci associated with these phenotypes have been identified in genetic mapping studies. We analyzed the population genomic signatures observed around the causal mutations for 12 of these loci in 25 dog breeds, for which we genotyped 25 individuals in each breed. By measuring the population frequencies of the causal mutations in each breed, we identified those breeds in which specific mutations most likely experienced positive selection. These instances were then used as positive controls for assessing the performance of popular statistics to detect selection from population genomic data. We found that artificial selection during dog domestication has left characteristic signatures in the haplotype and nucleotide polymorphism patterns around selected loci that can be detected in the genotype data from a single population sample. However, the sensitivity and accuracy at which such signatures were detected varied widely between loci, the particular statistic used, and the choice of analysis parameters. We observed examples of both hard and soft selective sweeps and detected strong selective events that removed genetic diversity almost entirely over regions >10 Mbp. Our study demonstrates the power and limitations of selection scans in populations with high levels of linkage disequilibrium due to severe founder effects and recent population bottlenecks. PMID:26589239

Evaluating the performance of selection scans to detect selective sweeps in domestic dogs.

PubMed

Schlamp, Florencia; van der Made, Julian; Stambler, Rebecca; Chesebrough, Lewis; Boyko, Adam R; Messer, Philipp W

2016-01-01

Selective breeding of dogs has resulted in repeated artificial selection on breed-specific morphological phenotypes. A number of quantitative trait loci associated with these phenotypes have been identified in genetic mapping studies. We analysed the population genomic signatures observed around the causal mutations for 12 of these loci in 25 dog breeds, for which we genotyped 25 individuals in each breed. By measuring the population frequencies of the causal mutations in each breed, we identified those breeds in which specific mutations most likely experienced positive selection. These instances were then used as positive controls for assessing the performance of popular statistics to detect selection from population genomic data. We found that artificial selection during dog domestication has left characteristic signatures in the haplotype and nucleotide polymorphism patterns around selected loci that can be detected in the genotype data from a single population sample. However, the sensitivity and accuracy at which such signatures were detected varied widely between loci, the particular statistic used and the choice of analysis parameters. We observed examples of both hard and soft selective sweeps and detected strong selective events that removed genetic diversity almost entirely over regions >10 Mbp. Our study demonstrates the power and limitations of selection scans in populations with high levels of linkage disequilibrium due to severe founder effects and recent population bottlenecks. © 2015 John Wiley & Sons Ltd.

Accuracy of ultra-wide-field fundus ophthalmoscopy-assisted deep learning, a machine-learning technology, for detecting age-related macular degeneration.

PubMed

Matsuba, Shinji; Tabuchi, Hitoshi; Ohsugi, Hideharu; Enno, Hiroki; Ishitobi, Naofumi; Masumoto, Hiroki; Kiuchi, Yoshiaki

2018-05-09

To predict exudative age-related macular degeneration (AMD), we combined a deep convolutional neural network (DCNN), a machine-learning algorithm, with Optos, an ultra-wide-field fundus imaging system. First, to evaluate the diagnostic accuracy of DCNN, 364 photographic images (AMD: 137) were amplified and the area under the curve (AUC), sensitivity and specificity were examined. Furthermore, in order to compare the diagnostic abilities between DCNN and six ophthalmologists, we prepared yield 84 sheets comprising 50% of normal and wet-AMD data each, and calculated the correct answer rate, specificity, sensitivity, and response times. DCNN exhibited 100% sensitivity and 97.31% specificity for wet-AMD images, with an average AUC of 99.76%. Moreover, comparing the diagnostic abilities of DCNN versus six ophthalmologists, the average accuracy of the DCNN was 100%. On the other hand, the accuracy of ophthalmologists, determined only by Optos images without a fundus examination, was 81.9%. A combination of DCNN with Optos images is not better than a medical examination; however, it can identify exudative AMD with a high level of accuracy. Our system is considered useful for screening and telemedicine.

Verification and classification bias interactions in diagnostic test accuracy studies for fine-needle aspiration biopsy.

PubMed

Schmidt, Robert L; Walker, Brandon S; Cohen, Michael B

2015-03-01

Reliable estimates of accuracy are important for any diagnostic test. Diagnostic accuracy studies are subject to unique sources of bias. Verification bias and classification bias are 2 sources of bias that commonly occur in diagnostic accuracy studies. Statistical methods are available to estimate the impact of these sources of bias when they occur alone. The impact of interactions when these types of bias occur together has not been investigated. We developed mathematical relationships to show the combined effect of verification bias and classification bias. A wide range of case scenarios were generated to assess the impact of bias components and interactions on total bias. Interactions between verification bias and classification bias caused overestimation of sensitivity and underestimation of specificity. Interactions had more effect on sensitivity than specificity. Sensitivity was overestimated by at least 7% in approximately 6% of the tested scenarios. Specificity was underestimated by at least 7% in less than 0.1% of the scenarios. Interactions between verification bias and classification bias create distortions in accuracy estimates that are greater than would be predicted from each source of bias acting independently. © 2014 American Cancer Society.

Dynamic sensitivity analysis of biological systems

PubMed Central

Wu, Wu Hsiung; Wang, Feng Sheng; Chang, Maw Shang

2008-01-01

Background A mathematical model to understand, predict, control, or even design a real biological system is a central theme in systems biology. A dynamic biological system is always modeled as a nonlinear ordinary differential equation (ODE) system. How to simulate the dynamic behavior and dynamic parameter sensitivities of systems described by ODEs efficiently and accurately is a critical job. In many practical applications, e.g., the fed-batch fermentation systems, the system admissible input (corresponding to independent variables of the system) can be time-dependent. The main difficulty for investigating the dynamic log gains of these systems is the infinite dimension due to the time-dependent input. The classical dynamic sensitivity analysis does not take into account this case for the dynamic log gains. Results We present an algorithm with an adaptive step size control that can be used for computing the solution and dynamic sensitivities of an autonomous ODE system simultaneously. Although our algorithm is one of the decouple direct methods in computing dynamic sensitivities of an ODE system, the step size determined by model equations can be used on the computations of the time profile and dynamic sensitivities with moderate accuracy even when sensitivity equations are more stiff than model equations. To show this algorithm can perform the dynamic sensitivity analysis on very stiff ODE systems with moderate accuracy, it is implemented and applied to two sets of chemical reactions: pyrolysis of ethane and oxidation of formaldehyde. The accuracy of this algorithm is demonstrated by comparing the dynamic parameter sensitivities obtained from this new algorithm and from the direct method with Rosenbrock stiff integrator based on the indirect method. The same dynamic sensitivity analysis was performed on an ethanol fed-batch fermentation system with a time-varying feed rate to evaluate the applicability of the algorithm to realistic models with time-dependent admissible input. Conclusion By combining the accuracy we show with the efficiency of being a decouple direct method, our algorithm is an excellent method for computing dynamic parameter sensitivities in stiff problems. We extend the scope of classical dynamic sensitivity analysis to the investigation of dynamic log gains of models with time-dependent admissible input. PMID:19091016

A surface enhanced Raman scattering quantitative analytical platform for detection of trace Cu coupled the catalytic reaction and gold nanoparticle aggregation with label-free Victoria blue B molecular probe.

PubMed

Li, Chongning; Ouyang, Huixiang; Tang, Xueping; Wen, Guiqing; Liang, Aihui; Jiang, Zhiliang

2017-01-15

With development of economy and society, there is an urgent need to develop convenient and sensitive methods for detection of Cu 2+ pollution in water. In this article, a simple and sensitive SERS sensor was proposed to quantitative analysis of trace Cu 2+ in water. The SERS sensor platform was prepared a common gold nanoparticle (AuNP)-SiO 2 sol substrate platform by adsorbing HSA, coupling with the catalytic reaction of Cu 2+ -ascorbic acid (H 2 A)-dissolved oxygen, and using label-free Victoria blue B (VBB) as SERS molecular probes. The SERS sensor platform response to the AuNP aggregations by hydroxyl radicals (•OH) oxidizing from the Cu 2+ catalytic reaction, which caused the SERS signal enhancement. Therefore, by monitoring the increase of SERS signal, Cu 2+ in water can be determined accurately. The results show that the SERS sensor platforms owns a linear response with a range from 0.025 to 25μmol/L Cu 2+ , and with a detection limit of 0.008μmol/L. In addition, the SERS method demonstrated good specificity for Cu 2+ , which can determined accurately trace Cu 2+ in water samples, and good recovery and accuracy are obtained for the water samples. With its high selectivity and good accuracy, the sensitive SERS quantitative analysis method is expected to be a promising candidate for determining copper ions in environmental monitoring and food safety. Copyright © 2016 Elsevier B.V. All rights reserved.

Research on bathymetry estimation by Worldview-2 based with the semi-analytical model

NASA Astrophysics Data System (ADS)

Sheng, L.; Bai, J.; Zhou, G.-W.; Zhao, Y.; Li, Y.-C.

2015-04-01

South Sea Islands of China are far away from the mainland, the reefs takes more than 95% of south sea, and most reefs scatter over interested dispute sensitive area. Thus, the methods of obtaining the reefs bathymetry accurately are urgent to be developed. Common used method, including sonar, airborne laser and remote sensing estimation, are limited by the long distance, large area and sensitive location. Remote sensing data provides an effective way for bathymetry estimation without touching over large area, by the relationship between spectrum information and bathymetry. Aimed at the water quality of the south sea of China, our paper develops a bathymetry estimation method without measured water depth. Firstly the semi-analytical optimization model of the theoretical interpretation models has been studied based on the genetic algorithm to optimize the model. Meanwhile, OpenMP parallel computing algorithm has been introduced to greatly increase the speed of the semi-analytical optimization model. One island of south sea in China is selected as our study area, the measured water depth are used to evaluate the accuracy of bathymetry estimation from Worldview-2 multispectral images. The results show that: the semi-analytical optimization model based on genetic algorithm has good results in our study area;the accuracy of estimated bathymetry in the 0-20 meters shallow water area is accepted.Semi-analytical optimization model based on genetic algorithm solves the problem of the bathymetry estimation without water depth measurement. Generally, our paper provides a new bathymetry estimation method for the sensitive reefs far away from mainland.

Integrated genetic and epigenetic prediction of coronary heart disease in the Framingham Heart Study.

PubMed

Dogan, Meeshanthini V; Grumbach, Isabella M; Michaelson, Jacob J; Philibert, Robert A

2018-01-01

An improved method for detecting coronary heart disease (CHD) could have substantial clinical impact. Building on the idea that systemic effects of CHD risk factors are a conglomeration of genetic and environmental factors, we use machine learning techniques and integrate genetic, epigenetic and phenotype data from the Framingham Heart Study to build and test a Random Forest classification model for symptomatic CHD. Our classifier was trained on n = 1,545 individuals and consisted of four DNA methylation sites, two SNPs, age and gender. The methylation sites and SNPs were selected during the training phase. The final trained model was then tested on n = 142 individuals. The test data comprised of individuals removed based on relatedness to those in the training dataset. This integrated classifier was capable of classifying symptomatic CHD status of those in the test set with an accuracy, sensitivity and specificity of 78%, 0.75 and 0.80, respectively. In contrast, a model using only conventional CHD risk factors as predictors had an accuracy and sensitivity of only 65% and 0.42, respectively, but with a specificity of 0.89 in the test set. Regression analyses of the methylation signatures illustrate our ability to map these signatures to known risk factors in CHD pathogenesis. These results demonstrate the capability of an integrated approach to effectively model symptomatic CHD status. These results also suggest that future studies of biomaterial collected from longitudinally informative cohorts that are specifically characterized for cardiac disease at follow-up could lead to the introduction of sensitive, readily employable integrated genetic-epigenetic algorithms for predicting onset of future symptomatic CHD.

New potentiometric transducer based on a Mn(II) [2-formylquinoline thiosemicarbazone] complex for static and hydrodynamic assessment of azides.

PubMed

Kamel, Ayman H

2015-11-01

A new potentiometric transducer for selective recognition of azide is characterized and developed. The PVC plasticized based sensor incorporates Mn(II) [2-formylquinoline thiosemicarbazone] complex in the presence of tri dodecyl methyl ammonium chloride (TDMAC) as a lipophilic cationic additive. The sensor displayed a near-Nernstian response for azide over 1.0×10(-2)-1.0×10(-5) mol L(-1), with an anionic slope of -55.8±0.6 mV decade(-1) and lower limit of detection 0.34 µg mL(-1). The sensor was pH independent in the range 5.5-9 and presented good selectivity features towards several inorganic anions, and it is easily used in a flow injection system and compared with a tubular detector. The intrinsic characteristics of the detector in a low dispersion manifold were determined and compared with data obtained under a hydrodynamic mode of operation. This simple and inexpensive automation, with a good potentiometric detector, enabled the analysis of ~33 samples h(-1) without requiring pre-treatment procedures. The proposed method is also applied to the analysis of trace levels of azide in primer mixtures. Significantly improved accuracy, precision, response time, stability and selectivity were offered by these simple and cost-effective potentiometric sensor compared with other standard techniques. The method has the requisite accuracy, sensitivity and precision to determine azide ions. Copyright © 2015 Elsevier B.V. All rights reserved.

DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues.

PubMed

Ma, Xin; Guo, Jing; Sun, Xiao

2016-01-01

DNA-binding proteins are fundamentally important in cellular processes. Several computational-based methods have been developed to improve the prediction of DNA-binding proteins in previous years. However, insufficient work has been done on the prediction of DNA-binding proteins from protein sequence information. In this paper, a novel predictor, DNABP (DNA-binding proteins), was designed to predict DNA-binding proteins using the random forest (RF) classifier with a hybrid feature. The hybrid feature contains two types of novel sequence features, which reflect information about the conservation of physicochemical properties of the amino acids, and the binding propensity of DNA-binding residues and non-binding propensities of non-binding residues. The comparisons with each feature demonstrated that these two novel features contributed most to the improvement in predictive ability. Furthermore, to improve the prediction performance of the DNABP model, feature selection using the minimum redundancy maximum relevance (mRMR) method combined with incremental feature selection (IFS) was carried out during the model construction. The results showed that the DNABP model could achieve 86.90% accuracy, 83.76% sensitivity, 90.03% specificity and a Matthews correlation coefficient of 0.727. High prediction accuracy and performance comparisons with previous research suggested that DNABP could be a useful approach to identify DNA-binding proteins from sequence information. The DNABP web server system is freely available at http://www.cbi.seu.edu.cn/DNABP/.

Local staging and assessment of colon cancer with 1.5-T magnetic resonance imaging

PubMed Central

Blake, Helena; Jeyadevan, Nelesh; Abulafi, Muti; Swift, Ian; Toomey, Paul; Brown, Gina

2016-01-01

Objective: The aim of this study was to assess the accuracy of 1.5-T MRI in the pre-operative local T and N staging of colon cancer and identification of extramural vascular invasion (EMVI). Methods: Between 2010 and 2012, 60 patients with adenocarcinoma of the colon were prospectively recruited at 2 centres. 55 patients were included for final analysis. Patients received pre-operative 1.5-T MRI with high-resolution T2 weighted, gadolinium-enhanced T1 weighted and diffusion-weighted images. These were blindly assessed by two expert radiologists. Accuracy of the T-stage, N-stage and EMVI assessment was evaluated using post-operative histology as the gold standard. Results: Results are reported for two readers. Identification of T3 disease demonstrated an accuracy of 71% and 51%, sensitivity of 74% and 42% and specificity of 74% and 83%. Identification of N1 disease demonstrated an accuracy of 57% for both readers, sensitivity of 26% and 35% and specificity of 81% and 74%. Identification of EMVI demonstrated an accuracy of 74% and 69%, sensitivity 63% and 26% and specificity 80% and 91%. Conclusion: 1.5-T MRI achieved a moderate accuracy in the local evaluation of colon cancer, but cannot be recommended to replace CT on the basis of this study. Advances in knowledge: This study confirms that MRI is a viable alternative to CT for the local assessment of colon cancer, but this study does not reproduce the very high accuracy reported in the only other study to assess the accuracy of MRI in colon cancer staging. PMID:27226219

An ensemble-of-classifiers based approach for early diagnosis of Alzheimer's disease: classification using structural features of brain images.

PubMed

Farhan, Saima; Fahiem, Muhammad Abuzar; Tauseef, Huma

2014-01-01

Structural brain imaging is playing a vital role in identification of changes that occur in brain associated with Alzheimer's disease. This paper proposes an automated image processing based approach for the identification of AD from MRI of the brain. The proposed approach is novel in a sense that it has higher specificity/accuracy values despite the use of smaller feature set as compared to existing approaches. Moreover, the proposed approach is capable of identifying AD patients in early stages. The dataset selected consists of 85 age and gender matched individuals from OASIS database. The features selected are volume of GM, WM, and CSF and size of hippocampus. Three different classification models (SVM, MLP, and J48) are used for identification of patients and controls. In addition, an ensemble of classifiers, based on majority voting, is adopted to overcome the error caused by an independent base classifier. Ten-fold cross validation strategy is applied for the evaluation of our scheme. Moreover, to evaluate the performance of proposed approach, individual features and combination of features are fed to individual classifiers and ensemble based classifier. Using size of left hippocampus as feature, the accuracy achieved with ensemble of classifiers is 93.75%, with 100% specificity and 87.5% sensitivity.

Species distribution model transferability and model grain size - finer may not always be better.

PubMed

Manzoor, Syed Amir; Griffiths, Geoffrey; Lukac, Martin

2018-05-08

Species distribution models have been used to predict the distribution of invasive species for conservation planning. Understanding spatial transferability of niche predictions is critical to promote species-habitat conservation and forecasting areas vulnerable to invasion. Grain size of predictor variables is an important factor affecting the accuracy and transferability of species distribution models. Choice of grain size is often dependent on the type of predictor variables used and the selection of predictors sometimes rely on data availability. This study employed the MAXENT species distribution model to investigate the effect of the grain size on model transferability for an invasive plant species. We modelled the distribution of Rhododendron ponticum in Wales, U.K. and tested model performance and transferability by varying grain size (50 m, 300 m, and 1 km). MAXENT-based models are sensitive to grain size and selection of variables. We found that over-reliance on the commonly used bioclimatic variables may lead to less accurate models as it often compromises the finer grain size of biophysical variables which may be more important determinants of species distribution at small spatial scales. Model accuracy is likely to increase with decreasing grain size. However, successful model transferability may require optimization of model grain size.

Design standards for experimental and field studies to evaluate diagnostic accuracy of tests for infectious diseases in aquatic animals.

PubMed

Laurin, E; Thakur, K K; Gardner, I A; Hick, P; Moody, N J G; Crane, M S J; Ernst, I

2018-05-01

Design and reporting quality of diagnostic accuracy studies (DAS) are important metrics for assessing utility of tests used in animal and human health. Following standards for designing DAS will assist in appropriate test selection for specific testing purposes and minimize the risk of reporting biased sensitivity and specificity estimates. To examine the benefits of recommending standards, design information from published DAS literature was assessed for 10 finfish, seven mollusc, nine crustacean and two amphibian diseases listed in the 2017 OIE Manual of Diagnostic Tests for Aquatic Animals. Of the 56 DAS identified, 41 were based on field testing, eight on experimental challenge studies and seven on both. Also, we adapted human and terrestrial-animal standards and guidelines for DAS structure for use in aquatic animal diagnostic research. Through this process, we identified and addressed important metrics for consideration at the design phase: study purpose, targeted disease state, selection of appropriate samples and specimens, laboratory analytical methods, statistical methods and data interpretation. These recommended design standards for DAS are presented as a checklist including risk-of-failure points and actions to mitigate bias at each critical step. Adherence to standards when designing DAS will also facilitate future systematic review and meta-analyses of DAS research literature. © 2018 John Wiley & Sons Ltd.

Image Stability Requirements For a Geostationary Imaging Fourier Transform Spectrometer (GIFTS)

NASA Technical Reports Server (NTRS)

Bingham, G. E.; Cantwell, G.; Robinson, R. C.; Revercomb, H. E.; Smith, W. L.

2001-01-01

A Geostationary Imaging Fourier Transform Spectrometer (GIFTS) has been selected for the NASA New Millennium Program (NMP) Earth Observing-3 (EO-3) mission. Our paper will discuss one of the key GIFTS measurement requirements, Field of View (FOV) stability, and its impact on required system performance. The GIFTS NMP mission is designed to demonstrate new and emerging sensor and data processing technologies with the goal of making revolutionary improvements in meteorological observational capability and forecasting accuracy. The GIFTS payload is a versatile imaging FTS with programmable spectral resolution and spatial scene selection that allows radiometric accuracy and atmospheric sounding precision to be traded in near real time for area coverage. The GIFTS sensor combines high sensitivity with a massively parallel spatial data collection scheme to allow high spatial resolution measurement of the Earth's atmosphere and rapid broad area coverage. An objective of the GIFTS mission is to demonstrate the advantages of high spatial resolution (4 km ground sample distance - gsd) on temperature and water vapor retrieval by allowing sampling in broken cloud regions. This small gsd, combined with the relatively long scan time required (approximately 10 s) to collect high resolution spectra from geostationary (GEO) orbit, may require extremely good pointing control. This paper discusses the analysis of this requirement.

Least-Squares Regression and Spectral Residual Augmented Classical Least-Squares Chemometric Models for Stability-Indicating Analysis of Agomelatine and Its Degradation Products: A Comparative Study.

PubMed

Naguib, Ibrahim A; Abdelrahman, Maha M; El Ghobashy, Mohamed R; Ali, Nesma A

2016-01-01

Two accurate, sensitive, and selective stability-indicating methods are developed and validated for simultaneous quantitative determination of agomelatine (AGM) and its forced degradation products (Deg I and Deg II), whether in pure forms or in pharmaceutical formulations. Partial least-squares regression (PLSR) and spectral residual augmented classical least-squares (SRACLS) are two chemometric models that are being subjected to a comparative study through handling UV spectral data in range (215-350 nm). For proper analysis, a three-factor, four-level experimental design was established, resulting in a training set consisting of 16 mixtures containing different ratios of interfering species. An independent test set consisting of eight mixtures was used to validate the prediction ability of the suggested models. The results presented indicate the ability of mentioned multivariate calibration models to analyze AGM, Deg I, and Deg II with high selectivity and accuracy. The analysis results of the pharmaceutical formulations were statistically compared to the reference HPLC method, with no significant differences observed regarding accuracy and precision. The SRACLS model gives comparable results to the PLSR model; however, it keeps the qualitative spectral information of the classical least-squares algorithm for analyzed components.

Lipidomics by ultrahigh performance liquid chromatography-high resolution mass spectrometry and its application to complex biological samples

PubMed Central

Triebl, Alexander; Trötzmüller, Martin; Hartler, Jürgen; Stojakovic, Tatjana; Köfeler, Harald C

2018-01-01

An improved approach for selective and sensitive identification and quantitation of lipid molecular species using reversed phase chromatography coupled to high resolution mass spectrometry was developed. The method is applicable to a wide variety of biological matrices using a simple liquid-liquid extraction procedure. Together, this approach combines three selectivity criteria: Reversed phase chromatography separates lipids according to their acyl chain length and degree of unsaturation and is capable of resolving positional isomers of lysophospholipids, as well as structural isomers of diacyl phospholipids and glycerolipids. Orbitrap mass spectrometry delivers the elemental composition of both positive and negative ions with high mass accuracy. Finally, automatically generated tandem mass spectra provide structural insight into numerous glycerolipids, phospholipids, and sphingolipids within a single run. Method validation resulted in a linearity range of more than four orders of magnitude, good values for accuracy and precision at biologically relevant concentration levels, and limits of quantitation of a few femtomoles on column. Hundreds of lipid molecular species were detected and quantified in three different biological matrices, which cover well the wide variety and complexity of various model organisms in lipidomic research. Together with a reliable software package, this method is a prime choice for global lipidomic analysis of even the most complex biological samples. PMID:28415015

Lipidomics by ultrahigh performance liquid chromatography-high resolution mass spectrometry and its application to complex biological samples.

PubMed

Triebl, Alexander; Trötzmüller, Martin; Hartler, Jürgen; Stojakovic, Tatjana; Köfeler, Harald C

2017-05-15

An improved approach for selective and sensitive identification and quantitation of lipid molecular species using reversed phase chromatography coupled to high resolution mass spectrometry was developed. The method is applicable to a wide variety of biological matrices using a simple liquid-liquid extraction procedure. Together, this approach combines multiple selectivity criteria: Reversed phase chromatography separates lipids according to their acyl chain length and degree of unsaturation and is capable of resolving positional isomers of lysophospholipids, as well as structural isomers of diacyl phospholipids and glycerolipids. Orbitrap mass spectrometry delivers the elemental composition of both positive and negative ions with high mass accuracy. Finally, automatically generated tandem mass spectra provide structural insight into numerous glycerolipids, phospholipids, and sphingolipids within a single run. Calibration showed linearity ranges of more than four orders of magnitude, good values for accuracy and precision at biologically relevant concentration levels, and limits of quantitation of a few femtomoles on column. Hundreds of lipid molecular species were detected and quantified in three different biological matrices, which cover well the wide variety and complexity of various model organisms in lipidomic research. Together with a software package, this method is a prime choice for global lipidomic analysis of even the most complex biological samples. Copyright © 2017 Elsevier B.V. All rights reserved.

absorption sensor for sensitive temperature and species measurements in high-temperature gases

NASA Astrophysics Data System (ADS)

Spearrin, R. M.; Ren, W.; Jeffries, J. B.; Hanson, R. K.

2014-09-01

A continuous-wave laser absorption diagnostic, based on the infrared CO2 bands near 4.2 and 2.7 μm, was developed for sensitive temperature and concentration measurements in high-temperature gas systems using fixed-wavelength methods. Transitions in the respective R-branches of both the fundamental υ 3 band (~2,350 cm-1) and combination υ 1 + υ 3 band (~3,610 cm-1) were chosen based on absorption line-strength, spectral isolation, and temperature sensitivity. The R(76) line near 2,390.52 cm-1 was selected for sensitive CO2 concentration measurements, and a detection limit of <5 ppm was achieved in shock tube kinetics experiments (~1,300 K). A cross-band, two-line thermometry technique was also established utilizing the R(96) line near 2,395.14 cm-1, paired with the R(28) line near 3,633.08 cm-1. This combination yields high temperature sensitivity (ΔE" = 3,305 cm-1) and expanded range compared with previous intra-band CO2 sensors. Thermometry performance was validated in a shock tube over a range of temperatures (600-1,800 K) important for combustion. Measured temperature accuracy was demonstrated to be better than 1 % over the entire range of conditions, with a standard error of ~0.5 % and µs temporal resolution.

Development and Psychometric Evaluation of the Brief Adolescent Gambling Screen (BAGS)

PubMed Central

Stinchfield, Randy; Wynne, Harold; Wiebe, Jamie; Tremblay, Joel

2017-01-01

The purpose of this study was to develop and evaluate the initial reliability, validity and classification accuracy of a new brief screen for adolescent problem gambling. The three-item Brief Adolescent Gambling Screen (BAGS) was derived from the nine-item Gambling Problem Severity Subscale (GPSS) of the Canadian Adolescent Gambling Inventory (CAGI) using a secondary analysis of existing CAGI data. The sample of 105 adolescents included 49 females and 56 males from Canada who completed the CAGI, a self-administered measure of DSM-IV diagnostic criteria for Pathological Gambling, and a clinician-administered diagnostic interview including the DSM-IV diagnostic criteria for Pathological Gambling (both of which were adapted to yield DSM-5 Gambling Disorder diagnosis). A stepwise multivariate discriminant function analysis selected three GPSS items as the best predictors of a diagnosis of Gambling Disorder. The BAGS demonstrated satisfactory estimates of reliability, validity and classification accuracy and was equivalent to the nine-item GPSS of the CAGI and the BAGS was more accurate than the SOGS-RA. The BAGS estimates of classification accuracy include hit rate = 0.95, sensitivity = 0.88, specificity = 0.98, false positive rate = 0.02, and false negative rate = 0.12. Since these classification estimates are preliminary, derived from a relatively small sample size, and based upon the same sample from which the items were selected, it will be important to cross-validate the BAGS with larger and more diverse samples. The BAGS should be evaluated for use as a screening tool in both clinical and school settings as well as epidemiological surveys. PMID:29312064

Utilization of nuclear methods for materials analysis and the determination of concentration gradients

NASA Technical Reports Server (NTRS)

Darras, R.

1979-01-01

The various types of nuclear chemical analysis methods are discussed. The possibilities of analysis through activation and direct observation of nuclear reactions are described. Such methods make it possible to analyze trace elements and impurities with selectivity, accuracy, and a high degree of sensitivity. Such methods are used in measuring major elements present in materials which are available for analysis only in small quantities. These methods are well suited to superficial analyses and to determination of concentration gradients; provided the nature and energy of the incident particles are chosen judiciously. Typical examples of steels, pure iron and refractory metals are illustrated.

Alzheimer's Disease Detection by Pseudo Zernike Moment and Linear Regression Classification.

PubMed

Wang, Shui-Hua; Du, Sidan; Zhang, Yin; Phillips, Preetha; Wu, Le-Nan; Chen, Xian-Qing; Zhang, Yu-Dong

2017-01-01

This study presents an improved method based on "Gorji et al. Neuroscience. 2015" by introducing a relatively new classifier-linear regression classification. Our method selects one axial slice from 3D brain image, and employed pseudo Zernike moment with maximum order of 15 to extract 256 features from each image. Finally, linear regression classification was harnessed as the classifier. The proposed approach obtains an accuracy of 97.51%, a sensitivity of 96.71%, and a specificity of 97.73%. Our method performs better than Gorji's approach and five other state-of-the-art approaches. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Land suitability assessment for wind power plant site selection using ANP-DEMATEL in a GIS environment: case study of Ardabil province, Iran.

PubMed

Azizi, Ali; Malekmohammadi, Bahram; Jafari, Hamid Reza; Nasiri, Hossein; Amini Parsa, Vahid

2014-10-01

Wind energy is a renewable energy resource that has increased in usage in most countries. Site selection for the establishment of large wind turbines, called wind farms, like any other engineering project, requires basic information and careful planning. This study assessed the possibility of establishing wind farms in Ardabil province in northwestern Iran by using a combination of analytic network process (ANP) and decision making trial and evaluation laboratory (DEMATEL) methods in a geographical information system (GIS) environment. DEMATEL was used to determine the criteria relationships. The weights of the criteria were determined using ANP and the overlaying process was done on GIS. Using 13 information layers in three main criteria including environmental, technical and economical, the land suitability map was produced and reclassified into 5 equally scored divisions from least suitable to most suitable areas. The results showed that about 6.68% of the area of Ardabil province is most suitable for establishment of wind turbines. Sensitivity analysis shows that significant portions of these most suitable zones coincide with suitable divisions of the input layers. The efficiency and accuracy of the hybrid model (ANP-DEMATEL) was evaluated and the results were compared to the ANP model. The sensitivity analysis, map classification, and factor weights for the two methods showed satisfactory results for the ANP-DEMATEL model in wind power plant site selection.

Comparison and validation of International Consensus Diagnostic Criteria for diagnosis of autoimmune pancreatitis from pancreatic cancer in a Taiwanese cohort.

PubMed

Chang, Ming-Chu; Liang, Po-Chin; Jan, I-Shiow; Yang, Ching-Yao; Tien, Yu-Wen; Wei, Shu-Chen; Wong, Jau-Min; Chang, Yu-Ting

2014-08-18

The International Consensus Diagnostic Criteria (ICDC) designed to diagnosis autoimmune pancreatitis (AIP) has been proposed recently. The diagnostic performance of ICDC has not been previously evaluated in diffuse-type and focal-type AIP, respectively, in comparison with the revised HISORt and Asian criteria in Taiwan. Prospective, consecutive patient cohort. Largest tertiary referred centre hospital managing pancreatic disease in Taiwan. 188 patients with AIP and 130 with tissue proofed pancreatic adenocarcinoma were consecutively recruited. The ICDC, as well as revised HISORt and Asian criteria, was applied for each participant. Each diagnostic criterion of ICDC was validated with special reference to levels 1 and 2 in diffuse-type and focal-type AIP. Sensitivity, specificity and accuracy. Each diagnostic criterion of ICDC was validated with special reference to levels 1 and 2 in AIP and focal-type AIP. The sensitivity, specificity and accuracy of ICDC for all AIP were the best: 89.4%, 100% and 93.7%, respectively, in these three criteria. The sensitivity, specificity and accuracy of ICDC for focal-type AIP (84.9%, 100% and 93.8%) were also the best among these three criteria. The area under the curve of receiver-operator characteristic of ICDC was 0.95 (95% CI 0.92 to 0.97) in all AIP and 0.93 (95% CI 0.88 to 0.97) in focal-type AIP. The sensitivity, specificity and accuracy of ICDC are higher than the revised HISORt and Asian criteria. The sensitivity, specificity and accuracy of each criterion are higher in diffuse-type AIP compared with focal-type AIP. Under the same specificity, the sensitivity and accuracy of ICDC are higher than other diagnostic criteria in focal-type AIP. ICDC has better diagnostic performance compared with previously proposed diagnostic criteria in diffuse-type and focal-type AIP. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Application of Anaerobic Digestion Model No. 1 for simulating anaerobic mesophilic sludge digestion

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

Mendes, Carlos, E-mail: carllosmendez@gmail.com; Esquerre, Karla, E-mail: karlaesquerre@ufba.br; Matos Queiroz, Luciano, E-mail: lmqueiroz@ufba.br

2015-01-15

Highlights: • The behavior of a anaerobic reactor was evaluated through modeling. • Parametric sensitivity analysis was used to select most sensitive of the ADM1. • The results indicate that the ADM1 was able to predict the experimental results. • Organic load rate above of 35 kg/m{sup 3} day affects the performance of the process. - Abstract: Improving anaerobic digestion of sewage sludge by monitoring common indicators such as volatile fatty acids (VFAs), gas composition and pH is a suitable solution for better sludge management. Modeling is an important tool to assess and to predict process performance. The present studymore » focuses on the application of the Anaerobic Digestion Model No. 1 (ADM1) to simulate the dynamic behavior of a reactor fed with sewage sludge under mesophilic conditions. Parametric sensitivity analysis is used to select the most sensitive ADM1 parameters for estimation using a numerical procedure while other parameters are applied without any modification to the original values presented in the ADM1 report. The results indicate that the ADM1 model after parameter estimation was able to predict the experimental results of effluent acetate, propionate, composites and biogas flows and pH with reasonable accuracy. The simulation of the effect of organic shock loading clearly showed that an organic shock loading rate above of 35 kg/m{sup 3} day affects the performance of the reactor. The results demonstrate that simulations can be helpful to support decisions on predicting the anaerobic digestion process of sewage sludge.« less

Validation of various adaptive threshold methods of segmentation applied to follicular lymphoma digital images stained with 3,3’-Diaminobenzidine&Haematoxylin

PubMed Central

2013-01-01

The comparative study of the results of various segmentation methods for the digital images of the follicular lymphoma cancer tissue section is described in this paper. The sensitivity and specificity and some other parameters of the following adaptive threshold methods of segmentation: the Niblack method, the Sauvola method, the White method, the Bernsen method, the Yasuda method and the Palumbo method, are calculated. Methods are applied to three types of images constructed by extraction of the brown colour information from the artificial images synthesized based on counterpart experimentally captured images. This paper presents usefulness of the microscopic image synthesis method in evaluation as well as comparison of the image processing results. The results of thoughtful analysis of broad range of adaptive threshold methods applied to: (1) the blue channel of RGB, (2) the brown colour extracted by deconvolution and (3) the ’brown component’ extracted from RGB allows to select some pairs: method and type of image for which this method is most efficient considering various criteria e.g. accuracy and precision in area detection or accuracy in number of objects detection and so on. The comparison shows that the White, the Bernsen and the Sauvola methods results are better than the results of the rest of the methods for all types of monochromatic images. All three methods segments the immunopositive nuclei with the mean accuracy of 0.9952, 0.9942 and 0.9944 respectively, when treated totally. However the best results are achieved for monochromatic image in which intensity shows brown colour map constructed by colour deconvolution algorithm. The specificity in the cases of the Bernsen and the White methods is 1 and sensitivities are: 0.74 for White and 0.91 for Bernsen methods while the Sauvola method achieves sensitivity value of 0.74 and the specificity value of 0.99. According to Bland-Altman plot the Sauvola method selected objects are segmented without undercutting the area for true positive objects but with extra false positive objects. The Sauvola and the Bernsen methods gives complementary results what will be exploited when the new method of virtual tissue slides segmentation be develop. Virtual Slides The virtual slides for this article can be found here: slide 1: http://diagnosticpathology.slidepath.com/dih/webViewer.php?snapshotId=13617947952577 and slide 2: http://diagnosticpathology.slidepath.com/dih/webViewer.php?snapshotId=13617948230017. PMID:23531405

Validation of various adaptive threshold methods of segmentation applied to follicular lymphoma digital images stained with 3,3'-Diaminobenzidine&Haematoxylin.

PubMed

Korzynska, Anna; Roszkowiak, Lukasz; Lopez, Carlos; Bosch, Ramon; Witkowski, Lukasz; Lejeune, Marylene

2013-03-25

The comparative study of the results of various segmentation methods for the digital images of the follicular lymphoma cancer tissue section is described in this paper. The sensitivity and specificity and some other parameters of the following adaptive threshold methods of segmentation: the Niblack method, the Sauvola method, the White method, the Bernsen method, the Yasuda method and the Palumbo method, are calculated. Methods are applied to three types of images constructed by extraction of the brown colour information from the artificial images synthesized based on counterpart experimentally captured images. This paper presents usefulness of the microscopic image synthesis method in evaluation as well as comparison of the image processing results. The results of thoughtful analysis of broad range of adaptive threshold methods applied to: (1) the blue channel of RGB, (2) the brown colour extracted by deconvolution and (3) the 'brown component' extracted from RGB allows to select some pairs: method and type of image for which this method is most efficient considering various criteria e.g. accuracy and precision in area detection or accuracy in number of objects detection and so on. The comparison shows that the White, the Bernsen and the Sauvola methods results are better than the results of the rest of the methods for all types of monochromatic images. All three methods segments the immunopositive nuclei with the mean accuracy of 0.9952, 0.9942 and 0.9944 respectively, when treated totally. However the best results are achieved for monochromatic image in which intensity shows brown colour map constructed by colour deconvolution algorithm. The specificity in the cases of the Bernsen and the White methods is 1 and sensitivities are: 0.74 for White and 0.91 for Bernsen methods while the Sauvola method achieves sensitivity value of 0.74 and the specificity value of 0.99. According to Bland-Altman plot the Sauvola method selected objects are segmented without undercutting the area for true positive objects but with extra false positive objects. The Sauvola and the Bernsen methods gives complementary results what will be exploited when the new method of virtual tissue slides segmentation be develop. The virtual slides for this article can be found here: slide 1: http://diagnosticpathology.slidepath.com/dih/webViewer.php?snapshotId=13617947952577 and slide 2: http://diagnosticpathology.slidepath.com/dih/webViewer.php?snapshotId=13617948230017.

Systematic Review and Meta-Analysis of Studies Evaluating Diagnostic Test Accuracy: A Practical Review for Clinical Researchers-Part II. Statistical Methods of Meta-Analysis

PubMed Central

Lee, Juneyoung; Kim, Kyung Won; Choi, Sang Hyun; Huh, Jimi

2015-01-01

Meta-analysis of diagnostic test accuracy studies differs from the usual meta-analysis of therapeutic/interventional studies in that, it is required to simultaneously analyze a pair of two outcome measures such as sensitivity and specificity, instead of a single outcome. Since sensitivity and specificity are generally inversely correlated and could be affected by a threshold effect, more sophisticated statistical methods are required for the meta-analysis of diagnostic test accuracy. Hierarchical models including the bivariate model and the hierarchical summary receiver operating characteristic model are increasingly being accepted as standard methods for meta-analysis of diagnostic test accuracy studies. We provide a conceptual review of statistical methods currently used and recommended for meta-analysis of diagnostic test accuracy studies. This article could serve as a methodological reference for those who perform systematic review and meta-analysis of diagnostic test accuracy studies. PMID:26576107

Multivariate models for prediction of human skin sensitization hazard.

PubMed

Strickland, Judy; Zang, Qingda; Paris, Michael; Lehmann, David M; Allen, David; Choksi, Neepa; Matheson, Joanna; Jacobs, Abigail; Casey, Warren; Kleinstreuer, Nicole

2017-03-01

One of the Interagency Coordinating Committee on the Validation of Alternative Method's (ICCVAM) top priorities is the development and evaluation of non-animal approaches to identify potential skin sensitizers. The complexity of biological events necessary to produce skin sensitization suggests that no single alternative method will replace the currently accepted animal tests. ICCVAM is evaluating an integrated approach to testing and assessment based on the adverse outcome pathway for skin sensitization that uses machine learning approaches to predict human skin sensitization hazard. We combined data from three in chemico or in vitro assays - the direct peptide reactivity assay (DPRA), human cell line activation test (h-CLAT) and KeratinoSens™ assay - six physicochemical properties and an in silico read-across prediction of skin sensitization hazard into 12 variable groups. The variable groups were evaluated using two machine learning approaches, logistic regression and support vector machine, to predict human skin sensitization hazard. Models were trained on 72 substances and tested on an external set of 24 substances. The six models (three logistic regression and three support vector machine) with the highest accuracy (92%) used: (1) DPRA, h-CLAT and read-across; (2) DPRA, h-CLAT, read-across and KeratinoSens; or (3) DPRA, h-CLAT, read-across, KeratinoSens and log P. The models performed better at predicting human skin sensitization hazard than the murine local lymph node assay (accuracy 88%), any of the alternative methods alone (accuracy 63-79%) or test batteries combining data from the individual methods (accuracy 75%). These results suggest that computational methods are promising tools to identify effectively the potential human skin sensitizers without animal testing. Published 2016. This article has been contributed to by US Government employees and their work is in the public domain in the USA. Published 2016. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Evaluation of failing hemodialysis fistulas with multidetector CT angiography: comparison of different 3D planes.

PubMed

Karadeli, E; Tarhan, N C; Ulu, E M Kayahan; Tutar, N U; Basaran, O; Coskun, M; Niron, E A

2009-01-01

To evaluate failing hemodialysis fistula complications using 16-detector MDCTA, and to assess the accuracies of different 3D planes. Thirty patients (16 men, 14 women, aged 27-79 years) were referred for hemodialysis access dysfunction. Thirty-one MDCTA exams were done prior to fistulography. For MDCTA, contrast was administered (2mL/kg at 5mL/s) via a peripheral vein in the contralateral arm. Axial MIP, coronal MIP, and VRT images were constructed. Venous complications were evaluated on axial source images, on each 3D plane, and on all-planes together. Results were analyzed using McNemar test. Axial MIP, VRT and all-planes evaluations were most sensitive for fistula site detection (93%). Coronal MIP had the highest sensitivity, specificity and accuracy (35%, 96%, and 85%, respectively) for detecting venous stenosis. VRT and all-planes had the highest sensitivity and accuracy for detecting aneurysms (100%). All-planes and axial MIP were most sensitive for detecting venous occlusion (61% and 54%). Comparisons of detection frequencies for each venous pathology between the five categories of MDCTA revealed no significant differences (P>0.05). MDCTA additionally showed 3 partially thrombosed aneurysms, 4 anastomosis site stenosis and 12 arterial complications. MDCTA overall gives low sensitivity for detection of central vein stenosis and moderate sensitivity for occlusion. For most pathology, all-planes evaluation of MDCTA gives highest sensitivity and accuracy rates when compared to other planes. For venous stenosis and occlusion, MDCTA should be considered when ultrasonography and fistulography are inconclusive. MDCTA is helpful in identifying aneurysms, collaterals, partial venous thromboses and additional arterial, anastomosis site pathologies.

SLOWLY REPEATED EVOKED PAIN (SREP) AS A MARKER OF CENTRAL SENSITIZATION IN FIBROMYALGIA: DIAGNOSTIC ACCURACY AND RELIABILITY IN COMPARISON WITH TEMPORAL SUMMATION OF PAIN.

PubMed

de la Coba, Pablo; Bruehl, Stephen; Gálvez-Sánchez, Carmen María; Reyes Del Paso, Gustavo A

2018-05-01

This study examined the diagnostic accuracy and test-retest reliability of a novel dynamic evoked pain protocol (slowly repeated evoked pain; SREP) compared to temporal summation of pain (TSP), a standard index of central sensitization. Thirty-five fibromyalgia (FM) and 30 rheumatoid arthritis (RA) patients completed, in pseudorandomized order, a standard mechanical TSP protocol (10 stimuli of 1s duration at the thenar eminence using a 300g monofilament with 1s interstimulus interval) and the SREP protocol (9 suprathreshold pressure stimuli of 5s duration applied to the fingernail with a 30s interstimulus interval). In order to evaluate reliability for both protocols, they were repeated in a second session 4-7 days later. Evidence for significant pain sensitization over trials (increasing pain intensity ratings) was observed for SREP in FM (p<.001) but not in RA (p=.35), whereas significant sensitization was observed in both diagnostic groups for the TSP protocol (p's<.008). Compared to TSP, SREP demonstrated higher overall diagnostic accuracy (87.7% vs. 64.6%), greater sensitivity (0.89 vs. 0.57), and greater specificity (0.87 vs. 0.73) in discriminating between FM and RA patients. Test-retest reliability of SREP sensitization was good in FM (ICCs: 0.80), and moderate in RA (ICC: 0.68). SREP seems to be a dynamic evoked pain index tapping into pain sensitization that allows for greater diagnostic accuracy in identifying FM patients compared to a standard TSP protocol. Further research is needed to study mechanisms underlying SREP and the potential utility of adding SREP to standard pain evaluation protocols.

Detection of motion artifact patterns in photoplethysmographic signals based on time and period domain analysis.

PubMed

Couceiro, R; Carvalho, P; Paiva, R P; Henriques, J; Muehlsteff, J

2014-12-01

The presence of motion artifacts in photoplethysmographic (PPG) signals is one of the major obstacles in the extraction of reliable cardiovascular parameters in continuous monitoring applications. In the current paper we present an algorithm for motion artifact detection based on the analysis of the variations in the time and the period domain characteristics of the PPG signal. The extracted features are ranked using a normalized mutual information feature selection algorithm and the best features are used in a support vector machine classification model to distinguish between clean and corrupted sections of the PPG signal. The proposed method has been tested in healthy and cardiovascular diseased volunteers, considering 11 different motion artifact sources. The results achieved by the current algorithm (sensitivity--SE: 84.3%, specificity--SP: 91.5% and accuracy--ACC: 88.5%) show that the current methodology is able to identify both corrupted and clean PPG sections with high accuracy in both healthy (ACC: 87.5%) and cardiovascular diseases (ACC: 89.5%) context.

Identification of misspelled words without a comprehensive dictionary using prevalence analysis.

PubMed

Turchin, Alexander; Chu, Julia T; Shubina, Maria; Einbinder, Jonathan S

2007-10-11

Misspellings are common in medical documents and can be an obstacle to information retrieval. We evaluated an algorithm to identify misspelled words through analysis of their prevalence in a representative body of text. We evaluated the algorithm's accuracy of identifying misspellings of 200 anti-hypertensive medication names on 2,000 potentially misspelled words randomly selected from narrative medical documents. Prevalence ratios (the frequency of the potentially misspelled word divided by the frequency of the non-misspelled word) in physician notes were computed by the software for each of the words. The software results were compared to the manual assessment by an independent reviewer. Area under the ROC curve for identification of misspelled words was 0.96. Sensitivity, specificity, and positive predictive value were 99.25%, 89.72% and 82.9% for the prevalence ratio threshold (0.32768) with the highest F-measure (0.903). Prevalence analysis can be used to identify and correct misspellings with high accuracy.

The Diagnostic Accuracy of Cytology for the Diagnosis of Hepatobiliary and Pancreatic Cancers.

PubMed

Al-Hajeili, Marwan; Alqassas, Maryam; Alomran, Astabraq; Batarfi, Bashaer; Basunaid, Bashaer; Alshail, Reem; Alaydarous, Shahad; Bokhary, Rana; Mosli, Mahmoud

2018-06-13

Although cytology testing is considered a valuable method to diagnose tumors that are difficult to access such as hepato-biliary-pancreatic (HBP) malignancies, its diagnostic accuracy remains unclear. We therefore aimed to investigate the diagnostic accuracy of cytology testing for HBP tumors. We performed a retrospective study of all cytology samples that were used to confirm radiologically detected HBP tumors between 2002 and 2016. The cytology techniques used in our center included fine needle aspiration (FNA), brush cytology, and aspiration of bile. Sensitivity, specificity, positive and negative predictive values, and likelihood ratios were calculated in comparison to histological confirmation. From a total of 133 medical records, we calculated an overall sensitivity of 76%, specificity of 74%, a negative likelihood ratio of 0.30, and a positive likelihood ratio of 2.9. Cytology was more accurate in diagnosing lesions of the liver (sensitivity 79%, specificity 57%) and biliary tree (sensitivity 100%, specificity 50%) compared to pancreatic (sensitivity 60%, specificity 83%) and gallbladder lesions (sensitivity 50%, specificity 85%). Cytology was more accurate in detecting primary cancers (sensitivity 77%, specificity 73%) when compared to metastatic cancers (sensitivity 73%, specificity 100%). FNA was the most frequently used cytological technique to diagnose HBP lesions (sensitivity 78.8%). Cytological testing is efficient in diagnosing HBP cancers, especially for hepatobiliary tumors. Given its relative simplicity, cost-effectiveness, and paucity of alternative diagnostic methods, cytology should still be considered as a first-line tool for diagnosing HBP malignancies. © 2018 S. Karger AG, Basel.

[Rapid identification of potato cultivars using NIR-excited fluorescence and Raman spectroscopy].

PubMed

Dai, Fen; Bergholt, Mads Sylvest; Benjamin, Arnold Julian Vinoj; Hong, Tian-Sheng; Zhiwei, Huang

2014-03-01

Potato is one of the most important food in the world. Rapid and noninvasive identification of potato cultivars plays a important role in the better use of varieties. In this study, The identification ability of optical spectroscopy techniques, including near-infrared (NIR) Raman spectroscopy and NIR fluorescence spectroscopy, for invasive detection of potato cultivars was evaluated. A rapid NIR Raman spectroscopy system was applied to measure the composite Raman and NIR fluorescence spectroscopy of 3 different species of potatoes (98 samples in total) under 785 nm laser light excitation. Then pure Raman and NIR fluorescence spectroscopy were abstracted from the composite spectroscopy, respectively. At last, the partial least squares-discriminant analysis (PLS-DA) was utilized to analyze and classify Raman spectra of 3 different types of potatoes. All the samples were divided into two sets at random: the calibration set (74samples) and prediction set (24 samples), the model was validated using a leave-one-out, cross-validation method. The results showed that both the NIR-excited fluorescence spectra and pure Raman spectra could be used to identify three cultivars of potatoes. The fluorescence spectrum could distinguish the Favorita variety well (sensitivity: 1, specificity: 0.86 and accuracy: 0.92), but the result for Diamant (sensitivity: 0.75, specificity: 0.75 and accuracy: 0. 75) and Granola (sensitivity: 0.16, specificity: 0.89 and accuracy: 0.71) cultivars identification were a bit poorer. We demonstrated that Raman spectroscopy uncovered the main biochemical compositions contained in potato species, and provided a better classification sensitivity, specificity and accuracy (sensitivity: 1, specificity: 1 and accuracy: 1 for all 3 potato cultivars identification) among the three types of potatoes as compared to fluorescence spectroscopy.

Accuracy of Focused Assessment with Sonography for Trauma (FAST) in Blunt Trauma Abdomen-A Prospective Study.

PubMed

Kumar, Subodh; Bansal, Virinder Kumar; Muduly, Dillip Kumar; Sharma, Pawan; Misra, Mahesh C; Chumber, Sunil; Singh, Saraman; Bhardwaj, D N

2015-12-01

Focused assessment with sonography for trauma (FAST) is a limited ultrasound examination, primarily aimed at the identification of the presence of free intraperitoneal or pericardial fluid. In the context of blunt trauma abdomen (BTA), free fluid is usually due to hemorrhage, bowel contents, or both; contributes towards the timely diagnosis of potentially life-threatening hemorrhage; and is a decision-making tool to help determine the need for further evaluation or operative intervention. Fifty patients with blunt trauma abdomen were evaluated prospectively with FAST. The findings of FAST were compared with contrast-enhanced computed tomography (CECT), laparotomy, and autopsy. Any free fluid in the abdomen was presumed to be hemoperitoneum. Sonographic findings of intra-abdominal free fluid were confirmed by CECT, laparotomy, or autopsy wherever indicated. In comparing with CECT scan, FAST had a sensitivity, specificity, and accuracy of 77.27, 100, and 79.16 %, respectively, in the detection of free fluid. When compared with surgical findings, it had a sensitivity, specificity, and accuracy of 94.44, 50, and 90 %, respectively. The sensitivity of FAST was 75 % in determining free fluid in patients who died when compared with autopsy findings. Overall sensitivity, specificity, and accuracy of FAST were 80.43, 75 and 80 %, respectively, for the detection of free fluid in the abdomen. From this study, we can safely conclude that FAST is a rapid, reliable, and feasible investigation in patients with BTA, and it can be performed easily, safely, and quickly in the emergency room with a reasonable sensitivity, specificity, and accuracy. It helps in the initial triage of patients for assessing the need for urgent surgery.

The diagnostic accuracy of neck ultrasound, 4D-Computed tomographyand sestamibi imaging in parathyroid carcinoma.

PubMed

Christakis, Ioannis; Vu, Thinh; Chuang, Hubert H; Fellman, Bryan; Figueroa, Angelica M Silva; Williams, Michelle D; Busaidy, Naifa L; Perrier, Nancy D

2017-10-01

Our aim was to investigate the accuracy of available imaging modalities for parathyroid carcinoma (PC) in our institution and to identify which imaging modality, or combination thereof, is optimal in preoperative determination of precise tumor location. All operated PC patients in our institution between 2000 and 2015 that had at least one of the following in-house preoperative scans: neck ultrasonography (US), neck 4D-Computed Tomography (4DCT) and 99mTc Sestamibi SPECT/CT (MIBI). Sensitivity, specificity and accuracy of PC tumor localization were assessed individually and in combination. 20 patients fulfilled the inclusion criteria and were analysed. There were 18 US, 18 CT and 9 MIBI scans. The sensitivity and accuracy for tumor localisation of US was 80% (CI 56-94%) and 73% respectively, of 4DCT was 79% (CI 58-93%) and 82%, and of MIBI was 81% (CI 54-96%) and 78%. The sensitivity and accuracy of the combination of CT and MIBI was 94% (CI 73-100%) and 95% and for the combination of US, CT and MIBI was 100% (CI 72-100%) and 100% respectively. The wash-out of the PC lesions, expressed as a percentage change in Hounsfield Units from the arterial phase to early delayed phase was -9.29% and to the late delayed phase was -16.88% (n=11). The sensitivity of solitary preoperative imaging of PC patients, whether by US, CT or MIBI, is approximately 80%. Combinations of CT with MIBI and US increase the sensitivity to 95% or better. Combined preoperative imaging of patients with clinical possibility of PC is therefore recommended. Copyright © 2017 Elsevier B.V. All rights reserved.

Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks

NASA Astrophysics Data System (ADS)

Halicek, Martin; Little, James V.; Wang, Xu; Patel, Mihir; Griffith, Christopher C.; El-Deiry, Mark W.; Chen, Amy Y.; Fei, Baowei

2018-02-01

Successful outcomes of surgical cancer resection necessitate negative, cancer-free surgical margins. Currently, tissue samples are sent to pathology for diagnostic confirmation. Hyperspectral imaging (HSI) is an emerging, non-contact optical imaging technique. A reliable optical method could serve to diagnose and biopsy specimens in real-time. Using convolutional neural networks (CNNs) as a tissue classifier, we developed a method to use HSI to perform an optical biopsy of ex-vivo surgical specimens, collected from 21 patients undergoing surgical cancer resection. Training and testing on samples from different patients, the CNN can distinguish squamous cell carcinoma (SCCa) from normal aerodigestive tract tissues with an area under the curve (AUC) of 0.82, 81% accuracy, 81% sensitivity, and 80% specificity. Additionally, normal oral tissues can be sub-classified into epithelium, muscle, and glandular mucosa using a decision tree method, with an average AUC of 0.94, 90% accuracy, 93% sensitivity, and 89% specificity. After separately training on thyroid tissue, the CNN differentiates between thyroid carcinoma and normal thyroid with an AUC of 0.95, 92% accuracy, 92% sensitivity, and 92% specificity. Moreover, the CNN can discriminate medullary thyroid carcinoma from benign multi-nodular goiter (MNG) with an AUC of 0.93, 87% accuracy, 88% sensitivity, and 85% specificity. Classical-type papillary thyroid carcinoma is differentiated from benign MNG with an AUC of 0.91, 86% accuracy, 86% sensitivity, and 86% specificity. Our preliminary results demonstrate that an HSI-based optical biopsy method using CNNs can provide multi-category diagnostic information for normal head-and-neck tissue, SCCa, and thyroid carcinomas. More patient data are needed in order to fully investigate the proposed technique to establish reliability and generalizability of the work.

Assessment of the predictive accuracy of five in silico prediction tools, alone or in combination, and two metaservers to classify long QT syndrome gene mutations.

PubMed

Leong, Ivone U S; Stuckey, Alexander; Lai, Daniel; Skinner, Jonathan R; Love, Donald R

2015-05-13

Long QT syndrome (LQTS) is an autosomal dominant condition predisposing to sudden death from malignant arrhythmia. Genetic testing identifies many missense single nucleotide variants of uncertain pathogenicity. Establishing genetic pathogenicity is an essential prerequisite to family cascade screening. Many laboratories use in silico prediction tools, either alone or in combination, or metaservers, in order to predict pathogenicity; however, their accuracy in the context of LQTS is unknown. We evaluated the accuracy of five in silico programs and two metaservers in the analysis of LQTS 1-3 gene variants. The in silico tools SIFT, PolyPhen-2, PROVEAN, SNPs&GO and SNAP, either alone or in all possible combinations, and the metaservers Meta-SNP and PredictSNP, were tested on 312 KCNQ1, KCNH2 and SCN5A gene variants that have previously been characterised by either in vitro or co-segregation studies as either "pathogenic" (283) or "benign" (29). The accuracy, sensitivity, specificity and Matthews Correlation Coefficient (MCC) were calculated to determine the best combination of in silico tools for each LQTS gene, and when all genes are combined. The best combination of in silico tools for KCNQ1 is PROVEAN, SNPs&GO and SIFT (accuracy 92.7%, sensitivity 93.1%, specificity 100% and MCC 0.70). The best combination of in silico tools for KCNH2 is SIFT and PROVEAN or PROVEAN, SNPs&GO and SIFT. Both combinations have the same scores for accuracy (91.1%), sensitivity (91.5%), specificity (87.5%) and MCC (0.62). In the case of SCN5A, SNAP and PROVEAN provided the best combination (accuracy 81.4%, sensitivity 86.9%, specificity 50.0%, and MCC 0.32). When all three LQT genes are combined, SIFT, PROVEAN and SNAP is the combination with the best performance (accuracy 82.7%, sensitivity 83.0%, specificity 80.0%, and MCC 0.44). Both metaservers performed better than the single in silico tools; however, they did not perform better than the best performing combination of in silico tools. The combination of in silico tools with the best performance is gene-dependent. The in silico tools reported here may have some value in assessing variants in the KCNQ1 and KCNH2 genes, but caution should be taken when the analysis is applied to SCN5A gene variants.

Genomic selection accuracies within and between environments and small breeding groups in white spruce.

PubMed

Beaulieu, Jean; Doerksen, Trevor K; MacKay, John; Rainville, André; Bousquet, Jean

2014-12-02

Genomic selection (GS) may improve selection response over conventional pedigree-based selection if markers capture more detailed information than pedigrees in recently domesticated tree species and/or make it more cost effective. Genomic prediction accuracies using 1748 trees and 6932 SNPs representative of as many distinct gene loci were determined for growth and wood traits in white spruce, within and between environments and breeding groups (BG), each with an effective size of Ne ≈ 20. Marker subsets were also tested. Model fits and/or cross-validation (CV) prediction accuracies for ridge regression (RR) and the least absolute shrinkage and selection operator models approached those of pedigree-based models. With strong relatedness between CV sets, prediction accuracies for RR within environment and BG were high for wood (r = 0.71-0.79) and moderately high for growth (r = 0.52-0.69) traits, in line with trends in heritabilities. For both classes of traits, these accuracies achieved between 83% and 92% of those obtained with phenotypes and pedigree information. Prediction into untested environments remained moderately high for wood (r ≥ 0.61) but dropped significantly for growth (r ≥ 0.24) traits, emphasizing the need to phenotype in all test environments and model genotype-by-environment interactions for growth traits. Removing relatedness between CV sets sharply decreased prediction accuracies for all traits and subpopulations, falling near zero between BGs with no known shared ancestry. For marker subsets, similar patterns were observed but with lower prediction accuracies. Given the need for high relatedness between CV sets to obtain good prediction accuracies, we recommend to build GS models for prediction within the same breeding population only. Breeding groups could be merged to build genomic prediction models as long as the total effective population size does not exceed 50 individuals in order to obtain high prediction accuracy such as that obtained in the present study. A number of markers limited to a few hundred would not negatively impact prediction accuracies, but these could decrease more rapidly over generations. The most promising short-term approach for genomic selection would likely be the selection of superior individuals within large full-sib families vegetatively propagated to implement multiclonal forestry.

Towards a Uniform Metrological Assessment of Grating-Based Optical Fiber Sensors: From Refractometers to Biosensors

PubMed Central

Chiavaioli, Francesco; Gouveia, Carlos A. J.; Jorge, Pedro A. S.; Baldini, Francesco

2017-01-01

A metrological assessment of grating-based optical fiber sensors is proposed with the aim of providing an objective evaluation of the performance of this sensor category. Attention was focused on the most common parameters, used to describe the performance of both optical refractometers and biosensors, which encompassed sensitivity, with a distinction between volume or bulk sensitivity and surface sensitivity, resolution, response time, limit of detection, specificity (or selectivity), reusability (or regenerability) and some other parameters of generic interest, such as measurement uncertainty, accuracy, precision, stability, drift, repeatability and reproducibility. Clearly, the concepts discussed here can also be applied to any resonance-based sensor, thus providing the basis for an easier and direct performance comparison of a great number of sensors published in the literature up to now. In addition, common mistakes present in the literature made for the evaluation of sensor performance are highlighted, and lastly a uniform performance assessment is discussed and provided. Finally, some design strategies will be proposed to develop a grating-based optical fiber sensing scheme with improved performance. PMID:28635665

Optimization and Validation of a Sensitive Method for HPLC-PDA Simultaneous Determination of Torasemide and Spironolactone in Human Plasma using Central Composite Design.

PubMed

Subramanian, Venkatesan; Nagappan, Kannappan; Sandeep Mannemala, Sai

2015-01-01

A sensitive, accurate, precise and rapid HPLC-PDA method was developed and validated for the simultaneous determination of torasemide and spironolactone in human plasma using Design of experiments. Central composite design was used to optimize the method using content of acetonitrile, concentration of buffer and pH of mobile phase as independent variables, while the retention factor of spironolactone, resolution between torasemide and phenobarbitone; and retention time of phenobarbitone were chosen as dependent variables. The chromatographic separation was achieved on Phenomenex C(18) column and the mobile phase comprising 20 mM potassium dihydrogen ortho phosphate buffer (pH-3.2) and acetonitrile in 82.5:17.5 v/v pumped at a flow rate of 1.0 mL min(-1). The method was validated according to USFDA guidelines in terms of selectivity, linearity, accuracy, precision, recovery and stability. The limit of quantitation values were 80 and 50 ng mL(-1) for torasemide and spironolactone respectively. Furthermore, the sensitivity and simplicity of the method suggests the validity of method for routine clinical studies.

Towards a Uniform Metrological Assessment of Grating-Based Optical Fiber Sensors: From Refractometers to Biosensors.

PubMed

Chiavaioli, Francesco; Gouveia, Carlos A J; Jorge, Pedro A S; Baldini, Francesco

2017-06-21

A metrological assessment of grating-based optical fiber sensors is proposed with the aim of providing an objective evaluation of the performance of this sensor category. Attention was focused on the most common parameters, used to describe the performance of both optical refractometers and biosensors, which encompassed sensitivity, with a distinction between volume or bulk sensitivity and surface sensitivity, resolution, response time, limit of detection, specificity (or selectivity), reusability (or regenerability) and some other parameters of generic interest, such as measurement uncertainty, accuracy, precision, stability, drift, repeatability and reproducibility. Clearly, the concepts discussed here can also be applied to any resonance-based sensor, thus providing the basis for an easier and direct performance comparison of a great number of sensors published in the literature up to now. In addition, common mistakes present in the literature made for the evaluation of sensor performance are highlighted, and lastly a uniform performance assessment is discussed and provided. Finally, some design strategies will be proposed to develop a grating-based optical fiber sensing scheme with improved performance.

A soft computing based approach using modified selection strategy for feature reduction of medical systems.

PubMed

Zuhtuogullari, Kursat; Allahverdi, Novruz; Arikan, Nihat

2013-01-01

The systems consisting high input spaces require high processing times and memory usage. Most of the attribute selection algorithms have the problems of input dimensions limits and information storage problems. These problems are eliminated by means of developed feature reduction software using new modified selection mechanism with middle region solution candidates adding. The hybrid system software is constructed for reducing the input attributes of the systems with large number of input variables. The designed software also supports the roulette wheel selection mechanism. Linear order crossover is used as the recombination operator. In the genetic algorithm based soft computing methods, locking to the local solutions is also a problem which is eliminated by using developed software. Faster and effective results are obtained in the test procedures. Twelve input variables of the urological system have been reduced to the reducts (reduced input attributes) with seven, six, and five elements. It can be seen from the obtained results that the developed software with modified selection has the advantages in the fields of memory allocation, execution time, classification accuracy, sensitivity, and specificity values when compared with the other reduction algorithms by using the urological test data.

Bionanomaterials and Bioinspired Nanostructures for Selective Vapor Sensing

NASA Astrophysics Data System (ADS)

Potyrailo, Radislav; Naik, Rajesh R.

2013-07-01

At present, monitoring of air at the workplace, in urban environments, and on battlefields; exhaled air from medical patients; air in packaged food containers; and so forth can be accomplished with different types of analytical instruments. Vapor sensors have their niche in these measurements when an unobtrusive, low-power, and cost-sensitive technical solution is required. Unfortunately, existing vapor sensors often degrade their vapor-quantitation accuracy in the presence of high levels of interferences and cannot quantitate several components in complex gas mixtures. Thus, new sensing approaches with improved sensor selectivity are required. This technological task can be accomplished by the careful design of sensing materials with new performance properties and by coupling these materials with the suitable physical transducers. This review is focused on the assessment of the capabilities of bionanomaterials and bioinspired nanostructures for selective vapor sensing. We demonstrate that these sensing materials can operate with diverse transducers based on electrical, mechanical, and optical readout principles and can provide vapor-response selectivity previously unattainable by using other sensing materials. This ability for selective vapor sensing provides opportunities to significantly impact the major directions in development and application scenarios of vapor sensors.

A Soft Computing Based Approach Using Modified Selection Strategy for Feature Reduction of Medical Systems

PubMed Central

Zuhtuogullari, Kursat; Allahverdi, Novruz; Arikan, Nihat

2013-01-01

The systems consisting high input spaces require high processing times and memory usage. Most of the attribute selection algorithms have the problems of input dimensions limits and information storage problems. These problems are eliminated by means of developed feature reduction software using new modified selection mechanism with middle region solution candidates adding. The hybrid system software is constructed for reducing the input attributes of the systems with large number of input variables. The designed software also supports the roulette wheel selection mechanism. Linear order crossover is used as the recombination operator. In the genetic algorithm based soft computing methods, locking to the local solutions is also a problem which is eliminated by using developed software. Faster and effective results are obtained in the test procedures. Twelve input variables of the urological system have been reduced to the reducts (reduced input attributes) with seven, six, and five elements. It can be seen from the obtained results that the developed software with modified selection has the advantages in the fields of memory allocation, execution time, classification accuracy, sensitivity, and specificity values when compared with the other reduction algorithms by using the urological test data. PMID:23573172

Non-invasive diagnosis of acute rejection in renal transplant patients using mass spectrometry of urine samples - a multicentre phase 3 diagnostic accuracy study.

PubMed

Zapf, Antonia; Gwinner, Wilfried; Karch, Annika; Metzger, Jochen; Haller, Hermann; Koch, Armin

2015-09-15

Reliable and timely detection of acute rejection in renal transplant patients is important to preserve the allograft function and to prevent premature allograft failure. The current gold standard for the rejection diagnosis is an allograft biopsy which is usually performed upon an unexplained decline in allograft function. Because of the invasiveness of the biopsy, non-invasive tests have been suggested to diagnose acute rejection including mass spectrometry analysis of urine samples. The aim of this study is to examine the diagnostic accuracy of mass spectrometry analysis in urine for the diagnosis of acute rejections using the biopsy as gold-standard. The study is an ongoing prospective, single-arm, multicentre, phase 3 diagnostic accuracy study. It started in October 2011 and will be concluded in December 2015. Patient within the first year after transplantation who are scheduled for a biopsy to clarify unexplained impairment of the allograft are consecutively recruited into the study. The overall sample size (n = 600) was calculated to demonstrate a sensitivity of 83 % and a specificity of 70 % for a one-sided type one error of 2.5 % and a power of 80 % per hypothesis. Biopsy evaluation and mass spectrometry analysis of urine samples (obtained immediately before biopsy) are performed independently by different readers without knowledge from the respective other assessment. The follow-up observation period is 6 months. For the primary analysis, the lower limits of the two-sided 95 % Wald confidence intervals for sensitivity and specificity will be compared with the pre-specified thresholds (83 % for sensitivity and 70 % for specificity). In secondary analyses the predictive values, the diagnostic measures in subgroups, and the clinical course will be assessed. Previous phase 2 diagnostic accuracy studies (in small selected study populations) provided sufficient evidence to suggest mass spectrometry on urine samples as a promising approach to detect acute rejections. This study determines the diagnostic performance of the test in the routine setting of post-transplant patient care, compared to the biopsy-based rejection diagnosis. The next step would be a randomized trial to compare the two diagnostic strategies (including the urine test or not) in relation to patient relevant endpoints. NCT01315067 ; March 14, 2011.

Diagnostic accuracy of physical examination for anterior knee instability: a systematic review.

PubMed

Leblanc, Marie-Claude; Kowalczuk, Marcin; Andruszkiewicz, Nicole; Simunovic, Nicole; Farrokhyar, Forough; Turnbull, Travis Lee; Debski, Richard E; Ayeni, Olufemi R

2015-10-01

Determining diagnostic accuracy of Lachman, pivot shift and anterior drawer tests versus gold standard diagnosis (magnetic resonance imaging or arthroscopy) for anterior cruciate ligament (ACL) insufficiency cases. Secondarily, evaluating effects of: chronicity, partial rupture, awake versus anaesthetized evaluation. Searching MEDLINE, EMBASE and PubMed identified studies on diagnostic accuracy for ACL insufficiency. Studies identification and data extraction were performed in duplicate. Quality assessment used QUADAS tool, and statistical analyses were completed for pooled sensitivity and specificity. Eight studies were included. Given insufficient data, pooled analysis was only possible for sensitivity on Lachman and pivot shift test. During awake evaluation, sensitivity for the Lachman test was 89 % (95 % CI 0.76, 0.98) for all rupture types, 96 % (95 % CI 0.90, 1.00) for complete ruptures and 68 % (95 % CI 0.25, 0.98) for partial ruptures. For pivot shift in awake evaluation, results were 79 % (95 % CI 0.63, 0.91) for all rupture types, 86 % (95 % CI 0.68, 0.99) for complete ruptures and 67 % (95 % CI 0.47, 0.83) for partial ruptures. Decreased sensitivity of Lachman and pivot shift tests for partial rupture cases and for awake patients raised suspicions regarding the accuracy of these tests for diagnosis of ACL insufficiency. This may lead to further research aiming to improve the understanding of the true accuracy of these physical diagnostic tests and increase the reliability of clinical investigation for this pathology. IV.

Glenohumeral articular cartilage lesions: prospective comparison of non-contrast magnetic resonance imaging and findings at arthroscopy.

PubMed

Spencer, Brian A; Dolinskas, Carol A; Seymour, Peter A; Thomas, Stephen J; Abboud, Joseph A

2013-09-01

The purpose of this study was to assess the diagnostic sensitivity, specificity, accuracy, negative predictive value (NPV), positive predictive value (PPV), and test-retest reliability of magnetic resonance imaging (MRI) for detecting cartilage abnormalities of the glenohumeral joint in comparison with the gold standard of diagnostic arthroscopy. Forty-four patients with a preoperative non-contrast MRI study of their affected shoulder underwent arthroscopy by one surgeon for rotator cuff tendinopathy from 2009 to 2010. Articular cartilage defects were prospectively recorded and graded according to the International Cartilage Repair Society classification system at the time of arthroscopy. One year after surgery, the preoperative MRI were reviewed by a board-certified radiologist and the treating surgeon for articular cartilage defects of both the humeral head and the glenoid. Sensitivity, specificity, accuracy, and test-retest reliability were calculated. At arthroscopy, 43% of the shoulders were found to have articular cartilage defects. When the readers' findings were combined, the sensitivity of detecting humeral lesions on MRI was 32%; specificity, 80%; accuracy, 63%; PPV, 57%; and NPV, 66%. The sensitivity of detecting glenoid lesions was 31%; specificity, 86%; accuracy, 76%; PPV, 33%; and NPV, 85%. This study finds that the overall accuracy of MRI in detecting articular cartilage damage in patients with the clinical diagnosis of subacromial pathology is moderate. Level II, development of diagnostic criteria based on consecutive patients with universally applied reference "gold" standard. Copyright © 2013 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.

Fabrication and Structural Design of Micro Pressure Sensors for Tire Pressure Measurement Systems (TPMS)

PubMed Central

Tian, Bian; Zhao, Yulong; Jiang, Zhuangde; Zhang, Ling; Liao, Nansheng; Liu, Yuanhao; Meng, Chao

2009-01-01

In this paper we describe the design and testing of a micro piezoresistive pressure sensor for a Tire Pressure Measurement System (TPMS) which has the advantages of a minimized structure, high sensitivity, linearity and accuracy. Through analysis of the stress distribution of the diaphragm using the ANSYS software, a model of the structure was established. The fabrication on a single silicon substrate utilizes the technologies of anisotropic chemical etching and packaging through glass anodic bonding. The performance of this type of piezoresistive sensor, including size, sensitivity, and long-term stability, were investigated. The results indicate that the accuracy is 0.5% FS, therefore this design meets the requirements for a TPMS, and not only has a smaller size and simplicity of preparation, but also has high sensitivity and accuracy. PMID:22573960

Diagnostic performance of an automated analysis software for the diagnosis of Alzheimer’s dementia with 18F FDG PET

PubMed Central

Partovi, Sasan; Yuh, Roger; Pirozzi, Sara; Lu, Ziang; Couturier, Spencer; Grosse, Ulrich; Schluchter, Mark D; Nelson, Aaron; Jones, Robert; O’Donnell, James K; Faulhaber, Peter

2017-01-01

The objective of this study was to assess the ability of a quantitative software-aided approach to improve the diagnostic accuracy of 18F FDG PET for Alzheimer’s dementia over visual analysis alone. Twenty normal subjects (M:F-12:8; mean age 80.6 years) and twenty mild AD subjects (M:F-12:8; mean age 70.6 years) with 18F FDG PET scans were obtained from the ADNI database. Three blinded readers interpreted these PET images first using a visual qualitative approach and then using a quantitative software-aided approach. Images were classified on two five-point scales based on normal/abnormal (1-definitely normal; 5-definitely abnormal) and presence of AD (1-definitely not AD; 5-definitely AD). Diagnostic sensitivity, specificity, and accuracy for both approaches were compared based on the aforementioned scales. The sensitivity, specificity, and accuracy for the normal vs. abnormal readings of all readers combined were higher when comparing the software-aided vs. visual approach (sensitivity 0.93 vs. 0.83 P = 0.0466; specificity 0.85 vs. 0.60 P = 0.0005; accuracy 0.89 vs. 0.72 P<0.0001). The specificity and accuracy for absence vs. presence of AD of all readers combined were higher when comparing the software-aided vs. visual approach (specificity 0.90 vs. 0.70 P = 0.0008; accuracy 0.81 vs. 0.72 P = 0.0356). Sensitivities of the software-aided and visual approaches did not differ significantly (0.72 vs. 0.73 P = 0.74). The quantitative software-aided approach appears to improve the performance of 18F FDG PET for the diagnosis of mild AD. It may be helpful for experienced 18F FDG PET readers analyzing challenging cases. PMID:28123864

The diagnostic test accuracy of magnetic resonance imaging, magnetic resonance arthrography and computer tomography in the detection of chondral lesions of the hip.

PubMed

Smith, Toby O; Simpson, Michael; Ejindu, Vivian; Hing, Caroline B

2013-04-01

The purpose of this study was to assess the diagnostic test accuracy of magnetic resonance imaging (MRI), magnetic resonance arthrography (MRA) and multidetector arrays in CT arthrography (MDCT) for assessing chondral lesions in the hip joint. A review of the published and unpublished literature databases was performed to identify all studies reporting the diagnostic test accuracy (sensitivity/specificity) of MRI, MRA or MDCT for the assessment of adults with chondral (cartilage) lesions of the hip with surgical comparison (arthroscopic or open) as the reference test. All included studies were reviewed using the quality assessment of diagnostic accuracy studies appraisal tool. Pooled sensitivity, specificity, likelihood ratios and diagnostic odds ratios were calculated with 95 % confidence intervals using a random-effects meta-analysis for MRI, MRA and MDCT imaging. Eighteen studies satisfied the eligibility criteria. These included 648 hips from 637 patients. MRI indicated a pooled sensitivity of 0.59 (95 % CI: 0.49-0.70) and specificity of 0.94 (95 % CI: 0.90-0.97), and MRA sensitivity and specificity values were 0.62 (95 % CI: 0.57-0.66) and 0.86 (95 % CI: 0.83-0.89), respectively. The diagnostic test accuracy for the detection of hip joint cartilage lesions is currently superior for MRI compared with MRA. There were insufficient data to perform meta-analysis for MDCT or CTA protocols. Based on the current limited diagnostic test accuracy of the use of magnetic resonance or CT, arthroscopy remains the most accurate method of assessing chondral lesions in the hip joint.

Variability of Diabetes Alert Dog Accuracy in a Real-World Setting

PubMed Central

Gonder-Frederick, Linda A.; Grabman, Jesse H.; Shepard, Jaclyn A.; Tripathi, Anand V.; Ducar, Dallas M.; McElgunn, Zachary R.

2017-01-01

Background: Diabetes alert dogs (DADs) are growing in popularity as an alternative method of glucose monitoring for individuals with type 1 diabetes (T1D). Only a few empirical studies have assessed DAD accuracy, with inconsistent results. The present study examined DAD accuracy and variability in performance in real-world conditions using a convenience sample of owner-report diaries. Method: Eighteen DAD owners (44.4% female; 77.8% youth) with T1D completed diaries of DAD alerts during the first year after placement. Diary entries included daily BG readings and DAD alerts. For each DAD, percentage hits (alert with BG ≤ 5.0 or ≥ 11.1 mmol/L; ≤90 or ≥200 mg/dl), percentage misses (no alert with BG out of range), and percentage false alarms (alert with BG in range) were computed. Sensitivity, specificity, positive likelihood ratio (PLR), and true positive rates were also calculated. Results: Overall comparison of DAD Hits to Misses yielded significantly more Hits for both low and high BG. Total sensitivity was 57.0%, with increased sensitivity to low BG (59.2%) compared to high BG (56.1%). Total specificity was 49.3% and PLR = 1.12. However, high variability in accuracy was observed across DADs, with low BG sensitivity ranging from 33% to 100%. Number of DADs achieving ≥ 60%, 65% and 70% true positive rates was 71%, 50% and 44%, respectively. Conclusions: DADs may be able to detect out-of-range BG, but variability across DADs is evident. Larger trials are needed to further assess DAD accuracy and to identify factors influencing the complexity of DAD accuracy in BG detection. PMID:28627305

Role of endoscopic ultrasound-guided fine needle aspiration and ultrasound-guided fine-needle aspiration in diagnosis of cystic pancreatic lesions

PubMed Central

Okasha, Hussein Hassan; Ashry, Mahmoud; Imam, Hala M. K.; Ezzat, Reem; Naguib, Mohamed; Farag, Ali H.; Gemeie, Emad H.; Khattab, Hani M.

2015-01-01

Background and Objective: The addition of fine-needle aspiration (FNA) to different imaging modalities has raised the accuracy for diagnosis of cystic pancreatic lesions. We aim to differentiate benign from neoplastic pancreatic cysts by evaluating cyst fluid carcinoembryonic antigen (CEA), carbohydrate antigen (CA19-9), and amylase levels and cytopathological examination, including mucin stain. Patients and Methods: This prospective study included 77 patients with pancreatic cystic lesions. Ultrasound-FNA (US-FNA) or endoscopic ultrasound-FNA (EUS-FNA) was done according to the accessibility of the lesion. The aspirated specimens were subjected to cytopathological examination (including mucin staining), tumor markers (CEA, CA19-9), and amylase level. Results: Cyst CEA value of 279 or more showed high statistical significance in differentiating mucinous from nonmucinous lesions with sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of 73%, 60%, 50%, 80%, and 65%, respectively. Cyst amylase could differentiate between neoplastic and nonneoplastic cysts at a level of 1043 with sensitivity of 58%, specificity of 75%, PPV of 73%, NPV of 60%, and accuracy of 66%. CA19-9 could not differentiate between neoplastic and nonneoplastic cysts. Mucin examination showed a sensitivity of 85%, specificity of 95%, PPV of 92%, NPV of 91%, and accuracy of 91% in differentiating mucinous from non-mucinous lesions. Cytopathological examination showed a sensitivity of 81%, specificity of 94%, PPV of 94%, NPV of 83%, and accuracy of 88%. Conclusion: US or EUS-FNA with analysis of cyst CEA level, CA19-9, amylase, mucin stain, and cytopathological examination increases the diagnostic accuracy of cystic pancreatic lesions. PMID:26020048

Accuracy of enhanced and unenhanced MRI in diagnosing scaphoid proximal pole avascular necrosis and predicting surgical outcome.

PubMed

Fox, M G; Wang, D T; Chhabra, A B

2015-11-01

Determine the sensitivity, specificity and accuracy of unenhanced and enhanced MRI in diagnosing scaphoid proximal pole (PP) avascular necrosis (AVN) and correlate whether MRI can help guide the selection of a vascularized or nonvascularized bone graft. The study was approved by the IRB. Two MSK radiologists independently performed a retrospective review of unenhanced and enhanced MRIs from 18 patients (16 males, 2 females; median age, 17.5 years) with scaphoid nonunions and surgery performed within 65 days of the MRI. AVN was diagnosed on the unenhanced MRI when a diffusely decreased T1-W signal was present in the PP and on the enhanced MRI when PP enhancement was less than distal pole enhancement. Surgical absence of PP bleeding was diagnostic of PP AVN. Postoperative osseous union (OU) was assessed with computed tomography and/or radiographs. Sensitivity, specificity and accuracy for PP AVN were 71, 82 and 78% for unenhanced and 43, 82 and 67% for enhanced MRI. Patients with PP AVN on unenhanced MRI had 86% (6/7) OU; 100% (5/5) OU with vascularized bone grafts and 50% (1/2) OU with nonvascularized grafts. Patients with PP AVN on enhanced MRI had 80% (4/5) OU; 100% (3/3) OU with vascularized bone grafts and 50% (1/2) OU with nonvascularized grafts. Patients with viable PP on unenhanced and enhanced MRI had 91% (10/11) and 92% (12/13) OU, respectively, all but one with nonvascularized graft. When PP AVN is evident on MRI, OU is best achieved with vascularized grafts. If PP AVN is absent, OU is successful with nonvascularized grafts.

Automation-assisted cervical cancer screening in manual liquid-based cytology with hematoxylin and eosin staining.

PubMed

Zhang, Ling; Kong, Hui; Ting Chin, Chien; Liu, Shaoxiong; Fan, Xinmin; Wang, Tianfu; Chen, Siping

2014-03-01

Current automation-assisted technologies for screening cervical cancer mainly rely on automated liquid-based cytology slides with proprietary stain. This is not a cost-efficient approach to be utilized in developing countries. In this article, we propose the first automation-assisted system to screen cervical cancer in manual liquid-based cytology (MLBC) slides with hematoxylin and eosin (H&E) stain, which is inexpensive and more applicable in developing countries. This system consists of three main modules: image acquisition, cell segmentation, and cell classification. First, an autofocusing scheme is proposed to find the global maximum of the focus curve by iteratively comparing image qualities of specific locations. On the autofocused images, the multiway graph cut (GC) is performed globally on the a* channel enhanced image to obtain cytoplasm segmentation. The nuclei, especially abnormal nuclei, are robustly segmented by using GC adaptively and locally. Two concave-based approaches are integrated to split the touching nuclei. To classify the segmented cells, features are selected and preprocessed to improve the sensitivity, and contextual and cytoplasm information are introduced to improve the specificity. Experiments on 26 consecutive image stacks demonstrated that the dynamic autofocusing accuracy was 2.06 μm. On 21 cervical cell images with nonideal imaging condition and pathology, our segmentation method achieved a 93% accuracy for cytoplasm, and a 87.3% F-measure for nuclei, both outperformed state of the art works in terms of accuracy. Additional clinical trials showed that both the sensitivity (88.1%) and the specificity (100%) of our system are satisfyingly high. These results proved the feasibility of automation-assisted cervical cancer screening in MLBC slides with H&E stain, which is highly desirable in community health centers and small hospitals. © 2013 International Society for Advancement of Cytometry.

Differentiation of Uterine Leiomyosarcoma from Atypical Leiomyoma: Diagnostic Accuracy of Qualitative MR Imaging Features and Feasibility of Texture Analysis.

PubMed

Lakhman, Yulia; Veeraraghavan, Harini; Chaim, Joshua; Feier, Diana; Goldman, Debra A; Moskowitz, Chaya S; Nougaret, Stephanie; Sosa, Ramon E; Vargas, Hebert Alberto; Soslow, Robert A; Abu-Rustum, Nadeem R; Hricak, Hedvig; Sala, Evis

2017-07-01

To investigate whether qualitative magnetic resonance (MR) features can distinguish leiomyosarcoma (LMS) from atypical leiomyoma (ALM) and assess the feasibility of texture analysis (TA). This retrospective study included 41 women (ALM = 22, LMS = 19) imaged with MRI prior to surgery. Two readers (R1, R2) evaluated each lesion for qualitative MR features. Associations between MR features and LMS were evaluated with Fisher's exact test. Accuracy measures were calculated for the four most significant features. TA was performed for 24 patients (ALM = 14, LMS = 10) with uniform imaging following lesion segmentation on axial T2-weighted images. Texture features were pre-selected using Wilcoxon signed-rank test with Bonferroni correction and analyzed with unsupervised clustering to separate LMS from ALM. Four qualitative MR features most strongly associated with LMS were nodular borders, haemorrhage, "T2 dark" area(s), and central unenhanced area(s) (p ≤ 0.0001 each feature/reader). The highest sensitivity [1.00 (95%CI:0.82-1.00)/0.95 (95%CI: 0.74-1.00)] and specificity [0.95 (95%CI:0.77-1.00)/1.00 (95%CI:0.85-1.00)] were achieved for R1/R2, respectively, when a lesion had ≥3 of these four features. Sixteen texture features differed significantly between LMS and ALM (p-values: <0.001-0.036). Unsupervised clustering achieved accuracy of 0.75 (sensitivity: 0.70; specificity: 0.79). Combination of ≥3 qualitative MR features accurately distinguished LMS from ALM. TA was feasible. • Four qualitative MR features demonstrated the strongest statistical association with LMS. • Combination of ≥3 these features could accurately differentiate LMS from ALM. • Texture analysis was a feasible semi-automated approach for lesion categorization.

Sensitivity and specificity of normality tests and consequences on reference interval accuracy at small sample size: a computer-simulation study.

PubMed

Le Boedec, Kevin

2016-12-01

According to international guidelines, parametric methods must be chosen for RI construction when the sample size is small and the distribution is Gaussian. However, normality tests may not be accurate at small sample size. The purpose of the study was to evaluate normality test performance to properly identify samples extracted from a Gaussian population at small sample sizes, and assess the consequences on RI accuracy of applying parametric methods to samples that falsely identified the parent population as Gaussian. Samples of n = 60 and n = 30 values were randomly selected 100 times from simulated Gaussian, lognormal, and asymmetric populations of 10,000 values. The sensitivity and specificity of 4 normality tests were compared. Reference intervals were calculated using 6 different statistical methods from samples that falsely identified the parent population as Gaussian, and their accuracy was compared. Shapiro-Wilk and D'Agostino-Pearson tests were the best performing normality tests. However, their specificity was poor at sample size n = 30 (specificity for P < .05: .51 and .50, respectively). The best significance levels identified when n = 30 were 0.19 for Shapiro-Wilk test and 0.18 for D'Agostino-Pearson test. Using parametric methods on samples extracted from a lognormal population but falsely identified as Gaussian led to clinically relevant inaccuracies. At small sample size, normality tests may lead to erroneous use of parametric methods to build RI. Using nonparametric methods (or alternatively Box-Cox transformation) on all samples regardless of their distribution or adjusting, the significance level of normality tests depending on sample size would limit the risk of constructing inaccurate RI. © 2016 American Society for Veterinary Clinical Pathology.

Use of diagnostic accuracy as a metric for evaluating laboratory proficiency with microarray assays using mixed-tissue RNA reference samples.

PubMed

Pine, P S; Boedigheimer, M; Rosenzweig, B A; Turpaz, Y; He, Y D; Delenstarr, G; Ganter, B; Jarnagin, K; Jones, W D; Reid, L H; Thompson, K L

2008-11-01

Effective use of microarray technology in clinical and regulatory settings is contingent on the adoption of standard methods for assessing performance. The MicroArray Quality Control project evaluated the repeatability and comparability of microarray data on the major commercial platforms and laid the groundwork for the application of microarray technology to regulatory assessments. However, methods for assessing performance that are commonly applied to diagnostic assays used in laboratory medicine remain to be developed for microarray assays. A reference system for microarray performance evaluation and process improvement was developed that includes reference samples, metrics and reference datasets. The reference material is composed of two mixes of four different rat tissue RNAs that allow defined target ratios to be assayed using a set of tissue-selective analytes that are distributed along the dynamic range of measurement. The diagnostic accuracy of detected changes in expression ratios, measured as the area under the curve from receiver operating characteristic plots, provides a single commutable value for comparing assay specificity and sensitivity. The utility of this system for assessing overall performance was evaluated for relevant applications like multi-laboratory proficiency testing programs and single-laboratory process drift monitoring. The diagnostic accuracy of detection of a 1.5-fold change in signal level was found to be a sensitive metric for comparing overall performance. This test approaches the technical limit for reliable discrimination of differences between two samples using this technology. We describe a reference system that provides a mechanism for internal and external assessment of laboratory proficiency with microarray technology and is translatable to performance assessments on other whole-genome expression arrays used for basic and clinical research.

Evaluation of computer-aided diagnosis (CAD) software for the detection of lung nodules on multidetector row computed tomography (MDCT): JAFROC study for the improvement in radiologists' diagnostic accuracy.

PubMed

Hirose, Tomohiro; Nitta, Norihisa; Shiraishi, Junji; Nagatani, Yukihiro; Takahashi, Masashi; Murata, Kiyoshi

2008-12-01

The aim of this study was to evaluate the usefulness of computer-aided diagnosis (CAD) software for the detection of lung nodules on multidetector-row computed tomography (MDCT) in terms of improvement in radiologists' diagnostic accuracy in detecting lung nodules, using jackknife free-response receiver-operating characteristic (JAFROC) analysis. Twenty-one patients (6 without and 15 with lung nodules) were selected randomly from 120 consecutive thoracic computed tomographic examinations. The gold standard for the presence or absence of nodules in the observer study was determined by consensus of two radiologists. Six expert radiologists participated in a free-response receiver operating characteristic study for the detection of lung nodules on MDCT, in which cases were interpreted first without and then with the output of CAD software. Radiologists were asked to indicate the locations of lung nodule candidates on the monitor with their confidence ratings for the presence of lung nodules. The performance of the CAD software indicated that the sensitivity in detecting lung nodules was 71.4%, with 0.95 false-positive results per case. When radiologists used the CAD software, the average sensitivity improved from 39.5% to 81.0%, with an increase in the average number of false-positive results from 0.14 to 0.89 per case. The average figure-of-merit values for the six radiologists were 0.390 without and 0.845 with the output of the CAD software, and there was a statistically significant difference (P < .0001) using the JAFROC analysis. The CAD software for the detection of lung nodules on MDCT has the potential to assist radiologists by increasing their accuracy.

Diaphragm and Lung Ultrasound to Predict Weaning Outcome: Systematic Review and Meta-Analysis.

PubMed

Llamas-Álvarez, Ana M; Tenza-Lozano, Eva M; Latour-Pérez, Jaime

2017-12-01

Deciding the optimal timing for extubation in patients who are mechanically ventilated can be challenging, and traditional weaning predictor tools are not very accurate. The aim of this systematic review and meta-analysis was to assess the accuracy of lung and diaphragm ultrasound for predicting weaning outcomes in critically ill adults. MEDLINE, the Cochrane Library, Web of Science, Scopus, LILACS, Teseo, Tesis Doctorales en Red, and OpenGrey were searched, and the bibliographies of relevant studies were reviewed. Two researchers independently selected studies that met the inclusion criteria and assessed study quality in accordance with the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The summary receiver-operating characteristic curve and pooled diagnostic OR (DOR) were estimated by using a bivariate random effects analysis. Sources of heterogeneity were explored by using predefined subgroup analyses and bivariate meta-regression. Nineteen studies involving 1,071 people were included in the study. For diaphragm thickening fraction, the area under the summary receiver-operating characteristic curve was 0.87, and DOR was 21 (95% CI, 11-40). Regarding diaphragmatic excursion, pooled sensitivity was 75% (95% CI, 65-85); pooled specificity, 75% (95% CI, 60-85); and DOR, 10 (95% CI, 4-24). For lung ultrasound, the area under the summary receiver-operating characteristic curve was 0.77, and DOR was 38 (95% CI, 7-198). Based on bivariate meta-regression analysis, a significantly higher specificity for diaphragm thickening fraction and higher sensitivity for diaphragmatic excursion was detected in studies with applicability concerns. Lung and diaphragm ultrasound can help predict weaning outcome, but its accuracy may vary depending on the patient subpopulation. Copyright © 2017 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

Accuracy of administrative data for surveillance of healthcare-associated infections: a systematic review

PubMed Central

van Mourik, Maaike S M; van Duijn, Pleun Joppe; Moons, Karel G M; Bonten, Marc J M; Lee, Grace M

2015-01-01

Objective Measuring the incidence of healthcare-associated infections (HAI) is of increasing importance in current healthcare delivery systems. Administrative data algorithms, including (combinations of) diagnosis codes, are commonly used to determine the occurrence of HAI, either to support within-hospital surveillance programmes or as free-standing quality indicators. We conducted a systematic review evaluating the diagnostic accuracy of administrative data for the detection of HAI. Methods Systematic search of Medline, Embase, CINAHL and Cochrane for relevant studies (1995–2013). Methodological quality assessment was performed using QUADAS-2 criteria; diagnostic accuracy estimates were stratified by HAI type and key study characteristics. Results 57 studies were included, the majority aiming to detect surgical site or bloodstream infections. Study designs were very diverse regarding the specification of their administrative data algorithm (code selections, follow-up) and definitions of HAI presence. One-third of studies had important methodological limitations including differential or incomplete HAI ascertainment or lack of blinding of assessors. Observed sensitivity and positive predictive values of administrative data algorithms for HAI detection were very heterogeneous and generally modest at best, both for within-hospital algorithms and for formal quality indicators; accuracy was particularly poor for the identification of device-associated HAI such as central line associated bloodstream infections. The large heterogeneity in study designs across the included studies precluded formal calculation of summary diagnostic accuracy estimates in most instances. Conclusions Administrative data had limited and highly variable accuracy for the detection of HAI, and their judicious use for internal surveillance efforts and external quality assessment is recommended. If hospitals and policymakers choose to rely on administrative data for HAI surveillance, continued improvements to existing algorithms and their robust validation are imperative. PMID:26316651

Airway physical examination tests for detection of difficult airway management in apparently normal adult patients.

PubMed

Roth, Dominik; Pace, Nathan L; Lee, Anna; Hovhannisyan, Karen; Warenits, Alexandra-Maria; Arrich, Jasmin; Herkner, Harald

2018-05-15

The unanticipated difficult airway is a potentially life-threatening event during anaesthesia or acute conditions. An unsuccessfully managed upper airway is associated with serious morbidity and mortality. Several bedside screening tests are used in clinical practice to identify those at high risk of difficult airway. Their accuracy and benefit however, remains unclear. The objective of this review was to characterize and compare the diagnostic accuracy of the Mallampati classification and other commonly used airway examination tests for assessing the physical status of the airway in adult patients with no apparent anatomical airway abnormalities. We performed this individually for each of the four descriptors of the difficult airway: difficult face mask ventilation, difficult laryngoscopy, difficult tracheal intubation, and failed intubation. We searched major electronic databases including CENTRAL, MEDLINE, Embase, ISI Web of Science, CINAHL, as well as regional, subject specific, and dissertation and theses databases from inception to 16 December 2016, without language restrictions. In addition, we searched the Science Citation Index and checked the references of all the relevant studies. We also handsearched selected journals, conference proceedings, and relevant guidelines. We updated this search in March 2018, but we have not yet incorporated these results. We considered full-text diagnostic test accuracy studies of any individual index test, or a combination of tests, against a reference standard. Participants were adults without obvious airway abnormalities, who were having laryngoscopy performed with a standard laryngoscope and the trachea intubated with a standard tracheal tube. Index tests included the Mallampati test, modified Mallampati test, Wilson risk score, thyromental distance, sternomental distance, mouth opening test, upper lip bite test, or any combination of these. The target condition was difficult airway, with one of the following reference standards: difficult face mask ventilation, difficult laryngoscopy, difficult tracheal intubation, and failed intubation. We performed screening and selection of the studies, data extraction and assessment of methodological quality (using QUADAS-2) independently and in duplicate. We designed a Microsoft Access database for data collection and used Review Manager 5 and R for data analysis. For each index test and each reference standard, we assessed sensitivity and specificity. We produced forest plots and summary receiver operating characteristic (ROC) plots to summarize the data. Where possible, we performed meta-analyses to calculate pooled estimates and compare test accuracy indirectly using bivariate models. We investigated heterogeneity and performed sensitivity analyses. We included 133 (127 cohort type and 6 case-control) studies involving 844,206 participants. We evaluated a total of seven different prespecified index tests in the 133 studies, as well as 69 non-prespecified, and 32 combinations. For the prespecified index tests, we found six studies for the Mallampati test, 105 for the modified Mallampati test, six for the Wilson risk score, 52 for thyromental distance, 18 for sternomental distance, 34 for the mouth opening test, and 30 for the upper lip bite test. Difficult face mask ventilation was the reference standard in seven studies, difficult laryngoscopy in 92 studies, difficult tracheal intubation in 50 studies, and failed intubation in two studies. Across all studies, we judged the risk of bias to be variable for the different domains; we mostly observed low risk of bias for patient selection, flow and timing, and unclear risk of bias for reference standard and index test. Applicability concerns were generally low for all domains. For difficult laryngoscopy, the summary sensitivity ranged from 0.22 (95% confidence interval (CI) 0.13 to 0.33; mouth opening test) to 0.67 (95% CI 0.45 to 0.83; upper lip bite test) and the summary specificity ranged from 0.80 (95% CI 0.74 to 0.85; modified Mallampati test) to 0.95 (95% CI 0.88 to 0.98; Wilson risk score). The upper lip bite test for diagnosing difficult laryngoscopy provided the highest sensitivity compared to the other tests (P < 0.001). For difficult tracheal intubation, summary sensitivity ranged from 0.24 (95% CI 0.12 to 0.43; thyromental distance) to 0.51 (95% CI 0.40 to 0.61; modified Mallampati test) and the summary specificity ranged from 0.87 (95% CI 0.82 to 0.91; modified Mallampati test) to 0.93 (0.87 to 0.96; mouth opening test). The modified Mallampati test had the highest sensitivity for diagnosing difficult tracheal intubation compared to the other tests (P < 0.001). For difficult face mask ventilation, we could only estimate summary sensitivity (0.17, 95% CI 0.06 to 0.39) and specificity (0.90, 95% CI 0.81 to 0.95) for the modified Mallampati test. Bedside airway examination tests, for assessing the physical status of the airway in adults with no apparent anatomical airway abnormalities, are designed as screening tests. Screening tests are expected to have high sensitivities. We found that all investigated index tests had relatively low sensitivities with high variability. In contrast, specificities were consistently and markedly higher than sensitivities across all tests. The standard bedside airway examination tests should be interpreted with caution, as they do not appear to be good screening tests. Among the tests we examined, the upper lip bite test showed the most favourable diagnostic test accuracy properties. Given the paucity of available data, future research is needed to develop tests with high sensitivities to make them useful, and to consider their use for screening difficult face mask ventilation and failed intubation. The 27 studies in 'Studies awaiting classification' may alter the conclusions of the review, once we have assessed them.

Accuracy of different Xpert MTB/Rif implementation strategies in programmatic settings at the regional referral hospitals in Uganda: Evidence for country wide roll out

PubMed Central

Sekibira, Rogers; Kirenga, Bruce; Katamba, Achilles; Joloba, Moses

2018-01-01

Background Xpert MTB/RIF assay is a highly sensitive test for TB diagnosis, but still costly to most low-income countries. Several implementation strategies instead of frontline have been suggested; however with scarce data. We assessed accuracy of different Xpert MTB/RIF implementation strategies to inform national roll-out. Methods This was a cross-sectional study of 1,924 adult presumptive TB patients in five regional referral hospitals of Uganda. Two sputum samples were collected, one for fluorescent microscopy (FM) and Xpert MTB/RIF examined at the study site laboratories. The second sample was sent to the Uganda Supra National TB reference laboratory for culture using both Lowenstein Jensen (LJ) and liquid culture (MGIT). We compared the sensitivities of FM, Xpert MTB/RIF and the incremental sensitivity of Xpert MTB/RIF among patients negative on FM using LJ and/or MGIT as a reference standard. Results A total 1924 patients were enrolled of which 1596 (83%) patients had at least one laboratory result and 1083 respondents had a complete set of all the laboratory results. A total of 328 (30%) were TB positive on LJ and /or MGIT culture. The sensitivity of FM was n (%; 95% confidence interval) 246 (63.5%; 57.9–68.7) overall compared to 52 (55.4%; 44.1–66.3) among HIV positive individuals, while the sensitivity of Xpert MTB/RIF was 300 (76.2%; 71.7–80.7) and 69 (71.6%; 60.5–81.1) overall and among HIV positive individuals respectively. Overall incremental sensitivity of Xpert MTB/RIF was 60 (36.5%; 27.7–46.0) and 20 (41.7%; 25.5–59.2) among HIV positive individuals. Conclusion Xpert MTB/RIF has a higher sensitivity than FM both in general population and HIV positive population. Xpert MTB/RIF offers a significant increase in terms of diagnostic sensitivity even when it is deployed selectively i.e. among smear negative presumptive TB patients. Our results support frontline use of Xpert MTB/RIF assay in high HIV/TB prevalent countries. In settings with limited access, mechanisms to refer smear negative sputum samples to Xpert MTB/RIF hubs are recommended. PMID:29566056

Quantification of M13 and T7 bacteriophages by TaqMan and SYBR green qPCR.

PubMed

Peng, Xiujuan; Nguyen, Alex; Ghosh, Debadyuti

2018-02-01

TaqMan and SYBR Green quantitative PCR (qPCR) methods were developed as DNA-based approaches to reproducibly enumerate M13 and T7 phages from phage display selection experiments individually and simultaneously. The genome copies of M13 and T7 phages were quantified by TaqMan or SYBR Green qPCR referenced against M13 and T7 DNA standard curves of known concentrations. TaqMan qPCR was capable of quantifying M13 and T7 phage DNA simultaneously with a detection range of 2.75*10 1 -2.75*10 8 genome copies(gc)/μL and 2.66*10 1 -2.66*10 8 genome copies(gc)/μL respectively. TaqMan qPCR demonstrated an efficient amplification efficiency (E s ) of 0.97 and 0.90 for M13 and T7 phage DNA, respectively. SYBR Green qPCR was ten-fold more sensitive than TaqMan qPCR, able to quantify 2.75-2.75*10 7 gc/μL and 2.66*10 1 -2.66*10 7 gc/μL of M13 and T7 phage DNA, with an amplification efficiency E s of 1.06 and 0.78, respectively. Due to its superior sensitivity, SYBR Green qPCR was used to enumerate M13 and T7 phage display clones selected against a cell line, and quantified titers demonstrated accuracy comparable to titers from traditional double-layer plaque assay. Compared to enzyme linked immunosorbent assay, both qPCR methods exhibited increased detection sensitivity and reproducibility. These qPCR methods are reproducible, sensitive, and time-saving to determine their titers and to quantify a large number of phage samples individually or simultaneously, thus avoiding the need for time-intensive double-layer plaque assay. These findings highlight the attractiveness of qPCR for phage enumeration for applications ranging from selection to next-generation sequencing (NGS). Copyright © 2017 Elsevier B.V. All rights reserved.

Recent advances on aptamer-based biosensors to detection of platelet-derived growth factor.

PubMed

Razmi, Nasrin; Baradaran, Behzad; Hejazi, Maryam; Hasanzadeh, Mohammad; Mosafer, Jafar; Mokhtarzadeh, Ahad; de la Guardia, Miguel

2018-08-15

Platelet-derived growth factor (PDGF-BB), a significant serum cytokine, is an important protein biomarker in diagnosis and recognition of cancer, which straightly rolled in proceeding of various cell transformations, including tumor growth and its development. Fibrosis, atherosclerosis are certain appalling diseases, which PDGF-BB is near to them. Generally, the expression amount of PDGF-BB increases in human life-threatening tumors serving as an indicator for tumor angiogenesis. Thus, identification and quantification of PDGF-BB in biomedical fields are particularly important. Affinity chromatography, immunohistochemical methods and enzyme-linked immunosorbent assay (ELISA), conventional methods for PDGF-BB detection, requiring high-cost and complicated instrumentation, take too much time and offer deficient sensitivity and selectivity, which restrict their usage in real applications. Hence, it is essential to design and build enhanced systems and platforms for the recognition and quantification of protein biomarkers. In the past few years, biosensors especially aptasensors have been received noticeable attention for the detection of PDGF-BB owing to their high sensitivity, selectivity, accuracy, fast response, and low cost. Since the role and importance of developing aptasensors in cancer diagnosis is undeniable. In this review, optical and electrochemical aptasensors, which have been applied by many researchers for PDGF-BB cancer biomarker detection, have been mentioned and merits and demerits of them have been explained and compared. Efforts related to design and development of aptamer-based biosensors using nanoparticles for sensitive and selective detection of PDGF-BB have been reviewed considering: Aptamer importance as recognition elements, principal, application and the recent improvements and developments of aptamer based optical and electrochemical methods. In addition, commercial biosensors and future perspectives for rapid and on-site detection of PDGF-BB have been summarized. Copyright © 2018 Elsevier B.V. All rights reserved.

Repeated significance tests of linear combinations of sensitivity and specificity of a diagnostic biomarker

PubMed Central

Wu, Mixia; Shu, Yu; Li, Zhaohai; Liu, Aiyi

2016-01-01

A sequential design is proposed to test whether the accuracy of a binary diagnostic biomarker meets the minimal level of acceptance. The accuracy of a binary diagnostic biomarker is a linear combination of the marker’s sensitivity and specificity. The objective of the sequential method is to minimize the maximum expected sample size under the null hypothesis that the marker’s accuracy is below the minimal level of acceptance. The exact results of two-stage designs based on Youden’s index and efficiency indicate that the maximum expected sample sizes are smaller than the sample sizes of the fixed designs. Exact methods are also developed for estimation, confidence interval and p-value concerning the proposed accuracy index upon termination of the sequential testing. PMID:26947768

Development and validation of a stability-indicating RP-HPLC-FLD method for determination of 5-[(4-chlorophenoxy) methyl]-1, 3, 4-oxadiazole-2-thiol; A novel drug candidate.

PubMed

Shehzadi, Naureen; Hussain, Khalid; Islam, Muhammad; Bukhari, Nadeem Irfan; Asif, Noman; Khan, Muhammad Tanveer; Salman, Muhammad; Qamar, Shaista; Parveen, Sajida; Zahid, Fakhra; Shah, Arshad Ali; Bilal, Abida; Abbasi, Muhammad Athar; Siddiqui, Sabahat Zahra; Rehman, Azizur

2018-03-01

The present study describes the development and validation of a simple high performance liquid chromatographic method for the determination of a novel drug candidate, 5-[(4-chlorophenoxy) methyl]-1, 3, 4-oxadiazole-2-thiol. The stability-indicating capacity of the method was evaluated by subjecting the compound's solution to hydrolytic, oxidative, photolytic, transition metal- and thermal- stress. The chromatographic separation was achieved over a C18 column (Promosil, 5 µm, 4.60 × 250 mm), maintained at 25°C, using an isocratic mobile phase comprising a mixture of acetonitrile and acidified water of pH 2.67 (1:1, v/v), at a flow rate of 1.00 mL/min and detection using a fluorescent light detector (excitation at 250 nm and emission at 410 nm). The Beer's law was followed over the concentration range 2.50-50.00 μg/ml. The recovery (98.56-100.19%, SD <5%), intraday accuracy and precision (97.31-100.81%, RSD <5%), inter-day accuracy and precision (97.50-100.75%, RSD <5%) and intermediate accuracy and precision (98.10-99.91%, RSD <5%) indicated that the method was reliable, repeatable, reproducible and rugged. The resolution and selectivity factors of the compound's peak from the nearest resolving peak, particularly in case of dry heat and copper metal stress, were found to be greater than 2 and 1, respectively, which indicated specificity and selectivity. The compound was extensively decomposed in alkaline-hydrolytic, oxidative, metal- and dry heat- stress. However, the compound in acidic and neutral conditions was resistant to photolysis. The results of the present study indicate that the developed method is specific, selective, sensitive and suitable, hence, may be used for quality control, stability testing and preformulation studies.

Breath analysis using external cavity diode lasers: a review

NASA Astrophysics Data System (ADS)

Bayrakli, Ismail

2017-04-01

Most techniques that are used for diagnosis and therapy of diseases are invasive. Reliable noninvasive methods are always needed for the comfort of patients. Owing to its noninvasiveness, ease of use, and easy repeatability, exhaled breath analysis is a very good candidate for this purpose. Breath analysis can be performed using different techniques, such as gas chromatography mass spectrometry (MS), proton transfer reaction-MS, and selected ion flow tube-MS. However, these devices are bulky and require complicated procedures for sample collection and preconcentration. Therefore, these are not practical for routine applications in hospitals. Laser-based techniques with small size, robustness, low cost, low response time, accuracy, precision, high sensitivity, selectivity, low detection limit, real-time, and point-of-care detection have a great potential for routine use in hospitals. In this review paper, the recent advances in the fields of external cavity lasers and breath analysis for detection of diseases are presented.

PrAS: Prediction of amidation sites using multiple feature extraction.

PubMed

Wang, Tong; Zheng, Wei; Wuyun, Qiqige; Wu, Zhenfeng; Ruan, Jishou; Hu, Gang; Gao, Jianzhao

2017-02-01

Amidation plays an important role in a variety of pathological processes and serious diseases like neural dysfunction and hypertension. However, identification of protein amidation sites through traditional experimental methods is time consuming and expensive. In this paper, we proposed a novel predictor for Prediction of Amidation Sites (PrAS), which is the first software package for academic users. The method incorporated four representative feature types, which are position-based features, physicochemical and biochemical properties features, predicted structure-based features and evolutionary information features. A novel feature selection method, positive contribution feature selection was proposed to optimize features. PrAS achieved AUC of 0.96, accuracy of 92.1%, sensitivity of 81.2%, specificity of 94.9% and MCC of 0.76 on the independent test set. PrAS is freely available at https://sourceforge.net/p/praspkg. Copyright © 2016 Elsevier Ltd. All rights reserved.

Investigation of an enhanced resolution triple quadrupole mass spectrometer for high-throughput liquid chromatography/tandem mass spectrometry assays.

PubMed

Yang, Liyu; Amad, Ma'an; Winnik, Witold M; Schoen, Alan E; Schweingruber, Hans; Mylchreest, Iain; Rudewicz, Patrick J

2002-01-01

Triple quadrupole mass spectrometers, when operated in multiple reaction monitoring (MRM) mode, offer a unique combination of sensitivity, specificity, and dynamic range. Consequently, the triple quadrupole is the workhorse for high-throughput quantitation within the pharmaceutical industry. However, in the past, the unit mass resolution of quadrupole instruments has been a limitation when interference from matrix or metabolites cannot be eliminated. With recent advances in instrument design, triple quadrupole instruments now afford mass resolution of less than 0.1 Dalton (Da) full width at half maximum (FWHM). This paper describes the evaluation of an enhanced resolution triple quadrupole mass spectrometer for high-throughput bioanalysis with emphasis on comparison of selectivity, sensitivity, dynamic range, precision, accuracy, and stability under both unit mass (1 Da FWHM) and enhanced (

Exploring a Three-Level Model of Calibration Accuracy

ERIC Educational Resources Information Center

Schraw, Gregory; Kuch, Fred; Gutierrez, Antonio P.; Richmond, Aaron S.

2014-01-01

We compared 5 different statistics (i.e., G index, gamma, "d'", sensitivity, specificity) used in the social sciences and medical diagnosis literatures to assess calibration accuracy in order to examine the relationship among them and to explore whether one statistic provided a best fitting general measure of accuracy. College…

Detection of artifacts from high energy bursts in neonatal EEG.

PubMed

Bhattacharyya, Sourya; Biswas, Arunava; Mukherjee, Jayanta; Majumdar, Arun Kumar; Majumdar, Bandana; Mukherjee, Suchandra; Singh, Arun Kumar

2013-11-01

Detection of non-cerebral activities or artifacts, intermixed within the background EEG, is essential to discard them from subsequent pattern analysis. The problem is much harder in neonatal EEG, where the background EEG contains spikes, waves, and rapid fluctuations in amplitude and frequency. Existing artifact detection methods are mostly limited to detect only a subset of artifacts such as ocular, muscle or power line artifacts. Few methods integrate different modules, each for detection of one specific category of artifact. Furthermore, most of the reference approaches are implemented and tested on adult EEG recordings. Direct application of those methods on neonatal EEG causes performance deterioration, due to greater pattern variation and inherent complexity. A method for detection of a wide range of artifact categories in neonatal EEG is thus required. At the same time, the method should be specific enough to preserve the background EEG information. The current study describes a feature based classification approach to detect both repetitive (generated from ECG, EMG, pulse, respiration, etc.) and transient (generated from eye blinking, eye movement, patient movement, etc.) artifacts. It focuses on artifact detection within high energy burst patterns, instead of detecting artifacts within the complete background EEG with wide pattern variation. The objective is to find true burst patterns, which can later be used to identify the Burst-Suppression (BS) pattern, which is commonly observed during newborn seizure. Such selective artifact detection is proven to be more sensitive to artifacts and specific to bursts, compared to the existing artifact detection approaches applied on the complete background EEG. Several time domain, frequency domain, statistical features, and features generated by wavelet decomposition are analyzed to model the proposed bi-classification between burst and artifact segments. A feature selection method is also applied to select the feature subset producing highest classification accuracy. The suggested feature based classification method is executed using our recorded neonatal EEG dataset, consisting of burst and artifact segments. We obtain 78% sensitivity and 72% specificity as the accuracy measures. The accuracy obtained using the proposed method is found to be about 20% higher than that of the reference approaches. Joint use of the proposed method with our previous work on burst detection outperforms reference methods on simultaneous burst and artifact detection. As the proposed method supports detection of a wide range of artifact patterns, it can be improved to incorporate the detection of artifacts within other seizure patterns and background EEG information as well. © 2013 Elsevier Ltd. All rights reserved.

Sensitivity Analysis of the Static Aeroelastic Response of a Wing

NASA Technical Reports Server (NTRS)

Eldred, Lloyd B.

1993-01-01

A technique to obtain the sensitivity of the static aeroelastic response of a three dimensional wing model is designed and implemented. The formulation is quite general and accepts any aerodynamic and structural analysis capability. A program to combine the discipline level, or local, sensitivities into global sensitivity derivatives is developed. A variety of representations of the wing pressure field are developed and tested to determine the most accurate and efficient scheme for representing the field outside of the aerodynamic code. Chebyshev polynomials are used to globally fit the pressure field. This approach had some difficulties in representing local variations in the field, so a variety of local interpolation polynomial pressure representations are also implemented. These panel based representations use a constant pressure value, a bilinearly interpolated value. or a biquadraticallv interpolated value. The interpolation polynomial approaches do an excellent job of reducing the numerical problems of the global approach for comparable computational effort. Regardless of the pressure representation used. sensitivity and response results with excellent accuracy have been produced for large integrated quantities such as wing tip deflection and trim angle of attack. The sensitivities of such things as individual generalized displacements have been found with fair accuracy. In general, accuracy is found to be proportional to the relative size of the derivatives to the quantity itself.

Fibronectin on circulating extracellular vesicles as a liquid biopsy to detect breast cancer.

PubMed

Moon, Pyong-Gon; Lee, Jeong-Eun; Cho, Young-Eun; Lee, Soo Jung; Chae, Yee Soo; Jung, Jin Hyang; Kim, In-San; Park, Ho Yong; Baek, Moon-Chang

2016-06-28

Extracellular vesicles (EVs) secreted from cancer cells have potential for generating cancer biomarker signatures. Fibronectin (FN) was selected as a biomarker candidate, due to the presence in surface on EVs secreted from human breast cancer cell lines. A subsequent study used two types of enzyme-linked immunosorbent assays (ELISA) to determine the presence of these proteins in plasma samples from disease-free individuals (n=70), patients with BC (n=240), BC patients after surgical resection (n=40), patients with benign breast tumor (n=55), and patients with non-cancerous diseases (thyroiditis, gastritis, hepatitis B, and rheumatoid arthritis; n=80). FN levels were significantly elevated (p< .0001) at all stages of BC, and returned to normal after tumor removal. The diagnostic accuracy for FN detection in extracellular vesicles (ELISA method 1) (area under the curve, 0.81; 95% CI, 0.76 to 0.86; sensitivity of 65.1% and specificity of 83.2%) were also better than those for FN detection in the plasma (ELISA method 2) (area under the curve, 0.77; 95% CI, 0.72 to 0.83; sensitivity of 69.2% and specificity of 73.3%) in BC. The diagnostic accuracy of plasma FN was similar in both the early-stage BC and all BC patients, as well as in the two sets. This liquid biopsy to detect FN on circulating EVs could be a promising method to detect early breast cancer.

Role of ascitic fluid adenosine deaminase (ADA) and serum CA-125 in the diagnosis of tuberculous peritonitis.

PubMed

Ali, N; Nath, N C; Parvin, R; Rahman, A; Bhuiyan, T M; Rahman, M; Mohsin M N

2014-12-01

This cross sectional study was carried out in the department of gastroenterology, BIRDEM, Dhaka from January 2010 to May 2011 to determine the role of ascitic fluid ADA and serum CA-125 in the diagnosis of clinically suspected tubercular peritonitis. Total 30 patients (age 39.69 ± 21.26, 18M/12F) with clinical suspicion of tuberculosis peritonitis were included in this study after analyzing selection criteria. Laparoscopic peritoneal biopsy with 'histopathological diagnosis' was considered gold standard against which accuracics of two biomarkers (ADA & CA-125) were compared. Cut off value of ADA and CA-125 are 24 u/l, 35 U/ml respectively. Sensitivity, specificity, positive predictive value, negative predictive value and accuracy of ADA as a diagnostic modality in tuberculos peritonitis were 87.5%, 83.33%, 95.45%, 62.5% and 86.67% respectively where as CA-125 was found to have 83.33% sensitivity, 50% specificity, 86.9% positive predictive value, 42.85% negative predictive value and 76.6% accuracy. Both biomarkers are simple, non-invasive, rapid and relatively cheap diagnostic test where as laparoscopy is an invasive procedure, costly & requires trained staff and not without risk and also not feasible in all the centre in our country. So ascitic fluid ADA and serum CA-125 are important diagnostic test for peritoneal tuberculosis.

Screening Questionnaires for Obstructive Sleep Apnea: An Updated Systematic Review.

PubMed

Amra, Babak; Rahmati, Behzad; Soltaninejad, Forogh; Feizi, Awat

2018-05-01

Obstructive sleep apnea (OSA) is the most common sleep-related breathing disorder and is associated with significant morbidity. We sought to present an updated systematic review of the literature on the accuracy of screening questionnaires for OSA against polysomnography (PSG) as the reference test. Using the main databases (including Medline, Cochrane Database of Systematic Reviews and Scopus) we used a combination of relevant keywords to filter studies published between January 2010 and April 2017. Population-based studies evaluating the accuracy of screening questionnaires for OSA against PSG were included in the review. Thirty-nine studies comprising 18 068 subjects were included. Four screening questionnaires for OSA had been validated in selected studies including the Berlin questionnaire (BQ), STOP-Bang Questionnaire (SBQ), STOP Questionnaire (SQ), and Epworth Sleepiness Scale (ESS). The sensitivity of SBQ in detecting mild (apnea-hypopnea index (AHI) ≥ 5 events/hour) and severe (AHI ≥ 30 events/hour) OSA was higher compared to other screening questionnaires (range from 81.08% to 97.55% and 69.2% to 98.7%, respectively). However, SQ had the highest sensitivity in predicting moderate OSA (AHI ≥ 15 events/hour; range = 41.3% to 100%). SQ and SBQ are reliable tools for screening OSA among sleep clinic patients. Although further validation studies on the screening abilities of these questionnaires on general populations are required.

Accurate identification of motor unit discharge patterns from high-density surface EMG and validation with a novel signal-based performance metric

NASA Astrophysics Data System (ADS)

Holobar, A.; Minetto, M. A.; Farina, D.

2014-02-01

Objective. A signal-based metric for assessment of accuracy of motor unit (MU) identification from high-density surface electromyograms (EMG) is introduced. This metric, so-called pulse-to-noise-ratio (PNR), is computationally efficient, does not require any additional experimental costs and can be applied to every MU that is identified by the previously developed convolution kernel compensation technique. Approach. The analytical derivation of the newly introduced metric is provided, along with its extensive experimental validation on both synthetic and experimental surface EMG signals with signal-to-noise ratios ranging from 0 to 20 dB and muscle contraction forces from 5% to 70% of the maximum voluntary contraction. Main results. In all the experimental and simulated signals, the newly introduced metric correlated significantly with both sensitivity and false alarm rate in identification of MU discharges. Practically all the MUs with PNR > 30 dB exhibited sensitivity >90% and false alarm rates <2%. Therefore, a threshold of 30 dB in PNR can be used as a simple method for selecting only reliably decomposed units. Significance. The newly introduced metric is considered a robust and reliable indicator of accuracy of MU identification. The study also shows that high-density surface EMG can be reliably decomposed at contraction forces as high as 70% of the maximum.

Performance of an artificial neural network for vertical root fracture detection: an ex vivo study.

PubMed

Kositbowornchai, Suwadee; Plermkamon, Supattra; Tangkosol, Tawan

2013-04-01

To develop an artificial neural network for vertical root fracture detection. A probabilistic neural network design was used to clarify whether a tooth root was sound or had a vertical root fracture. Two hundred images (50 sound and 150 vertical root fractures) derived from digital radiography--used to train and test the artificial neural network--were divided into three groups according to the number of training and test data sets: 80/120,105/95 and 130/70, respectively. Either training or tested data were evaluated using grey-scale data per line passing through the root. These data were normalized to reduce the grey-scale variance and fed as input data of the neural network. The variance of function in recognition data was calculated between 0 and 1 to select the best performance of neural network. The performance of the neural network was evaluated using a diagnostic test. After testing data under several variances of function, we found the highest sensitivity (98%), specificity (90.5%) and accuracy (95.7%) occurred in Group three, for which the variance of function in recognition data was between 0.025 and 0.005. The neural network designed in this study has sufficient sensitivity, specificity and accuracy to be a model for vertical root fracture detection. © 2012 John Wiley & Sons A/S.

Determination of Land Use/ Land Cover Changes in Igneada Alluvial (Longos) Forest Ecosystem, Turkey

NASA Astrophysics Data System (ADS)

Bektas Balcik, F.

2012-12-01

Alluvial (Longos) forests are one of the most fragile and threatened ecosystems in the world. Typically, these types of ecosystems have high biological diversity, high productivity, and high habitat dynamism. In this study, Igneada, Kirklareli was selected as study area. The region, lies between latitudes 41° 46' N and 41° 59' N and stretches between longitudes 27° 50' E and 28° 02' E and it covers approximately 24000 (ha). Igneada Longos ecosystems include mixed forests, streams, flooded (alluvial) forests, marshes, wetlands, lakes and coastal sand dunes with different types of flora and fauna. Igneada was classified by Conservation International as one of the world's top 122 Important Plant Areas, and 185 Important Bird Areas. These types of wild forest in other parts of Turkey and in Europe have been damaged due to anthropogenic effects. Remote sensing is very effective tool to monitor these types of sensitive regions for sustainable management. In this study, 1984 and 2011 dated Landsat 5 TM data were used to determine land cover/land use change detection of the selected region by using six vegetation indices such as Tasseled Cap index of greenness (TCG), brightness (TCB), and wetness (TCW), ratios of near-infrared to red image (RVI), normalized difference vegetation index (NDVI), and soil-adjusted vegetation index (SAVI). Geometric and radiometric corrections were applied in image pre-processing step. Selective Principle Component Analysis (PCA) change detection method was applied to the selected vegetation index imagery to generate change imagery for extracting the changed features between the year of 1984 and 2011. Accuracy assessment was applied based on error matrix by calculating overall accuracy and Kappa statistics.

Derivation of an artificial gene to improve classification accuracy upon gene selection.

PubMed

Seo, Minseok; Oh, Sejong

2012-02-01

Classification analysis has been developed continuously since 1936. This research field has advanced as a result of development of classifiers such as KNN, ANN, and SVM, as well as through data preprocessing areas. Feature (gene) selection is required for very high dimensional data such as microarray before classification work. The goal of feature selection is to choose a subset of informative features that reduces processing time and provides higher classification accuracy. In this study, we devised a method of artificial gene making (AGM) for microarray data to improve classification accuracy. Our artificial gene was derived from a whole microarray dataset, and combined with a result of gene selection for classification analysis. We experimentally confirmed a clear improvement of classification accuracy after inserting artificial gene. Our artificial gene worked well for popular feature (gene) selection algorithms and classifiers. The proposed approach can be applied to any type of high dimensional dataset. Copyright © 2011 Elsevier Ltd. All rights reserved.

Accuracy of genomic selection in European maize elite breeding populations.

PubMed

Zhao, Yusheng; Gowda, Manje; Liu, Wenxin; Würschum, Tobias; Maurer, Hans P; Longin, Friedrich H; Ranc, Nicolas; Reif, Jochen C

2012-03-01

Genomic selection is a promising breeding strategy for rapid improvement of complex traits. The objective of our study was to investigate the prediction accuracy of genomic breeding values through cross validation. The study was based on experimental data of six segregating populations from a half-diallel mating design with 788 testcross progenies from an elite maize breeding program. The plants were intensively phenotyped in multi-location field trials and fingerprinted with 960 SNP markers. We used random regression best linear unbiased prediction in combination with fivefold cross validation. The prediction accuracy across populations was higher for grain moisture (0.90) than for grain yield (0.58). The accuracy of genomic selection realized for grain yield corresponds to the precision of phenotyping at unreplicated field trials in 3-4 locations. As for maize up to three generations are feasible per year, selection gain per unit time is high and, consequently, genomic selection holds great promise for maize breeding programs.