Combining clinical variables to optimize prediction of antidepressant treatment outcomes.

PubMed

Iniesta, Raquel; Malki, Karim; Maier, Wolfgang; Rietschel, Marcella; Mors, Ole; Hauser, Joanna; Henigsberg, Neven; Dernovsek, Mojca Zvezdana; Souery, Daniel; Stahl, Daniel; Dobson, Richard; Aitchison, Katherine J; Farmer, Anne; Lewis, Cathryn M; McGuffin, Peter; Uher, Rudolf

2016-07-01

The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remission during treatment with escitalopram or nortriptyline and to identify contributing predictors from a range of demographic and clinical variables in 793 adults with major depressive disorder. A combination of demographic and clinical variables, with strong contributions from symptoms of depressed mood, reduced interest, decreased activity, indecisiveness, pessimism and anxiety significantly predicted treatment outcomes, explaining 5-10% of variance in symptom improvement with escitalopram. Similar combinations of variables predicted remission with area under the curve 0.72, explaining approximately 15% of variance (pseudo R(2)) in who achieves remission, with strong contributions from body mass index, appetite, interest-activity symptom dimension and anxious-somatizing depression subtype. Escitalopram-specific outcome prediction was more accurate than generic outcome prediction, and reached effect sizes that were near or above a previously established benchmark for clinical significance. Outcome prediction on the nortriptyline arm did not significantly differ from chance. These results suggest that easily obtained demographic and clinical variables can predict therapeutic response to escitalopram with clinically meaningful accuracy, suggesting a potential for individualized prescription of this antidepressant drug. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Optimization and real-time control for laser treatment of heterogeneous soft tissues.

PubMed

Feng, Yusheng; Fuentes, David; Hawkins, Andrea; Bass, Jon M; Rylander, Marissa Nichole

2009-01-01

Predicting the outcome of thermotherapies in cancer treatment requires an accurate characterization of the bioheat transfer processes in soft tissues. Due to the biological and structural complexity of tumor (soft tissue) composition and vasculature, it is often very difficult to obtain reliable tissue properties that is one of the key factors for the accurate treatment outcome prediction. Efficient algorithms employing in vivo thermal measurements to determine heterogeneous thermal tissues properties in conjunction with a detailed sensitivity analysis can produce essential information for model development and optimal control. The goals of this paper are to present a general formulation of the bioheat transfer equation for heterogeneous soft tissues, review models and algorithms developed for cell damage, heat shock proteins, and soft tissues with nanoparticle inclusion, and demonstrate an overall computational strategy for developing a laser treatment framework with the ability to perform real-time robust calibrations and optimal control. This computational strategy can be applied to other thermotherapies using the heat source such as radio frequency or high intensity focused ultrasound.

Prediction of Contact Fatigue Life of Alloy Cast Steel Rolls Using Back-Propagation Neural Network

NASA Astrophysics Data System (ADS)

Jin, Huijin; Wu, Sujun; Peng, Yuncheng

2013-12-01

In this study, an artificial neural network (ANN) was employed to predict the contact fatigue life of alloy cast steel rolls (ACSRs) as a function of alloy composition, heat treatment parameters, and contact stress by utilizing the back-propagation algorithm. The ANN was trained and tested using experimental data and a very good performance of the neural network was achieved. The well-trained neural network was then adopted to predict the contact fatigue life of chromium alloyed cast steel rolls with different alloy compositions and heat treatment processes. The prediction results showed that the maximum value of contact fatigue life was obtained with quenching at 960 °C, tempering at 520 °C, and under the contact stress of 2355 MPa. The optimal alloy composition was C-0.54, Si-0.66, Mn-0.67, Cr-4.74, Mo-0.46, V-0.13, Ni-0.34, and Fe-balance (wt.%). Some explanations of the predicted results from the metallurgical viewpoints are given. A convenient and powerful method of optimizing alloy composition and heat treatment parameters of ACSRs has been developed.

Automatic CT simulation optimization for radiation therapy: A general strategy.

PubMed

Li, Hua; Yu, Lifeng; Anastasio, Mark A; Chen, Hsin-Chen; Tan, Jun; Gay, Hiram; Michalski, Jeff M; Low, Daniel A; Mutic, Sasa

2014-03-01

In radiation therapy, x-ray computed tomography (CT) simulation protocol specifications should be driven by the treatment planning requirements in lieu of duplicating diagnostic CT screening protocols. The purpose of this study was to develop a general strategy that allows for automatically, prospectively, and objectively determining the optimal patient-specific CT simulation protocols based on radiation-therapy goals, namely, maintenance of contouring quality and integrity while minimizing patient CT simulation dose. The authors proposed a general prediction strategy that provides automatic optimal CT simulation protocol selection as a function of patient size and treatment planning task. The optimal protocol is the one that delivers the minimum dose required to provide a CT simulation scan that yields accurate contours. Accurate treatment plans depend on accurate contours in order to conform the dose to actual tumor and normal organ positions. An image quality index, defined to characterize how simulation scan quality affects contour delineation, was developed and used to benchmark the contouring accuracy and treatment plan quality within the predication strategy. A clinical workflow was developed to select the optimal CT simulation protocols incorporating patient size, target delineation, and radiation dose efficiency. An experimental study using an anthropomorphic pelvis phantom with added-bolus layers was used to demonstrate how the proposed prediction strategy could be implemented and how the optimal CT simulation protocols could be selected for prostate cancer patients based on patient size and treatment planning task. Clinical IMRT prostate treatment plans for seven CT scans with varied image quality indices were separately optimized and compared to verify the trace of target and organ dosimetry coverage. Based on the phantom study, the optimal image quality index for accurate manual prostate contouring was 4.4. The optimal tube potentials for patient sizes of 38, 43, 48, 53, and 58 cm were 120, 140, 140, 140, and 140 kVp, respectively, and the corresponding minimum CTDIvol for achieving the optimal image quality index 4.4 were 9.8, 32.2, 100.9, 241.4, and 274.1 mGy, respectively. For patients with lateral sizes of 43-58 cm, 120-kVp scan protocols yielded up to 165% greater radiation dose relative to 140-kVp protocols, and 140-kVp protocols always yielded a greater image quality index compared to the same dose-level 120-kVp protocols. The trace of target and organ dosimetry coverage and the γ passing rates of seven IMRT dose distribution pairs indicated the feasibility of the proposed image quality index for the predication strategy. A general strategy to predict the optimal CT simulation protocols in a flexible and quantitative way was developed that takes into account patient size, treatment planning task, and radiation dose. The experimental study indicated that the optimal CT simulation protocol and the corresponding radiation dose varied significantly for different patient sizes, contouring accuracy, and radiation treatment planning tasks.

Model-based optimization of G-CSF treatment during cytotoxic chemotherapy.

PubMed

Schirm, Sibylle; Engel, Christoph; Loibl, Sibylle; Loeffler, Markus; Scholz, Markus

2018-02-01

Although G-CSF is widely used to prevent or ameliorate leukopenia during cytotoxic chemotherapies, its optimal use is still under debate and depends on many therapy parameters such as dosing and timing of cytotoxic drugs and G-CSF, G-CSF pharmaceuticals used and individual risk factors of patients. We integrate available biological knowledge and clinical data regarding cell kinetics of bone marrow granulopoiesis, the cytotoxic effects of chemotherapy and pharmacokinetics and pharmacodynamics of G-CSF applications (filgrastim or pegfilgrastim) into a comprehensive model. The model explains leukocyte time courses of more than 70 therapy scenarios comprising 10 different cytotoxic drugs. It is applied to develop optimized G-CSF schedules for a variety of clinical scenarios. Clinical trial results showed validity of model predictions regarding alternative G-CSF schedules. We propose modifications of G-CSF treatment for the chemotherapies 'BEACOPP escalated' (Hodgkin's disease), 'ETC' (breast cancer), and risk-adapted schedules for 'CHOP-14' (aggressive non-Hodgkin's lymphoma in elderly patients). We conclude that we established a model of human granulopoiesis under chemotherapy which allows predictions of yet untested G-CSF schedules, comparisons between them, and optimization of filgrastim and pegfilgrastim treatment. As a general rule of thumb, G-CSF treatment should not be started too early and patients could profit from filgrastim treatment continued until the end of the chemotherapy cycle.

Impact of database quality in knowledge-based treatment planning for prostate cancer.

PubMed

Wall, Phillip D H; Carver, Robert L; Fontenot, Jonas D

2018-03-13

This article investigates dose-volume prediction improvements in a common knowledge-based planning (KBP) method using a Pareto plan database compared with using a conventional, clinical plan database. Two plan databases were created using retrospective, anonymized data of 124 volumetric modulated arc therapy (VMAT) prostate cancer patients. The clinical plan database (CPD) contained planning data from each patient's clinically treated VMAT plan, which were manually optimized by various planners. The multicriteria optimization database (MCOD) contained Pareto-optimal plan data from VMAT plans created using a standardized multicriteria optimization protocol. Overlap volume histograms, incorporating fractional organ at risk volumes only within the treatment fields, were computed for each patient and used to match new patient anatomy to similar database patients. For each database patient, CPD and MCOD KBP predictions were generated for D 10 , D 30 , D 50 , D 65 , and D 80 of the bladder and rectum in a leave-one-out manner. Prediction achievability was evaluated through a replanning study on a subset of 31 randomly selected database patients using the best KBP predictions, regardless of plan database origin, as planning goals. MCOD predictions were significantly lower than CPD predictions for all 5 bladder dose-volumes and rectum D 50 (P = .004) and D 65 (P < .001), whereas CPD predictions for rectum D 10 (P = .005) and D 30 (P < .001) were significantly less than MCOD predictions. KBP predictions were statistically achievable in the replans for all predicted dose-volumes, excluding D 10 of bladder (P = .03) and rectum (P = .04). Compared with clinical plans, replans showed significant average reductions in D mean for bladder (7.8 Gy; P < .001) and rectum (9.4 Gy; P < .001), while maintaining statistically similar planning target volume, femoral head, and penile bulb dose. KBP dose-volume predictions derived from Pareto plans were more optimal overall than those resulting from manually optimized clinical plans, which significantly improved KBP-assisted plan quality. This work investigates how the plan quality of knowledge databases affects the performance and achievability of dose-volume predictions from a common knowledge-based planning approach for prostate cancer. Bladder and rectum dose-volume predictions derived from a database of standardized Pareto-optimal plans were compared with those derived from clinical plans manually designed by various planners. Dose-volume predictions from the Pareto plan database were significantly lower overall than those from the clinical plan database, without compromising achievability. Copyright © 2018 Elsevier Inc. All rights reserved.

Geometric parameter analysis to predetermine optimal radiosurgery technique for the treatment of arteriovenous malformation

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

Mestrovic, Ante; Clark, Brenda G.; Department of Medical Physics, British Columbia Cancer Agency, Vancouver, British Columbia

2005-11-01

Purpose: To develop a method of predicting the values of dose distribution parameters of different radiosurgery techniques for treatment of arteriovenous malformation (AVM) based on internal geometric parameters. Methods and Materials: For each of 18 previously treated AVM patients, four treatment plans were created: circular collimator arcs, dynamic conformal arcs, fixed conformal fields, and intensity-modulated radiosurgery. An algorithm was developed to characterize the target and critical structure shape complexity and the position of the critical structures with respect to the target. Multiple regression was employed to establish the correlation between the internal geometric parameters and the dose distribution for differentmore » treatment techniques. The results from the model were applied to predict the dosimetric outcomes of different radiosurgery techniques and select the optimal radiosurgery technique for a number of AVM patients. Results: Several internal geometric parameters showing statistically significant correlation (p < 0.05) with the treatment planning results for each technique were identified. The target volume and the average minimum distance between the target and the critical structures were the most effective predictors for normal tissue dose distribution. The structure overlap volume with the target and the mean distance between the target and the critical structure were the most effective predictors for critical structure dose distribution. The predicted values of dose distribution parameters of different radiosurgery techniques were in close agreement with the original data. Conclusions: A statistical model has been described that successfully predicts the values of dose distribution parameters of different radiosurgery techniques and may be used to predetermine the optimal technique on a patient-to-patient basis.« less

Prediction of wastewater treatment plants performance based on artificial fish school neural network

NASA Astrophysics Data System (ADS)

Zhang, Ruicheng; Li, Chong

2011-10-01

A reliable model for wastewater treatment plant is essential in providing a tool for predicting its performance and to form a basis for controlling the operation of the process. This would minimize the operation costs and assess the stability of environmental balance. For the multi-variable, uncertainty, non-linear characteristics of the wastewater treatment system, an artificial fish school neural network prediction model is established standing on actual operation data in the wastewater treatment system. The model overcomes several disadvantages of the conventional BP neural network. The results of model calculation show that the predicted value can better match measured value, played an effect on simulating and predicting and be able to optimize the operation status. The establishment of the predicting model provides a simple and practical way for the operation and management in wastewater treatment plant, and has good research and engineering practical value.

Analysis of the tenderisation of jumbo squid (Dosidicus gigas) meat by ultrasonic treatment using response surface methodology.

PubMed

Hu, Yaqin; Yu, Hiaxia; Dong, Kaicheng; Yang, Shuibing; Ye, Xingqian; Chen, Shiguo

2014-10-01

Due to its unique structure, jumbo squid (Dosidicus gigas) meat is sensitive to heat treatment, which makes the traditional squid products taste tough and hard. This study aimed to tenderise jumbo squid meat through ultrasonic treatment. Response surface methodology (RSM) was used to predict the tenderising effect of various treatment conditions. According to the results of RSM, the optimal conditions appeared to be a power of 186.9 W, a frequency of 25.6 kHz, and a time of 30.8 min, and the predicted values of flexibility and firmness under these optimal conditions were 2.40 mm and 435.1 g, respectively. Protein degradation and a broken muscle fibre structure were observed through histological assay and SDS-PAGE, which suggests a satisfactory tenderisation effect. Copyright © 2014. Published by Elsevier Ltd.

Optimal designs for prediction studies of whiplash.

PubMed

Kamper, Steven J; Hancock, Mark J; Maher, Christopher G

2011-12-01

Commentary. To provide guidance for the design and interpretation of predictive studies of whiplash associated disorders (WAD). Numerous studies have sought to define and explain the clinical course and response to treatment of people with WAD. Design of these studies is often suboptimal, which can lead to biased findings and issues with interpreting the results. Literature review and commentary. Predictive studies can be grouped into four broad categories; studies of symptomatic course, studies that aim to identify factors that predict outcome, studies that aim to isolate variables that are causally responsible for outcome, and studies that aim to identify patients who respond best to particular treatments. Although the specific research question will determine the optimal methods, there are a number of generic features that should be incorporated into design of such studies. The aim of these features is to minimize bias, generate adequately precise prognostic estimates, and ensure generalizability of the findings. This paper provides a summary of important considerations in the design, conduct, and reporting of prediction studies in the field of whiplash.

Prediction of chemo-response in serous ovarian cancer.

PubMed

Gonzalez Bosquet, Jesus; Newtson, Andreea M; Chung, Rebecca K; Thiel, Kristina W; Ginader, Timothy; Goodheart, Michael J; Leslie, Kimberly K; Smith, Brian J

2016-10-19

Nearly one-third of serous ovarian cancer (OVCA) patients will not respond to initial treatment with surgery and chemotherapy and die within one year of diagnosis. If patients who are unlikely to respond to current standard therapy can be identified up front, enhanced tumor analyses and treatment regimens could potentially be offered. Using the Cancer Genome Atlas (TCGA) serous OVCA database, we previously identified a robust molecular signature of 422-genes associated with chemo-response. Our objective was to test whether this signature is an accurate and sensitive predictor of chemo-response in serous OVCA. We first constructed prediction models to predict chemo-response using our previously described 422-gene signature that was associated with response to treatment in serous OVCA. Performance of all prediction models were measured with area under the curves (AUCs, a measure of the model's accuracy) and their respective confidence intervals (CIs). To optimize the prediction process, we determined which elements of the signature most contributed to chemo-response prediction. All prediction models were replicated and validated using six publicly available independent gene expression datasets. The 422-gene signature prediction models predicted chemo-response with AUCs of ~70 %. Optimization of prediction models identified the 34 most important genes in chemo-response prediction. These 34-gene models had improved performance, with AUCs approaching 80 %. Both 422-gene and 34-gene prediction models were replicated and validated in six independent datasets. These prediction models serve as the foundation for the future development and implementation of a diagnostic tool to predict response to chemotherapy for serous OVCA patients.

Treatment selection in a randomized clinical trial via covariate-specific treatment effect curves.

PubMed

Ma, Yunbei; Zhou, Xiao-Hua

2017-02-01

For time-to-event data in a randomized clinical trial, we proposed two new methods for selecting an optimal treatment for a patient based on the covariate-specific treatment effect curve, which is used to represent the clinical utility of a predictive biomarker. To select an optimal treatment for a patient with a specific biomarker value, we proposed pointwise confidence intervals for each covariate-specific treatment effect curve and the difference between covariate-specific treatment effect curves of two treatments. Furthermore, to select an optimal treatment for a future biomarker-defined subpopulation of patients, we proposed confidence bands for each covariate-specific treatment effect curve and the difference between each pair of covariate-specific treatment effect curve over a fixed interval of biomarker values. We constructed the confidence bands based on a resampling technique. We also conducted simulation studies to evaluate finite-sample properties of the proposed estimation methods. Finally, we illustrated the application of the proposed method in a real-world data set.

Mathematical Modeling and Optimizing of in Vitro Hormonal Combination for G × N15 Vegetative Rootstock Proliferation Using Artificial Neural Network-Genetic Algorithm (ANN-GA)

PubMed Central

Arab, Mohammad M.; Yadollahi, Abbas; Ahmadi, Hamed; Eftekhari, Maliheh; Maleki, Masoud

2017-01-01

The efficiency of a hybrid systems method which combined artificial neural networks (ANNs) as a modeling tool and genetic algorithms (GAs) as an optimizing method for input variables used in ANN modeling was assessed. Hence, as a new technique, it was applied for the prediction and optimization of the plant hormones concentrations and combinations for in vitro proliferation of Garnem (G × N15) rootstock as a case study. Optimizing hormones combination was surveyed by modeling the effects of various concentrations of cytokinin–auxin, i.e., BAP, KIN, TDZ, IBA, and NAA combinations (inputs) on four growth parameters (outputs), i.e., micro-shoots number per explant, length of micro-shoots, developed callus weight (CW) and the quality index (QI) of plantlets. Calculation of statistical values such as R2 (coefficient of determination) related to the accuracy of ANN-GA models showed a considerably higher prediction accuracy for ANN models, i.e., micro-shoots number: R2 = 0.81, length of micro-shoots: R2 = 0.87, CW: R2 = 0.88, QI: R2 = 0.87. According to the results, among the input variables, BAP (19.3), KIN (9.64), and IBA (2.63) showed the highest values of variable sensitivity ratio for proliferation rate. The GA showed that media containing 1.02 mg/l BAP in combination with 0.098 mg/l IBA could lead to the optimal proliferation rate (10.53) for G × N15 rootstock. Another objective of the present study was to compare the performance of predicted and optimized cytokinin–auxin combination with the best optimized obtained concentrations of our other experiments. Considering three growth parameters (length of micro-shoots, micro-shoots number, and proliferation rate), the last treatment was found to be superior to the rest of treatments for G × N15 rootstock in vitro multiplication. Very little difference between the ANN predicted and experimental data confirmed high capability of ANN-GA method in predicting new optimized protocols for plant in vitro propagation. PMID:29163583

Fast Biological Modeling for Voxel-based Heavy Ion Treatment Planning Using the Mechanistic Repair-Misrepair-Fixation Model and Nuclear Fragment Spectra

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

Kamp, Florian; Department of Radiation Oncology, Technische Universität München, Klinikum Rechts der Isar, München; Physik-Department, Technische Universität München, Garching

2015-11-01

Purpose: The physical and biological differences between heavy ions and photons have not been fully exploited and could improve treatment outcomes. In carbon ion therapy, treatment planning must account for physical properties, such as the absorbed dose and nuclear fragmentation, and for differences in the relative biological effectiveness (RBE) of ions compared with photons. We combined the mechanistic repair-misrepair-fixation (RMF) model with Monte Carlo-generated fragmentation spectra for biological optimization of carbon ion treatment plans. Methods and Materials: Relative changes in double-strand break yields and radiosensitivity parameters with particle type and energy were determined using the independently benchmarked Monte Carlo damagemore » simulation and the RMF model to estimate the RBE values for primary carbon ions and secondary fragments. Depth-dependent energy spectra were generated with the Monte Carlo code FLUKA for clinically relevant initial carbon ion energies. The predicted trends in RBE were compared with the published experimental data. Biological optimization for carbon ions was implemented in a 3-dimensional research treatment planning tool. Results: We compared the RBE and RBE-weighted dose (RWD) distributions of different carbon ion treatment scenarios with and without nuclear fragments. The inclusion of fragments in the simulations led to smaller RBE predictions. A validation of RMF against measured cell survival data reported in published studies showed reasonable agreement. We calculated and optimized the RWD distributions on patient data and compared the RMF predictions with those from other biological models. The RBE values in an astrocytoma tumor ranged from 2.2 to 4.9 (mean 2.8) for a RWD of 3 Gy(RBE) assuming (α/β){sub X} = 2 Gy. Conclusions: These studies provide new information to quantify and assess uncertainties in the clinically relevant RBE values for carbon ion therapy based on biophysical mechanisms. We present results from the first biological optimization of carbon ion radiation therapy beams on patient data using a combined RMF and Monte Carlo damage simulation modeling approach. The presented method is advantageous for fast biological optimization.« less

Fast Biological Modeling for Voxel-based Heavy Ion Treatment Planning Using the Mechanistic Repair-Misrepair-Fixation Model and Nuclear Fragment Spectra.

PubMed

Kamp, Florian; Cabal, Gonzalo; Mairani, Andrea; Parodi, Katia; Wilkens, Jan J; Carlson, David J

2015-11-01

The physical and biological differences between heavy ions and photons have not been fully exploited and could improve treatment outcomes. In carbon ion therapy, treatment planning must account for physical properties, such as the absorbed dose and nuclear fragmentation, and for differences in the relative biological effectiveness (RBE) of ions compared with photons. We combined the mechanistic repair-misrepair-fixation (RMF) model with Monte Carlo-generated fragmentation spectra for biological optimization of carbon ion treatment plans. Relative changes in double-strand break yields and radiosensitivity parameters with particle type and energy were determined using the independently benchmarked Monte Carlo damage simulation and the RMF model to estimate the RBE values for primary carbon ions and secondary fragments. Depth-dependent energy spectra were generated with the Monte Carlo code FLUKA for clinically relevant initial carbon ion energies. The predicted trends in RBE were compared with the published experimental data. Biological optimization for carbon ions was implemented in a 3-dimensional research treatment planning tool. We compared the RBE and RBE-weighted dose (RWD) distributions of different carbon ion treatment scenarios with and without nuclear fragments. The inclusion of fragments in the simulations led to smaller RBE predictions. A validation of RMF against measured cell survival data reported in published studies showed reasonable agreement. We calculated and optimized the RWD distributions on patient data and compared the RMF predictions with those from other biological models. The RBE values in an astrocytoma tumor ranged from 2.2 to 4.9 (mean 2.8) for a RWD of 3 Gy(RBE) assuming (α/β)X = 2 Gy. These studies provide new information to quantify and assess uncertainties in the clinically relevant RBE values for carbon ion therapy based on biophysical mechanisms. We present results from the first biological optimization of carbon ion radiation therapy beams on patient data using a combined RMF and Monte Carlo damage simulation modeling approach. The presented method is advantageous for fast biological optimization. Copyright © 2015 Elsevier Inc. All rights reserved.

Metabolomics biomarkers to predict acamprosate treatment response in alcohol-dependent subjects.

PubMed

Hinton, David J; Vázquez, Marely Santiago; Geske, Jennifer R; Hitschfeld, Mario J; Ho, Ada M C; Karpyak, Victor M; Biernacka, Joanna M; Choi, Doo-Sup

2017-05-31

Precision medicine for alcohol use disorder (AUD) allows optimal treatment of the right patient with the right drug at the right time. Here, we generated multivariable models incorporating clinical information and serum metabolite levels to predict acamprosate treatment response. The sample of 120 patients was randomly split into a training set (n = 80) and test set (n = 40) five independent times. Treatment response was defined as complete abstinence (no alcohol consumption during 3 months of acamprosate treatment) while nonresponse was defined as any alcohol consumption during this period. In each of the five training sets, we built a predictive model using a least absolute shrinkage and section operator (LASSO) penalized selection method and then evaluated the predictive performance of each model in the corresponding test set. The models predicted acamprosate treatment response with a mean sensitivity and specificity in the test sets of 0.83 and 0.31, respectively, suggesting our model performed well at predicting responders, but not non-responders (i.e. many non-responders were predicted to respond). Studies with larger sample sizes and additional biomarkers will expand the clinical utility of predictive algorithms for pharmaceutical response in AUD.

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

Boutilier, Justin J., E-mail: j.boutilier@mail.utoronto.ca; Lee, Taewoo; Craig, Tim

Purpose: To develop and evaluate the clinical applicability of advanced machine learning models that simultaneously predict multiple optimization objective function weights from patient geometry for intensity-modulated radiation therapy of prostate cancer. Methods: A previously developed inverse optimization method was applied retrospectively to determine optimal objective function weights for 315 treated patients. The authors used an overlap volume ratio (OV) of bladder and rectum for different PTV expansions and overlap volume histogram slopes (OVSR and OVSB for the rectum and bladder, respectively) as explanatory variables that quantify patient geometry. Using the optimal weights as ground truth, the authors trained and appliedmore » three prediction models: logistic regression (LR), multinomial logistic regression (MLR), and weighted K-nearest neighbor (KNN). The population average of the optimal objective function weights was also calculated. Results: The OV at 0.4 cm and OVSR at 0.1 cm features were found to be the most predictive of the weights. The authors observed comparable performance (i.e., no statistically significant difference) between LR, MLR, and KNN methodologies, with LR appearing to perform the best. All three machine learning models outperformed the population average by a statistically significant amount over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and dose to the bladder, rectum, CTV, and PTV. When comparing the weights directly, the LR model predicted bladder and rectum weights that had, on average, a 73% and 74% relative improvement over the population average weights, respectively. The treatment plans resulting from the LR weights had, on average, a rectum V70Gy that was 35% closer to the clinical plan and a bladder V70Gy that was 29% closer, compared to the population average weights. Similar results were observed for all other clinical metrics. Conclusions: The authors demonstrated that the KNN and MLR weight prediction methodologies perform comparably to the LR model and can produce clinical quality treatment plans by simultaneously predicting multiple weights that capture trade-offs associated with sparing multiple OARs.« less

Model-Based Individualized Treatment of Chemotherapeutics: Bayesian Population Modeling and Dose Optimization

PubMed Central

Jayachandran, Devaraj; Laínez-Aguirre, José; Rundell, Ann; Vik, Terry; Hannemann, Robert; Reklaitis, Gintaras; Ramkrishna, Doraiswami

2015-01-01

6-Mercaptopurine (6-MP) is one of the key drugs in the treatment of many pediatric cancers, auto immune diseases and inflammatory bowel disease. 6-MP is a prodrug, converted to an active metabolite 6-thioguanine nucleotide (6-TGN) through enzymatic reaction involving thiopurine methyltransferase (TPMT). Pharmacogenomic variation observed in the TPMT enzyme produces a significant variation in drug response among the patient population. Despite 6-MP’s widespread use and observed variation in treatment response, efforts at quantitative optimization of dose regimens for individual patients are limited. In addition, research efforts devoted on pharmacogenomics to predict clinical responses are proving far from ideal. In this work, we present a Bayesian population modeling approach to develop a pharmacological model for 6-MP metabolism in humans. In the face of scarcity of data in clinical settings, a global sensitivity analysis based model reduction approach is used to minimize the parameter space. For accurate estimation of sensitive parameters, robust optimal experimental design based on D-optimality criteria was exploited. With the patient-specific model, a model predictive control algorithm is used to optimize the dose scheduling with the objective of maintaining the 6-TGN concentration within its therapeutic window. More importantly, for the first time, we show how the incorporation of information from different levels of biological chain-of response (i.e. gene expression-enzyme phenotype-drug phenotype) plays a critical role in determining the uncertainty in predicting therapeutic target. The model and the control approach can be utilized in the clinical setting to individualize 6-MP dosing based on the patient’s ability to metabolize the drug instead of the traditional standard-dose-for-all approach. PMID:26226448

Do treatment quality indicators predict cardiovascular outcomes in patients with diabetes?

PubMed

Sidorenkov, Grigory; Voorham, Jaco; de Zeeuw, Dick; Haaijer-Ruskamp, Flora M; Denig, Petra

2013-01-01

Landmark clinical trials have led to optimal treatment recommendations for patients with diabetes. Whether optimal treatment is actually delivered in practice is even more important than the efficacy of the drugs tested in trials. To this end, treatment quality indicators have been developed and tested against intermediate outcomes. No studies have tested whether these treatment quality indicators also predict hard patient outcomes. A cohort study was conducted using data collected from >10.000 diabetes patients in the Groningen Initiative to Analyze Type 2 Treatment (GIANTT) database and Dutch Hospital Data register. Included quality indicators measured glucose-, lipid-, blood pressure- and albuminuria-lowering treatment status and treatment intensification. Hard patient outcome was the composite of cardiovascular events and all-cause death. Associations were tested using Cox regression adjusting for confounding, reporting hazard ratios (HR) with 95% confidence intervals. Lipid and albuminuria treatment status, but not blood pressure lowering treatment status, were associated with the composite outcome (HR = 0.77, 0.67-0.88; HR = 0.75, 0.59-0.94). Glucose lowering treatment status was associated with the composite outcome only in patients with an elevated HbA1c level (HR = 0.72, 0.56-0.93). Treatment intensification with glucose-lowering but not with lipid-, blood pressure- and albuminuria-lowering drugs was associated with the outcome (HR = 0.73, 0.60-0.89). Treatment quality indicators measuring lipid- and albuminuria-lowering treatment status are valid quality measures, since they predict a lower risk of cardiovascular events and mortality in patients with diabetes. The quality indicators for glucose-lowering treatment should only be used for restricted populations with elevated HbA1c levels. Intriguingly, the tested indicators for blood pressure-lowering treatment did not predict patient outcomes. These results question whether all treatment indicators are valid measures to judge quality of health care and its economics.

Specific attitudes which predict psychology students' intentions to seek help for psychological distress.

PubMed

Thomas, Susan J; Caputi, Peter; Wilson, Coralie J

2014-03-01

Although many postgraduate psychology programs address students' mental health, there are compelling indications that earlier, undergraduate, interventions may be optimal. We investigated specific attitudes that predict students' intentions to seek treatment for psychological distress to inform targeted interventions. Psychology students (N = 289; mean age = 19.75 years) were surveyed about attitudes and intentions to seek treatment for stress, anxiety, or depression. Less than one quarter of students reported that they would be likely to seek treatment should they develop psychological distress. Attitudes that predicted help-seeking intentions related to recognition of symptoms and the benefits of professional help, and openness to treatment for emotional problems. The current study identified specific attitudes which predict help-seeking intentions in psychology students. These attitudes could be strengthened in undergraduate educational interventions promoting well-being and appropriate treatment uptake among psychology students. © 2013 Wiley Periodicals, Inc.

Causes and Management of Treatment-Resistant Panic Disorder and Agoraphobia: A Survey of Expert Therapists

ERIC Educational Resources Information Center

Sanderson, William C.; Bruce, Timothy J.

2007-01-01

Cognitive behavior therapy (CBT) is recognized as an effective psychological treatment for panic disorder (PD). Despite its efficacy, some clients do not respond optimally to this treatment. Unfortunately, literatures on the prediction, prevention, and management of suboptimal response are not well developed. Considering this lack of empirical…

Design of optimal hyperthermia protocols for prostate cancer by controlling HSP expression through computer modeling (Invited Paper)

NASA Astrophysics Data System (ADS)

Rylander, Marissa N.; Feng, Yusheng; Diller, Kenneth; Bass, J.

2005-04-01

Heat shock proteins (HSP) are critical components of a complex defense mechanism essential for preserving cell survival under adverse environmental conditions. It is inevitable that hyperthermia will enhance tumor tissue viability, due to HSP expression in regions where temperatures are insufficient to coagulate proteins, and would likely increase the probability of cancer recurrence. Although hyperthermia therapy is commonly used in conjunction with radiotherapy, chemotherapy, and gene therapy to increase therapeutic effectiveness, the efficacy of these therapies can be substantially hindered due to HSP expression when hyperthermia is applied prior to these procedures. Therefore, in planning hyperthermia protocols, prediction of the HSP response of the tumor must be incorporated into the treatment plan to optimize the thermal dose delivery and permit prediction of overall tissue response. In this paper, we present a highly accurate, adaptive, finite element tumor model capable of predicting the HSP expression distribution and tissue damage region based on measured cellular data when hyperthermia protocols are specified. Cubic spline representations of HSP27 and HSP70, and Arrhenius damage models were integrated into the finite element model to enable prediction of the HSP expression and damage distribution in the tissue following laser heating. Application of the model can enable optimized treatment planning by controlling of the tissue response to therapy based on accurate prediction of the HSP expression and cell damage distribution.

Inflammatory and Other Biomarkers: Role in Pathophysiology and Prediction of Gestational Diabetes Mellitus

PubMed Central

Abell, Sally K.; De Courten, Barbora; Boyle, Jacqueline A.; Teede, Helena J.

2015-01-01

Understanding pathophysiology and identifying mothers at risk of major pregnancy complications is vital to effective prevention and optimal management. However, in current antenatal care, understanding of pathophysiology of complications is limited. In gestational diabetes mellitus (GDM), risk prediction is mostly based on maternal history and clinical risk factors and may not optimally identify high risk pregnancies. Hence, universal screening is widely recommended. Here, we will explore the literature on GDM and biomarkers including inflammatory markers, adipokines, endothelial function and lipids to advance understanding of pathophysiology and explore risk prediction, with a goal to guide prevention and treatment of GDM. PMID:26110385

Feasibility of predicting tumor motion using online data acquired during treatment and a generalized neural network optimized with offline patient tumor trajectories.

PubMed

Teo, Troy P; Ahmed, Syed Bilal; Kawalec, Philip; Alayoubi, Nadia; Bruce, Neil; Lyn, Ethan; Pistorius, Stephen

2018-02-01

The accurate prediction of intrafraction lung tumor motion is required to compensate for system latency in image-guided adaptive radiotherapy systems. The goal of this study was to identify an optimal prediction model that has a short learning period so that prediction and adaptation can commence soon after treatment begins, and requires minimal reoptimization for individual patients. Specifically, the feasibility of predicting tumor position using a combination of a generalized (i.e., averaged) neural network, optimized using historical patient data (i.e., tumor trajectories) obtained offline, coupled with the use of real-time online tumor positions (obtained during treatment delivery) was examined. A 3-layer perceptron neural network was implemented to predict tumor motion for a prediction horizon of 650 ms. A backpropagation algorithm and batch gradient descent approach were used to train the model. Twenty-seven 1-min lung tumor motion samples (selected from a CyberKnife patient dataset) were sampled at a rate of 7.5 Hz (0.133 s) to emulate the frame rate of an electronic portal imaging device (EPID). A sliding temporal window was used to sample the data for learning. The sliding window length was set to be equivalent to the first breathing cycle detected from each trajectory. Performing a parametric sweep, an averaged error surface of mean square errors (MSE) was obtained from the prediction responses of seven trajectories used for the training of the model (Group 1). An optimal input data size and number of hidden neurons were selected to represent the generalized model. To evaluate the prediction performance of the generalized model on unseen data, twenty tumor traces (Group 2) that were not involved in the training of the model were used for the leave-one-out cross-validation purposes. An input data size of 35 samples (4.6 s) and 20 hidden neurons were selected for the generalized neural network. An average sliding window length of 28 data samples was used. The average initial learning period prior to the availability of the first predicted tumor position was 8.53 ± 1.03 s. Average mean absolute error (MAE) of 0.59 ± 0.13 mm and 0.56 ± 0.18 mm were obtained from Groups 1 and 2, respectively, giving an overall MAE of 0.57 ± 0.17 mm. Average root-mean-square-error (RMSE) of 0.67 ± 0.36 for all the traces (0.76 ± 0.34 mm, Group 1 and 0.63 ± 0.36 mm, Group 2), is comparable to previously published results. Prediction errors are mainly due to the irregular periodicities between cycles. Since the errors from Groups 1 and 2 are within the same range, it demonstrates that this model can generalize and predict on unseen data. This is a first attempt to use an averaged MSE error surface (obtained from the prediction of different patients' tumor trajectories) to determine the parameters of a generalized neural network. This network could be deployed as a plug-and-play predictor for tumor trajectory during treatment delivery, eliminating the need for optimizing individual networks with pretreatment patient data. © 2017 American Association of Physicists in Medicine.

How outcome prediction could affect patient decision making in knee replacements: a qualitative study.

PubMed

Barlow, Timothy; Scott, Patricia; Griffin, Damian; Realpe, Alba

2016-07-22

There is approximately a 17 % dissatisfaction rate with knee replacements. Calls for tools that can pre-operatively identify patients at risk of being dissatisfied have been widespread. However, it is not known how to present such information to patients, how it would affect their decision making process, and at what part of the pathway such a tool should be used. Using focus groups involving 12 participants and in-depth interviews with 10 participants, we examined how individual predictions of outcome could affect patients' decision making by providing fictitious predictions to patients at different stages of treatment. A thematic analysis was used to analyse the data. Our results demonstrate several interesting findings. Firstly, patients who have received information from friends and family are unwilling to adjust their expectation of outcome down (i.e. to a worse outcome), but highly willing to adjust it up (to a better outcome). This is an example of the optimism bias, and suggests that the effect on expectation of a poor outcome prediction would be blunted. Secondly, patients generally wanted a "bottom line" outcome, rather than lots of detail. Thirdly, patients who were earlier in their treatment for osteoarthritis were more likely to find the information useful, and it was more likely to affect their decision, than patients later in their treatment pathway. This research suggest that an outcome prediction tool would have most effect targeted towards people at the start of their treatment pathway, with a "bottom line" prediction of outcome. However, any effect on expectation and decision making of a poor outcome prediction is likely to be blunted by the optimism bias. These findings merit replication in a larger sample size.

A Complete Procedure for Predicting and Improving the Performance of HAWT's

NASA Astrophysics Data System (ADS)

Al-Abadi, Ali; Ertunç, Özgür; Sittig, Florian; Delgado, Antonio

2014-06-01

A complete procedure for predicting and improving the performance of the horizontal axis wind turbine (HAWT) has been developed. The first process is predicting the power extracted by the turbine and the derived rotor torque, which should be identical to that of the drive unit. The BEM method and a developed post-stall treatment for resolving stall-regulated HAWT is incorporated in the prediction. For that, a modified stall-regulated prediction model, which can predict the HAWT performance over the operating range of oncoming wind velocity, is derived from existing models. The model involves radius and chord, which has made it more general in applications for predicting the performance of different scales and rotor shapes of HAWTs. The second process is modifying the rotor shape by an optimization process, which can be applied to any existing HAWT, to improve its performance. A gradient- based optimization is used for adjusting the chord and twist angle distribution of the rotor blade to increase the extraction of the power while keeping the drive torque constant, thus the same drive unit can be kept. The final process is testing the modified turbine to predict its enhanced performance. The procedure is applied to NREL phase-VI 10kW as a baseline turbine. The study has proven the applicability of the developed model in predicting the performance of the baseline as well as the optimized turbine. In addition, the optimization method has shown that the power coefficient can be increased while keeping same design rotational speed.

Optimizing SGLT inhibitor treatment for diabetes with chronic kidney diseases.

PubMed

Layton, Anita T

2018-06-28

Diabetes induces glomerular hyperfiltration, affects kidney function, and may lead to chronic kidney diseases. A novel therapeutic treatment for diabetic patients targets the sodium-glucose cotransporter isoform 2 (SGLT2) in the kidney. SGLT2 inhibitors enhance urinary glucose, [Formula: see text] and fluid excretion and lower hyperglycemia in diabetes by inhibiting [Formula: see text] and glucose reabsorption along the proximal convoluted tubule. A goal of this study is to predict the effects of SGLT2 inhibitors in diabetic patients with and without chronic kidney diseases. To that end, we applied computational rat kidney models to assess how SGLT2 inhibition affects renal solute transport and metabolism when nephron population are normal or reduced (the latter simulates chronic kidney disease). The model predicts that SGLT2 inhibition induces glucosuria and natriuresis, with those effects enhanced in a remnant kidney. The model also predicts that the [Formula: see text] transport load and thus oxygen consumption of the S3 segment are increased under SGLT2 inhibition, a consequence that may increase the risk of hypoxia for that segment. To protect the vulnerable S3 segment, we explore dual SGLT2/SGLT1 inhibition and seek to determine the optimal combination that would yield sufficient urinary glucose excretion while limiting the metabolic load on the S3 segment. The model predicts that the optimal combination of SGLT2/SGLT1 inhibition lowers the oxygen requirements of key tubular segments, but decreases urine flow and [Formula: see text] excretion; the latter effect may limit the cardiovascular protection of the treatment.

SU-F-BRD-01: A Logistic Regression Model to Predict Objective Function Weights in Prostate Cancer IMRT

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

Boutilier, J; Chan, T; Lee, T

2014-06-15

Purpose: To develop a statistical model that predicts optimization objective function weights from patient geometry for intensity-modulation radiotherapy (IMRT) of prostate cancer. Methods: A previously developed inverse optimization method (IOM) is applied retrospectively to determine optimal weights for 51 treated patients. We use an overlap volume ratio (OVR) of bladder and rectum for different PTV expansions in order to quantify patient geometry in explanatory variables. Using the optimal weights as ground truth, we develop and train a logistic regression (LR) model to predict the rectum weight and thus the bladder weight. Post hoc, we fix the weights of the leftmore » femoral head, right femoral head, and an artificial structure that encourages conformity to the population average while normalizing the bladder and rectum weights accordingly. The population average of objective function weights is used for comparison. Results: The OVR at 0.7cm was found to be the most predictive of the rectum weights. The LR model performance is statistically significant when compared to the population average over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and mean voxel dose to the bladder, rectum, CTV, and PTV. On average, the LR model predicted bladder and rectum weights that are both 63% closer to the optimal weights compared to the population average. The treatment plans resulting from the LR weights have, on average, a rectum V70Gy that is 35% closer to the clinical plan and a bladder V70Gy that is 43% closer. Similar results are seen for bladder V54Gy and rectum V54Gy. Conclusion: Statistical modelling from patient anatomy can be used to determine objective function weights in IMRT for prostate cancer. Our method allows the treatment planners to begin the personalization process from an informed starting point, which may lead to more consistent clinical plans and reduce overall planning time.« less

Robust optimization based upon statistical theory.

PubMed

Sobotta, B; Söhn, M; Alber, M

2010-08-01

Organ movement is still the biggest challenge in cancer treatment despite advances in online imaging. Due to the resulting geometric uncertainties, the delivered dose cannot be predicted precisely at treatment planning time. Consequently, all associated dose metrics (e.g., EUD and maxDose) are random variables with a patient-specific probability distribution. The method that the authors propose makes these distributions the basis of the optimization and evaluation process. The authors start from a model of motion derived from patient-specific imaging. On a multitude of geometry instances sampled from this model, a dose metric is evaluated. The resulting pdf of this dose metric is termed outcome distribution. The approach optimizes the shape of the outcome distribution based on its mean and variance. This is in contrast to the conventional optimization of a nominal value (e.g., PTV EUD) computed on a single geometry instance. The mean and variance allow for an estimate of the expected treatment outcome along with the residual uncertainty. Besides being applicable to the target, the proposed method also seamlessly includes the organs at risk (OARs). The likelihood that a given value of a metric is reached in the treatment is predicted quantitatively. This information reveals potential hazards that may occur during the course of the treatment, thus helping the expert to find the right balance between the risk of insufficient normal tissue sparing and the risk of insufficient tumor control. By feeding this information to the optimizer, outcome distributions can be obtained where the probability of exceeding a given OAR maximum and that of falling short of a given target goal can be minimized simultaneously. The method is applicable to any source of residual motion uncertainty in treatment delivery. Any model that quantifies organ movement and deformation in terms of probability distributions can be used as basis for the algorithm. Thus, it can generate dose distributions that are robust against interfraction and intrafraction motion alike, effectively removing the need for indiscriminate safety margins.

Effects of temperature on the life history parameters of Anoplophora Glabripennis (Coleoptera: Cerambycidae)

Treesearch

Melody A. Keena; Paul M. Moore; Steve M. Ulanecki

2003-01-01

There is a critical need for information on the basic biology of the Asian longhorned beetle, Anoplophora glabripennis (Motschulsky), to provide the biological basis for predicting developmental phenology in order to optimize the timing of exclusion and eradication treatments and to predict attack rates under different environmental conditions. In...

Functional Recovery in Major Depressive Disorder: Focus on Early Optimized Treatment.

PubMed

Habert, Jeffrey; Katzman, Martin A; Oluboka, Oloruntoba J; McIntyre, Roger S; McIntosh, Diane; MacQueen, Glenda M; Khullar, Atul; Milev, Roumen V; Kjernisted, Kevin D; Chokka, Pratap R; Kennedy, Sidney H

2016-09-01

This article presents the case that a more rapid, individualized approach to treating major depressive disorder (MDD) may increase the likelihood of achieving full symptomatic and functional recovery for individual patients and that studies show it is possible to make earlier decisions about appropriateness of treatment in order to rapidly optimize that treatment. A PubMed search was conducted using terms including major depressive disorder, early improvement, predictor, duration of untreated illness, and function. English-language articles published before September 2015 were included. Additional studies were found within identified research articles and reviews. Thirty antidepressant studies reporting predictor criteria and outcome measures are included in this review. Studies were reviewed to extract definitions of predictors, outcome measures, and results of the predictor analysis. Results were summarized separately for studies reporting effects of early improvement, baseline characteristics, and duration of untreated depression. Shorter duration of the current depressive episode and duration of untreated depression are associated with better symptomatic and functional outcomes in MDD. Early improvement of depressive symptoms predicts positive symptomatic outcomes (response and remission), and early functional improvement predicts an increased likelihood of functional remission. The approach to treatment of depression that exhibits the greatest potential for achieving full symptomatic and functional recovery is early optimized treatment: early diagnosis followed by rapid individualized treatment. Monitoring symptoms and function early in treatment is crucial to ensuring that patients do not remain on ineffective or poorly tolerated treatment, which may delay recovery and heighten the risk of residual functional deficits. © Copyright 2016 Physicians Postgraduate Press, Inc.

SU-F-J-06: Optimized Patient Inclusion for NaF PET Response-Based Biopsies

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

Roth, A; Harmon, S; Perk, T

Purpose: A method to guide mid-treatment biopsies using quantitative [F-18]NaF PET/CT response is being investigated in a clinical trial. This study aims to develop methodology to identify patients amenable to mid-treatment biopsy based on pre-treatment imaging characteristics. Methods: 35 metastatic prostate cancer patients had NaF PET/CT scans taken prior to the start of treatment and 9–12 weeks into treatment. For mid-treatment biopsy targeting, lesions must be at least 1.5 cm{sup 3} and located in a clinically feasible region (lumbar/sacral spine, pelvis, humerus, or femur). Three methods were developed based on number of lesions present prior to treatment: a feasibility-restricted method,more » a location-restricted method, and an unrestricted method. The feasibility restricted method only utilizes information from lesions meeting biopsy requirements in the pre-treatment scan. The unrestricted method accounts for all lesions present in the pre-treatment scan. For each method, optimized classification cutoffs for candidate patients were determined. Results: 13 of the 35 patients had enough lesions at the mid-treatment for biopsy candidacy. Of 1749 lesions identified in all 35 patients at mid-treatment, only 9.8% were amenable to biopsy. Optimizing the feasibility-restricted method required 4 lesions at pre-treatment meeting volume and region requirements for biopsy, resulting patient identification sensitivity of 0.8 and specificity of 0.7. Of 6 false positive patients, only one patient lacked lesions for biopsy. Restricting for location alone showed poor results (sensitivity 0.2 and specificity 0.3). The optimized unrestricted method required patients have at least 37 lesions in pretreatment scan, resulting in a sensitivity of 0.8 and specificity of 0.8. There were 5 false positives, only one lacked lesions for biopsy. Conclusion: Incorporating the overall pre-treatment number of NaF PET/CT identified lesions provided best prediction for identifying candidate patients for mid-treatment biopsy. This study provides validity for prediction-based inclusion criteria that can be extended to various clinical trial scenarios. Funded by Prostate Cancer Foundation.« less

Performance analysis and optimization of an advanced pharmaceutical wastewater treatment plant through a visual basic software tool (PWWT.VB).

PubMed

Pal, Parimal; Thakura, Ritwik; Chakrabortty, Sankha

2016-05-01

A user-friendly, menu-driven simulation software tool has been developed for the first time to optimize and analyze the system performance of an advanced continuous membrane-integrated pharmaceutical wastewater treatment plant. The software allows pre-analysis and manipulation of input data which helps in optimization and shows the software performance visually on a graphical platform. Moreover, the software helps the user to "visualize" the effects of the operating parameters through its model-predicted output profiles. The software is based on a dynamic mathematical model, developed for a systematically integrated forward osmosis-nanofiltration process for removal of toxic organic compounds from pharmaceutical wastewater. The model-predicted values have been observed to corroborate well with the extensive experimental investigations which were found to be consistent under varying operating conditions like operating pressure, operating flow rate, and draw solute concentration. Low values of the relative error (RE = 0.09) and high values of Willmott-d-index (d will = 0.981) reflected a high degree of accuracy and reliability of the software. This software is likely to be a very efficient tool for system design or simulation of an advanced membrane-integrated treatment plant for hazardous wastewater.

Computer-Aided Design/Computer-Assisted Manufacture Monolithic Restorations for Severely Worn Dentition: A Case History Report.

PubMed

Abou-Ayash, Samir; Boldt, Johannes; Vuck, Alexander

Full-arch rehabilitation of patients with severe tooth wear due to parafunctional behavior is a challenge for dentists and dental technicians, especially when a highly esthetic outcome is desired. A variety of different treatment options and prosthetic materials are available for such a clinical undertaking. The ongoing progress of computer-aided design/computer-assisted manufacture technologies in combination with all-ceramic materials provides a predictable workflow for these complex cases. This case history report describes a comprehensive, step-by-step treatment protocol leading to an optimally predictable treatment outcome for an esthetically compromised patient.

The comparative oncologic effectiveness of available management strategies for clinically localized prostate cancer.

PubMed

Tyson, Mark D; Penson, David F; Resnick, Matthew J

2017-02-01

The primary goal of modern prostate cancer treatment paradigms is to optimize the balance of predicted benefits associated with prostate cancer treatment against the predicted harms of therapy. However, given the limitations in the existing evidence as well as the significant tradeoffs posed by each treatment, there remain myriad challenges associated with individualized prostate cancer treatment decision-making. In this review, we summarize the existing comparative effectiveness evidence of treatments for localized prostate cancer with an emphasis on oncologic control. While we focus on the major treatment categories of radical prostatectomy, radiation therapy, and observation, we also provide a review of emerging therapies such as cryotherapy and high-intensity frequency ultrasound (HIFU). Copyright © 2017 Elsevier Inc. All rights reserved.

A stochastic model for optimizing composite predictors based on gene expression profiles.

PubMed

Ramanathan, Murali

2003-07-01

This project was done to develop a mathematical model for optimizing composite predictors based on gene expression profiles from DNA arrays and proteomics. The problem was amenable to a formulation and solution analogous to the portfolio optimization problem in mathematical finance: it requires the optimization of a quadratic function subject to linear constraints. The performance of the approach was compared to that of neighborhood analysis using a data set containing cDNA array-derived gene expression profiles from 14 multiple sclerosis patients receiving intramuscular inteferon-beta1a. The Markowitz portfolio model predicts that the covariance between genes can be exploited to construct an efficient composite. The model predicts that a composite is not needed for maximizing the mean value of a treatment effect: only a single gene is needed, but the usefulness of the effect measure may be compromised by high variability. The model optimized the composite to yield the highest mean for a given level of variability or the least variability for a given mean level. The choices that meet this optimization criteria lie on a curve of composite mean vs. composite variability plot referred to as the "efficient frontier." When a composite is constructed using the model, it outperforms the composite constructed using the neighborhood analysis method. The Markowitz portfolio model may find potential applications in constructing composite biomarkers and in the pharmacogenomic modeling of treatment effects derived from gene expression endpoints.

2018 update to the HIV-TRePS system: the development of new computational models to predict HIV treatment outcomes, with or without a genotype, with enhanced usability for low-income settings.

PubMed

Revell, Andrew D; Wang, Dechao; Perez-Elias, Maria-Jesus; Wood, Robin; Cogill, Dolphina; Tempelman, Hugo; Hamers, Raph L; Reiss, Peter; van Sighem, Ard I; Rehm, Catherine A; Pozniak, Anton; Montaner, Julio S G; Lane, H Clifford; Larder, Brendan A

2018-06-08

Optimizing antiretroviral drug combination on an individual basis can be challenging, particularly in settings with limited access to drugs and genotypic resistance testing. Here we describe our latest computational models to predict treatment responses, with or without a genotype, and compare their predictive accuracy with that of genotyping. Random forest models were trained to predict the probability of virological response to a new therapy introduced following virological failure using up to 50 000 treatment change episodes (TCEs) without a genotype and 18 000 TCEs including genotypes. Independent data sets were used to evaluate the models. This study tested the effects on model accuracy of relaxing the baseline data timing windows, the use of a new filter to exclude probable non-adherent cases and the addition of maraviroc, tipranavir and elvitegravir to the system. The no-genotype models achieved area under the receiver operator characteristic curve (AUC) values of 0.82 and 0.81 using the standard and relaxed baseline data windows, respectively. The genotype models achieved AUC values of 0.86 with the new non-adherence filter and 0.84 without. Both sets of models were significantly more accurate than genotyping with rules-based interpretation, which achieved AUC values of only 0.55-0.63, and were marginally more accurate than previous models. The models were able to identify alternative regimens that were predicted to be effective for the vast majority of cases in which the new regimen prescribed in the clinic failed. These latest global models predict treatment responses accurately even without a genotype and have the potential to help optimize therapy, particularly in resource-limited settings.

Using Chemical Reaction Kinetics to Predict Optimal Antibiotic Treatment Strategies.

PubMed

Abel Zur Wiesch, Pia; Clarelli, Fabrizio; Cohen, Ted

2017-01-01

Identifying optimal dosing of antibiotics has proven challenging-some antibiotics are most effective when they are administered periodically at high doses, while others work best when minimizing concentration fluctuations. Mechanistic explanations for why antibiotics differ in their optimal dosing are lacking, limiting our ability to predict optimal therapy and leading to long and costly experiments. We use mathematical models that describe both bacterial growth and intracellular antibiotic-target binding to investigate the effects of fluctuating antibiotic concentrations on individual bacterial cells and bacterial populations. We show that physicochemical parameters, e.g. the rate of drug transmembrane diffusion and the antibiotic-target complex half-life are sufficient to explain which treatment strategy is most effective. If the drug-target complex dissociates rapidly, the antibiotic must be kept constantly at a concentration that prevents bacterial replication. If antibiotics cross bacterial cell envelopes slowly to reach their target, there is a delay in the onset of action that may be reduced by increasing initial antibiotic concentration. Finally, slow drug-target dissociation and slow diffusion out of cells act to prolong antibiotic effects, thereby allowing for less frequent dosing. Our model can be used as a tool in the rational design of treatment for bacterial infections. It is easily adaptable to other biological systems, e.g. HIV, malaria and cancer, where the effects of physiological fluctuations of drug concentration are also poorly understood.

Using Chemical Reaction Kinetics to Predict Optimal Antibiotic Treatment Strategies

PubMed Central

Abel zur Wiesch, Pia; Cohen, Ted

2017-01-01

Identifying optimal dosing of antibiotics has proven challenging—some antibiotics are most effective when they are administered periodically at high doses, while others work best when minimizing concentration fluctuations. Mechanistic explanations for why antibiotics differ in their optimal dosing are lacking, limiting our ability to predict optimal therapy and leading to long and costly experiments. We use mathematical models that describe both bacterial growth and intracellular antibiotic-target binding to investigate the effects of fluctuating antibiotic concentrations on individual bacterial cells and bacterial populations. We show that physicochemical parameters, e.g. the rate of drug transmembrane diffusion and the antibiotic-target complex half-life are sufficient to explain which treatment strategy is most effective. If the drug-target complex dissociates rapidly, the antibiotic must be kept constantly at a concentration that prevents bacterial replication. If antibiotics cross bacterial cell envelopes slowly to reach their target, there is a delay in the onset of action that may be reduced by increasing initial antibiotic concentration. Finally, slow drug-target dissociation and slow diffusion out of cells act to prolong antibiotic effects, thereby allowing for less frequent dosing. Our model can be used as a tool in the rational design of treatment for bacterial infections. It is easily adaptable to other biological systems, e.g. HIV, malaria and cancer, where the effects of physiological fluctuations of drug concentration are also poorly understood. PMID:28060813

WE-B-304-02: Treatment Planning Evaluation and Optimization Should Be Biologically and Not Dose/volume Based

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

Deasy, J.

The ultimate goal of radiotherapy treatment planning is to find a treatment that will yield a high tumor control probability (TCP) with an acceptable normal tissue complication probability (NTCP). Yet most treatment planning today is not based upon optimization of TCPs and NTCPs, but rather upon meeting physical dose and volume constraints defined by the planner. It has been suggested that treatment planning evaluation and optimization would be more effective if they were biologically and not dose/volume based, and this is the claim debated in this month’s Point/Counterpoint. After a brief overview of biologically and DVH based treatment planning bymore » the Moderator Colin Orton, Joseph Deasy (for biological planning) and Charles Mayo (against biological planning) will begin the debate. Some of the arguments in support of biological planning include: this will result in more effective dose distributions for many patients DVH-based measures of plan quality are known to have little predictive value there is little evidence that either D95 or D98 of the PTV is a good predictor of tumor control sufficient validated outcome prediction models are now becoming available and should be used to drive planning and optimization Some of the arguments against biological planning include: several decades of experience with DVH-based planning should not be discarded we do not know enough about the reliability and errors associated with biological models the radiotherapy community in general has little direct experience with side by side comparisons of DVH vs biological metrics and outcomes it is unlikely that a clinician would accept extremely cold regions in a CTV or hot regions in a PTV, despite having acceptable TCP values Learning Objectives: To understand dose/volume based treatment planning and its potential limitations To understand biological metrics such as EUD, TCP, and NTCP To understand biologically based treatment planning and its potential limitations.« less

WE-B-304-01: Treatment Planning Evaluation and Optimization Should Be Dose/volume and Not Biologically Based

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

Mayo, C.

The ultimate goal of radiotherapy treatment planning is to find a treatment that will yield a high tumor control probability (TCP) with an acceptable normal tissue complication probability (NTCP). Yet most treatment planning today is not based upon optimization of TCPs and NTCPs, but rather upon meeting physical dose and volume constraints defined by the planner. It has been suggested that treatment planning evaluation and optimization would be more effective if they were biologically and not dose/volume based, and this is the claim debated in this month’s Point/Counterpoint. After a brief overview of biologically and DVH based treatment planning bymore » the Moderator Colin Orton, Joseph Deasy (for biological planning) and Charles Mayo (against biological planning) will begin the debate. Some of the arguments in support of biological planning include: this will result in more effective dose distributions for many patients DVH-based measures of plan quality are known to have little predictive value there is little evidence that either D95 or D98 of the PTV is a good predictor of tumor control sufficient validated outcome prediction models are now becoming available and should be used to drive planning and optimization Some of the arguments against biological planning include: several decades of experience with DVH-based planning should not be discarded we do not know enough about the reliability and errors associated with biological models the radiotherapy community in general has little direct experience with side by side comparisons of DVH vs biological metrics and outcomes it is unlikely that a clinician would accept extremely cold regions in a CTV or hot regions in a PTV, despite having acceptable TCP values Learning Objectives: To understand dose/volume based treatment planning and its potential limitations To understand biological metrics such as EUD, TCP, and NTCP To understand biologically based treatment planning and its potential limitations.« less

Accumulating Data to Optimally Predict Obesity Treatment (ADOPT): Recommendations from the Biological Domain.

PubMed

Rosenbaum, Michael; Agurs-Collins, Tanya; Bray, Molly S; Hall, Kevin D; Hopkins, Mark; Laughlin, Maren; MacLean, Paul S; Maruvada, Padma; Savage, Cary R; Small, Dana M; Stoeckel, Luke

2018-04-01

The responses to behavioral, pharmacological, or surgical obesity treatments are highly individualized. The Accumulating Data to Optimally Predict obesity Treatment (ADOPT) project provides a framework for how obesity researchers, working collectively, can generate the evidence base needed to guide the development of tailored, and potentially more effective, strategies for obesity treatment. The objective of the ADOPT biological domain subgroup is to create a list of high-priority biological measures for weight-loss studies that will advance the understanding of individual variability in response to adult obesity treatments. This list includes measures of body composition, energy homeostasis (energy intake and output), brain structure and function, and biomarkers, as well as biobanking procedures, which could feasibly be included in most, if not all, studies of obesity treatment. The recommended high-priority measures are selected to balance needs for sensitivity, specificity, and/or comprehensiveness with feasibility to achieve a commonality of usage and increase the breadth and impact of obesity research. The accumulation of data on key biological factors, along with behavioral, psychosocial, and environmental factors, can generate a more precise description of the interplay and synergy among them and their impact on treatment responses, which can ultimately inform the design and delivery of effective, tailored obesity treatments. © 2018 The Obesity Society.

Optimization of physico-chemical properties of gelatin extracted from fish skin of rainbow trout (Onchorhynchus mykiss).

PubMed

Tabarestani, H Shahiri; Maghsoudlou, Y; Motamedzadegan, A; Mahoonak, A R Sadeghi

2010-08-01

Physico-chemical properties of gelatin extracted from rainbow trout (Onchorhynchus mykiss) skin were optimized using response surface methodology (RSM). Central rotatable composite design was applied to study the combined effects of NaOH concentration (0.01-0.21 N), acetic acid concentration (0.01-0.21 N) and pre-treatment time (1-3h) on yield, molecular weight distribution, gel strength, viscosity and melting point of gelatin. Regression models were developed to predict the variables. Predict values of multiple response at optimal condition were that yield=9.36%, alpha(1)/alpha(2) chain ratio=1.76, beta chain percent=32.81, gel strength=459 g, viscosity=3.2 mPa s and melting point=20.4 degrees C. The optimal condition was obtained using 0.19 N NaOH and 0.121 N acetic acid for 3h. The results showed that the concentration of H(+) during pre-treatment had significant effect on molecular weight distribution, melting point and gel strength. The concentration of OH(-) had significant effect on viscosity and for extraction yield, pretreatment time was the critical factor. (c) 2010 Elsevier Ltd. All rights reserved.

Self-reported physical health of inmates: Impact of incarceration and relation to optimism

PubMed Central

Heigel, Caron P.; Stuewig, Jeffrey; Tangney, June P.

2011-01-01

This study investigated the relationship between inmates’ physical health concerns and optimism. Optimism has been consistently associated with physical health in community samples, but little research has examined this potentially malleable variable in an inmate population. This study of 502 male and female jail inmates attempts to bridge this gap. Results showed optimism was negatively associated with physical health concerns upon entry to jail and prior to release or transfer. Additionally, optimism assessed upon entry to jail predicted modest decreases in physical health concerns over incarceration. Results suggest that optimism is a health-related variable that may be beneficial when optimism-increasing components are integrated into treatment. PMID:20339128

SU-D-BRB-02: Combining a Commercial Autoplanning Engine with Database Dose Predictions to Further Improve Plan Quality

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

Robertson, SP; Moore, JA; Hui, X

Purpose: Database dose predictions and a commercial autoplanning engine both improve treatment plan quality in different but complimentary ways. The combination of these planning techniques is hypothesized to further improve plan quality. Methods: Four treatment plans were generated for each of 10 head and neck (HN) and 10 prostate cancer patients, including Plan-A: traditional IMRT optimization using clinically relevant default objectives; Plan-B: traditional IMRT optimization using database dose predictions; Plan-C: autoplanning using default objectives; and Plan-D: autoplanning using database dose predictions. One optimization was used for each planning method. Dose distributions were normalized to 95% of the planning target volumemore » (prostate: 8000 cGy; HN: 7000 cGy). Objectives used in plan optimization and analysis were the larynx (25%, 50%, 90%), left and right parotid glands (50%, 85%), spinal cord (0%, 50%), rectum and bladder (0%, 20%, 50%, 80%), and left and right femoral heads (0%, 70%). Results: All objectives except larynx 25% and 50% resulted in statistically significant differences between plans (Friedman’s χ{sup 2} ≥ 11.2; p ≤ 0.011). Maximum dose to the rectum (Plans A-D: 8328, 8395, 8489, 8537 cGy) and bladder (Plans A-D: 8403, 8448, 8527, 8569 cGy) were significantly increased. All other significant differences reflected a decrease in dose. Plans B-D were significantly different from Plan-A for 3, 17, and 19 objectives, respectively. Plans C-D were also significantly different from Plan-B for 8 and 13 objectives, respectively. In one case (cord 50%), Plan-D provided significantly lower dose than plan C (p = 0.003). Conclusion: Combining database dose predictions with a commercial autoplanning engine resulted in significant plan quality differences for the greatest number of objectives. This translated to plan quality improvements in most cases, although special care may be needed for maximum dose constraints. Further evaluation is warranted in a larger cohort across HN, prostate, and other treatment sites. This work is supported by Philips Radiation Oncology Systems.« less

Mathematical modeling analysis of intratumoral disposition of anticancer agents and drug delivery systems.

PubMed

Popilski, Hen; Stepensky, David

2015-05-01

Solid tumors are characterized by complex morphology. Numerous factors relating to the composition of the cells and tumor stroma, vascularization and drainage of fluids affect the local microenvironment within a specific location inside the tumor. As a result, the intratumoral drug/drug delivery system (DDS) disposition following systemic or local administration is non-homogeneous and its complexity reflects the differences in the local microenvironment. Mathematical models can be used to analyze the intratumoral drug/DDS disposition and pharmacological effects and to assist in choice of optimal anticancer treatment strategies. The mathematical models that have been applied by different research groups to describe the intratumoral disposition of anticancer drugs/DDSs are summarized in this article. The properties of these models and of their suitability for prediction of the drug/DDS intratumoral disposition and pharmacological effects are reviewed. Currently available mathematical models appear to neglect some of the major factors that govern the drug/DDS intratumoral disposition, and apparently possess limited prediction capabilities. More sophisticated and detailed mathematical models and their extensive validation are needed for reliable prediction of different treatment scenarios and for optimization of drug treatment in the individual cancer patients.

Prospective multicenter validation of the Glasgow Blatchford bleeding score in the management of patients with upper gastrointestinal hemorrhage presenting at an emergency department.

PubMed

Aquarius, Michel; Smeets, Fabiënne G M; Konijn, Helena W; Stassen, Patricia M; Keulen, Eric T; Van Deursen, Cees T; Masclee, Ad A M; Keulemans, Yolande C

2015-09-01

The Glasgow Blatchford Bleeding Score (GBS) has been developed to assess the need for treatment in patients with acute upper gastrointestinal hemorrhage (UGIH) presenting at emergency departments (EDs). We aimed (a) to determine the validity of the GBS and Rockall scoring systems for prediction of need for treatment and (b) to identify the optimal cut-off value of the GBS. We carried out a population-based, prospective multicenter study of 520 consecutive patients presenting with acute UGIH at EDs of three hospitals. The accuracy of GBS and Rockall scores in predicting the need for treatment (i.e. endoscopic, surgical, or radiological intervention and blood transfusion) was analyzed using receiver operating characteristic curves. Receiver operating characteristic curve analysis showed that the GBS had a good discriminative ability to determine the need for treatment in patients with acute UGIH (area under the curve: 0.88; 95% confidence interval: 0.85-0.91). The GBS was superior to both the clinical Rockall and the full Rockall score in predicting the need for treatment (area under the curve: 0.86 vs. 0.70 vs. 0.77). At a cut-off value of up to 2, the GBS had the optimal combination of sensitivity (99.4%) and specificity (42.4%). The GBS is superior compared with both Rockall scores in predicting the need for treatment in patients with suspected acute UGIH presenting at EDs in the Netherlands. Patients with a GBS of 2 or less form a subgroup of low-risk patients. These low-risk patients are eligible for outpatient management, which might reduce hospital admissions and healthcare costs.

The Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures Project: Rationale and Approach.

PubMed

MacLean, Paul S; Rothman, Alexander J; Nicastro, Holly L; Czajkowski, Susan M; Agurs-Collins, Tanya; Rice, Elise L; Courcoulas, Anita P; Ryan, Donna H; Bessesen, Daniel H; Loria, Catherine M

2018-04-01

Individual variability in response to multiple modalities of obesity treatment is well documented, yet our understanding of why some individuals respond while others do not is limited. The etiology of this variability is multifactorial; however, at present, we lack a comprehensive evidence base to identify which factors or combination of factors influence treatment response. This paper provides an overview and rationale of the Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures Project, which aims to advance the understanding of individual variability in response to adult obesity treatment. This project provides an integrated model for how factors in the behavioral, biological, environmental, and psychosocial domains may influence obesity treatment responses and identify a core set of measures to be used consistently across adult weight-loss trials. This paper provides the foundation for four companion papers that describe the core measures in detail. The accumulation of data on factors across the four ADOPT domains can inform the design and delivery of effective, tailored obesity treatments. ADOPT provides a framework for how obesity researchers can collectively generate this evidence base and is a first step in an ongoing process that can be refined as the science advances. © 2018 The Obesity Society.

Measurement of the main and critical parameters for optimal laser treatment of heart disease

NASA Astrophysics Data System (ADS)

Kabeya, FB; Abrahamse, H.; Karsten, AE

2017-10-01

Laser light is frequently used in the diagnosis and treatment of patients. As in traditional treatments such as medication, bypass surgery, and minimally invasive ways, laser treatment can also fail and present serious side effects. The true reason for laser treatment failure or the side effects thereof, remains unknown. From the literature review conducted, and experimental results generated we conclude that an optimal laser treatment for coronary artery disease (named heart disease) can be obtained if certain critical parameters are correctly measured and understood. These parameters include the laser power, the laser beam profile, the fluence rate, the treatment time, as well as the absorption and scattering coefficients of the target treatment tissue. Therefore, this paper proposes different, accurate methods for the measurement of these critical parameters to determine the optimal laser treatment of heart disease with a minimal risk of side effects. The results from the measurement of absorption and scattering properties can be used in a computer simulation package to predict the fluence rate. The computing technique is a program based on the random number (Monte Carlo) process and probability statistics to track the propagation of photons through a biological tissue.

Clinical utility of therapeutic drug monitoring in biological disease modifying anti-rheumatic drug treatment of rheumatic disorders: a systematic narrative review.

PubMed

Van Herwaarden, Noortje; Van Den Bemt, Bart J F; Wientjes, Maike H M; Kramers, Cornelis; Den Broeder, Alfons A

2017-08-01

Biological Disease Modifying Anti-Rheumatic Drugs (bDMARDs) have improved the treatment outcomes of inflammatory rheumatic diseases including Rheumatoid Arthritis and spondyloarthropathies. Inter-individual variation exists in (maintenance of) response to bDMARDs. Therapeutic Drug Monitoring (TDM) of bDMARDs could potentially help in optimizing treatment for the individual patient. Areas covered: Evidence of clinical utility of TDM in bDMARD treatment is reviewed. Different clinical scenarios will be discussed, including: prediction of response after start of treatment, prediction of response to a next bDMARD in case of treatment failure of the first, prediction of successful dose reduction or discontinuation in case of low disease activity, prediction of response to dose-escalation in case of active disease and prediction of response to bDMARD in case of flare in disease activity. Expert opinion: The limited available evidence does often not report important outcomes for diagnostic studies, such as sensitivity and specificity. In most clinical relevant scenarios, predictive value of serum (anti-) drug levels is absent, therefore the use of TDM of bDMARDs cannot be advocated. Well-designed prospective studies should be done to further investigate the promising scenarios to determine the place of TDM in clinical practice.

Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures: Behavioral Domain.

PubMed

Lytle, Leslie A; Nicastro, Holly L; Roberts, Susan B; Evans, Mary; Jakicic, John M; Laposky, Aaron D; Loria, Catherine M

2018-04-01

The ability to identify and measure behaviors that are related to weight loss and the prevention of weight regain is crucial to understanding the variability in response to obesity treatment and the development of tailored treatments. The overarching goal of the Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures Project is to provide obesity researchers with guidance on a set of constructs and measures that are related to weight control and that span and integrate obesity-related behavioral, biological, environmental, and psychosocial domains. This article describes how the behavioral domain subgroup identified the initial list of high-priority constructs and measures to be included, and it describes practical considerations for assessing the following four behavioral areas: eating, activity, sleep, and self-monitoring of weight. Challenges and considerations for advancing the science related to weight loss and maintenance behaviors are also discussed. Assessing a set of core behavioral measures in combination with those from other ADOPT domains is critical to improve our understanding of individual variability in response to adult obesity treatment. The selection of behavioral measures is based on the current science, although there continues to be much work needed in this field. © 2018 The Obesity Society.

Dynamic analysis and optimal control for a model of hepatitis C with treatment

NASA Astrophysics Data System (ADS)

Zhang, Suxia; Xu, Xiaxia

2017-05-01

A model for hepatitis C is formulated to study the effects of treatment and public concern on HCV transmission dynamics. The stability of equilibria and persistence of the model are analyzed, and an optimal control measure is performed to prevent the spread of HCV with minimal infected individuals and cost. The dynamical analysis reveals that the disease-free equilibrium of the model is asymptotically stable if the basic reproductive number R0 is less than unity. On the other hand, if R0 > 1 , the disease is uniformly persistent. Numerical simulations are conducted to investigate the influence of different vital parameters on R0. For the corresponding optimality system, the optimal solution is discussed by Pontryagin Maximum Principle, and the comparisons of model-predicted consequences with control or not are presented.

Testing a hydraulic trait based model of stomatal control: results from a controlled drought experiment on aspen (Populus tremuloides, Michx.) and ponderosa pine (Pinus ponderosa, Douglas)

NASA Astrophysics Data System (ADS)

Love, D. M.; Venturas, M.; Sperry, J.; Wang, Y.; Anderegg, W.

2017-12-01

Modeling approaches for tree stomatal control often rely on empirical fitting to provide accurate estimates of whole tree transpiration (E) and assimilation (A), which are limited in their predictive power by the data envelope used to calibrate model parameters. Optimization based models hold promise as a means to predict stomatal behavior under novel climate conditions. We designed an experiment to test a hydraulic trait based optimization model, which predicts stomatal conductance from a gain/risk approach. Optimal stomatal conductance is expected to maximize the potential carbon gain by photosynthesis, and minimize the risk to hydraulic transport imposed by cavitation. The modeled risk to the hydraulic network is assessed from cavitation vulnerability curves, a commonly measured physiological trait in woody plant species. Over a growing season garden grown plots of aspen (Populus tremuloides, Michx.) and ponderosa pine (Pinus ponderosa, Douglas) were subjected to three distinct drought treatments (moderate, severe, severe with rehydration) relative to a control plot to test model predictions. Model outputs of predicted E, A, and xylem pressure can be directly compared to both continuous data (whole tree sapflux, soil moisture) and point measurements (leaf level E, A, xylem pressure). The model also predicts levels of whole tree hydraulic impairment expected to increase mortality risk. This threshold is used to estimate survivorship in the drought treatment plots. The model can be run at two scales, either entirely from climate (meteorological inputs, irrigation) or using the physiological measurements as a starting point. These data will be used to study model performance and utility, and aid in developing the model for larger scale applications.

Voxel-based dose prediction with multi-patient atlas selection for automated radiotherapy treatment planning

NASA Astrophysics Data System (ADS)

McIntosh, Chris; Purdie, Thomas G.

2017-01-01

Automating the radiotherapy treatment planning process is a technically challenging problem. The majority of automated approaches have focused on customizing and inferring dose volume objectives to be used in plan optimization. In this work we outline a multi-patient atlas-based dose prediction approach that learns to predict the dose-per-voxel for a novel patient directly from the computed tomography planning scan without the requirement of specifying any objectives. Our method learns to automatically select the most effective atlases for a novel patient, and then map the dose from those atlases onto the novel patient. We extend our previous work to include a conditional random field for the optimization of a joint distribution prior that matches the complementary goals of an accurately spatially distributed dose distribution while still adhering to the desired dose volume histograms. The resulting distribution can then be used for inverse-planning with a new spatial dose objective, or to create typical dose volume objectives for the canonical optimization pipeline. We investigated six treatment sites (633 patients for training and 113 patients for testing) and evaluated the mean absolute difference in all DVHs for the clinical and predicted dose distribution. The results on average are favorable in comparison to our previous approach (1.91 versus 2.57). Comparing our method with and without atlas-selection further validates that atlas-selection improved dose prediction on average in whole breast (0.64 versus 1.59), prostate (2.13 versus 4.07), and rectum (1.46 versus 3.29) while it is less important in breast cavity (0.79 versus 0.92) and lung (1.33 versus 1.27) for which there is high conformity and minimal dose shaping. In CNS brain, atlas-selection has the potential to be impactful (3.65 versus 5.09), but selecting the ideal atlas is the most challenging.

Prediction of Pathological Stage in Patients with Prostate Cancer: A Neuro-Fuzzy Model

PubMed Central

Acampora, Giovanni; Brown, David; Rees, Robert C.

2016-01-01

The prediction of cancer staging in prostate cancer is a process for estimating the likelihood that the cancer has spread before treatment is given to the patient. Although important for determining the most suitable treatment and optimal management strategy for patients, staging continues to present significant challenges to clinicians. Clinical test results such as the pre-treatment Prostate-Specific Antigen (PSA) level, the biopsy most common tumor pattern (Primary Gleason pattern) and the second most common tumor pattern (Secondary Gleason pattern) in tissue biopsies, and the clinical T stage can be used by clinicians to predict the pathological stage of cancer. However, not every patient will return abnormal results in all tests. This significantly influences the capacity to effectively predict the stage of prostate cancer. Herein we have developed a neuro-fuzzy computational intelligence model for classifying and predicting the likelihood of a patient having Organ-Confined Disease (OCD) or Extra-Prostatic Disease (ED) using a prostate cancer patient dataset obtained from The Cancer Genome Atlas (TCGA) Research Network. The system input consisted of the following variables: Primary and Secondary Gleason biopsy patterns, PSA levels, age at diagnosis, and clinical T stage. The performance of the neuro-fuzzy system was compared to other computational intelligence based approaches, namely the Artificial Neural Network, Fuzzy C-Means, Support Vector Machine, the Naive Bayes classifiers, and also the AJCC pTNM Staging Nomogram which is commonly used by clinicians. A comparison of the optimal Receiver Operating Characteristic (ROC) points that were identified using these approaches, revealed that the neuro-fuzzy system, at its optimal point, returns the largest Area Under the ROC Curve (AUC), with a low number of false positives (FPR = 0.274, TPR = 0.789, AUC = 0.812). The proposed approach is also an improvement over the AJCC pTNM Staging Nomogram (FPR = 0.032, TPR = 0.197, AUC = 0.582). PMID:27258119

Neuromusculoskeletal Model Calibration Significantly Affects Predicted Knee Contact Forces for Walking

PubMed Central

Serrancolí, Gil; Kinney, Allison L.; Fregly, Benjamin J.; Font-Llagunes, Josep M.

2016-01-01

Though walking impairments are prevalent in society, clinical treatments are often ineffective at restoring lost function. For this reason, researchers have begun to explore the use of patient-specific computational walking models to develop more effective treatments. However, the accuracy with which models can predict internal body forces in muscles and across joints depends on how well relevant model parameter values can be calibrated for the patient. This study investigated how knowledge of internal knee contact forces affects calibration of neuromusculoskeletal model parameter values and subsequent prediction of internal knee contact and leg muscle forces during walking. Model calibration was performed using a novel two-level optimization procedure applied to six normal walking trials from the Fourth Grand Challenge Competition to Predict In Vivo Knee Loads. The outer-level optimization adjusted time-invariant model parameter values to minimize passive muscle forces, reserve actuator moments, and model parameter value changes with (Approach A) and without (Approach B) tracking of experimental knee contact forces. Using the current guess for model parameter values but no knee contact force information, the inner-level optimization predicted time-varying muscle activations that were close to experimental muscle synergy patterns and consistent with the experimental inverse dynamic loads (both approaches). For all the six gait trials, Approach A predicted knee contact forces with high accuracy for both compartments (average correlation coefficient r = 0.99 and root mean square error (RMSE) = 52.6 N medial; average r = 0.95 and RMSE = 56.6 N lateral). In contrast, Approach B overpredicted contact force magnitude for both compartments (average RMSE = 323 N medial and 348 N lateral) and poorly matched contact force shape for the lateral compartment (average r = 0.90 medial and −0.10 lateral). Approach B had statistically higher lateral muscle forces and lateral optimal muscle fiber lengths but lower medial, central, and lateral normalized muscle fiber lengths compared to Approach A. These findings suggest that poorly calibrated model parameter values may be a major factor limiting the ability of neuromusculoskeletal models to predict knee contact and leg muscle forces accurately for walking. PMID:27210105

Optimal waist circumference cutoff value for defining the metabolic syndrome in postmenopausal Latin American women.

PubMed

Blümel, Juan E; Legorreta, Deborah; Chedraui, Peter; Ayala, Felix; Bencosme, Ascanio; Danckers, Luis; Lange, Diego; Espinoza, Maria T; Gomez, Gustavo; Grandia, Elena; Izaguirre, Humberto; Manriquez, Valentin; Martino, Mabel; Navarro, Daysi; Ojeda, Eliana; Onatra, William; Pozzo, Estela; Prada, Mariela; Royer, Monique; Saavedra, Javier M; Sayegh, Fabiana; Tserotas, Konstantinos; Vallejo, Maria S; Zuñiga, Cristina

2012-04-01

The aim of this study was to determine an optimal waist circumference (WC) cutoff value for defining the metabolic syndrome (METS) in postmenopausal Latin American women. A total of 3,965 postmenopausal women (age, 45-64 y), with self-reported good health, attending routine consultation at 12 gynecological centers in major Latin American cities were included in this cross-sectional study. Modified guidelines of the US National Cholesterol Education Program, Adult Treatment Panel III were used to assess METS risk factors. Receiver operator characteristic curve analysis was used to obtain an optimal WC cutoff value best predicting at least two other METS components. Optimal cutoff values were calculated by plotting the true-positive rate (sensitivity) against the false-positive rate (1 - specificity). In addition, total accuracy, distance to receiver operator characteristic curve, and the Youden Index were calculated. Of the participants, 51.6% (n = 2,047) were identified as having two or more nonadipose METS risk components (excluding a positive WC component). These women were older, had more years since menopause onset, used hormone therapy less frequently, and had higher body mass indices than women with fewer metabolic risk factors. The optimal WC cutoff value best predicting at least two other METS components was determined to be 88 cm, equal to that defined by the Adult Treatment Panel III. A WC cutoff value of 88 cm is optimal for defining METS in this postmenopausal Latin American series.

Methods to model and predict the ViewRay treatment deliveries to aid patient scheduling and treatment planning.

PubMed

Liu, Shi; Wu, Yu; Wooten, H Omar; Green, Olga; Archer, Brent; Li, Harold; Yang, Deshan

2016-03-08

A software tool is developed, given a new treatment plan, to predict treatment delivery time for radiation therapy (RT) treatments of patients on ViewRay magnetic resonance image-guided radiation therapy (MR-IGRT) delivery system. This tool is necessary for managing patient treatment scheduling in our clinic. The predicted treatment delivery time and the assessment of plan complexities could also be useful to aid treatment planning. A patient's total treatment delivery time, not including time required for localization, is modeled as the sum of four components: 1) the treatment initialization time; 2) the total beam-on time; 3) the gantry rotation time; and 4) the multileaf collimator (MLC) motion time. Each of the four components is predicted separately. The total beam-on time can be calculated using both the planned beam-on time and the decay-corrected dose rate. To predict the remain-ing components, we retrospectively analyzed the patient treatment delivery record files. The initialization time is demonstrated to be random since it depends on the final gantry angle of the previous treatment. Based on modeling the relationships between the gantry rotation angles and the corresponding rotation time, linear regression is applied to predict the gantry rotation time. The MLC motion time is calculated using the leaves delay modeling method and the leaf motion speed. A quantitative analysis was performed to understand the correlation between the total treatment time and the plan complexity. The proposed algorithm is able to predict the ViewRay treatment delivery time with the average prediction error 0.22min or 1.82%, and the maximal prediction error 0.89 min or 7.88%. The analysis has shown the correlation between the plan modulation (PM) factor and the total treatment delivery time, as well as the treatment delivery duty cycle. A possibility has been identified to significantly reduce MLC motion time by optimizing the positions of closed MLC pairs. The accuracy of the proposed prediction algorithm is sufficient to support patient treatment appointment scheduling. This developed software tool is currently applied in use on a daily basis in our clinic, and could also be used as an important indicator for treatment plan complexity.

Imaging diagnostics in ovarian cancer: magnetic resonance imaging and a scoring system guiding choice of primary treatment.

PubMed

Kasper, Sigrid M; Dueholm, Margit; Marinovskij, Edvard; Blaakær, Jan

2017-03-01

To analyze the ability of magnetic resonance imaging (MRI) and systematic evaluation at surgery to predict optimal cytoreduction in primary advanced ovarian cancer and to develop a preoperative scoring system for cancer staging. Preoperative MRI and standard laparotomy were performed in 99 women with either ovarian or primary peritoneal cancer. Using univariate and multivariate logistic regression analysis of a systematic description of the tumor in nine abdominal compartments obtained by MRI and during surgery plus clinical parameters, a scoring system was designed that predicted non-optimal cytoreduction. Non-optimal cytoreduction at operation was predicted by the following: (A) presence of comorbidities group 3 or 4 (ASA); (B) tumor presence in multiple numbers of different compartments, and (C) numbers of specified sites of organ involvement. The score includes: number of compartments involved (1-9 points), >1 subdiaphragmal location with presence of tumor (1 point); deep organ involvement of liver (1 point), porta hepatis (1 point), spleen (1 point), mesentery/vessel (1 point), cecum/ileocecal (1 point), rectum/vessels (1 point): ASA groups 3 and 4 (2 points). Use of the scoring system based on operative findings gave an area under the curve (AUC) of 91% (85-98%) for patients in whom optimal cytoreduction could not be achieved. The score AUC obtained by MRI was 84% (76-92%), and 43% of non-optimal cytoreduction patients were identified, with only 8% of potentially operable patients being falsely evaluated as suitable for non-optimal cytoreduction at the most optimal cut-off value. Tumor in individual locations did not predict operability. This systematic scoring system based on operative findings and MRI may predict non-optimal cytoreduction. MRI is able to assess ovarian cancer with peritoneal carcinomatosis with satisfactory concordance with laparotomic findings. This scoring system could be useful as a clinical guideline and should be evaluated and developed further in larger studies. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Callous-unemotional traits and early life stress predict treatment effects on stress and sex hormone functioning in incarcerated male adolescents.

PubMed

Johnson, Megan; Vitacco, Michael J; Shirtcliff, Elizabeth A

2018-03-01

The stress response system is highly plastic, and hormone rhythms may "adaptively calibrate" in response to treatment. This investigation assessed whether stress and sex hormone diurnal rhythms changed over the course of behavioral treatment, and whether callous-unemotional (CU) traits and history of early adversity affected treatment results on diurnal hormone functioning in a sample of 28 incarcerated adolescent males. It was hypothesized that the treatment would have beneficial effects, such that healthier diurnal rhythms would emerge post-treatment. Diurnal cortisol, testosterone, and dehydroepiandrosterone (DHEA) were sampled two weeks after admission to the correctional/treatment facility, and again approximately four months later. Positive treatment effects were detected for the whole sample, such that testosterone dampened across treatment. CU traits predicted a non-optimal hormone response to treatment, potentially indicating biological preparedness to respond to acts of social dominance and aggression. The interaction between CU traits and adversity predicted a promising and sensitized response to treatment including increased cortisol and a steeper testosterone drop across treatment. Results suggest that stress and sex hormones are highly receptive to treatment during this window of development.

Optimizing Chemotherapy Dose and Schedule by Norton-Simon Mathematical Modeling

PubMed Central

Traina, Tiffany A.; Dugan, Ute; Higgins, Brian; Kolinsky, Kenneth; Theodoulou, Maria; Hudis, Clifford A.; Norton, Larry

2011-01-01

Background To hasten and improve anticancer drug development, we created a novel approach to generating and analyzing preclinical dose-scheduling data so as to optimize benefit-to-toxicity ratios. Methods We applied mathematical methods based upon Norton-Simon growth kinetic modeling to tumor-volume data from breast cancer xenografts treated with capecitabine (Xeloda®, Roche) at the conventional schedule of 14 days of treatment followed by a 7-day rest (14 - 7). Results The model predicted that 7 days of treatment followed by a 7-day rest (7 - 7) would be superior. Subsequent preclinical studies demonstrated that this biweekly capecitabine schedule allowed for safe delivery of higher daily doses, improved tumor response, and prolonged animal survival. Conclusions We demonstrated that the application of Norton-Simon modeling to the design and analysis of preclinical data predicts an improved capecitabine dosing schedule in xenograft models. This method warrants further investigation and application in clinical drug development. PMID:20519801

Substance abuse intensive outpatient treatment: does program graduation matter?

PubMed

Wallace, Amy E; Weeks, William B

2004-07-01

Program graduation, even after controlling for length of stay, may predict for improved outcomes in some substance abuse treatment settings. We investigated the role of program graduation by comparing social outcomes and inpatient utilization the years before and after treatment among graduates and dropouts of a Veterans Administration substance abuse intensive outpatient program. At enrollment, graduates and dropouts were similar in all spheres measured. Patients who completed the treatment program used significantly fewer psychiatric inpatient bed days of care the year after they completed the program, both in comparison to their own prior use and in comparison to program dropouts. Graduates were more likely to be abstinent and less likely to fully relapse or be incarcerated at 6-month followup. Further research is needed to discern optimal treatment length-that which maximizes both length of stay and completion rates, while optimizing use of limited treatment resources.

Reducing Dropout in Treatment for Depression: Translating Dropout Predictors Into Individualized Treatment Recommendations.

PubMed

Zilcha-Mano, Sigal; Keefe, John R; Chui, Harold; Rubin, Avinadav; Barrett, Marna S; Barber, Jacques P

2016-12-01

Premature discontinuation of therapy is a widespread problem that hampers the delivery of mental health treatment. A high degree of variability has been found among rates of premature treatment discontinuation, suggesting that rates may differ depending on potential moderators. In the current study, our aim was to identify demographic and interpersonal variables that moderate the association between treatment assignment and dropout. Data from a randomized controlled trial conducted from November 2001 through June 2007 (N = 156) comparing supportive-expressive therapy, antidepressant medication, and placebo for the treatment of depression (based on DSM-IV criteria) were used. Twenty prerandomization variables were chosen based on previous literature. These variables were subjected to exploratory bootstrapped variable selection and included in the logistic regression models if they passed variable selection. Three variables were found to moderate the association between treatment assignment and dropout: age, pretreatment therapeutic alliance expectations, and the presence of vindictive tendencies in interpersonal relationships. When patients were divided into those randomly assigned to their optimal treatment and those assigned to their least optimal treatment, dropout rates in the optimal treatment group (24.4%) were significantly lower than those in the least optimal treatment group (47.4%; P = .03). Present findings suggest that a patient's age and pretreatment interpersonal characteristics predict the association between common depression treatments and dropout rate. If validated by further studies, these characteristics can assist in reducing dropout through targeted treatment assignment. Secondary analysis of data from ClinicalTrials.gov identifier: NCT00043550. © Copyright 2016 Physicians Postgraduate Press, Inc.

A prospective development study of software-guided radio-frequency ablation of primary and secondary liver tumors: Clinical intervention modelling, planning and proof for ablation cancer treatment (ClinicIMPPACT).

PubMed

Reinhardt, Martin; Brandmaier, Philipp; Seider, Daniel; Kolesnik, Marina; Jenniskens, Sjoerd; Sequeiros, Roberto Blanco; Eibisberger, Martin; Voglreiter, Philip; Flanagan, Ronan; Mariappan, Panchatcharam; Busse, Harald; Moche, Michael

2017-12-01

Radio-frequency ablation (RFA) is a promising minimal-invasive treatment option for early liver cancer, however monitoring or predicting the size of the resulting tissue necrosis during the RFA-procedure is a challenging task, potentially resulting in a significant rate of under- or over treatments. Currently there is no reliable lesion size prediction method commercially available. ClinicIMPPACT is designed as multicenter-, prospective-, non-randomized clinical trial to evaluate the accuracy and efficiency of innovative planning and simulation software. 60 patients with early liver cancer will be included at four European clinical institutions and treated with the same RFA system. The preinterventional imaging datasets will be used for computational planning of the RFA treatment. All ablations will be simulated simultaneously to the actual RFA procedure, using the software environment developed in this project. The primary outcome measure is the comparison of the simulated ablation zones with the true lesions shown in follow-up imaging after one month, to assess accuracy of the lesion prediction. This unique multicenter clinical trial aims at the clinical integration of a dedicated software solution to accurately predict lesion size and shape after radiofrequency ablation of liver tumors. Accelerated and optimized workflow integration, and real-time intraoperative image processing, as well as inclusion of patient specific information, e.g. organ perfusion and registration of the real RFA needle position might make the introduced software a powerful tool for interventional radiologists to optimize patient outcomes.

Governing Laws of Complex System Predictability under Co-evolving Uncertainty Sources: Theory and Nonlinear Geophysical Applications

NASA Astrophysics Data System (ADS)

Perdigão, R. A. P.

2017-12-01

Predictability assessments are traditionally made on a case-by-case basis, often by running the particular model of interest with randomly perturbed initial/boundary conditions and parameters, producing computationally expensive ensembles. These approaches provide a lumped statistical view of uncertainty evolution, without eliciting the fundamental processes and interactions at play in the uncertainty dynamics. In order to address these limitations, we introduce a systematic dynamical framework for predictability assessment and forecast, by analytically deriving governing equations of predictability in terms of the fundamental architecture of dynamical systems, independent of any particular problem under consideration. The framework further relates multiple uncertainty sources along with their coevolutionary interplay, enabling a comprehensive and explicit treatment of uncertainty dynamics along time, without requiring the actual model to be run. In doing so, computational resources are freed and a quick and effective a-priori systematic dynamic evaluation is made of predictability evolution and its challenges, including aspects in the model architecture and intervening variables that may require optimization ahead of initiating any model runs. It further brings out universal dynamic features in the error dynamics elusive to any case specific treatment, ultimately shedding fundamental light on the challenging issue of predictability. The formulated approach, framed with broad mathematical physics generality in mind, is then implemented in dynamic models of nonlinear geophysical systems with various degrees of complexity, in order to evaluate their limitations and provide informed assistance on how to optimize their design and improve their predictability in fundamental dynamical terms.

Predictive and preventive strategies to advance the treatments of cardiovascular and cerebrovascular diseases: the Ukrainian context

PubMed Central

2012-01-01

Despite great efforts in treatments of cardiovascular diseases, the field requires innovative strategies because of high rates of morbidity, mortality and disability, indicating evident deficits in predictive vascular diagnosis and individualized treatment approaches. Talking about the vascular system, currently, physicians are not provided with integrated medical approaches to diagnose and treat vascular diseases. Only an individual global approach to the analysis of all segments in the vascular system of a patient allows finding the optimal way for vascular disease treatment. As for the existing methodology, there is a dominance of static methods such as X-ray contrast angiography and magnetic resonance imaging in angiomode. Taking into account the world experience, this article deals with innovative strategies, aiming at predictive diagnosis in vascular system, personalization of the biomedical treatment approaches, and targeted prevention of individual patient cohorts. Clinical examples illustrate the advances in corresponding healthcare sectors. Recommendations are provided to promote the field. PMID:23083430

Optimization of process parameters for pilot-scale liquid-state bioconversion of sewage sludge by mixed fungal inoculation.

PubMed

Rahman, Roshanida A; Molla, Abul Hossain; Barghash, Hind F A; Fakhru'l-Razi, Ahmadun

2016-01-01

Liquid-state bioconversion (LSB) technique has great potential for application in bioremediation of sewage sludge. The purpose of this study is to determine the optimum level of LSB process of sewage sludge treatment by mixed fungal (Aspergillus niger and Penicillium corylophilum) inoculation in a pilot-scale bioreactor. The optimization of process factors was investigated using response surface methodology based on Box-Behnken design considering hydraulic retention time (HRT) and substrate influent concentration (S0) on nine responses for optimizing and fitted to the regression model. The optimum region was successfully depicted by optimized conditions, which was identified as the best fit for convenient multiple responses. The results from process verification were in close agreement with those obtained through predictions. Considering five runs of different conditions of HRT (low, medium and high 3.62, 6.13 and 8.27 days, respectively) with the range of S0 value (the highest 12.56 and the lowest 7.85 g L(-1)), it was monitored as the lower HRT was considered as the best option because it required minimum days of treatment than the others with influent concentration around 10 g L(-1). Therefore, optimum process factors of 3.62 days for HRT and 10.12 g L(-1) for S0 were identified as the best fit for LSB process and its performance was deviated by less than 5% in most of the cases compared to the predicted values. The recorded optimized results address a dynamic development in commercial-scale biological treatment of wastewater for safe and environment-friendly disposal in near future.

Defining Treatment Response and Remission in Child Anxiety: Signal Detection Analysis Using the Pediatric Anxiety Rating Scale

ERIC Educational Resources Information Center

Caporino, Nicole E.; Brodman, Douglas M.; Kendall, Philip C.; Albano, Anne Marie; Sherrill, Joel; Piacentini, John; Sakolsky, Dara; Birmaher, Boris; Compton, Scott N.; Ginsburg, Golda; Rynn, Moira; McCracken, James; Gosch, Elizabeth; Keeton, Courtney; March, John; Walkup, John T.

2013-01-01

Objective: To determine optimal Pediatric Anxiety Rating Scale (PARS) percent reduction and raw score cut-offs for predicting treatment response and remission among children and adolescents with anxiety disorders. Method: Data were from a subset of youth (N = 438; 7-17 years of age) who participated in the Child/Adolescent Anxiety Multimodal Study…

Optimization of drug-drug interaction study design: comparison of minimal physiologically based pharmacokinetic models on prediction of CYP3A inhibition by ketoconazole.

PubMed

Han, Bing; Mao, Jialin; Chien, Jenny Y; Hall, Stephen D

2013-07-01

Ketoconazole is a potent CYP3A inhibitor used to assess the contribution of CYP3A to drug clearance and quantify the increase in drug exposure due to a strong inhibitor. Physiologically based pharmacokinetic (PBPK) models have been used to evaluate treatment regimens resulting in maximal CYP3A inhibition by ketoconazole but have reached different conclusions. We compare two PBPK models of the ketoconazole-midazolam interaction, model 1 (Chien et al., 2006) and model 2 implemented in Simcyp (version 11), to predict 16 published treatment regimens. With use of model 2, 41% of the study point estimates of area under the curve (AUC) ratio and 71% of the 90% confidence intervals were predicted within 1.5-fold of the observed, but these increased to 82 and 100%, respectively, with model 1. For midazolam, model 2 predicted a maximal midazolam AUC ratio of 8 and a hepatic fraction metabolized by CYP3A (f(m)) of 0.97, whereas model 1 predicted 17 and 0.90, respectively, which are more consistent with observed data. On the basis of model 1, ketoconazole (400 mg QD) for at least 3 days and substrate administration within 2 hours is required for maximal CYP3A inhibition. Ketoconazole treatment regimens that use 200 mg BID underestimate the systemic fraction metabolized by CYP3A (0.86 versus 0.90) for midazolam. The systematic underprediction also applies to CYP3A substrates with high bioavailability and long half-lives. The superior predictive performance of model 1 reflects the need for accumulation of ketoconazole at enzyme site and protracted inhibition. Model 2 is not recommended for inferring optimal study design and estimation of fraction metabolized by CYP3A.

Bayesian Phase II optimization for time-to-event data based on historical information.

PubMed

Bertsche, Anja; Fleischer, Frank; Beyersmann, Jan; Nehmiz, Gerhard

2017-01-01

After exploratory drug development, companies face the decision whether to initiate confirmatory trials based on limited efficacy information. This proof-of-concept decision is typically performed after a Phase II trial studying a novel treatment versus either placebo or an active comparator. The article aims to optimize the design of such a proof-of-concept trial with respect to decision making. We incorporate historical information and develop pre-specified decision criteria accounting for the uncertainty of the observed treatment effect. We optimize these criteria based on sensitivity and specificity, given the historical information. Specifically, time-to-event data are considered in a randomized 2-arm trial with additional prior information on the control treatment. The proof-of-concept criterion uses treatment effect size, rather than significance. Criteria are defined on the posterior distribution of the hazard ratio given the Phase II data and the historical control information. Event times are exponentially modeled within groups, allowing for group-specific conjugate prior-to-posterior calculation. While a non-informative prior is placed on the investigational treatment, the control prior is constructed via the meta-analytic-predictive approach. The design parameters including sample size and allocation ratio are then optimized, maximizing the probability of taking the right decision. The approach is illustrated with an example in lung cancer.

Failure-probability driven dose painting

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

Vogelius, Ivan R.; Håkansson, Katrin; Due, Anne K.

Purpose: To demonstrate a data-driven dose-painting strategy based on the spatial distribution of recurrences in previously treated patients. The result is a quantitative way to define a dose prescription function, optimizing the predicted local control at constant treatment intensity. A dose planning study using the optimized dose prescription in 20 patients is performed.Methods: Patients treated at our center have five tumor subvolumes from the center of the tumor (PET positive volume) and out delineated. The spatial distribution of 48 failures in patients with complete clinical response after (chemo)radiation is used to derive a model for tumor control probability (TCP). Themore » total TCP is fixed to the clinically observed 70% actuarial TCP at five years. Additionally, the authors match the distribution of failures between the five subvolumes to the observed distribution. The steepness of the dose–response is extracted from the literature and the authors assume 30% and 20% risk of subclinical involvement in the elective volumes. The result is a five-compartment dose response model matching the observed distribution of failures. The model is used to optimize the distribution of dose in individual patients, while keeping the treatment intensity constant and the maximum prescribed dose below 85 Gy.Results: The vast majority of failures occur centrally despite the small volumes of the central regions. Thus, optimizing the dose prescription yields higher doses to the central target volumes and lower doses to the elective volumes. The dose planning study shows that the modified prescription is clinically feasible. The optimized TCP is 89% (range: 82%–91%) as compared to the observed TCP of 70%.Conclusions: The observed distribution of locoregional failures was used to derive an objective, data-driven dose prescription function. The optimized dose is predicted to result in a substantial increase in local control without increasing the predicted risk of toxicity.« less

KCNN4 and S100A14 act as predictors of recurrence in optimally debulked patients with serous ovarian cancer

PubMed Central

Hu, Ting; Sun, Qian; Wu, Jianli; Lin, Xingguang; Luo, Danfeng; Sun, Chaoyang; Wang, Changyu; Zhou, Bo; Li, Na; Xia, Meng; Lu, Hao; Meng, Li; Xu, Xiaoyan; Hu, Junbo; Ma, Ding; Chen, Gang; Zhu, Tao

2016-01-01

Approximately 50-75% of patients with serous ovarian carcinoma (SOC) experience recurrence within 18 months after first-line treatment. Current clinical indicators are inadequate for predicting the risk of recurrence. In this study, we used 7 publicly available microarray datasets to identify gene signatures related to recurrence in optimally debulked SOC patients, and validated their expressions in an independent clinic cohort of 127 patients using immunohistochemistry (IHC). We identified a two-gene signature including KCNN4 and S100A14 which was related to recurrence in optimally debulked SOC patients. Their mRNA expression levels were positively correlated and regulated by DNA copy number alterations (CNA) (KCNN4: p=1.918e-05) and DNA promotermethylation (KCNN4: p=0.0179; S100A14: p=2.787e-13). Recurrence prediction models built in the TCGA dataset based on KCNN4 and S100A14 individually and in combination showed good prediction performance in the other 6 datasets (AUC:0.5442-0.9524). The independent cohort supported the expression difference between SOC recurrences. Also, a KCNN4 and S100A14-centered protein interaction subnetwork was built from the STRING database, and the shortest regulation path between them, called the KCNN4-UBA52-KLF4-S100A14 axis, was identified. This discovery might facilitate individualized treatment of SOC. PMID:27270322

Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures: Psychosocial Domain.

PubMed

Sutin, Angelina R; Boutelle, Kerri; Czajkowski, Susan M; Epel, Elissa S; Green, Paige A; Hunter, Christine M; Rice, Elise L; Williams, David M; Young-Hyman, Deborah; Rothman, Alexander J

2018-04-01

Within the Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures Project, the psychosocial domain addresses how psychosocial processes underlie the influence of obesity treatment strategies on weight loss and weight maintenance. The subgroup for the psychosocial domain identified an initial list of high-priority constructs and measures that ranged from relatively stable characteristics about the person (cognitive function, personality) to dynamic characteristics that may change over time (motivation, affect). This paper describes (a) how the psychosocial domain fits into the broader model of weight loss and weight maintenance as conceptualized by ADOPT; (b) the guiding principles used to select constructs and measures for recommendation; (c) the high-priority constructs recommended for inclusion; (d) domain-specific issues for advancing the science; and (e) recommendations for future research. The inclusion of similar measures across trials will help to better identify how psychosocial factors mediate and moderate the weight loss and weight maintenance process, facilitate research into dynamic interactions with factors in the other ADOPT domains, and ultimately improve the design and delivery of effective interventions. © 2018 The Obesity Society.

A Predictive Framework to Elucidate Venous Stenosis: CFD & Shape Optimization.

PubMed

Javid Mahmoudzadeh Akherat, S M; Cassel, Kevin; Boghosian, Michael; Hammes, Mary; Coe, Fredric

2017-07-01

The surgical creation of vascular accesses for renal failure patients provides an abnormally high flow rate conduit in the patient's upper arm vasculature that facilitates the hemodialysis treatment. These vascular accesses, however, are very often associated with complications that lead to access failure and thrombotic incidents, mainly due to excessive neointimal hyperplasia (NH) and subsequently stenosis. Development of a framework to monitor and predict the evolution of the venous system post access creation can greatly contribute to maintaining access patency. Computational fluid dynamics (CFD) has been exploited to inspect the non-homeostatic wall shear stress (WSS) distribution that is speculated to trigger NH in the patient cohort under investigation. Thereafter, CFD in liaison with a gradient-free shape optimization method has been employed to analyze the deformation modes of the venous system enduring non-physiological hemodynamics. It is observed that the optimally evolved shapes and their corresponding hemodynamics strive to restore the homeostatic state of the venous system to a normal, pre-surgery condition. It is concluded that a CFD-shape optimization coupling that seeks to regulate the WSS back to a well-defined physiological WSS target range can accurately predict the mode of patient-specific access failure.

Childhood trauma predicts antidepressant response in adults with major depression: data from the randomized international study to predict optimized treatment for depression.

PubMed

Williams, L M; Debattista, C; Duchemin, A-M; Schatzberg, A F; Nemeroff, C B

2016-05-03

Few reliable predictors indicate which depressed individuals respond to antidepressants. Several studies suggest that a history of early-life trauma predicts poorer response to antidepressant therapy but results are variable and limited in adults. The major goal of the present study was to evaluate the role of early-life trauma in predicting acute response outcomes to antidepressants in a large sample of well-characterized patients with major depressive disorder (MDD). The international Study to Predict Optimized Treatment for Depression (iSPOT-D) is a randomized clinical trial with enrollment from December 2008 to January 2012 at eight academic and nine private clinical settings in five countries. Patients (n=1008) meeting DSM-IV criteria for MDD and 336 matched healthy controls comprised the study sample. Six participants withdrew due to serious adverse events. Randomization was to 8 weeks of treatment with escitalopram, sertraline or venlafaxine with dosage adjusted by the participant's treating clinician per routine clinical practice. Exposure to 18 types of traumatic events before the age of 18 was assessed using the Early-Life Stress Questionnaire. Impact of early-life stressors-overall trauma 'load' and specific type of abuse-on treatment outcomes measures: response: (⩾50% improvement on the 17-item Hamilton Rating Scale for Depression, HRSD17 or on the 16-item Quick Inventory of Depressive Symptomatology-Self-Rated, QIDS_SR16) and remission (score ⩽7 on the HRSD17 and ⩽5 on the QIDS_SR16). Trauma prevalence in MDD was compared with controls. Depressed participants were significantly more likely to report early-life stress than controls; 62.5% of MDD participants reported more than two traumatic events compared with 28.4% of controls. The higher rate of early-life trauma was most apparent for experiences of interpersonal violation (emotional, sexual and physical abuses). Abuse and notably abuse occurring at ⩽7 years of age predicted poorer outcomes after 8 weeks of antidepressants, across the three treatment arms. In addition, the abuses occurring between ages 4 and 7 years differentially predicted the poorest outcome following the treatment with sertraline. Specific types of early-life trauma, particularly physical, emotional and sexual abuse, especially when occurring at ⩽7 years of age are important moderators of subsequent response to antidepressant therapy for MDD.

Childhood trauma predicts antidepressant response in adults with major depression: data from the randomized international study to predict optimized treatment for depression

PubMed Central

Williams, L M; Debattista, C; Duchemin, A-M; Schatzberg, A F; Nemeroff, C B

2016-01-01

Few reliable predictors indicate which depressed individuals respond to antidepressants. Several studies suggest that a history of early-life trauma predicts poorer response to antidepressant therapy but results are variable and limited in adults. The major goal of the present study was to evaluate the role of early-life trauma in predicting acute response outcomes to antidepressants in a large sample of well-characterized patients with major depressive disorder (MDD). The international Study to Predict Optimized Treatment for Depression (iSPOT-D) is a randomized clinical trial with enrollment from December 2008 to January 2012 at eight academic and nine private clinical settings in five countries. Patients (n=1008) meeting DSM-IV criteria for MDD and 336 matched healthy controls comprised the study sample. Six participants withdrew due to serious adverse events. Randomization was to 8 weeks of treatment with escitalopram, sertraline or venlafaxine with dosage adjusted by the participant's treating clinician per routine clinical practice. Exposure to 18 types of traumatic events before the age of 18 was assessed using the Early-Life Stress Questionnaire. Impact of early-life stressors—overall trauma ‘load' and specific type of abuse—on treatment outcomes measures: response: (⩾50% improvement on the 17-item Hamilton Rating Scale for Depression, HRSD17 or on the 16-item Quick Inventory of Depressive Symptomatology—Self-Rated, QIDS_SR16) and remission (score ⩽7 on the HRSD17 and ⩽5 on the QIDS_SR16). Trauma prevalence in MDD was compared with controls. Depressed participants were significantly more likely to report early-life stress than controls; 62.5% of MDD participants reported more than two traumatic events compared with 28.4% of controls. The higher rate of early-life trauma was most apparent for experiences of interpersonal violation (emotional, sexual and physical abuses). Abuse and notably abuse occurring at ⩽7 years of age predicted poorer outcomes after 8 weeks of antidepressants, across the three treatment arms. In addition, the abuses occurring between ages 4 and 7 years differentially predicted the poorest outcome following the treatment with sertraline. Specific types of early-life trauma, particularly physical, emotional and sexual abuse, especially when occurring at ⩽7 years of age are important moderators of subsequent response to antidepressant therapy for MDD. PMID:27138798

Optimizing hydroxyurea therapy for sickle cell anemia.

PubMed

Ware, Russell E

2015-01-01

Hydroxyurea has proven efficacy in numerous clinical trials as a disease-modifying treatment for patients with sickle cell anemia (SCA) but is currently under-used in clinical practice. To improve the effectiveness of hydroxyurea therapy, efforts should be directed toward broadening the clinical treatment indications, optimizing the daily dosage, and emphasizing the benefits of early and extended treatment. Here, various issues related to hydroxyurea treatment are discussed, focusing on both published evidence and clinical experience. Specific guidance is provided regarding important but potentially unfamiliar aspects of hydroxyurea treatment for SCA, such as escalating to maximum tolerated dose, treating in the setting of cerebrovascular disease, switching from chronic transfusions to hydroxyurea, and using serial phlebotomy to alleviate iron overload. Future research directions to optimize hydroxyurea therapy are also discussed, including personalized dosing based on pharmacokinetic modeling, prediction of fetal hemoglobin responses based on pharmacogenomics, and the risks and benefits of hydroxyurea for non-SCA genotypes and during pregnancy/lactation. Another critical initiative is the introduction of hydroxyurea safely and effectively into global regions that have a high disease burden of SCA but limited resources, such as sub-Saharan Africa, the Caribbean, and India. Final considerations emphasize the long-term goal of optimizing hydroxyurea therapy, which is to help treatment become accepted as standard of care for all patients with SCA. © 2015 by The American Society of Hematology. All rights reserved.

Predicting objective function weights from patient anatomy in prostate IMRT treatment planning

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

Lee, Taewoo, E-mail: taewoo.lee@utoronto.ca; Hammad, Muhannad; Chan, Timothy C. Y.

2013-12-15

Purpose: Intensity-modulated radiation therapy (IMRT) treatment planning typically combines multiple criteria into a single objective function by taking a weighted sum. The authors propose a statistical model that predicts objective function weights from patient anatomy for prostate IMRT treatment planning. This study provides a proof of concept for geometry-driven weight determination. Methods: A previously developed inverse optimization method (IOM) was used to generate optimal objective function weights for 24 patients using their historical treatment plans (i.e., dose distributions). These IOM weights were around 1% for each of the femoral heads, while bladder and rectum weights varied greatly between patients. Amore » regression model was developed to predict a patient's rectum weight using the ratio of the overlap volume of the rectum and bladder with the planning target volume at a 1 cm expansion as the independent variable. The femoral head weights were fixed to 1% each and the bladder weight was calculated as one minus the rectum and femoral head weights. The model was validated using leave-one-out cross validation. Objective values and dose distributions generated through inverse planning using the predicted weights were compared to those generated using the original IOM weights, as well as an average of the IOM weights across all patients. Results: The IOM weight vectors were on average six times closer to the predicted weight vectors than to the average weight vector, usingl{sub 2} distance. Likewise, the bladder and rectum objective values achieved by the predicted weights were more similar to the objective values achieved by the IOM weights. The difference in objective value performance between the predicted and average weights was statistically significant according to a one-sided sign test. For all patients, the difference in rectum V54.3 Gy, rectum V70.0 Gy, bladder V54.3 Gy, and bladder V70.0 Gy values between the dose distributions generated by the predicted weights and IOM weights was less than 5 percentage points. Similarly, the difference in femoral head V54.3 Gy values between the two dose distributions was less than 5 percentage points for all but one patient. Conclusions: This study demonstrates a proof of concept that patient anatomy can be used to predict appropriate objective function weights for treatment planning. In the long term, such geometry-driven weights may serve as a starting point for iterative treatment plan design or may provide information about the most clinically relevant region of the Pareto surface to explore.« less

Predicting objective function weights from patient anatomy in prostate IMRT treatment planning

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

Lee, Taewoo, E-mail: taewoo.lee@utoronto.ca; Hammad, Muhannad; Chan, Timothy C. Y.

Purpose: Intensity-modulated radiation therapy (IMRT) treatment planning typically combines multiple criteria into a single objective function by taking a weighted sum. The authors propose a statistical model that predicts objective function weights from patient anatomy for prostate IMRT treatment planning. This study provides a proof of concept for geometry-driven weight determination. Methods: A previously developed inverse optimization method (IOM) was used to generate optimal objective function weights for 24 patients using their historical treatment plans (i.e., dose distributions). These IOM weights were around 1% for each of the femoral heads, while bladder and rectum weights varied greatly between patients. Amore » regression model was developed to predict a patient's rectum weight using the ratio of the overlap volume of the rectum and bladder with the planning target volume at a 1 cm expansion as the independent variable. The femoral head weights were fixed to 1% each and the bladder weight was calculated as one minus the rectum and femoral head weights. The model was validated using leave-one-out cross validation. Objective values and dose distributions generated through inverse planning using the predicted weights were compared to those generated using the original IOM weights, as well as an average of the IOM weights across all patients. Results: The IOM weight vectors were on average six times closer to the predicted weight vectors than to the average weight vector, usingl{sub 2} distance. Likewise, the bladder and rectum objective values achieved by the predicted weights were more similar to the objective values achieved by the IOM weights. The difference in objective value performance between the predicted and average weights was statistically significant according to a one-sided sign test. For all patients, the difference in rectum V54.3 Gy, rectum V70.0 Gy, bladder V54.3 Gy, and bladder V70.0 Gy values between the dose distributions generated by the predicted weights and IOM weights was less than 5 percentage points. Similarly, the difference in femoral head V54.3 Gy values between the two dose distributions was less than 5 percentage points for all but one patient. Conclusions: This study demonstrates a proof of concept that patient anatomy can be used to predict appropriate objective function weights for treatment planning. In the long term, such geometry-driven weights may serve as a starting point for iterative treatment plan design or may provide information about the most clinically relevant region of the Pareto surface to explore.« less

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder

PubMed Central

Kessler, R.C.; van Loo, H.M.; Wardenaar, K.J.; Bossarte, R.M.; Brenner, L.A.; Ebert, D.D; de Jonge, P.; Nierenberg, A.A.; Rosellini, A.J.; Sampson, N.A.; Schoevers, R.A.; Wilcox, M.A.; Zaslavsky, A.M.

2016-01-01

Aims Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful. Methods We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalized) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments. Results Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention versus control) or differential treatment outcomes (i.e., intervention A versus intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalized treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials. Conclusions Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists. PMID:26810628

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder.

PubMed

Kessler, R C; van Loo, H M; Wardenaar, K J; Bossarte, R M; Brenner, L A; Ebert, D D; de Jonge, P; Nierenberg, A A; Rosellini, A J; Sampson, N A; Schoevers, R A; Wilcox, M A; Zaslavsky, A M

2017-02-01

Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful. We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments. Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials. Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.

Methods to model and predict the ViewRay treatment deliveries to aid patient scheduling and treatment planning

PubMed Central

Liu, Shi; Wu, Yu; Wooten, H. Omar; Green, Olga; Archer, Brent; Li, Harold

2016-01-01

A software tool is developed, given a new treatment plan, to predict treatment delivery time for radiation therapy (RT) treatments of patients on ViewRay magnetic resonance image‐guided radiation therapy (MR‐IGRT) delivery system. This tool is necessary for managing patient treatment scheduling in our clinic. The predicted treatment delivery time and the assessment of plan complexities could also be useful to aid treatment planning. A patient's total treatment delivery time, not including time required for localization, is modeled as the sum of four components: 1) the treatment initialization time; 2) the total beam‐on time; 3) the gantry rotation time; and 4) the multileaf collimator (MLC) motion time. Each of the four components is predicted separately. The total beam‐on time can be calculated using both the planned beam‐on time and the decay‐corrected dose rate. To predict the remain‐ing components, we retrospectively analyzed the patient treatment delivery record files. The initialization time is demonstrated to be random since it depends on the final gantry angle of the previous treatment. Based on modeling the relationships between the gantry rotation angles and the corresponding rotation time, linear regression is applied to predict the gantry rotation time. The MLC motion time is calculated using the leaves delay modeling method and the leaf motion speed. A quantitative analysis was performed to understand the correlation between the total treatment time and the plan complexity. The proposed algorithm is able to predict the ViewRay treatment delivery time with the average prediction error 0.22 min or 1.82%, and the maximal prediction error 0.89 min or 7.88%. The analysis has shown the correlation between the plan modulation (PM) factor and the total treatment delivery time, as well as the treatment delivery duty cycle. A possibility has been identified to significantly reduce MLC motion time by optimizing the positions of closed MLC pairs. The accuracy of the proposed prediction algorithm is sufficient to support patient treatment appointment scheduling. This developed software tool is currently applied in use on a daily basis in our clinic, and could also be used as an important indicator for treatment plan complexity. PACS number(s): 87.55.N PMID:27074472

EEG gamma synchronization is associated with response to paroxetine treatment.

PubMed

Arikan, Mehmet Kemal; Metin, Baris; Tarhan, Nevzat

2018-08-01

Resistance to medication is a significant problem in psychiatric practice, and effective methods for predicting response are needed to optimize treatment efficacy and limit morbidity. Gamma oscillations are considered as an index of the brain's general cognitive activity; however, the role of gamma oscillations in disease has not been studied sufficiently. This study aimed to determine if gamma power during rest can be used to predict response to anti-depressant medication treatment. Hamilton Depression Rating Scale (HDRS) score and resting state gamma power was measured in 18 medication-free patients during an episode of major depression. After 6 weeks of paroxetine monotherapy HDRS was administered again. Baseline gamma power at frontal, central and temporal electrodes before treatment was significantly related to post-treatment change in HDRS scores. The results indicate that gamma oscillations could be considered a marker of response to paroxetine treatment in patients with major depression. Copyright © 2018. Published by Elsevier B.V.

Electroconvulsive Therapy Intervention for Parkinson's Disease.

PubMed

Narang, Puneet; Glowacki, Anna; Lippmann, Steven

2015-01-01

Electroconvulsive therapy is an established means to improve function in a variety of psychiatric and neurologic conditions, particularly for patients who remain treatment-refractory. Parkinson's disease is a neurodegenerative disorder that sometimes does not respond well to conventional pharmacotherapies. Reports have indicated that electroconvulsive therapy may be an effective and safe treatment for those patients with Parkinson's disease who are not optimally responding to first-line treatments. Despite these reports, however, electroconvulsive therapy is not often used by clinicians in patients with treatment-resistant Parkinson's disease, perhaps due to stigma, lack of knowledge regarding its safety and efficacy, and/or inability to predict the duration of therapeutic benefit. Our objective was to determine if the available literature on ECT supports it as a safe and effective treatment option in patients with treatment-refractory Parkinson's disease. Motoric improvement induced by electroconvulsive therapy has been documented for decades in persons with Parkinson's disease. Efficacy and safety are reported following electroconvulsive therapy in people with Parkinson's disease who have sub-optimal response to medicines or experience the "on/off" phenomenon to L-dopa. Electroconvulsive therapy is an effective option for acute and maintenance treatment of Parkinson's disease in select patients. Inability to predict how long the beneficial effects of ECT therapy will last in patients with Parkinson's disease may be a reason why this treatment is underutilized by clinicians. More research is warranted to clarify parameters for application and duration of therapeutic benefit in individuals with difficult-to-treat Parkinson's disease.

Process Optimization of Eco-Friendly Flame Retardant Finish for Cotton Fabric: a Response Surface Methodology Approach

NASA Astrophysics Data System (ADS)

Yasin, Sohail; Curti, Massimo; Behary, Nemeshwaree; Perwuelz, Anne; Giraud, Stephane; Rovero, Giorgio; Guan, Jinping; Chen, Guoqiang

The n-methylol dimethyl phosphono propionamide (MDPA) flame retardant compounds are predominantly used for cotton fabric treatments with trimethylol melamine (TMM) to obtain better crosslinking and enhanced flame retardant properties. Nevertheless, such treatments are associated with a toxic issue of cancer-causing formaldehyde release. An eco-friendly finishing was used to get formaldehyde-free fixation of flame retardant to the cotton fabric. Citric acid as a crosslinking agent along with the sodium hypophosphite as a catalyst in the treatment was utilized. The process parameters of the treatment were enhanced for optimized flame retardant properties, in addition, low mechanical loss to the fabric by response surface methodology using Box-Behnken statistical design experiment methodology was achieved. The effects of concentrations on the fabric’s properties (flame retardancy and mechanical properties) were evaluated. The regression equations for the prediction of concentrations and mechanical properties of the fabric were also obtained for the eco-friendly treatment. The R-squared values of all the responses were above 0.95 for the reagents used, indicating the degree of relationship between the predicted values by the Box-Behnken design and the actual experimental results. It was also found that the concentration parameters (crosslinking reagents and catalysts) in the treatment formulation have a prime role in the overall performance of flame retardant cotton fabrics.

Muscle Synergies May Improve Optimization Prediction of Knee Contact Forces During Walking

PubMed Central

Walter, Jonathan P.; Kinney, Allison L.; Banks, Scott A.; D'Lima, Darryl D.; Besier, Thor F.; Lloyd, David G.; Fregly, Benjamin J.

2014-01-01

The ability to predict patient-specific joint contact and muscle forces accurately could improve the treatment of walking-related disorders. Muscle synergy analysis, which decomposes a large number of muscle electromyographic (EMG) signals into a small number of synergy control signals, could reduce the dimensionality and thus redundancy of the muscle and contact force prediction process. This study investigated whether use of subject-specific synergy controls can improve optimization prediction of knee contact forces during walking. To generate the predictions, we performed mixed dynamic muscle force optimizations (i.e., inverse skeletal dynamics with forward muscle activation and contraction dynamics) using data collected from a subject implanted with a force-measuring knee replacement. Twelve optimization problems (three cases with four subcases each) that minimized the sum of squares of muscle excitations were formulated to investigate how synergy controls affect knee contact force predictions. The three cases were: (1) Calibrate+Match where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously matched, (2) Precalibrate+Predict where experimental knee contact forces were predicted using precalibrated muscle model parameters values from the first case, and (3) Calibrate+Predict where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously predicted, all while matching inverse dynamic loads at the hip, knee, and ankle. The four subcases used either 44 independent controls or five synergy controls with and without EMG shape tracking. For the Calibrate+Match case, all four subcases closely reproduced the measured medial and lateral knee contact forces (R2 ≥ 0.94, root-mean-square (RMS) error < 66 N), indicating sufficient model fidelity for contact force prediction. For the Precalibrate+Predict and Calibrate+Predict cases, synergy controls yielded better contact force predictions (0.61 < R2 < 0.90, 83 N < RMS error < 161 N) than did independent controls (-0.15 < R2 < 0.79, 124 N < RMS error < 343 N) for corresponding subcases. For independent controls, contact force predictions improved when precalibrated model parameter values or EMG shape tracking was used. For synergy controls, contact force predictions were relatively insensitive to how model parameter values were calibrated, while EMG shape tracking made lateral (but not medial) contact force predictions worse. For the subject and optimization cost function analyzed in this study, use of subject-specific synergy controls improved the accuracy of knee contact force predictions, especially for lateral contact force when EMG shape tracking was omitted, and reduced prediction sensitivity to uncertainties in muscle model parameter values. PMID:24402438

Muscle synergies may improve optimization prediction of knee contact forces during walking.

PubMed

Walter, Jonathan P; Kinney, Allison L; Banks, Scott A; D'Lima, Darryl D; Besier, Thor F; Lloyd, David G; Fregly, Benjamin J

2014-02-01

The ability to predict patient-specific joint contact and muscle forces accurately could improve the treatment of walking-related disorders. Muscle synergy analysis, which decomposes a large number of muscle electromyographic (EMG) signals into a small number of synergy control signals, could reduce the dimensionality and thus redundancy of the muscle and contact force prediction process. This study investigated whether use of subject-specific synergy controls can improve optimization prediction of knee contact forces during walking. To generate the predictions, we performed mixed dynamic muscle force optimizations (i.e., inverse skeletal dynamics with forward muscle activation and contraction dynamics) using data collected from a subject implanted with a force-measuring knee replacement. Twelve optimization problems (three cases with four subcases each) that minimized the sum of squares of muscle excitations were formulated to investigate how synergy controls affect knee contact force predictions. The three cases were: (1) Calibrate+Match where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously matched, (2) Precalibrate+Predict where experimental knee contact forces were predicted using precalibrated muscle model parameters values from the first case, and (3) Calibrate+Predict where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously predicted, all while matching inverse dynamic loads at the hip, knee, and ankle. The four subcases used either 44 independent controls or five synergy controls with and without EMG shape tracking. For the Calibrate+Match case, all four subcases closely reproduced the measured medial and lateral knee contact forces (R2 ≥ 0.94, root-mean-square (RMS) error < 66 N), indicating sufficient model fidelity for contact force prediction. For the Precalibrate+Predict and Calibrate+Predict cases, synergy controls yielded better contact force predictions (0.61 < R2 < 0.90, 83 N < RMS error < 161 N) than did independent controls (-0.15 < R2 < 0.79, 124 N < RMS error < 343 N) for corresponding subcases. For independent controls, contact force predictions improved when precalibrated model parameter values or EMG shape tracking was used. For synergy controls, contact force predictions were relatively insensitive to how model parameter values were calibrated, while EMG shape tracking made lateral (but not medial) contact force predictions worse. For the subject and optimization cost function analyzed in this study, use of subject-specific synergy controls improved the accuracy of knee contact force predictions, especially for lateral contact force when EMG shape tracking was omitted, and reduced prediction sensitivity to uncertainties in muscle model parameter values.

A new threshold of apparent diffusion coefficient values in white matter after successful tissue plasminogen activator treatment for acute brain ischemia.

PubMed

Sato, Atsushi; Shimizu, Yusaku; Koyama, Junichi; Hongo, Kazuhiro

2017-06-01

Tissue plasminogen activator (tPA) is effective for the treatment of acute brain ischemia, but may trigger fatal brain edema or hemorrhage if the brain ischemia results in a large infarct. Herein, we attempted to predict the extent of infarcts by determining the optimal threshold of ADC values on DWI that predictively distinguishes between infarct and reversible areas, and by reconstructing color-coded images based on this threshold. The study subjects consisted of 36 patients with acute brain ischemia in whom MRA had confirmed reopening of the occluded arteries in a short time (mean: 99min) after tPA treatment. We measured the apparetnt diffusion coefficient (ADC) values in several small regions of interest over the white matter within high-intensity areas on the initial diffusion weighted image (DWI); then, by comparing the findings to the follow-up images, we obtained the optimal threshold of ADC values using receiver-operating characteristic analysis. The threshold obtained (583×10 -6 m 2 /s) was lower than those previously reported; this threshold could distinguish between infarct and reversible areas with considerable accuracy (sensitivity: 0.87, specificity: 0.94). The threshold obtained and the reconstructed images were predictive of the final radiological result of tPA treatment, and this threshold may be helpful in determining the appropriate management of patients with acute brain ischemia. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Predicting Treatment Response in Social Anxiety Disorder From Functional Magnetic Resonance Imaging

PubMed Central

Doehrmann, Oliver; Ghosh, Satrajit S.; Polli, Frida E.; Reynolds, Gretchen O.; Horn, Franziska; Keshavan, Anisha; Triantafyllou, Christina; Saygin, Zeynep M.; Whitfield-Gabrieli, Susan; Hofmann, Stefan G.; Pollack, Mark; Gabrieli, John D.

2013-01-01

Context Current behavioral measures poorly predict treatment outcome in social anxiety disorder (SAD). To our knowledge, this is the first study to examine neuroimaging-based treatment prediction in SAD. Objective To measure brain activation in patients with SAD as a biomarker to predict subsequent response to cognitive behavioral therapy (CBT). Design Functional magnetic resonance imaging (fMRI) data were collected prior to CBT intervention. Changes in clinical status were regressed on brain responses and tested for selectivity for social stimuli. Setting Patients were treated with protocol-based CBT at anxiety disorder programs at Boston University or Massachusetts General Hospital and underwent neuroimaging data collection at Massachusetts Institute of Technology. Patients Thirty-nine medication-free patients meeting DSM-IV criteria for the generalized subtype of SAD. Interventions Brain responses to angry vs neutral faces or emotional vs neutral scenes were examined with fMRI prior to initiation of CBT. Main Outcome Measures Whole-brain regression analyses with differential fMRI responses for angry vs neutral faces and changes in Liebowitz Social Anxiety Scale score as the treatment outcome measure. Results Pretreatment responses significantly predicted subsequent treatment outcome of patients selectively for social stimuli and particularly in regions of higher-order visual cortex. Combining the brain measures with information on clinical severity accounted for more than 40% of the variance in treatment response and substantially exceeded predictions based on clinical measures at baseline. Prediction success was unaffected by testing for potential confounding factors such as depression severity at baseline. Conclusions The results suggest that brain imaging can provide biomarkers that substantially improve predictions for the success of cognitive behavioral interventions and more generally suggest that such biomarkers may offer evidence-based, personalized medicine approaches for optimally selecting among treatment options for a patient. PMID:22945462

"SABER": A new software tool for radiotherapy treatment plan evaluation.

PubMed

Zhao, Bo; Joiner, Michael C; Orton, Colin G; Burmeister, Jay

2010-11-01

Both spatial and biological information are necessary in order to perform true optimization of a treatment plan and for predicting clinical outcome. The goal of this work is to develop an enhanced treatment plan evaluation tool which incorporates biological parameters and retains spatial dose information. A software system is developed which provides biological plan evaluation with a novel combination of features. It incorporates hyper-radiosensitivity using the induced-repair model and applies the new concept of dose convolution filter (DCF) to simulate dose wash-out effects due to cell migration, bystander effect, and/or tissue motion during treatment. Further, the concept of spatial DVH (sDVH) is introduced to evaluate and potentially optimize the spatial dose distribution in the target volume. Finally, generalized equivalent uniform dose is derived from both the physical dose distribution (gEUD) and the distribution of equivalent dose in 2 Gy fractions (gEUD2) and the software provides three separate models for calculation of tumor control probability (TCP), normal tissue complication probability (NTCP), and probability of uncomplicated tumor control (P+). TCP, NTCP, and P+ are provided as a function of prescribed dose and multivariable TCP, NTCP, and P+ plots are provided to illustrate the dependence on individual parameters used to calculate these quantities. Ten plans from two clinical treatment sites are selected to test the three calculation models provided by this software. By retaining both spatial and biological information about the dose distribution, the software is able to distinguish features of radiotherapy treatment plans not discernible using commercial systems. Plans that have similar DVHs may have different spatial and biological characteristics and the application of novel tools such as sDVH and DCF within the software may substantially change the apparent plan quality or predicted plan metrics such as TCP and NTCP. For the cases examined, both the calculation method and the application of DCF can change the ranking order of competing plans. The voxel-by-voxel TCP model makes it feasible to incorporate spatial variations of clonogen densities (n), radiosensitivities (SF2), and fractionation sensitivities (alpha/beta) as those data become available. The new software incorporates both spatial and biological information into the treatment planning process. The application of multiple methods for the incorporation of biological and spatial information has demonstrated that the order of application of biological models can change the order of plan ranking. Thus, the results of plan evaluation and optimization are dependent not only on the models used but also on the order in which they are applied. This software can help the planner choose more biologically optimal treatment plans and potentially predict treatment outcome more accurately.

WE-B-304-03: Biological Treatment Planning

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

Orton, C.

The ultimate goal of radiotherapy treatment planning is to find a treatment that will yield a high tumor control probability (TCP) with an acceptable normal tissue complication probability (NTCP). Yet most treatment planning today is not based upon optimization of TCPs and NTCPs, but rather upon meeting physical dose and volume constraints defined by the planner. It has been suggested that treatment planning evaluation and optimization would be more effective if they were biologically and not dose/volume based, and this is the claim debated in this month’s Point/Counterpoint. After a brief overview of biologically and DVH based treatment planning bymore » the Moderator Colin Orton, Joseph Deasy (for biological planning) and Charles Mayo (against biological planning) will begin the debate. Some of the arguments in support of biological planning include: this will result in more effective dose distributions for many patients DVH-based measures of plan quality are known to have little predictive value there is little evidence that either D95 or D98 of the PTV is a good predictor of tumor control sufficient validated outcome prediction models are now becoming available and should be used to drive planning and optimization Some of the arguments against biological planning include: several decades of experience with DVH-based planning should not be discarded we do not know enough about the reliability and errors associated with biological models the radiotherapy community in general has little direct experience with side by side comparisons of DVH vs biological metrics and outcomes it is unlikely that a clinician would accept extremely cold regions in a CTV or hot regions in a PTV, despite having acceptable TCP values Learning Objectives: To understand dose/volume based treatment planning and its potential limitations To understand biological metrics such as EUD, TCP, and NTCP To understand biologically based treatment planning and its potential limitations.« less

Gamma-oryzanol-loaded calcium pectinate microparticles reinforced with chitosan: optimization and release characteristics.

PubMed

Lee, Ji-Soo; Kim, Jong Soo; Lee, Hyeon Gyu

2009-05-01

Response surface methodology was used to optimize microparticle preparation conditions, including the ratio of pectin:gamma-oryzanol (OZ) (X(1)), agitation speed (X(2)), and the concentration of emulsifier (X(3)), for maximal entrapment efficiency (EE) of OZ-loaded Ca pectinate microparticles. The optimized values of X(1), X(2), and X(3) were found to be 2.72:5.28, 1143.5 rpm, and 2.61%, respectively. Experimental results obtained for the optimum formulation agreed favorably with the predicted results, indicating the usefulness of predicting models for EE. In order to evaluate the effect of chitosan-coating and blending on the release pattern of the entrapped OZ from microparticles, chitosan-coated and blended Ca pectinate microparticles were prepared. Release studies revealed that the chitosan treatments, especially the chitosan-coating, were effective in suppressing the release in both simulated gastric fluid (SGF) and intestinal fluid (SIF).

Optimization of treatment with interferon beta in multiple sclerosis. Usefulness of automatic system application criteria

PubMed Central

Ruiz-Peña, Juan Luís; Duque, Pablo; Izquierdo, Guillermo

2008-01-01

Background A software based tool has been developed (Optem) to allow automatize the recommendations of the Canadian Multiple Sclerosis Working Group for optimizing MS treatment in order to avoid subjective interpretation. Methods Treatment Optimization Recommendations (TORs) were applied to our database of patients treated with IFN β1a IM. Patient data were assessed during year 1 for disease activity, and patients were assigned to 2 groups according to TOR: "change treatment" (CH) and "no change treatment" (NCH). These assessments were then compared to observed clinical outcomes for disease activity over the following years. Results We have data on 55 patients. The "change treatment" status was assigned to 22 patients, and "no change treatment" to 33 patients. The estimated sensitivity and specificity according to last visit status were 73.9% and 84.4%. During the following years, the Relapse Rate was always higher in the "change treatment" group than in the "no change treatment" group (5 y; CH: 0.7, NCH: 0.07; p < 0.001, 12 m – last visit; CH: 0.536, NCH: 0.34). We obtained the same results with the EDSS (4 y; CH: 3.53, NCH: 2.55, annual progression rate in 12 m – last visit; CH: 0.29, NCH: 0.13). Conclusion Applying TOR at the first year of therapy allowed accurate prediction of continued disease activity in relapses and disability progression. PMID:18325088

A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy.

PubMed

Ji, Zhiwei; Wang, Bing; Yan, Ke; Dong, Ligang; Meng, Guanmin; Shi, Lei

2017-12-21

In recent years, the integration of 'omics' technologies, high performance computation, and mathematical modeling of biological processes marks that the systems biology has started to fundamentally impact the way of approaching drug discovery. The LINCS public data warehouse provides detailed information about cell responses with various genetic and environmental stressors. It can be greatly helpful in developing new drugs and therapeutics, as well as improving the situations of lacking effective drugs, drug resistance and relapse in cancer therapies, etc. In this study, we developed a Ternary status based Integer Linear Programming (TILP) method to infer cell-specific signaling pathway network and predict compounds' treatment efficacy. The novelty of our study is that phosphor-proteomic data and prior knowledge are combined for modeling and optimizing the signaling network. To test the power of our approach, a generic pathway network was constructed for a human breast cancer cell line MCF7; and the TILP model was used to infer MCF7-specific pathways with a set of phosphor-proteomic data collected from ten representative small molecule chemical compounds (most of them were studied in breast cancer treatment). Cross-validation indicated that the MCF7-specific pathway network inferred by TILP were reliable predicting a compound's efficacy. Finally, we applied TILP to re-optimize the inferred cell-specific pathways and predict the outcomes of five small compounds (carmustine, doxorubicin, GW-8510, daunorubicin, and verapamil), which were rarely used in clinic for breast cancer. In the simulation, the proposed approach facilitates us to identify a compound's treatment efficacy qualitatively and quantitatively, and the cross validation analysis indicated good accuracy in predicting effects of five compounds. In summary, the TILP model is useful for discovering new drugs for clinic use, and also elucidating the potential mechanisms of a compound to targets.

Clinical trial: factors associated with freedom from relapse of heartburn in patients with healed reflux oesophagitis--results from the maintenance phase of the EXPO study.

PubMed

Labenz, J; Armstrong, D; Zetterstrand, S; Eklund, S; Leodolter, A

2009-06-01

Ability to predict freedom from heartburn relapse during maintenance therapy for healed reflux oesophagitis may facilitate optimal treatment choices for individual patients. To determine factors predicting freedom from heartburn relapse during maintenance proton pump inhibitor therapy in patients with healed reflux oesophagitis. This post-hoc analysis used data from the maintenance phase of the EXPO study (AstraZeneca study code: SH-NEG-0008); 2766 patients with healed reflux oesophagitis and resolved heartburn received once-daily esomeprazole 20 mg or pantoprazole 20 mg for 6 months. Multiple logistic regression analysis determined factors associated with freedom from heartburn relapse. Heartburn relapse rates were lower with esomeprazole than pantoprazole in all subgroups analysed. Esomeprazole treatment was the factor most strongly associated with freedom from heartburn relapse (odds ratio 2.08; P < 0.0001). Other factors significantly associated with freedom from heartburn relapse were Helicobacter pylori infection, greater age, non-obesity, absence of epigastric pain at baseline, pre-treatment nonsevere heartburn and GERD symptom duration < or =5 years. Several factors predict freedom from heartburn relapse during maintenance proton pump inhibitor therapy for healed reflux oesophagitis, the strongest being choice of proton pump inhibitor. These findings outline the importance of optimizing acid control and identifying predictors of relapse for effective long-term symptom management in reflux oesophagitis patients.

A study on the predictability of acute lymphoblastic leukaemia response to treatment using a hybrid oncosimulator.

PubMed

Ouzounoglou, Eleftherios; Kolokotroni, Eleni; Stanulla, Martin; Stamatakos, Georgios S

2018-02-06

Efficient use of Virtual Physiological Human (VPH)-type models for personalized treatment response prediction purposes requires a precise model parameterization. In the case where the available personalized data are not sufficient to fully determine the parameter values, an appropriate prediction task may be followed. This study, a hybrid combination of computational optimization and machine learning methods with an already developed mechanistic model called the acute lymphoblastic leukaemia (ALL) Oncosimulator which simulates ALL progression and treatment response is presented. These methods are used in order for the parameters of the model to be estimated for retrospective cases and to be predicted for prospective ones. The parameter value prediction is based on a regression model trained on retrospective cases. The proposed Hybrid ALL Oncosimulator system has been evaluated when predicting the pre-phase treatment outcome in ALL. This has been correctly achieved for a significant percentage of patient cases tested (approx. 70% of patients). Moreover, the system is capable of denying the classification of cases for which the results are not trustworthy enough. In that case, potentially misleading predictions for a number of patients are avoided, while the classification accuracy for the remaining patient cases further increases. The results obtained are particularly encouraging regarding the soundness of the proposed methodologies and their relevance to the process of achieving clinical applicability of the proposed Hybrid ALL Oncosimulator system and VPH models in general.

A Visual Basic simulation software tool for performance analysis of a membrane-based advanced water treatment plant.

PubMed

Pal, P; Kumar, R; Srivastava, N; Chaudhuri, J

2014-02-01

A Visual Basic simulation software (WATTPPA) has been developed to analyse the performance of an advanced wastewater treatment plant. This user-friendly and menu-driven software is based on the dynamic mathematical model for an industrial wastewater treatment scheme that integrates chemical, biological and membrane-based unit operations. The software-predicted results corroborate very well with the experimental findings as indicated in the overall correlation coefficient of the order of 0.99. The software permits pre-analysis and manipulation of input data, helps in optimization and exhibits performance of an integrated plant visually on a graphical platform. It allows quick performance analysis of the whole system as well as the individual units. The software first of its kind in its domain and in the well-known Microsoft Excel environment is likely to be very useful in successful design, optimization and operation of an advanced hybrid treatment plant for hazardous wastewater.

Enhancement of multimodality texture-based prediction models via optimization of PET and MR image acquisition protocols: a proof of concept

NASA Astrophysics Data System (ADS)

Vallières, Martin; Laberge, Sébastien; Diamant, André; El Naqa, Issam

2017-11-01

Texture-based radiomic models constructed from medical images have the potential to support cancer treatment management via personalized assessment of tumour aggressiveness. While the identification of stable texture features under varying imaging settings is crucial for the translation of radiomics analysis into routine clinical practice, we hypothesize in this work that a complementary optimization of image acquisition parameters prior to texture feature extraction could enhance the predictive performance of texture-based radiomic models. As a proof of concept, we evaluated the possibility of enhancing a model constructed for the early prediction of lung metastases in soft-tissue sarcomas by optimizing PET and MR image acquisition protocols via computerized simulations of image acquisitions with varying parameters. Simulated PET images from 30 STS patients were acquired by varying the extent of axial data combined per slice (‘span’). Simulated T 1-weighted and T 2-weighted MR images were acquired by varying the repetition time and echo time in a spin-echo pulse sequence, respectively. We analyzed the impact of the variations of PET and MR image acquisition parameters on individual textures, and we investigated how these variations could enhance the global response and the predictive properties of a texture-based model. Our results suggest that it is feasible to identify an optimal set of image acquisition parameters to improve prediction performance. The model constructed with textures extracted from simulated images acquired with a standard clinical set of acquisition parameters reached an average AUC of 0.84 +/- 0.01 in bootstrap testing experiments. In comparison, the model performance significantly increased using an optimal set of image acquisition parameters (p = 0.04 ), with an average AUC of 0.89 +/- 0.01 . Ultimately, specific acquisition protocols optimized to generate superior radiomics measurements for a given clinical problem could be developed and standardized via dedicated computer simulations and thereafter validated using clinical scanners.

Predictive features associated with thyrotoxic storm and management.

PubMed

Bacuzzi, Alessandro; Dionigi, Gianlorenzo; Guzzetti, Luca; De Martino, Alessandro Ivan; Severgnini, Paolo; Cuffari, Salvatore

2017-10-01

Thyroid storm (TS) is an endocrine emergency characterized by rapid deterioration, associated with high mortality rate therefore rapid diagnosis and emergent treatment is mandatory. In the past, thyroid surgery was the most common cause of TS, but recent preoperative medication creates a euthyroid state before performing surgery. An active approach during perioperative period could determine an effective clinical treatment of this life-threating diseases. Recently, the Japan Thyroid Association and Japan Endocrine Society developed diagnostic criteria for TS focusing on premature and prompt diagnosis avoiding inopportune e useless drugs. This review analyses predictive features associated with thyrotoxic storm highlighting recent literature to optimize the patient quality of care.

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

NONE

The ultimate goal of radiotherapy treatment planning is to find a treatment that will yield a high tumor control probability (TCP) with an acceptable normal tissue complication probability (NTCP). Yet most treatment planning today is not based upon optimization of TCPs and NTCPs, but rather upon meeting physical dose and volume constraints defined by the planner. It has been suggested that treatment planning evaluation and optimization would be more effective if they were biologically and not dose/volume based, and this is the claim debated in this month’s Point/Counterpoint. After a brief overview of biologically and DVH based treatment planning bymore » the Moderator Colin Orton, Joseph Deasy (for biological planning) and Charles Mayo (against biological planning) will begin the debate. Some of the arguments in support of biological planning include: this will result in more effective dose distributions for many patients DVH-based measures of plan quality are known to have little predictive value there is little evidence that either D95 or D98 of the PTV is a good predictor of tumor control sufficient validated outcome prediction models are now becoming available and should be used to drive planning and optimization Some of the arguments against biological planning include: several decades of experience with DVH-based planning should not be discarded we do not know enough about the reliability and errors associated with biological models the radiotherapy community in general has little direct experience with side by side comparisons of DVH vs biological metrics and outcomes it is unlikely that a clinician would accept extremely cold regions in a CTV or hot regions in a PTV, despite having acceptable TCP values Learning Objectives: To understand dose/volume based treatment planning and its potential limitations To understand biological metrics such as EUD, TCP, and NTCP To understand biologically based treatment planning and its potential limitations.« less

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

Ranganathan, V; Kumar, P; Bzdusek, K

Purpose: We propose a novel data-driven method to predict the achievability of clinical objectives upfront before invoking the IMRT optimization. Methods: A new metric called “Geometric Complexity (GC)” is used to estimate the achievability of clinical objectives. Here, GC is the measure of the number of “unmodulated” beamlets or rays that intersect the Region-of-interest (ROI) and the target volume. We first compute the geometric complexity ratio (GCratio) between the GC of a ROI (say, parotid) in a reference plan and the GC of the same ROI in a given plan. The GCratio of a ROI indicates the relative geometric complexitymore » of the ROI as compared to the same ROI in the reference plan. Hence GCratio can be used to predict if a defined clinical objective associated with the ROI can be met by the optimizer for a given case. Basically a higher GCratio indicates a lesser likelihood for the optimizer to achieve the clinical objective defined for a given ROI. Similarly, a lower GCratio indicates a higher likelihood for the optimizer to achieve the clinical objective defined for the given ROI. We have evaluated the proposed method on four Head and Neck cases using Pinnacle3 (version 9.10.0) Treatment Planning System (TPS). Results: Out of the total of 28 clinical objectives from four head and neck cases included in the study, 25 were in agreement with the prediction, which implies an agreement of about 85% between predicted and obtained results. The Pearson correlation test shows a positive correlation between predicted and obtained results (Correlation = 0.82, r2 = 0.64, p < 0.005). Conclusion: The study demonstrates the feasibility of the proposed method in head and neck cases for predicting the achievability of clinical objectives with reasonable accuracy.« less

Complaint-adaptive power density optimization as a tool for HTP-guided steering in deep hyperthermia treatment of pelvic tumors

NASA Astrophysics Data System (ADS)

Canters, R. A. M.; Franckena, M.; van der Zee, J.; Van Rhoon, G. C.

2008-12-01

For an efficient clinical use of HTP (hyperthermia treatment planning), optimization methods are needed. In this study, a complaint-adaptive PD (power density) optimization as a tool for HTP-guided steering in deep hyperthermia of pelvic tumors is developed and tested. PD distribution in patients is predicted using FE-models. Two goal functions, Opt1 and Opt2, are applied to optimize PD distributions. Optimization consists of three steps: initial optimization, adaptive optimization after a first complaint and increasing the weight of a region after recurring complaints. Opt1 initially considers only target PD whereas Opt2 also takes into account hot spots. After patient complaints though, both limit PD in a region. Opt1 and Opt2 are evaluated in a phantom test, using patient models and during hyperthermia treatment. The phantom test and a sensitivity study in ten patient models, show that HTP-guided steering is most effective in peripheral complaint regions. Clinical evaluation in two groups of five patients shows that time between complaints is longer using Opt2 (p = 0.007). However, this does not lead to significantly different temperatures (T50s of 40.3 (Opt1) versus 40.1 °C (Opt2) (p = 0.898)). HTP-guided steering is feasible in terms of PD reduction in complaint regions and in time consumption. Opt2 is preferable in future use, because of better complaint reduction and control.

Rapeseed-straw enzymatic digestibility enhancement by sodium hydroxide treatment under ultrasound irradiation.

PubMed

Kang, Kyeong Eop; Jeong, Gwi-Taek; Park, Don-Hee

2013-08-01

In this study, we carried out sodium hydroxide and sonication pretreatments of rapeseed straw (Brassica napus) to obtain monosugar suitable for production of biofuels. To optimize the pretreatment conditions, we applied a statistical response-surface methodology. The optimal pretreatment conditions using sodium hydroxide under sonication irradiation were determined to be 75.0 °C, 7.0 % sodium hydroxide, and 6.8 h. For these conditions, we predicted 97.3 % enzymatic digestibility. In repeated experiments to validate the predicted value, 98.9 ± 0.3 % enzymatic digestibility was obtained, which was well within the range of the predicted model. Moreover, sonication irradiation was found to have a good effect on pretreatment in the lower temperature range and at all concentrations of sodium hydroxide. According to scanning electron microscopy images, the surface area and pore size of the pretreated rapeseed straw were modified by the sodium hydroxide pretreatment under sonication irradiation.

A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence.

PubMed

Fan, Mingyi; Hu, Jiwei; Cao, Rensheng; Ruan, Wenqian; Wei, Xionghui

2018-06-01

Water pollution occurs mainly due to inorganic and organic pollutants, such as nutrients, heavy metals and persistent organic pollutants. For the modeling and optimization of pollutants removal, artificial intelligence (AI) has been used as a major tool in the experimental design that can generate the optimal operational variables, since AI has recently gained a tremendous advance. The present review describes the fundamentals, advantages and limitations of AI tools. Artificial neural networks (ANNs) are the AI tools frequently adopted to predict the pollutants removal processes because of their capabilities of self-learning and self-adapting, while genetic algorithm (GA) and particle swarm optimization (PSO) are also useful AI methodologies in efficient search for the global optima. This article summarizes the modeling and optimization of pollutants removal processes in water treatment by using multilayer perception, fuzzy neural, radial basis function and self-organizing map networks. Furthermore, the results conclude that the hybrid models of ANNs with GA and PSO can be successfully applied in water treatment with satisfactory accuracies. Finally, the limitations of current AI tools and their new developments are also highlighted for prospective applications in the environmental protection. Copyright © 2018 Elsevier Ltd. All rights reserved.

Accumulating Data to Optimally Predict Obesity Treatment (ADOPT) Core Measures: Environmental Domain.

PubMed

Saelens, Brian E; Arteaga, S Sonia; Berrigan, David; Ballard, Rachel M; Gorin, Amy A; Powell-Wiley, Tiffany M; Pratt, Charlotte; Reedy, Jill; Zenk, Shannon N

2018-04-01

There is growing interest in how environment is related to adults' weight and activity and eating behaviors. However, little is known about whether environmental factors are related to the individual variability seen in adults' intentional weight loss or maintenance outcomes. The environmental domain subgroup of the Accumulating Data to Optimally Predict obesity Treatment (ADOPT) Core Measures Project sought to identify a parsimonious set of objective and perceived neighborhood and social environment constructs and corresponding measures to include in the assessment of response to adult weight-loss treatment. Starting with the home address, the environmental domain subgroup recommended for inclusion in future weight-loss or maintenance studies constructs and measures related to walkability, perceived land use mix, food outlet accessibility (perceived and objective), perceived food availability, socioeconomics, and crime-related safety (perceived and objective) to characterize the home neighborhood environment. The subgroup also recommended constructs and measures related to social norms (perceived and objective) and perceived support to characterize an individual's social environment. The 12 neighborhood and social environment constructs and corresponding measures provide a succinct and comprehensive set to allow for more systematic examination of the impact of environment on adults' weight loss and maintenance. © 2018 The Obesity Society.

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

Lu, Bo, E-mail: luboufl@gmail.com; Park, Justin C.; Fan, Qiyong

Purpose: Accurately localizing lung tumor localization is essential for high-precision radiation therapy techniques such as stereotactic body radiation therapy (SBRT). Since direct monitoring of tumor motion is not always achievable due to the limitation of imaging modalities for treatment guidance, placement of fiducial markers on the patient’s body surface to act as a surrogate for tumor position prediction is a practical alternative for tracking lung tumor motion during SBRT treatments. In this work, the authors propose an innovative and robust model to solve the multimarker position optimization problem. The model is able to overcome the major drawbacks of the sparsemore » optimization approach (SOA) model. Methods: The principle-component-analysis (PCA) method was employed as the framework to build the authors’ statistical prediction model. The method can be divided into two stages. The first stage is to build the surrogate tumor matrix and calculate its eigenvalues and associated eigenvectors. The second stage is to determine the “best represented” columns of the eigenvector matrix obtained from stage one and subsequently acquire the optimal marker positions as well as numbers. Using 4-dimensional CT (4DCT) and breath hold CT imaging data, the PCA method was compared to the SOA method with respect to calculation time, average prediction accuracy, prediction stability, noise resistance, marker position consistency, and marker distribution. Results: The PCA and SOA methods which were both tested were on all 11 patients for a total of 130 cases including 4DCT and breath-hold CT scenarios. The maximum calculation time for the PCA method was less than 1 s with 64 752 surface points, whereas the average calculation time for the SOA method was over 12 min with 400 surface points. Overall, the tumor center position prediction errors were comparable between the two methods, and all were less than 1.5 mm. However, for the extreme scenarios (breath hold), the prediction errors for the PCA method were not only smaller, but were also more stable than for the SOA method. Results obtained by imposing a series of random noises to the surrogates indicated that the PCA method was much more noise resistant than the SOA method. The marker position consistency tests using various combinations of 4DCT phases to construct the surrogates suggested that the marker position predictions of the PCA method were more consistent than those of the SOA method, in spite of surrogate construction. Marker distribution tests indicated that greater than 80% of the calculated marker positions fell into the high cross correlation and high motion magnitude regions for both of the algorithms. Conclusions: The PCA model is an accurate, efficient, robust, and practical model for solving the multimarker position optimization problem to predict lung tumor motion during SBRT treatments. Due to its generality, PCA model can also be applied to other imaging guidance system whichever using surface motion as the surrogates.« less

Reduced tyrosine kinase inhibitor dose is predicted to be as effective as standard dose in chronic myeloid leukemia: A simulation study based on phase 3 trial data.

PubMed

Fassoni, Artur C; Baldow, Christoph; Roeder, Ingo; Glauche, Ingmar

2018-06-28

Continuing tyrosine kinase inhibitor mediated targeting of the BCR-ABL1 oncoprotein is the standard therapy for chronic myeloid leukemia and allows for a sustained disease control in the majority of patients. While therapy cessation for patients appeared as a safe option for about half of the optimally responding patients, a systematic assessment of long-term tyrosine kinase inhibitor dose de-escalation is missing. We use a mathematical model to analyze and consistently describe biphasic treatment responses from tyrosine kinase inhibitor treated patients from two independent clinical phase-3 trials. Scale estimates reveal that drug efficiency determines the initial response while the long-term behavior is limited by the rare activation of leukemic stem cells. We use this mathematical framework to investigate the influence of different dosing regimens on the treatment outcome. We provide strong evidence suggesting that tyrosine kinase inhibitor dose de-escalation (at least 50%) does not lead to a reduction of long-term treatment efficiency for most patients, which have already achieved sustained remission, and maintains the secondary decline of BCR-ABL1 levels. We demonstrate that continuous BCR-ABL1 monitoring provides patient-specific predictions of an optimal reduced dose not decreasing the anti-leukemic effect on residual leukemic stem cells. Our results are consistent with the interim results of the DESTINY trial and provide clinically testable predictions. Our results suggest that dose halving should be considered as a long-term treatment option for well-responding chronic myeloid leukemia patients under continuing maintenance therapy with tyrosine kinase inhibitors. We emphasize the clinical potential of this approach to reduce treatment-related side-effects and therapy costs. Copyright © 2018, Ferrata Storti Foundation.

Electroconvulsive Therapy Intervention for Parkinson’s Disease

PubMed Central

Glowacki, Anna; Lippmann, Steven

2015-01-01

Background: Electroconvulsive therapy is an established means to improve function in a variety of psychiatric and neurologic conditions, particularly for patients who remain treatment-refractory. Parkinson’s disease is a neurodegenerative disorder that sometimes does not respond well to conventional pharmacotherapies. Reports have indicated that electroconvulsive therapy may be an effective and safe treatment for those patients with Parkinson’s disease who are not optimally responding to first-line treatments. Despite these reports, however, electroconvulsive therapy is not often used by clinicians in patients with treatment-resistant Parkinson’s disease, perhaps due to stigma, lack of knowledge regarding its safety and efficacy, and/or inability to predict the duration of therapeutic benefit. Objective: Our objective was to determine if the available literature on ECT supports it as a safe and effective treatment option in patients with treatment-refractory Parkinson’s disease. Conclusion: Motoric improvement induced by electroconvulsive therapy has been documented for decades in persons with Parkinson’s disease. Efficacy and safety are reported following electroconvulsive therapy in people with Parkinson’s disease who have sub-optimal response to medicines or experience the “on/off” phenomenon to L-dopa. Electroconvulsive therapy is an effective option for acute and maintenance treatment of Parkinson’s disease in select patients. Inability to predict how long the beneficial effects of ECT therapy will last in patients with Parkinson’s disease may be a reason why this treatment is underutilized by clinicians. More research is warranted to clarify parameters for application and duration of therapeutic benefit in individuals with difficult-to-treat Parkinson’s disease. PMID:26634178

Development of a model with which to predict the life expectancy of patients with spinal epidural metastasis.

PubMed

Bartels, Ronald H M A; Feuth, Ton; van der Maazen, Richard; Verbeek, André L M; Kappelle, Arnoud C; André Grotenhuis, J; Leer, Jan Willem

2007-11-01

The surgical treatment of spinal epidural metastasis is evolving. To be a surgical candidate, a patient should have a life expectancy of at least 3 months. Estimation of survival by experienced specialists has proven to be unreliable. The Cox proportional hazards model was used to make a prediction model. To validate the model, Efron optimism correction by bootstrapping was performed. Retrospective data of patients treated for a spinal metastasis were used. Possible predictive factors were defined based on clinical experience and the literature. Statistical methods and clinical knowledge were also used to reveal an optimal set of predictors of survival. Data from patients treated at the Department of Radiation Oncology for spinal metastasis between 1998 and 2005 were evaluated. The case notes of 219 patients form the base of this study. In the final model, only 5 variables were required to predict the survival of a patient with spinal metastasis: sex, location of the primary lesion, intentional curative treatment of the primary tumor, cervical location of the spinal metastasis, and Karnofsky performance score. Examples with different predictors are given. The R(2) (N) index of Nagelkerke was 0.36 (95% confidence interval [95% CI], 0.28-0.48) and the c-index 0.72 (95% CI, 0.68-0.77). A reliable and simple model with which to predict the survival of a patient with spinal epidural metastasis is presented. Without the need for extensive investigations, survival can be predicted and only 5 easily obtainable parameters are required.

A physiologically based pharmacokinetic (PBPK) model for predicting the efficacy of drug overdose treatment with liposomes in man.

PubMed

Howell, Brett A; Chauhan, Anuj

2010-08-01

Physiologically based pharmacokinetic (PBPK) models were developed for design and optimization of liposome therapy for treatment of overdoses of tricyclic antidepressants and local anesthetics. In vitro drug-binding data for pegylated, anionic liposomes and published mechanistic equations for partition coefficients were used to develop the models. The models were proven reliable through comparisons to intravenous data. The liposomes were predicted to be highly effective at treating amitriptyline overdoses, with reductions in the area under the concentration versus time curves (AUC) of 64% for the heart and brain. Peak heart and brain drug concentrations were predicted to drop by 20%. Bupivacaine AUC and peak concentration reductions were lower at 15.4% and 17.3%, respectively, for the heart and brain. The predicted pharmacokinetic profiles following liposome administration agreed well with data from clinical studies where protein fragments were administered to patients for overdose treatment. Published data on local cardiac function were used to relate the predicted concentrations in the body to local pharmacodynamic effects in the heart. While the results offer encouragement for future liposome therapies geared toward overdose, it is imperative to point out that animal experiments and phase I clinical trials are the next steps to ensuring the efficacy of the treatment. (c) 2010 Wiley-Liss, Inc. and the American Pharmacists Association

Present and future breast cancer management--bench to bedside and back: a positioning paper of academia, regulatory authorities and pharmaceutical industry.

PubMed

Bartsch, R; Frings, S; Marty, M; Awada, A; Berghoff, A S; Conte, P; Dickin, S; Enzmann, H; Gnant, M; Hasmann, M; Hendriks, H R; Llombart, A; Massacesi, C; von Minckwitz, G; Penault-Llorca, F; Scaltriti, M; Yarden, Y; Zwierzina, H; Zielinski, C C

2014-04-01

Insights into tumour biology of breast cancer have led the path towards the introduction of targeted treatment approaches; still, breast cancer-related mortality remains relatively high. Efforts in the field of basic research revealed new druggable targets which now await validation within the context of clinical trials. Therefore, questions concerning the optimal design of future studies are becoming even more pertinent. Aspects such as the ideal end point, availability of predictive markers to identify the optimal cohort for drug testing, or potential mechanisms of resistance need to be resolved. An expert panel representing the academic community, the pharmaceutical industry, as well as European Regulatory Authorities met in Vienna, Austria, in November 2012, in order to discuss breast cancer biology, identification of novel biological targets and optimal drug development with the aim of treatment individualization. This article summarizes statements and perspectives provided by the meeting participants.

[State of the art molecular diagnostics and therapy of chronic lymphocytic leukaemia in the era of new targeted therapies].

PubMed

Gurbity Pálfi, Tímea; Fésüs, Viktória; Bödör, Csaba; Borbényi, Zita

2017-10-01

Chronic lymphoid leukaemia (CLL) has a heterogeneous clinical course depending on many clinical and molecular prognostic markers, which play an important role in the selection of the best treatment option. So far, TP53 disruption is the key prognostic and predictive factor assisting treatment decisions, especially in the era of novel therapies. Asymptomatic patients in early stages of the disease will still benefit from watchful waiting. In the frontline setting, chemoimmunotherapy is still the standard care in the majority of standard risk CLL patients. New classes of drugs like kinase inhibitors and BCL-2 inhibitors (ibrutinib, idelalisib and venetoclax) are the treatment of choice in CLL patients with relapsed/refractory disease, with the exception of high risk disease, where the optimal treatment is frontline ibrutinib monotherapy. In the near future, integrating next generation sequencing into the routine diagnostics would help the development of individual CLL patient management and to choose an optimal treatment strategy. Orv Hetil. 2017; 158(41): 1620-1629.

Personality and Differential Treatment Response in Major Depression: A Randomized Controlled Trial Comparing Cognitive-Behavioural Therapy and Pharmacotherapy

PubMed Central

Bagby, R Michael; Quilty, Lena C; Segal, Zindel V; McBride, Carolina C; Kennedy, Sidney H; Costa, Paul T

2008-01-01

Objective Effective treatments for major depressive disorder exist, yet some patients fail to respond, or achieve only partial response. One approach to optimizing treatment success is to identify which patients are more likely to respond best to which treatments. The objective of this investigation was to determine if patient personality characteristics are predictive of response to either cognitive-behavioural therapy (CBT) or pharmacotherapy (PHT). Method Depressed patients completed the Revised NEO Personality Inventory, which measures the higher-order domain and lower-order facet traits of the Five-Factor Model of Personality, and were randomized to receive either CBT or PHT. Result Four personality traits—the higher-order domain neuroticism and 3 lower-order facet traits: trust, straightforwardness, and tendermindedness—were able to distinguish a differential response rate to CBT, compared with PHT. Conclusion The assessment of patient dimensional personality traits can assist in the selection and optimization of treatment response for depressed patients. PMID:18616856

Relapse prediction in Graves´ disease: Towards mathematical modeling of clinical, immune and genetic markers.

PubMed

Langenstein, Christoph; Schork, Diana; Badenhoop, Klaus; Herrmann, Eva

2016-12-01

Graves' disease (GD) is an important and prevalent thyroid autoimmune disorder. Standard therapy for GD consists of antithyroid drugs (ATD) with treatment periods of around 12 months but relapse is frequent. Since predictors for relapse are difficult to identify the individual decision making for optimal treatment is often arbitrary. After reviewing the literature on this topic we summarize important factors involved in GD and with respect to their potential for relapse prediction from markers before and after treatment. This information was used to design a mathematical model integrating thyroid hormone parameters, thyroid size, antibody titers and a complex algorithm encompassing genetic predisposition, environmental exposures and current immune activity in order to arrive at a prognostic index for relapse risk after treatment. In the search for a tool to analyze and predict relapse in GD mathematical modeling is a promising approach. In analogy to mathematical modeling approaches in other diseases such as viral infections, we developed a differential equation model on the basis of published clinical trials in patients with GD. Although our model needs further evaluation to be applicable in a clinical context, it provides a perspective for an important contribution to a final statistical prediction model.

Modeling how reversal of immune exhaustion elicits cure of chronic hepatitis C after the end of treatment with direct-acting antiviral agents.

PubMed

Baral, Subhasish; Roy, Rahul; Dixit, Narendra M

2018-05-09

A fraction of chronic hepatitis C patients treated with direct-acting antivirals (DAAs) achieved sustained virological responses (SVR), or cure, despite having detectable viremia at the end of treatment (EOT). This observation, termed EOT + /SVR, remains puzzling and precludes rational optimization of treatment durations. One hypothesis to explain EOT + /SVR, the immunologic hypothesis, argues that the viral decline induced by DAAs during treatment reverses the exhaustion of cytotoxic T lymphocytes (CTLs), which then clear the infection after treatment. Whether the hypothesis is consistent with data of viral load changes in patients who experienced EOT + /SVR is unknown. Here, we constructed a mathematical model of viral kinetics incorporating the immunologic hypothesis and compared its predictions with patient data. We found the predictions to be in quantitative agreement with patient data. Using the model, we unraveled an underlying bistability that gives rise to EOT + /SVR and presents a new avenue to optimize treatment durations. Infected cells trigger both activation and exhaustion of CTLs. CTLs in turn kill infected cells. Due to these competing interactions, two stable steady states, chronic infection and viral clearance, emerge, separated by an unstable steady state with intermediate viremia. When treatment during chronic infection drives viremia sufficiently below the unstable state, spontaneous viral clearance results post-treatment, marking EOT + /SVR. The duration to achieve this desired reduction in viremia defines the minimum treatment duration required for ensuring SVR, which our model can quantify. Estimating parameters defining the CTL response of individuals to HCV infection would enable the application of our model to personalize treatment durations. © 2018 The Authors Immunology & Cell Biology published by John Wiley & Sons Australia, Ltd on behalf of Australasian Society for Immunology Inc.

[Study on application of SVM in prediction of coronary heart disease].

PubMed

Zhu, Yue; Wu, Jianghua; Fang, Ying

2013-12-01

Base on the data of blood pressure, plasma lipid, Glu and UA by physical test, Support Vector Machine (SVM) was applied to identify coronary heart disease (CHD) in patients and non-CHD individuals in south China population for guide of further prevention and treatment of the disease. Firstly, the SVM classifier was built using radial basis kernel function, liner kernel function and polynomial kernel function, respectively. Secondly, the SVM penalty factor C and kernel parameter sigma were optimized by particle swarm optimization (PSO) and then employed to diagnose and predict the CHD. By comparison with those from artificial neural network with the back propagation (BP) model, linear discriminant analysis, logistic regression method and non-optimized SVM, the overall results of our calculation demonstrated that the classification performance of optimized RBF-SVM model could be superior to other classifier algorithm with higher accuracy rate, sensitivity and specificity, which were 94.51%, 92.31% and 96.67%, respectively. So, it is well concluded that SVM could be used as a valid method for assisting diagnosis of CHD.

Using Central Composite Experimental Design to Optimize the Degradation of Tylosin from Aqueous Solution by Photo-Fenton Reaction

PubMed Central

Sarrai, Abd Elaziz; Hanini, Salah; Merzouk, Nachida Kasbadji; Tassalit, Djilali; Szabó, Tibor; Hernádi, Klára; Nagy, László

2016-01-01

The feasibility of the application of the Photo-Fenton process in the treatment of aqueous solution contaminated by Tylosin antibiotic was evaluated. The Response Surface Methodology (RSM) based on Central Composite Design (CCD) was used to evaluate and optimize the effect of hydrogen peroxide, ferrous ion concentration and initial pH as independent variables on the total organic carbon (TOC) removal as the response function. The interaction effects and optimal parameters were obtained by using MODDE software. The significance of the independent variables and their interactions was tested by means of analysis of variance (ANOVA) with a 95% confidence level. Results show that the concentration of the ferrous ion and pH were the main parameters affecting TOC removal, while peroxide concentration had a slight effect on the reaction. The optimum operating conditions to achieve maximum TOC removal were determined. The model prediction for maximum TOC removal was compared to the experimental result at optimal operating conditions. A good agreement between the model prediction and experimental results confirms the soundness of the developed model. PMID:28773551

Process-time Optimization of Vacuum Degassing Using a Genetic Alloy Design Approach

PubMed Central

Dilner, David; Lu, Qi; Mao, Huahai; Xu, Wei; van der Zwaag, Sybrand; Selleby, Malin

2014-01-01

This paper demonstrates the use of a new model consisting of a genetic algorithm in combination with thermodynamic calculations and analytical process models to minimize the processing time during a vacuum degassing treatment of liquid steel. The model sets multiple simultaneous targets for final S, N, O, Si and Al levels and uses the total slag mass, the slag composition, the steel composition and the start temperature as optimization variables. The predicted optimal conditions agree well with industrial practice. For those conditions leading to the shortest process time the target compositions for S, N and O are reached almost simultaneously. PMID:28788286

Ultimate open pit stochastic optimization

NASA Astrophysics Data System (ADS)

Marcotte, Denis; Caron, Josiane

2013-02-01

Classical open pit optimization (maximum closure problem) is made on block estimates, without directly considering the block grades uncertainty. We propose an alternative approach of stochastic optimization. The stochastic optimization is taken as the optimal pit computed on the block expected profits, rather than expected grades, computed from a series of conditional simulations. The stochastic optimization generates, by construction, larger ore and waste tonnages than the classical optimization. Contrary to the classical approach, the stochastic optimization is conditionally unbiased for the realized profit given the predicted profit. A series of simulated deposits with different variograms are used to compare the stochastic approach, the classical approach and the simulated approach that maximizes expected profit among simulated designs. Profits obtained with the stochastic optimization are generally larger than the classical or simulated pit. The main factor controlling the relative gain of stochastic optimization compared to classical approach and simulated pit is shown to be the information level as measured by the boreholes spacing/range ratio. The relative gains of the stochastic approach over the classical approach increase with the treatment costs but decrease with mining costs. The relative gains of the stochastic approach over the simulated pit approach increase both with the treatment and mining costs. At early stages of an open pit project, when uncertainty is large, the stochastic optimization approach appears preferable to the classical approach or the simulated pit approach for fair comparison of the values of alternative projects and for the initial design and planning of the open pit.

Framework for Smart Electronic Health Record- Linked Predictive Models to Optimize Care for Complex Digestive Diseases

DTIC Science & Technology

2015-03-01

data against previous published outcomes in AP and Chronic Pancreatitis (CP). This served as useful validation of our data set before entering the...These patients can develop multiple complications from their disease. In addition, the treatments for CD (both medical and surgical ) can impose...years of diagnosis. The treatment for CD can sometimes involve very expensive medications with potentially serious side effects, as well as surgical

Optimization of the Temporal Pattern of Applied Radiation Dose: Implication for the Treatment of Prostate Cancer

DTIC Science & Technology

2009-03-01

environment II.A: Characterization of dosimetry in IMRT radiobiological experiment phantom using TLDs and film. (7-10 mos.) Objectives: 1... dosimetry with TLDs and film. (8-10 mos.) 4. Analysis of measured dosimetry with TLDs and film compared to predicted dosimetry from treatment...cells were). Dosimetry in the phantom was assessed with film and monitor units were calculated accordingly to deliver the desired dose. Once in

Discussion of skill improvement in marine ecosystem dynamic models based on parameter optimization and skill assessment

NASA Astrophysics Data System (ADS)

Shen, Chengcheng; Shi, Honghua; Liu, Yongzhi; Li, Fen; Ding, Dewen

2016-07-01

Marine ecosystem dynamic models (MEDMs) are important tools for the simulation and prediction of marine ecosystems. This article summarizes the methods and strategies used for the improvement and assessment of MEDM skill, and it attempts to establish a technical framework to inspire further ideas concerning MEDM skill improvement. The skill of MEDMs can be improved by parameter optimization (PO), which is an important step in model calibration. An efficient approach to solve the problem of PO constrained by MEDMs is the global treatment of both sensitivity analysis and PO. Model validation is an essential step following PO, which validates the efficiency of model calibration by analyzing and estimating the goodness-of-fit of the optimized model. Additionally, by focusing on the degree of impact of various factors on model skill, model uncertainty analysis can supply model users with a quantitative assessment of model confidence. Research on MEDMs is ongoing; however, improvement in model skill still lacks global treatments and its assessment is not integrated. Thus, the predictive performance of MEDMs is not strong and model uncertainties lack quantitative descriptions, limiting their application. Therefore, a large number of case studies concerning model skill should be performed to promote the development of a scientific and normative technical framework for the improvement of MEDM skill.

Prediction of lithium response in first-episode mania using the LITHium Intelligent Agent (LITHIA): Pilot data and proof-of-concept.

PubMed

Fleck, David E; Ernest, Nicholas; Adler, Caleb M; Cohen, Kelly; Eliassen, James C; Norris, Matthew; Komoroski, Richard A; Chu, Wen-Jang; Welge, Jeffrey A; Blom, Thomas J; DelBello, Melissa P; Strakowski, Stephen M

2017-06-01

Individualized treatment for bipolar disorder based on neuroimaging treatment targets remains elusive. To address this shortcoming, we developed a linguistic machine learning system based on a cascading genetic fuzzy tree (GFT) design called the LITHium Intelligent Agent (LITHIA). Using multiple objectively defined functional magnetic resonance imaging (fMRI) and proton magnetic resonance spectroscopy ( 1 H-MRS) inputs, we tested whether LITHIA could accurately predict the lithium response in participants with first-episode bipolar mania. We identified 20 subjects with first-episode bipolar mania who received an adequate trial of lithium over 8 weeks and both fMRI and 1 H-MRS scans at baseline pre-treatment. We trained LITHIA using 18 1 H-MRS and 90 fMRI inputs over four training runs to classify treatment response and predict symptom reductions. Each training run contained a randomly selected 80% of the total sample and was followed by a 20% validation run. Over a different randomly selected distribution of the sample, we then compared LITHIA to eight common classification methods. LITHIA demonstrated nearly perfect classification accuracy and was able to predict post-treatment symptom reductions at 8 weeks with at least 88% accuracy in training and 80% accuracy in validation. Moreover, LITHIA exceeded the predictive capacity of the eight comparator methods and showed little tendency towards overfitting. The results provided proof-of-concept that a novel GFT is capable of providing control to a multidimensional bioinformatics problem-namely, prediction of the lithium response-in a pilot data set. Future work on this, and similar machine learning systems, could help assign psychiatric treatments more efficiently, thereby optimizing outcomes and limiting unnecessary treatment. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Specific expectancies are associated with symptomatic outcomes and side effect burden in a trial of chamomile extract for Generalized Anxiety Disorder

PubMed Central

Keefe, John R.; Amsterdam, Jay; Li, Qing S; Soeller, Irene; DeRubeis, Robert; Mao, Jun J

2017-01-01

Objective Patient expectancies are hypothesized to contribute to the efficacy and side effects of psychiatric treatments, but little research has investigated this hypothesis in the context of psychopharmacological therapies for anxiety. We prospectively investigated whether expectancies predicted efficacy and adverse events in oral therapy for Generalized Anxiety Disorder (GAD), controlling for confounding patient characteristics correlating with outcomes. Methods Expectancies regarding treatment efficacy and side effects were assessed at baseline of an eight week open-label phase of a trial of chamomile for Generalized Anxiety Disorder (GAD). The primary outcome was patient-reported GAD-7 scores, with clinical response and treatment-emergent side-effects as secondary outcomes. Expectancies were used to predict symptomatic and side-effect outcomes. Results Very few baseline patient characteristics predicted either type of expectancy. Controlling for a patient’s predicted recovery based on their baseline characteristics, higher efficacy expectancies at baseline predicted greater change on the GAD-7 (adjusted β = −0.19, p = 0.011). Efficacy expectancies also predicted a higher likelihood of attaining clinical response (adjusted odds ratio = 1.69, p = 0.002). Patients with higher side effect expectancies reported more side effects (adjusted log expected count = 0.26, p = 0.038). Efficacy expectancies were unrelated to side effect reports (log expected count = −0.05, p = 0.680), and side effect expectancies were unrelated to treatment efficacy (β = 0.08, p = 0.306). Conclusions Patients entering chamomile treatment for GAD with more favorable self-generated expectancies for the treatment experience greater improvement and fewer adverse events. Aligning patient expectancies with treatment selections may optimize outcomes. PMID:27716513

Predictors of Sustained Response to Rivastigmine in Patients With Alzheimer's Disease: A Retrospective Analysis

PubMed Central

Grossberg, George T.; Somogyi, Monique; Meng, Xiangyi

2011-01-01

Objective: The cholinesterase inhibitor rivastigmine is approved for the treatment of mild to moderate Alzheimer's disease. However, it is not possible to predict which individuals will benefit from treatment. This retrospective analysis of an international, 24-week, randomized, double-blind trial aimed to identify the percentage of persons with Alzheimer's disease who have a sustained response with rivastigmine patch, rivastigmine capsules, or placebo; to determine the magnitude of the sustained treatment response; and to investigate baseline patient characteristics predictive of the observed sustained response. Method: Patients who improved on the Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-cog) and Alzheimer's Disease Cooperative Study-Activities of Daily Living scale (ADCS-ADL) at week 16 and maintained at least the week 16 improvement at week 24 were identified as sustained responders. Treatment differences and baseline predictive factors were assessed in patients demonstrating a 1-, 2-, 3-, 4-, or 5-point sustained improvement. The first patient was screened in November 2003 and the last patient completed the study in January 2006. Results: More persons with Alzheimer's disease had sustained improvements on the ADAS-cog and ADCS-ADL with rivastigmine versus placebo. Sustained improvements of 4 or 5 points on the ADAS-cog or ADCS-ADL were demonstrated in the 9.5-mg/24-h rivastigmine patch (24% and 36% of patients, respectively) and 12-mg/d capsule groups (28% on both outcome measures). Factors predictive of a sustained response to treatment included baseline Mini-Mental State Examination, ADAS-cog, and ADCS-ADL scores and treatment, country of treatment, and time since first symptom was diagnosed by a physician. Conclusions: Understanding factors predictive of sustained cholinesterase inhibitor treatment response should help to optimize Alzheimer's disease management and encourage compliance by allowing more realistic expectations of treatment effects. PMID:21977379

Predictors of sustained response to rivastigmine in patients with Alzheimer's disease: a retrospective analysis.

PubMed

Sadowsky, Carl H; Grossberg, George T; Somogyi, Monique; Meng, Xiangyi

2011-01-01

The cholinesterase inhibitor rivastigmine is approved for the treatment of mild to moderate Alzheimer's disease. However, it is not possible to predict which individuals will benefit from treatment. This retrospective analysis of an international, 24-week, randomized, double-blind trial aimed to identify the percentage of persons with Alzheimer's disease who have a sustained response with rivastigmine patch, rivastigmine capsules, or placebo; to determine the magnitude of the sustained treatment response; and to investigate baseline patient characteristics predictive of the observed sustained response. Patients who improved on the Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-cog) and Alzheimer's Disease Cooperative Study-Activities of Daily Living scale (ADCS-ADL) at week 16 and maintained at least the week 16 improvement at week 24 were identified as sustained responders. Treatment differences and baseline predictive factors were assessed in patients demonstrating a 1-, 2-, 3-, 4-, or 5-point sustained improvement. The first patient was screened in November 2003 and the last patient completed the study in January 2006. More persons with Alzheimer's disease had sustained improvements on the ADAS-cog and ADCS-ADL with rivastigmine versus placebo. Sustained improvements of 4 or 5 points on the ADAS-cog or ADCS-ADL were demonstrated in the 9.5-mg/24-h rivastigmine patch (24% and 36% of patients, respectively) and 12-mg/d capsule groups (28% on both outcome measures). Factors predictive of a sustained response to treatment included baseline Mini-Mental State Examination, ADAS-cog, and ADCS-ADL scores and treatment, country of treatment, and time since first symptom was diagnosed by a physician. Understanding factors predictive of sustained cholinesterase inhibitor treatment response should help to optimize Alzheimer's disease management and encourage compliance by allowing more realistic expectations of treatment effects.

Terbinafine in combination with other antifungal agents for treatment of resistant or refractory mycoses: investigating optimal dosing regimens using a physiologically based pharmacokinetic model.

PubMed

Dolton, Michael J; Perera, Vidya; Pont, Lisa G; McLachlan, Andrew J

2014-01-01

Terbinafine is increasingly used in combination with other antifungal agents to treat resistant or refractory mycoses due to synergistic in vitro antifungal activity; high doses are commonly used, but limited data are available on systemic exposure, and no assessment of pharmacodynamic target attainment has been made. Using a physiologically based pharmacokinetic (PBPK) model for terbinafine, this study aimed to predict total and unbound terbinafine concentrations in plasma with a range of high-dose regimens and also calculate predicted pharmacodynamic parameters for terbinafine. Predicted terbinafine concentrations accumulated significantly during the first 28 days of treatment; the area under the concentration-time curve (AUC)/MIC ratios and AUC for the free, unbound fraction (fAUC)/MIC ratios increased by 54 to 62% on day 7 of treatment and by 80 to 92% on day 28 compared to day 1, depending on the dose regimen. Of the high-dose regimens investigated, 500 mg of terbinafine taken every 12 h provided the highest systemic exposure; on day 7 of treatment, the predicted AUC, maximum concentration (Cmax), and minimum concentration (Cmin) were approximately 4-fold, 1.9-fold, and 4.4-fold higher than with a standard-dose regimen of 250 mg once daily. Close agreement was seen between the concentrations predicted by the PBPK model and the observed concentrations, indicating good predictive performance. This study provides the first report of predicted terbinafine exposure in plasma with a range of high-dose regimens.

TU-G-210-03: Acoustic Simulations in Transcranial MRgFUS: Treatment Prediction and Analysis

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

Vyas, U.

Modeling can play a vital role in predicting, optimizing and analyzing the results of therapeutic ultrasound treatments. Simulating the propagating acoustic beam in various targeted regions of the body allows for the prediction of the resulting power deposition and temperature profiles. In this session we will apply various modeling approaches to breast, abdominal organ and brain treatments. Of particular interest is the effectiveness of procedures for correcting for phase aberrations caused by intervening irregular tissues, such as the skull in transcranial applications or inhomogeneous breast tissues. Also described are methods to compensate for motion in targeted abdominal organs such asmore » the liver or kidney. Douglas Christensen – Modeling for Breast and Brain HIFU Treatment Planning Tobias Preusser – TRANS-FUSIMO - An Integrative Approach to Model-Based Treatment Planning of Liver FUS Urvi Vyas – Acoustic Simulations in Transcranial MRgFUS: Treatment Prediction and Analysis Learning Objectives: Understand the role of acoustic beam modeling for predicting the effectiveness of therapeutic ultrasound treatments. Apply acoustic modeling to specific breast, liver, kidney and transcranial anatomies. Determine how to obtain appropriate acoustic modeling parameters from clinical images. Understand the separate role of absorption and scattering in energy delivery to tissues. See how organ motion can be compensated for in ultrasound therapies. Compare simulated data with clinical temperature measurements in transcranial applications. Supported by NIH R01 HL172787 and R01 EB013433 (DC); EU Seventh Framework Programme (FP7/2007-2013) under 270186 (FUSIMO) and 611889 (TRANS-FUSIMO)(TP); and P01 CA159992, GE, FUSF and InSightec (UV)« less

Consensus in chronic ankle instability: aetiology, assessment, surgical indications and place for arthroscopy.

PubMed

Guillo, S; Bauer, T; Lee, J W; Takao, M; Kong, S W; Stone, J W; Mangone, P G; Molloy, A; Perera, A; Pearce, C J; Michels, F; Tourné, Y; Ghorbani, A; Calder, J

2013-12-01

Ankle sprains are the most common injuries sustained during sports activities. Most ankle sprains recover fully with non-operative treatment but 20-30% develop chronic ankle instability. Predicting which patients who sustain an ankle sprain will develop instability is difficult. This paper summarises a consensus on identifying which patients may require surgery, the optimal surgical intervention along with treatment of concomitant pathology given the evidence available today. It also discusses the role of arthroscopic treatment and the anatomical basis for individual procedures. Copyright © 2013. Published by Elsevier Masson SAS.

Optimizing global liver function in radiation therapy treatment planning

NASA Astrophysics Data System (ADS)

Wu, Victor W.; Epelman, Marina A.; Wang, Hesheng; Romeijn, H. Edwin; Feng, Mary; Cao, Yue; Ten Haken, Randall K.; Matuszak, Martha M.

2016-09-01

Liver stereotactic body radiation therapy (SBRT) patients differ in both pre-treatment liver function (e.g. due to degree of cirrhosis and/or prior treatment) and radiosensitivity, leading to high variability in potential liver toxicity with similar doses. This work investigates three treatment planning optimization models that minimize risk of toxicity: two consider both voxel-based pre-treatment liver function and local-function-based radiosensitivity with dose; one considers only dose. Each model optimizes different objective functions (varying in complexity of capturing the influence of dose on liver function) subject to the same dose constraints and are tested on 2D synthesized and 3D clinical cases. The normal-liver-based objective functions are the linearized equivalent uniform dose (\\ell \\text{EUD} ) (conventional ‘\\ell \\text{EUD} model’), the so-called perfusion-weighted \\ell \\text{EUD} (\\text{fEUD} ) (proposed ‘fEUD model’), and post-treatment global liver function (GLF) (proposed ‘GLF model’), predicted by a new liver-perfusion-based dose-response model. The resulting \\ell \\text{EUD} , fEUD, and GLF plans delivering the same target \\ell \\text{EUD} are compared with respect to their post-treatment function and various dose-based metrics. Voxel-based portal venous liver perfusion, used as a measure of local function, is computed using DCE-MRI. In cases used in our experiments, the GLF plan preserves up to 4.6 % ≤ft(7.5 % \\right) more liver function than the fEUD (\\ell \\text{EUD} ) plan does in 2D cases, and up to 4.5 % ≤ft(5.6 % \\right) in 3D cases. The GLF and fEUD plans worsen in \\ell \\text{EUD} of functional liver on average by 1.0 Gy and 0.5 Gy in 2D and 3D cases, respectively. Liver perfusion information can be used during treatment planning to minimize the risk of toxicity by improving expected GLF; the degree of benefit varies with perfusion pattern. Although fEUD model optimization is computationally inexpensive and often achieves better GLF than \\ell \\text{EUD} model optimization does, the GLF model directly optimizes a more clinically relevant metric and can further improve fEUD plan quality.

Predictors of response to Systems Training for Emotional Predictability and Problem Solving (STEPPS) for borderline personality disorder: an exploratory study.

PubMed

Black, D W; Allen, J; St John, D; Pfohl, B; McCormick, B; Blum, N

2009-07-01

Few predictors of treatment outcome or early discontinuation have been identified in persons with borderline personality disorder (BPD). The aim of the study was to examine the relationship between baseline clinical variables and treatment response and early discontinuation in a randomized controlled trial of System Training for Emotional Predictability and Problem Solving, a new cognitive group treatment. Improvement was rated using the Zanarini Rating Scale for BPD, the Clinical Global Impression Scale, the Global Assessment Scale and the Beck Depression Inventory. Subjects were assessed during the 20 week trial and a 1-year follow-up. Higher baseline severity was associated with greater improvement in global functioning and BPD-related symptoms. Higher impulsivity was predictive of early discontinuation. Optimal improvement was associated with attending > or = 15 sessions. Subjects likely to improve have the more severe BPD symptoms at baseline, while high levels of impulsivity are associated with early discontinuation.

Tool Steel Heat Treatment Optimization Using Neural Network Modeling

NASA Astrophysics Data System (ADS)

Podgornik, Bojan; Belič, Igor; Leskovšek, Vojteh; Godec, Matjaz

2016-11-01

Optimization of tool steel properties and corresponding heat treatment is mainly based on trial and error approach, which requires tremendous experimental work and resources. Therefore, there is a huge need for tools allowing prediction of mechanical properties of tool steels as a function of composition and heat treatment process variables. The aim of the present work was to explore the potential and possibilities of artificial neural network-based modeling to select and optimize vacuum heat treatment conditions depending on the hot work tool steel composition and required properties. In the current case training of the feedforward neural network with error backpropagation training scheme and four layers of neurons (8-20-20-2) scheme was based on the experimentally obtained tempering diagrams for ten different hot work tool steel compositions and at least two austenitizing temperatures. Results show that this type of modeling can be successfully used for detailed and multifunctional analysis of different influential parameters as well as to optimize heat treatment process of hot work tool steels depending on the composition. In terms of composition, V was found as the most beneficial alloying element increasing hardness and fracture toughness of hot work tool steel; Si, Mn, and Cr increase hardness but lead to reduced fracture toughness, while Mo has the opposite effect. Optimum concentration providing high KIc/HRC ratios would include 0.75 pct Si, 0.4 pct Mn, 5.1 pct Cr, 1.5 pct Mo, and 0.5 pct V, with the optimum heat treatment performed at lower austenitizing and intermediate tempering temperatures.

Mammalian cell culture monitoring using in situ spectroscopy: Is your method really optimised?

PubMed

André, Silvère; Lagresle, Sylvain; Hannas, Zahia; Calvosa, Éric; Duponchel, Ludovic

2017-03-01

In recent years, as a result of the process analytical technology initiative of the US Food and Drug Administration, many different works have been carried out on direct and in situ monitoring of critical parameters for mammalian cell cultures by Raman spectroscopy and multivariate regression techniques. However, despite interesting results, it cannot be said that the proposed monitoring strategies, which will reduce errors of the regression models and thus confidence limits of the predictions, are really optimized. Hence, the aim of this article is to optimize some critical steps of spectroscopic acquisition and data treatment in order to reach a higher level of accuracy and robustness of bioprocess monitoring. In this way, we propose first an original strategy to assess the most suited Raman acquisition time for the processes involved. In a second part, we demonstrate the importance of the interbatch variability on the accuracy of the predictive models with a particular focus on the optical probes adjustment. Finally, we propose a methodology for the optimization of the spectral variables selection in order to decrease prediction errors of multivariate regressions. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:308-316, 2017. © 2017 American Institute of Chemical Engineers.

Spatial photosensitizer fluorescence emission predictive analysis for photodynamic therapy monitoring applied to a skin disease

NASA Astrophysics Data System (ADS)

Salas-García, Irene; Fanjul-Vélez, Félix; Arce-Diego, José Luis

2012-03-01

The development of Photodynamic Therapy (PDT) predictive models has become a valuable tool for an optimal treatment planning, monitoring and dosimetry adjustment. A few attempts have achieved a quite complete characterization of the complex photochemical and photophysical processes involved, even taking into account superficial fluorescence in the target tissue. The present work is devoted to the application of a predictive PDT model to obtain fluorescence tomography information during PDT when applied to a skin disease. The model takes into account the optical radiation distribution, a non-homogeneous topical photosensitizer distribution, the time dependent photochemical interaction and the photosensitizer fluorescence emission. The results show the spatial evolution of the photosensitizer fluorescence emission and the amount of singlet oxygen produced during PDT. The depth dependent photosensitizer fluorescence emission obtained is essential to estimate the spatial photosensitizer concentration and its degradation due to photobleaching. As a consequence the proposed approach could be used to predict the photosensitizer fluorescence tomographic measurements during PDT. The singlet oxygen prediction could also be employed as a valuable tool to predict the short term treatment outcome.

Prediction of methylphenidate treatment outcome in adults with attention-deficit/hyperactivity disorder (ADHD).

PubMed

Retz, Wolfgang; Retz-Junginger, Petra

2014-11-01

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent mental disorder of childhood, which often persists in adulthood. Methylphenidate (MPH) is one of the most effective medications to treat ADHD, but also few adult patients show no sufficient response to this drug. In this paper, we give an overview regarding genetic, neuroimaging, clinical and other studies which have tried to reveal the reasons for non-response in adults with ADHD, based on a systematic literature search. Although MPH is a well-established treatment for adults with ADHD, research regarding the prediction of treatment outcome is still limited and has resulted in inconsistent findings. No reliable neurobiological markers of treatment response have been identified so far. Some findings from clinical studies suggest that comorbidity with substance use disorders and personality disorders has an impact on treatment course and outcome. As MPH is widely used in the treatment of adults with ADHD, much more work is needed regarding positive and negative predictors of long-term treatment outcome in order to optimize the pharmacological treatment of adult ADHD patients.

TH-E-BRF-06: Kinetic Modeling of Tumor Response to Fractionated Radiotherapy

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

Zhong, H; Gordon, J; Chetty, I

2014-06-15

Purpose: Accurate calibration of radiobiological parameters is crucial to predicting radiation treatment response. Modeling differences may have a significant impact on calibrated parameters. In this study, we have integrated two existing models with kinetic differential equations to formulate a new tumor regression model for calibrating radiobiological parameters for individual patients. Methods: A system of differential equations that characterizes the birth-and-death process of tumor cells in radiation treatment was analytically solved. The solution of this system was used to construct an iterative model (Z-model). The model consists of three parameters: tumor doubling time Td, half-life of dying cells Tr and cellmore » survival fraction SFD under dose D. The Jacobian determinant of this model was proposed as a constraint to optimize the three parameters for six head and neck cancer patients. The derived parameters were compared with those generated from the two existing models, Chvetsov model (C-model) and Lim model (L-model). The C-model and L-model were optimized with the parameter Td fixed. Results: With the Jacobian-constrained Z-model, the mean of the optimized cell survival fractions is 0.43±0.08, and the half-life of dying cells averaged over the six patients is 17.5±3.2 days. The parameters Tr and SFD optimized with the Z-model differ by 1.2% and 20.3% from those optimized with the Td-fixed C-model, and by 32.1% and 112.3% from those optimized with the Td-fixed L-model, respectively. Conclusion: The Z-model was analytically constructed from the cellpopulation differential equations to describe changes in the number of different tumor cells during the course of fractionated radiation treatment. The Jacobian constraints were proposed to optimize the three radiobiological parameters. The developed modeling and optimization methods may help develop high-quality treatment regimens for individual patients.« less

Determination of the optimal tolerance for MLC positioning in sliding window and VMAT techniques

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

Hernandez, V., E-mail: vhernandezmasgrau@gmail.com; Abella, R.; Calvo, J. F.

2015-04-15

Purpose: Several authors have recommended a 2 mm tolerance for multileaf collimator (MLC) positioning in sliding window treatments. In volumetric modulated arc therapy (VMAT) treatments, however, the optimal tolerance for MLC positioning remains unknown. In this paper, the authors present the results of a multicenter study to determine the optimal tolerance for both techniques. Methods: The procedure used is based on dynalog file analysis. The study was carried out using seven Varian linear accelerators from five different centers. Dynalogs were collected from over 100 000 clinical treatments and in-house software was used to compute the number of tolerance faults as amore » function of the user-defined tolerance. Thus, the optimal value for this tolerance, defined as the lowest achievable value, was investigated. Results: Dynalog files accurately predict the number of tolerance faults as a function of the tolerance value, especially for low fault incidences. All MLCs behaved similarly and the Millennium120 and the HD120 models yielded comparable results. In sliding window techniques, the number of beams with an incidence of hold-offs >1% rapidly decreases for a tolerance of 1.5 mm. In VMAT techniques, the number of tolerance faults sharply drops for tolerances around 2 mm. For a tolerance of 2.5 mm, less than 0.1% of the VMAT arcs presented tolerance faults. Conclusions: Dynalog analysis provides a feasible method for investigating the optimal tolerance for MLC positioning in dynamic fields. In sliding window treatments, the tolerance of 2 mm was found to be adequate, although it can be reduced to 1.5 mm. In VMAT treatments, the typically used 5 mm tolerance is excessively high. Instead, a tolerance of 2.5 mm is recommended.« less

Evaluating spatially explicit burn probabilities for strategic fire management planning

Treesearch

C. Miller; M.-A. Parisien; A. A. Ager; M. A. Finney

2008-01-01

Spatially explicit information on the probability of burning is necessary for virtually all strategic fire and fuels management planning activities, including conducting wildland fire risk assessments, optimizing fuel treatments, and prevention planning. Predictive models providing a reliable estimate of the annual likelihood of fire at each point on the landscape have...

Predicting costs of alien species surveillance across varying transportation networks

Treesearch

Laura Blackburn; Rebecca Epanchin-Niell; Alexandra Thompson; Andrew Liebhold; Jacqueline Beggs

2017-01-01

Efforts to detect and eradicate invading populations before they establish are a critical component of national biosecurity programmes. An essential element for maximizing the efficiency of these efforts is the balancing of expenditures on surveillance (e.g. trapping) versus treatment (e.g. eradication). Identifying the optimal allocation of resources towards...

Wall Shear Stress Restoration in Dialysis Patient's Venous Stenosis: Elucidation via 3D CFD and Shape Optimization

NASA Astrophysics Data System (ADS)

Mahmoudzadeh Akherat, S. M. Javid; Cassel, Kevin; Hammes, Mary; Boghosian, Michael; Illinois Institute of Technology Team; University of Chicago Team

2016-11-01

Venous stenosis developed after the growth of excessive neointimal hyperplasia (NH) in chronic dialysis treatment is a major cause of mortality in renal failure patients. It has been hypothesized that the low wall shear stress (WSS) triggers an adaptive response in patients' venous system that through the growth of neointimal hyperplastic lesions restores WSS and transmural pressure, which also regulates the blood flow rate back to physiologically acceptable values which is violated by dialysis treatment. A strong coupling of three-dimensional CFD and shape optimization analyses were exploited to elucidate and forecast this adaptive response which correlates very well topographically with patient-specific clinical data. Based on the framework developed, a medical protocol is suggested to predict and prevent dialysis treatment failure in clinical practice. Supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health (R01 DK90769).

Optimal combinations of control strategies and cost-effective analysis for visceral leishmaniasis disease transmission.

PubMed

Biswas, Santanu; Subramanian, Abhishek; ELMojtaba, Ibrahim M; Chattopadhyay, Joydev; Sarkar, Ram Rup

2017-01-01

Visceral leishmaniasis (VL) is a deadly neglected tropical disease that poses a serious problem in various countries all over the world. Implementation of various intervention strategies fail in controlling the spread of this disease due to issues of parasite drug resistance and resistance of sandfly vectors to insecticide sprays. Due to this, policy makers need to develop novel strategies or resort to a combination of multiple intervention strategies to control the spread of the disease. To address this issue, we propose an extensive SIR-type model for anthroponotic visceral leishmaniasis transmission with seasonal fluctuations modeled in the form of periodic sandfly biting rate. Fitting the model for real data reported in South Sudan, we estimate the model parameters and compare the model predictions with known VL cases. Using optimal control theory, we study the effects of popular control strategies namely, drug-based treatment of symptomatic and PKDL-infected individuals, insecticide treated bednets and spray of insecticides on the dynamics of infected human and vector populations. We propose that the strategies remain ineffective in curbing the disease individually, as opposed to the use of optimal combinations of the mentioned strategies. Testing the model for different optimal combinations while considering periodic seasonal fluctuations, we find that the optimal combination of treatment of individuals and insecticide sprays perform well in controlling the disease for the time period of intervention introduced. Performing a cost-effective analysis we identify that the same strategy also proves to be efficacious and cost-effective. Finally, we suggest that our model would be helpful for policy makers to predict the best intervention strategies for specific time periods and their appropriate implementation for elimination of visceral leishmaniasis.

Drug delivery optimization through Bayesian networks.

PubMed Central

Bellazzi, R.

1992-01-01

This paper describes how Bayesian Networks can be used in combination with compartmental models to plan Recombinant Human Erythropoietin (r-HuEPO) delivery in the treatment of anemia of chronic uremic patients. Past measurements of hematocrit or hemoglobin concentration in a patient during the therapy can be exploited to adjust the parameters of a compartmental model of the erythropoiesis. This adaptive process allows more accurate patient-specific predictions, and hence a more rational dosage planning. We describe a drug delivery optimization protocol, based on our approach. Some results obtained on real data are presented. PMID:1482938

Prediction of light aircraft interior noise

NASA Technical Reports Server (NTRS)

Howlett, J. T.; Morales, D. A.

1976-01-01

At the present time, predictions of aircraft interior noise depend heavily on empirical correction factors derived from previous flight measurements. However, to design for acceptable interior noise levels and to optimize acoustic treatments, analytical techniques which do not depend on empirical data are needed. This paper describes a computerized interior noise prediction method for light aircraft. An existing analytical program (developed for commercial jets by Cockburn and Jolly in 1968) forms the basis of some modal analysis work which is described. The accuracy of this modal analysis technique for predicting low-frequency coupled acoustic-structural natural frequencies is discussed along with trends indicating the effects of varying parameters such as fuselage length and diameter, structural stiffness, and interior acoustic absorption.

Optimal Design for Informative Protocols in Xenograft Tumor Growth Inhibition Experiments in Mice.

PubMed

Lestini, Giulia; Mentré, France; Magni, Paolo

2016-09-01

Tumor growth inhibition (TGI) models are increasingly used during preclinical drug development in oncology for the in vivo evaluation of antitumor effect. Tumor sizes are measured in xenografted mice, often only during and shortly after treatment, thus preventing correct identification of some TGI model parameters. Our aims were (i) to evaluate the importance of including measurements during tumor regrowth and (ii) to investigate the proportions of mice included in each arm. For these purposes, optimal design theory based on the Fisher information matrix implemented in PFIM4.0 was applied. Published xenograft experiments, involving different drugs, schedules, and cell lines, were used to help optimize experimental settings and parameters using the Simeoni TGI model. For each experiment, a two-arm design, i.e., control versus treatment, was optimized with or without the constraint of not sampling during tumor regrowth, i.e., "short" and "long" studies, respectively. In long studies, measurements could be taken up to 6 g of tumor weight, whereas in short studies the experiment was stopped 3 days after the end of treatment. Predicted relative standard errors were smaller in long studies than in corresponding short studies. Some optimal measurement times were located in the regrowth phase, highlighting the importance of continuing the experiment after the end of treatment. In the four-arm designs, the results showed that the proportions of control and treated mice can differ. To conclude, making measurements during tumor regrowth should become a general rule for informative preclinical studies in oncology, especially when a delayed drug effect is suspected.

Optimal design for informative protocols in xenograft tumor growth inhibition experiments in mice

PubMed Central

Lestini, Giulia; Mentré, France; Magni, Paolo

2016-01-01

Tumor growth inhibition (TGI) models are increasingly used during preclinical drug development in oncology for the in vivo evaluation of antitumor effect. Tumor sizes are measured in xenografted mice, often only during and shortly after treatment, thus preventing correct identification of some TGI model parameters. Our aims were i) to evaluate the importance of including measurements during tumor regrowth; ii) to investigate the proportions of mice included in each arm. For these purposes, optimal design theory based on the Fisher information matrix implemented in PFIM4.0 was applied. Published xenograft experiments, involving different drugs, schedules and cell lines, were used to help optimize experimental settings and parameters using the Simeoni TGI model. For each experiment, a two-arm design, i.e. control vs treatment, was optimized with or without the constraint of not sampling during tumor regrowth, i.e. “short” and “long” studies, respectively. In long studies, measurements could be taken up to 6 grams of tumor weight, whereas in short studies the experiment was stopped three days after the end of treatment. Predicted relative standard errors were smaller in long studies than in corresponding short studies. Some optimal measurement times were located in the regrowth phase, highlighting the importance of continuing the experiment after the end of treatment. In the four-arm designs, the results showed that the proportions of control and treated mice can differ. To conclude, making measurements during tumor regrowth should become a general rule for informative preclinical studies in oncology, especially when a delayed drug effect is suspected. PMID:27306546

Regulatory element-based prediction identifies new susceptibility regulatory variants for osteoporosis.

PubMed

Yao, Shi; Guo, Yan; Dong, Shan-Shan; Hao, Ruo-Han; Chen, Xiao-Feng; Chen, Yi-Xiao; Chen, Jia-Bin; Tian, Qing; Deng, Hong-Wen; Yang, Tie-Lin

2017-08-01

Despite genome-wide association studies (GWASs) have identified many susceptibility genes for osteoporosis, it still leaves a large part of missing heritability to be discovered. Integrating regulatory information and GWASs could offer new insights into the biological link between the susceptibility SNPs and osteoporosis. We generated five machine learning classifiers with osteoporosis-associated variants and regulatory features data. We gained the optimal classifier and predicted genome-wide SNPs to discover susceptibility regulatory variants. We further utilized Genetic Factors for Osteoporosis Consortium (GEFOS) and three in-house GWASs samples to validate the associations for predicted positive SNPs. The random forest classifier performed best among all machine learning methods with the F1 score of 0.8871. Using the optimized model, we predicted 37,584 candidate SNPs for osteoporosis. According to the meta-analysis results, a list of regulatory variants was significantly associated with osteoporosis after multiple testing corrections and contributed to the expression of known osteoporosis-associated protein-coding genes. In summary, combining GWASs and regulatory elements through machine learning could provide additional information for understanding the mechanism of osteoporosis. The regulatory variants we predicted will provide novel targets for etiology research and treatment of osteoporosis.

Artificial Intelligence Based Optimization for the Se(IV) Removal from Aqueous Solution by Reduced Graphene Oxide-Supported Nanoscale Zero-Valent Iron Composites

PubMed Central

Cao, Rensheng; Ruan, Wenqian; Wu, Xianliang; Wei, Xionghui

2018-01-01

Highly promising artificial intelligence tools, including neural network (ANN), genetic algorithm (GA) and particle swarm optimization (PSO), were applied in the present study to develop an approach for the evaluation of Se(IV) removal from aqueous solutions by reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) composites. Both GA and PSO were used to optimize the parameters of ANN. The effect of operational parameters (i.e., initial pH, temperature, contact time and initial Se(IV) concentration) on the removal efficiency was examined using response surface methodology (RSM), which was also utilized to obtain a dataset for the ANN training. The ANN-GA model results (with a prediction error of 2.88%) showed a better agreement with the experimental data than the ANN-PSO model results (with a prediction error of 4.63%) and the RSM model results (with a prediction error of 5.56%), thus the ANN-GA model was an ideal choice for modeling and optimizing the Se(IV) removal by the nZVI/rGO composites due to its low prediction error. The analysis of the experimental data illustrates that the removal process of Se(IV) obeyed the Langmuir isotherm and the pseudo-second-order kinetic model. Furthermore, the Se 3d and 3p peaks found in XPS spectra for the nZVI/rGO composites after removing treatment illustrates that the removal of Se(IV) was mainly through the adsorption and reduction mechanisms. PMID:29543753

Artificial Intelligence Based Optimization for the Se(IV) Removal from Aqueous Solution by Reduced Graphene Oxide-Supported Nanoscale Zero-Valent Iron Composites.

PubMed

Cao, Rensheng; Fan, Mingyi; Hu, Jiwei; Ruan, Wenqian; Wu, Xianliang; Wei, Xionghui

2018-03-15

Highly promising artificial intelligence tools, including neural network (ANN), genetic algorithm (GA) and particle swarm optimization (PSO), were applied in the present study to develop an approach for the evaluation of Se(IV) removal from aqueous solutions by reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) composites. Both GA and PSO were used to optimize the parameters of ANN. The effect of operational parameters (i.e., initial pH, temperature, contact time and initial Se(IV) concentration) on the removal efficiency was examined using response surface methodology (RSM), which was also utilized to obtain a dataset for the ANN training. The ANN-GA model results (with a prediction error of 2.88%) showed a better agreement with the experimental data than the ANN-PSO model results (with a prediction error of 4.63%) and the RSM model results (with a prediction error of 5.56%), thus the ANN-GA model was an ideal choice for modeling and optimizing the Se(IV) removal by the nZVI/rGO composites due to its low prediction error. The analysis of the experimental data illustrates that the removal process of Se(IV) obeyed the Langmuir isotherm and the pseudo-second-order kinetic model. Furthermore, the Se 3d and 3p peaks found in XPS spectra for the nZVI/rGO composites after removing treatment illustrates that the removal of Se(IV) was mainly through the adsorption and reduction mechanisms.

Immunotherapy for metastatic urothelial carcinoma: status quo and the future.

PubMed

Necchi, Andrea; Rink, Michael; Giannatempo, Patrizia; Raggi, Daniele; Xylinas, Evanguelos

2018-01-01

The treatment paradigm of urothelial carcinoma has been revolutionized by the advent of multiple anti-programmed-cell death-1/ligand-1 (PD-1/PD-L1) antibodies. Significant improvements have been obtained in the locally advanced or metastatic stage, which was lacking of therapeutic standards. This review reports key findings from completed and ongoing clinical trials that highlight the potential of PD-1/PD-L1 blockade in this disease. Anti-PD-1/PD-L1 monoclonal antibodies have shown efficacy and safety in patients with urothelial carcinoma, regardless of their prognostic features. Efficacy was similar across different compounds, with objective responses that approximate 20%, with some differences favoring PD-L1-expressing patients. Typically, responding patients have good chances of achieving durable response, but biomarkers predictive of therapeutic effect are lacking. To date, evidences from randomized studies are limited to the second-line, postplatinum therapy. Despite the activity of PD-1/PD-L1 inhibitors is well established in metastatic urothelial carcinoma, multiple gray zones still exist regarding their optimal use in clinical practice. These uncertainties are related to patient and treatment-related criteria, to the optimal duration of treatment, including combination or sequence with standard chemotherapy. Special issues are represented by pseudoprogression or hyperprogression. Generally, enhanced predictive tools are needed and a myriad of further investigations are underway.

Prediction of treatment response and metastatic disease in soft tissue sarcoma

NASA Astrophysics Data System (ADS)

Farhidzadeh, Hamidreza; Zhou, Mu; Goldgof, Dmitry B.; Hall, Lawrence O.; Raghavan, Meera.; Gatenby, Robert A.

2014-03-01

Soft tissue sarcomas (STS) are a heterogenous group of malignant tumors comprised of more than 50 histologic subtypes. Based on spatial variations of the tumor, predictions of the development of necrosis in response to therapy as well as eventual progression to metastatic disease are made. Optimization of treatment, as well as management of therapy-related side effects, may be improved using progression information earlier in the course of therapy. Multimodality pre- and post-gadolinium enhanced magnetic resonance images (MRI) were taken before and after treatment for 30 patients. Regional variations in the tumor bed were measured quantitatively. The voxel values from the tumor region were used as features and a fuzzy clustering algorithm was used to segment the tumor into three spatial regions. The regions were given labels of high, intermediate and low based on the average signal intensity of pixels from the post-contrast T1 modality. These spatially distinct regions were viewed as essential meta-features to predict the response of the tumor to therapy based on necrosis (dead tissue in tumor bed) and metastatic disease (spread of tumor to sites other than primary). The best feature was the difference in the number of pixels in the highest intensity regions of tumors before and after treatment. This enabled prediction of patients with metastatic disease and lack of positive treatment response (i.e. less necrosis). The best accuracy, 73.33%, was achieved by a Support Vector Machine in a leave-one-out cross validation on 30 cases predicting necrosis < 90% post treatment and metastasis.

Comparison of modelling accuracy with and without exploiting automated optical monitoring information in predicting the treated wastewater quality.

PubMed

Tomperi, Jani; Leiviskä, Kauko

2018-06-01

Traditionally the modelling in an activated sludge process has been based on solely the process measurements, but as the interest to optically monitor wastewater samples to characterize the floc morphology has increased, in the recent years the results of image analyses have been more frequently utilized to predict the characteristics of wastewater. This study shows that the traditional process measurements or the automated optical monitoring variables by themselves are not capable of developing the best predictive models for the treated wastewater quality in a full-scale wastewater treatment plant, but utilizing these variables together the optimal models, which show the level and changes in the treated wastewater quality, are achieved. By this early warning, process operation can be optimized to avoid environmental damages and economic losses. The study also shows that specific optical monitoring variables are important in modelling a certain quality parameter, regardless of the other input variables available.

Creation of Novel Cores for β-Secretase (BACE-1) Inhibitors: A Multiparameter Lead Generation Strategy

PubMed Central

2014-01-01

In order to find optimal core structures as starting points for lead optimization, a multiparameter lead generation workflow was designed with the goal of finding BACE-1 inhibitors as a treatment for Alzheimer’s disease. De novo design of core fragments was connected with three predictive in silico models addressing target affinity, permeability, and hERG activity, in order to guide synthesis. Taking advantage of an additive SAR, the prioritized cores were decorated with a few, well-characterized substituents from known BACE-1 inhibitors in order to allow for core-to-core comparisons. Prediction methods and analyses of how physicochemical properties of the core structures correlate to in vitro data are described. The syntheses and in vitro data of the test compounds are reported in a separate paper by Ginman et al. [J. Med. Chem.2013, 56, 4181–4205]. The affinity predictions are described in detail by Roos et al. [J. Chem. Inf.2014, DOI: 10.1021/ci400374z]. PMID:24900855

Creation of Novel Cores for β-Secretase (BACE-1) Inhibitors: A Multiparameter Lead Generation Strategy.

PubMed

Viklund, Jenny; Kolmodin, Karin; Nordvall, Gunnar; Swahn, Britt-Marie; Svensson, Mats; Gravenfors, Ylva; Rahm, Fredrik

2014-04-10

In order to find optimal core structures as starting points for lead optimization, a multiparameter lead generation workflow was designed with the goal of finding BACE-1 inhibitors as a treatment for Alzheimer's disease. De novo design of core fragments was connected with three predictive in silico models addressing target affinity, permeability, and hERG activity, in order to guide synthesis. Taking advantage of an additive SAR, the prioritized cores were decorated with a few, well-characterized substituents from known BACE-1 inhibitors in order to allow for core-to-core comparisons. Prediction methods and analyses of how physicochemical properties of the core structures correlate to in vitro data are described. The syntheses and in vitro data of the test compounds are reported in a separate paper by Ginman et al. [J. Med. Chem. 2013, 56, 4181-4205]. The affinity predictions are described in detail by Roos et al. [J. Chem. Inf. 2014, DOI: 10.1021/ci400374z].

Sci-Thur AM: YIS – 05: Prediction of lung tumor motion using a generalized neural network optimized from the average prediction outcome of a group of patients

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

Teo, Troy; Alayoubi, Nadia; Bruce, Neil

Purpose: In image-guided adaptive radiotherapy systems, prediction of tumor motion is required to compensate for system latencies. However, due to the non-stationary nature of respiration, it is a challenge to predict the associated tumor motions. In this work, a systematic design of the neural network (NN) using a mixture of online data acquired during the initial period of the tumor trajectory, coupled with a generalized model optimized using a group of patient data (obtained offline) is presented. Methods: The average error surface obtained from seven patients was used to determine the input data size and number of hidden neurons formore » the generalized NN. To reduce training time, instead of using random weights to initialize learning (method 1), weights inherited from previous training batches (method 2) were used to predict tumor position for each sliding window. Results: The generalized network was established with 35 input data (∼4.66s) and 20 hidden nodes. For a prediction horizon of 650 ms, mean absolute errors of 0.73 mm and 0.59 mm were obtained for method 1 and 2 respectively. An average initial learning period of 8.82 s is obtained. Conclusions: A network with a relatively short initial learning time was achieved. Its accuracy is comparable to previous studies. This network could be used as a plug-and play predictor in which (a) tumor positions can be predicted as soon as treatment begins and (b) the need for pretreatment data and optimization for individual patients can be avoided.« less

Replacement of missing teeth with fiber-reinforced composite FPDs: clinical protocol.

PubMed

Bouillaguet, Serge; Schütt, Andrea; Marin, Isabelle; Etechami, Leila; Di Salvo, Giancarlo; Krejci, Ivo

2003-04-01

The concept of minimally invasive preparation protocols has resulted in reduced loss of critical tooth structures and maintenance of optimal strength, form, and aesthetics. While various treatment options have been described for single-tooth replacement, fiber-reinforced composite (FRC) fixed partial dentures (FPDs) provide a viable treatment alternative with proven mechanical properties, aesthetics, and function. This article presents several clinical scenarios in which minimally invasive adhesive FRC FPDs are provided to deliver enhanced predictability, strength, and durability.

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

Swanson, K; Corwin, D; Rockne, R

Purpose: To demonstrate a method of generating patient-specific, biologically-guided radiation therapy (RT) plans and to quantify and predict response to RT in glioblastoma. We investigate the biological correlates and imaging physics driving T2-MRI based response to radiation therapy using an MRI simulator. Methods: We have integrated a patient-specific biomathematical model of glioblastoma proliferation, invasion and radiotherapy with a multiobjective evolutionary algorithm for intensity-modulated RT optimization to construct individualized, biologically-guided plans. Patient-individualized simulations of the standard-of-care and optimized plans are compared in terms of several biological metrics quantified on MRI. An extension of the PI model is used to investigate themore » role of angiogenesis and its correlates in glioma response to therapy with the Proliferation-Invasion-Hypoxia- Necrosis-Angiogenesis model (PIHNA). The PIHNA model is used with a brain tissue phantom to predict tumor-induced vasogenic edema, tumor and tissue density that is used in a multi-compartmental MRI signal equation for generation of simulated T2- weighted MRIs. Results: Applying a novel metric of treatment response (Days Gained) to the patient-individualized simulation results predicted that the optimized RT plans would have a significant impact on delaying tumor progression, with Days Gained increases from 21% to 105%. For the T2- MRI simulations, initial validation tests compared average simulated T2 values for white matter, tumor, and peripheral edema to values cited in the literature. Simulated results closely match the characteristic T2 value for each tissue. Conclusion: Patient-individualized simulations using the combination of a biomathematical model with an optimization algorithm for RT generated biologically-guided doses that decreased normal tissue dose and increased therapeutic ratio with the potential to improve survival outcomes for treatment of glioblastoma. Simulated T2-MRI is shown to be consistent with known physics of MRI and can be used to further investigate biological drivers of imaging-based response to RT.« less

Terbinafine in Combination with Other Antifungal Agents for Treatment of Resistant or Refractory Mycoses: Investigating Optimal Dosing Regimens Using a Physiologically Based Pharmacokinetic Model

PubMed Central

Dolton, Michael J.; Perera, Vidya; Pont, Lisa G.

2014-01-01

Terbinafine is increasingly used in combination with other antifungal agents to treat resistant or refractory mycoses due to synergistic in vitro antifungal activity; high doses are commonly used, but limited data are available on systemic exposure, and no assessment of pharmacodynamic target attainment has been made. Using a physiologically based pharmacokinetic (PBPK) model for terbinafine, this study aimed to predict total and unbound terbinafine concentrations in plasma with a range of high-dose regimens and also calculate predicted pharmacodynamic parameters for terbinafine. Predicted terbinafine concentrations accumulated significantly during the first 28 days of treatment; the area under the concentration-time curve (AUC)/MIC ratios and AUC for the free, unbound fraction (fAUC)/MIC ratios increased by 54 to 62% on day 7 of treatment and by 80 to 92% on day 28 compared to day 1, depending on the dose regimen. Of the high-dose regimens investigated, 500 mg of terbinafine taken every 12 h provided the highest systemic exposure; on day 7 of treatment, the predicted AUC, maximum concentration (Cmax), and minimum concentration (Cmin) were approximately 4-fold, 1.9-fold, and 4.4-fold higher than with a standard-dose regimen of 250 mg once daily. Close agreement was seen between the concentrations predicted by the PBPK model and the observed concentrations, indicating good predictive performance. This study provides the first report of predicted terbinafine exposure in plasma with a range of high-dose regimens. PMID:24126579

Development of a Multicomponent Prediction Model for Acute Esophagitis in Lung Cancer Patients Receiving Chemoradiotherapy

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

De Ruyck, Kim, E-mail: kim.deruyck@UGent.be; Sabbe, Nick; Oberije, Cary

2011-10-01

Purpose: To construct a model for the prediction of acute esophagitis in lung cancer patients receiving chemoradiotherapy by combining clinical data, treatment parameters, and genotyping profile. Patients and Methods: Data were available for 273 lung cancer patients treated with curative chemoradiotherapy. Clinical data included gender, age, World Health Organization performance score, nicotine use, diabetes, chronic disease, tumor type, tumor stage, lymph node stage, tumor location, and medical center. Treatment parameters included chemotherapy, surgery, radiotherapy technique, tumor dose, mean fractionation size, mean and maximal esophageal dose, and overall treatment time. A total of 332 genetic polymorphisms were considered in 112 candidatemore » genes. The predicting model was achieved by lasso logistic regression for predictor selection, followed by classic logistic regression for unbiased estimation of the coefficients. Performance of the model was expressed as the area under the curve of the receiver operating characteristic and as the false-negative rate in the optimal point on the receiver operating characteristic curve. Results: A total of 110 patients (40%) developed acute esophagitis Grade {>=}2 (Common Terminology Criteria for Adverse Events v3.0). The final model contained chemotherapy treatment, lymph node stage, mean esophageal dose, gender, overall treatment time, radiotherapy technique, rs2302535 (EGFR), rs16930129 (ENG), rs1131877 (TRAF3), and rs2230528 (ITGB2). The area under the curve was 0.87, and the false-negative rate was 16%. Conclusion: Prediction of acute esophagitis can be improved by combining clinical, treatment, and genetic factors. A multicomponent prediction model for acute esophagitis with a sensitivity of 84% was constructed with two clinical parameters, four treatment parameters, and four genetic polymorphisms.« less

A New Prediction Model for Evaluating Treatment-Resistant Depression.

PubMed

Kautzky, Alexander; Baldinger-Melich, Pia; Kranz, Georg S; Vanicek, Thomas; Souery, Daniel; Montgomery, Stuart; Mendlewicz, Julien; Zohar, Joseph; Serretti, Alessandro; Lanzenberger, Rupert; Kasper, Siegfried

2017-02-01

Despite a broad arsenal of antidepressants, about a third of patients suffering from major depressive disorder (MDD) do not respond sufficiently to adequate treatment. Using the data pool of the Group for the Study of Resistant Depression and machine learning, we intended to draw new insights featuring 48 clinical, sociodemographic, and psychosocial predictors for treatment outcome. Patients were enrolled starting from January 2000 and diagnosed according to DSM-IV. Treatment-resistant depression (TRD) was defined by a 17-item Hamilton Depression Rating Scale (HDRS) score ≥ 17 after at least 2 antidepressant trials of adequate dosage and length. Remission was defined by an HDRS score < 8. Stepwise predictor reduction using randomForest was performed to find the optimal number for classification of treatment outcome. After importance values were generated, prediction for remission and resistance was performed in a training sample of 400 patients. For prediction, we used a set of 80 patients not featured in the training sample and computed receiver operating characteristics. The most useful predictors for treatment outcome were the timespan between first and last depressive episode, age at first antidepressant treatment, response to first antidepressant treatment, severity, suicidality, melancholia, number of lifetime depressive episodes, patients' admittance type, education, occupation, and comorbid diabetes, panic, and thyroid disorder. While single predictors could not reach a prediction accuracy much different from random guessing, by combining all predictors, we could detect resistance with an accuracy of 0.737 and remission with an accuracy of 0.850. Consequently, 65.5% of predictions for TRD and 77.7% for remission can be expected to be accurate. Using machine learning algorithms, we could demonstrate success rates of 0.737 for predicting TRD and 0.850 for predicting remission, surpassing predictive capabilities of clinicians. Our results strengthen data mining and suggest the benefit of focus on interaction-based statistics. Considering that all predictors can easily be obtained in a clinical setting, we hope that our model can be tested by other research groups. © Copyright 2017 Physicians Postgraduate Press, Inc.

Simple tool for prediction of parotid gland sparing in intensity-modulated radiation therapy.

PubMed

Gensheimer, Michael F; Hummel-Kramer, Sharon M; Cain, David; Quang, Tony S

2015-01-01

Sparing one or both parotid glands is a key goal when planning head and neck cancer radiation treatment. If the planning target volume (PTV) overlaps one or both parotid glands substantially, it may not be possible to achieve adequate gland sparing. This finding results in physicians revising their PTV contours after an intensity-modulated radiation therapy (IMRT) plan has been run and reduces workflow efficiency. We devised a simple formula for predicting mean parotid gland dose from the overlap of the parotid gland and isotropically expanded PTV contours. We tested the tool using 44 patients from 2 institutions and found agreement between predicted and actual parotid gland doses (mean absolute error = 5.3 Gy). This simple method could increase treatment planning efficiency by improving the chance that the first plan presented to the physician will have optimal parotid gland sparing. Published by Elsevier Inc.

Simple tool for prediction of parotid gland sparing in intensity-modulated radiation therapy

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

Gensheimer, Michael F.; Hummel-Kramer, Sharon M., E-mail: sharonhummel@comcast.net; Cain, David

Sparing one or both parotid glands is a key goal when planning head and neck cancer radiation treatment. If the planning target volume (PTV) overlaps one or both parotid glands substantially, it may not be possible to achieve adequate gland sparing. This finding results in physicians revising their PTV contours after an intensity-modulated radiation therapy (IMRT) plan has been run and reduces workflow efficiency. We devised a simple formula for predicting mean parotid gland dose from the overlap of the parotid gland and isotropically expanded PTV contours. We tested the tool using 44 patients from 2 institutions and found agreementmore » between predicted and actual parotid gland doses (mean absolute error = 5.3 Gy). This simple method could increase treatment planning efficiency by improving the chance that the first plan presented to the physician will have optimal parotid gland sparing.« less

Optimization of pulsed electric field pre-treatments to enhance health-promoting glucosinolates in broccoli flowers and stalk.

PubMed

Aguiló-Aguayo, Ingrid; Suarez, Manuel; Plaza, Lucia; Hossain, Mohammad B; Brunton, Nigel; Lyng, James G; Rai, Dilip K

2015-07-01

The effect of pulsed electric field (PEF) treatment variables (electric field strength and treatment time) on the glucosinolate content of broccoli flowers and stalks was evaluated. Samples were subjected to electric field strengths from 1 to 4 kV cm(-1) and treatment times from 50 to 1000 µs at 5 Hz. Data fitted significantly (P < 0.0014) the proposed second-order response functions. The results showed that PEF combined treatment conditions of 4 kV cm(-1) for 525 and 1000 µs were optimal to maximize glucosinolate levels in broccoli flowers (ranging from 187.1 to 212.5%) and stalks (ranging from 110.6 to 203.0%) respectively. The predicted values from the developed quadratic polynomial equation were in close agreement with the actual experimental values, with low average mean deviations (E%) ranging from 0.59 to 8.80%. The use of PEF processing at moderate conditions could be a suitable method to stimulate production of broccoli with high health-promoting glucosinolate content. © 2014 Society of Chemical Industry.

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

Chen, X; Wang, J; Hu, W

Purpose: The Varian RapidPlan™ is a commercial knowledge-based optimization process which uses a set of clinically used treatment plans to train a model that can predict individualized dose-volume objectives. The purpose of this study is to evaluate the performance of RapidPlan to generate intensity modulated radiation therapy (IMRT) plans for cervical cancer. Methods: Totally 70 IMRT plans for cervical cancer with varying clinical and physiological indications were enrolled in this study. These patients were all previously treated in our institution. There were two prescription levels usually used in our institution: 45Gy/25 fractions and 50.4Gy/28 fractions. 50 of these plans weremore » selected to train the RapidPlan model for predicting dose-volume constraints. After model training, this model was validated with 10 plans from training pool(internal validation) and additional other 20 new plans(external validation). All plans used for the validation were re-optimized with the original beam configuration and the generated priorities from RapidPlan were manually adjusted to ensure that re-optimized DVH located in the range of the model prediction. DVH quantitative analysis was performed to compare the RapidPlan generated and the original manual optimized plans. Results: For all the validation cases, RapidPlan based plans (RapidPlan) showed similar or superior results compared to the manual optimized ones. RapidPlan increased the result of D98% and homogeneity in both two validations. For organs at risk, the RapidPlan decreased mean doses of bladder by 1.25Gy/1.13Gy (internal/external validation) on average, with p=0.12/p<0.01. The mean dose of rectum and bowel were also decreased by an average of 2.64Gy/0.83Gy and 0.66Gy/1.05Gy,with p<0.01/ p<0.01and p=0.04/<0.01 for the internal/external validation, respectively. Conclusion: The RapidPlan model based cervical cancer plans shows ability to systematically improve the IMRT plan quality. It suggests that RapidPlan has great potential to make the treatment planning process more efficient.« less

Monte Carlo treatment of resonance-radiation imprisonment in fluorescent lamps—revisited

NASA Astrophysics Data System (ADS)

Anderson, James B.

2016-12-01

We reported in 1985 a Monte Carlo treatment of the imprisonment of the 253.7 nm resonance radiation from mercury in the mercury-argon discharge of fluorescent lamps. The calculated spectra of the emitted radiation were found in good agreement with measured spectra. The addition of the isotope mercury-196 to natural mercury was found, also in agreement with experiments, to increase lamp efficiency. In this paper we report the extension of the earlier work with increased accuracy, analysis of photon exit-time distributions, recycling of energy released in quenching, analysis of dynamic similarity for different lamp sizes, variation of Mrozowski transfer rates, prediction and analysis of the hyperfine ultra-violet spectra, and optimization of tailored mercury isotope mixtures for increased lamp efficiency. The spectra were found insensitive to the extent of quenching and recycling. The optimized mixtures were found to increase efficiencies by as much as 5% for several lamp configurations. Optimization without increasing the mercury-196 fraction was found to increase efficiencies by nearly 1% for several configurations.

TH-E-BRF-01: Exploiting Tumor Shrinkage in Split-Course Radiotherapy

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

Unkelbach, J; Craft, D; Hong, T

2014-06-15

Purpose: In split-course radiotherapy, a patient is treated in several stages separated by weeks or months. This regimen has been motivated by radiobiological considerations. However, using modern image-guidance, it also provides an approach to reduce normal tissue dose by exploiting tumor shrinkage. In this work, we consider the optimal design of split-course treatments, motivated by the clinical management of large liver tumors for which normal liver dose constraints prohibit the administration of an ablative radiation dose in a single treatment. Methods: We introduce a dynamic tumor model that incorporates three factors: radiation induced cell kill, tumor shrinkage, and tumor cellmore » repopulation. The design of splitcourse radiotherapy is formulated as a mathematical optimization problem in which the total dose to the liver is minimized, subject to delivering the prescribed dose to the tumor. Based on the model, we gain insight into the optimal administration of radiation over time, i.e. the optimal treatment gaps and dose levels. Results: We analyze treatments consisting of two stages in detail. The analysis confirms the intuition that the second stage should be delivered just before the tumor size reaches a minimum and repopulation overcompensates shrinking. Furthermore, it was found that, for a large range of model parameters, approximately one third of the dose should be delivered in the first stage. The projected benefit of split-course treatments in terms of liver sparing depends on model assumptions. However, the model predicts large liver dose reductions by more than a factor of two for plausible model parameters. Conclusion: The analysis of the tumor model suggests that substantial reduction in normal tissue dose can be achieved by exploiting tumor shrinkage via an optimal design of multi-stage treatments. This suggests taking a fresh look at split-course radiotherapy for selected disease sites where substantial tumor regression translates into reduced target volumes.« less

Prioritizing CD4 Count Monitoring in Response to ART in Resource-Constrained Settings: A Retrospective Application of Prediction-Based Classification

PubMed Central

Liu, Yan; Li, Xiaohong; Johnson, Margaret; Smith, Collette; Kamarulzaman, Adeeba bte; Montaner, Julio; Mounzer, Karam; Saag, Michael; Cahn, Pedro; Cesar, Carina; Krolewiecki, Alejandro; Sanne, Ian; Montaner, Luis J.

2012-01-01

Background Global programs of anti-HIV treatment depend on sustained laboratory capacity to assess treatment initiation thresholds and treatment response over time. Currently, there is no valid alternative to CD4 count testing for monitoring immunologic responses to treatment, but laboratory cost and capacity limit access to CD4 testing in resource-constrained settings. Thus, methods to prioritize patients for CD4 count testing could improve treatment monitoring by optimizing resource allocation. Methods and Findings Using a prospective cohort of HIV-infected patients (n = 1,956) monitored upon antiretroviral therapy initiation in seven clinical sites with distinct geographical and socio-economic settings, we retrospectively apply a novel prediction-based classification (PBC) modeling method. The model uses repeatedly measured biomarkers (white blood cell count and lymphocyte percent) to predict CD4+ T cell outcome through first-stage modeling and subsequent classification based on clinically relevant thresholds (CD4+ T cell count of 200 or 350 cells/µl). The algorithm correctly classified 90% (cross-validation estimate = 91.5%, standard deviation [SD] = 4.5%) of CD4 count measurements <200 cells/µl in the first year of follow-up; if laboratory testing is applied only to patients predicted to be below the 200-cells/µl threshold, we estimate a potential savings of 54.3% (SD = 4.2%) in CD4 testing capacity. A capacity savings of 34% (SD = 3.9%) is predicted using a CD4 threshold of 350 cells/µl. Similar results were obtained over the 3 y of follow-up available (n = 619). Limitations include a need for future economic healthcare outcome analysis, a need for assessment of extensibility beyond the 3-y observation time, and the need to assign a false positive threshold. Conclusions Our results support the use of PBC modeling as a triage point at the laboratory, lessening the need for laboratory-based CD4+ T cell count testing; implementation of this tool could help optimize the use of laboratory resources, directing CD4 testing towards higher-risk patients. However, further prospective studies and economic analyses are needed to demonstrate that the PBC model can be effectively applied in clinical settings. Please see later in the article for the Editors' Summary PMID:22529752

Helplessness/hopelessness, minimization and optimism predict survival in women with invasive ovarian cancer: a role for targeted support during initial treatment decision-making?

PubMed

Price, Melanie A; Butow, Phyllis N; Bell, Melanie L; deFazio, Anna; Friedlander, Michael; Fardell, Joanna E; Protani, Melinda M; Webb, Penelope M

2016-06-01

Women with advanced ovarian cancer generally have a poor prognosis but there is significant variability in survival despite similar disease characteristics and treatment regimens. The aim of this study was to determine whether psychosocial factors predict survival in women with ovarian cancer, controlling for potential confounders. The sample comprised 798 women with invasive ovarian cancer recruited into the Australian Ovarian Cancer Study and a subsequent quality of life study. Validated measures of depression, optimism, minimization, helplessness/hopelessness, and social support were completed 3-6 monthly for up to 2 years. Four hundred nineteen women (52.5 %) died over the follow-up period. Associations between time-varying psychosocial variables and survival were tested using adjusted Cox proportional hazard models. There was a significant interaction of psychosocial variables measured prior to first progression and overall survival, with higher optimism (adjusted hazard ratio per 1 standard deviation (HR) = 0.80, 95 % confidence interval (CI) 0.65-0.97), higher minimization (HR = 0.79, CI 0.66-0.94), and lower helplessness/hopelessness (HR = 1.40, CI 1.15-1.71) associated with longer survival. After disease progression, these variables were not associated with survival (optimism HR = 1.10, CI 0.95-1.27; minimization HR = 1.12, CI 0.95-1.31; and helplessness/hopelessness HR = 0.86, CI 0.74-1.00). Depression and social support were not associated with survival. In women with invasive ovarian cancer, psychosocial variables prior to disease progression appear to impact on overall survival, suggesting a preventive rather than modifying role. Addressing psychosocial responses to cancer and their potential impact on treatment decision-making early in the disease trajectory may benefit survival and quality of life.

Adaptive model-predictive controller for magnetic resonance guided focused ultrasound therapy.

PubMed

de Bever, Joshua; Todd, Nick; Payne, Allison; Christensen, Douglas A; Roemer, Robert B

2014-11-01

Minimising treatment time and protecting healthy tissues are conflicting goals that play major roles in making magnetic resonance image-guided focused ultrasound (MRgFUS) therapies clinically practical. We have developed and tested in vivo an adaptive model-predictive controller (AMPC) that reduces treatment time, ensures safety and efficacy, and provides flexibility in treatment set-up. The controller realises time savings by modelling the heated treatment cell's future temperatures and thermal dose accumulation in order to anticipate the optimal time to switch to the next cell. Selected tissues are safeguarded by a configurable temperature constraint. Simulations quantified the time savings realised by each controller feature as well as the trade-offs between competing safety and treatment time parameters. In vivo experiments in rabbit thighs established the controller's effectiveness and reliability. In all in vivo experiments the target thermal dose of at least 240 CEM43 was delivered everywhere in the treatment volume. The controller's temperature safety limit reliably activated and constrained all protected tissues to <9 CEM43. Simulations demonstrated the path independence of the controller, and that a path which successively proceeds to the hottest untreated neighbouring cell leads to significant time savings, e.g. when compared to a concentric spiral path. Use of the AMPC produced a compounding time-saving effect; reducing the treatment cells' heating times concurrently reduced heating of normal tissues, which eliminated cooling periods. Adaptive model-predictive control can automatically deliver safe, effective MRgFUS treatments while significantly reducing treatment times.

Distinct work-related, clinical and psychological factors predict return to work following treatment in four different cancer types.

PubMed

Cooper, Alethea F; Hankins, Matthew; Rixon, Lorna; Eaton, Emma; Grunfeld, Elizabeth A

2013-03-01

Many factors influence return to work (RTW) following cancer treatment. However specific factors affecting RTW across different cancer types are unclear. This study examined the role of clinical, sociodemographic, work and psychological factors in RTW following treatment for breast, gynaecological, head and neck, and urological cancer. A 12-month prospective questionnaire study was conducted with 290 patients. Cox regression analyses were conducted to calculate hazard ratios (HR) for time to RTW. Between 89-94% of cancer survivors returned to work. Breast cancer survivors took the longest to return (median 30 weeks), and urology cancer survivors returned the soonest (median 5 weeks). Earlier return among breast cancer survivors was predicted by a greater sense of control over their cancer at work (HR 1.2; 95% CI: 1.09-1.37) and by full-time work (HR 2.1; CI: 1.24-3.4). Predictive of a longer return among gynaecological cancer survivors was a belief that cancer treatment may impair ability to work (HR 0.75; CI: 0.62-0.91). Among urological cancer survivors constipation was predictive of longer RTW (HR 0.99; CI: 0.97-1.00), whereas undertaking flexible working was predictive of returning sooner (HR 1.70; CI: 1.07-2.7). Head and neck cancer survivors who perceived greater negative consequences of their cancer took longer to return (HR 0.27; CI: 0.11-0.68). Those reporting better physical functioning returned sooner (HR1.04; CI: 1.01-1.08). A different profile of predictive factors emerged for the four cancer types. In addition to optimal symptom management and workplace adaptations, the findings suggest that eliciting and challenging specific cancer and treatment-related perceptions may facilitate RTW. Copyright © 2012 John Wiley & Sons, Ltd.

Swing of the Surgical Pendulum: A Return to Surgery for Treatment of Head and Neck Cancer in the 21st Century?

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

Holsinger, F. Christopher; Weber, Randal S.

Treatment for head and neck cancer has evolved significantly during the past 100 years. Beginning with Bilroth's total laryngectomy on New Year's Day in 1873, 'radical' surgery remained the only accepted treatment for head and neck cancer when optimal local and regional control was the goal. Bigger was still better when it came to managing the primary tumor and the neck. The 'commando' procedure and radical neck dissection were the hallmarks of this first generation of treatments of head-and-neck cancer. With the advent of microvascular reconstructive techniques, larger and more comprehensive resections could be performed. Despite these large resections andmore » their 'mutilating' sequelae, overall survival did not improve. Even for intermediate-stage disease in head-and-neck cancer, the 5-year survival rate did not improve >50%. Many concluded that more than the scalpel was needed for optimal local and regional control, especially for intermediate- and advanced-stage disease. Most important, the multidisciplinary teams must identify and correlate biomarkers in the tumor and host that predict for a response to therapy and for optimal functional recovery. As the pendulum swings back, a scientific approach using tissue biomarkers for the response to treatment in the setting of multidisciplinary trials must emerge as the new paradigm. In the postgenomic era, treatment decisions should be made based on functional and oncologic parameters-not just to avoid perceived morbidity.« less

Multimodality Tumor Delineation and Predictive Modelling via Fuzzy-Fusion Deformable Models and Biological Potential Functions

NASA Astrophysics Data System (ADS)

Wasserman, Richard Marc

The radiation therapy treatment planning (RTTP) process may be subdivided into three planning stages: gross tumor delineation, clinical target delineation, and modality dependent target definition. The research presented will focus on the first two planning tasks. A gross tumor target delineation methodology is proposed which focuses on the integration of MRI, CT, and PET imaging data towards the generation of a mathematically optimal tumor boundary. The solution to this problem is formulated within a framework integrating concepts from the fields of deformable modelling, region growing, fuzzy logic, and data fusion. The resulting fuzzy fusion algorithm can integrate both edge and region information from multiple medical modalities to delineate optimal regions of pathological tissue content. The subclinical boundaries of an infiltrating neoplasm cannot be determined explicitly via traditional imaging methods and are often defined to extend a fixed distance from the gross tumor boundary. In order to improve the clinical target definition process an estimation technique is proposed via which tumor growth may be modelled and subclinical growth predicted. An in vivo, macroscopic primary brain tumor growth model is presented, which may be fit to each patient undergoing treatment, allowing for the prediction of future growth and consequently the ability to estimate subclinical local invasion. Additionally, the patient specific in vivo tumor model will be of significant utility in multiple diagnostic clinical applications.

[An analysis of status of personnel in occupational disease prevention and treatment institutions in Hunan Province, China, from 1996 to 2015].

PubMed

Liu, X L; Xiao, Y L; Tang, H Q; Chen, B L; Yang, L H; Xiao, Y L; Lv, S J

2018-01-20

Objective: To analyze the status of personnel in occupational disease prevention and treatment institutions in Hunan Province, China, from 1996 to 2015, to predict staff composition using grey model (GM) (1, 1) , and to provide a scientific basis and reference for optimizing human resource planning of occupational disease prevention and treatment in other provinces and regions and promoting the service capacity of the institutions. Methods: The data of the staff in occupational disease prevention and treatment institutions in Hunan Province, China, from 1996 to 2015 were obtained from the established basic information management system. The descriptive analysis method was used to analyze the dynamic changes in number and composition of the staff and the GM (1, 1) was used to predict the staff composition. Results: The numbers of the staff members in 1996 and 2015 in occupational disease prevention and treatment institutions in Hunan Province, China were 1591 and 1429, respectively. In the twenty years, the main education level of the staff transformed from "technical secondary school education and non-academic qualifications" to "bachelor degree or above and college degree"; the main major of the staff transformed from "other majors" to "public health and clinical medicine"; the proportion of the staff members without professional titles changed from >1/3 to 5%; and the proportions of the staff members with senior, intermediate, and junior professional titles were steadily rising. GM prediction showed that the proportions of highly educated staff members in 2018 and 2020 would be up to 41.00% and 45.61%, respectively; and the proportions of the staff members with a major in public health in 2018 and 2020 would be up to 44.15% and 46.60%, respectively. Conclusion: The staff in occupational disease prevention and treatment institutions in Hunan Province, China, in the twenty years have slight changes in staff size and great improvement in staff quality, which is beneficial to sustainable development of the occupational disease prevention and treatment undertakings. The education level and major will be further optimized in the next five years.

Prospective treatment planning to improve locoregional hyperthermia for oesophageal cancer.

PubMed

Kok, H P; van Haaren, P M A; van de Kamer, J B; Zum Vörde Sive Vörding, P J; Wiersma, J; Hulshof, M C C M; Geijsen, E D; van Lanschot, J J B; Crezee, J

2006-08-01

In the Academic Medical Center (AMC) Amsterdam, locoregional hyperthermia for oesophageal tumours is applied using the 70 MHz AMC-4 phased array system. Due to the occurrence of treatment-limiting hot spots in normal tissue and systemic stress at high power, the thermal dose achieved in the tumour can be sub-optimal. The large number of degrees of freedom of the heating device, i.e. the amplitudes and phases of the antennae, makes it difficult to avoid treatment-limiting hot spots by intuitive amplitude/phase steering. Prospective hyperthermia treatment planning combined with high resolution temperature-based optimization was applied to improve hyperthermia treatment of patients with oesophageal cancer. All hyperthermia treatments were performed with 'standard' clinical settings. Temperatures were measured systemically, at the location of the tumour and near the spinal cord, which is an organ at risk. For 16 patients numerically optimized settings were obtained from treatment planning with temperature-based optimization. Steady state tumour temperatures were maximized, subject to constraints to normal tissue temperatures. At the start of 48 hyperthermia treatments in these 16 patients temperature rise (DeltaT) measurements were performed by applying a short power pulse with the numerically optimized amplitude/phase settings, with the clinical settings and with mixed settings, i.e. numerically optimized amplitudes combined with clinical phases. The heating efficiency of the three settings was determined by the measured DeltaT values and the DeltaT-ratio between the DeltaT in the tumour (DeltaToes) and near the spinal cord (DeltaTcord). For a single patient the steady state temperature distribution was computed retrospectively for all three settings, since the temperature distributions may be quite different. To illustrate that the choice of the optimization strategy is decisive for the obtained settings, a numerical optimization on DeltaT-ratio was performed for this patient and the steady state temperature distribution for the obtained settings was computed. A higher DeltaToes was measured with the mixed settings compared to the calculated and clinical settings; DeltaTcord was higher with the mixed settings compared to the clinical settings. The DeltaT-ratio was approximately 1.5 for all three settings. These results indicate that the most effective tumour heating can be achieved with the mixed settings. DeltaT is proportional to the Specific Absorption Rate (SAR) and a higher SAR results in a higher steady state temperature, which implies that mixed settings are likely to provide the most effective heating at steady state as well. The steady state temperature distributions for the clinical and mixed settings, computed for the single patient, showed some locations where temperatures exceeded the normal tissue constraints used in the optimization. This demonstrates that the numerical optimization did not prescribe the mixed settings, because it had to comply with the constraints set to the normal tissue temperatures. However, the predicted hot spots are not necessarily clinically relevant. Numerical optimization on DeltaT-ratio for this patient yielded a very high DeltaT-ratio ( approximately 380), albeit at the cost of excessive heating of normal tissue and lower steady state tumour temperatures compared to the conventional optimization. Treatment planning can be valuable to improve hyperthermia treatments. A thorough discussion on clinically relevant objectives and constraints is essential.

Specific expectancies are associated with symptomatic outcomes and side effect burden in a trial of chamomile extract for generalized anxiety disorder.

PubMed

Keefe, John R; Amsterdam, Jay; Li, Qing S; Soeller, Irene; DeRubeis, Robert; Mao, Jun J

2017-01-01

Patient expectancies are hypothesized to contribute to the efficacy and side effects of psychiatric treatments, but little research has investigated this hypothesis in the context of psychopharmacological therapies for anxiety. We prospectively investigated whether expectancies predicted efficacy and adverse events in oral therapy for Generalized Anxiety Disorder (GAD), controlling for confounding patient characteristics correlating with outcomes. Expectancies regarding treatment efficacy and side effects were assessed at baseline of an eight week open-label phase of a trial of chamomile for Generalized Anxiety Disorder (GAD). The primary outcome was patient-reported GAD-7 scores, with clinical response and treatment-emergent side-effects as secondary outcomes. Expectancies were used to predict symptomatic and side-effect outcomes. Very few baseline patient characteristics predicted either type of expectancy. Controlling for a patient's predicted recovery based on their baseline characteristics, higher efficacy expectancies at baseline predicted greater change on the GAD-7 (adjusted β = -0.19, p = 0.011). Efficacy expectancies also predicted a higher likelihood of attaining clinical response (adjusted odds ratio = 1.69, p = 0.002). Patients with higher side effect expectancies reported more side effects (adjusted log expected count = 0.26, p = 0.038). Efficacy expectancies were unrelated to side effect reports (log expected count = -0.05, p = 0.680), and side effect expectancies were unrelated to treatment efficacy (β = 0.08, p = 0.306). Patients entering chamomile treatment for GAD with more favorable self-generated expectancies for the treatment experience greater improvement and fewer adverse events. Aligning patient expectancies with treatment selections may optimize outcomes. Trial Number NCT01072344 at ClinicalTrials.gov. Copyright © 2016 Elsevier Ltd. All rights reserved.

Pharmacokinetics and Drug Interactions Determine Optimum Combination Strategies in Computational Models of Cancer Evolution.

PubMed

Chakrabarti, Shaon; Michor, Franziska

2017-07-15

The identification of optimal drug administration schedules to battle the emergence of resistance is a major challenge in cancer research. The existence of a multitude of resistance mechanisms necessitates administering drugs in combination, significantly complicating the endeavor of predicting the evolutionary dynamics of cancers and optimal intervention strategies. A thorough understanding of the important determinants of cancer evolution under combination therapies is therefore crucial for correctly predicting treatment outcomes. Here we developed the first computational strategy to explore pharmacokinetic and drug interaction effects in evolutionary models of cancer progression, a crucial step towards making clinically relevant predictions. We found that incorporating these phenomena into our multiscale stochastic modeling framework significantly changes the optimum drug administration schedules identified, often predicting nonintuitive strategies for combination therapies. We applied our approach to an ongoing phase Ib clinical trial (TATTON) administering AZD9291 and selumetinib to EGFR-mutant lung cancer patients. Our results suggest that the schedules used in the three trial arms have almost identical efficacies, but slight modifications in the dosing frequencies of the two drugs can significantly increase tumor cell eradication. Interestingly, we also predict that drug concentrations lower than the MTD are as efficacious, suggesting that lowering the total amount of drug administered could lower toxicities while not compromising on the effectiveness of the drugs. Our approach highlights the fact that quantitative knowledge of pharmacokinetic, drug interaction, and evolutionary processes is essential for identifying best intervention strategies. Our method is applicable to diverse cancer and treatment types and allows for a rational design of clinical trials. Cancer Res; 77(14); 3908-21. ©2017 AACR . ©2017 American Association for Cancer Research.

[Imaging center - optimization of the imaging process].

PubMed

Busch, H-P

2013-04-01

Hospitals around the world are under increasing pressure to optimize the economic efficiency of treatment processes. Imaging is responsible for a great part of the success but also of the costs of treatment. In routine work an excessive supply of imaging methods leads to an "as well as" strategy up to the limit of the capacity without critical reflection. Exams that have no predictable influence on the clinical outcome are an unjustified burden for the patient. They are useless and threaten the financial situation and existence of the hospital. In recent years the focus of process optimization was exclusively on the quality and efficiency of performed single examinations. In the future critical discussion of the effectiveness of single exams in relation to the clinical outcome will be more important. Unnecessary exams can be avoided, only if in addition to the optimization of single exams (efficiency) there is an optimization strategy for the total imaging process (efficiency and effectiveness). This requires a new definition of processes (Imaging Pathway), new structures for organization (Imaging Center) and a new kind of thinking on the part of the medical staff. Motivation has to be changed from gratification of performed exams to gratification of process quality (medical quality, service quality, economics), including the avoidance of additional (unnecessary) exams. © Georg Thieme Verlag KG Stuttgart · New York.

Pharmacogenetics-based area-under-curve model can predict efficacy and adverse events from axitinib in individual patients with advanced renal cell carcinoma.

PubMed

Yamamoto, Yoshiaki; Tsunedomi, Ryouichi; Fujita, Yusuke; Otori, Toru; Ohba, Mitsuyoshi; Kawai, Yoshihisa; Hirata, Hiroshi; Matsumoto, Hiroaki; Haginaka, Jun; Suzuki, Shigeo; Dahiya, Rajvir; Hamamoto, Yoshihiko; Matsuyama, Kenji; Hazama, Shoichi; Nagano, Hiroaki; Matsuyama, Hideyasu

2018-03-30

We investigated the relationship between axitinib pharmacogenetics and clinical efficacy/adverse events in advanced renal cell carcinoma (RCC) and established a model to predict clinical efficacy and adverse events using pharmacokinetic and gene polymorphisms related to drug metabolism and efflux in a phase II trial. We prospectively evaluated the area under the plasma concentration-time curve (AUC) of axitinib, objective response rate, and adverse events in 44 consecutive advanced RCC patients treated with axitinib. To establish a model for predicting clinical efficacy and adverse events, polymorphisms in genes including ABC transporters ( ABCB1 and ABCG2 ), UGT1A , and OR2B11 were analyzed by whole-exome sequencing, Sanger sequencing, and DNA microarray. To validate this prediction model, calculated AUC by 6 gene polymorphisms was compared with actual AUC in 16 additional consecutive patients prospectively. Actual AUC significantly correlated with the objective response rate ( P = 0.0002) and adverse events (hand-foot syndrome, P = 0.0055; and hypothyroidism, P = 0.0381). Calculated AUC significantly correlated with actual AUC ( P < 0.0001), and correctly predicted objective response rate ( P = 0.0044) as well as adverse events ( P = 0.0191 and 0.0082, respectively). In the validation study, calculated AUC prior to axitinib treatment precisely predicted actual AUC after axitinib treatment ( P = 0.0066). Our pharmacogenetics-based AUC prediction model may determine the optimal initial dose of axitinib, and thus facilitate better treatment of patients with advanced RCC.

Pharmacogenetics-based area-under-curve model can predict efficacy and adverse events from axitinib in individual patients with advanced renal cell carcinoma

PubMed Central

Yamamoto, Yoshiaki; Tsunedomi, Ryouichi; Fujita, Yusuke; Otori, Toru; Ohba, Mitsuyoshi; Kawai, Yoshihisa; Hirata, Hiroshi; Matsumoto, Hiroaki; Haginaka, Jun; Suzuki, Shigeo; Dahiya, Rajvir; Hamamoto, Yoshihiko; Matsuyama, Kenji; Hazama, Shoichi; Nagano, Hiroaki; Matsuyama, Hideyasu

2018-01-01

We investigated the relationship between axitinib pharmacogenetics and clinical efficacy/adverse events in advanced renal cell carcinoma (RCC) and established a model to predict clinical efficacy and adverse events using pharmacokinetic and gene polymorphisms related to drug metabolism and efflux in a phase II trial. We prospectively evaluated the area under the plasma concentration–time curve (AUC) of axitinib, objective response rate, and adverse events in 44 consecutive advanced RCC patients treated with axitinib. To establish a model for predicting clinical efficacy and adverse events, polymorphisms in genes including ABC transporters (ABCB1 and ABCG2), UGT1A, and OR2B11 were analyzed by whole-exome sequencing, Sanger sequencing, and DNA microarray. To validate this prediction model, calculated AUC by 6 gene polymorphisms was compared with actual AUC in 16 additional consecutive patients prospectively. Actual AUC significantly correlated with the objective response rate (P = 0.0002) and adverse events (hand-foot syndrome, P = 0.0055; and hypothyroidism, P = 0.0381). Calculated AUC significantly correlated with actual AUC (P < 0.0001), and correctly predicted objective response rate (P = 0.0044) as well as adverse events (P = 0.0191 and 0.0082, respectively). In the validation study, calculated AUC prior to axitinib treatment precisely predicted actual AUC after axitinib treatment (P = 0.0066). Our pharmacogenetics-based AUC prediction model may determine the optimal initial dose of axitinib, and thus facilitate better treatment of patients with advanced RCC. PMID:29682213

Selecting the minimum prediction base of historical data to perform 5-year predictions of the cancer burden: The GoF-optimal method.

PubMed

Valls, Joan; Castellà, Gerard; Dyba, Tadeusz; Clèries, Ramon

2015-06-01

Predicting the future burden of cancer is a key issue for health services planning, where a method for selecting the predictive model and the prediction base is a challenge. A method, named here Goodness-of-Fit optimal (GoF-optimal), is presented to determine the minimum prediction base of historical data to perform 5-year predictions of the number of new cancer cases or deaths. An empirical ex-post evaluation exercise for cancer mortality data in Spain and cancer incidence in Finland using simple linear and log-linear Poisson models was performed. Prediction bases were considered within the time periods 1951-2006 in Spain and 1975-2007 in Finland, and then predictions were made for 37 and 33 single years in these periods, respectively. The performance of three fixed different prediction bases (last 5, 10, and 20 years of historical data) was compared to that of the prediction base determined by the GoF-optimal method. The coverage (COV) of the 95% prediction interval and the discrepancy ratio (DR) were calculated to assess the success of the prediction. The results showed that (i) models using the prediction base selected through GoF-optimal method reached the highest COV and the lowest DR and (ii) the best alternative strategy to GoF-optimal was the one using the base of prediction of 5-years. The GoF-optimal approach can be used as a selection criterion in order to find an adequate base of prediction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Approaches to drug therapy for COPD in Russia: a proposed therapeutic algorithm.

PubMed

Zykov, Kirill A; Ovcharenko, Svetlana I

2017-01-01

Until recently, there have been few clinical algorithms for the management of patients with COPD. Current evidence-based clinical management guidelines can appear to be complex, and they lack clear step-by-step instructions. For these reasons, we chose to create a simple and practical clinical algorithm for the management of patients with COPD, which would be applicable to real-world clinical practice, and which was based on clinical symptoms and spirometric parameters that would take into account the pathophysiological heterogeneity of COPD. This optimized algorithm has two main fields, one for nonspecialist treatment by primary care and general physicians and the other for treatment by specialized pulmonologists. Patients with COPD are treated with long-acting bronchodilators and short-acting drugs on a demand basis. If the forced expiratory volume in one second (FEV 1 ) is ≥50% of predicted and symptoms are mild, treatment with a single long-acting muscarinic antagonist or long-acting beta-agonist is proposed. When FEV 1 is <50% of predicted and/or the COPD assessment test score is ≥10, the use of combined bronchodilators is advised. If there is no response to treatment after three months, referral to a pulmonary specialist is recommended for pathophysiological endotyping: 1) eosinophilic endotype with peripheral blood or sputum eosinophilia >3%; 2) neutrophilic endotype with peripheral blood neutrophilia >60% or green sputum; or 3) pauci-granulocytic endotype. It is hoped that this simple, optimized, step-by-step algorithm will help to individualize the treatment of COPD in real-world clinical practice. This algorithm has yet to be evaluated prospectively or by comparison with other COPD management algorithms, including its effects on patient treatment outcomes. However, it is hoped that this algorithm may be useful in daily clinical practice for physicians treating patients with COPD in Russia.

Revisiting Bevacizumab + Cytotoxics Scheduling Using Mathematical Modeling: Proof of Concept Study in Experimental Non-Small Cell Lung Carcinoma.

PubMed

Imbs, Diane-Charlotte; El Cheikh, Raouf; Boyer, Arnaud; Ciccolini, Joseph; Mascaux, Céline; Lacarelle, Bruno; Barlesi, Fabrice; Barbolosi, Dominique; Benzekry, Sébastien

2018-01-01

Concomitant administration of bevacizumab and pemetrexed-cisplatin is a common treatment for advanced nonsquamous non-small cell lung cancer (NSCLC). Vascular normalization following bevacizumab administration may transiently enhance drug delivery, suggesting improved efficacy with sequential administration. To investigate optimal scheduling, we conducted a study in NSCLC-bearing mice. First, experiments demonstrated improved efficacy when using sequential vs. concomitant scheduling of bevacizumab and chemotherapy. Combining this data with a mathematical model of tumor growth under therapy accounting for the normalization effect, we predicted an optimal delay of 2.8 days between bevacizumab and chemotherapy. This prediction was confirmed experimentally, with reduced tumor growth of 38% as compared to concomitant scheduling, and prolonged survival (74 vs. 70 days). Alternate sequencing of 8 days failed in achieving a similar increase in efficacy, thus emphasizing the utility of modeling support to identify optimal scheduling. The model could also be a useful tool in the clinic to personally tailor regimen sequences. © 2017 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

A new predictive model for continuous positive airway pressure in the treatment of obstructive sleep apnea.

PubMed

Ebben, Matthew R; Narizhnaya, Mariya; Krieger, Ana C

2017-05-01

Numerous mathematical formulas have been developed to determine continuous positive airway pressure (CPAP) without an in-laboratory titration study. Recent studies have shown that style of CPAP mask can affect the optimal pressure requirement. However, none of the current models take mask style into account. Therefore, the goal of this study was to develop new predictive models of CPAP that take into account the style of mask interface. Data from 200 subjects with attended CPAP titrations during overnight polysomnograms using nasal masks and 132 subjects using oronasal masks were randomized and split into either a model development or validation group. Predictive models were then created in each model development group and the accuracy of the models was then tested in the model validation groups. The correlation between our new oronasal model and laboratory determined optimal CPAP was significant, r = 0.61, p < 0.001. Our nasal formula was also significantly related to laboratory determined optimal CPAP, r = 0.35, p < 0.001. The oronasal model created in our study significantly outperformed the original CPAP predictive model developed by Miljeteig and Hoffstein, z = 1.99, p < 0.05. The predictive performance of our new nasal model did not differ significantly from Miljeteig and Hoffstein's original model, z = -0.16, p < 0.90. The best predictors for the nasal mask group were AHI, lowest SaO2, and neck size, whereas the top predictors in the oronasal group were AHI and lowest SaO2. Our data show that predictive models of CPAP that take into account mask style can significantly improve the formula's accuracy. Most of the past models likely focused on model development with nasal masks (mask style used for model development was not typically reported in previous investigations) and are not well suited for patients using an oronasal interface. Our new oronasal CPAP prediction equation produced significantly improved performance compared to the well-known Miljeteig and Hoffstein formula in patients titrated on CPAP with an oronasal mask and was also significantly related to laboratory determined optimal CPAP.

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

PubMed

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

2011-04-01

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

A New Predictive Tool for Optimization of the Treatment of Brain Metastases from Colorectal Cancer After Stereotactic Radiosurgery.

PubMed

Rades, Dirk; Dahlke, Markus; Gebauer, Niklas; Bartscht, Tobias; Hornung, Dagmar; Trang, Ngo Thuy; Phuong, Pham Cam; Khoa, Mai Trong; Gliemroth, Jan

2015-10-01

To develop a predictive tool for survival after stereotactic radiosurgery of brain metastases from colorectal cancer. Out of nine factors analyzed for survival, those showing significance (p<0.05) or a trend (p≤0.06) were included. For each factor, 0 (worse survival) or 1 (better survival) point was assigned. Total scores represented the sum of the factor scores. Performance status (p=0.010) and interval from diagnosis of colorectal cancer until radiosurgery (p=0.026) achieved significance, extracranial metastases showed a trend (p=0.06). These factors were included in the tool. Total scores were 0-3 points. Six-month survival rates were 17% for patients with 0, 25% for those with 1, 67% for those with 2 and 100% for those with 3 points; 12-month rates were 0%, 0%, 33% and 67%, respectively. Two groups were created: 0-1 and 2-3 points. Six- and 12-month survival rates were 20% vs. 78% and 0% vs. 44% (p=0.002), respectively. This tool helps optimize the treatment of patients after stereotactic radiosurgery for brain metastases from colorectal cancer. Copyright© 2015 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Application of SBA-15 in Adsorption-Fenton Oxidation Process for Simultaneous Remediation of Dehp and As(iii)

NASA Astrophysics Data System (ADS)

Latorre, I.; Hwang, S.

2013-12-01

Di-2-ethylhexyl phthalate (DEHP) has been widely used as plasticizer in the manufacturing of polymeric materials to enhance flexibility, transparency and softness, particularly, in polyvinyl chloride (PVC) production. Several studies elucidated that DEHP could be linked to hepatocellular tumors and pre-term birth and may be a developmental and reproductive toxicant. Arsenic (As) contamination has been widespread in the environment and because of its toxicity and prevalence in nature; it also has become a significant environmental health concern. Most solid waste materials containing DEHP and As(III) are disposed of in landfills and may migrate to groundwater and soil environments representing a threat to human receptors. Therefore, the application of adsorption-Fenton oxidation process with Fe adsorbed to SBA-15 matrix was assessed for simultaneous remediation of DEHP and As(III). Three sequences were run to assess the regeneration efficiency of the SBA-15. A response surface methodology was employed to optimize adsorption and Fenton regeneration. Adsorption optimization was evaluated with regard to SBA-15 doses and the extent of As(III) and Fe concentrations. Optimization of Fenton regeneration, in addition, assessed initial H2O2 concentration. Global optimization for maximum reduction of DEHP and As(III) was performed by D-Optimal. Highest adsorption of DEHP (90-95%) and As (40-95%) into the SBA-15 was predicted at 1.16 mM Fe, 18.74 mg SBA-15 and 3.71 mg/L As(III). Highest reduction of As (78-99%) and DEHP (90-97%) was predicted with 0.50 mM Fe, 22 mg SBA-15, 3.02 mg/L As(III) and 22.50 mM H2O2. Global optimal treatments were validated and SBA-15 regenerated material was characterized via SEM and XPS. The efficiency of DEHP and As(III) remediation by adsorption-Fenton oxidation process, applying optimal treatment combinations, was evaluated using leachate from a lab scale bioreactor monofill (i.e., filled with PVC materials). Capability of As(III) and DEHP adsorption into SBA-15 was affected by the preferentiality adsorption of Fe and other compounds present in the monofill leachate.

Nomogram for suboptimal cytoreduction at primary surgery for advanced stage ovarian cancer.

PubMed

Gerestein, Cornelis G; Eijkemans, Marinus J; Bakker, Jeanette; Elgersma, Otto E; van der Burg, Maria E L; Kooi, Geertruida S; Burger, Curt W

2011-11-01

Maximal cytoreduction to minimal residual tumor is the most important determinant of prognosis in patients with advanced stage epithelial ovarian cancer (EOC). Preoperative prediction of suboptimal cytoreduction, defined as residual tumor >1 cm, could guide treatment decisions and improve counseling. The objective of this study was to identify predictive computed tomographic (CT) scan and clinical parameters for suboptimal cytoreduction at primary cytoreductive surgery for advanced stage EOC and to generate a nomogram with the identified parameters, which would be easy to use in daily clinical practice. Between October 2005 and December 2008, all patients with primary surgery for suspected advanced stage EOC at six participating teaching hospitals in the South Western part of the Netherlands entered the study protocol. To investigate independent predictors of suboptimal cytoreduction, a Cox proportional hazard model with backward stepwise elimination was utilized. One hundred and fifteen patients with FIGO stage III/IV EOC entered the study protocol. Optimal cytoreduction was achieved in 52 (45%) patients. A suboptimal cytoreduction was predicted by preoperative blood platelet count (p=0.1990; odds ratio (OR)=1.002), diffuse peritoneal thickening (DPT) (p=0.0074; OR=3.021), and presence of ascites on at least two thirds of CT scan slices (p=0.0385; OR=2.294) with a for-optimism corrected c-statistic of 0.67. Suboptimal cytoreduction was predicted by preoperative platelet count, DPT and presence of ascites. The generated nomogram can, after external validation, be used to estimate surgical outcome and to identify those patients, who might benefit from alternative treatment approaches.

Prediction of two month modified Rankin Scale with an ordinal prediction model in patients with aneurysmal subarachnoid haemorrhage

PubMed Central

2010-01-01

Background Aneurysmal subarachnoid haemorrhage (aSAH) is a devastating event with a frequently disabling outcome. Our aim was to develop a prognostic model to predict an ordinal clinical outcome at two months in patients with aSAH. Methods We studied patients enrolled in the International Subarachnoid Aneurysm Trial (ISAT), a randomized multicentre trial to compare coiling and clipping in aSAH patients. Several models were explored to estimate a patient's outcome according to the modified Rankin Scale (mRS) at two months after aSAH. Our final model was validated internally with bootstrapping techniques. Results The study population comprised of 2,128 patients of whom 159 patients died within 2 months (8%). Multivariable proportional odds analysis identified World Federation of Neurosurgical Societies (WFNS) grade as the most important predictor, followed by age, sex, lumen size of the aneurysm, Fisher grade, vasospasm on angiography, and treatment modality. The model discriminated moderately between those with poor and good mRS scores (c statistic = 0.65), with minor optimism according to bootstrap re-sampling (optimism corrected c statistic = 0.64). Conclusion We presented a calibrated and internally validated ordinal prognostic model to predict two month mRS in aSAH patients who survived the early stage up till a treatment decision. Although generalizability of the model is limited due to the selected population in which it was developed, this model could eventually be used to support clinical decision making after external validation. Trial Registration International Standard Randomised Controlled Trial, Number ISRCTN49866681 PMID:20920243

Coupling of EIT with computational lung modeling for predicting patient-specific ventilatory responses.

PubMed

Roth, Christian J; Becher, Tobias; Frerichs, Inéz; Weiler, Norbert; Wall, Wolfgang A

2017-04-01

Providing optimal personalized mechanical ventilation for patients with acute or chronic respiratory failure is still a challenge within a clinical setting for each case anew. In this article, we integrate electrical impedance tomography (EIT) monitoring into a powerful patient-specific computational lung model to create an approach for personalizing protective ventilatory treatment. The underlying computational lung model is based on a single computed tomography scan and able to predict global airflow quantities, as well as local tissue aeration and strains for any ventilation maneuver. For validation, a novel "virtual EIT" module is added to our computational lung model, allowing to simulate EIT images based on the patient's thorax geometry and the results of our numerically predicted tissue aeration. Clinically measured EIT images are not used to calibrate the computational model. Thus they provide an independent method to validate the computational predictions at high temporal resolution. The performance of this coupling approach has been tested in an example patient with acute respiratory distress syndrome. The method shows good agreement between computationally predicted and clinically measured airflow data and EIT images. These results imply that the proposed framework can be used for numerical prediction of patient-specific responses to certain therapeutic measures before applying them to an actual patient. In the long run, definition of patient-specific optimal ventilation protocols might be assisted by computational modeling. NEW & NOTEWORTHY In this work, we present a patient-specific computational lung model that is able to predict global and local ventilatory quantities for a given patient and any selected ventilation protocol. For the first time, such a predictive lung model is equipped with a virtual electrical impedance tomography module allowing real-time validation of the computed results with the patient measurements. First promising results obtained in an acute respiratory distress syndrome patient show the potential of this approach for personalized computationally guided optimization of mechanical ventilation in future. Copyright © 2017 the American Physiological Society.

Optimal pharmacological therapy in ST-elevation myocardial infarction-a review : A review of antithrombotic therapies in STEMI.

PubMed

Hermanides, R S; Kilic, S; van 't Hof, A W J

2018-04-23

Antithrombotic therapy is an essential component in the optimisation of clinical outcomes in patients with ST-elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention. There are currently several intravenous anticoagulant drugs available for primary percutaneous coronary intervention. Dual antiplatelet therapy comprising aspirin and P2Y12 inhibitor represents the cornerstone treatment for STEMI. However, these effective treatment strategies may be associated with bleeding complications. Compared with clopidogrel, prasugrel and ticagrelor are more potent and predictable, which translates into better clinical outcomes. Therefore, these agents are the first-line treatment in primary percutaneous coronary intervention. However, patients can still experience adverse ischaemic events, which might be in part attributed to alternative pathways triggering thrombosis. In this review, we provide a critical and updated review of currently available antithrombotic therapies used in patients with STEMI undergoing primary PCI. Finding a balance that minimises both thrombotic and bleeding risk is difficult, but crucial. Further randomised trials for this optimal balance are needed.

Treatment of an actual slaughterhouse wastewater by integration of biological and advanced oxidation processes: Modeling, optimization, and cost-effectiveness analysis.

PubMed

Bustillo-Lecompte, Ciro Fernando; Mehrvar, Mehrab

2016-11-01

Biological and advanced oxidation processes are combined to treat an actual slaughterhouse wastewater (SWW) by a sequence of an anaerobic baffled reactor, an aerobic activated sludge reactor, and a UV/H2O2 photoreactor with recycle in continuous mode at laboratory scale. In the first part of this study, quadratic modeling along with response surface methodology are used for the statistical analysis and optimization of the combined process. The effects of the influent total organic carbon (TOC) concentration, the flow rate, the pH, the inlet H2O2 concentration, and their interaction on the overall treatment efficiency, CH4 yield, and H2O2 residual in the effluent of the photoreactor are investigated. The models are validated at different operating conditions using experimental data. Maximum TOC and total nitrogen (TN) removals of 91.29 and 86.05%, respectively, maximum CH4 yield of 55.72%, and minimum H2O2 residual of 1.45% in the photoreactor effluent were found at optimal operating conditions. In the second part of this study, continuous distribution kinetics is applied to establish a mathematical model for the degradation of SWW as a function of time. The agreement between model predictions and experimental values indicates that the proposed model could describe the performance of the combined anaerobic-aerobic-UV/H2O2 processes for the treatment of SWW. In the final part of the study, the optimized combined anaerobic-aerobic-UV/H2O2 processes with recycle were evaluated using a cost-effectiveness analysis to minimize the retention time, the electrical energy consumption, and the overall incurred treatment costs required for the efficient treatment of slaughterhouse wastewater effluents. Copyright © 2016 Elsevier Ltd. All rights reserved.

Study of cabin noise control for twin engine general aviation aircraft

NASA Astrophysics Data System (ADS)

Vaicaitis, R.; Slazak, M.

1982-02-01

An analytical model based on modal analysis was developed to predict the noise transmission into a twin-engine light aircraft. The model was applied to optimize the interior noise to an A-weighted level of 85 dBA. To achieve the required noise attenuation, add-on treatments in the form of honeycomb panels, damping tapes, acoustic blankets, septum barriers and limp trim panels were added to the existing structure. The added weight of the noise control treatment is about 1.1 percent of the total gross take-off weight of the aircraft.

Cabin Noise Control for Twin Engine General Aviation Aircraft

NASA Technical Reports Server (NTRS)

Vaicaitis, R.; Slazak, M.

1982-01-01

An analytical model based on modal analysis was developed to predict the noise transmission into a twin-engine light aircraft. The model was applied to optimize the interior noise to an A-weighted level of 85 dBA. To achieve the required noise attenuation, add-on treatments in the form of honeycomb panels, damping tapes, acoustic blankets, septum barriers and limp trim panels were added to the existing structure. The added weight of the noise control treatment is about 1.1 percent of the total gross take-off weight of the aircraft.

SU-E-J-04: A Data-Driven, Response-Based, Multi-Criteria Decision Support System for Personalized Lung Radiation Treatment Planning

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

Luo, Y; McShan, D; Schipper, M

2014-06-01

Purpose: To develop a decision support tool to predict a patient's potential overall survival (OS) and radiation induced toxicity (RIT) based on clinical factors and responses during the course of radiotherapy, and suggest appropriate radiation dose adjustments to improve therapeutic effect. Methods: Important relationships between a patient's basic information and their clinical features before and during the radiation treatment are identified from historical clinical data by using statistical learning and data mining approaches. During each treatment period, a data analysis (DA) module predicts radiotherapy features such as time to local progression (TTLP), time to distant metastases (TTDM), radiation toxicity tomore » different organs, etc., under possible future treatment plans based on patient specifics or responses. An information fusion (IF) module estimates intervals for a patient's OS and the probabilities of RIT from a treatment plan by integrating the outcomes of module DA. A decision making (DM) module calculates “satisfaction” with the predicted radiation outcome based on trade-offs between OS and RIT, and finds the best treatment plan for the next time period via multi-criteria optimization. Results: Using physical and biological data from 130 lung cancer patients as our test bed, we were able to train and implement the 3 modules of our decision support tool. Examples demonstrate how it can help predict a new patient's potential OS and RIT with different radiation dose plans along with how these combinations change with dose, thus presenting a range of satisfaction/utility for use in individualized decision support. Conclusion: Although the decision support tool is currently developed from a small patient sample size, it shows the potential for the improvement of each patient's satisfaction in personalized radiation therapy. The radiation treatment outcome prediction and decision making model needs to be evaluated with more patients and demonstrated for use in radiation treatments for other cancers. P01-CA59827;R01CA142840.« less

SU-E-T-280: Reconstructed Rectal Wall Dose Map-Based Verification of Rectal Dose Sparing Effect According to Rectum Definition Methods and Dose Perturbation by Air Cavity in Endo-Rectal Balloon

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

Park, J; Research Institute of Biomedical Engineering, The Catholic University of Korea, Seoul; Park, H

Purpose: Dosimetric effect and discrepancy according to the rectum definition methods and dose perturbation by air cavity in an endo-rectal balloon (ERB) were verified using rectal-wall (Rwall) dose maps considering systematic errors in dose optimization and calculation accuracy in intensity-modulated radiation treatment (IMRT) for prostate cancer patients. Methods: When the inflated ERB having average diameter of 4.5 cm and air volume of 100 cc is used for patient, Rwall doses were predicted by pencil-beam convolution (PBC), anisotropic analytic algorithm (AAA), and AcurosXB (AXB) with material assignment function. The errors of dose optimization and calculation by separating air cavity from themore » whole rectum (Rwhole) were verified with measured rectal doses. The Rwall doses affected by the dose perturbation of air cavity were evaluated using a featured rectal phantom allowing insert of rolled-up gafchromic films and glass rod detectors placed along the rectum perimeter. Inner and outer Rwall doses were verified with reconstructed predicted rectal wall dose maps. Dose errors and extent at dose levels were evaluated with estimated rectal toxicity. Results: While AXB showed insignificant difference of target dose coverage, Rwall doses underestimated by up to 20% in dose optimization for the Rwhole than Rwall at all dose range except for the maximum dose. As dose optimization for Rwall was applied, the Rwall doses presented dose error less than 3% between dose calculation algorithm except for overestimation of maximum rectal dose up to 5% in PBC. Dose optimization for Rwhole caused dose difference of Rwall especially at intermediate doses. Conclusion: Dose optimization for Rwall could be suggested for more accurate prediction of rectal wall dose prediction and dose perturbation effect by air cavity in IMRT for prostate cancer. This research was supported by the Leading Foreign Research Institute Recruitment Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (MSIP) (Grant No. 200900420)« less

An auxiliary optimization method for complex public transit route network based on link prediction

NASA Astrophysics Data System (ADS)

Zhang, Lin; Lu, Jian; Yue, Xianfei; Zhou, Jialin; Li, Yunxuan; Wan, Qian

2018-02-01

Inspired by the missing (new) link prediction and the spurious existing link identification in link prediction theory, this paper establishes an auxiliary optimization method for public transit route network (PTRN) based on link prediction. First, link prediction applied to PTRN is described, and based on reviewing the previous studies, the summary indices set and its algorithms set are collected for the link prediction experiment. Second, through analyzing the topological properties of Jinan’s PTRN established by the Space R method, we found that this is a typical small-world network with a relatively large average clustering coefficient. This phenomenon indicates that the structural similarity-based link prediction will show a good performance in this network. Then, based on the link prediction experiment of the summary indices set, three indices with maximum accuracy are selected for auxiliary optimization of Jinan’s PTRN. Furthermore, these link prediction results show that the overall layout of Jinan’s PTRN is stable and orderly, except for a partial area that requires optimization and reconstruction. The above pattern conforms to the general pattern of the optimal development stage of PTRN in China. Finally, based on the missing (new) link prediction and the spurious existing link identification, we propose optimization schemes that can be used not only to optimize current PTRN but also to evaluate PTRN planning.

Why the dim light melatonin onset (DLMO) should be measured before treatment of patients with circadian rhythm sleep disorders.

PubMed

Keijzer, Henry; Smits, Marcel G; Duffy, Jeanne F; Curfs, Leopold M G

2014-08-01

Treatment of circadian rhythm sleep disorders (CRSD) may include light therapy, chronotherapy and melatonin. Exogenous melatonin is increasingly being used in patients with insomnia or CRSD. Although pharmacopoeias and the European food safety authority (EFSA) recommend administering melatonin 1-2 h before desired bedtime, several studies have shown that melatonin is not always effective if administered according to that recommendation. Crucial for optimal treatment of CRSD, melatonin and other treatments should be administered at a time related to individual circadian timing (typically assessed using the dim light melatonin onset (DLMO)). If not administered according to the individual patient's circadian timing, melatonin and other treatments may not only be ineffective, they may even result in contrary effects. Endogenous melatonin levels can be measured reliably in saliva collected at the patient's home. A clinically reliably DLMO can be calculated using a fixed threshold. Diary and polysomnographic sleep-onset time do not reliably predict DLMO or circadian timing in patients with CRSD. Knowing the patient's individual circadian timing by assessing DLMO can improve diagnosis and treatment of CRSD with melatonin as well as other therapies such as light or chronotherapy, and optimizing treatment timing will shorten the time required to achieve results. Copyright © 2013 Elsevier Ltd. All rights reserved.

Threshold-driven optimization for reference-based auto-planning

NASA Astrophysics Data System (ADS)

Long, Troy; Chen, Mingli; Jiang, Steve; Lu, Weiguo

2018-02-01

We study threshold-driven optimization methodology for automatically generating a treatment plan that is motivated by a reference DVH for IMRT treatment planning. We present a framework for threshold-driven optimization for reference-based auto-planning (TORA). Commonly used voxel-based quadratic penalties have two components for penalizing under- and over-dosing of voxels: a reference dose threshold and associated penalty weight. Conventional manual- and auto-planning using such a function involves iteratively updating the preference weights while keeping the thresholds constant, an unintuitive and often inconsistent method for planning toward some reference DVH. However, driving a dose distribution by threshold values instead of preference weights can achieve similar plans with less computational effort. The proposed methodology spatially assigns reference DVH information to threshold values, and iteratively improves the quality of that assignment. The methodology effectively handles both sub-optimal and infeasible DVHs. TORA was applied to a prostate case and a liver case as a proof-of-concept. Reference DVHs were generated using a conventional voxel-based objective, then altered to be either infeasible or easy-to-achieve. TORA was able to closely recreate reference DVHs in 5-15 iterations of solving a simple convex sub-problem. TORA has the potential to be effective for auto-planning based on reference DVHs. As dose prediction and knowledge-based planning becomes more prevalent in the clinical setting, incorporating such data into the treatment planning model in a clear, efficient way will be crucial for automated planning. A threshold-focused objective tuning should be explored over conventional methods of updating preference weights for DVH-guided treatment planning.

Optimization of the Alkaline Pretreatment of Rice Straw for Enhanced Methane Yield

PubMed Central

Song, Zilin; Yang, Gaihe; Han, Xinhui; Feng, Yongzhong; Ren, Guangxin

2013-01-01

The lime pretreatment process for rice straw was optimized to enhance the biodegradation performance and increase biogas yield. The optimization was implemented using response surface methodology (RSM) and Box-Behnken experimental design. The effects of biodegradation, as well as the interactive effects of Ca(OH)2 concentration, pretreatment time, and inoculum amount on biogas improvement, were investigated. Rice straw compounds, such as lignin, cellulose, and hemicellulose, were significantly degraded with increasing Ca(OH)2 concentration. The optimal conditions for the use of pretreated rice straw in anaerobic digestion were 9.81% Ca(OH)2 (w/w TS), 5.89 d treatment time, and 45.12% inoculum content, which resulted in a methane yield of 225.3 mL/g VS. A determination coefficient (R 2) of 96% was obtained, indicating that the model used to predict the anabolic digestion process shows a favorable fit with the experimental parameters. PMID:23509824

Extraction of gelatin from salmon (Salmo salar) fish skin using trypsin-aided process: optimization by Plackett-Burman and response surface methodological approaches.

PubMed

Fan, HuiYin; Dumont, Marie-Josée; Simpson, Benjamin K

2017-11-01

Gelatin from salmon ( Salmo salar ) skin with high molecular weight protein chains ( α -chains) was extracted using trypsin-aided process. Response surface methodology was used to optimise the extraction parameters. Yield, hydroxyproline content and protein electrophoretic profile via sodium dodecyl sulfate-polyacrylamide gel electrophoresis analysis of gelatin were used as responses in the optimization study. The optimum conditions were determined as: trypsin concentration at 1.49 U/g; extraction temperature at 45 °C; and extraction time at 6 h 16 min. This response surface optimized model was significant and produced an experimental value (202.04 ± 8.64%) in good agreement with the predicted value (204.19%). Twofold higher yields of gelatin with high molecular weight protein chains were achieved in the optimized process with trypsin treatment when compared to the process without trypsin.

Mathematical modeling for novel cancer drug discovery and development.

PubMed

Zhang, Ping; Brusic, Vladimir

2014-10-01

Mathematical modeling enables: the in silico classification of cancers, the prediction of disease outcomes, optimization of therapy, identification of promising drug targets and prediction of resistance to anticancer drugs. In silico pre-screened drug targets can be validated by a small number of carefully selected experiments. This review discusses the basics of mathematical modeling in cancer drug discovery and development. The topics include in silico discovery of novel molecular drug targets, optimization of immunotherapies, personalized medicine and guiding preclinical and clinical trials. Breast cancer has been used to demonstrate the applications of mathematical modeling in cancer diagnostics, the identification of high-risk population, cancer screening strategies, prediction of tumor growth and guiding cancer treatment. Mathematical models are the key components of the toolkit used in the fight against cancer. The combinatorial complexity of new drugs discovery is enormous, making systematic drug discovery, by experimentation, alone difficult if not impossible. The biggest challenges include seamless integration of growing data, information and knowledge, and making them available for a multiplicity of analyses. Mathematical models are essential for bringing cancer drug discovery into the era of Omics, Big Data and personalized medicine.

Systems oncology: towards patient-specific treatment regimes informed by multiscale mathematical modelling.

PubMed

Powathil, Gibin G; Swat, Maciej; Chaplain, Mark A J

2015-02-01

The multiscale complexity of cancer as a disease necessitates a corresponding multiscale modelling approach to produce truly predictive mathematical models capable of improving existing treatment protocols. To capture all the dynamics of solid tumour growth and its progression, mathematical modellers need to couple biological processes occurring at various spatial and temporal scales (from genes to tissues). Because effectiveness of cancer therapy is considerably affected by intracellular and extracellular heterogeneities as well as by the dynamical changes in the tissue microenvironment, any model attempt to optimise existing protocols must consider these factors ultimately leading to improved multimodal treatment regimes. By improving existing and building new mathematical models of cancer, modellers can play important role in preventing the use of potentially sub-optimal treatment combinations. In this paper, we analyse a multiscale computational mathematical model for cancer growth and spread, incorporating the multiple effects of radiation therapy and chemotherapy in the patient survival probability and implement the model using two different cell based modelling techniques. We show that the insights provided by such multiscale modelling approaches can ultimately help in designing optimal patient-specific multi-modality treatment protocols that may increase patients quality of life. Copyright © 2014 Elsevier Ltd. All rights reserved.

Use of an in vitro pharmacodynamic model to derive a moxifloxacin regimen that optimizes kill of Yersinia pestis and prevents emergence of resistance.

PubMed

Louie, A; Heine, H S; VanScoy, B; Eichas, A; Files, K; Fikes, S; Brown, D L; Liu, W; Kinzig-Schippers, M; Sörgel, F; Drusano, G L

2011-02-01

Yersinia pestis, the causative agent of bubonic, septicemic, and pneumonic plague, is classified as a CDC category A bioterrorism pathogen. Streptomycin and doxycycline are the "gold standards" for the treatment of plague. However, streptomycin is not available in many countries, and Y. pestis isolates resistant to streptomycin and doxycycline occur naturally and have been generated in laboratories. Moxifloxacin is a fluoroquinolone antibiotic that demonstrates potent activity against Y. pestis in in vitro and animal infection models. However, the dose and frequency of administration of moxifloxacin that would be predicted to optimize treatment efficacy in humans while preventing the emergence of resistance are unknown. Therefore, dose range and dose fractionation studies for moxifloxacin were conducted for Y. pestis in an in vitro pharmacodynamic model in which the half-lives of moxifloxacin in human serum were simulated so as to identify the lowest drug exposure and the schedule of administration that are linked with killing of Y. pestis and with the suppression of resistance. In the dose range studies, simulated moxifloxacin regimens of ≥175 mg/day killed drug-susceptible bacteria without resistance amplification. Dose fractionation studies demonstrated that the AUC (area under the concentration-time curve)/MIC ratio predicted kill of drug-susceptible Y. pestis, while the C(max) (maximum concentration of the drug in serum)/MIC ratio was linked to resistance prevention. Monte Carlo simulations predicted that moxifloxacin at 400 mg/day would successfully treat human infection due to Y. pestis in 99.8% of subjects and would prevent resistance amplification. We conclude that in an in vitro pharmacodynamic model, the clinically prescribed moxifloxacin regimen of 400 mg/day is predicted to be highly effective for the treatment of Y. pestis infections in humans. Studies of moxifloxacin in animal models of plague are warranted.

Plasma microRNA profile as a predictor of early virological response to interferon treatment in chronic hepatitis B patients.

PubMed

Zhang, Xiaonan; Chen, Cuncun; Wu, Min; Chen, Liang; Zhang, Jiming; Zhang, Xinxin; Zhang, Zhanqin; Wu, Jingdi; Wang, Jiefei; Chen, Xiaorong; Huang, Tao; Chen, Lixiang; Yuan, Zhenghong

2012-01-01

Interferon (IFN) and pegylated interferon (PEG-IFN) treatment of chronic hepatitis B leads to a sustained virological response in a limited proportion of patients and has considerable side effects. To find novel markers associated with prognosis of IFN therapy, we investigated whether a pretreatment plasma microRNA profile could be used to predict early virological response to IFN. We performed microRNA microarray analysis of plasma samples from 94 patients with chronic hepatitis B who received IFN therapy. The microRNA profiles from 13 liver biopsy samples were also measured. The OneR feature ranking and incremental feature selection method were used to rank and optimize the number of features in the model. Support vector machine prediction engine and jack-knife cross-validation were used to generate and evaluate the prediction model. The optimized model consisting of 11 microRNAs yielded a 74.2% overall accuracy in the training group and was independently confirmed in the test group (71.4% accuracy). Univariate and multivariate logistic regression analyses confirmed its independent association with early virological response (OR=7.35; P=2.12×10(-5)). Combining the microRNA profile with the alanine aminotransferase level improved the overall accuracy from 73.4% to 77.3%. Co-transfection of an HBV replicative construct with microRNA mimics revealed that let-7f, miR-939 and miR-638 were functionally associated with the HBV life cycle. The 11 microRNA signatures in plasma, together with basic clinical variables, might provide an accurate method to assist in medication decisions and improve the overall sustained response to IFN treatment.

Population-based V3 genotypic tropism assay: a retrospective analysis using screening samples from the A4001029 and MOTIVATE studies.

PubMed

McGovern, Rachel A; Thielen, Alexander; Mo, Theresa; Dong, Winnie; Woods, Conan K; Chapman, Douglass; Lewis, Marilyn; James, Ian; Heera, Jayvant; Valdez, Hernan; Harrigan, P Richard

2010-10-23

The MOTIVATE-1 and 2 studies compared maraviroc (MVC) along with optimized background therapy (OBT) vs. placebo along with OBT in treatment-experienced patients screened as having R5-HIV (original Monogram Trofile). A subset screened with non-R5 HIV were treated with MVC or placebo along with OBT in a sister safety trial, A4001029. This analysis retrospectively examined the performance of population-based sequence analysis of HIV-1 env V3-loop to predict coreceptor tropism. Triplicate V3-loop sequences were generated using stored screening plasma samples and data was processed using custom software ('ReCall'), blinded to clinical response. Tropism was inferred using geno2pheno ('g2p'; 5% false positive rate). Primary outcomes were viral load changes after starting maraviroc; and concordance with prior screening Trofile results. Genotype and Trofile results were available for 1164 individuals with virological outcome data (N = 169 non-R5 by Trofile). Compared with Trofile, V3 genotyping had a specificity of 92.6% and a sensitivity of 67.4% for detecting non-R5 virus. However, when compared with clinical outcome, virological responses were consistently similar between Trofile and V3 genotype at weeks 8 and 24 following the initiation of therapy for patients categorized as R5. Despite differences in sensitivity for predicting non-R5 HIV, week 8 and 24 week virological responses were similar in this treatment-experienced population. These findings suggest the potential utility of V3 genotyping as an accessible assay to select patients who may benefit from maraviroc treatment. Optimization of the predictive tropism algorithm may lead to further improvement in the clinical utility of HIV genotypic tropism assays.

Optimization of immunoglobulin substitution therapy by a stochastic immune response model.

PubMed

Figge, Marc Thilo

2009-05-28

The immune system is a complex adaptive system of cells and molecules that are interwoven in a highly organized communication network. Primary immune deficiencies are disorders in which essential parts of the immune system are absent or do not function according to plan. X-linked agammaglobulinemia is a B-lymphocyte maturation disorder in which the production of immunoglobulin is prohibited by a genetic defect. Patients have to be put on life-long immunoglobulin substitution therapy in order to prevent recurrent and persistent opportunistic infections. We formulate an immune response model in terms of stochastic differential equations and perform a systematic analysis of empirical therapy protocols that differ in the treatment frequency. The model accounts for the immunoglobulin reduction by natural degradation and by antigenic consumption, as well as for the periodic immunoglobulin replenishment that gives rise to an inhomogeneous distribution of immunoglobulin specificities in the shape space. Results are obtained from computer simulations and from analytical calculations within the framework of the Fokker-Planck formalism, which enables us to derive closed expressions for undetermined model parameters such as the infection clearance rate. We find that the critical value of the clearance rate, below which a chronic infection develops, is strongly dependent on the strength of fluctuations in the administered immunoglobulin dose per treatment and is an increasing function of the treatment frequency. The comparative analysis of therapy protocols with regard to the treatment frequency yields quantitative predictions of therapeutic relevance, where the choice of the optimal treatment frequency reveals a conflict of competing interests: In order to diminish immunomodulatory effects and to make good economic sense, therapeutic immunoglobulin levels should be kept close to physiological levels, implying high treatment frequencies. However, clearing infections without additional medication is more reliably achieved by substitution therapies with low treatment frequencies. Our immune response model predicts that the compromise solution of immunoglobulin substitution therapy has a treatment frequency in the range from one infusion per week to one infusion per two weeks.

Retreatment Predictions in Odontology by means of CBR Systems.

PubMed

Campo, Livia; Aliaga, Ignacio J; De Paz, Juan F; García, Alvaro Enrique; Bajo, Javier; Villarubia, Gabriel; Corchado, Juan M

2016-01-01

The field of odontology requires an appropriate adjustment of treatments according to the circumstances of each patient. A follow-up treatment for a patient experiencing problems from a previous procedure such as endodontic therapy, for example, may not necessarily preclude the possibility of extraction. It is therefore necessary to investigate new solutions aimed at analyzing data and, with regard to the given values, determine whether dental retreatment is required. In this work, we present a decision support system which applies the case-based reasoning (CBR) paradigm, specifically designed to predict the practicality of performing or not performing a retreatment. Thus, the system uses previous experiences to provide new predictions, which is completely innovative in the field of odontology. The proposed prediction technique includes an innovative combination of methods that minimizes false negatives to the greatest possible extent. False negatives refer to a prediction favoring a retreatment when in fact it would be ineffective. The combination of methods is performed by applying an optimization problem to reduce incorrect classifications and takes into account different parameters, such as precision, recall, and statistical probabilities. The proposed system was tested in a real environment and the results obtained are promising.

Retreatment Predictions in Odontology by means of CBR Systems

PubMed Central

Campo, Livia; Aliaga, Ignacio J.; García, Alvaro Enrique; Villarubia, Gabriel; Corchado, Juan M.

2016-01-01

The field of odontology requires an appropriate adjustment of treatments according to the circumstances of each patient. A follow-up treatment for a patient experiencing problems from a previous procedure such as endodontic therapy, for example, may not necessarily preclude the possibility of extraction. It is therefore necessary to investigate new solutions aimed at analyzing data and, with regard to the given values, determine whether dental retreatment is required. In this work, we present a decision support system which applies the case-based reasoning (CBR) paradigm, specifically designed to predict the practicality of performing or not performing a retreatment. Thus, the system uses previous experiences to provide new predictions, which is completely innovative in the field of odontology. The proposed prediction technique includes an innovative combination of methods that minimizes false negatives to the greatest possible extent. False negatives refer to a prediction favoring a retreatment when in fact it would be ineffective. The combination of methods is performed by applying an optimization problem to reduce incorrect classifications and takes into account different parameters, such as precision, recall, and statistical probabilities. The proposed system was tested in a real environment and the results obtained are promising. PMID:26884749

Is optimism real?

PubMed

Simmons, Joseph P; Massey, Cade

2012-11-01

Is optimism real, or are optimistic forecasts just cheap talk? To help answer this question, we investigated whether optimistic predictions persist in the face of large incentives to be accurate. We asked National Football League football fans to predict the winner of a single game. Roughly half (the partisans) predicted a game involving their favorite team, and the other half (the neutrals) predicted a game involving 2 teams they were neutral about. Participants were promised either a small incentive ($5) or a large incentive ($50) for correctly predicting the game's winner. Optimism emerged even when incentives were large, as partisans were much more likely than neutrals to predict partisans' favorite teams to win. Strong optimism also emerged among participants whose responses to follow-up questions strongly suggested that they believed the predictions they made. This research supports the claim that optimism is real. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

Optimal use of novel agents in chronic lymphocytic leukemia.

PubMed

Smith, Mitchell R; Weiss, Robert F

2018-05-07

Novel agents are changing therapy for patients with CLL, but their optimal use remains unclear. We model the clinical situation in which CLL responds to therapy, but resistant clones, generally carrying del17p, progress and lead to relapse. Sub-clones of varying growth rates and treatment sensitivity affect predicted therapy outcomes. We explore effects of different approaches to starting novel agent in relation to bendamustine-rituximab induction therapy: at initiation of therapy, at the end of chemo-immunotherapy, at molecular relapse, or at clinical detection of relapse. The outcomes differ depending on the underlying clonal architecture, raising the concept that personalized approaches based on clinical evaluation of each patient's clonal architecture might optimize outcomes while minimizing toxicity and cost. Copyright © 2018 Elsevier Ltd. All rights reserved.

Cross-sectoral optimization and visualization of transformation processes in urban water infrastructures in rural areas.

PubMed

Baron, S; Kaufmann Alves, I; Schmitt, T G; Schöffel, S; Schwank, J

2015-01-01

Predicted demographic, climatic and socio-economic changes will require adaptations of existing water supply and wastewater disposal systems. Especially in rural areas, these new challenges will affect the functionality of the present systems. This paper presents a joint interdisciplinary research project with the objective of developing an innovative software-based optimization and decision support system for the implementation of long-term transformations of existing infrastructures of water supply, wastewater and energy. The concept of the decision support and optimization tool is described and visualization methods for the presentation of results are illustrated. The model is tested in a rural case study region in the Southwest of Germany. A transformation strategy for a decentralized wastewater treatment concept and its visualization are presented for a model village.

A PFI mill can be used to predict biomechanical pulp strength properties

Treesearch

Gary F. Leatham; Gary C. Myers

1990-01-01

Recently, we showed that a biomechanical pulping process in which aspen chips are pretreated with a white-rot fungus can give energy savings and can increase paper sheet strength. To optimize this process, we need more efficient ways to evaluate the fungal treatments. Here, we examine a method that consists of treating coarse refiner mechanical pulp, refining in a PFI...

Fluid therapy in vomiting and diarrhea.

PubMed

Brown, Andrew J; Otto, Cynthia M

2008-05-01

Fluid therapy in the patient with vomiting and diarrhea is essential to correct hypovolemia, dehydration, acid-base imbalance, and serum electrolyte abnormalities. Prediction of acid-base or electrolyte disturbances is difficult; therefore, point of care testing is beneficial to optimize therapy. This article focuses on the pathophysiology and treatment of hypovolemia, dehydration, electrolyte disturbances, and acid-base derangements resulting from and associated with vomiting and diarrhea.

Large-scale optimization-based classification models in medicine and biology.

PubMed

Lee, Eva K

2007-06-01

We present novel optimization-based classification models that are general purpose and suitable for developing predictive rules for large heterogeneous biological and medical data sets. Our predictive model simultaneously incorporates (1) the ability to classify any number of distinct groups; (2) the ability to incorporate heterogeneous types of attributes as input; (3) a high-dimensional data transformation that eliminates noise and errors in biological data; (4) the ability to incorporate constraints to limit the rate of misclassification, and a reserved-judgment region that provides a safeguard against over-training (which tends to lead to high misclassification rates from the resulting predictive rule); and (5) successive multi-stage classification capability to handle data points placed in the reserved-judgment region. To illustrate the power and flexibility of the classification model and solution engine, and its multi-group prediction capability, application of the predictive model to a broad class of biological and medical problems is described. Applications include: the differential diagnosis of the type of erythemato-squamous diseases; predicting presence/absence of heart disease; genomic analysis and prediction of aberrant CpG island meythlation in human cancer; discriminant analysis of motility and morphology data in human lung carcinoma; prediction of ultrasonic cell disruption for drug delivery; identification of tumor shape and volume in treatment of sarcoma; discriminant analysis of biomarkers for prediction of early atherosclerois; fingerprinting of native and angiogenic microvascular networks for early diagnosis of diabetes, aging, macular degeneracy and tumor metastasis; prediction of protein localization sites; and pattern recognition of satellite images in classification of soil types. In all these applications, the predictive model yields correct classification rates ranging from 80 to 100%. This provides motivation for pursuing its use as a medical diagnostic, monitoring and decision-making tool.

Application of PK/PD Modeling in Veterinary Field: Dose Optimization and Drug Resistance Prediction

PubMed Central

Ahmad, Ijaz; Huang, Lingli; Hao, Haihong; Sanders, Pascal; Yuan, Zonghui

2016-01-01

Among veterinary drugs, antibiotics are frequently used. The true mean of antibiotic treatment is to administer dose of drug that will have enough high possibility of attaining the preferred curative effect, with adequately low chance of concentration associated toxicity. Rising of antibacterial resistance and lack of novel antibiotic is a global crisis; therefore there is an urgent need to overcome this problem. Inappropriate antibiotic selection, group treatment, and suboptimal dosing are mostly responsible for the mentioned problem. One approach to minimizing the antibacterial resistance is to optimize the dosage regimen. PK/PD model is important realm to be used for that purpose from several years. PK/PD model describes the relationship between drug potency, microorganism exposed to drug, and the effect observed. Proper use of the most modern PK/PD modeling approaches in veterinary medicine can optimize the dosage for patient, which in turn reduce toxicity and reduce the emergence of resistance. The aim of this review is to look at the existing state and application of PK/PD in veterinary medicine based on in vitro, in vivo, healthy, and disease model. PMID:26989688

Pharmacokinetic modeling of gentamicin in treatment of infective endocarditis: Model development and validation of existing models.

PubMed

Gomes, Anna; van der Wijk, Lars; Proost, Johannes H; Sinha, Bhanu; Touw, Daan J

2017-01-01

Gentamicin shows large variations in half-life and volume of distribution (Vd) within and between individuals. Thus, monitoring and accurately predicting serum levels are required to optimize effectiveness and minimize toxicity. Currently, two population pharmacokinetic models are applied for predicting gentamicin doses in adults. For endocarditis patients the optimal model is unknown. We aimed at: 1) creating an optimal model for endocarditis patients; and 2) assessing whether the endocarditis and existing models can accurately predict serum levels. We performed a retrospective observational two-cohort study: one cohort to parameterize the endocarditis model by iterative two-stage Bayesian analysis, and a second cohort to validate and compare all three models. The Akaike Information Criterion and the weighted sum of squares of the residuals divided by the degrees of freedom were used to select the endocarditis model. Median Prediction Error (MDPE) and Median Absolute Prediction Error (MDAPE) were used to test all models with the validation dataset. We built the endocarditis model based on data from the modeling cohort (65 patients) with a fixed 0.277 L/h/70kg metabolic clearance, 0.698 (±0.358) renal clearance as fraction of creatinine clearance, and Vd 0.312 (±0.076) L/kg corrected lean body mass. External validation with data from 14 validation cohort patients showed a similar predictive power of the endocarditis model (MDPE -1.77%, MDAPE 4.68%) as compared to the intensive-care (MDPE -1.33%, MDAPE 4.37%) and standard (MDPE -0.90%, MDAPE 4.82%) models. All models acceptably predicted pharmacokinetic parameters for gentamicin in endocarditis patients. However, these patients appear to have an increased Vd, similar to intensive care patients. Vd mainly determines the height of peak serum levels, which in turn correlate with bactericidal activity. In order to maintain simplicity, we advise to use the existing intensive-care model in clinical practice to avoid potential underdosing of gentamicin in endocarditis patients.

Pharmacokinetic modeling of gentamicin in treatment of infective endocarditis: Model development and validation of existing models

PubMed Central

van der Wijk, Lars; Proost, Johannes H.; Sinha, Bhanu; Touw, Daan J.

2017-01-01

Gentamicin shows large variations in half-life and volume of distribution (Vd) within and between individuals. Thus, monitoring and accurately predicting serum levels are required to optimize effectiveness and minimize toxicity. Currently, two population pharmacokinetic models are applied for predicting gentamicin doses in adults. For endocarditis patients the optimal model is unknown. We aimed at: 1) creating an optimal model for endocarditis patients; and 2) assessing whether the endocarditis and existing models can accurately predict serum levels. We performed a retrospective observational two-cohort study: one cohort to parameterize the endocarditis model by iterative two-stage Bayesian analysis, and a second cohort to validate and compare all three models. The Akaike Information Criterion and the weighted sum of squares of the residuals divided by the degrees of freedom were used to select the endocarditis model. Median Prediction Error (MDPE) and Median Absolute Prediction Error (MDAPE) were used to test all models with the validation dataset. We built the endocarditis model based on data from the modeling cohort (65 patients) with a fixed 0.277 L/h/70kg metabolic clearance, 0.698 (±0.358) renal clearance as fraction of creatinine clearance, and Vd 0.312 (±0.076) L/kg corrected lean body mass. External validation with data from 14 validation cohort patients showed a similar predictive power of the endocarditis model (MDPE -1.77%, MDAPE 4.68%) as compared to the intensive-care (MDPE -1.33%, MDAPE 4.37%) and standard (MDPE -0.90%, MDAPE 4.82%) models. All models acceptably predicted pharmacokinetic parameters for gentamicin in endocarditis patients. However, these patients appear to have an increased Vd, similar to intensive care patients. Vd mainly determines the height of peak serum levels, which in turn correlate with bactericidal activity. In order to maintain simplicity, we advise to use the existing intensive-care model in clinical practice to avoid potential underdosing of gentamicin in endocarditis patients. PMID:28475651

A feasibility investigation for modeling and optimization of temperature in bone drilling using fuzzy logic and Taguchi optimization methodology.

PubMed

Pandey, Rupesh Kumar; Panda, Sudhansu Sekhar

2014-11-01

Drilling of bone is a common procedure in orthopedic surgery to produce hole for screw insertion to fixate the fracture devices and implants. The increase in temperature during such a procedure increases the chances of thermal invasion of bone which can cause thermal osteonecrosis resulting in the increase of healing time or reduction in the stability and strength of the fixation. Therefore, drilling of bone with minimum temperature is a major challenge for orthopedic fracture treatment. This investigation discusses the use of fuzzy logic and Taguchi methodology for predicting and minimizing the temperature produced during bone drilling. The drilling experiments have been conducted on bovine bone using Taguchi's L25 experimental design. A fuzzy model is developed for predicting the temperature during orthopedic drilling as a function of the drilling process parameters (point angle, helix angle, feed rate and cutting speed). Optimum bone drilling process parameters for minimizing the temperature are determined using Taguchi method. The effect of individual cutting parameters on the temperature produced is evaluated using analysis of variance. The fuzzy model using triangular and trapezoidal membership predicts the temperature within a maximum error of ±7%. Taguchi analysis of the obtained results determined the optimal drilling conditions for minimizing the temperature as A3B5C1.The developed system will simplify the tedious task of modeling and determination of the optimal process parameters to minimize the bone drilling temperature. It will reduce the risk of thermal osteonecrosis and can be very effective for the online condition monitoring of the process. © IMechE 2014.

Collagen gel droplet-embedded culture drug sensitivity testing in squamous cell carcinoma cell lines derived from human oral cancers: Optimal contact concentrations of cisplatin and fluorouracil.

PubMed

Sakuma, Kaname; Tanaka, Akira; Mataga, Izumi

2016-12-01

The collagen gel droplet-embedded culture drug sensitivity test (CD-DST) is an anticancer drug sensitivity test that uses a method of three-dimensional culture of extremely small samples, and it is suited to primary cultures of human cancer cells. It is a useful method for oral squamous cell carcinoma (OSCC), in which the cancer tissues available for testing are limited. However, since the optimal contact concentrations of anticancer drugs have yet to be established in OSCC, CD-DST for detecting drug sensitivities of OSCC is currently performed by applying the optimal contact concentrations for stomach cancer. In the present study, squamous carcinoma cell lines from human oral cancer were used to investigate the optimal contact concentrations of cisplatin (CDDP) and fluorouracil (5-FU) during CD-DST for OSCC. CD-DST was performed in 7 squamous cell carcinoma cell lines derived from human oral cancers (Ca9-22, HSC-3, HSC-4, HO-1-N-1, KON, OSC-19 and SAS) using CDDP (0.15, 0.3, 1.25, 2.5, 5.0 and 10.0 µg/ml) and 5-FU (0.4, 0.9, 1.8, 3.8, 7.5, 15.0 and 30.0 µg/ml), and the optimal contact concentrations were calculated from the clinical response rate of OSCC to single-drug treatment and the in vitro efficacy rate curve. The optimal concentrations were 0.5 µg/ml for CDDP and 0.7 µg/ml for 5-FU. The antitumor efficacy of CDDP at this optimal contact concentration in CD-DST was compared to the antitumor efficacy in the nude mouse method. The T/C values, which were calculated as the ratio of the colony volume of the treatment group and the colony volume of the control group, at the optimal contact concentration of CDDP and of the nude mouse method were almost in agreement (P<0.05) and predicted clinical efficacy, indicating that the calculated optimal contact concentration is valid. Therefore, chemotherapy for OSCC based on anticancer drug sensitivity tests offers patients a greater freedom of choice and is likely to assume a greater importance in the selection of treatment from the perspectives of function preservation and quality of life, as well as representing a treatment option for unresectable, intractable or recurrent cases.

Artificial intelligence based model for optimization of COD removal efficiency of an up-flow anaerobic sludge blanket reactor in the saline wastewater treatment.

PubMed

Picos-Benítez, Alain R; López-Hincapié, Juan D; Chávez-Ramírez, Abraham U; Rodríguez-García, Adrián

2017-03-01

The complex non-linear behavior presented in the biological treatment of wastewater requires an accurate model to predict the system performance. This study evaluates the effectiveness of an artificial intelligence (AI) model, based on the combination of artificial neural networks (ANNs) and genetic algorithms (GAs), to find the optimum performance of an up-flow anaerobic sludge blanket reactor (UASB) for saline wastewater treatment. Chemical oxygen demand (COD) removal was predicted using conductivity, organic loading rate (OLR) and temperature as input variables. The ANN model was built from experimental data and performance was assessed through the maximum mean absolute percentage error (= 9.226%) computed from the measured and model predicted values of the COD. Accordingly, the ANN model was used as a fitness function in a GA to find the best operational condition. In the worst case scenario (low energy requirements, high OLR usage and high salinity) this model guaranteed COD removal efficiency values above 70%. This result is consistent and was validated experimentally, confirming that this ANN-GA model can be used as a tool to achieve the best performance of a UASB reactor with the minimum requirement of energy for saline wastewater treatment.

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

Espinoza, I; Peschke, P; Karger, C

Purpose: In radiotherapy, it is important to predict the response of tumour to irradiation prior to the treatment. Mathematical modelling of tumour control probability (TCP) based on the dose distribution, medical imaging and other biological information may help to improve this prediction and to optimize the treatment plan. The aim of this work is to develop an image based 3D multiscale radiobiological model, which describes the growth and the response to radiotherapy of hypoxic tumors. Methods: The computer model is based on voxels, containing tumour, normal (including capillary) and dead cells. Killing of tumour cells due to irradiation is calculatedmore » by the Linear Quadratic Model (extended for hypoxia), and the proliferation and resorption of cells are modelled by exponential laws. The initial shape of the tumours is taken from CT images and the initial vascular and cell density information from PET and/or MR images. Including the fractionation regime and the physical dose distribution of the radiation treatment, the model simulates the spatial-temporal evolution of the tumor. Additionally, the dose distribution may be biologically optimized. Results: The model describes the appearance of hypoxia during tumour growth and the reoxygenation processes during radiotherapy. Among other parameters, the TCP is calculated for different dose distributions. The results are in accordance with published results. Conclusion: The simulation model may contribute to the understanding of the influence of biological parameters on tumor response during treatment, and specifically on TCP. It may be used to implement dose-painting approaches. Experimental and clinical validation is needed. This study is supported by a grant from the Ministry of Education of Chile, Programa Mece Educacion Superior (2)« less

Fully automated treatment planning for head and neck radiotherapy using a voxel-based dose prediction and dose mimicking method

NASA Astrophysics Data System (ADS)

McIntosh, Chris; Welch, Mattea; McNiven, Andrea; Jaffray, David A.; Purdie, Thomas G.

2017-08-01

Recent works in automated radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient directly from the planning image. We present a probabilistic, atlas-based approach which predicts the dose for novel patients using a set of automatically selected most similar patients (atlases). The output is a spatial dose objective, which specifies the desired dose-per-voxel, and therefore replaces the need to specify and tune dose-volume objectives. Voxel-based dose mimicking optimization then converts the predicted dose distribution to a complete treatment plan with dose calculation using a collapsed cone convolution dose engine. In this study, we investigated automated planning for right-sided oropharaynx head and neck patients treated with IMRT and VMAT. We compare four versions of our dose prediction pipeline using a database of 54 training and 12 independent testing patients by evaluating 14 clinical dose evaluation criteria. Our preliminary results are promising and demonstrate that automated methods can generate comparable dose distributions to clinical. Overall, automated plans achieved an average of 0.6% higher dose for target coverage evaluation criteria, and 2.4% lower dose at the organs at risk criteria levels evaluated compared with clinical. There was no statistically significant difference detected in high-dose conformity between automated and clinical plans as measured by the conformation number. Automated plans achieved nine more unique criteria than clinical across the 12 patients tested and automated plans scored a significantly higher dose at the evaluation limit for two high-risk target coverage criteria and a significantly lower dose in one critical organ maximum dose. The novel dose prediction method with dose mimicking can generate complete treatment plans in 12-13 min without user interaction. It is a promising approach for fully automated treatment planning and can be readily applied to different treatment sites and modalities.

Fully automated treatment planning for head and neck radiotherapy using a voxel-based dose prediction and dose mimicking method.

PubMed

McIntosh, Chris; Welch, Mattea; McNiven, Andrea; Jaffray, David A; Purdie, Thomas G

2017-07-06

Recent works in automated radiotherapy treatment planning have used machine learning based on historical treatment plans to infer the spatial dose distribution for a novel patient directly from the planning image. We present a probabilistic, atlas-based approach which predicts the dose for novel patients using a set of automatically selected most similar patients (atlases). The output is a spatial dose objective, which specifies the desired dose-per-voxel, and therefore replaces the need to specify and tune dose-volume objectives. Voxel-based dose mimicking optimization then converts the predicted dose distribution to a complete treatment plan with dose calculation using a collapsed cone convolution dose engine. In this study, we investigated automated planning for right-sided oropharaynx head and neck patients treated with IMRT and VMAT. We compare four versions of our dose prediction pipeline using a database of 54 training and 12 independent testing patients by evaluating 14 clinical dose evaluation criteria. Our preliminary results are promising and demonstrate that automated methods can generate comparable dose distributions to clinical. Overall, automated plans achieved an average of 0.6% higher dose for target coverage evaluation criteria, and 2.4% lower dose at the organs at risk criteria levels evaluated compared with clinical. There was no statistically significant difference detected in high-dose conformity between automated and clinical plans as measured by the conformation number. Automated plans achieved nine more unique criteria than clinical across the 12 patients tested and automated plans scored a significantly higher dose at the evaluation limit for two high-risk target coverage criteria and a significantly lower dose in one critical organ maximum dose. The novel dose prediction method with dose mimicking can generate complete treatment plans in 12-13 min without user interaction. It is a promising approach for fully automated treatment planning and can be readily applied to different treatment sites and modalities.

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

Zhang, P; Kuo, L; Yorke, E

Purpose: To develop a biological modeling strategy which incorporates the response observed on the mid-treatment PET/CT into a dose escalation design for adaptive radiotherapy of non-small-cell lung cancer. Method: FDG-PET/CT was acquired midway through standard fractionated treatment and registered to pre-treatment planning PET/CT to evaluate radiation response of lung cancer. Each mid-treatment PET voxel was assigned the median SUV inside a concentric 1cm-diameter sphere to account for registration and imaging uncertainties. For each voxel, the planned radiation dose, pre- and mid-treatment SUVs were used to parameterize the linear-quadratic model, which was then utilized to predict the SUV distribution after themore » full prescribed dose. Voxels with predicted post-treatment SUV≥2 were identified as the resistant target (response arm). An adaptive simultaneous integrated boost was designed to escalate dose to the resistant target as high as possible, while keeping prescription dose to the original target and lung toxicity intact. In contrast, an adaptive target volume was delineated based only on the intensity of mid-treatment PET/CT (intensity arm), and a similar adaptive boost plan was optimized. The dose escalation capability of the two approaches was compared. Result: Images of three patients were used in this planning study. For one patient, SUV prediction indicated complete response and no necessary dose escalation. For the other two, resistant targets defined in the response arm were multifocal, and on average accounted for 25% of the pre-treatment target, compared to 67% in the intensity arm. The smaller response arm targets led to a 6Gy higher mean target dose in the adaptive escalation design. Conclusion: This pilot study suggests that adaptive dose escalation to a biologically resistant target predicted from a pre- and mid-treatment PET/CT may be more effective than escalation based on the mid-treatment PET/CT alone. More plans and ultimately clinical protocols are needed to validate this approach. MSKCC has a research agreement with Varian Medical System.« less

Computer-aided global breast MR image feature analysis for prediction of tumor response to chemotherapy: performance assessment

NASA Astrophysics Data System (ADS)

Aghaei, Faranak; Tan, Maxine; Hollingsworth, Alan B.; Zheng, Bin; Cheng, Samuel

2016-03-01

Dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI) has been used increasingly in breast cancer diagnosis and assessment of cancer treatment efficacy. In this study, we applied a computer-aided detection (CAD) scheme to automatically segment breast regions depicting on MR images and used the kinetic image features computed from the global breast MR images acquired before neoadjuvant chemotherapy to build a new quantitative model to predict response of the breast cancer patients to the chemotherapy. To assess performance and robustness of this new prediction model, an image dataset involving breast MR images acquired from 151 cancer patients before undergoing neoadjuvant chemotherapy was retrospectively assembled and used. Among them, 63 patients had "complete response" (CR) to chemotherapy in which the enhanced contrast levels inside the tumor volume (pre-treatment) was reduced to the level as the normal enhanced background parenchymal tissues (post-treatment), while 88 patients had "partially response" (PR) in which the high contrast enhancement remain in the tumor regions after treatment. We performed the studies to analyze the correlation among the 22 global kinetic image features and then select a set of 4 optimal features. Applying an artificial neural network trained with the fusion of these 4 kinetic image features, the prediction model yielded an area under ROC curve (AUC) of 0.83+/-0.04. This study demonstrated that by avoiding tumor segmentation, which is often difficult and unreliable, fusion of kinetic image features computed from global breast MR images without tumor segmentation can also generate a useful clinical marker in predicting efficacy of chemotherapy.

Integrating qPLM and biomechanical test data with an anisotropic fiber distribution model and predictions of TGF-β1 and IGF-1 regulation of articular cartilage fiber modulus

PubMed Central

Stender, Michael E.; Raub, Christopher B.; Yamauchi, Kevin A.; Shirazi, Reza; Vena, Pasquale; Sah, Robert L.; Hazelwood, Scott J.; Klisch, Stephen M.

2013-01-01

A continuum mixture model with distinct collagen (COL) and glycosaminoglycan (GAG) elastic constituents was developed for the solid matrix of immature bovine articular cartilage. A continuous COL fiber volume fraction distribution function and a true COL fiber elastic modulus (Ef) were used. Quantitative polarized light microscopy (qPLM) methods were developed to account for the relatively high cell density of immature articular cartilage and used with a novel algorithm that constructs a 3D distribution function from 2D qPLM data. For specimens untreated and cultured in vitro, most model parameters were specified from qPLM analysis and biochemical assay results; consequently, Ef was predicted using an optimization to measured mechanical properties in uniaxial tension and unconfined compression. Analysis of qPLM data revealed a highly anisotropic fiber distribution, with principal fiber orientation parallel to the surface layer. For untreated samples, predicted Ef values were 175 and 422 MPa for superficial (S) and middle (M) zone layers, respectively. TGF-β1 treatment was predicted to increase and decrease Ef values for the S and M layers to 281 and 309 MPa, respectively. IGF-1 treatment was predicted to decrease Ef values for the S and M layers to 22 and 26 MPa, respectively. A novel finding was that distinct native depth-dependent fiber modulus properties were modulated to nearly homogeneous values by TGF-β1 and IGF-1 treatments, with modulated values strongly dependent on treatment. PMID:23266906

Prediction of settled water turbidity and optimal coagulant dosage in drinking water treatment plant using a hybrid model of k-means clustering and adaptive neuro-fuzzy inference system

NASA Astrophysics Data System (ADS)

Kim, Chan Moon; Parnichkun, Manukid

2017-11-01

Coagulation is an important process in drinking water treatment to attain acceptable treated water quality. However, the determination of coagulant dosage is still a challenging task for operators, because coagulation is nonlinear and complicated process. Feedback control to achieve the desired treated water quality is difficult due to lengthy process time. In this research, a hybrid of k-means clustering and adaptive neuro-fuzzy inference system ( k-means-ANFIS) is proposed for the settled water turbidity prediction and the optimal coagulant dosage determination using full-scale historical data. To build a well-adaptive model to different process states from influent water, raw water quality data are classified into four clusters according to its properties by a k-means clustering technique. The sub-models are developed individually on the basis of each clustered data set. Results reveal that the sub-models constructed by a hybrid k-means-ANFIS perform better than not only a single ANFIS model, but also seasonal models by artificial neural network (ANN). The finally completed model consisting of sub-models shows more accurate and consistent prediction ability than a single model of ANFIS and a single model of ANN based on all five evaluation indices. Therefore, the hybrid model of k-means-ANFIS can be employed as a robust tool for managing both treated water quality and production costs simultaneously.

Experimental Research and Mathematical Modeling of Parameters Effecting on Cutting Force and SurfaceRoughness in CNC Turning Process

NASA Astrophysics Data System (ADS)

Zeqiri, F.; Alkan, M.; Kaya, B.; Toros, S.

2018-01-01

In this paper, the effects of cutting parameters on cutting forces and surface roughness based on Taguchi experimental design method are determined. Taguchi L9 orthogonal array is used to investigate the effects of machining parameters. Optimal cutting conditions are determined using the signal/noise (S/N) ratio which is calculated by average surface roughness and cutting force. Using results of analysis, effects of parameters on both average surface roughness and cutting forces are calculated on Minitab 17 using ANOVA method. The material that was investigated is Inconel 625 steel for two cases with heat treatment and without heat treatment. The predicted and calculated values with measurement are very close to each other. Confirmation test of results showed that the Taguchi method was very successful in the optimization of machining parameters for maximum surface roughness and cutting forces in the CNC turning process.

Structured Set Intra Prediction With Discriminative Learning in a Max-Margin Markov Network for High Efficiency Video Coding

PubMed Central

Dai, Wenrui; Xiong, Hongkai; Jiang, Xiaoqian; Chen, Chang Wen

2014-01-01

This paper proposes a novel model on intra coding for High Efficiency Video Coding (HEVC), which simultaneously predicts blocks of pixels with optimal rate distortion. It utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, in addition to formulating the data-driven structural interdependences to make the prediction error coherent with the probability distribution, which is desirable for successful transform and coding. The structured set prediction model incorporates a max-margin Markov network (M3N) to regulate and optimize multiple block predictions. The model parameters are learned by discriminating the actual pixel value from other possible estimates to maximize the margin (i.e., decision boundary bandwidth). Compared to existing methods that focus on minimizing prediction error, the M3N-based model adaptively maintains the coherence for a set of predictions. Specifically, the proposed model concurrently optimizes a set of predictions by associating the loss for individual blocks to the joint distribution of succeeding discrete cosine transform coefficients. When the sample size grows, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. As an internal step, we optimize the underlying Markov network structure to find states that achieve the maximal energy using expectation propagation. For validation, we integrate the proposed model into HEVC for optimal mode selection on rate-distortion optimization. The proposed prediction model obtains up to 2.85% bit rate reduction and achieves better visual quality in comparison to the HEVC intra coding. PMID:25505829

Maternal syphilis: pathophysiology and treatment.

PubMed Central

Berman, Stuart M.

2004-01-01

Despite the long history of medical interest in syphilis and its effects on pregnancy outcome, many fundamental questions about the pathophysiology and treatment of syphilis during pregnancy remain unanswered. However, understanding has been advanced by recent scientific reports such as those which delineate the complete sequence of the genome of the syphilis spirochaete, provide a more precise description of fetal and neonate infection by use of rabbit infectivity tests and describe the gestational age distribution of fetal death secondary to syphilis. It appears that fetal syphilitic involvement progresses in a rather predictable fashion, and although there is disagreement about the optimal prenatal treatment regimen, programmatic efforts to prevent fetal death must provide seropositive pregnant women with a recommended treatment early in pregnancy, and certainly before the third trimester. PMID:15356936

Amygdala Reactivity to Emotional Faces in the Prediction of General and Medication-Specific Responses to Antidepressant Treatment in the Randomized iSPOT-D Trial.

PubMed

Williams, Leanne M; Korgaonkar, Mayuresh S; Song, Yun C; Paton, Rebecca; Eagles, Sarah; Goldstein-Piekarski, Andrea; Grieve, Stuart M; Harris, Anthony W F; Usherwood, Tim; Etkin, Amit

2015-09-01

Although the cost of poor treatment outcomes of depression is staggering, we do not yet have clinically useful methods for selecting the most effective antidepressant for each depressed person. Emotional brain activation is altered in major depressive disorder (MDD) and implicated in treatment response. Identifying which aspects of emotional brain activation are predictive of general and specific responses to antidepressants may help clinicians and patients when making treatment decisions. We examined whether amygdala activation probed by emotion stimuli is a general or differential predictor of response to three commonly prescribed antidepressants, using functional magnetic resonance imaging (fMRI). A test-retest design was used to assess patients with MDD in an academic setting as part of the International Study to Predict Optimized Treatment in Depression. A total of 80 MDD outpatients were scanned prior to treatment and 8 weeks after randomization to the selective serotonin reuptake inhibitors escitalopram and sertraline and the serotonin-norepinephrine reuptake inhibitor, venlafaxine-extended release (XR). A total of 34 matched controls were scanned at the same timepoints. We quantified the blood oxygen level-dependent signal of the amygdala during subliminal and supraliminal viewing of facial expressions of emotion. Response to treatment was defined by ⩾50% symptom improvement on the 17-item Hamilton Depression Rating Scale. Pre-treatment amygdala hypo-reactivity to subliminal happy and threat was a general predictor of treatment response, regardless of medication type (Cohen's d effect size 0.63 to 0.77; classification accuracy, 75%). Responders showed hypo-reactivity compared to controls at baseline, and an increase toward 'normalization' post-treatment. Pre-treatment amygdala reactivity to subliminal sadness was a differential moderator of non-response to venlafaxine-XR (Cohen's d effect size 1.5; classification accuracy, 81%). Non-responders to venlafaxine-XR showed pre-treatment hyper-reactivity, which progressed to hypo-reactivity rather than normalization post-treatment, and hypo-reactivity post-treatment was abnormal compared to controls. Impaired amygdala activation has not previously been highlighted in the general vs differential prediction of antidepressant outcomes. Amygdala hypo-reactivity to emotions signaling reward and threat predicts the general capacity to respond to antidepressants. Amygdala hyper-reactivity to sad emotion is involved in a specific non-response to a serotonin-norepinephrine reuptake inhibitor. The findings suggest amygdala probes may help inform the personal selection of antidepressant treatments.

Variable porosity of the pipeline embolization device in straight and curved vessels: a guide for optimal deployment strategy.

PubMed

Shapiro, M; Raz, E; Becske, T; Nelson, P K

2014-04-01

Low-porosity endoluminal devices for the treatment of intracranial aneurysms, also known as flow diverters, have been in experimental and clinical use for close to 10 years. Despite rigorous evidence of their safety and efficacy in well-controlled trials, a number of key factors concerning their use remain poorly defined. Among these, none has received more attention to date than the debate on how many devices are optimally required to achieve a safe, effective, and economical outcome. Additional, related questions concern device sizing relative to the parent artery and optimal method of deployment of the devices. While some or all of these issues may be ultimately answered on an empiric basis via subgroup analysis of growing treatment cohorts, we believe that careful in vitro examination of relevant device properties can also help guide its in vivo use. We conducted a number of benchtop experiments to investigate the varied porosity of Pipeline Embolization Devices deployed in a simulated range of parent vessel diameters and applied these results toward conceptualizing optimal treatment strategies of fusiform and wide-neck aneurysms. The results of our studies confirm a predictable parabolic variability in device porosity based on the respective comparative sizes of the device and recipient artery, as well as device curvature. Even modest oversizing leads to a significant increase in porosity. The experiments demonstrate various deleterious effects of device oversizing relative to the parent artery and provide strategies for addressing size mismatches when they are unavoidable.

Selection of optimal sensors for predicting performance of polymer electrolyte membrane fuel cell

NASA Astrophysics Data System (ADS)

Mao, Lei; Jackson, Lisa

2016-10-01

In this paper, sensor selection algorithms are investigated based on a sensitivity analysis, and the capability of optimal sensors in predicting PEM fuel cell performance is also studied using test data. The fuel cell model is developed for generating the sensitivity matrix relating sensor measurements and fuel cell health parameters. From the sensitivity matrix, two sensor selection approaches, including the largest gap method, and exhaustive brute force searching technique, are applied to find the optimal sensors providing reliable predictions. Based on the results, a sensor selection approach considering both sensor sensitivity and noise resistance is proposed to find the optimal sensor set with minimum size. Furthermore, the performance of the optimal sensor set is studied to predict fuel cell performance using test data from a PEM fuel cell system. Results demonstrate that with optimal sensors, the performance of PEM fuel cell can be predicted with good quality.

Clinical predictors of effective continuous positive airway pressure in patients with obstructive sleep apnea/hypopnea syndrome.

PubMed

Lai, Chi-Chih; Friedman, Michael; Lin, Hsin-Ching; Wang, Pa-Chun; Hwang, Michelle S; Hsu, Cheng-Ming; Lin, Meng-Chih; Chin, Chien-Hung

2015-08-01

To identify standard clinical parameters that may predict the optimal level of continuous positive airway pressure (CPAP) in adult patients with obstructive sleep apnea/hypopnea syndrome (OSAHS). This is a retrospective study in a tertiary academic medical center that included 129 adult patients (117 males and 12 females) with OSAHS confirmed by diagnostic polysomnography (PSG). All OSAHS patients underwent successful full-night manual titration to determine the optimal CPAP pressure level for OSAHS treatment. The PSG parameters and completed physical examination, including body mass index, tonsil size grading, modified Mallampati grade (also known as updated Friedman's tongue position [uFTP]), uvular length, neck circumference, waist circumference, hip circumference, thyroid-mental distance, and hyoid-mental distance (HMD) were recorded. When the physical examination variables and OSAHS disease were correlated singly with the optimal CPAP pressure, we found that uFTP, HMD, and apnea/hypopnea index (AHI) were reliable predictors of CPAP pressures (P = .013, P = .002, and P < .001, respectively, by multiple regression). When all important factors were considered in a stepwise multiple linear regression analysis, a significant correlation with optimal CPAP pressure was formulated by factoring the uFTP, HMD, and AHI (optimal CPAP pressure = 1.01 uFTP + 0.74 HMD + 0.059 AHI - 1.603). This study distinguished the correlation between uFTP, HMD, and AHI with the optimal CPAP pressure. The structure of the upper airway (especially tongue base obstruction) and disease severity may predict the effective level of CPAP pressure. 4. © 2015 The American Laryngological, Rhinological and Otological Society, Inc.

Reduction and prediction of N2O emission from an Anoxic/Oxic wastewater treatment plant upon DO control and model simulation.

PubMed

Sun, Shichang; Bao, Zhiyuan; Li, Ruoyu; Sun, Dezhi; Geng, Haihong; Huang, Xiaofei; Lin, Junhao; Zhang, Peixin; Ma, Rui; Fang, Lin; Zhang, Xianghua; Zhao, Xuxin

2017-11-01

In order to make a better understanding of the characteristics of N 2 O emission in A/O wastewater treatment plant, full-scale and pilot-scale experiments were carried out and a back propagation artificial neural network model based on the experimental data was constructed to make a precise prediction of N 2 O emission. Results showed that, N 2 O flux from different units followed a descending order: aerated grit tank>oxic zone≫anoxic zone>final clarifier>primary clarifier, but 99.4% of the total emission of N 2 O (1.60% of N-load) was monitored from the oxic zone due to its big surface area. A proper DO control could reduce N 2 O emission down to 0.21% of N-load in A/O process, and a two-hidden-layers back propagation model with an optimized structure of 4:3:9:1 could achieve a good simulation of N 2 O emission, which provided a new method for the prediction of N 2 O emission during wastewater treatment. Copyright © 2017. Published by Elsevier Ltd.

A TCP model for external beam treatment of intermediate-risk prostate cancer

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

Walsh, Sean; Putten, Wil van der

2013-03-15

Purpose: Biological models offer the ability to predict clinical outcomes. The authors describe a model to predict the clinical response of intermediate-risk prostate cancer to external beam radiotherapy for a variety of fractionation regimes. Methods: A fully heterogeneous population averaged tumor control probability model was fit to clinical outcome data for hyper, standard, and hypofractionated treatments. The tumor control probability model was then employed to predict the clinical outcome of extreme hypofractionation regimes, as utilized in stereotactic body radiotherapy. Results: The tumor control probability model achieves an excellent level of fit, R{sup 2} value of 0.93 and a root meanmore » squared error of 1.31%, to the clinical outcome data for hyper, standard, and hypofractionated treatments using realistic values for biological input parameters. Residuals Less-Than-Or-Slanted-Equal-To 1.0% are produced by the tumor control probability model when compared to clinical outcome data for stereotactic body radiotherapy. Conclusions: The authors conclude that this tumor control probability model, used with the optimized radiosensitivity values obtained from the fit, is an appropriate mechanistic model for the analysis and evaluation of external beam RT plans with regard to tumor control for these clinical conditions.« less

Potential biomarker panels in overall breast cancer management: advancements by multilevel diagnostics.

PubMed

Girotra, Shantanu; Yeghiazaryan, Kristina; Golubnitschaja, Olga

2016-09-01

Breast cancer (BC) prevalence has reached an epidemic scale with half a million deaths annually. Current deficits in BC management include predictive and preventive approaches, optimized screening programs, individualized patient profiling, highly sensitive detection technologies for more precise diagnostics and therapy monitoring, individualized prediction and effective treatment of BC metastatic disease. To advance BC management, paradigm shift from delayed to predictive, preventive and personalized medical services is essential. Corresponding step forwards requires innovative multilevel diagnostics procuring specific panels of validated biomarkers. Here, we discuss current instrumental advancements including genomics, proteomics, epigenetics, miRNA, metabolomics, circulating tumor cells and cancer stem cells with a focus on biomarker discovery and multilevel diagnostic panels. A list of the recommended biomarker candidates is provided.

Surgical treatment of secondary peritonitis : A continuing problem.

PubMed

van Ruler, O; Boermeester, M A

2017-01-01

Secondary peritonitis remains associated with high mortality and morbidity rates. Treatment of secondary peritonitis is challenging even in modern medicine. Surgical intervention for source control remains the cornerstone of treatment, beside adequate antimicrobial therapy and resuscitation. A randomized clinical trial showed that relaparotomy on demand (ROD) after initial emergency surgery is the preferred treatment strategy, irrespective of the severity and extent of peritonitis. The effective and safe use of ROD requires intensive monitoring of the patient in a setting where diagnostic tests and decision making about relaparotomy are guaranteed round the clock. The lack of knowledge on timely and adequate patient selection, together with the lack of use of easy but reliable monitoring tools, seems to hamper full implementation of ROD. The accuracy of the relap decision tool is reasonable for prediction of ongoing peritonitis and selection for computer tomography (CT). The value of CT in an early postoperative phase is unclear. Future research and innovative technologies should focus on the additive value of CT in cases of operated secondary peritonitis and on the further optimization of bedside prediction tools to enhance adequate patient selection for intervention in a multidisciplinary setting.

Breast Radiotherapy with Mixed Energy Photons; a Model for Optimal Beam Weighting.

PubMed

Birgani, Mohammadjavad Tahmasebi; Fatahiasl, Jafar; Hosseini, Seyed Mohammad; Bagheri, Ali; Behrooz, Mohammad Ali; Zabiehzadeh, Mansour; Meskani, Reza; Gomari, Maryam Talaei

2015-01-01

Utilization of high energy photons (>10 MV) with an optimal weight using a mixed energy technique is a practical way to generate a homogenous dose distribution while maintaining adequate target coverage in intact breast radiotherapy. This study represents a model for estimation of this optimal weight for day to day clinical usage. For this purpose, treatment planning computed tomography scans of thirty-three consecutive early stage breast cancer patients following breast conservation surgery were analyzed. After delineation of the breast clinical target volume (CTV) and placing opposed wedge paired isocenteric tangential portals, dosimeteric calculations were conducted and dose volume histograms (DVHs) were generated, first with pure 6 MV photons and then these calculations were repeated ten times with incorporating 18 MV photons (ten percent increase in weight per step) in each individual patient. For each calculation two indexes including maximum dose in the breast CTV (Dmax) and the volume of CTV which covered with 95% Isodose line (VCTV, 95%IDL) were measured according to the DVH data and then normalized values were plotted in a graph. The optimal weight of 18 MV photons was defined as the intersection point of Dmax and VCTV, 95%IDL graphs. For creating a model to predict this optimal weight multiple linear regression analysis was used based on some of the breast and tangential field parameters. The best fitting model for prediction of 18 MV photons optimal weight in breast radiotherapy using mixed energy technique, incorporated chest wall separation plus central lung distance (Adjusted R2=0.776). In conclusion, this study represents a model for the estimation of optimal beam weighting in breast radiotherapy using mixed photon energy technique for routine day to day clinical usage.

Design of Biomedical Robots for Phenotype Prediction Problems

PubMed Central

deAndrés-Galiana, Enrique J.; Sonis, Stephen T.

2016-01-01

Abstract Genomics has been used with varying degrees of success in the context of drug discovery and in defining mechanisms of action for diseases like cancer and neurodegenerative and rare diseases in the quest for orphan drugs. To improve its utility, accuracy, and cost-effectiveness optimization of analytical methods, especially those that translate to clinically relevant outcomes, is critical. Here we define a novel tool for genomic analysis termed a biomedical robot in order to improve phenotype prediction, identifying disease pathogenesis and significantly defining therapeutic targets. Biomedical robot analytics differ from historical methods in that they are based on melding feature selection methods and ensemble learning techniques. The biomedical robot mathematically exploits the structure of the uncertainty space of any classification problem conceived as an ill-posed optimization problem. Given a classifier, there exist different equivalent small-scale genetic signatures that provide similar predictive accuracies. We perform the sensitivity analysis to noise of the biomedical robot concept using synthetic microarrays perturbed by different kinds of noises in expression and class assignment. Finally, we show the application of this concept to the analysis of different diseases, inferring the pathways and the correlation networks. The final aim of a biomedical robot is to improve knowledge discovery and provide decision systems to optimize diagnosis, treatment, and prognosis. This analysis shows that the biomedical robots are robust against different kinds of noises and particularly to a wrong class assignment of the samples. Assessing the uncertainty that is inherent to any phenotype prediction problem is the right way to address this kind of problem. PMID:27347715

Design of Biomedical Robots for Phenotype Prediction Problems.

PubMed

deAndrés-Galiana, Enrique J; Fernández-Martínez, Juan Luis; Sonis, Stephen T

2016-08-01

Genomics has been used with varying degrees of success in the context of drug discovery and in defining mechanisms of action for diseases like cancer and neurodegenerative and rare diseases in the quest for orphan drugs. To improve its utility, accuracy, and cost-effectiveness optimization of analytical methods, especially those that translate to clinically relevant outcomes, is critical. Here we define a novel tool for genomic analysis termed a biomedical robot in order to improve phenotype prediction, identifying disease pathogenesis and significantly defining therapeutic targets. Biomedical robot analytics differ from historical methods in that they are based on melding feature selection methods and ensemble learning techniques. The biomedical robot mathematically exploits the structure of the uncertainty space of any classification problem conceived as an ill-posed optimization problem. Given a classifier, there exist different equivalent small-scale genetic signatures that provide similar predictive accuracies. We perform the sensitivity analysis to noise of the biomedical robot concept using synthetic microarrays perturbed by different kinds of noises in expression and class assignment. Finally, we show the application of this concept to the analysis of different diseases, inferring the pathways and the correlation networks. The final aim of a biomedical robot is to improve knowledge discovery and provide decision systems to optimize diagnosis, treatment, and prognosis. This analysis shows that the biomedical robots are robust against different kinds of noises and particularly to a wrong class assignment of the samples. Assessing the uncertainty that is inherent to any phenotype prediction problem is the right way to address this kind of problem.

The Prevalence Rate of Tuberculin Skin Test Positive by Contacts Group to Predict the Development of Active Tuberculosis After School Outbreaks.

PubMed

Kim, Hee Jin; Chun, Byung Chul; Kwon, AmyM; Lee, Gyeong-Ho; Ryu, Sungweon; Oh, Soo Yeon; Lee, Jin Beom; Yoo, Se Hwa; Kim, Eui Sook; Kim, Je Hyeong; Shin, Chol; Lee, Seung Heon

2015-10-01

The tuberculin skin test (TST) is the standard tool to diagnose latent tuberculosis infection (LTBI) in mass screening. The aim of this study is to find an optimal cut-off point of the TST+ rate within tuberculosis (TB) contacts to predict the active TB development among adolescents in school TB outbreaks. The Korean National Health Insurance Review and Assessment database was used to identify active TB development in relation to the initial TST (cut-off, 10 mm). The 7,475 contacts in 89 schools were divided into two groups: Incident TB group (43 schools) and no incident TB group (46 schools). LTBI treatment was initiated in 607 of the 1,761 TST+ contacts. The association with active TB progression was examined at different cut-off points of the TST+ rate. The mean duration of follow-up was 3.9±0.9 years. Thirty-three contacts developed active TB during the 4,504 person-years among the TST+ contacts without LTBI treatment (n=1,154). The average TST+ rate for the incident TB group (n=43) and no incident TB group (n=46) were 31.0% and 15.5%, respectively. The TST+ rate per group was related with TB progression (odds ratio [OR], 1.025; 95% confidence interval [CI], 1.001-1.050; p=0.037). Based on the TST+ rate per group, active TB was best predicted at TST+ ≥ 16% (OR, 3.11; 95% CI, 1.29-7.51; area under curve, 0.64). Sixteen percent of the TST+ rate per group within the same grade students can be suggested as an optimal cut-off to predict active TB development in middle and high schools TB outbreaks.

Baseline psychophysiological and cortisol reactivity as a predictor of PTSD treatment outcome in virtual reality exposure therapy

PubMed Central

Norrholm, Seth Davin; Jovanovic, Tanja; Gerardi, Maryrose; Breazeale, Kathryn G.; Price, Matthew; Davis, Michael; Duncan, Erica; Ressler, Kerry J.; Bradley, Bekh; Rizzo, Albert; Tuerk, Peter W.; Rothbaum, Barbara O.

2017-01-01

Baseline cue-dependent physiological reactivity may serve as an objective measure of posttraumatic stress disorder (PTSD) symptoms. Additionally, prior animal model and psychological studies would suggest that subjects with greatest symptoms at baseline may have the greatest violation of expectancy to danger when undergoing exposure based psychotherapy; thus treatment approaches which enhanced the learning under these conditions would be optimal for those with maximal baseline cue-dependent reactivity. However methods to study this hypothesis objectively are lacking. Virtual reality (VR) methodologies have been successfully employed as an enhanced form of imaginal prolonged exposure therapy for the treatment of PTSD. Our goal was to examine the predictive nature of initial psychophysiological (e.g., startle, skin conductance, heart rate) and stress hormone responses (e.g., cortisol) during presentation of VR-based combat-related stimuli on PTSD treatment outcome. Combat veterans with PTSD underwent 6 weeks of VR exposure therapy combined with either D-cycloserine (DCS), alprazolam (ALP), or placebo (PBO). In the DCS group, startle response to VR scenes prior to initiation of treatment accounted for 76% of the variance in CAPS change scores, p < 0.001, in that higher responses predicted greater changes in symptom severity over time. Additionally, baseline cortisol reactivity was inversely associated with treatment response in the ALP group, p = 0.04. We propose that baseline cue-activated physiological measures will be sensitive to predicting patients’ level of response to exposure therapy, in particular in the presence of enhancement (e.g., DCS). PMID:27183343

Relaxation-induced anxiety: Effects of peak and trajectories of change on treatment outcome for generalized anxiety disorder.

PubMed

Newman, Michelle G; Lafreniere, Lucas S; Jacobson, Nicholas C

2018-07-01

Evidence is mixed regarding whether relaxation-induced anxiety (RIA) impedes relaxation training (RT) efficacy. Unlike past studies that averaged RIA across sessions, we examined peak RIA, change in RIA level across sessions, and timing of peak RIA with outcome. This was a secondary analysis of Borkovec, Newman, Pincus, and Lytle [2002. A component analysis of cognitive-behavioral therapy for generalized anxiety disorder and the role of interpersonal problems. Journal of Consulting and Clinical Psychology, 70, 288-298. doi: 10.1037/0022-006X.70.2.288 ]. Forty-one GAD participants were assigned randomly to CBT (n = 22) or BT (n = 19). Both treatments contained RT and RIA ratings within 13/14 sessions. Analyses used generalized additive mixed models (GAMMs), which accounted for longitudinal nonindependence and examined nonlinear trajectories of change. All participants improved significantly regardless of RIA. "Change trajectory of RIA level did not predict outcome". Instead, lower peak RIA predicted fewer GAD symptoms at post-treatment and greater likelihood to continue to improve during follow-up. Also, timing of peak was important. Whereas lower peak early in therapy did not predict outcome, lower peak during the last third of treatment did. Peak RIA's effect was neither accounted for by baseline symptom severity, treatment condition, comorbidity, nor by preceding or concurrent anxiety symptom change. People with consistently low peak RIA and/or who fully habituate to RIA by the end of therapy respond optimally to relaxation-based treatments.

WE-F-BRB-03: Inclusion of Data-Driven Risk Predictions in Radiation Treatment Planning in the Context of a Local Level Learning Health System

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

McNutt, T.

Advancements in informatics in radiotherapy are opening up opportunities to improve our ability to assess treatment plans. Models on individualizing patient dose constraints from prior patient data and shape relationships have been extensively researched and are now making their way into commercial products. New developments in knowledge based treatment planning involve understanding the impact of the radiation dosimetry on the patient. Akin to radiobiology models that have driven intensity modulated radiotherapy optimization, toxicity and outcome predictions based on treatment plans and prior patient experiences may be the next step in knowledge based planning. In order to realize these predictions, itmore » is necessary to understand how the clinical information can be captured, structured and organized with ontologies and databases designed for recall. Large databases containing radiation dosimetry and outcomes present the opportunity to evaluate treatment plans against predictions of toxicity and disease response. Such evaluations can be based on dose volume histogram or even the full 3-dimensional dose distribution and its relation to the critical anatomy. This session will provide an understanding of ontologies and standard terminologies used to capture clinical knowledge into structured databases; How data can be organized and accessed to utilize the knowledge in planning; and examples of research and clinical efforts to incorporate that clinical knowledge into planning for improved care for our patients. Learning Objectives: Understand the role of standard terminologies, ontologies and data organization in oncology Understand methods to capture clinical toxicity and outcomes in a clinical setting Understand opportunities to learn from clinical data and its application to treatment planning Todd McNutt receives funding from Philips, Elekta and Toshiba for some of the work presented.« less

Predictors of treatment response to strengthening and stretching exercises for patellofemoral pain: An examination of patellar alignment.

PubMed

Peng, Hsien-Te; Song, Chen-Yi

2015-12-01

Closed kinetic chain and quadriceps strengthening, combined with flexibility exercises of the lower limb musculature, is a common treatment for patellofemoral pain syndrome (PFPS). The effectiveness has been well documented; however, very little is known about which factors predict treatment success. A total of 43 female subjects with PFPS participated in an eight-week progressive leg press (LP) strengthening and stretching exercise program. A decrease of 1.5 cm on a 10 cm visual analog scale (VAS) score was used as an indicator for treatment success. The baseline patellar tilt angle difference (PTA-d) due to quadriceps contraction prior to treatment was evaluated as a predictor of treatment success. The logistic regression and receiver operating characteristics (ROC) curve analysis were performed to investigate the predictive value of PTA-d. PTA-d could significantly predict the treatment success of LP strengthening and stretching exercises. The odds ratio (OR) for having an unsuccessful outcome was 1.19 (95% confidence interval (CI), 1.03-1.39, P<0.021) per degree increment of PTA-d. The most optimal cut-off value for the clinical discrimination of treatment success after LP strengthening and stretching exercise was -1.5° of PTA-d (sensitivity=0.74, specificity=0.71). The area under the ROC curve was 0.73 (standard error=0.08). Female patients with PFPS whose quadriceps contraction reduced the lateral patellar tilt prior to LP strengthening and stretching exercise treatment are more likely to experience pain relief. It seems clinically important to check dynamic patellar tilt characteristics before treatment to aid in clinical decision making. Copyright © 2014 Elsevier B.V. All rights reserved.

Recent progress in translational cystic fibrosis research using precision medicine strategies.

PubMed

Cholon, Deborah M; Gentzsch, Martina

2018-03-01

Significant progress has been achieved in developing precision therapies for cystic fibrosis; however, highly effective treatments that target the ion channel, CFTR, are not yet available for many patients. As numerous CFTR therapeutics are currently in the clinical pipeline, reliable screening tools capable of predicting drug efficacy to support individualized treatment plans and translational research are essential. The utilization of bronchial, nasal, and rectal tissues from individual cystic fibrosis patients for drug testing using in vitro assays such as electrophysiological measurements of CFTR activity and evaluation of fluid movement in spheroid cultures, has advanced the prediction of patient-specific responses. However, for precise prediction of drug effects, in vitro models of CFTR rescue should incorporate the inflamed cystic fibrosis airway environment and mimic the complex tissue structures of airway epithelia. Furthermore, novel assays that monitor other aspects of successful CFTR rescue such as restoration of mucus characteristics, which is important for predicting mucociliary clearance, will allow for better prognoses of successful therapies in vivo. Additional cystic fibrosis treatment strategies are being intensively explored, such as development of drugs that target other ion channels, and novel technologies including pluripotent stem cells, gene therapy, and gene editing. The multiple therapeutic approaches available to treat the basic defect in cystic fibrosis combined with relevant precision medicine models provide a framework for identifying optimal and sustained treatments that will benefit all cystic fibrosis patients. Copyright © 2017 European Cystic Fibrosis Society. Published by Elsevier B.V. All rights reserved.

Numerical Simulation of Callus Healing for Optimization of Fracture Fixation Stiffness

PubMed Central

Steiner, Malte; Claes, Lutz; Ignatius, Anita; Simon, Ulrich; Wehner, Tim

2014-01-01

The stiffness of fracture fixation devices together with musculoskeletal loading defines the mechanical environment within a long bone fracture, and can be quantified by the interfragmentary movement. In vivo results suggested that this can have acceleratory or inhibitory influences, depending on direction and magnitude of motion, indicating that some complications in fracture treatment could be avoided by optimizing the fixation stiffness. However, general statements are difficult to make due to the limited number of experimental findings. The aim of this study was therefore to numerically investigate healing outcomes under various combinations of shear and axial fixation stiffness, and to detect the optimal configuration. A calibrated and established numerical model was used to predict fracture healing for numerous combinations of axial and shear fixation stiffness under physiological, superimposed, axial compressive and translational shear loading in sheep. Characteristic maps of healing outcome versus fixation stiffness (axial and shear) were created. The results suggest that delayed healing of 3 mm transversal fracture gaps will occur for highly flexible or very rigid axial fixation, which was corroborated by in vivo findings. The optimal fixation stiffness for ovine long bone fractures was predicted to be 1000–2500 N/mm in the axial and >300 N/mm in the shear direction. In summary, an optimized, moderate axial stiffness together with certain shear stiffness enhances fracture healing processes. The negative influence of one improper stiffness can be compensated by adjustment of the stiffness in the other direction. PMID:24991809

Expression and Secretion of Endostar Protein by Escherichia Coli: Optimization of Culture Conditions Using the Response Surface Methodology.

PubMed

Mohajeri, Abbas; Abdolalizadeh, Jalal; Pilehvar-Soltanahmadi, Younes; Kiafar, Farhad; Zarghami, Nosratollah

2016-10-01

Endostar as a specific drug in treatment of the nonsmall cell lung cancer is produced using Escherichia coli expression system. Plackett-Burman design (PBD) and response surface methodology (RSM) are statistical tools for experimental design and optimization of biotechnological processes. This investigation aimed to predict and develop the optimal culture condition and its components for expression and secretion of endostar into the culture medium of E. coli. The synthetic endostar coding sequence was fused with PhoA signal peptide. The nine factors involved in the production of recombinant protein-postinduction temperature, cell density, rotation speed, postinduction time, concentration of glycerol, IPTG, peptone, glycine, and triton X-100-were evaluated using PBD. Four significant factors were selected based on PBD results for optimizing culture condition using RSM. Endostar was purified using cation exchange chromatography and size exclusion chromatography. The maximum level of endostar was obtained under the following condition: 13.57-h postinduction time, 0.76 % glycine, 0.7 % triton X-100, and 4.87 % glycerol. The predicted levels of endostar was significantly correlated with experimental levels (R 2 = 0.982, P = 0.00). The obtained results indicated that PBD and RSM are effective tools for optimization of culture condition and its components for endostar production in E. coli. The most important factors in the enhancement of the protein production are glycerol, glycine, and postinduction time.

Numerical simulation of callus healing for optimization of fracture fixation stiffness.

PubMed

Steiner, Malte; Claes, Lutz; Ignatius, Anita; Simon, Ulrich; Wehner, Tim

2014-01-01

The stiffness of fracture fixation devices together with musculoskeletal loading defines the mechanical environment within a long bone fracture, and can be quantified by the interfragmentary movement. In vivo results suggested that this can have acceleratory or inhibitory influences, depending on direction and magnitude of motion, indicating that some complications in fracture treatment could be avoided by optimizing the fixation stiffness. However, general statements are difficult to make due to the limited number of experimental findings. The aim of this study was therefore to numerically investigate healing outcomes under various combinations of shear and axial fixation stiffness, and to detect the optimal configuration. A calibrated and established numerical model was used to predict fracture healing for numerous combinations of axial and shear fixation stiffness under physiological, superimposed, axial compressive and translational shear loading in sheep. Characteristic maps of healing outcome versus fixation stiffness (axial and shear) were created. The results suggest that delayed healing of 3 mm transversal fracture gaps will occur for highly flexible or very rigid axial fixation, which was corroborated by in vivo findings. The optimal fixation stiffness for ovine long bone fractures was predicted to be 1000-2500 N/mm in the axial and >300 N/mm in the shear direction. In summary, an optimized, moderate axial stiffness together with certain shear stiffness enhances fracture healing processes. The negative influence of one improper stiffness can be compensated by adjustment of the stiffness in the other direction.

WE-FG-BRB-00: The Challenges of Predicting RBE Effects in Particle Therapy and Opportunities for Improving Cancer Therapy

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

NONE

The physical pattern of energy deposition and the enhanced relative biological effectiveness (RBE) of protons and carbon ions compared to photons offer unique and not fully understood or exploited opportunities to improve the efficacy of radiation therapy. Variations in RBE within a pristine or spread out Bragg peak and between particle types may be exploited to enhance cell killing in target regions without a corresponding increase in damage to normal tissue structures. In addition, the decreased sensitivity of hypoxic tumors to photon-based therapies may be partially overcome through the use of more densely ionizing radiations. These and other differences betweenmore » particle and photon beams may be used to generate biologically optimized treatments that reduce normal tissue complications. In this symposium, speakers will examine the impact of the RBE of charged particles on measurable biological endpoints, treatment plan optimization, and the prediction or retrospective assessment of treatment outcomes. In particular, an AAPM task group was formed to critically examine the evidence for a spatially-variant RBE in proton therapy. Current knowledge of proton RBE variation with respect to dose, biological endpoint, and physics parameters will be reviewed. Further, the clinical relevance of these variations will be discussed. Recent work focused on improving simulations of radiation physics and biological response in proton and carbon ion therapy will also be presented. Finally, relevant biology research and areas of research needs will be highlighted, including the dependence of RBE on genetic factors including status of DNA repair pathways, the sensitivity of cancer stem-like cells to charged particles, the role of charged particles in hypoxic tumors, and the importance of fractionation effects. In addition to the physical advantages of protons and more massive ions over photons, the future application of biologically optimized treatment plans and their potential to provide higher levels of local tumor control and improved normal tissue sparing will be discussed. Learning Objectives: To assess whether the current practice of a constant RBE of 1.1 should be revised or maintained in proton therapy and to evaluate the potential clinical consequences of delivering RBE-weighted dose distributions based on variable RBE To review current research on biological models used to predict the increased biological effectiveness of proton and carbon ions to help move towards a practical understanding and implementation of biological optimization in particle therapy To discuss potential differences in biological mechanisms between photons and charged particles (light and heavy ions) that could impact clinical cancer therapy H. Paganetti, NCI U19 CA21239D. Grosshans, Our research is supported by the NCIK. Held, Funding Support: National Cancer Institute of the National Institutes of Health, USA, under Award Number R21CA182259 and Federal Share of Program Income Earned by Massachusetts General Hospital on C06CA059267, Proton Therapy Research and Treatment Center.« less

TU-FG-201-07: Development of SRS Conical Collimator Collision Prediction Software for Radiation Treatment Safety

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

Gutti, V; Morrow, A; Kim, S

Purpose: Stereotactic radiosurgery (SRS) treatments using conical collimators can potentially result in gantry collision with treatment table due to limited collision-clear spaces. An in-house software was developed to help the SRS treatment planner mitigate potential SRS conical collimator (Varian Medical System, Palo Alto, CA) collisions with the treatment table. This software was designed to remove treatment re-planning secondary to unexpected collisions. Methods: A BrainLAB SRS ICT Frameless Extension used for SRS treatments in our clinic was mathematically modelled using surface points registered to the 3D co-ordinate space of the couch extension. The surface points are transformed based on the treatmentmore » isocenter point and potential collisions are determined in 3D space for couch and gantry angle combinations. The distance between the SRS conical collimators and LINAC isocenter is known. The collision detection model was programmed in MATLAB (Mathwork, Natick, MA) to display graphical plots of the calculations, and the plotted data is used to avoid the gantry and couch angle combinations that would likely result in a collision. We have utilized the cone collision tool for 23 SRS cone treatment plans (8 retrospective and 15 prospective for 10 patients). Results: Twenty one plans strongly agreed with the software tool prediction for collision. However, in two plans, a collision was observed with a 0.5 cm margin when the software predicted no collision. Therefore, additional margins were added to the clearance criteria in the program to achieve a lower risk of actual collisions. Conclusion: Our in-house developed collision check software successfully avoided SRS cone re-planning by 91.3% due to a reduction in cone collisions with the treatment table. Future developments to our software will include a CT image data set based collision prediction model as well as a beam angle optimization tool to avoid normal critical tissues as well as previously treated lesions.« less

Fast detection of peroxidase (POD) activity in tomato leaves which infected with Botrytis cinerea using hyperspectral imaging

NASA Astrophysics Data System (ADS)

Kong, Wenwen; Liu, Fei; Zhang, Chu; Bao, Yidan; Yu, Jiajia; He, Yong

2014-01-01

Tomatoes are cultivated around the world and gray mold is one of its most prominent and destructive diseases. An early disease detection method can decrease losses caused by plant diseases and prevent the spread of diseases. The activity of peroxidase (POD) is very important indicator of disease stress for plants. The objective of this study is to examine the possibility of fast detection of POD activity in tomato leaves which infected with Botrytis cinerea using hyperspectral imaging data. Five pre-treatment methods were investigated. Genetic algorithm-partial least squares (GA-PLS) was applied to select optimal wavelengths. A new fast learning neural algorithm named extreme learning machine (ELM) was employed as multivariate analytical tool in this study. 21 optimal wavelengths were selected by GA-PLS and used as inputs of three calibration models. The optimal prediction result was achieved by ELM model with selected wavelengths, and the r and RMSEP in validation were 0.8647 and 465.9880 respectively. The results indicated that hyperspectral imaging could be considered as a valuable tool for POD activity prediction. The selected wavelengths could be potential resources for instrument development.

Optimization of cyanide extraction from wastewater using emulsion liquid membrane system by response surface methodology.

PubMed

Xue, Juan Qin; Liu, Ni Na; Li, Guo Ping; Dang, Long Tao

To solve the disposal problem of cyanide wastewater, removal of cyanide from wastewater using a water-in-oil emulsion type of emulsion liquid membrane (ELM) was studied in this work. Specifically, the effects of surfactant Span-80, carrier trioctylamine (TOA), stripping agent NaOH solution and the emulsion-to-external-phase-volume ratio on removal of cyanide were investigated. Removal of total cyanide was determined using the silver nitrate titration method. Regression analysis and optimization of the conditions were conducted using the Design-Expert software and response surface methodology (RSM). The actual cyanide removals and the removals predicted using RSM analysis were in close agreement, and the optimal conditions were determined to be as follows: the volume fraction of Span-80, 4% (v/v); the volume fraction of TOA, 4% (v/v); the concentration of NaOH, 1% (w/v); and the emulsion-to-external-phase volume ratio, 1:7. Under the optimum conditions, the removal of total cyanide was 95.07%, and the RSM predicted removal was 94.90%, with a small exception. The treatment of cyanide wastewater using an ELM is an effective technique for application in industry.

Annual Research Review: New Frontiers in Developmental Neuropharmacology--Can Long-Term Therapeutic Effects of Drugs Be Optimized through Carefully Timed Early Intervention?

ERIC Educational Resources Information Center

Andersen, Susan L.; Navalta, Carryl P.

2011-01-01

Our aim is to present a working model that may serve as a valuable heuristic to predict enduring effects of drugs when administered during development. Our primary tenet is that a greater understanding of neurodevelopment can lead to improved treatment that intervenes early in the progression of a given disorder and prevents symptoms from…

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

Lian, J; Yuan, L; Wu, Q

Purpose: The quality and efficiency of radiotherapy treatment planning are highly planer dependent. Previously we have developed a statistical model to correlate anatomical features with dosimetry features of head and neck Tomotherapy treatment. The model enables us to predict the best achievable dosimetry for individual patient prior to treatment planning. The purpose of this work is to study if the prediction model can facilitate the treatment planning in both the efficiency and dosimetric quality. Methods: The anatomy-dosimetry correlation model was used to calculate the expected DVH for nine patients formerly treated. In Group A (3 patients), the model prediction agreedmore » with the clinic plan; in Group B (3 patients), the model predicted lower larynx mean dose than the clinic plan; in Group C (3 patients), the model suggested the brainstem could be further spared. Guided by the prior knowledge, we re-planned all 9 cases. The number of interactions during the optimization process and dosimetric endpoints between the original clinical plan and model-guided re-plan were compared. Results: For Group A, the difference of target coverage and organs-at-risk sparing is insignificant (p>0.05) between the replan and the clinical plan. For Group B, the clinical plan larynx median dose is 49.4±4.7 Gy, while the prediction suggesting 40.0±6.2 Gy (p<0.05). The re-plan achieved 41.5±6.6 Gy, with similar dose on other structures as clinical plan. For Group C, the clinical plan brainstem maximum dose is 44.7±5.5 Gy. The model predicted lower value 32.2±3.8 Gy (p<0.05). The re-plans reduced brainstem maximum dose to 31.8±4.1 Gy without affecting the dosimetry of other structures. In the replanning of the 9 cases, the times operator interacted with TPS are reduced on average about 50% compared to the clinical plan. Conclusion: We have demonstrated that the prior expert knowledge embedded model improved the efficiency and quality of Tomotherapy treatment planning.« less

SU-F-T-344: Commissioning Constant Dose Rate VMAT in the Raystation Treatment Planning System for a Varian Clinac IX

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

Pursley, J; Gueorguiev, G; Prichard, H

Purpose: To demonstrate the commissioning of constant dose rate volumetric modulated arc therapy (VMAT) in the Raystation treatment planning system for a Varian Clinac iX with Exact couch. Methods: Constant dose rate (CDR) VMAT is an option in the Raystation treatment planning system, enabling VMAT delivery on Varian linacs without a RapidArc upgrade. Raystation 4.7 was used to commission CDR-VMAT for a Varian Clinac iX. Raystation arc model parameters were selected to match machine deliverability characteristics. A Varian Exact couch model was added to Raystation 4.7 and commissioned for use in VMAT optimization. CDR-VMAT commissioning checks were performed on themore » linac, including patient-specific QA measurements for 10 test patients using both the ArcCHECK from Sun Nuclear Corporation and COMPASS from IBA Dosimetry. Multi-criteria optimization (MCO) in Raystation was used for CDR-VMAT planning. Results: Raystation 4.7 generated clinically acceptable and deliverable CDR-VMAT plans for the Varian Clinac. VMAT plans were optimized including a model of the Exact couch with both rails in the out positions. CDR-VMAT plans generated with MCO in Raystation were dosimetrically comparable to Raystation MCO-generated IMRT plans. Patient-specific QA measurements with the ArcCHECK on the couch showed good agreement with the treatment planning system prediction. Patient-specific, structure-specific, multi-statistical parameter 3D QA measurements with gantry-mounted COMPASS also showed good agreement. Conclusion: Constant dose rate VMAT was successfully modeled in Raystation 4.7 for a Varian Clinac iX, and Raystation’s multicriteria optimization generated constant dose rate VMAT plans which were deliverable and dosimetrically comparable to IMRT plans.« less

Selection of the optimal completion of horizontal wells with multi-stage hydraulic fracturing of the low-permeable formation, field C

NASA Astrophysics Data System (ADS)

Bozoev, A. M.; Demidova, E. A.

2016-03-01

At the moment, many fields of Western Siberia are in the later stages of development. In this regard, the multilayer fields are actually involved in the development of hard to recover reserves by conducting well interventions. However, most of these assets may not to be economical profitable without application of horizontal drilling and multi-stage hydraulic fracturing treatment. Moreover, location of frac ports in relative to each other, number of stages, volume of proppant per one stage are the main issues due to the fact that the interference effect could lead to the loss of oil production. The optimal arrangement of horizontal wells with multi-stage hydraulic fracture was defined in this paper. Several analytical approaches have been used to predict the started oil flow rate and chose the most appropriate for field C reservoir J1. However, none of the analytical equations could not take into account the interference effect and determine the optimum number of fractures. Therefore, the simulation modelling was used. Finally, the universal equation is derived for this field C, the reservoir J1. This tool could be used to predict the flow rate of the horizontal well with hydraulic fracturing treatment on the qualitative level without simulation model.

A computational framework for simultaneous estimation of muscle and joint contact forces and body motion using optimization and surrogate modeling.

PubMed

Eskinazi, Ilan; Fregly, Benjamin J

2018-04-01

Concurrent estimation of muscle activations, joint contact forces, and joint kinematics by means of gradient-based optimization of musculoskeletal models is hindered by computationally expensive and non-smooth joint contact and muscle wrapping algorithms. We present a framework that simultaneously speeds up computation and removes sources of non-smoothness from muscle force optimizations using a combination of parallelization and surrogate modeling, with special emphasis on a novel method for modeling joint contact as a surrogate model of a static analysis. The approach allows one to efficiently introduce elastic joint contact models within static and dynamic optimizations of human motion. We demonstrate the approach by performing two optimizations, one static and one dynamic, using a pelvis-leg musculoskeletal model undergoing a gait cycle. We observed convergence on the order of seconds for a static optimization time frame and on the order of minutes for an entire dynamic optimization. The presented framework may facilitate model-based efforts to predict how planned surgical or rehabilitation interventions will affect post-treatment joint and muscle function. Copyright © 2018 IPEM. Published by Elsevier Ltd. All rights reserved.

A Novel Method of Supplying Nutrients Permits Predictable Shoot Growth and Root : Shoot Ratios of Pre-transplant Bedding Plants

PubMed Central

Greenwood, Duncan J.; Mckee, John M. T.; Fuller, Deborah P.; Burns, Ian G.; Mulholland, Barry J.

2007-01-01

Background and Aims Growth of bedding plants, in small peat plugs, relies on nutrients in the irrigation solution. The object of the study was to find a way of modifying the nutrient supply so that good-quality seedlings can be grown rapidly and yet have the high root : shoot ratios essential for efficient transplanting. Methods A new procedure was devised in which the concentrations of nutrients in the irrigation solution were modified during growth according to changing plant demand, instead of maintaining the same concentrations throughout growth. The new procedure depends on published algorithms for the dependence of growth rate and optimal plant nutrient concentrations on shoot dry weight Ws (g m−2), and on measuring evapotranspiration rates and shoot dry weights at weekly intervals. Pansy, Viola tricola ‘Universal plus yellow’ and petunia, Petunia hybrida ‘Multiflora light salmon vein’ were grown in four independent experiments with the expected optimum nutrient concentration and fractions of the optimum. Root and shoot weights were measured during growth. Key Results For each level of nutrient supply Ws increased with time (t) in days, according to the equation ΔWs/Δt=K2Ws/(100+Ws) in which the growth rate coefficient (K2) remained approximately constant throughout growth. The value of K2 for the optimum treatment was defined by incoming radiation and temperature. The value of K2 for each sub-optimum treatment relative to that for the optimum treatment was logarithmically related to the sub-optimal nutrient supply. Provided the aerial environment was optimal, Rsb/Ro≈Wo/Wsb where R is the root : shoot ratio, W is the shoot dry weight, and sb and o indicate sub-optimum and optimum nutrient supplies, respectively. Sub-optimal nutrient concentrations also depressed shoot growth without appreciably affecting root growth when the aerial environment was non-limiting. Conclusion The new procedure can predict the effects of nutrient supply, incoming radiation and temperature on the time course of shoot growth and the root : shoot ratio for a range of growing conditions. PMID:17210608

Acoustic Treatment Design Scaling Methods. Volume 1; Overview, Results, and Recommendations

NASA Technical Reports Server (NTRS)

Kraft, R. E.; Yu, J.

1999-01-01

Scale model fan rigs that simulate new generation ultra-high-bypass engines at about 1/5-scale are achieving increased importance as development vehicles for the design of low-noise aircraft engines. Testing at small scale allows the tests to be performed in existing anechoic wind tunnels, which provides an accurate simulation of the important effects of aircraft forward motion on the noise generation. The ability to design, build, and test miniaturized acoustic treatment panels on scale model fan rigs representative of the fullscale engine provides not only a cost-savings, but an opportunity to optimize the treatment by allowing tests of different designs. The primary objective of this study was to develop methods that will allow scale model fan rigs to be successfully used as acoustic treatment design tools. The study focuses on finding methods to extend the upper limit of the frequency range of impedance prediction models and acoustic impedance measurement methods for subscale treatment liner designs, and confirm the predictions by correlation with measured data. This phase of the program had as a goal doubling the upper limit of impedance measurement from 6 kHz to 12 kHz. The program utilizes combined analytical and experimental methods to achieve the objectives.

Optimizing Fracture Treatments in a Mississippian "Chat" Reservoir, South-Central Kansas

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

K. David Newell; Saibal Bhattacharya; Alan Byrnes

2005-10-01

This project is a collaboration of Woolsey Petroleum Corporation (a small independent operator) and the Kansas Geological Survey. The project will investigate geologic and engineering factors critical for designing hydraulic fracture treatments in Mississippian ''chat'' reservoirs. Mississippian reservoirs, including the chat, account for 159 million m3 (1 billion barrels) of the cumulative oil produced in Kansas. Mississippian reservoirs presently represent {approx}40% of the state's 5.6*106m3 (35 million barrels) annual production. Although geographically widespread, the ''chat'' is a heterogeneous reservoir composed of chert, cherty dolomite, and argillaceous limestone. Fractured chert with micro-moldic porosity is the best reservoir in this 18- tomore » 30-m-thick (60- to 100-ft) unit. The chat will be cored in an infill well in the Medicine Lodge North field (417,638 m3 [2,626,858 bbls] oil; 217,811,000 m3 [7,692,010 mcf] gas cumulative production; discovered 1954). The core and modern wireline logs will provide geological and petrophysical data for designing a fracture treatment. Optimum hydraulic fracturing design is poorly defined in the chat, with poor correlation of treatment size to production increase. To establish new geologic and petrophysical guidelines for these treatments, data from core petrophysics, wireline logs, and oil-field maps will be input to a fracture-treatment simulation program. Parameters will be established for optimal size of the treatment and geologic characteristics of the predicted fracturing. The fracturing will be performed and subsequent wellsite tests will ascertain the results for comparison to predictions. A reservoir simulation program will then predict the rate and volumetric increase in production. Comparison of the predicted increase in production with that of reality, and the hypothetical fracturing behavior of the reservoir with that of its actual behavior, will serve as tests of the geologic and petrophysical characterization of the oil field. After this feedback, a second well will be cored and logged, and procedure will be repeated to test characteristics determined to be critical for designing cost-effective fracture treatments. Most oil and gas production in Kansas, and that of the Midcontinent oil industry, is dominated by small companies. The overwhelming majority of these independent operators employ less than 20 people. These companies have limited scientific and engineering expertise and they are increasingly needing guidelines and technical examples that will help them to not be wasteful of their limited financial resources and petroleum reserves. To aid these operators, the technology transfer capabilities of the Kansas Geological Survey will disseminate the results of this study to the local, regional, and national oil industry. Internet access, seminars, presentations, and publications by Woolsey Petroleum Company and Kansas Geological Survey geologists and engineers are anticipated.« less

Chiral stationary phase optimized selectivity liquid chromatography: A strategy for the separation of chiral isomers.

PubMed

Hegade, Ravindra Suryakant; De Beer, Maarten; Lynen, Frederic

2017-09-15

Chiral Stationary-Phase Optimized Selectivity Liquid Chromatography (SOSLC) is proposed as a tool to optimally separate mixtures of enantiomers on a set of commercially available coupled chiral columns. This approach allows for the prediction of the separation profiles on any possible combination of the chiral stationary phases based on a limited number of preliminary analyses, followed by automated selection of the optimal column combination. Both the isocratic and gradient SOSLC approach were implemented for prediction of the retention times for a mixture of 4 chiral pairs on all possible combinations of the 5 commercial chiral columns. Predictions in isocratic and gradient mode were performed with a commercially available and with an in-house developed Microsoft visual basic algorithm, respectively. Optimal predictions in the isocratic mode required the coupling of 4 columns whereby relative deviations between the predicted and experimental retention times ranged between 2 and 7%. Gradient predictions led to the coupling of 3 chiral columns allowing baseline separation of all solutes, whereby differences between predictions and experiments ranged between 0 and 12%. The methodology is a novel tool allowing optimizing the separation of mixtures of optical isomers. Copyright © 2017 Elsevier B.V. All rights reserved.

Microwave-assisted chemical pre-treatment of waste sorghum leaves: Process optimization and development of an intelligent model for determination of volatile compound fractions.

PubMed

Rorke, Daneal C S; Suinyuy, Terence N; Gueguim Kana, E B

2017-01-01

This study reports the profiling of volatile compounds generated during microwave-assisted chemical pre-treatment of sorghum leaves. Compounds including acetic acid (0-186.26ng/g SL), furfural (0-240.80ng/g SL), 5-hydroxymethylfurfural (HMF) (0-19.20ng/g SL) and phenol (0-7.76ng/g SL) were detected. The reducing sugar production was optimized. An intelligent model based on Artificial Neural Networks (ANNs) was developed and validated to predict a profile of 21 volatile compounds under novel pre-treatment conditions. This model gave R 2 -values of up to 0.93. Knowledge extraction revealed furfural and phenol exhibited high sensitivity to acid- and alkali concentration and S:L ratio, while phenol showed high sensitivity to microwave duration and intensity. Furthermore, furfural production was majorly dependent on acid concentration and fit a dosage-response relationship model with a 2.5% HCl threshold. Significant non-linearities were observed between pre-treatment conditions and the profile of various compounds. This tool reduces analytical costs through virtual analytical instrumentation, improving process economics. Copyright © 2016 Elsevier Ltd. All rights reserved.

Fighting Cancer with Mathematics and Viruses.

PubMed

Santiago, Daniel N; Heidbuechel, Johannes P W; Kandell, Wendy M; Walker, Rachel; Djeu, Julie; Engeland, Christine E; Abate-Daga, Daniel; Enderling, Heiko

2017-08-23

After decades of research, oncolytic virotherapy has recently advanced to clinical application, and currently a multitude of novel agents and combination treatments are being evaluated for cancer therapy. Oncolytic agents preferentially replicate in tumor cells, inducing tumor cell lysis and complex antitumor effects, such as innate and adaptive immune responses and the destruction of tumor vasculature. With the availability of different vector platforms and the potential of both genetic engineering and combination regimens to enhance particular aspects of safety and efficacy, the identification of optimal treatments for patient subpopulations or even individual patients becomes a top priority. Mathematical modeling can provide support in this arena by making use of experimental and clinical data to generate hypotheses about the mechanisms underlying complex biology and, ultimately, predict optimal treatment protocols. Increasingly complex models can be applied to account for therapeutically relevant parameters such as components of the immune system. In this review, we describe current developments in oncolytic virotherapy and mathematical modeling to discuss the benefit of integrating different modeling approaches into biological and clinical experimentation. Conclusively, we propose a mutual combination of these research fields to increase the value of the preclinical development and the therapeutic efficacy of the resulting treatments.

Fighting Cancer with Mathematics and Viruses

PubMed Central

Santiago, Daniel N.; Heidbuechel, Johannes P. W.; Kandell, Wendy M.; Walker, Rachel; Djeu, Julie; Abate-Daga, Daniel; Enderling, Heiko

2017-01-01

After decades of research, oncolytic virotherapy has recently advanced to clinical application, and currently a multitude of novel agents and combination treatments are being evaluated for cancer therapy. Oncolytic agents preferentially replicate in tumor cells, inducing tumor cell lysis and complex antitumor effects, such as innate and adaptive immune responses and the destruction of tumor vasculature. With the availability of different vector platforms and the potential of both genetic engineering and combination regimens to enhance particular aspects of safety and efficacy, the identification of optimal treatments for patient subpopulations or even individual patients becomes a top priority. Mathematical modeling can provide support in this arena by making use of experimental and clinical data to generate hypotheses about the mechanisms underlying complex biology and, ultimately, predict optimal treatment protocols. Increasingly complex models can be applied to account for therapeutically relevant parameters such as components of the immune system. In this review, we describe current developments in oncolytic virotherapy and mathematical modeling to discuss the benefit of integrating different modeling approaches into biological and clinical experimentation. Conclusively, we propose a mutual combination of these research fields to increase the value of the preclinical development and the therapeutic efficacy of the resulting treatments. PMID:28832539

Predictive models for customizing chemotherapy in advanced non-small cell lung cancer (NSCLC).

PubMed

Bonanno, Laura

2013-06-01

The backbone of first-line treatment for Epidermal Growth Factor (EGFR) wild-type (wt) advanced Non-small cell lung cancer (NSCLC) patients is the use of a platinum-based chemotherapy combination. The treatment is characterized by great inter-individual variability in outcome. Molecular predictive markers are extremely needed in order to identify patients most likely to benefit from platinum-based treatment and resistant ones, thus optimizing chemotherapy approach in NSCLC. Several components of DNA repair response (DRR) have been investigated as potential predictive markers. Among them, high levels of expression of ERCC1, both at protein and mRNA levels, have been associated with resistance to cisplatin in NSCLC. In addition, low levels of expression of RRM1, a target for gemcitabine, have been associated with improved OS in advanced NSCLC patients treated with cisplatin and gemcitabine. Preclinical data and retrospective analyses showed that BRCA1 is able to induce resistance to cisplatin and sensitivity to antimicrotubule agents. In addition, the mRNA levels of expression of RAP80, encoding for a protein cooperating with BRCA1 in homologous recombination (HR), have demonstrated to further sub-classify low BRCA1 NSCLC tumors, improving the predictive model. On the basis of biological knowledge on DNA repair pathway and recent controversial results from clinical validation of potential molecular markers, integrated analysis of multiple DNA repair components could improve predictive information and pave the way to a new approach to customized chemotherapy clinical trials.

“How Will It Help Me?”: Reasons Underlying Treatment Preferences Between Sertraline and Prolonged Exposure in PTSD

PubMed Central

Chen, Jessica A.; Keller, Stephanie M.; Zoellner, Lori A.; Feeny, Norah C.

2014-01-01

Individuals with posttraumatic stress disorder (PTSD) often wait years before seeking treatment. Improving treatment initiation and adherence requires a better understanding patient beliefs that lead to treatment preferences. Using a treatment-seeking sample (N = 200) with chronic PTSD, qualitative reasons underlying treatment preferences for either prolonged exposure (PE) or sertraline (SER) were examined. Reasons for treatment preference primarily focused on how the treatment was perceived to reduce PTSD symptoms rather than practical ones. Patients were more positive about PE than SER. Individual differences did not reliably predict underlying preference reasons, suggesting that what makes a treatment desirable is not strongly determined by current functioning, treatment, or trauma history. Taken together, this information is critical for treatment providers, arguing for enhancing psychoeducation about how treatment works and acknowledging pre-existing biases against pharmacotherapy for PTSD that should be addressed. This knowledge has the potential to optimize and better personalize PTSD patient care. PMID:23896851

Evolutionary Dynamic Multiobjective Optimization Via Kalman Filter Prediction.

PubMed

Muruganantham, Arrchana; Tan, Kay Chen; Vadakkepat, Prahlad

2016-12-01

Evolutionary algorithms are effective in solving static multiobjective optimization problems resulting in the emergence of a number of state-of-the-art multiobjective evolutionary algorithms (MOEAs). Nevertheless, the interest in applying them to solve dynamic multiobjective optimization problems has only been tepid. Benchmark problems, appropriate performance metrics, as well as efficient algorithms are required to further the research in this field. One or more objectives may change with time in dynamic optimization problems. The optimization algorithm must be able to track the moving optima efficiently. A prediction model can learn the patterns from past experience and predict future changes. In this paper, a new dynamic MOEA using Kalman filter (KF) predictions in decision space is proposed to solve the aforementioned problems. The predictions help to guide the search toward the changed optima, thereby accelerating convergence. A scoring scheme is devised to hybridize the KF prediction with a random reinitialization method. Experimental results and performance comparisons with other state-of-the-art algorithms demonstrate that the proposed algorithm is capable of significantly improving the dynamic optimization performance.

Optimal management of immune-related adverse events resulting from treatment with immune checkpoint inhibitors: a review and update.

PubMed

Nagai, Hiroki; Muto, Manabu

2018-06-01

Over the last two decades, molecular-targeted agents have become mainstream treatment for many types of malignancies and have improved the overall survival of patients. However, most patients eventually develop resistance to these targeted therapies. Recently, immunotherapies such as immune checkpoint inhibitors have revolutionized the treatment paradigm for many types of malignancies. Immune checkpoint inhibitors have been approved for treatment of melanoma, non-small cell lung cancer, renal cell carcinoma, head and neck squamous cell carcinoma, Hodgkin's lymphoma, bladder cancer and gastric cancer. However, oncologists have been faced with immune-related adverse events caused by immune checkpoint inhibitors; these are generally mild but can be fatal in some cases. Because immune checkpoint inhibitors have distinct toxicity profiles from those of chemotherapy or targeted therapy, many oncologists are not familiar with the principles for optimal management of immune-related adverse events, which require early recognition and appropriate treatment without delay. To achieve this, oncologists must educate patients and health-care workers, develop checklists of appropriate tests for immune-related adverse events and collaborate closely with organ specialists. Clinical questions that remain include whether immune checkpoint inhibitors should be administered to patients with autoimmune disease and whether patients for whom immune-related adverse events lead to delays in immunotherapy should be retreated. In addition, the predicted use of combination immunotherapies in the near future means that oncologists will face a higher incidence and severity of immune-related adverse events. This review provides an overview of the optimal management of immune-related adverse events attributed to immune checkpoint inhibitors.

A randomized phase 3 study on the optimization of the combination of bevacizumab with FOLFOX/OXXEL in the treatment of patients with metastatic colorectal cancer-OBELICS (Optimization of BEvacizumab scheduLIng within Chemotherapy Scheme).

PubMed

Avallone, Antonio; Piccirillo, Maria Carmela; Aloj, Luigi; Nasti, Guglielmo; Delrio, Paolo; Izzo, Francesco; Di Gennaro, Elena; Tatangelo, Fabiana; Granata, Vincenza; Cavalcanti, Ernesta; Maiolino, Piera; Bianco, Francesco; Aprea, Pasquale; De Bellis, Mario; Pecori, Biagio; Rosati, Gerardo; Carlomagno, Chiara; Bertolini, Alessandro; Gallo, Ciro; Romano, Carmela; Leone, Alessandra; Caracò, Corradina; de Lutio di Castelguidone, Elisabetta; Daniele, Gennaro; Catalano, Orlando; Botti, Gerardo; Petrillo, Antonella; Romano, Giovanni M; Iaffaioli, Vincenzo R; Lastoria, Secondo; Perrone, Francesco; Budillon, Alfredo

2016-02-08

Despite the improvements in diagnosis and treatment, colorectal cancer (CRC) is the second cause of cancer deaths in both sexes. Therefore, research in this field remains of great interest. The approval of bevacizumab, a humanized anti-vascular endothelial growth factor (VEGF) monoclonal antibody, in combination with a fluoropyrimidine-based chemotherapy in the treatment of metastatic CRC has changed the oncology practice in this disease. However, the efficacy of bevacizumab-based treatment, has thus far been rather modest. Efforts are ongoing to understand the better way to combine bevacizumab and chemotherapy, and to identify valid predictive biomarkers of benefit to avoid unnecessary and costly therapy to nonresponder patients. The BRANCH study in high-risk locally advanced rectal cancer patients showed that varying bevacizumab schedule may impact on the feasibility and efficacy of chemo-radiotherapy. OBELICS is a multicentre, open-label, randomised phase 3 trial comparing in mCRC patients two treatment arms (1:1): standard concomitant administration of bevacizumab with chemotherapy (mFOLFOX/OXXEL regimen) vs experimental sequential bevacizumab given 4 days before chemotherapy, as first or second treatment line. Primary end point is the objective response rate (ORR) measured according to RECIST criteria. A sample size of 230 patients was calculated allowing reliable assessment in all plausible first-second line case-mix conditions, with a 80% statistical power and 2-sided alpha error of 0.05. Secondary endpoints are progression free-survival (PFS), overall survival (OS), toxicity and quality of life. The evaluation of the potential predictive role of several circulating biomarkers (circulating endothelial cells and progenitors, VEGF and VEGF-R SNPs, cytokines, microRNAs, free circulating DNA) as well as the value of the early [(18)F]-Fluorodeoxyglucose positron emission tomography (FDG-PET) response, are the objectives of the traslational project. Overall this study could optimize bevacizumab scheduling in combination with chemotherapy in mCRC patients. Moreover, correlative studies could improve the knowledge of the mechanisms by which bevacizumab enhance chemotherapy effect and could identify early predictors of response. EudraCT Number: 2011-004997-27 TRIAL REGISTRATION: ClinicalTrials.gove number, NCT01718873.

Investigating the relationship between predictability and imbalance in minimisation: a simulation study

PubMed Central

2013-01-01

Background The use of restricted randomisation methods such as minimisation is increasing. This paper investigates under what conditions it is preferable to use restricted randomisation in order to achieve balance between treatment groups at baseline with regard to important prognostic factors and whether trialists should be concerned that minimisation may be considered deterministic. Methods Using minimisation as the randomisation algorithm, treatment allocation was simulated for hypothetical patients entering a theoretical study having values for prognostic factors randomly assigned with a stipulated probability. The number of times the allocation could have been determined with certainty and the imbalances which might occur following randomisation using minimisation were examined. Results Overall treatment balance is relatively unaffected by reducing the probability of allocation to optimal treatment group (P) but within-variable balance can be affected by any P <1. This effect is magnified by increased numbers of prognostic variables, the number of categories within them and the prevalence of these categories within the study population. Conclusions In general, for smaller trials, probability of treatment allocation to the treatment group with fewer numbers requires a larger value P to keep treatment and variable groups balanced. For larger trials probability of allocation values from P = 0.5 to P = 0.8 can be used while still maintaining balance. For one prognostic variable there is no significant benefit in terms of predictability in reducing the value of P. However, for more than one prognostic variable, significant reduction in levels of predictability can be achieved with the appropriate choice of P for the given trial design. PMID:23537389

Investigating the relationship between predictability and imbalance in minimisation: a simulation study.

PubMed

McPherson, Gladys C; Campbell, Marion K; Elbourne, Diana R

2013-03-27

The use of restricted randomisation methods such as minimisation is increasing. This paper investigates under what conditions it is preferable to use restricted randomisation in order to achieve balance between treatment groups at baseline with regard to important prognostic factors and whether trialists should be concerned that minimisation may be considered deterministic. Using minimisation as the randomisation algorithm, treatment allocation was simulated for hypothetical patients entering a theoretical study having values for prognostic factors randomly assigned with a stipulated probability. The number of times the allocation could have been determined with certainty and the imbalances which might occur following randomisation using minimisation were examined. Overall treatment balance is relatively unaffected by reducing the probability of allocation to optimal treatment group (P) but within-variable balance can be affected by any P <1. This effect is magnified by increased numbers of prognostic variables, the number of categories within them and the prevalence of these categories within the study population. In general, for smaller trials, probability of treatment allocation to the treatment group with fewer numbers requires a larger value P to keep treatment and variable groups balanced. For larger trials probability of allocation values from P = 0.5 to P = 0.8 can be used while still maintaining balance. For one prognostic variable there is no significant benefit in terms of predictability in reducing the value of P. However, for more than one prognostic variable, significant reduction in levels of predictability can be achieved with the appropriate choice of P for the given trial design.

TU-G-210-00: Treatment Planning Strategies, Modeling, Control

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

NONE

Modeling can play a vital role in predicting, optimizing and analyzing the results of therapeutic ultrasound treatments. Simulating the propagating acoustic beam in various targeted regions of the body allows for the prediction of the resulting power deposition and temperature profiles. In this session we will apply various modeling approaches to breast, abdominal organ and brain treatments. Of particular interest is the effectiveness of procedures for correcting for phase aberrations caused by intervening irregular tissues, such as the skull in transcranial applications or inhomogeneous breast tissues. Also described are methods to compensate for motion in targeted abdominal organs such asmore » the liver or kidney. Douglas Christensen – Modeling for Breast and Brain HIFU Treatment Planning Tobias Preusser – TRANS-FUSIMO – An Integrative Approach to Model-Based Treatment Planning of Liver FUS Tobias Preusser – TRANS-FUSIMO – An Integrative Approach to Model-Based Treatment Planning of Liver FUS Learning Objectives: Understand the role of acoustic beam modeling for predicting the effectiveness of therapeutic ultrasound treatments. Apply acoustic modeling to specific breast, liver, kidney and transcranial anatomies. Determine how to obtain appropriate acoustic modeling parameters from clinical images. Understand the separate role of absorption and scattering in energy delivery to tissues. See how organ motion can be compensated for in ultrasound therapies. Compare simulated data with clinical temperature measurements in transcranial applications. Supported by NIH R01 HL172787 and R01 EB013433 (DC); EU Seventh Framework Programme (FP7/2007-2013) under 270186 (FUSIMO) and 611889 (TRANS-FUSIMO)(TP); and P01 CA159992, GE, FUSF and InSightec (UV)« less

TU-G-210-01: Modeling for Breast and Brain HIFU Treatment Planning

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

Christensen, D.

Modeling can play a vital role in predicting, optimizing and analyzing the results of therapeutic ultrasound treatments. Simulating the propagating acoustic beam in various targeted regions of the body allows for the prediction of the resulting power deposition and temperature profiles. In this session we will apply various modeling approaches to breast, abdominal organ and brain treatments. Of particular interest is the effectiveness of procedures for correcting for phase aberrations caused by intervening irregular tissues, such as the skull in transcranial applications or inhomogeneous breast tissues. Also described are methods to compensate for motion in targeted abdominal organs such asmore » the liver or kidney. Douglas Christensen – Modeling for Breast and Brain HIFU Treatment Planning Tobias Preusser – TRANS-FUSIMO – An Integrative Approach to Model-Based Treatment Planning of Liver FUS Tobias Preusser – TRANS-FUSIMO – An Integrative Approach to Model-Based Treatment Planning of Liver FUS Learning Objectives: Understand the role of acoustic beam modeling for predicting the effectiveness of therapeutic ultrasound treatments. Apply acoustic modeling to specific breast, liver, kidney and transcranial anatomies. Determine how to obtain appropriate acoustic modeling parameters from clinical images. Understand the separate role of absorption and scattering in energy delivery to tissues. See how organ motion can be compensated for in ultrasound therapies. Compare simulated data with clinical temperature measurements in transcranial applications. Supported by NIH R01 HL172787 and R01 EB013433 (DC); EU Seventh Framework Programme (FP7/2007-2013) under 270186 (FUSIMO) and 611889 (TRANS-FUSIMO)(TP); and P01 CA159992, GE, FUSF and InSightec (UV)« less

Positioning the principles of precision medicine in care pathways for allergic rhinitis and chronic rhinosinusitis - A EUFOREA-ARIA-EPOS-AIRWAYS ICP statement.

PubMed

Hellings, P W; Fokkens, W J; Bachert, C; Akdis, C A; Bieber, T; Agache, I; Bernal-Sprekelsen, M; Canonica, G W; Gevaert, P; Joos, G; Lund, V; Muraro, A; Onerci, M; Zuberbier, T; Pugin, B; Seys, S F; Bousquet, J

2017-09-01

Precision medicine (PM) is increasingly recognized as the way forward for optimizing patient care. Introduced in the field of oncology, it is now considered of major interest in other medical domains like allergy and chronic airway diseases, which face an urgent need to improve the level of disease control, enhance patient satisfaction and increase effectiveness of preventive interventions. The combination of personalized care, prediction of treatment success, prevention of disease and patient participation in the elaboration of the treatment plan is expected to substantially improve the therapeutic approach for individuals suffering from chronic disabling conditions. Given the emerging data on the impact of patient stratification on treatment outcomes, European and American regulatory bodies support the principles of PM and its potential advantage over current treatment strategies. The aim of the current document was to propose a consensus on the position and gradual implementation of the principles of PM within existing adult treatment algorithms for allergic rhinitis (AR) and chronic rhinosinusitis (CRS). At the time of diagnosis, prediction of success of the initiated treatment and patient participation in the decision of the treatment plan can be implemented. The second-level approach ideally involves strategies to prevent progression of disease, in addition to prediction of success of therapy, and patient participation in the long-term therapeutic strategy. Endotype-driven treatment is part of a personalized approach and should be positioned at the tertiary level of care, given the efforts needed for its implementation and the high cost of molecular diagnosis and biological treatment. © 2017 EAACI and John Wiley and Sons A/S. Published by John Wiley and Sons Ltd.

Development of an in vitro skin sensitization test using human cell lines; human Cell Line Activation Test (h-CLAT). II. An inter-laboratory study of the h-CLAT.

PubMed

Sakaguchi, H; Ashikaga, T; Miyazawa, M; Yoshida, Y; Ito, Y; Yoneyama, K; Hirota, M; Itagaki, H; Toyoda, H; Suzuki, H

2006-08-01

Recent regulatory changes have placed a major emphasis on in vitro safety testing and alternative models. In regard to skin sensitization tests, dendritic cells (DCs) derived from human peripheral blood have been considered in the development of new in vitro alternatives. Human cell lines have been also reported recently. In our previous study, we suggested that measuring CD86 and/or CD54 expression on THP-1 cells (human monocytic leukemia cell line) could be used as an in vitro skin sensitization method. An inter-laboratory study among two laboratories was undertaken in Japan in order to further develop an in vitro skin sensitization model. In the present study, we used two human cell lines: THP-1 and U-937 (human histiocytic lymphoma cell line). First we optimized our test protocol (refer to the related paper entitled "optimization of the h-CLAT protocol" within this journal) and then we did an inter-laboratory validation with nine chemicals using the optimized protocol. We measured the expression of CD86 and CD54 on the above cells using flow cytometry after a 24h and 48h exposure to six known allergens (e.g., DNCB, pPD, NiSO(4)) and three non-allergens (e.g., SLS, tween 80). For the sample test concentration, four doses (0.1x, 0.5x, 1x, and 2x of the 50% inhibitory concentration (IC(50))) were evaluated. IC(50) was calculated using MTT assay. We found that allergens/non-allergens were better predicted using THP-1 cells compared to U-937 cells following a 24 h and a 48 h exposure. We also found that the 24h treatment time tended to have a better accuracy than the 48 h treatment time for THP-1 cells. Expression of CD86 and CD54 were good predictive markers for THP-1 cells, but for U-937 cells, expression of CD86 was a better predictor than CD54, at the 24h and the 48 h treatment time. The accuracy also improved when both markers (CD86 and CD54) were used as compared with a single marker for THP-1 cells. Both laboratories gave a good prediction of allergen/non-allergen, especially using THP-1 cells. These results suggest that our method, human Cell Line Activation Test (h-CLAT), using human cell lines THP-1 and U-937, but especially THP-1 cells at 24h treatment, may be a useful in vitro skin sensitization model to predict various contact allergens.

Genome-scale metabolic modeling to provide insight into the production of storage compounds during feast-famine cycles of activated sludge.

PubMed

Tajparast, Mohammad; Frigon, Dominic

2013-01-01

Studying storage metabolism during feast-famine cycles of activated sludge treatment systems provides profound insight in terms of both operational issues (e.g., foaming and bulking) and process optimization for the production of value added by-products (e.g., bioplastics). We examined the storage metabolism (including poly-β-hydroxybutyrate [PHB], glycogen, and triacylglycerols [TAGs]) during feast-famine cycles using two genome-scale metabolic models: Rhodococcus jostii RHA1 (iMT1174) and Escherichia coli K-12 (iAF1260) for growth on glucose, acetate, and succinate. The goal was to develop the proper objective function (OF) for the prediction of the main storage compound produced in activated sludge for given feast-famine cycle conditions. For the flux balance analysis, combinations of three OFs were tested. For all of them, the main OF was to maximize growth rates. Two additional sub-OFs were used: (1) minimization of biochemical fluxes, and (2) minimization of metabolic adjustments (MoMA) between the feast and famine periods. All (sub-)OFs predicted identical substrate-storage associations for the feast-famine growth of the above-mentioned metabolic models on a given substrate when glucose and acetate were set as sole carbon sources (i.e., glucose-glycogen and acetate-PHB), in agreement with experimental observations. However, in the case of succinate as substrate, the predictions depended on the network structure of the metabolic models such that the E. coli model predicted glycogen accumulation and the R. jostii model predicted PHB accumulation. While the accumulation of both PHB and glycogen was observed experimentally, PHB showed higher dynamics during an activated sludge feast-famine growth cycle with succinate as substrate. These results suggest that new modeling insights between metabolic predictions and population ecology will be necessary to properly predict metabolisms likely to emerge within the niches of activated sludge communities. Nonetheless, we believe that the development of this approach will help guide the optimization of the production of storage compounds as valuable by-products of wastewater treatment.

Prediction of Tibial Rotation Pathologies Using Particle Swarm Optimization and K-Means Algorithms.

PubMed

Sari, Murat; Tuna, Can; Akogul, Serkan

2018-03-28

The aim of this article is to investigate pathological subjects from a population through different physical factors. To achieve this, particle swarm optimization (PSO) and K-means (KM) clustering algorithms have been combined (PSO-KM). Datasets provided by the literature were divided into three clusters based on age and weight parameters and each one of right tibial external rotation (RTER), right tibial internal rotation (RTIR), left tibial external rotation (LTER), and left tibial internal rotation (LTIR) values were divided into three types as Type 1, Type 2 and Type 3 (Type 2 is non-pathological (normal) and the other two types are pathological (abnormal)), respectively. The rotation values of every subject in any cluster were noted. Then the algorithm was run and the produced values were also considered. The values of the produced algorithm, the PSO-KM, have been compared with the real values. The hybrid PSO-KM algorithm has been very successful on the optimal clustering of the tibial rotation types through the physical criteria. In this investigation, Type 2 (pathological subjects) is of especially high predictability and the PSO-KM algorithm has been very successful as an operation system for clustering and optimizing the tibial motion data assessments. These research findings are expected to be very useful for health providers, such as physiotherapists, orthopedists, and so on, in which this consequence may help clinicians to appropriately designing proper treatment schedules for patients.

Optimization of pectin extraction and antioxidant activities from Jerusalem artichoke

NASA Astrophysics Data System (ADS)

Liu, Shengyi; Shi, Xuejie; Xu, Lanlan; Yi, Yuetao

2016-03-01

Jerusalem artichoke is an economic crop widely planted in saline-alkaline soil. The use of Jerusalem artichoke is of great significance. In this study, the response surface method was employed to optimize the effects of processing variables (extraction temperature, pH, extraction time, and liquid-to-solid ratio) on the yield of Jerusalem artichoke pectin. Under the optimal extraction conditions: pH 1.52, 63.62 min, 100°C and a liquid-to-solid ratio of 44.4 mL/g, the maximum pectin yield was predicted to be 18.76%. Experiments were conducted under these optimal conditions and a pectin yield of 18.52±0.90% was obtained, which validated the model prediction. The effects of diff erent drying methods (freeze drying, spray drying and vacuum drying) on the properties of Jerusalem artichoke pectin were evaluated and they were compared with apple pectin. FTIR spectral analysis showed no major structural diff erences in Jerusalem artichoke pectin samples produced by various drying treatments. The antioxidant activities of pectin dried by diff erent methods were investigated using in vitro hydroxyl and DPPH radical scavenging systems. The results revealed that the activities of spray dried pectin (SDP) and apple pectin (AP) were stronger than those of vacuum oven dried pectin (ODP) and vacuum freeze dried pectin (FDP). Therefore compared with the other two drying methods, the spray drying method was the best.

Uniform tissue lesion formation induced by high-intensity focused ultrasound along a spiral pathway.

PubMed

Qian, Kui; Li, Chenghai; Ni, Zhengyang; Tu, Juan; Guo, Xiasheng; Zhang, Dong

2017-05-01

Both theoretical and experimental studies were performed here to investigate the lesion formation induced by high-intensity focused ultrasound (HIFU) operating in continuous scanning mode along a spiral pathway. The Khokhlov-Zabolotskaya-Kuznetsov equation and bio-heat equation were combined in the current model to predict HIFU-induced temperature distribution and lesion formation. The shape of lesion and treatment efficiency were assessed for a given scanning speed at two different grid spacing (3mm and 4mm) in the gel phantom studies and further researched in ex vivo studies. The results show that uniform lesions can be generated with continuous HIFU scanning along a spiral pathway. The complete coverage of the entire treated volume can be achieved as long as the spacing grid of the spiral pathway is small enough for heat to diffuse and deposit, and the treatment efficiency can be optimized by selecting an appropriate scanning speed. This study can provide guidance for further optimization of the treatment efficiency and safety of HIFU therapy. Copyright © 2017 Elsevier B.V. All rights reserved.

Current advances in mathematical modeling of anti-cancer drug penetration into tumor tissues.

PubMed

Kim, Munju; Gillies, Robert J; Rejniak, Katarzyna A

2013-11-18

Delivery of anti-cancer drugs to tumor tissues, including their interstitial transport and cellular uptake, is a complex process involving various biochemical, mechanical, and biophysical factors. Mathematical modeling provides a means through which to understand this complexity better, as well as to examine interactions between contributing components in a systematic way via computational simulations and quantitative analyses. In this review, we present the current state of mathematical modeling approaches that address phenomena related to drug delivery. We describe how various types of models were used to predict spatio-temporal distributions of drugs within the tumor tissue, to simulate different ways to overcome barriers to drug transport, or to optimize treatment schedules. Finally, we discuss how integration of mathematical modeling with experimental or clinical data can provide better tools to understand the drug delivery process, in particular to examine the specific tissue- or compound-related factors that limit drug penetration through tumors. Such tools will be important in designing new chemotherapy targets and optimal treatment strategies, as well as in developing non-invasive diagnosis to monitor treatment response and detect tumor recurrence.

[Addictive potential in man: methodological aspects].

PubMed

Warot, D; Marra, D

1995-01-01

Different methods have been developed in clinical abuse liability testing in man. Tolerance, psychic and/or physical dependence must be investigated through clinical studies during drug development of a new substance. Adequate methodology is needed using double-blind, time-blind evaluations, comparisons of different dose levels and duration of treatment for a given drug, abrupt and gradual interruption of treatment, appropriate period of observation after treatment cessation ... The optimal scale to evaluate properly the symptoms occurring after drug discontinuation is still under investigation. These studies will or should permit the differentiation of rebound, withdrawal and recurrence. Methods developed to study reinforcing effects in post-addicts and healthy subjects are self-administration and choice procedures. In addition, the more traditional approach has been through assessing self-reported effects in which standardized questionnaires are used (Addiction Research Center Inventory or A.R.C.I.; Single Dose Questionnaire or S.D.Q.). A third focus of measurement has been discrimination studies performed in individuals with histories of drug abuse as well as healthy subjects. Abuse-liability testing of a new compound needs a multidimensional assessment to optimize the predictivity in defining the relative risk.

A three-dimensional inverse finite element analysis of the heel pad.

PubMed

Chokhandre, Snehal; Halloran, Jason P; van den Bogert, Antonie J; Erdemir, Ahmet

2012-03-01

Quantification of plantar tissue behavior of the heel pad is essential in developing computational models for predictive analysis of preventive treatment options such as footwear for patients with diabetes. Simulation based studies in the past have generally adopted heel pad properties from the literature, in return using heel-specific geometry with material properties of a different heel. In exceptional cases, patient-specific material characterization was performed with simplified two-dimensional models, without further evaluation of a heel-specific response under different loading conditions. The aim of this study was to conduct an inverse finite element analysis of the heel in order to calculate heel-specific material properties in situ. Multidimensional experimental data available from a previous cadaver study by Erdemir et al. ("An Elaborate Data Set Characterizing the Mechanical Response of the Foot," ASME J. Biomech. Eng., 131(9), pp. 094502) was used for model development, optimization, and evaluation of material properties. A specimen-specific three-dimensional finite element representation was developed. Heel pad material properties were determined using inverse finite element analysis by fitting the model behavior to the experimental data. Compression dominant loading, applied using a spherical indenter, was used for optimization of the material properties. The optimized material properties were evaluated through simulations representative of a combined loading scenario (compression and anterior-posterior shear) with a spherical indenter and also of a compression dominant loading applied using an elevated platform. Optimized heel pad material coefficients were 0.001084 MPa (μ), 9.780 (α) (with an effective Poisson's ratio (ν) of 0.475), for a first-order nearly incompressible Ogden material model. The model predicted structural response of the heel pad was in good agreement for both the optimization (<1.05% maximum tool force, 0.9% maximum tool displacement) and validation cases (6.5% maximum tool force, 15% maximum tool displacement). The inverse analysis successfully predicted the material properties for the given specimen-specific heel pad using the experimental data for the specimen. The modeling framework and results can be used for accurate predictions of the three-dimensional interaction of the heel pad with its surroundings.

Optimal Chemotherapy for Leukemia: A Model-Based Strategy for Individualized Treatment

PubMed Central

Jayachandran, Devaraj; Rundell, Ann E.; Hannemann, Robert E.; Vik, Terry A.; Ramkrishna, Doraiswami

2014-01-01

Acute Lymphoblastic Leukemia, commonly known as ALL, is a predominant form of cancer during childhood. With the advent of modern healthcare support, the 5-year survival rate has been impressive in the recent past. However, long-term ALL survivors embattle several treatment-related medical and socio-economic complications due to excessive and inordinate chemotherapy doses received during treatment. In this work, we present a model-based approach to personalize 6-Mercaptopurine (6-MP) treatment for childhood ALL with a provision for incorporating the pharmacogenomic variations among patients. Semi-mechanistic mathematical models were developed and validated for i) 6-MP metabolism, ii) red blood cell mean corpuscular volume (MCV) dynamics, a surrogate marker for treatment efficacy, and iii) leukopenia, a major side-effect. With the constraint of getting limited data from clinics, a global sensitivity analysis based model reduction technique was employed to reduce the parameter space arising from semi-mechanistic models. The reduced, sensitive parameters were used to individualize the average patient model to a specific patient so as to minimize the model uncertainty. Models fit the data well and mimic diverse behavior observed among patients with minimum parameters. The model was validated with real patient data obtained from literature and Riley Hospital for Children in Indianapolis. Patient models were used to optimize the dose for an individual patient through nonlinear model predictive control. The implementation of our approach in clinical practice is realizable with routinely measured complete blood counts (CBC) and a few additional metabolite measurements. The proposed approach promises to achieve model-based individualized treatment to a specific patient, as opposed to a standard-dose-for-all, and to prescribe an optimal dose for a desired outcome with minimum side-effects. PMID:25310465

Computationally Optimizing the Compliance of a Biopolymer Based Tissue Engineered Vascular Graft

PubMed Central

Harrison, Scott; Tamimi, Ehab; Uhlorn, Josh; Leach, Tim; Vande Geest, Jonathan P.

2016-01-01

Coronary heart disease is a leading cause of death among Americans for which coronary artery bypass graft (CABG) surgery is a standard surgical treatment. The success of CABG surgery is impaired by a compliance mismatch between vascular grafts and native vessels. Tissue engineered vascular grafts (TEVGs) have the potential to be compliance matched and thereby reduce the risk of graft failure. Glutaraldehyde (GLUT) vapor-crosslinked gelatin/fibrinogen constructs were fabricated and mechanically tested in a previous study by our research group at 2, 8, and 24 hrs of GLUT vapor exposure. The current study details a computational method that was developed to predict the material properties of our constructs for crosslinking times between 2 and 24 hrs by interpolating the 2, 8, and 24 hrs crosslinking time data. matlab and abaqus were used to determine the optimal combination of fabrication parameters to produce a compliance matched construct. The validity of the method was tested by creating a 16-hr crosslinked construct of 130 μm thickness and comparing its compliance to that predicted by the optimization algorithm. The predicted compliance of the 16-hr construct was 0.00059 mm Hg−1 while the experimentally determined compliance was 0.00065 mm Hg−1, a relative difference of 9.2%. Prior data in our laboratory has shown the compliance of the left anterior descending porcine coronary (LADC) artery to be 0.00071 ± 0.0003 mm Hg−1. Our optimization algorithm predicts that a 258-μm-thick construct that is GLUT vapor crosslinked for 8.1 hrs would match LADC compliance. This result is consistent with our previous work demonstrating that an 8-hr GLUT vapor crosslinked construct produces a compliance that is not significantly different from a porcine coronary LADC. PMID:26593773

Application of Neural Network Optimized by Mind Evolutionary Computation in Building Energy Prediction

NASA Astrophysics Data System (ADS)

Song, Chen; Zhong-Cheng, Wu; Hong, Lv

2018-03-01

Building Energy forecasting plays an important role in energy management and plan. Using mind evolutionary algorithm to find the optimal network weights and threshold, to optimize the BP neural network, can overcome the problem of the BP neural network into a local minimum point. The optimized network is used for time series prediction, and the same month forecast, to get two predictive values. Then two kinds of predictive values are put into neural network, to get the final forecast value. The effectiveness of the method was verified by experiment with the energy value of three buildings in Hefei.

Restless legs syndrome augmentation among Japanese patients receiving pramipexole therapy: Rate and risk factors in a retrospective study

PubMed Central

Takahashi, Masayoshi; Nishida, Shingo; Nakamura, Masaki; Kobayashi, Mina; Matsui, Kentaro; Ito, Eiki; Usui, Akira; Inoue, Yuichi

2017-01-01

To investigate the rate of and risk factors for restless legs syndrome (RLS) augmentation in Japanese patients receiving pramipexole (PPX) treatment. Records of 231 consecutive patients with idiopathic RLS who received PPX therapy for more than one month in a single sleep disorder center were analyzed retrospectively. Augmentation was diagnosed based on the Max Planck Institute criteria; associated factors were identified by logistic regression analysis. Mean age at PPX initiation was 60.6 ± 14.9 years and mean treatment duration was 48.5 ± 26.4 months. Augmentation was diagnosed in 21 patients (9.1%). Daily PPX dose and treatment duration were significantly associated with augmentation. By analyzing the receiver operating characteristic curve, a PPX dose of 0.375 mg/day was found to be the optimal cut-off value for predicting augmentation. After stratifying patients according to PPX treatment duration, at median treatment duration of 46 months, optimal cut-off values for daily doses were 0.375 and 0.500 mg/day for <46 months and ≥46 months of treatment, respectively. The RLS augmentation with PPX treatment in Japanese patients was occurred at rate of 9.1%, being quite compatible with previously reported rates in Caucasian patients. The symptom could appear within a relatively short period after starting the treatment in possibly vulnerable cases even with a smaller drug dose. Our results support the importance of keeping doses of PPX low throughout the RLS treatment course to prevent augmentation. PMID:28264052

Computer-guided design of optimal microbial consortia for immune system modulation

PubMed Central

Szabady, Rose L; Bhattarai, Shakti K; Olle, Bernat; Norman, Jason M; Suda, Wataru; Oshima, Kenshiro; Hattori, Masahira; Gerber, Georg K; Sander, Chris; Honda, Kenya

2018-01-01

Manipulation of the gut microbiota holds great promise for the treatment of diseases. However, a major challenge is the identification of therapeutically potent microbial consortia that colonize the host effectively while maximizing immunologic outcome. Here, we propose a novel workflow to select optimal immune-inducing consortia from microbiome compositicon and immune effectors measurements. Using published and newly generated microbial and regulatory T-cell (Treg) data from germ-free mice, we estimate the contributions of twelve Clostridia strains with known immune-modulating effect to Treg induction. Combining this with a longitudinal data-constrained ecological model, we predict the ability of every attainable and ecologically stable subconsortium in promoting Treg activation and rank them by the Treg Induction Score (TrIS). Experimental validation of selected consortia indicates a strong and statistically significant correlation between predicted TrIS and measured Treg. We argue that computational indexes, such as the TrIS, are valuable tools for the systematic selection of immune-modulating bacteriotherapeutics. PMID:29664397

Computer-guided design of optimal microbial consortia for immune system modulation.

PubMed

Stein, Richard R; Tanoue, Takeshi; Szabady, Rose L; Bhattarai, Shakti K; Olle, Bernat; Norman, Jason M; Suda, Wataru; Oshima, Kenshiro; Hattori, Masahira; Gerber, Georg K; Sander, Chris; Honda, Kenya; Bucci, Vanni

2018-04-17

Manipulation of the gut microbiota holds great promise for the treatment of diseases. However, a major challenge is the identification of therapeutically potent microbial consortia that colonize the host effectively while maximizing immunologic outcome. Here, we propose a novel workflow to select optimal immune-inducing consortia from microbiome compositicon and immune effectors measurements. Using published and newly generated microbial and regulatory T-cell (T reg ) data from germ-free mice, we estimate the contributions of twelve Clostridia strains with known immune-modulating effect to T reg induction. Combining this with a longitudinal data-constrained ecological model, we predict the ability of every attainable and ecologically stable subconsortium in promoting T reg activation and rank them by the T reg Induction Score (TrIS). Experimental validation of selected consortia indicates a strong and statistically significant correlation between predicted TrIS and measured T reg . We argue that computational indexes, such as the TrIS, are valuable tools for the systematic selection of immune-modulating bacteriotherapeutics. © 2018, Stein et al.

A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications

PubMed Central

Slama, Matous; Benes, Peter M.; Bila, Jiri

2015-01-01

During radiotherapy treatment for thoracic and abdomen cancers, for example, lung cancers, respiratory motion moves the target tumor and thus badly affects the accuracy of radiation dose delivery into the target. A real-time image-guided technique can be used to monitor such lung tumor motion for accurate dose delivery, but the system latency up to several hundred milliseconds for repositioning the radiation beam also affects the accuracy. In order to compensate the latency, neural network prediction technique with real-time retraining can be used. We have investigated real-time prediction of 3D time series of lung tumor motion on a classical linear model, perceptron model, and on a class of higher-order neural network model that has more attractive attributes regarding its optimization convergence and computational efficiency. The implemented static feed-forward neural architectures are compared when using gradient descent adaptation and primarily the Levenberg-Marquardt batch algorithm as the ones of the most common and most comprehensible learning algorithms. The proposed technique resulted in fast real-time retraining, so the total computational time on a PC platform was equal to or even less than the real treatment time. For one-second prediction horizon, the proposed techniques achieved accuracy less than one millimeter of 3D mean absolute error in one hundred seconds of total treatment time. PMID:25893194

A fast neural network approach to predict lung tumor motion during respiration for radiation therapy applications.

PubMed

Bukovsky, Ivo; Homma, Noriyasu; Ichiji, Kei; Cejnek, Matous; Slama, Matous; Benes, Peter M; Bila, Jiri

2015-01-01

During radiotherapy treatment for thoracic and abdomen cancers, for example, lung cancers, respiratory motion moves the target tumor and thus badly affects the accuracy of radiation dose delivery into the target. A real-time image-guided technique can be used to monitor such lung tumor motion for accurate dose delivery, but the system latency up to several hundred milliseconds for repositioning the radiation beam also affects the accuracy. In order to compensate the latency, neural network prediction technique with real-time retraining can be used. We have investigated real-time prediction of 3D time series of lung tumor motion on a classical linear model, perceptron model, and on a class of higher-order neural network model that has more attractive attributes regarding its optimization convergence and computational efficiency. The implemented static feed-forward neural architectures are compared when using gradient descent adaptation and primarily the Levenberg-Marquardt batch algorithm as the ones of the most common and most comprehensible learning algorithms. The proposed technique resulted in fast real-time retraining, so the total computational time on a PC platform was equal to or even less than the real treatment time. For one-second prediction horizon, the proposed techniques achieved accuracy less than one millimeter of 3D mean absolute error in one hundred seconds of total treatment time.

An effective tool to manage the distribution of medicines and monitor the treatment in hospital pharmacies.

PubMed

Franzoso, Gianpaolo

2014-01-01

Introduction The purpose of the article is to share a modus operandi and a tool that allows the recruitment and management of thousands of patients and their treatment by using a simple software created by the author and made freely available to all colleague-pharmacists. The author, a pharmacist, created this database because there were no tools on the market with all the features needed to manage the treatment of patients and the orders of drugs to ensure continuity of care without waste of public money. Methods The data collection is facilitated by the software and allows the monitoring of treatment of the patients and their re-evaluation. This tool can create a table containing all the information needed to predict the demand for drugs, the timing of therapies and of the treatment plans. It is an effective instrument to calculate the optimal purchase of drugs and the delivery of therapies to patients. Conclusions A simple tool that allows the management of many patients, reduces research time and facilitates the control of therapies. It allows us to optimize inventory and minimize the stock of drugs. It allows the pharmacist to focus attention on the clinical management of the patient by helping him to follow therapy and respond to his needs.

Optimal design of low-density SNP arrays for genomic prediction: algorithm and applications

USDA-ARS?s Scientific Manuscript database

Low-density (LD) single nucleotide polymorphism (SNP) arrays provide a cost-effective solution for genomic prediction and selection, but algorithms and computational tools are needed for their optimal design. A multiple-objective, local optimization (MOLO) algorithm was developed for design of optim...

Collision prediction software for radiotherapy treatments.

PubMed

Padilla, Laura; Pearson, Erik A; Pelizzari, Charles A

2015-11-01

This work presents a method of collision predictions for external beam radiotherapy using surface imaging. The present methodology focuses on collision prediction during treatment simulation to evaluate the clearance of a patient's treatment position and allow for its modification if necessary. A Kinect camera (Microsoft, Redmond, WA) is used to scan the patient and immobilization devices in the treatment position at the simulator. The surface is reconstructed using the skanect software (Occipital, Inc., San Francisco, CA). The treatment isocenter is marked using simulated orthogonal lasers projected on the surface scan. The point cloud of this surface is then shifted to isocenter and converted from Cartesian to cylindrical coordinates. A slab models the treatment couch. A cylinder with a radius equal to the normal distance from isocenter to the collimator plate, and a height defined by the collimator diameter is used to estimate collisions. Points within the cylinder clear through a full gantry rotation with the treatment couch at 0°, while points outside of it collide. The angles of collision are reported. This methodology was experimentally verified using a mannequin positioned in an alpha cradle with both arms up. A planning CT scan of the mannequin was performed, two isocenters were marked in pinnacle, and this information was exported to AlignRT (VisionRT, London, UK)--a surface imaging system for patient positioning. This was used to ensure accurate positioning of the mannequin in the treatment room, when available. Collision calculations were performed for the two treatment isocenters and the results compared to the collisions detected the room. The accuracy of the Kinect-Skanect surface was evaluated by comparing it to the external surface of the planning CT scan. Experimental verification results showed that the predicted angles of collision matched those recorded in the room within 0.5°, in most cases (largest deviation -1.2°). The accuracy study for the Kinect-Skanect surface showed an average discrepancy between the CT external contour and the surface scan of 2.2 mm. This methodology provides fast and reliable collision predictions using surface imaging. The use of the Kinect-Skanect system allows for a comprehensive modeling of the patient topography including all the relevant anatomy and immobilization devices that may lead to collisions. The use of this tool at the treatment simulation stage may allow therapists to evaluate the clearance of a patient's treatment position and optimize it before the planning CT scan is performed. This can allow for safer treatments for the patients due to better collision predictions and improved clinical workflow by minimizing replanning and resimulations due to unforeseen clearance issues.

Orthodontic Considerations for Maxillary Distraction Osteogenesis in Growing Patients with Cleft Lip and Palate Using Internal Distractors

PubMed Central

Silveira, Adriana da; Moura, Pollyana Marques de; Harshbarger, Raymond J.

2014-01-01

The orthodontist plays a key role in the selection of the optimal treatment for patients followed by a craniofacial team. For patients with cleft lip and palate, the need for multidisciplinary treatment planning and sequentially staged treatment is essential for successful patient outcomes. The technique of Le Fort I distraction osteogenesis of the maxilla using an internal device is potentially a predictable, stable, and convenient option for the correction of severe maxillary hypoplasia. It is an alternative option for treatment of maxillary hypoplasia in growing patients. In this article, the authors describe the orthodontist's approach to the management of cleft patients with severe maxillary deficiency with the use of an internal distraction device. The information is presented with a focus on the clinical aspects of treatment, using case illustrations and appropriate literature. PMID:25383056

Orthodontic considerations for maxillary distraction osteogenesis in growing patients with cleft lip and palate using internal distractors.

PubMed

Silveira, Adriana da; Moura, Pollyana Marques de; Harshbarger, Raymond J

2014-11-01

The orthodontist plays a key role in the selection of the optimal treatment for patients followed by a craniofacial team. For patients with cleft lip and palate, the need for multidisciplinary treatment planning and sequentially staged treatment is essential for successful patient outcomes. The technique of Le Fort I distraction osteogenesis of the maxilla using an internal device is potentially a predictable, stable, and convenient option for the correction of severe maxillary hypoplasia. It is an alternative option for treatment of maxillary hypoplasia in growing patients. In this article, the authors describe the orthodontist's approach to the management of cleft patients with severe maxillary deficiency with the use of an internal distraction device. The information is presented with a focus on the clinical aspects of treatment, using case illustrations and appropriate literature.

Evaluation of port-wine stain treatment outcomes using multispectral imaging

NASA Astrophysics Data System (ADS)

Samatham, Ravikant; Choudhury, Niloy; Krol, Alfons L.; Jacques, Steven L.

2012-02-01

Port-wine Stain (PWS) is a vascular malformation characterized by ectasia of superficial dermal capillaries. The flash-lamp pumped pulsed dye laser (PDL) treatment has been the mainstay of PWS for the last decade. Despite the success of the PDL in significantly fading the PWS, the overall cure rate is less than 10%. The precise efficacy of an individual PDL treatment is hard to evaluate and the treatment outcome is measured by visual observation of clinical fading. A hand-held multi-spectral imaging system was developed to image PWS before and after PDL treatment. In an NIH-funded pilot study multi-spectral camera was used to image PWS in children (2- 17 years). Oxygen saturation (S) and blood content (B) of PWS before and after the treatment was determined by analysis of the reflectance spectra. The outcome of the treatment was evaluated during follow up visits of the patients. One of the major causes of failure of laser therapy of port-wine stains (PWS) is reperfusion of the lesion after laser treatment. Oxygen saturation and blood content maps of PWS before and after treatment can predict regions of reperfusion and subsequent failure of the treatment. The ability to measure reperfusion and to predict lesions or areas susceptible to reperfusion, will help in selection of patients/lesions for laser treatment and help to optimize laser dosimetry for maximum effect. The current studies also should provide a basis for monitoring of future alternative therapies or enhancers of laser treatment in resistant cases.

Modeling and optimization of lime-based stabilization in high alkaline arsenic-bearing sludges with a central composite design.

PubMed

Lei, Jie; Peng, Bing; Min, Xiaobo; Liang, Yanjie; You, Yang; Chai, Liyuan

2017-04-16

This study focuses on the modeling and optimization of lime-based stabilization in high alkaline arsenic-bearing sludges (HAABS) and describes the relationship between the arsenic leachate concentration (ALC) and stabilization parameters to develop a prediction model for obtaining the optimal process parameters and conditions. A central composite design (CCD) along with response surface methodology (RSM) was conducted to model and investigate the stabilization process with three independent variables: the Ca/As mole ratio, reaction time and liquid/solid ratio, along with their interactions. The obvious characteristic changes of the HAABS before and after stabilization were verified by X-ray diffraction (XRD), scanning electron microscopy (SEM), particle size distribution (PSD) and the community bureau of reference (BCR) sequential extraction procedure. A prediction model Y (ALC) with a statistically significant P-value <0.01 and high correlation coefficient R 2 = 93.22% was obtained. The optimal parameters were successfully predicted by the model for the minimum ALC of 0.312 mg/L, which was validated with the experimental result (0.306 mg/L). The XRD, SEM and PSD results indicated that crystal calcium arsenate Ca 5 (AsO 4 ) 3 OH and Ca 4 (OH) 2 (AsO 4 ) 2 ·4H 2 O formation played an important role in minimizing the ALC. The BCR sequential extraction results demonstrated that the treated HAABS were stable in a weak acidic environment for a short time but posed a potential environmental risk after a long time. The results clearly confirm that the proposed three-factor CCD is an effective approach for modeling the stabilization of HAABS. However, further solidification technology is suggested for use after lime-based stabilization treatment of arsenic-bearing sludges.

Modeling and Optimization of NLDH/PVDF Ultrafiltration Nanocomposite Membrane Using Artificial Neural Network-Genetic Algorithm Hybrid.

PubMed

Arefi-Oskoui, Samira; Khataee, Alireza; Vatanpour, Vahid

2017-07-10

In this research, MgAl-CO 3 2- nanolayered double hydroxide (NLDH) was synthesized through a facile coprecipitation method, followed by a hydrothermal treatment. The prepared NLDHs were used as a hydrophilic nanofiller for improving the performance of the PVDF-based ultrafiltration membranes. The main objective of this research was to obtain the optimized formula of NLDH/PVDF nanocomposite membrane presenting the best performance using computational techniques as a cost-effective method. For this aim, an artificial neural network (ANN) model was developed for modeling and expressing the relationship between the performance of the nanocomposite membrane (pure water flux, protein flux and flux recovery ratio) and the affecting parameters including the NLDH, PVP 29000 and polymer concentrations. The effects of the mentioned parameters and the interaction between the parameters were investigated using the contour plot predicted with the developed model. Scanning electron microscopy (SEM), atomic force microscopy (AFM), and water contact angle techniques were applied to characterize the nanocomposite membranes and to interpret the predictions of the ANN model. The developed ANN model was introduced to genetic algorithm (GA) as a bioinspired optimizer to determine the optimum values of input parameters leading to high pure water flux, protein flux, and flux recovery ratio. The optimum values for NLDH, PVP 29000 and the PVDF concentration were determined to be 0.54, 1, and 18 wt %, respectively. The performance of the nanocomposite membrane prepared using the optimum values proposed by GA was investigated experimentally, in which the results were in good agreement with the values predicted by ANN model with error lower than 6%. This good agreement confirmed that the nanocomposite membranes prformance could be successfully modeled and optimized by ANN-GA system.

A global quality assurance system for personalized radiation therapy treatment planning for the prostate (or other sites)

NASA Astrophysics Data System (ADS)

Nwankwo, Obioma; Sihono, Dwi Seno K.; Schneider, Frank; Wenz, Frederik

2014-09-01

Introduction: the quality of radiotherapy treatment plans varies across institutions and depends on the experience of the planner. For the purpose of intra- and inter-institutional homogenization of treatment plan quality, we present an algorithm that learns the organs-at-risk (OARs) sparing patterns from a database of high quality plans. Thereafter, the algorithm predicts the dose that similar organs will receive in future radiotherapy plans prior to treatment planning on the basis of the anatomies of the organs. The predicted dose provides the basis for the individualized specification of planning objectives, and for the objective assessment of the quality of radiotherapy plans. Materials and method: one hundred and twenty eight (128) Volumetric Modulated Arc Therapy (VMAT) plans were selected from a database of prostate cancer plans. The plans were divided into two groups, namely a training set that is made up of 95 plans and a validation set that consists of 33 plans. A multivariate analysis technique was used to determine the relationships between the positions of voxels and their dose. This information was used to predict the likely sparing of the OARs of the plans of the validation set. The predicted doses were visually and quantitatively compared to the reference data using dose volume histograms, the 3D dose distribution, and a novel evaluation metric that is based on the dose different test. Results: a voxel of the bladder on the average receives a higher dose than a voxel of the rectum in optimized radiotherapy plans for the treatment of prostate cancer in our institution if both voxels are at the same distance to the PTV. Based on our evaluation metric, the predicted and reference dose to the bladder agree to within 5% of the prescribed dose to the PTV in 18 out of 33 cases, while the predicted and reference doses to the rectum agree to within 5% in 28 out of the 33 plans of the validation set. Conclusion: We have described a method to predict the likely dose that OARs will receive before treatment planning. This prospective knowledge could be used to implement a global quality assurance system for personalized radiation therapy treatment planning.

Predicting optimal response to B-cell depletion with rituximab in multiple sclerosis using CXCL13 index, magnetic resonance imaging and clinical measures

PubMed Central

Alvarez, Enrique; Piccio, Laura; Mikesell, Robert J; Trinkaus, Kathryn; Parks, Becky J; Naismith, Robert T

2015-01-01

Background B-cell depleting drugs show promise for treating multiple sclerosis. Objective We sought predictors of optimal response to rituximab, a B-cell depleting antibody, to help guide therapy selection. Methods We performed a post hoc study of 30 relapsing multiple sclerosis patients with breakthrough disease while on beta-interferon or glatiramer acetate who were treated with add-on rituximab. Standardized neurologic examinations, brain magnetic resonance imaging, and cerebrospinal fluid were obtained before and after rituximab. Tissue biomarkers were measured. Optimal responders were defined as having no evidence of disease activity. Results At baseline, optimal responders with no evidence of disease activity had higher IgG indices (P = 0.041), and higher CXCL13 indices ((cerebrospinal fluid CXCL13/serum CXCL13)/albumin index; P = 0.024), more contrast enhancing lesions (P = 0.002), better 25 foot timed walk (P = 0.001), and Expanded Disability Status Scale (P = 0.002). Rituximab treatment led to reduced cerebrospinal fluid biomarkers of tissue destruction: myelin basic protein (P = 0.046), neurofilament light chain (P < 0.001), and of inflammation (CXCL13 index; P = 0.042). Conclusions Multiple sclerosis patients with optimal response to rituximab had higher cerebrospinal fluid IgG and CXCL13 indices, more gadolinium-enhancing lesions, and less disability at baseline. Rituximab treatment led to decreased markers of inflammation and tissue damage. If validated, these results will help identify multiple sclerosis patients who will respond optimally to B-cell depletion. PMID:28607711

SU-E-T-413: Examining Acquisition Rate for Using MatriXX Ion Chamber Array to Measure HDR Brachytherapy Treatments

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

Wagar, M; Bhagwat, M; O’Farrell, D

2015-06-15

Purpose: There are unique obstacles to implementing the MatriXX ionchamber array as a QA tool in Brachytherapy given that the device is designed for use in the MV energy range. One of the challenges we investigate is the affect of acquisition rates on dose measurement accuracy for HDR treatment plans. Methods: A treatment plan was optimized in Oncentra Brachy TPS to deliver a planar dose to a 5×5cm region at 10mm depth. The applicator was affixed to the surface of the MatriXX array. The plan was delivered multiple times using a Nucleatron HDR afterloader with a 2.9Ci Ir192 source. Formore » each measurement the sampling rate of the MatriXX movie mode was varied (30ms and 500ms). This experiment was repeated with identical parameters, following a source exchange, with an 11.2Ci Ir192 source. Finally, a single snap measurement was acquired. Analysis was preformed to evaluate the fidelity of the dose delivery for each iteration of the experiment. Evaluation was based on the comparison between the measured and TPS predicted dose. Results: Higher sample rates induce a greater discrepancy between the predicted and measured dose. Delivering the plan using a lower activity source also produced greater discrepancy in the measurement due to the increased delivery time. Analyzing the single snap measurement showed little difference from the 500ms integral dose measurement. Conclusion: The advantage of using movie mode for HDR treatment delivery QA is the ability for real time source tracking in addition to dose measurement. Our analysis indicates that 500ms is an optimal frame rate.« less

Bayesian Optimization for Neuroimaging Pre-processing in Brain Age Classification and Prediction

PubMed Central

Lancaster, Jenessa; Lorenz, Romy; Leech, Rob; Cole, James H.

2018-01-01

Neuroimaging-based age prediction using machine learning is proposed as a biomarker of brain aging, relating to cognitive performance, health outcomes and progression of neurodegenerative disease. However, even leading age-prediction algorithms contain measurement error, motivating efforts to improve experimental pipelines. T1-weighted MRI is commonly used for age prediction, and the pre-processing of these scans involves normalization to a common template and resampling to a common voxel size, followed by spatial smoothing. Resampling parameters are often selected arbitrarily. Here, we sought to improve brain-age prediction accuracy by optimizing resampling parameters using Bayesian optimization. Using data on N = 2003 healthy individuals (aged 16–90 years) we trained support vector machines to (i) distinguish between young (<22 years) and old (>50 years) brains (classification) and (ii) predict chronological age (regression). We also evaluated generalisability of the age-regression model to an independent dataset (CamCAN, N = 648, aged 18–88 years). Bayesian optimization was used to identify optimal voxel size and smoothing kernel size for each task. This procedure adaptively samples the parameter space to evaluate accuracy across a range of possible parameters, using independent sub-samples to iteratively assess different parameter combinations to arrive at optimal values. When distinguishing between young and old brains a classification accuracy of 88.1% was achieved, (optimal voxel size = 11.5 mm3, smoothing kernel = 2.3 mm). For predicting chronological age, a mean absolute error (MAE) of 5.08 years was achieved, (optimal voxel size = 3.73 mm3, smoothing kernel = 3.68 mm). This was compared to performance using default values of 1.5 mm3 and 4mm respectively, resulting in MAE = 5.48 years, though this 7.3% improvement was not statistically significant. When assessing generalisability, best performance was achieved when applying the entire Bayesian optimization framework to the new dataset, out-performing the parameters optimized for the initial training dataset. Our study outlines the proof-of-principle that neuroimaging models for brain-age prediction can use Bayesian optimization to derive case-specific pre-processing parameters. Our results suggest that different pre-processing parameters are selected when optimization is conducted in specific contexts. This potentially motivates use of optimization techniques at many different points during the experimental process, which may improve statistical sensitivity and reduce opportunities for experimenter-led bias. PMID:29483870

TU-AB-BRB-03: Coverage-Based Treatment Planning to Accommodate Organ Deformable Motions and Contouring Uncertainties for Prostate Treatment

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

Xu, H.

The accepted clinical method to accommodate targeting uncertainties inherent in fractionated external beam radiation therapy is to utilize GTV-to-CTV and CTV-to-PTV margins during the planning process to design a PTV-conformal static dose distribution on the planning image set. Ideally, margins are selected to ensure a high (e.g. >95%) target coverage probability (CP) in spite of inherent inter- and intra-fractional positional variations, tissue motions, and initial contouring uncertainties. Robust optimization techniques, also known as probabilistic treatment planning techniques, explicitly incorporate the dosimetric consequences of targeting uncertainties by including CP evaluation into the planning optimization process along with coverage-based planning objectives. Themore » treatment planner no longer needs to use PTV and/or PRV margins; instead robust optimization utilizes probability distributions of the underlying uncertainties in conjunction with CP-evaluation for the underlying CTVs and OARs to design an optimal treated volume. This symposium will describe CP-evaluation methods as well as various robust planning techniques including use of probability-weighted dose distributions, probability-weighted objective functions, and coverage optimized planning. Methods to compute and display the effect of uncertainties on dose distributions will be presented. The use of robust planning to accommodate inter-fractional setup uncertainties, organ deformation, and contouring uncertainties will be examined as will its use to accommodate intra-fractional organ motion. Clinical examples will be used to inter-compare robust and margin-based planning, highlighting advantages of robust-plans in terms of target and normal tissue coverage. Robust-planning limitations as uncertainties approach zero and as the number of treatment fractions becomes small will be presented, as well as the factors limiting clinical implementation of robust planning. Learning Objectives: To understand robust-planning as a clinical alternative to using margin-based planning. To understand conceptual differences between uncertainty and predictable motion. To understand fundamental limitations of the PTV concept that probabilistic planning can overcome. To understand the major contributing factors to target and normal tissue coverage probability. To understand the similarities and differences of various robust planning techniques To understand the benefits and limitations of robust planning techniques.« less

Customer demand prediction of service-oriented manufacturing using the least square support vector machine optimized by particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Cao, Jin; Jiang, Zhibin; Wang, Kangzhou

2017-07-01

Many nonlinear customer satisfaction-related factors significantly influence the future customer demand for service-oriented manufacturing (SOM). To address this issue and enhance the prediction accuracy, this article develops a novel customer demand prediction approach for SOM. The approach combines the phase space reconstruction (PSR) technique with the optimized least square support vector machine (LSSVM). First, the prediction sample space is reconstructed by the PSR to enrich the time-series dynamics of the limited data sample. Then, the generalization and learning ability of the LSSVM are improved by the hybrid polynomial and radial basis function kernel. Finally, the key parameters of the LSSVM are optimized by the particle swarm optimization algorithm. In a real case study, the customer demand prediction of an air conditioner compressor is implemented. Furthermore, the effectiveness and validity of the proposed approach are demonstrated by comparison with other classical predication approaches.

Leflunomide in the treatment of rheumatoid arthritis. An analysis of predictors for treatment continuation.

PubMed

van Roon, E N; Hoekstra, M; Tobi, H; Jansen, T L Th A; Bernelot Moens, H J; Brouwers, J R B J; van de Laar, M A F J

2005-09-01

To determine factors predictive for leflunomide drug survival in an outpatient population with rheumatoid arthritis in a setting of care-as-usual. A standard dataset was collected from medical records of consecutive outpatients on leflunomide treatment for rheumatoid arthritis between January 2000 and June 2003. The dataset consisted of patient, disease and treatment characteristics at the start of leflunomide treatment, and data on leflunomide use. Leflunomide was started in 279 patients and 173 patients (62.0%) withdrew from treatment during follow-up. From univariate analysis, concomitant systemic corticosteroid use [hazard ratio (HR) (95% confidence interval) 1.35 (1.00, 1.83)] and an erythrocyte sedimentation rate <35 mm h(-1)[HR 1.38 (1.01, 1.88)] at start of leflunomide were found to be predictive for better leflunomide survival. Furthermore, the attending rheumatologist was correlated with leflunomide drug survival. Hazard ratios varied, depending on the individual rheumatologist, from 0.60 to 2.66. Multivariate analysis suggested attending rheumatologist (HR varying from 0.54 to 2.30 depending on the individual rheumatologist), concomitant systemic corticosteroid use [HR 1.58 (1.14 2.21)] and erythrocyte sedimentation rate <35 mm h(-1)[HR 1.42 (1.03, 1.96)] at start of leflunomide to be associated with leflunomide survival. Concomitant systemic corticosteroid use, erythrocyte sedimentation rate at the start of treatment and attending rheumatologist were found to be predictive for leflunomide survival. Information on these predictors at the start of leflunomide therapy may offer information on which patients are at an increased risk of withdrawal from leflunomide. Whether this information leads to optimization of leflunomide treatment outcomes remains to be studied.

SU-E-J-254: Evaluating the Role of Mid-Treatment and Post-Treatment FDG-PET/CT in Predicting Progression-Free Survival and Distant Metastasis of Anal Cancer Patients Treated with Chemoradiotherapy

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

Zhang, H; Wang, J; Chuong, M

2015-06-15

Purpose: To evaluate the role of mid-treatment and post-treatment FDG-PET/CT in predicting progression-free survival (PFS) and distant metastasis (DM) of anal cancer patients treated with chemoradiotherapy (CRT). Methods: 17 anal cancer patients treated with CRT were retrospectively studied. The median prescription dose was 56 Gy (range, 50–62.5 Gy). All patients underwent FDG-PET/CT scans before and after CRT. 16 of the 17 patients had an additional FDG-PET/CT image at 3–5 weeks into the treatment (denoted as mid-treatment FDG-PET/CT). 750 features were extracted from these three sets of scans, which included both traditional PET/CT measures (SUVmax, SUVpeak, tumor diameters, etc.) and spatialtemporalmore » PET/CT features (comprehensively quantify a tumor’s FDG uptake intensity and distribution, spatial variation (texture), geometric property and their temporal changes relative to baseline). 26 clinical parameters (age, gender, TNM stage, histology, GTV dose, etc.) were also analyzed. Advanced analytics including methods to select an optimal set of predictors and a model selection engine, which identifies the most accurate machine learning algorithm for predictive analysis was developed. Results: Comparing baseline + mid-treatment PET/CT set to baseline + posttreatment PET/CT set, 14 predictors were selected from each feature group. Same three clinical parameters (tumor size, T stage and whether 5-FU was held during any cycle of chemotherapy) and two traditional measures (pre- CRT SUVmin and SUVmedian) were selected by both predictor groups. Different mix of spatial-temporal PET/CT features was selected. Using the 14 predictors and Naive Bayes, mid-treatment PET/CT set achieved 87.5% accuracy (2 PFS patients misclassified, all local recurrence and DM patients correctly classified). Post-treatment PET/CT set achieved 94.0% accuracy (all PFS and DM patients correctly predicted, 1 local recurrence patient misclassified) with logistic regression, neural network or support vector machine model. Conclusion: Applying radiomics approach to either midtreatment or post-treatment PET/CT could achieve high accuracy in predicting anal cancer treatment outcomes. This work was supported in part by the National Cancer Institute Grant R01CA172638.« less

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM.

PubMed

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei; Song, Houbing

2018-01-15

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model's performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM's parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models' performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors.

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

Fukada, Junichi, E-mail: fukada@rad.med.keio.ac.jp; Shigematsu, Naoyuki; Takeuchi, Hiroya

Purpose: We investigated clinical and treatment-related factors as predictors of symptomatic pericardial effusion in esophageal cancer patients after concurrent chemoradiation therapy. Methods and Materials: We reviewed 214 consecutive primary esophageal cancer patients treated with concurrent chemoradiation therapy between 2001 and 2010 in our institute. Pericardial effusion was detected on follow-up computed tomography. Symptomatic effusion was defined as effusion ≥grade 3 according to Common Terminology Criteria for Adverse Events v4.0 criteria. Percent volume irradiated with 5 to 65 Gy (V5-V65) and mean dose to the pericardium were evaluated employing dose-volume histograms. To evaluate dosimetry for patients treated with two-dimensional planning inmore » the earlier period (2001-2005), computed tomography data at diagnosis were transferred to a treatment planning system to reconstruct three-dimensional plans without modification. Optimal dosimetric thresholds for symptomatic pericardial effusion were calculated by receiver operating characteristic curves. Associating clinical and treatment-related risk factors for symptomatic pericardial effusion were detected by univariate and multivariate analyses. Results: The median follow-up was 29 (range, 6-121) months for eligible 167 patients. Symptomatic pericardial effusion was observed in 14 (8.4%) patients. Dosimetric analyses revealed average values of V30 to V45 for the pericardium and mean pericardial doses were significantly higher in patients with symptomatic pericardial effusion than in those with asymptomatic pericardial effusion (P<.05). Pericardial V5 to V55 and mean pericardial doses were significantly higher in patients with symptomatic pericardial effusion than in those without pericardial effusion (P<.001). Mean pericardial doses of 36.5 Gy and V45 of 58% were selected as optimal cutoff values for predicting symptomatic pericardial effusion. Multivariate analysis identified mean pericardial dose as the strongest risk factor for symptomatic pericardial effusion. Conclusions: Dose-volume thresholds for the pericardium facilitate predicting symptomatic pericardial effusion. Mean pericardial dose was selected based not only on the optimal dose-volume threshold but also on the most significant risk factor for symptomatic pericardial effusion.« less

Symptomatic pericardial effusion after chemoradiation therapy in esophageal cancer patients.

PubMed

Fukada, Junichi; Shigematsu, Naoyuki; Takeuchi, Hiroya; Ohashi, Toshio; Saikawa, Yoshiro; Takaishi, Hiromasa; Hanada, Takashi; Shiraishi, Yutaka; Kitagawa, Yuko; Fukuda, Keiichi

2013-11-01

We investigated clinical and treatment-related factors as predictors of symptomatic pericardial effusion in esophageal cancer patients after concurrent chemoradiation therapy. We reviewed 214 consecutive primary esophageal cancer patients treated with concurrent chemoradiation therapy between 2001 and 2010 in our institute. Pericardial effusion was detected on follow-up computed tomography. Symptomatic effusion was defined as effusion ≥grade 3 according to Common Terminology Criteria for Adverse Events v4.0 criteria. Percent volume irradiated with 5 to 65 Gy (V5-V65) and mean dose to the pericardium were evaluated employing dose-volume histograms. To evaluate dosimetry for patients treated with two-dimensional planning in the earlier period (2001-2005), computed tomography data at diagnosis were transferred to a treatment planning system to reconstruct three-dimensional plans without modification. Optimal dosimetric thresholds for symptomatic pericardial effusion were calculated by receiver operating characteristic curves. Associating clinical and treatment-related risk factors for symptomatic pericardial effusion were detected by univariate and multivariate analyses. The median follow-up was 29 (range, 6-121) months for eligible 167 patients. Symptomatic pericardial effusion was observed in 14 (8.4%) patients. Dosimetric analyses revealed average values of V30 to V45 for the pericardium and mean pericardial doses were significantly higher in patients with symptomatic pericardial effusion than in those with asymptomatic pericardial effusion (P<.05). Pericardial V5 to V55 and mean pericardial doses were significantly higher in patients with symptomatic pericardial effusion than in those without pericardial effusion (P<.001). Mean pericardial doses of 36.5 Gy and V45 of 58% were selected as optimal cutoff values for predicting symptomatic pericardial effusion. Multivariate analysis identified mean pericardial dose as the strongest risk factor for symptomatic pericardial effusion. Dose-volume thresholds for the pericardium facilitate predicting symptomatic pericardial effusion. Mean pericardial dose was selected based not only on the optimal dose-volume threshold but also on the most significant risk factor for symptomatic pericardial effusion. Copyright © 2013 Elsevier Inc. All rights reserved.

An imaging informatics-based ePR (electronic patient record) system for providing decision support in evaluating dose optimization in stroke rehabilitation

NASA Astrophysics Data System (ADS)

Liu, Brent J.; Winstein, Carolee; Wang, Ximing; Konersman, Matt; Martinez, Clarisa; Schweighofer, Nicolas

2012-02-01

Stroke is one of the major causes of death and disability in America. After stroke, about 65% of survivors still suffer from severe paresis, while rehabilitation treatment strategy after stroke plays an essential role in recovery. Currently, there is a clinical trial (NIH award #HD065438) to determine the optimal dose of rehabilitation for persistent recovery of arm and hand paresis. For DOSE (Dose Optimization Stroke Evaluation), laboratory-based measurements, such as the Wolf Motor Function test, behavioral questionnaires (e.g. Motor Activity Log-MAL), and MR, DTI, and Transcranial Magnetic Stimulation (TMS) imaging studies are planned. Current data collection processes are tedious and reside in various standalone systems including hardcopy forms. In order to improve the efficiency of this clinical trial and facilitate decision support, a web-based imaging informatics system has been implemented together with utilizing mobile devices (eg, iPAD, tablet PC's, laptops) for collecting input data and integrating all multi-media data into a single system. The system aims to provide clinical imaging informatics management and a platform to develop tools to predict the treatment effect based on the imaging studies and the treatment dosage with mathematical models. Since there is a large amount of information to be recorded within the DOSE project, the system provides clinical data entry through mobile device applications thus allowing users to collect data at the point of patient interaction without typing into a desktop computer, which is inconvenient. Imaging analysis tools will also be developed for structural MRI, DTI, and TMS imaging studies that will be integrated within the system and correlated with the clinical and behavioral data. This system provides a research platform for future development of mathematical models to evaluate the differences between prediction and reality and thus improve and refine the models rapidly and efficiently.

Correlates of motivation to change in pathological gamblers completing cognitive-behavioral group therapy.

PubMed

Gómez-Peña, Mónica; Penelo, Eva; Granero, Roser; Fernández-Aranda, Fernando; Alvarez-Moya, Eva; Santamaría, Juan José; Moragas, Laura; Neus Aymamí, Maria; Gunnard, Katarina; Menchón, José M; Jimenez-Murcia, Susana

2012-07-01

The present study analyzes the association between the motivation to change and the cognitive-behavioral group intervention, in terms of dropouts and relapses, in a sample of male pathological gamblers. The specific objectives were as follows: (a) to estimate the predictive value of baseline University of Rhode Island Change Assessment scale (URICA) scores (i.e., at the start of the study) as regards the risk of relapse and dropout during treatment and (b) to assess the incremental predictive ability of URICA scores, as regards the mean change produced in the clinical status of patients between the start and finish of treatment. The relationship between the URICA and the response to treatment was analyzed by means of a pre-post design applied to a sample of 191 patients who were consecutively receiving cognitive-behavioral group therapy. The statistical analysis included logistic regression models and hierarchical multiple linear regression models. The discriminative ability of the models including the four URICA scores regarding the likelihood of relapse and dropout was acceptable (area under the receiver operating haracteristic curve: .73 and .71, respectively). No significant predictive ability was found as regards the differences between baseline and posttreatment scores (changes in R(2) below 5% in the multiple regression models). The availability of useful measures of motivation to change would enable treatment outcomes to be optimized through the application of specific therapeutic interventions. © 2012 Wiley Periodicals, Inc.

Predictors of improvement and progression of diabetic polyneuropathy following treatment with α-lipoic acid for 4 years in the NATHAN 1 trial.

PubMed

Ziegler, Dan; Low, Phillip A; Freeman, Roy; Tritschler, Hans; Vinik, Aaron I

2016-03-01

We aimed to analyze the impact of baseline factors on the efficacy of α-lipoic acid (ALA) over 4 years in the NATHAN 1 trial. This was a post-hoc analysis of the NATHAN 1 trial, a 4-year randomized study including 460 diabetic patients with mild-to-moderate polyneuropathy using ALA 600 mg qd or placebo. Amongst others, efficacy measures were the Neuropathy Impairment Score of the lower limbs (NIS-LL) and heart rate during deep breathing (HRDB). Improvement and prevention of progression of NIS-LL (ΔNIS-LL≥2 points) with ALA vs. placebo after 4 years was predicted by higher age, lower BMI, male sex, normal blood pressure, history of cardiovascular disease (CVD), insulin treatment, longer duration of diabetes and neuropathy, and higher neuropathy stage. Participants treated with ALA who received ACE inhibitors showed a better outcome in HRDB after 4 years. Better outcome in neuropathic impairments following 4-year treatment with α-lipoic acid was predicted by normal BMI and blood pressure and higher burden due to CVD, diabetes, and neuropathy, while improvement in cardiac autonomic function was predicted by ACE inhibitor treatment. Thus, optimal control of CVD risk factors could contribute to improved efficacy of α-lipoic acid in patients with higher disease burden. Copyright © 2016 Elsevier Inc. All rights reserved.

Synthesis and Size Dependent Reflectance Study of Water Soluble SnS Nanoparticles

PubMed Central

Xu, Ying; Al-Salim, Najeh; Tilley, Richard D.

2012-01-01

Near-monodispersed water soluble SnS nanoparticles in the diameter range of 3–6 nm are synthesized by a facile, solution based one-step approach using ethanolamine ligands. The optimal amount of triethanolamine is investigated. The effect of further heat treatment on the size of these SnS nanoparticles is discussed. Diffuse reflectance study of SnS nanoparticles agrees with predictions from quantum confinement model. PMID:28348295

Clinical versus actuarial judgment.

PubMed

Dawes, R M; Faust, D; Meehl, P E

1989-03-31

Professionals are frequently consulted to diagnose and predict human behavior; optimal treatment and planning often hinge on the consultant's judgmental accuracy. The consultant may rely on one of two contrasting approaches to decision-making--the clinical and actuarial methods. Research comparing these two approaches shows the actuarial method to be superior. Factors underlying the greater accuracy of actuarial methods, sources of resistance to the scientific findings, and the benefits of increased reliance on actuarial approaches are discussed.

Influence of patient mispositioning on SAR distribution and simulated temperature in regional deep hyperthermia

NASA Astrophysics Data System (ADS)

Aklan, Bassim; Gierse, Pia; Hartmann, Josefin; Ott, Oliver J.; Fietkau, Rainer; Bert, Christoph

2017-06-01

Patient positioning plays an important role in regional deep hyperthermia to obtain a successful hyperthermia treatment. In this study, the influence of possible patient mispositioning was systematically assessed on specific absorption rate (SAR) and temperature distribution. With a finite difference time domain approach, the SAR and temperature distributions were predicted for six patients at 312 positions. Patient displacements and rotations as well as the combination of both were considered inside the Sigma-Eye applicator. Position sensitivity is assessed for hyperthermia treatment planning -guided steering, which relies on model-based optimization of the SAR and temperature distribution. The evaluation of the patient mispositioning was done with and without optimization. The evaluation without optimization was made by creating a treatment plan for the patient reference position in the center of the applicator and applied for all other positions, while the evaluation with optimization was based on creating an individual plan for each position. The parameter T90 was used for the temperature evaluation, which was defined as the temperature that covers 90% of the gross tumor volume (GTV). Furthermore, the hotspot tumor quotient (HTQ) was used as a goal function to assess the quality of the SAR and temperature distribution. The T90 was shown considerably dependent on the position within the applicator. Without optimization, the T90 was clearly decreased below 40 °C by patient shifts and the combination of shifts and rotations. However, the application of optimization for each positon led to an increase of T90 in the GTV. Position inaccuracies of less than 1 cm in the X-and Y-directions and 2 cm in the Z-direction, resulted in an increase of HTQ of less than 5%, which does not significantly affect the SAR and temperature distribution. Current positioning precision is sufficient in the X (right-left)-direction, but position accuracy is required in the Y-and Z-directions.

Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters

PubMed Central

2014-01-01

This paper examined the efficiency of multivariate linear regression (MLR) and artificial neural network (ANN) models in prediction of two major water quality parameters in a wastewater treatment plant. Biochemical oxygen demand (BOD) and chemical oxygen demand (COD) as well as indirect indicators of organic matters are representative parameters for sewer water quality. Performance of the ANN models was evaluated using coefficient of correlation (r), root mean square error (RMSE) and bias values. The computed values of BOD and COD by model, ANN method and regression analysis were in close agreement with their respective measured values. Results showed that the ANN performance model was better than the MLR model. Comparative indices of the optimized ANN with input values of temperature (T), pH, total suspended solid (TSS) and total suspended (TS) for prediction of BOD was RMSE = 25.1 mg/L, r = 0.83 and for prediction of COD was RMSE = 49.4 mg/L, r = 0.81. It was found that the ANN model could be employed successfully in estimating the BOD and COD in the inlet of wastewater biochemical treatment plants. Moreover, sensitive examination results showed that pH parameter have more effect on BOD and COD predicting to another parameters. Also, both implemented models have predicted BOD better than COD. PMID:24456676

Considerations in Spinal Fusion Surgery for Chronic Lumbar Pain: Psychosocial Factors, Rating Scales, and Perioperative Patient Education-A Review of the Literature.

PubMed

Gaudin, Daniel; Krafcik, Brianna M; Mansour, Tarek R; Alnemari, Ahmed

2017-02-01

Despite widespread use of lumbar spinal fusion as a treatment for back pain, outcomes remain variable. Optimizing patient selection can help to reduce adverse outcomes. This literature review was conducted to better understand factors associated with optimal postoperative results after lumbar spinal fusion for chronic back pain and current tools used for evaluation. The PubMed database was searched for clinical trials related to psychosocial determinants of outcome after lumbar spinal fusion surgery; evaluation of commonly used patient subjective outcome measures; and perioperative cognitive, behavioral, and educational therapies. Reference lists of included studies were also searched by hand for additional studies meeting inclusion and exclusion criteria. Patients' perception of good health before surgery and low cardiovascular comorbidity predict improved postoperative physical functional capacity and greater patient satisfaction. Depression, tobacco use, and litigation predict poorer outcomes after lumbar fusion. Incorporation of cognitive-behavioral therapy perioperatively can address these psychosocial risk factors and improve outcomes. The 36-Item Short Form Health Survey, European Quality of Life five dimensions questionnaire, visual analog pain scale, brief pain inventory, and Oswestry Disability Index can provide specific feedback to track patient progress and are important to understand when evaluating the current literature. This review summarizes current information and explains commonly used assessment tools to guide clinicians in decision making when caring for patients with lower back pain. When determining a treatment algorithm, physicians must consider predictive psychosocial factors. Use of perioperative cognitive-behavioral therapy and patient education can improve outcomes after lumbar spinal fusion. Copyright © 2016 Elsevier Inc. All rights reserved.

Bayesian Decision Support for Adaptive Lung Treatments

NASA Astrophysics Data System (ADS)

McShan, Daniel; Luo, Yi; Schipper, Matt; TenHaken, Randall

2014-03-01

Purpose: A Bayesian Decision Network will be demonstrated to provide clinical decision support for adaptive lung response-driven treatment management based on evidence that physiologic metrics may correlate better with individual patient response than traditional (population-based) dose and volume-based metrics. Further, there is evidence that information obtained during the course of radiation therapy may further improve response predictions. Methods: Clinical factors were gathered for 58 patients including planned mean lung dose, and the bio-markers IL-8 and TGF-β1 obtained prior to treatment and two weeks into treatment along with complication outcomes for these patients. A Bayesian Decision Network was constructed using Netica 5.0.2 from Norsys linking these clinical factors to obtain a prediction of radiation induced lung disese (RILD) complication. A decision node was added to the network to provide a plan adaption recommendation based on the trade-off between the RILD prediction and complexity of replanning. A utility node provides the weighting cost between the competing factors. Results: The decision node predictions were optimized against the data for the 58 cases. With this decision network solution, one can consider the decision result for a new patient with specific findings to obtain a recommendation to adaptively modify the originally planned treatment course. Conclusions: A Bayesian approach allows handling and propagating probabilistic data in a logical and principled manner. Decision networks provide the further ability to provide utility-based trade-offs, reflecting non-medical but practical cost/benefit analysis. The network demonstrated illustrates the basic concept, but many other factors may affect these decisions and work on building better models are being designed and tested. Acknowledgement: Supported by NIH-P01-CA59827

Evaluating Response to High-Dose 13.3 mg/24 h Rivastigmine Patch in Patients with Severe Alzheimer's Disease.

PubMed

Farlow, Martin R; Sadowsky, Carl H; Velting, Drew M; Meng, Xiangyi; Islam, M Zahur

2015-06-01

To identify factors predicting improvement/stabilization on the Alzheimer's Disease Cooperative Study-Clinical Global Impression of Change (ADCS-CGIC) and investigate whether early treatment responses can predict long-term outcomes, during a trial of 13.3 mg/24 h versus 4.6 mg/24 h rivastigmine patch in patients with severe Alzheimer's disease (AD). Logistic regression was used to relate Week 24 ADCS-CGIC score to potential baseline predictors. Additional analyses based on receiver-operating characteristic curves were performed using Week 8/16 ADCS-CGIC scores to predict response (13.3 mg/24 h patch) at Week 24. ADCS-CGIC score of (1) 1-3 = "improvement," (2) 1-4 = "improvement or no change". "Treatment" (13.3 mg/24 h patch) and increased age were significant predictors of "improvement" (P = 0.01 and P = 0.003, respectively), and "treatment" (P = 0.001), increased age (P = 0.002), and prior AD treatment (P = 0.03) for "improvement or no change". At Week 8 and 16, ADCS-CGIC scores of 4 and 5 were optimal thresholds in predicting "improvement," and "improvement or no change," respectively, at Week 24. A significant therapeutic effect of high-dose rivastigmine patch on ADCS-CGIC response was observed. The 13.3 mg/24 h patch was identified as a predictor of "improvement" or "improvement or no change". Patients with minimal worsening/improvement/no change after treatment initiation may be more likely to respond following long-term therapy. © 2015 John Wiley & Sons Ltd.

Long-term response to recombinant human growth hormone treatment: a new predictive mathematical method.

PubMed

Migliaretti, G; Ditaranto, S; Guiot, C; Vannelli, S; Matarazzo, P; Cappello, N; Stura, I; Cavallo, F

2018-07-01

Recombinant GH has been offered to GH-deficient (GHD) subjects for more than 30 years, in order to improve height and growth velocity in children and to enhance metabolic effects in adults. The aim of our work is to describe the long-term effect of rhGH treatment in GHD pediatric patients, suggesting a growth prediction model. A homogeneous database is defined for diagnosis and treatment modalities, based on GHD patients afferent to Hospital Regina Margherita in Turin (Italy). In this study, 232 GHD patients are selected (204 idiopathic GHD and 28 organic GHD). Each measure is shown in terms of mean with relative standard deviations (SD) and 95% confidence interval (95% CI). To estimate the final height of each patient on the basis of few measures, a mathematical growth prediction model [based on Gompertzian function and a mixed method based on the radial basis functions (RBFs) and the particle swarm optimization (PSO) models] was performed. The results seem to highlight the benefits of an early start of treatment, further confirming what is suggested by the literature. Generally, the RBF-PSO method shows a good reliability in the prediction of the final height. Indeed, RMSE is always lower than 4, i.e., in average the forecast will differ at most of 4 cm to the real value. In conclusion, the large and accurate database of Italian GHD patients allowed us to assess the rhGH treatment efficacy and compare the results with those obtained in other Countries. Moreover, we proposed and validated a new mathematical model forecasting the expected final height after therapy which was validated on our cohort.

Relationship between compliance and persistence with osteoporosis medications and fracture risk in primary health care in France: a retrospective case-control analysis.

PubMed

Cotté, François-Emery; Mercier, Florence; De Pouvourville, Gérard

2008-12-01

Nonadherence to treatment is an important determinant of long-term outcomes in women with osteoporosis. This study was conducted to investigate the association between adherence and osteoporotic fracture risk and to identify optimal thresholds for good compliance and persistence. A secondary objective was to perform a preliminary evaluation of the cost consequences of adherence. This was a retrospective case-control analysis. Data were derived from the Thales prescription database, which contains information on >1.6 million patients in the primary health care setting in France. Cases were women aged >or=50 years who had an osteoporosis-related fracture in 2006. For each case, 5 matched controls were randomly selected. Both compliance and persistence aspects of treatment adherence were examined. Compliance was estimated based on the medication possession ratio (MPR). Persistence was calculated as the time from the initial filling of a prescription for osteoporosis medication until its discontinuation. The mean (SD) MPR was lower in cases compared with controls (58.8% [34.7%] vs 72.1% [28.8%], respectively; P < 0.001). Cases were more likely than controls to discontinue osteoporosis treatment (50.0% vs 25.3%; P < 0.001), yielding a significantly lower proportion of patients who were still persistent at 1 year (34.1% vs 40.9%; P < 0.001). MPR was the best predictor of fracture risk, with an area under the receiver-operating-characteristic curve that was higher than that for persistence (0.59 vs 0.55). The optimal MPR threshold for predicting fracture risk was >or=68.0%. Compared with less-compliant women, women who achieved this threshold had a 51% reduction in fracture risk. The difference in annual drug expenditure between women achieving this threshold and those who did not was approximately euro300. The optimal threshold for persistence with therapy was at least 6 months. Attaining this threshold was associated with a 28% reduction in fracture risk compared with less-persistent women. In this study, better treatment adherence was associated with a greater reduction in fracture risk. Compliance appeared to predict fracture risk better than did persistence.

SWAB/NVALT (Dutch Working Party on Antibiotic Policy and Dutch Association of Chest Physicians) guidelines on the management of community-acquired pneumonia in adults.

PubMed

Wiersinga, W J; Bonten, M J; Boersma, W G; Jonkers, R E; Aleva, R M; Kullberg, B J; Schouten, J A; Degener, J E; Janknegt, R; Verheij, T J; Sachs, A P E; Prins, J M

2012-03-01

The Dutch Working Party on Antibiotic Policy (SWAB) and the Dutch Association of Chest Physicians (NVALT) convened a joint committee to develop evidence-based guidelines on the diagnosis and treatment of community acquired pneumonia (CAP). The guidelines are intended for adult patients with CAP who present at the hospital and are treated as outpatients as well as for hospitalised patients up to 72 hours after admission. Areas covered include current patterns of epidemiology and antibiotic resistance of causative agents of CAP in the Netherlands, the possibility to predict the causative agent of CAP on the basis of clinical data at first presentation, risk factors associated with specific pathogens, the importance of the severity of disease upon presentation for choice of initial treatment, the role of rapid diagnostic tests in treatment decisions, the optimal initial empiric treatment and treatment when a specific pathogen has been identified, the timeframe in which the first dose of antibiotics should be given, optimal duration of antibiotic treatment and antibiotic switch from the intravenous to the oral route. Additional recommendations are made on the role of radiological investigations in the diagnostic work-up of patients with a clinical suspicion of CAP, on the potential benefit of adjunctive immunotherapy, and on the policy for patients with parapneumonic effusions.

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

Herschtal, Alan, E-mail: Alan.Herschtal@petermac.org; Faculty of Health, Arts and Design, Swinburne University of Technology, Melbourne; Te Marvelde, Luc

Objective: To develop a mathematical tool that can update a patient's planning target volume (PTV) partway through a course of radiation therapy to more precisely target the tumor for the remainder of treatment and reduce dose to surrounding healthy tissue. Methods and Materials: Daily on-board imaging was used to collect large datasets of displacements for patients undergoing external beam radiation therapy for solid tumors. Bayesian statistical modeling of these geometric uncertainties was used to optimally trade off between displacement data collected from previously treated patients and the progressively accumulating data from a patient currently partway through treatment, to optimally predictmore » future displacements for that patient. These predictions were used to update the PTV position and margin width for the remainder of treatment, such that the clinical target volume (CTV) was more precisely targeted. Results: Software simulation of dose to CTV and normal tissue for 2 real prostate displacement datasets consisting of 146 and 290 patients treated with a minimum of 30 fractions each showed that re-evaluating the PTV position and margin width after 8 treatment fractions reduced healthy tissue dose by 19% and 17%, respectively, while maintaining CTV dose. Conclusion: Incorporating patient-specific displacement patterns from early in a course of treatment allows PTV adaptation for the remainder of treatment. This substantially reduces the dose to healthy tissues and thus can reduce radiation therapy–induced toxicities, improving patient outcomes.« less

Novel Strategies on Personalized Medicine for Breast Cancer Treatment: An Update.

PubMed

Chan, Carmen W H; Law, Bernard M H; So, Winnie K W; Chow, Ka Ming; Waye, Mary M Y

2017-11-15

Breast cancer is the most common cancer type among women worldwide. With breast cancer patients and survivors being reported to experience a repertoire of symptoms that are detrimental to their quality of life, the development of breast cancer treatment strategies that are effective with minimal side effects is therefore required. Personalized medicine, the treatment process that is tailored to the individual needs of each patient, is recently gaining increasing attention for its prospect in the development of effective cancer treatment regimens. Indeed, recent studies have identified a number of genes and molecules that may be used as biomarkers for predicting drug response and severity of common cancer-associated symptoms. These would provide useful clues not only for the determination of the optimal drug choice/dosage to be used in personalized treatment, but also for the identification of gene or molecular targets for the development of novel symptom management strategies, which ultimately would lead to the development of more personalized therapies for effective cancer treatment. In this article, recent studies that would provide potential new options for personalized therapies for breast cancer patients and survivors are reviewed. We suggest novel strategies, including the optimization of drug choice/dosage and the identification of genetic changes that are associated with cancer symptom occurrence and severity, which may help in enhancing the effectiveness and acceptability of the currently available cancer therapies.

Optimal strategy analysis based on robust predictive control for inventory system with random demand

NASA Astrophysics Data System (ADS)

Saputra, Aditya; Widowati, Sutrisno

2017-12-01

In this paper, the optimal strategy for a single product single supplier inventory system with random demand is analyzed by using robust predictive control with additive random parameter. We formulate the dynamical system of this system as a linear state space with additive random parameter. To determine and analyze the optimal strategy for the given inventory system, we use robust predictive control approach which gives the optimal strategy i.e. the optimal product volume that should be purchased from the supplier for each time period so that the expected cost is minimal. A numerical simulation is performed with some generated random inventory data. We simulate in MATLAB software where the inventory level must be controlled as close as possible to a set point decided by us. From the results, robust predictive control model provides the optimal strategy i.e. the optimal product volume that should be purchased and the inventory level was followed the given set point.

Toward a science of tumor forecasting for clinical oncology

DOE PAGES

Yankeelov, Thomas E.; Quaranta, Vito; Evans, Katherine J.; ...

2015-03-15

We propose that the quantitative cancer biology community makes a concerted effort to apply lessons from weather forecasting to develop an analogous methodology for predicting and evaluating tumor growth and treatment response. Currently, the time course of tumor response is not predicted; instead, response is only assessed post hoc by physical examination or imaging methods. This fundamental practice within clinical oncology limits optimization of a treatment regimen for an individual patient, as well as to determine in real time whether the choice was in fact appropriate. This is especially frustrating at a time when a panoply of molecularly targeted therapiesmore » is available, and precision genetic or proteomic analyses of tumors are an established reality. By learning from the methods of weather and climate modeling, we submit that the forecasting power of biophysical and biomathematical modeling can be harnessed to hasten the arrival of a field of predictive oncology. Furthermore, with a successful methodology toward tumor forecasting, it should be possible to integrate large tumor-specific datasets of varied types and effectively defeat one cancer patient at a time.« less

Toward a science of tumor forecasting for clinical oncology.

PubMed

Yankeelov, Thomas E; Quaranta, Vito; Evans, Katherine J; Rericha, Erin C

2015-03-15

We propose that the quantitative cancer biology community makes a concerted effort to apply lessons from weather forecasting to develop an analogous methodology for predicting and evaluating tumor growth and treatment response. Currently, the time course of tumor response is not predicted; instead, response is only assessed post hoc by physical examination or imaging methods. This fundamental practice within clinical oncology limits optimization of a treatment regimen for an individual patient, as well as to determine in real time whether the choice was in fact appropriate. This is especially frustrating at a time when a panoply of molecularly targeted therapies is available, and precision genetic or proteomic analyses of tumors are an established reality. By learning from the methods of weather and climate modeling, we submit that the forecasting power of biophysical and biomathematical modeling can be harnessed to hasten the arrival of a field of predictive oncology. With a successful methodology toward tumor forecasting, it should be possible to integrate large tumor-specific datasets of varied types and effectively defeat one cancer patient at a time. ©2015 American Association for Cancer Research.

A neural network model to predict the wastewater inflow incorporating rainfall events.

PubMed

El-Din, Ahmed Gamal; Smith, Daniel W

2002-03-01

Under steady-state conditions, a wastewater treatment plant usually has a satisfactory performance because these conditions are similar to design conditions. However, load variations constitute a large portion of the operating life of a treatment facility and most of the observed problems in complying with permit requirements occur during these load transients. During storm events upsets to the different physical and biological processes may take place in a wastewater treatment plant, and therefore, the ability to predict the hydraulic load to a treatment facility during such events is very beneficial for the optimization of the treatment process. Most of the hydrologic and hydraulic models describing sewage collection systems are deterministic. Such models require detailed knowledge of the system and usually rely on a large number of parameters, some of which are uncertain or difficult to determine. Presented in this paper, an artificial neural network (ANN) model that is used to make short-term predictions of wastewater inflow rate that enters the Gold Bar Wastewater Treatment Plant (GBWWTP), the largest plant in the Edmonton area (Alberta, Canada). The neural model uses rainfall data, observed in the collection system discharging to the plant, as inputs. The building process of the model was conducted in a systematic way that allowed the identification of a parsimonious model that is able to learn (and not memorize) from past data and generalize very well to unseen data that was used to validate the model. The neural network model gave excellent results. The potential of using the model as part of a real-time process control system is also discussed.

Fronto-limbic effective connectivity as possible predictor of antidepressant response to SSRI administration.

PubMed

Vai, Benedetta; Bulgarelli, Chiara; Godlewska, Beata R; Cowen, Philip J; Benedetti, Francesco; Harmer, Catherine J

2016-12-01

The timely selection of the optimal treatment for depressed patients is critical to improve remission rates. The detection of pre-treatment variables able to predict differential treatment response may provide novel approaches for treatment selection. Selective serotonin reuptake inhibitors (SSRIs) modulate the fronto-limbic functional response and connectivity, an effect preceding the overt clinical antidepressant effects. Here we investigated whether the cortico-limbic connectivity associated with emotional bias measured before SSRI administration predicts the efficacy of antidepressant treatment in MDD patients. fMRI and Dynamic Causal Modeling (DCM) were combined to study if effective connectivity might differentiate healthy controls (HC) and patients affected by major depression who later responded (RMDD, n=21), or failed to respond (nRMDD, n=12), to 6 weeks of escitalopram administration. Sixteen DCMs exploring connectivity between anterior cingulate cortex (ACC), ventrolateral prefrontal cortex (VLPFC), Amygdala (Amy), and fusiform gyrus (FG) were constructed. Analyses revealed that nRMDD had reduced endogenous connectivity from Amy to VLPFC and to ACC, with an increased connectivity and modulation of the ACC to Amy connectivity when processing of fearful emotional stimuli compared to HC. RMDD and HC did not significantly differ among themselves. Pre-treatment effective connectivity in fronto-limbic circuitry could be an important factor affecting antidepressant response, and highlight the mechanisms which may be involved in recovery from depression. These results suggest that fronto-limbic connectivity might provide a neural biomarker to predict the clinical outcome to SSRIs administration in major depression. Copyright © 2016 Elsevier B.V. and ECNP. All rights reserved.

Baseline psychophysiological and cortisol reactivity as a predictor of PTSD treatment outcome in virtual reality exposure therapy.

PubMed

Norrholm, Seth Davin; Jovanovic, Tanja; Gerardi, Maryrose; Breazeale, Kathryn G; Price, Matthew; Davis, Michael; Duncan, Erica; Ressler, Kerry J; Bradley, Bekh; Rizzo, Albert; Tuerk, Peter W; Rothbaum, Barbara O

2016-07-01

Baseline cue-dependent physiological reactivity may serve as an objective measure of posttraumatic stress disorder (PTSD) symptoms. Additionally, prior animal model and psychological studies would suggest that subjects with greatest symptoms at baseline may have the greatest violation of expectancy to danger when undergoing exposure based psychotherapy; thus treatment approaches which enhanced the learning under these conditions would be optimal for those with maximal baseline cue-dependent reactivity. However methods to study this hypothesis objectively are lacking. Virtual reality (VR) methodologies have been successfully employed as an enhanced form of imaginal prolonged exposure therapy for the treatment of PTSD. Our goal was to examine the predictive nature of initial psychophysiological (e.g., startle, skin conductance, heart rate) and stress hormone responses (e.g., cortisol) during presentation of VR-based combat-related stimuli on PTSD treatment outcome. Combat veterans with PTSD underwent 6 weeks of VR exposure therapy combined with either d-cycloserine (DCS), alprazolam (ALP), or placebo (PBO). In the DCS group, startle response to VR scenes prior to initiation of treatment accounted for 76% of the variance in CAPS change scores, p < 0.001, in that higher responses predicted greater changes in symptom severity over time. Additionally, baseline cortisol reactivity was inversely associated with treatment response in the ALP group, p = 0.04. We propose that baseline cue-activated physiological measures will be sensitive to predicting patients' level of response to exposure therapy, in particular in the presence of enhancement (e.g., DCS). Published by Elsevier Ltd.

Amygdala Reactivity to Emotional Faces in the Prediction of General and Medication-Specific Responses to Antidepressant Treatment in the Randomized iSPOT-D Trial

PubMed Central

Williams, Leanne M; Korgaonkar, Mayuresh S; Song, Yun C; Paton, Rebecca; Eagles, Sarah; Goldstein-Piekarski, Andrea; Grieve, Stuart M; Harris, Anthony W F; Usherwood, Tim; Etkin, Amit

2015-01-01

Although the cost of poor treatment outcomes of depression is staggering, we do not yet have clinically useful methods for selecting the most effective antidepressant for each depressed person. Emotional brain activation is altered in major depressive disorder (MDD) and implicated in treatment response. Identifying which aspects of emotional brain activation are predictive of general and specific responses to antidepressants may help clinicians and patients when making treatment decisions. We examined whether amygdala activation probed by emotion stimuli is a general or differential predictor of response to three commonly prescribed antidepressants, using functional magnetic resonance imaging (fMRI). A test–retest design was used to assess patients with MDD in an academic setting as part of the International Study to Predict Optimized Treatment in Depression. A total of 80 MDD outpatients were scanned prior to treatment and 8 weeks after randomization to the selective serotonin reuptake inhibitors escitalopram and sertraline and the serotonin–norepinephrine reuptake inhibitor, venlafaxine-extended release (XR). A total of 34 matched controls were scanned at the same timepoints. We quantified the blood oxygen level-dependent signal of the amygdala during subliminal and supraliminal viewing of facial expressions of emotion. Response to treatment was defined by ⩾50% symptom improvement on the 17-item Hamilton Depression Rating Scale. Pre-treatment amygdala hypo-reactivity to subliminal happy and threat was a general predictor of treatment response, regardless of medication type (Cohen's d effect size 0.63 to 0.77; classification accuracy, 75%). Responders showed hypo-reactivity compared to controls at baseline, and an increase toward ‘normalization' post-treatment. Pre-treatment amygdala reactivity to subliminal sadness was a differential moderator of non-response to venlafaxine-XR (Cohen's d effect size 1.5; classification accuracy, 81%). Non-responders to venlafaxine-XR showed pre-treatment hyper-reactivity, which progressed to hypo-reactivity rather than normalization post-treatment, and hypo-reactivity post-treatment was abnormal compared to controls. Impaired amygdala activation has not previously been highlighted in the general vs differential prediction of antidepressant outcomes. Amygdala hypo-reactivity to emotions signaling reward and threat predicts the general capacity to respond to antidepressants. Amygdala hyper-reactivity to sad emotion is involved in a specific non-response to a serotonin–norepinephrine reuptake inhibitor. The findings suggest amygdala probes may help inform the personal selection of antidepressant treatments. PMID:25824424

[Prediction of the efficacy of the non-medicamental treatment with the use of the ensemble of classifiers].

PubMed

Zaĭtsev, A A; Khodashinskiĭ, I A; Plotnikov, O O

2011-01-01

The importance to have the most efficacious tools and methods for the prevention and treatment of various diseases and rehabilitation of the patients dictates the necessity of search for new means of optimal correction of individual reserves of the organism. One of the approaches to addressing this problem is simulation of prognostication of curative effects of non-medicamental therapy. It is proposed to choose the therapeutic program using an ensemble of classifiers. Two types of them are considered, one based on the solution trees, the other based on the fuzzy rule basis. The software was developed that ensures high accuracy of th e prognosis of the efficiency of the two programs of the spa and resort treatment.

Clinical decision tool for optimal delivery of liver stereotactic body radiation therapy: Photons versus protons.

PubMed

Gandhi, Saumil J; Liang, Xing; Ding, Xuanfeng; Zhu, Timothy C; Ben-Josef, Edgar; Plastaras, John P; Metz, James M; Both, Stefan; Apisarnthanarax, Smith

2015-01-01

Stereotactic body radiation therapy (SBRT) for treatment of liver tumors is often limited by liver dose constraints. Protons offer potential for more liver sparing, but clinical situations in which protons may be superior to photons are not well described. We developed and validated a treatment decision model to determine whether liver tumors of certain sizes and locations are more suited for photon versus proton SBRT. Six spherical mock tumors from 1 to 6 cm in diameter were contoured on computed tomography images of 1 patient at 4 locations: dome, caudal, left medial, and central. Photon and proton plans were generated to deliver 50 Gy in 5 fractions to each tumor and optimized to deliver equivalent target coverage and maximal liver sparing. Using these plans, we developed a hypothesis-generating model to predict the optimal modality for maximal liver sparing based on tumor size and location. We then validated this model in 10 patients with liver tumors. Protons spared significantly more liver than photons for dome or central tumors ≥3 cm (dome: 134 ± 21 cm(3), P = .03; central: 108 ± 4 cm(3), P = .01). Our model correctly predicted the optimal SBRT modality for all 10 patients. For patients with dome or central tumors ≥3 cm, protons significantly increased the volume of liver spared (176 ± 21 cm(3), P = .01) and decreased the mean liver dose (8.4 vs 12.2 Gy, P = .01) while offering no significant advantage for tumors <3 cm at any location or for caudal and left medial tumors of any size. When feasible, protons should be considered as the radiation modality of choice for dome and central tumors >3 cm to allow maximal liver sparing and potentially reduce radiation toxicity. Protons should also be considered for any tumor >5 cm if photon plans fail to achieve adequate coverage or exceed the mean liver threshold. Copyright © 2015 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.

Evaluating and Optimizing Online Advertising: Forget the Click, but There Are Good Proxies.

PubMed

Dalessandro, Brian; Hook, Rod; Perlich, Claudia; Provost, Foster

2015-06-01

Online systems promise to improve advertisement targeting via the massive and detailed data available. However, there often is too few data on exactly the outcome of interest, such as purchases, for accurate campaign evaluation and optimization (due to low conversion rates, cold start periods, lack of instrumentation of offline purchases, and long purchase cycles). This paper presents a detailed treatment of proxy modeling, which is based on the identification of a suitable alternative (proxy) target variable when data on the true objective is in short supply (or even completely nonexistent). The paper has a two-fold contribution. First, the potential of proxy modeling is demonstrated clearly, based on a massive-scale experiment across 58 real online advertising campaigns. Second, we assess the value of different specific proxies for evaluating and optimizing online display advertising, showing striking results. The results include bad news and good news. The most commonly cited and used proxy is a click on an ad. The bad news is that across a large number of campaigns, clicks are not good proxies for evaluation or for optimization: clickers do not resemble buyers. The good news is that an alternative sort of proxy performs remarkably well: observed visits to the brand's website. Specifically, predictive models built based on brand site visits-which are much more common than purchases-do a remarkably good job of predicting which browsers will make a purchase. The practical bottom line: evaluating and optimizing campaigns using clicks seems wrongheaded; however, there is an easy and attractive alternative-use a well-chosen site-visit proxy instead.

Optimization of flask culture medium and conditions for hyaluronic acid production by a Streptococcus equisimilis mutant nc2168.

PubMed

Chen, Yong-Hao; Li, Jun; Liu, Li; Liu, Hong-Zhi; Wang, Qiang

2012-10-01

A mutant designated NC2168, which was selected from wild-type Streptococcus equisimilis CVCC55116 by ultraviolet ray combined with(60)Co-γ ray treatment and does not produce streptolysin, was employed to produce hyaluronic acid (HA). In order to increase the output of HA in a flask, the culture medium and conditions for NC2168 were optimized in this study. The influence of culture medium ingredients including carbon sources, nitrogen sources and metal ions on HA production was evaluated using factional factorial design. The mathematical model, which represented the effect of each medium component and their interaction on the yield of HA, was established by the quadratic rotary combination design and response surface method. The model estimated that, a maximal yield of HA could be obtained when the concentrations of yeast extract, peptone, glucose, and MgSO4 were set at 3 g/100 mL, 2 g/100 mL, 0.5 g/100 mL and 0.15 g/100 mL, respectively. Compared with the values obtained by other runs in the experimental design, the optimized medium resulted in a remarkable increase in the output of HA and the maximum of the predicted HA production was 174.76 mg/L. The model developed was accurate and reliable for predicting the production of HA by NC2168.Cultivation conditions were optimized by an orthogonal experimental design and the optimal conditions were as follows: temperature 33°C, pH 7.8, agitation speed 200 rpm, medium volume 20 mL.

The usefulness of chief complaints to predict severity, ventilator dependence, treatment option, and short-term outcome of patients with Guillain-Barré syndrome: a retrospective study.

PubMed

Wang, Ying; Shang, Pei; Xin, Meiying; Bai, Jing; Zhou, Chunkui; Zhang, Hong-Liang

2017-11-21

It remains an urgent need for early recognition of disease severity, treatment option and outcome of Guillain-Barré syndrome (GBS). The chief complaint may be quickly obtained in clinic and is one of the candidates for early predictors. However, studies on the chief complaint are still lacking in GBS. The aim of the study is to describe the components of chief complaints of GBS patients, and to explore association between chief complaints and disease severity/treatment option/outcome of GBS, so as to aid the early prediction of the disease course and to assist the clinicians to prescribe an optimal early treatment. A total of 523 GBS patients admitted to the First Hospital of Jilin University from 2003 to 2013 were enrolled for retrospective analysis. The data of chief complaints, clinical manifestations, and treatment options, etc. were collected. The clinical severity was evaluated by the Medical Research Council sum score and the Hughes Functional Grading Scale. The prognosis at 6 month after discharge was described by modified Erasmus GBS outcome score. The clinic GBS severity evaluation scale (CGSES), a newly established model in our study, was used to explore the role of chief complaints to predict intravenous immunoglobulin (IVIg). The major components of the chief complaints of GBS patients were weakness, numbness, pain, cranial nerve involvement, dyspnea, ataxia and autonomic dysfunction. Chief complaint of weakness was a predictor of severe disease course and poor short-term outcome, while chief complaint of numbness and cranial nerve involvement were promising predictors. Cranial nerve involvement was the predictor of ventilator dependence. The percentages of 366 GBS patients, who need IVIg treatment at nadir with CGSES ranging from 1 to 4, were 50.00, 67.34, 80.61, and 90.67%, respectively. Chief complaints are clinic predictors of disease severity, ventilator dependence and short-term outcome. IVIg treatment during hospitalisation could be predicted in clinic using CGSES score.

Response surface methodology for optimization of medium for decolorization of textile dye Direct Black 22 by a novel bacterial consortium.

PubMed

Mohana, Sarayu; Shrivastava, Shalini; Divecha, Jyoti; Madamwar, Datta

2008-02-01

Decolorization and degradation of polyazo dye Direct Black 22 was carried out by distillery spent wash degrading mixed bacterial consortium, DMC. Response surface methodology (RSM) involving a central composite design (CCD) in four factors was successfully employed for the study and optimization of decolorization process. The hyper activities and interactions between glucose concentration, yeast extract concentration, dye concentration and inoculum size on dye decolorization were investigated and modeled. Under optimized conditions the bacterial consortium was able to decolorize the dye almost completely (>91%) within 12h. Bacterial consortium was able to decolorize 10 different azo dyes. The optimum combination of the four variables predicted through RSM was confirmed through confirmatory experiments and hence this bacterial consortium holds potential for the treatment of industrial waste water. Dye degradation products obtained during the course of decolorization were analyzed by HPTLC.

Adaptive adjustment of interval predictive control based on combined model and application in shell brand petroleum distillation tower

NASA Astrophysics Data System (ADS)

Sun, Chao; Zhang, Chunran; Gu, Xinfeng; Liu, Bin

2017-10-01

Constraints of the optimization objective are often unable to be met when predictive control is applied to industrial production process. Then, online predictive controller will not find a feasible solution or a global optimal solution. To solve this problem, based on Back Propagation-Auto Regressive with exogenous inputs (BP-ARX) combined control model, nonlinear programming method is used to discuss the feasibility of constrained predictive control, feasibility decision theorem of the optimization objective is proposed, and the solution method of soft constraint slack variables is given when the optimization objective is not feasible. Based on this, for the interval control requirements of the controlled variables, the slack variables that have been solved are introduced, the adaptive weighted interval predictive control algorithm is proposed, achieving adaptive regulation of the optimization objective and automatically adjust of the infeasible interval range, expanding the scope of the feasible region, and ensuring the feasibility of the interval optimization objective. Finally, feasibility and effectiveness of the algorithm is validated through the simulation comparative experiments.

Do we have biomarkers to predict response to neoadjuvant and adjuvant chemotherapy and immunotherapy in bladder cancer?

PubMed Central

Wezel, Felix; Vallo, Stefan

2017-01-01

Radical cystectomy (RC) is the standard of care treatment of localized muscle-invasive bladder cancer (BC). However, about 50% of patients develop metastases within 2 years after cystectomy. Neoadjuvant cisplatin-based chemotherapy before cystectomy improves the overall survival (OS) in patients with muscle-invasive BC. Pathological response to neoadjuvant treatment is a strong predictor of better disease-specific survival. Nevertheless, some patients do not benefit from chemotherapy. The identification of reliable biomarkers enabling clinicians to identify patients who might benefit from chemotherapy is a very important clinical task. An identification tool could lead to individualized therapy, optimizing response rates. In addition, unnecessary treatment with chemotherapy which potentially leads to a loss of quality of life and which might also might cause a delay of cystectomy in a neoadjuvant setting could be avoided. The present review aims to summarize and discuss the current literature on biomarkers for the prediction of response to systemic therapy in muscle-invasive BC. Tremendous efforts in genetic and molecular characterization have led to the identification of predictive candidate biomarkers in urothelial carcinoma (UC), although prospective validation is pending. Ongoing clinical trials examining the benefit of individual therapies in UC of the bladder (UCB) by molecular patient selection hold promise to shed light on this question. PMID:29354494

C-learning: A new classification framework to estimate optimal dynamic treatment regimes.

PubMed

Zhang, Baqun; Zhang, Min

2017-12-11

A dynamic treatment regime is a sequence of decision rules, each corresponding to a decision point, that determine that next treatment based on each individual's own available characteristics and treatment history up to that point. We show that identifying the optimal dynamic treatment regime can be recast as a sequential optimization problem and propose a direct sequential optimization method to estimate the optimal treatment regimes. In particular, at each decision point, the optimization is equivalent to sequentially minimizing a weighted expected misclassification error. Based on this classification perspective, we propose a powerful and flexible C-learning algorithm to learn the optimal dynamic treatment regimes backward sequentially from the last stage until the first stage. C-learning is a direct optimization method that directly targets optimizing decision rules by exploiting powerful optimization/classification techniques and it allows incorporation of patient's characteristics and treatment history to improve performance, hence enjoying advantages of both the traditional outcome regression-based methods (Q- and A-learning) and the more recent direct optimization methods. The superior performance and flexibility of the proposed methods are illustrated through extensive simulation studies. © 2017, The International Biometric Society.

Learning to wait: A laboratory investigation

USGS Publications Warehouse

Oprea, R.; Friedman, D.; Anderson, S.T.

2009-01-01

Human subjects decide when to sink a fixed cost C to seize an irreversible investment opportunity whose value V is governed by Brownian motion. The optimal policy is to invest when V first crosses a threshold V* = (1 + w*) C, where the wait option premium w* depends on drift, volatility, and expiration hazard parameters. Subjects in the Low w* treatment on average invest at values quite close to optimum. Subjects in the two Medium and the High w* treatments invested at values below optimum, but with the predicted ordering, and values approached the optimum by the last block of 20 periods. ?? 2009 The Review of Economic Studies Limited.

Optimal Predictions in Everyday Cognition: The Wisdom of Individuals or Crowds?

ERIC Educational Resources Information Center

Mozer, Michael C.; Pashler, Harold; Homaei, Hadjar

2008-01-01

Griffiths and Tenenbaum (2006) asked individuals to make predictions about the duration or extent of everyday events (e.g., cake baking times), and reported that predictions were optimal, employing Bayesian inference based on veridical prior distributions. Although the predictions conformed strikingly to statistics of the world, they reflect…

Optimization and modeling of reduction of wastewater sludge water content and turbidity removal using magnetic iron oxide nanoparticles (MION).

PubMed

Hwang, Jeong-Ha; Han, Dong-Woo

2015-01-01

Economic and rapid reduction of sludge water content in sewage wastewater is difficult and requires special advanced treatment technologies. This study focused on optimizing and modeling decreased sludge water content (Y1) and removing turbidity (Y2) with magnetic iron oxide nanoparticles (Fe3O4, MION) using a central composite design (CCD) and response surface methodology (RSM). CCD and RSM were applied to evaluate and optimize the interactive effects of mixing time (X1) and MION concentration (X2) on chemical flocculent performance. The results show that the optimum conditions were 14.1 min and 22.1 mg L(-1) for response Y1 and 16.8 min and 8.85 mg L(-1) for response Y2, respectively. The two responses were obtained experimentally under this optimal scheme and fit the model predictions well (R(2) = 97.2% for Y1 and R(2) = 96.9% for Y2). A 90.8% decrease in sludge water content and turbidity removal of 29.4% were demonstrated. These results confirm that the statistical models were reliable, and that the magnetic flocculation conditions for decreasing sludge water content and removing turbidity from sewage wastewater were appropriate. The results reveal that MION are efficient for rapid separation and are a suitable alterative to sediment sludge during the wastewater treatment process.

Gene expression markers in circulating tumor cells may predict bone metastasis and response to hormonal treatment in breast cancer

PubMed Central

WANG, HAIYING; MOLINA, JULIAN; JIANG, JOHN; FERBER, MATTHEW; PRUTHI, SANDHYA; JATKOE, TIMOTHY; DERECHO, CARLO; RAJPUROHIT, YASHODA; ZHENG, JIAN; WANG, YIXIN

2013-01-01

Circulating tumor cells (CTCs) have recently attracted attention due to their potential as prognostic and predictive markers for the clinical management of metastatic breast cancer patients. The isolation of CTCs from patients may enable the molecular characterization of these cells, which may help establish a minimally invasive assay for the prediction of metastasis and further optimization of treatment. Molecular markers of proven clinical value may therefore be useful in predicting disease aggressiveness and response to treatment. In our earlier study, we identified a gene signature in breast cancer that appears to be significantly associated with bone metastasis. Among the genes that constitute this signature, trefoil factor 1 (TFF1) was identified as the most differentially expressed gene associated with bone metastasis. In this study, we investigated 25 candidate gene markers in the CTCs of metastatic breast cancer patients with different metastatic sites. The panel of the 25 markers was investigated in 80 baseline samples (first blood draw of CTCs) and 30 follow-up samples. In addition, 40 healthy blood donors (HBDs) were analyzed as controls. The assay was performed using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) with RNA extracted from CTCs captured by the CellSearch system. Our study indicated that 12 of the genes were uniquely expressed in CTCs and 10 were highly expressed in the CTCs obtained from patients compared to those obtained from HBDs. Among these genes, the expression of keratin 19 was highly correlated with the CTC count. The TFF1 expression in CTCs was a strong predictor of bone metastasis and the patients with a high expression of estrogen receptor β in CTCs exhibited a better response to hormonal treatment. Molecular characterization of these genes in CTCs may provide a better understanding of the mechanism underlying tumor metastasis and identify gene markers in CTCs for predicting disease progression and response to treatment. PMID:24649289

Gene expression markers in circulating tumor cells may predict bone metastasis and response to hormonal treatment in breast cancer.

PubMed

Wang, Haiying; Molina, Julian; Jiang, John; Ferber, Matthew; Pruthi, Sandhya; Jatkoe, Timothy; Derecho, Carlo; Rajpurohit, Yashoda; Zheng, Jian; Wang, Yixin

2013-11-01

Circulating tumor cells (CTCs) have recently attracted attention due to their potential as prognostic and predictive markers for the clinical management of metastatic breast cancer patients. The isolation of CTCs from patients may enable the molecular characterization of these cells, which may help establish a minimally invasive assay for the prediction of metastasis and further optimization of treatment. Molecular markers of proven clinical value may therefore be useful in predicting disease aggressiveness and response to treatment. In our earlier study, we identified a gene signature in breast cancer that appears to be significantly associated with bone metastasis. Among the genes that constitute this signature, trefoil factor 1 (TFF1) was identified as the most differentially expressed gene associated with bone metastasis. In this study, we investigated 25 candidate gene markers in the CTCs of metastatic breast cancer patients with different metastatic sites. The panel of the 25 markers was investigated in 80 baseline samples (first blood draw of CTCs) and 30 follow-up samples. In addition, 40 healthy blood donors (HBDs) were analyzed as controls. The assay was performed using quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) with RNA extracted from CTCs captured by the CellSearch system. Our study indicated that 12 of the genes were uniquely expressed in CTCs and 10 were highly expressed in the CTCs obtained from patients compared to those obtained from HBDs. Among these genes, the expression of keratin 19 was highly correlated with the CTC count. The TFF1 expression in CTCs was a strong predictor of bone metastasis and the patients with a high expression of estrogen receptor β in CTCs exhibited a better response to hormonal treatment. Molecular characterization of these genes in CTCs may provide a better understanding of the mechanism underlying tumor metastasis and identify gene markers in CTCs for predicting disease progression and response to treatment.

Predicting successful long-term weight loss from short-term weight-loss outcomes: new insights from a dynamic energy balance model (the POUNDS Lost study)123

PubMed Central

Ivanescu, Andrada E; Martin, Corby K; Heymsfield, Steven B; Marshall, Kaitlyn; Bodrato, Victoria E; Williamson, Donald A; Anton, Stephen D; Sacks, Frank M; Ryan, Donna; Bray, George A

2015-01-01

Background: Currently, early weight-loss predictions of long-term weight-loss success rely on fixed percent-weight-loss thresholds. Objective: The objective was to develop thresholds during the first 3 mo of intervention that include the influence of age, sex, baseline weight, percent weight loss, and deviations from expected weight to predict whether a participant is likely to lose 5% or more body weight by year 1. Design: Data consisting of month 1, 2, 3, and 12 treatment weights were obtained from the 2-y Preventing Obesity Using Novel Dietary Strategies (POUNDS Lost) intervention. Logistic regression models that included covariates of age, height, sex, baseline weight, target energy intake, percent weight loss, and deviation of actual weight from expected were developed for months 1, 2, and 3 that predicted the probability of losing <5% of body weight in 1 y. Receiver operating characteristic (ROC) curves, area under the curve (AUC), and thresholds were calculated for each model. The AUC statistic quantified the ROC curve’s capacity to classify participants likely to lose <5% of their body weight at the end of 1 y. The models yielding the highest AUC were retained as optimal. For comparison with current practice, ROC curves relying solely on percent weight loss were also calculated. Results: Optimal models for months 1, 2, and 3 yielded ROC curves with AUCs of 0.68 (95% CI: 0.63, 0.74), 0.75 (95% CI: 0.71, 0.81), and 0.79 (95% CI: 0.74, 0.84), respectively. Percent weight loss alone was not better at identifying true positives than random chance (AUC ≤0.50). Conclusions: The newly derived models provide a personalized prediction of long-term success from early weight-loss variables. The predictions improve on existing fixed percent-weight-loss thresholds. Future research is needed to explore model application for informing treatment approaches during early intervention. The POUNDS Lost study was registered at clinicaltrials.gov as NCT00072995. PMID:25733628

Optimizing deep hyperthermia treatments: are locations of patient pain complaints correlated with modelled SAR peak locations?

NASA Astrophysics Data System (ADS)

Canters, R. A. M.; Franckena, M.; van der Zee, J.; van Rhoon, G. C.

2011-01-01

During deep hyperthermia treatment, patient pain complaints due to heating are common when maximizing power. Hence, there exists a good rationale to investigate whether the locations of predicted SAR peaks by hyperthermia treatment planning (HTP) are correlated with the locations of patient pain during treatment. A retrospective analysis was performed, using the treatment reports of 35 patients treated with deep hyperthermia controlled by extensive treatment planning. For various SAR indicators, the average distance from a SAR peak to a patient discomfort location was calculated, for each complaint. The investigated V0.1 closest (i.e. the part of the 0.1th SAR percentile closest to the patient complaint) performed the best, and leads to an average distance between the SAR peak and the complaint location of 3.9 cm. Other SAR indicators produced average distances that were all above 10 cm. Further, the predicted SAR peak location with V0.1 provides a 77% match with the region of complaint. The current study demonstrates that HTP is able to provide a global indication of the regions where hotspots during treatment will most likely occur. Further development of this technology is necessary in order to use HTP as a valuable toll for objective and advanced SAR steering. The latter is especially valid for applications that enable 3D SAR steering.

Utility of event-related potentials in predicting antidepressant treatment response: An iSPOT-D report.

PubMed

van Dinteren, Rik; Arns, Martijn; Kenemans, Leon; Jongsma, Marijtje L A; Kessels, Roy P C; Fitzgerald, Paul; Fallahpour, Kamran; Debattista, Charles; Gordon, Evian; Williams, Leanne M

2015-11-01

It is essential to improve antidepressant treatment of major depressive disorder (MDD) and one way this could be achieved is by reducing the number of treatment steps by employing biomarkers that can predict treatment outcome. This study investigated differences between MDD patients and healthy controls in the P3 and N1 component from the event-related potential (ERP) generated in a standard two-tone oddball paradigm. Furthermore, the P3 and N1 are investigated as predictors for treatment outcome to three different antidepressants. In the international Study to Predict Optimized Treatment in Depression (iSPOT-D)--a multi-center, international, randomized, prospective practical trial--1008 MDD participants were randomized to escitalopram, sertraline or venlafaxine-XR. The study also recruited 336 healthy controls. Treatment response and remission were established after eight weeks using the 17-item Hamilton Rating Scale for Depression. P3 and N1 latencies and amplitudes were analyzed using a peak-picking approach and further replicated by using exact low resolution tomography (eLORETA). A reduced P3 was found in MDD patients compared to controls by a peak-picking analysis. This was validated in a temporal global field power analysis. Source density analysis revealed that the difference in cortical activity originated from the posterior cingulate and parahippocampal gyrus. Male non-responders to venlafaxine-XR had significantly smaller N1 amplitudes than responders. This was demonstrated by both analytical methods. Male non-responders to venlafaxine-XR had less activity originating from the left insular cortex. The observed results are discussed from a neural network viewpoint. Copyright © 2015 Elsevier B.V. and ECNP. All rights reserved.

TU-G-210-02: TRANS-FUSIMO - An Integrative Approach to Model-Based Treatment Planning of Liver FUS

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

Preusser, T.

Modeling can play a vital role in predicting, optimizing and analyzing the results of therapeutic ultrasound treatments. Simulating the propagating acoustic beam in various targeted regions of the body allows for the prediction of the resulting power deposition and temperature profiles. In this session we will apply various modeling approaches to breast, abdominal organ and brain treatments. Of particular interest is the effectiveness of procedures for correcting for phase aberrations caused by intervening irregular tissues, such as the skull in transcranial applications or inhomogeneous breast tissues. Also described are methods to compensate for motion in targeted abdominal organs such asmore » the liver or kidney. Douglas Christensen – Modeling for Breast and Brain HIFU Treatment Planning Tobias Preusser – TRANS-FUSIMO – An Integrative Approach to Model-Based Treatment Planning of Liver FUS Tobias Preusser – TRANS-FUSIMO – An Integrative Approach to Model-Based Treatment Planning of Liver FUS Learning Objectives: Understand the role of acoustic beam modeling for predicting the effectiveness of therapeutic ultrasound treatments. Apply acoustic modeling to specific breast, liver, kidney and transcranial anatomies. Determine how to obtain appropriate acoustic modeling parameters from clinical images. Understand the separate role of absorption and scattering in energy delivery to tissues. See how organ motion can be compensated for in ultrasound therapies. Compare simulated data with clinical temperature measurements in transcranial applications. Supported by NIH R01 HL172787 and R01 EB013433 (DC); EU Seventh Framework Programme (FP7/2007-2013) under 270186 (FUSIMO) and 611889 (TRANS-FUSIMO)(TP); and P01 CA159992, GE, FUSF and InSightec (UV)« less

Dissolved oxygen content prediction in crab culture using a hybrid intelligent method

PubMed Central

Yu, Huihui; Chen, Yingyi; Hassan, ShahbazGul; Li, Daoliang

2016-01-01

A precise predictive model is needed to obtain a clear understanding of the changing dissolved oxygen content in outdoor crab ponds, to assess how to reduce risk and to optimize water quality management. The uncertainties in the data from multiple sensors are a significant factor when building a dissolved oxygen content prediction model. To increase prediction accuracy, a new hybrid dissolved oxygen content forecasting model based on the radial basis function neural networks (RBFNN) data fusion method and a least squares support vector machine (LSSVM) with an optimal improved particle swarm optimization(IPSO) is developed. In the modelling process, the RBFNN data fusion method is used to improve information accuracy and provide more trustworthy training samples for the IPSO-LSSVM prediction model. The LSSVM is a powerful tool for achieving nonlinear dissolved oxygen content forecasting. In addition, an improved particle swarm optimization algorithm is developed to determine the optimal parameters for the LSSVM with high accuracy and generalizability. In this study, the comparison of the prediction results of different traditional models validates the effectiveness and accuracy of the proposed hybrid RBFNN-IPSO-LSSVM model for dissolved oxygen content prediction in outdoor crab ponds. PMID:27270206

Dissolved oxygen content prediction in crab culture using a hybrid intelligent method.

PubMed

Yu, Huihui; Chen, Yingyi; Hassan, ShahbazGul; Li, Daoliang

2016-06-08

A precise predictive model is needed to obtain a clear understanding of the changing dissolved oxygen content in outdoor crab ponds, to assess how to reduce risk and to optimize water quality management. The uncertainties in the data from multiple sensors are a significant factor when building a dissolved oxygen content prediction model. To increase prediction accuracy, a new hybrid dissolved oxygen content forecasting model based on the radial basis function neural networks (RBFNN) data fusion method and a least squares support vector machine (LSSVM) with an optimal improved particle swarm optimization(IPSO) is developed. In the modelling process, the RBFNN data fusion method is used to improve information accuracy and provide more trustworthy training samples for the IPSO-LSSVM prediction model. The LSSVM is a powerful tool for achieving nonlinear dissolved oxygen content forecasting. In addition, an improved particle swarm optimization algorithm is developed to determine the optimal parameters for the LSSVM with high accuracy and generalizability. In this study, the comparison of the prediction results of different traditional models validates the effectiveness and accuracy of the proposed hybrid RBFNN-IPSO-LSSVM model for dissolved oxygen content prediction in outdoor crab ponds.

Soft Tissue Surgical Procedures for Optimizing Anterior Implant Esthetics

PubMed Central

Ioannou, Andreas L.; Kotsakis, Georgios A.; McHale, Michelle G.; Lareau, Donald E.; Hinrichs, James E.; Romanos, Georgios E.

2015-01-01

Implant dentistry has been established as a predictable treatment with excellent clinical success to replace missing or nonrestorable teeth. A successful esthetic implant reconstruction is predicated on two fundamental components: the reproduction of the natural tooth characteristics on the implant crown and the establishment of soft tissue housing that will simulate a healthy periodontium. In order for an implant to optimally rehabilitate esthetics, the peri-implant soft tissues must be preserved and/or augmented by means of periodontal surgical procedures. Clinicians who practice implant dentistry should strive to achieve an esthetically successful outcome beyond just osseointegration. Knowledge of a variety of available techniques and proper treatment planning enables the clinician to meet the ever-increasing esthetic demands as requested by patients. The purpose of this paper is to enhance the implant surgeon's rationale and techniques beyond that of simply placing a functional restoration in an edentulous site to a level whereby an implant-supported restoration is placed in reconstructed soft tissue, so the site is indiscernible from a natural tooth. PMID:26124837

Extinction Dynamics and Control in Adaptive Networks

NASA Astrophysics Data System (ADS)

Schwartz, Ira; Shaw, Leah; Hindes, Jason

Disease control is of paramount importance in public health. Moreover, models of disease spread are an important component in implementing effective vaccination and treatment campaigns. However, human behavior in response to an outbreak has only recently been included in epidemic models on networks. Here we develop the mathematical machinery to describe the dynamics of extinction in finite populations that include human adaptive behavior. The formalism enables us to compute the optimal, fluctuation-induced path to extinction, and predict the average extinction time in adaptive networks as a function of the adaptation rate. We find that both observables have several unique scalings depending on the relative speed of infection and adaptivity. Finally, we discuss how the theory can be used to design optimal control programs in general networks, by coupling the effective force of noise with treatment and human behavior. Research supported by U.S. Naval Research Laboratory funding (Grant No. N0001414WX00023) and the Office of Naval Research (Grant No. N0001414WX20610).

Interpolation of longitudinal shape and image data via optimal mass transport

NASA Astrophysics Data System (ADS)

Gao, Yi; Zhu, Liang-Jia; Bouix, Sylvain; Tannenbaum, Allen

2014-03-01

Longitudinal analysis of medical imaging data has become central to the study of many disorders. Unfortunately, various constraints (study design, patient availability, technological limitations) restrict the acquisition of data to only a few time points, limiting the study of continuous disease/treatment progression. Having the ability to produce a sensible time interpolation of the data can lead to improved analysis, such as intuitive visualizations of anatomical changes, or the creation of more samples to improve statistical analysis. In this work, we model interpolation of medical image data, in particular shape data, using the theory of optimal mass transport (OMT), which can construct a continuous transition from two time points while preserving "mass" (e.g., image intensity, shape volume) during the transition. The theory even allows a short extrapolation in time and may help predict short-term treatment impact or disease progression on anatomical structure. We apply the proposed method to the hippocampus-amygdala complex in schizophrenia, the heart in atrial fibrillation, and full head MR images in traumatic brain injury.

Optimizing management of actinic keratosis and photodamaged skin: utilizing a stepwise approach.

PubMed

Lee, Andrew D; Jorizzo, Joseph L

2009-09-01

The incidence of photodamaged skin and skin lesions of all degrees of severity, from actinic keratosis (AK) to skin cancers, has dramatically increased. Actinic keratoses are pathologic, reflecting damage of essential skin cell functions and potentially progressing to invasive squamous cell carcinoma (SCC). The rate of progression is uncertain but may be as high as 10%. Because it is impossible to predict which AKs will progress to SCC, all lesions should be treated. Options include topical therapies, cryotherapy, curettage, and photodynamic therapy. Unfortunately, many individuals do not seek treatment or avoid it because of irritation, discomfort, and concern for scarring. Combining field-directed therapy and cryotherapy has been more effective than cryotherapy alone. Incorporating patient education with treatment may optimize outcomes. We propose a comprehensive 5-step approach for managing AK lesions and photodamaged skin that includes periodic clinical skin examinations; treating AK lesions with a combination of field- and lesion-directed therapy; and patient education regarding sun-protective measures and regular skin self-examinations.

Fermentation performance optimization in an ectopic fermentation system.

PubMed

Yang, Xiaotong; Geng, Bing; Zhu, Changxiong; Li, Hongna; He, Buwei; Guo, Hui

2018-07-01

Ectopic fermentation systems (EFSs) were developed for wastewater treatment. Previous studies have investigated the ability of thermophilic bacteria to improve fermentation performance in EFS. Continuing this research, we evaluated EFS performance using principle component analysis and investigated the addition of different proportions of cow dung. Viable bacteria communities were clustered and identified using BOX-AIR-based repetitive extragenic palindromic-PCR and 16S rDNA analysis. The results revealed optimal conditions for the padding were maize straw inoculated with thermophilic bacteria. Adding 20% cow dung yielded the best pH values (6.94-8.56), higher temperatures, increased wastewater absorption, improved litter quality, and greater microbial quantities. The viable bacteria groups were enriched by the addition of thermophilic consortium, and exogenous strains G21, G14, G4-1, and CR-15 were detected in fermentation process. The proportion of Bacillus species in treatment groups reached 70.37% after fermentation, demonstrating that thermophilic bacteria, especially Bacillus, have an important role in EFS, supporting previous predictions. Copyright © 2018 Elsevier Ltd. All rights reserved.

Neuronal prediction of opponent's behavior during cooperative social interchange in primates.

PubMed

Haroush, Keren; Williams, Ziv M

2015-03-12

A cornerstone of successful social interchange is the ability to anticipate each other's intentions or actions. While generating these internal predictions is essential for constructive social behavior, their single neuronal basis and causal underpinnings are unknown. Here, we discover specific neurons in the primate dorsal anterior cingulate that selectively predict an opponent's yet unknown decision to invest in their common good or defect and distinct neurons that encode the monkey's own current decision based on prior outcomes. Mixed population predictions of the other was remarkably near optimal compared to behavioral decoders. Moreover, disrupting cingulate activity selectively biased mutually beneficial interactions between the monkeys but, surprisingly, had no influence on their decisions when no net-positive outcome was possible. These findings identify a group of other-predictive neurons in the primate anterior cingulate essential for enacting cooperative interactions and may pave a way toward the targeted treatment of social behavioral disorders. Copyright © 2015 Elsevier Inc. All rights reserved.

Changes in multimodality functional imaging parameters early during chemoradiation predict treatment response in patients with locally advanced head and neck cancer.

PubMed

Wong, Kee H; Panek, Rafal; Dunlop, Alex; Mcquaid, Dualta; Riddell, Angela; Welsh, Liam C; Murray, Iain; Koh, Dow-Mu; Leach, Martin O; Bhide, Shreerang A; Nutting, Christopher M; Oyen, Wim J; Harrington, Kevin J; Newbold, Kate L

2018-05-01

To assess the optimal timing and predictive value of early intra-treatment changes in multimodality functional and molecular imaging (FMI) parameters as biomarkers for clinical remission in patients receiving chemoradiation for head and neck squamous cell carcinoma (HNSCC). Thirty-five patients with stage III-IVb (AJCC 7th edition) HNSCC prospectively underwent 18 F-FDG-PET/CT, and diffusion-weighted (DW), dynamic contrast-enhanced (DCE) and susceptibility-weighted MRI at baseline, week 1 and week 2 of chemoradiation. Patients with evidence of persistent or recurrent disease during follow-up were classed as non-responders. Changes in FMI parameters at week 1 and week 2 were compared between responders and non-responders with the Mann-Whitney U test. The significance threshold was set at a p value of <0.05. There were 27 responders and 8 non-responders. Responders showed a greater reduction in PET-derived tumor total lesion glycolysis (TLG 40% ; p = 0.007) and maximum standardized uptake value (SUV max ; p = 0.034) after week 1 than non-responders but these differences were absent by week 2. In contrast, it was not until week 2 that MRI-derived parameters were able to discriminate between the two groups: larger fractional increases in primary tumor apparent diffusion coefficient (ADC; p < 0.001), volume transfer constant (K trans ; p = 0.012) and interstitial space volume fraction (V e ; p = 0.047) were observed in responders versus non-responders. ADC was the most powerful predictor (∆ >17%, AUC 0.937). Early intra-treatment changes in FDG-PET, DW and DCE MRI-derived parameters are predictive of ultimate response to chemoradiation in HNSCC. However, the optimal timing for assessment with FDG-PET parameters (week 1) differed from MRI parameters (week 2). This highlighted the importance of scanning time points for the design of FMI risk-stratified interventional studies.

The Validity of the Montgomery-Asberg Depression Rating Scale in an Inpatient Sample with Alcohol Dependence

PubMed Central

Hobden, Breanne; Schwandt, Melanie L.; Carey, Mariko; Lee, Mary R.; Farokhnia, Mehdi; Bouhlal, Sofia; Oldmeadow, Christopher; Leggio, Lorenzo

2017-01-01

Background The Montgomery-Asberg Depression Rating Scale (MADRS) is commonly used to examine depressive symptoms in clinical settings, including facilities treating patients for alcohol addiction. No studies have examined the validity of the MADRS compared to an established clinical diagnostic tool of depression in this population. This study aimed to examine: 1) the validity of the MADRS compared to a clinical diagnosis of a depressive disorder (using the Structured Clinical Interview for DSM-IV (SCID)) in patients seeking treatment for alcohol dependence (AD); 2) whether the validity of the MADRS differs by type of SCID-based diagnosis of depression; and 3) which items contribute to the optimal predictive model of the MADRS compared to a SCID diagnosis of a depressive disorder. Methods Individuals seeking treatment for AD and admitted to an inpatient unit were administered the MADRS at day 2 of their detoxification program. Clinical diagnoses of AD and depression were made via the Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders-IV at the beginning of treatment. Results In total, 803 participants were included in the study. The MADRS demonstrated low overall accuracy relative to the clinical diagnosis of depression with an area under the curve of 0.68. The optimal threshold for balancing sensitivity and specificity identified by the Euclidean distance was >14. This cut-point demonstrated a sensitivity of 66%, a specificity of 60%, a positive predictive value of 50% and a negative predictive value of 75%. The MADRS performed slightly better for major depressive disorders compared to alcohol-induced depression. Items related to lassitude, concentration and appetite slightly decreased the accuracy of the MADRS. Conclusion The MADRS does not appear to be an appropriate substitute for a diagnostic tool among alcohol-dependent patients. The MADRS may, however, still be a useful screening tool assuming careful consideration of cut-off scores. PMID:28421616

The Validity of the Montgomery-Asberg Depression Rating Scale in an Inpatient Sample with Alcohol Dependence.

PubMed

Hobden, Breanne; Schwandt, Melanie L; Carey, Mariko; Lee, Mary R; Farokhnia, Mehdi; Bouhlal, Sofia; Oldmeadow, Christopher; Leggio, Lorenzo

2017-06-01

The Montgomery-Asberg Depression Rating Scale (MADRS) is commonly used to examine depressive symptoms in clinical settings, including facilities treating patients for alcohol addiction. No studies have examined the validity of the MADRS compared to an established clinical diagnostic tool of depression in this population. This study aimed to examine the following: (i) the validity of the MADRS compared to a clinical diagnosis of a depressive disorder (using the Structured Clinical Interview for DSM-IV-TR [SCID-IV-TR]) in patients seeking treatment for alcohol dependence (AD); (ii) whether the validity of the MADRS differs by type of SCID-IV-TR-based diagnosis of depression; and (iii) which items contribute to the optimal predictive model of the MADRS compared to a SCID-IV-TR diagnosis of a depressive disorder. Individuals seeking treatment for AD and admitted to an inpatient unit were administered the MADRS at day 2 of their detoxification program. Clinical diagnoses of AD and depression were made via the SCID-IV-TR at the beginning of treatment. In total, 803 participants were included in the study. The MADRS demonstrated low overall accuracy relative to the clinical diagnosis of depression with an area under the receiver operating characteristic curve of 0.68. The optimal threshold for balancing sensitivity and specificity identified by the Euclidean distance was >14. This cut-point demonstrated a sensitivity of 66%, a specificity of 60%, a positive predictive value of 50%, and a negative predictive value of 75%. The MADRS performed slightly better for major depressive disorders compared to alcohol-induced depression. Items related to lassitude, concentration, and appetite slightly decreased the accuracy of the MADRS. The MADRS does not appear to be an appropriate substitute for a diagnostic tool among alcohol-dependent patients. The MADRS may, however, still be a useful screening tool assuming careful consideration of cut-points. Copyright © 2017 by the Research Society on Alcoholism.

Characterization of Neutropenia in Advanced Cancer Patients Following Palbociclib Treatment Using a Population Pharmacokinetic-Pharmacodynamic Modeling and Simulation Approach.

PubMed

Sun, Wan; O'Dwyer, Peter J; Finn, Richard S; Ruiz-Garcia, Ana; Shapiro, Geoffrey I; Schwartz, Gary K; DeMichele, Angela; Wang, Diane

2017-09-01

Neutropenia is the most commonly reported hematologic toxicity following treatment with palbociclib, a cyclin-dependent kinase 4/6 inhibitor approved for metastatic breast cancer. Using data from 185 advanced cancer patients receiving palbociclib in 3 clinical trials, a pharmacokinetic-pharmacodynamic model was developed to describe the time course of absolute neutrophil count (ANC) and quantify the exposure-response relationship for neutropenia. These analyses help in understanding neutropenia associated with palbociclib and its comparison with chemotherapy-induced neutropenia. In the model, palbociclib plasma concentration was related to its antiproliferative effect on precursor cells through drug-related parameters (ie, maximum estimated drug effect and concentration corresponding to 50% of the maximum effect), and neutrophil physiology was mimicked through system-related parameters (ie, mean transit time, baseline ANC, and feedback parameter). Sex and baseline albumin level were significant covariates for baseline ANC. It was demonstrated by different model evaluation approaches (eg, prediction-corrected visual predictive check and standardized visual predictive check) that the final model adequately described longitudinal ANC with good predictive capability. The established model suggested that higher palbociclib exposure was associated with lower longitudinal neutrophil counts. The ANC nadir was reached approximately 21 days after palbociclib treatment initiation. Consistent with their mechanisms of action, neutropenia associated with palbociclib (cytostatic) was rapidly reversible and noncumulative, with a notably weaker antiproliferative effect on precursor cells relative to chemotherapies (cytotoxic). This pharmacokinetic-pharmacodynamic model aids in predicting neutropenia and optimizing dosing for future palbociclib trials with different dosing regimen combinations. © 2017, The American College of Clinical Pharmacology.

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM

PubMed Central

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei

2018-01-01

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model’s performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models’ performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors. PMID:29342942

The PD-1 pathway as a therapeutic target to overcome immune escape mechanisms in cancer.

PubMed

Henick, Brian S; Herbst, Roy S; Goldberg, Sarah B

2014-12-01

Immunotherapy is emerging as a powerful approach in cancer treatment. Preclinical data predicted the antineoplastic effects seen in clinical trials of programmed death-1 (PD-1) pathway inhibitors, as well as their observed toxicities. The results of early clinical trials are extraordinarily promising in several cancer types and have shaped the direction of ongoing and future studies. This review describes the biological rationale for targeting the PD-1 pathway with monoclonal antibodies for the treatment of cancer as a context for examining the results of early clinical trials. It also surveys the landscape of ongoing clinical trials and discusses their anticipated strengths and limitations. PD-1 pathway inhibition represents a new frontier in cancer immunotherapy, which shows clear evidence of activity in various tumor types including NSCLC and melanoma. Ongoing and upcoming trials will examine optimal combinations of these agents, which should further define their role across tumor types. Current limitations include the absence of a reliable companion diagnostic to predict likely responders, as well as lack of data in early-stage cancer when treatment has the potential to increase cure rates.

Determination of patellofemoral pain sub-groups and development of a method for predicting treatment outcome using running gait kinematics.

PubMed

Watari, Ricky; Kobsar, Dylan; Phinyomark, Angkoon; Osis, Sean; Ferber, Reed

2016-10-01

Not all patients with patellofemoral pain exhibit successful outcomes following exercise therapy. Thus, the ability to identify patellofemoral pain subgroups related to treatment response is important for the development of optimal therapeutic strategies to improve rehabilitation outcomes. The purpose of this study was to use baseline running gait kinematic and clinical outcome variables to classify patellofemoral pain patients on treatment response retrospectively. Forty-one individuals with patellofemoral pain that underwent a 6-week exercise intervention program were sub-grouped as treatment Responders (n=28) and Non-responders (n=13) based on self-reported measures of pain and function. Baseline three-dimensional running kinematics, and self-reported measures underwent a linear discriminant analysis of the principal components of the variables to retrospectively classify participants based on treatment response. The significance of the discriminant function was verified with a Wilk's lambda test (α=0.05). The model selected 2 gait principal components and had a 78.1% classification accuracy. Overall, Non-responders exhibited greater ankle dorsiflexion, knee abduction and hip flexion during the swing phase and greater ankle inversion during the stance phase, compared to Responders. This is the first study to investigate an objective method to use baseline kinematic and self-report outcome variables to classify on patellofemoral pain treatment outcome. This study represents a significant first step towards a method to help clinicians make evidence-informed decisions regarding optimal treatment strategies for patients with patellofemoral pain. Copyright © 2016 Elsevier Ltd. All rights reserved.

Multiple model predictive control for optimal drug administration of mixed immunotherapy and chemotherapy of tumours.

PubMed

Sharifi, N; Ozgoli, S; Ramezani, A

2017-06-01

Mixed immunotherapy and chemotherapy of tumours is one of the most efficient ways to improve cancer treatment strategies. However, it is important to 'design' an effective treatment programme which can optimize the ways of combining immunotherapy and chemotherapy to diminish their imminent side effects. Control engineering techniques could be used for this. The method of multiple model predictive controller (MMPC) is applied to the modified Stepanova model to induce the best combination of drugs scheduling under a better health criteria profile. The proposed MMPC is a feedback scheme that can perform global optimization for both tumour volume and immune competent cell density by performing multiple constraints. Although current studies usually assume that immunotherapy has no side effect, this paper presents a new method of mixed drug administration by employing MMPC, which implements several constraints for chemotherapy and immunotherapy by considering both drug toxicity and autoimmune. With designed controller we need maximum 57% and 28% of full dosage of drugs for chemotherapy and immunotherapy in some instances, respectively. Therefore, through the proposed controller less dosage of drugs are needed, which contribute to suitable results with a perceptible reduction in medicine side effects. It is observed that in the presence of MMPC, the amount of required drugs is minimized, while the tumour volume is reduced. The efficiency of the presented method has been illustrated through simulations, as the system from an initial condition in the malignant region of the state space (macroscopic tumour volume) transfers into the benign region (microscopic tumour volume) in which the immune system can control tumour growth. Copyright © 2017 Elsevier B.V. All rights reserved.

A predictive control framework for optimal energy extraction of wind farms

NASA Astrophysics Data System (ADS)

Vali, M.; van Wingerden, J. W.; Boersma, S.; Petrović, V.; Kühn, M.

2016-09-01

This paper proposes an adjoint-based model predictive control for optimal energy extraction of wind farms. It employs the axial induction factor of wind turbines to influence their aerodynamic interactions through the wake. The performance index is defined here as the total power production of the wind farm over a finite prediction horizon. A medium-fidelity wind farm model is utilized to predict the inflow propagation in advance. The adjoint method is employed to solve the formulated optimization problem in a cost effective way and the first part of the optimal solution is implemented over the control horizon. This procedure is repeated at the next controller sample time providing the feedback into the optimization. The effectiveness and some key features of the proposed approach are studied for a two turbine test case through simulations.

The Bi-Directional Prediction of Carbon Fiber Production Using a Combination of Improved Particle Swarm Optimization and Support Vector Machine.

PubMed

Xiao, Chuncai; Hao, Kuangrong; Ding, Yongsheng

2014-12-30

This paper creates a bi-directional prediction model to predict the performance of carbon fiber and the productive parameters based on a support vector machine (SVM) and improved particle swarm optimization (IPSO) algorithm (SVM-IPSO). In the SVM, it is crucial to select the parameters that have an important impact on the performance of prediction. The IPSO is proposed to optimize them, and then the SVM-IPSO model is applied to the bi-directional prediction of carbon fiber production. The predictive accuracy of SVM is mainly dependent on its parameters, and IPSO is thus exploited to seek the optimal parameters for SVM in order to improve its prediction capability. Inspired by a cell communication mechanism, we propose IPSO by incorporating information of the global best solution into the search strategy to improve exploitation, and we employ IPSO to establish the bi-directional prediction model: in the direction of the forward prediction, we consider productive parameters as input and property indexes as output; in the direction of the backward prediction, we consider property indexes as input and productive parameters as output, and in this case, the model becomes a scheme design for novel style carbon fibers. The results from a set of the experimental data show that the proposed model can outperform the radial basis function neural network (RNN), the basic particle swarm optimization (PSO) method and the hybrid approach of genetic algorithm and improved particle swarm optimization (GA-IPSO) method in most of the experiments. In other words, simulation results demonstrate the effectiveness and advantages of the SVM-IPSO model in dealing with the problem of forecasting.

Decision-support models for empiric antibiotic selection in Gram-negative bloodstream infections.

PubMed

MacFadden, D R; Coburn, B; Shah, N; Robicsek, A; Savage, R; Elligsen, M; Daneman, N

2018-04-25

Early empiric antibiotic therapy in patients can improve clinical outcomes in Gram-negative bacteraemia. However, the widespread prevalence of antibiotic-resistant pathogens compromises our ability to provide adequate therapy while minimizing use of broad antibiotics. We sought to determine whether readily available electronic medical record data could be used to develop predictive models for decision support in Gram-negative bacteraemia. We performed a multi-centre cohort study, in Canada and the USA, of hospitalized patients with Gram-negative bloodstream infection from April 2010 to March 2015. We analysed multivariable models for prediction of antibiotic susceptibility at two empiric windows: Gram-stain-guided and pathogen-guided treatment. Decision-support models for empiric antibiotic selection were developed based on three clinical decision thresholds of acceptable adequate coverage (80%, 90% and 95%). A total of 1832 patients with Gram-negative bacteraemia were evaluated. Multivariable models showed good discrimination across countries and at both Gram-stain-guided (12 models, areas under the curve (AUCs) 0.68-0.89, optimism-corrected AUCs 0.63-0.85) and pathogen-guided (12 models, AUCs 0.75-0.98, optimism-corrected AUCs 0.64-0.95) windows. Compared to antibiogram-guided therapy, decision-support models of antibiotic selection incorporating individual patient characteristics and prior culture results have the potential to increase use of narrower-spectrum antibiotics (in up to 78% of patients) while reducing inadequate therapy. Multivariable models using readily available epidemiologic factors can be used to predict antimicrobial susceptibility in infecting pathogens with reasonable discriminatory ability. Implementation of sequential predictive models for real-time individualized empiric antibiotic decision-making has the potential to both optimize adequate coverage for patients while minimizing overuse of broad-spectrum antibiotics, and therefore requires further prospective evaluation. Readily available epidemiologic risk factors can be used to predict susceptibility of Gram-negative organisms among patients with bacteraemia, using automated decision-making models. Copyright © 2018 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

Does impulsivity predict outcome in treatment for binge eating disorder? A multimodal investigation.

PubMed

Manasse, Stephanie M; Espel, Hallie M; Schumacher, Leah M; Kerrigan, Stephanie G; Zhang, Fengqing; Forman, Evan M; Juarascio, Adrienne S

2016-10-01

Multiple dimensions of impulsivity (e.g., affect-driven impulsivity, impulsive inhibition - both general and food-specific, and impulsive decision-making) are associated with binge eating pathology cross-sectionally, yet the literature on whether impulsivity predicts treatment outcome is limited. The present pilot study explored impulsivity-related predictors of 20-week outcome in a small open trial (n = 17) of a novel treatment for binge eating disorder. Overall, dimensions of impulsivity related to emotions (i.e., negative urgency) and food cues emerged as predictors of treatment outcomes (i.e., binge eating frequency and global eating pathology as measured by the Eating Disorders Examination), while more general measures of impulsivity were statistically unrelated to global eating pathology or binge frequency. Specifically, those with higher levels of negative urgency at baseline experienced slower and less pronounced benefit from treatment, and those with higher food-specific impulsivity had more severe global eating pathology at baseline that was consistent at post-treatment and follow-up. These preliminary findings suggest that patients high in negative urgency and with poor response inhibition to food cues may benefit from augmentation of existing treatments to achieve optimal outcomes. Future research will benefit from replication with a larger sample, parsing out the role of different dimensions of impulsivity in treatment outcome for eating disorders, and identifying how treatment can be improved to accommodate higher levels of baseline impulsivity. Copyright © 2016 Elsevier Ltd. All rights reserved.

An objective function exploiting suboptimal solutions in metabolic networks

PubMed Central

2013-01-01

Background Flux Balance Analysis is a theoretically elegant, computationally efficient, genome-scale approach to predicting biochemical reaction fluxes. Yet FBA models exhibit persistent mathematical degeneracy that generally limits their predictive power. Results We propose a novel objective function for cellular metabolism that accounts for and exploits degeneracy in the metabolic network to improve flux predictions. In our model, regulation drives metabolism toward a region of flux space that allows nearly optimal growth. Metabolic mutants deviate minimally from this region, a function represented mathematically as a convex cone. Near-optimal flux configurations within this region are considered equally plausible and not subject to further optimizing regulation. Consistent with relaxed regulation near optimality, we find that the size of the near-optimal region predicts flux variability under experimental perturbation. Conclusion Accounting for suboptimal solutions can improve the predictive power of metabolic FBA models. Because fluctuations of enzyme and metabolite levels are inevitable, tolerance for suboptimality may support a functionally robust metabolic network. PMID:24088221

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction.

PubMed

Gao, Xiang-Ming; Yang, Shi-Feng; Pan, San-Bo

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

PubMed Central

2017-01-01

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization. PMID:28912803

Scaled signal intensity of uterine fibroids based on T2-weighted MR images: a potential objective method to determine the suitability for magnetic resonance-guided focused ultrasound surgery of uterine fibroids.

PubMed

Park, Hyun; Yoon, Sang-Wook; Sokolov, Amit

2015-12-01

Magnetic Resonance-guided Focused Ultrasound Surgery (MRgFUS) is a non-invasive method to treat uterine fibroids. To help determine the patient suitability for MRgFUS, we propose a new objective measure: the scaled signal intensity (SSI) of uterine fibroids in T2 weighted MR images (T2WI). Forty three uterine fibroids in 40 premenopausal women were included in this retrospective study. SSI of each fibroid was measured from the screening T2WI by standardizing its mean signal intensity to a 0-100 scale, using reference intensities of rectus abdominis muscle (0) and subcutaneous fat (100). Correlation between the SSI and the non-perfused volume (NPV) ratio (a measure for treatment success) was calculated. Pre-treatment SSI showed a significant inverse-correlation with post treatment NPV ratio (p < 0.05). When dichotomizing NPV ratio at 45 %, the optimal cut off value of the SSI was found to be 16.0. A fibroid with SSI value 16.0 or less can be expected to have optimal responses. The SSI of uterine fibroids in T2WI can be suggested as an objective parameter to help in patient selection for MRgFUS. • Signal intensity of fibroid in MR images predicts treatment response to MRgFUS. • Signal intensity is standardized into scaled form using adjacent tissues as references. • Fibroids with SSI less than 16.0 are expected to have optimal responses.

[Surgical treatment of secondary peritonitis: A continuing problem. German version].

PubMed

van Ruler, O; Boermeester, M A

2016-01-01

Secondary peritonitis remains associated with high mortality and morbidity rates. Treatment of secondary peritonitis is still challenging even in the era of modern medicine. Surgical intervention for source control remains the cornerstone of treatment besides adequate antimicrobial therapy and when necessary intensive medical care measures and resuscitation. A randomized clinical trial showed that relaparotomy on demand (ROD) after initial emergency surgery was the preferred treatment strategy, irrespective of the severity and extent of peritonitis. The effective and safe use of ROD requires intensive monitoring of the patient in a setting where diagnostic tests and decision making about relaparotomy are guaranteed round the clock. The lack of knowledge on timely and adequate patient selection, together with the lack of use of easy but reliable monitoring tools seem to hamper full implementation of ROD. The accuracy of the relaparotomy decision tool is reasonable for prediction of the formation of peritonitis and necessary selection of patients for computed tomography (CT). The value of CT in the early postoperative phase is unclear. Future research and innovative technologies should focus on the additive value of CT after surgical treatment for secondary peritonitis and on the further optimization of bedside prediction tools to enhance adequate patient selection for interventions in a multidisciplinary setting.

CBT Specific Process in Exposure-Based Treatments: Initial Examination in a Pediatric OCD Sample

PubMed Central

Benito, Kristen Grabill; Conelea, Christine; Garcia, Abbe M.; Freeman, Jennifer B.

2012-01-01

Cognitive-Behavioral theory and empirical support suggest that optimal activation of fear is a critical component for successful exposure treatment. Using this theory, we developed coding methodology for measuring CBT-specific process during exposure. We piloted this methodology in a sample of young children (N = 18) who previously received CBT as part of a randomized controlled trial. Results supported the preliminary reliability and predictive validity of coding variables with 12 week and 3 month treatment outcome data, generally showing results consistent with CBT theory. However, given our limited and restricted sample, additional testing is warranted. Measurement of CBT-specific process using this methodology may have implications for understanding mechanism of change in exposure-based treatments and for improving dissemination efforts through identification of therapist behaviors associated with improved outcome. PMID:22523609

A novel method to accelerate orthodontic tooth movement

PubMed Central

Buyuk, S. Kutalmış; Yavuz, Mustafa C.; Genc, Esra; Sunar, Oguzhan

2018-01-01

This clinical case report presents fixed orthodontic treatment of a patient with moderately crowded teeth. It was performed with a new technique called ‘discision’. Discision method that was described for the first time by the present authors yielded predictable outcomes, and orthodontic treatment was completed in a short period of time. The total duration of orthodontic treatment was 4 months. Class I molar and canine relationships were established at the end of the treatment. Moreover, crowding in the mandible and maxilla was corrected, and optimal overjet and overbite were established. No scar tissue was observed in any gingival region on which discision was performed. The discision technique was developed as a minimally invasive alternative method to piezocision technique, and the authors suggest that this new method yields good outcomes in achieving rapid tooth movement. PMID:29436571

Population pharmacokinetics of a three-day chloroquine treatment in patients with Plasmodium vivax infection on the Thai-Myanmar border.

PubMed

Höglund, Richard; Moussavi, Younis; Ruengweerayut, Ronnatrai; Cheomung, Anurak; Äbelö, Angela; Na-Bangchang, Kesara

2016-02-29

A three-day course of chloroquine remains a standard treatment of Plasmodium vivax infection in Thailand with satisfactory clinical efficacy and tolerability although a continuous decline in in vitro parasite sensitivity has been reported. Information on the pharmacokinetics of chloroquine and its active metabolite desethylchloroquine are required for optimization of treatment to attain therapeutic exposure and thus prevent drug resistance development. The study was conducted at Mae Tao Clinic for migrant worker, Tak province, Thailand. Blood samples were collected from a total of 75 (8 Thais and 67 Burmeses; 36 males and 39 females; aged 17-52 years) patients with mono-infection with P. vivax malaria [median (95 % CI) admission parasitaemia 4898 (1206-29,480)/µL] following treatment with a three-day course of chloroquine (25 mg/kg body weight chloroquine phosphate over 3 days). Whole blood concentrations of chloroquine and desethylchloroquine were measured using high performance liquid chromatography with UV detection. Concentration-time profiles of both compounds were analysed using a population-based pharmacokinetic approach. All patients showed satisfactory response to standard treatment with a three-day course of chloroquine with 100 % cure rate within the follow-up period of 42 days. Neither recurrence of P. vivax parasitaemia nor appearance of P. falciparum occurred. A total of 1045 observations from 75 participants were included in the pharmacokinetic analysis. Chloroquine disposition was most adequately described by the two-compartment model with one transit compartment absorption model into the central compartment and a first-order transformation of chloroquine into desethylchloroquine with an additional peripheral compartment added to desethylchloroquine. First-order elimination from the central compartment of chloroquine and desethylchloroquine was assumed. The model exhibited a strong predictive ability and the pharmacokinetic parameters were estimated with adequate precision. The developed population-based pharmacokinetic model could be applied for future prediction of optimal dosage regimen of chloroquine in patients with P. vivax infection.

Metabolic autofluorescence imaging of head and neck cancer organoids quantifies cellular heterogeneity and treatment response (Conference Presentation)

NASA Astrophysics Data System (ADS)

Shah, Amy T.; Heaster, Tiffany M.; Skala, Melissa C.

2017-02-01

Treatment options for head and neck cancer are limited, and can cause an impaired ability to eat, talk, and breathe. Therefore, optimized and personalized therapies could reduce unnecessary toxicities from ineffective treatments. Organoids are generated from primary tumor tissue and provide a physiologically-relevant in vitro model to measure drug response. Additionally, multiphoton fluorescence lifetime imaging (FLIM) of the metabolic cofactors NAD(P)H and FAD can resolve dynamic cellular response to anti-cancer treatment. This study applies FLIM of NAD(P)H and FAD to head and neck cancer organoids. Head and neck cancer tissue was digested and grown in culture as three-dimensional organoids. Gold standard measures of therapeutic response in vivo indicate stable disease after treatment with cetuximab (antibody therapy) or cisplatin (chemotherapy), and treatment response after combination treatment. In parallel, organoids were treated with cetuximab, cisplatin, or combination therapy for 24 hours. Treated organoids exhibit decreased NAD(P)H lifetime (p<0.05) and increased FAD lifetime (p<0.05) compared with control organoids. Additionally, analysis of cellular heterogeneity identifies distinct subpopulations of cells in response to treatment. A quantitative heterogeneity index predicts in vivo treatment response and demonstrates increased cellular heterogeneity in organoids treated with cetuximab or cisplatin compared with combination treatment. Mapping of cell subpopulations enables characterization of spatial relationships between cell subpopulations. Ultimately, an organoid model combined with metabolic fluorescence imaging could provide a high-throughput platform for drug discovery. Organoids grown from patient tissue could enable individualized treatment planning. These achievements could optimize quality of life and treatment outcomes for head and neck cancer patients.

Vibration control of beams using constrained layer damping with functionally graded viscoelastic cores: theory and experiments

NASA Astrophysics Data System (ADS)

El-Sabbagh, A.; Baz, A.

2006-03-01

Conventionally, the viscoelastic cores of Constrained Layer Damping (CLD) treatments are made of materials that have uniform shear modulus. Under such conditions, it is well-recognized that these treatments are only effective near their edges where the shear strains attain their highest values. In order to enhance the damping characteristics of the CLD treatments, we propose to manufacture the cores from Functionally Graded ViscoElastic Materials (FGVEM) that have optimally selected gradient of the shear modulus over the length of the treatments. With such optimized distribution of the shear modulus, the shear strain can be enhanced, and the energy dissipation can be maximized. The theory governing the vibration of beams treated with CLD, that has functionally graded viscoelastic cores, is presented using the finite element method (FEM). The predictions of the FEM are validated experimentally for plain beams, beams treated conventional CLD, and beams with CLD/FGVEM of different configurations. The obtained results indicate a close agreement between theory and experiments. Furthermore, the obtained results demonstrate the effectiveness of the new class of CLD with functionally graded cores in enhancing the energy dissipation over the conventional CLD over a broad frequency band. Extension of the proposed one-dimensional beam/CLD/FGVEM system to more complex structures is a natural extension to the present study.

Successes and limitations of targeted therapies in renal cell carcinoma.

PubMed

Pracht, Marc; Berthold, Dominik

2014-01-01

Until recently, the standard treatment for metastatic renal cell carcinoma (RCC) was nonspecific immunotherapy based on interleukin-2 or interferon-α. This was associated with a modest survival benefit and with significant clinical toxicities. The understanding of numerous molecular pathways in RCC, including HIF, VEGF, mTOR, and the consecutive use of targeted therapies since the beginning of 2005 have significantly improved outcomes for patients with metastatic RCC with an overall survival greater than 2 years. At present, at least 7 targeted agents are approved for first and consecutive lines of treatment of clear cell metastatic RCC. Long-term benefit and extended survival may be achieved through the optimal use of targeted therapies: optimal dosing, adverse event management and treatment duration and compliance. Advances in the finding of prognostic factors highlight the potential for personalizing treatment for patients with metastatic RCC. Data regarding the best sequencing of targeted therapies, predictive biomarkers, best timing of surgery, patient risk profiles, understanding of resistance mechanisms and safety of targeted therapies are growing and will provide a further step ahead in the management of advanced RCC. In parallel, a new class of therapeutics is emerging in RCC: immunotherapy; in particular check-point blockade antibodies are showing very promising results. © 2014 S. Karger AG, Basel.

Long-term psychosocial consequences of surgical congenital malformations.

PubMed

Diseth, Trond H; Emblem, Ragnhild

2017-10-01

Surgical congenital malformations often represent years of treatment, large number of hospital stays, treatment procedures, and long-term functional sequels affecting patients' psychosocial functioning. Both functional defects and psychosocial difficulties that occur commonly in childhood may pass through adolescence on to adulthood. This overview presents reports published over the past 3 decades to elucidate the long-term psychosocial consequences of surgical congenital malformations. Literature searches conducted on PubMed database revealed that less than 1% of all the records of surgical congenital malformations described long-term psychosocial consequences, but with diverse findings. This inconsistency may be due to methodological differences or deficiencies; especially in study design, patient sampling, and methods. Most of the studies revealed that the functional deficits may have great impact on patients' mental health, psychosocial functioning, and QoL; both short- and long-term negative consequences. Factors other than functional problems, e.g., repeated anesthesia, multiple hospitalization, traumatic treatment procedures, and parental dysfunctioning, may also predict long-term mental health and psychosocial functioning. Through multidisciplinary approach, pediatric surgeons should also be aware of deficits in emotional and psychosocial functioning. To achieve overall optimal psychosocial functioning, the challenge is to find a compromise between physically optimal treatment procedures and procedures that are not psychologically detrimental. Copyright © 2017. Published by Elsevier Inc.

Guideline on anterior cruciate ligament injury

PubMed Central

2012-01-01

The Dutch Orthopaedic Association has a long tradition of development of practical clinical guidelines. Here we present the recommendations from the multidisciplinary clinical guideline working group for anterior cruciate ligament injury. The following 8 clinical questions were formulated by a steering group of the Dutch Orthopaedic Association. What is the role of physical examination and additional diagnostic tools? Which patient-related outcome measures should be used? What are the relevant parameters that influence the indication for an ACL reconstruction? Which findings or complaints are predictive of a bad result of an ACL injury treatment? What is the optimal timing for surgery for an ACL injury? What is the outcome of different conservative treatment modalities? Which kind of graft gives the best result in an ACL reconstruction? What is the optimal postoperative treatment concerning rehabilitation, resumption of sports, and physiotherapy? These 8 questions were answered and recommendations were made, using the “Appraisal of Guidelines for Research and Evaluation” instrument. This instrument seeks to improve the quality and effectiveness of clinical practical guidelines by establishing a shared framework to develop, report, and assess. The steering group has also developed 7 internal indicators to aid in measuring and enhancing the quality of the treatment of patients with an ACL injury, for use in a hospital or practice. PMID:22900914

Applications of Fluorescence Spectroscopy for dissolved organic matter characterization in wastewater treatment plants

NASA Astrophysics Data System (ADS)

Goffin, Angélique; Guérin, Sabrina; Rocher, Vincent; Varrault, Gilles

2016-04-01

Dissolved organic matter (DOM) influences wastewater treatment plants efficiency (WTTP): variations in its quality and quantity can induce a foaming phenomenon and a fouling event inside biofiltration processes. Moreover, in order to manage denitrification step (control and optimization of the nitrate recirculation), it is important to be able to estimate biodegradable organic matter quantity before biological treatment. But the current methods used to characterize organic matter quality, like biological oxygen demand are laborious, time consuming and sometimes not applicable to directly monitor organic matter in situ. In the context of MOCOPEE research program (www.mocopee.com), this study aims to assess the use of optical techniques, such as UV-Visible absorbance and more specifically fluorescence spectroscopy in order to monitor and to optimize process efficiency in WWTP. Fluorescence excitation-emission matrix (EEM) spectroscopy was employed to prospect the possibility of using this technology online and in real time to characterize dissolved organic matter in different effluents of the WWTP Seine Centre (240,000 m3/day) in Paris, France. 35 sewage water influent samples were collected on 10 days at different hours. Data treatment were performed by two methods: peak picking and parallel factor analysis (PARAFAC). An evolution of DOM quality (position of excitation - emission peaks) and quantity (intensity of fluorescence) was observed between the different treatment steps (influent, primary treatment, biological treatment, effluent). Correlations were found between fluorescence indicators and different water quality key parameters in the sewage influents. We developed different multivariate linear regression models in order to predict a variety of water quality parameters by fluorescence intensity at specific excitation-emission wavelengths. For example dissolved biological oxygen demand (r2=0,900; p<0,0001) and ammonium concentration (r2=0,898; p<0,0001) present good correlation with specific fluorescence peaks and indicators. These indicators derived from 3D spectrofluorescence could be used in order to characterize DOM online and thus to optimize process efficiency in WWTP.

Predicting Short-Term Remembering as Boundedly Optimal Strategy Choice.

PubMed

Howes, Andrew; Duggan, Geoffrey B; Kalidindi, Kiran; Tseng, Yuan-Chi; Lewis, Richard L

2016-07-01

It is known that, on average, people adapt their choice of memory strategy to the subjective utility of interaction. What is not known is whether an individual's choices are boundedly optimal. Two experiments are reported that test the hypothesis that an individual's decisions about the distribution of remembering between internal and external resources are boundedly optimal where optimality is defined relative to experience, cognitive constraints, and reward. The theory makes predictions that are tested against data, not fitted to it. The experiments use a no-choice/choice utility learning paradigm where the no-choice phase is used to elicit a profile of each participant's performance across the strategy space and the choice phase is used to test predicted choices within this space. They show that the majority of individuals select strategies that are boundedly optimal. Further, individual differences in what people choose to do are successfully predicted by the analysis. Two issues are discussed: (a) the performance of the minority of participants who did not find boundedly optimal adaptations, and (b) the possibility that individuals anticipate what, with practice, will become a bounded optimal strategy, rather than what is boundedly optimal during training. Copyright © 2015 Cognitive Science Society, Inc.

Stochastic optimization of intensity modulated radiotherapy to account for uncertainties in patient sensitivity

NASA Astrophysics Data System (ADS)

Kåver, Gereon; Lind, Bengt K.; Löf, Johan; Liander, Anders; Brahme, Anders

1999-12-01

The aim of the present work is to better account for the known uncertainties in radiobiological response parameters when optimizing radiation therapy. The radiation sensitivity of a specific patient is usually unknown beyond the expectation value and possibly the standard deviation that may be derived from studies on groups of patients. Instead of trying to find the treatment with the highest possible probability of a desirable outcome for a patient of average sensitivity, it is more desirable to maximize the expectation value of the probability for the desirable outcome over the possible range of variation of the radiation sensitivity of the patient. Such a stochastic optimization will also have to consider the distribution function of the radiation sensitivity and the larger steepness of the response for the individual patient. The results of stochastic optimization are also compared with simpler methods such as using biological response `margins' to account for the range of sensitivity variation. By using stochastic optimization, the absolute gain will typically be of the order of a few per cent and the relative improvement compared with non-stochastic optimization is generally less than about 10 per cent. The extent of this gain varies with the level of interpatient variability as well as with the difficulty and complexity of the case studied. Although the dose changes are rather small (<5 Gy) there is a strong desire to make treatment plans more robust, and tolerant of the likely range of variation of the radiation sensitivity of each individual patient. When more accurate predictive assays of the radiation sensitivity for each patient become available, the need to consider the range of variations can be reduced considerably.

Can the generic antiretroviral industry support access to a universal antiretroviral regimen?

PubMed

Amole, Carolyn D; Middlecote, Caroline; Prabhu, Vineet R; Kumarasamy, N

2017-07-01

The generic antiretroviral (ARV) industry played a critical role in the massive scale-up of HIV treatment in low-income and middle-income countries since 2000. As the global community looks ahead to a universal antiretroviral regimen, this article considers the industry's role in supporting universal access to affordable, simpler, more durable, and tolerable HIV treatment regimens. Generic manufacturers made treatment scale-up in low-income and middle-income countries possible through reducing prices, combining molecules from different originator companies to develop optimal fixed-dose combinations, and investing in production capacity to meet escalating demand. Achieving scale-up of a universal regimen will require continued partnership in these areas. Collaboration on the demand and supply sides of the ARV marketplace will be required to foster a healthy and sustainable marketplace for new regimens. This includes clear priority setting from the global treatment community on priority products; predictable demand; regulatory prioritization of optimal products; effective tendering and procurement practices that enable multiple suppliers to participate in the market; coordinated product introduction efforts between Ministries of Health, partners, and civil society; and transparency from both buyers and suppliers to promote and monitor supply security. New regimens will benefit people living with HIV, as well as buyers and generic suppliers, by maximizing existing production capacity and treatment budgets to reach the 90-90-90 goals.

Design and Optimization of an Austenitic TRIP Steel for Blast and Fragment Protection

NASA Astrophysics Data System (ADS)

Feinberg, Zechariah Daniel

In light of the pervasive nature of terrorist attacks, there is a pressing need for the design and optimization of next generation materials for blast and fragment protection applications. Sadhukhan used computational tools and a systems-based approach to design TRIP-120---a fully austenitic transformation-induced plasticity (TRIP) steel. Current work more completely evaluates the mechanical properties of the prototype, optimizes the processing for high performance in tension and shear, and builds models for more predictive power of the mechanical behavior and austenite stability. Under quasi-static and dynamic tension and shear, the design exhibits high strength and high uniform ductility as a result of a strain hardening effect that arises with martensitic transformation. Significantly more martensitic transformation occurred under quasi-static loading conditions (69% in tension and 52% in shear) compared to dynamic loading conditions (13% tension and 5% in shear). Nonetheless, significant transformation occurs at high-strain rates which increases strain hardening, delays the onset of necking instability, and increases total energy absorption under adiabatic conditions. Although TRIP-120 effectively utilizes a TRIP effect to delay necking instability, a common trend of abrupt failure with limited fracture ductility was observed in tension and shear at all strain rates. Further characterization of the structure of TRIP-120 showed that an undesired grain boundary cellular reaction (η phase formation) consumed the fine dispersion of the metastable gamma' phase and limited the fracture ductility. A warm working procedure was added to the processing of TRIP-120 in order to eliminate the grain boundary cellular reaction from the structure. By eliminating η formation at the grain boundaries, warm-worked TRIP-120 exhibits a drastic improvement in the mechanical properties in tension and shear. In quasi-static tension, the optimized warm-worked TRIP-120 with an Mssigma( u.t.) of -13°C has a yield strength of 180 ksi (1241 MPa), uniform ductility of 0.303, and fracture ductility of 0.95, which corresponds to a 48% increase in yield strength, a 43% increase in uniform ductility, and a 254% increase in fracture ductility relative to the designed processing of TRIP-120. The highest performing condition of warm-worked TRIP-120 in quasi-static shear with an Mssigma( sh) of 58°C exhibits a shear yield strength of 95.1 ksi (656 MPa), shear fracture strain of 144%, and energy dissipation density of 1099 MJ/m3, which corresponds to a shear yield strength increase of 61%, a shear fracture strain increase of 55%, and an energy dissipation density increase of 76%. A wide range of austenite stabilities can be achieved by altering the heat treatment times and temperatures, which significantly alters the mechanical properties. Although performance cannot be optimized for tension and shear simultaneously, different heat treatments can be applied to warm-worked TRIP-120 to achieve high performance in tension or shear. Parametric models calibrated with three-dimensional atom probe data played a crucial role in guiding the predictive process optimization of TRIP-120. Such models have been built to provide the predictive capability of inputting warm working and aging conditions and outputting the resulting structure, austenite stability, and mechanical properties. The predictive power of computational models has helped identify processing conditions that have improved the performance of TRIP-120 in tension and shear and can be applied to future designs that optimize for adiabatic conditions.

Collision prediction software for radiotherapy treatments

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

Padilla, Laura; Pearson, Erik A.; Pelizzari, Charles A., E-mail: c-pelizzari@uchicago.edu

2015-11-15

Purpose: This work presents a method of collision predictions for external beam radiotherapy using surface imaging. The present methodology focuses on collision prediction during treatment simulation to evaluate the clearance of a patient’s treatment position and allow for its modification if necessary. Methods: A Kinect camera (Microsoft, Redmond, WA) is used to scan the patient and immobilization devices in the treatment position at the simulator. The surface is reconstructed using the SKANECT software (Occipital, Inc., San Francisco, CA). The treatment isocenter is marked using simulated orthogonal lasers projected on the surface scan. The point cloud of this surface is thenmore » shifted to isocenter and converted from Cartesian to cylindrical coordinates. A slab models the treatment couch. A cylinder with a radius equal to the normal distance from isocenter to the collimator plate, and a height defined by the collimator diameter is used to estimate collisions. Points within the cylinder clear through a full gantry rotation with the treatment couch at 0° , while points outside of it collide. The angles of collision are reported. This methodology was experimentally verified using a mannequin positioned in an alpha cradle with both arms up. A planning CT scan of the mannequin was performed, two isocenters were marked in PINNACLE, and this information was exported to AlignRT (VisionRT, London, UK)—a surface imaging system for patient positioning. This was used to ensure accurate positioning of the mannequin in the treatment room, when available. Collision calculations were performed for the two treatment isocenters and the results compared to the collisions detected the room. The accuracy of the Kinect-Skanect surface was evaluated by comparing it to the external surface of the planning CT scan. Results: Experimental verification results showed that the predicted angles of collision matched those recorded in the room within 0.5°, in most cases (largest deviation −1.2°). The accuracy study for the Kinect-Skanect surface showed an average discrepancy between the CT external contour and the surface scan of 2.2 mm. Conclusions: This methodology provides fast and reliable collision predictions using surface imaging. The use of the Kinect-Skanect system allows for a comprehensive modeling of the patient topography including all the relevant anatomy and immobilization devices that may lead to collisions. The use of this tool at the treatment simulation stage may allow therapists to evaluate the clearance of a patient’s treatment position and optimize it before the planning CT scan is performed. This can allow for safer treatments for the patients due to better collision predictions and improved clinical workflow by minimizing replanning and resimulations due to unforeseen clearance issues.« less

Mid-Treatment Sleep Duration Predicts Clinically Significant Knee Osteoarthritis Pain reduction at 6 months: Effects From a Behavioral Sleep Medicine Clinical Trial.

PubMed

Salwen, Jessica K; Smith, Michael T; Finan, Patrick H

2017-02-01

To determine the relative influence of sleep continuity (sleep efficiency, sleep onset latency, total sleep time [TST], and wake after sleep onset) on clinical pain outcomes within a trial of cognitive behavioral therapy for insomnia (CBT-I) for patients with comorbid knee osteoarthritis and insomnia. Secondary analyses were performed on data from 74 patients with comorbid insomnia and knee osteoarthritis who completed a randomized clinical trial of 8-session multicomponent CBT-I versus an active behavioral desensitization control condition (BD), including a 6-month follow-up assessment. Data used herein include daily diaries of sleep parameters, actigraphy data, and self-report questionnaires administered at specific time points. Patients who reported at least 30% improvement in self-reported pain from baseline to 6-month follow-up were considered responders (N = 31). Pain responders and nonresponders did not differ significantly at baseline across any sleep continuity measures. At mid-treatment, only TST predicted pain response via t tests and logistic regression, whereas other measures of sleep continuity were nonsignificant. Recursive partitioning analyses identified a minimum cut-point of 382 min of TST achieved at mid-treatment in order to best predict pain improvements 6-month posttreatment. Actigraphy results followed the same pattern as daily diary-based results. Clinically significant pain reductions in response to both CBT-I and BD were optimally predicted by achieving approximately 6.5 hr sleep duration by mid-treatment. Thus, tailoring interventions to increase TST early in treatment may be an effective strategy to promote long-term pain reductions. More comprehensive research on components of behavioral sleep medicine treatments that contribute to pain response is warranted. © Sleep Research Society 2016. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.

Streamflow Prediction based on Chaos Theory

NASA Astrophysics Data System (ADS)

Li, X.; Wang, X.; Babovic, V. M.

2015-12-01

Chaos theory is a popular method in hydrologic time series prediction. Local model (LM) based on this theory utilizes time-delay embedding to reconstruct the phase-space diagram. For this method, its efficacy is dependent on the embedding parameters, i.e. embedding dimension, time lag, and nearest neighbor number. The optimal estimation of these parameters is thus critical to the application of Local model. However, these embedding parameters are conventionally estimated using Average Mutual Information (AMI) and False Nearest Neighbors (FNN) separately. This may leads to local optimization and thus has limitation to its prediction accuracy. Considering about these limitation, this paper applies a local model combined with simulated annealing (SA) to find the global optimization of embedding parameters. It is also compared with another global optimization approach of Genetic Algorithm (GA). These proposed hybrid methods are applied in daily and monthly streamflow time series for examination. The results show that global optimization can contribute to the local model to provide more accurate prediction results compared with local optimization. The LM combined with SA shows more advantages in terms of its computational efficiency. The proposed scheme here can also be applied to other fields such as prediction of hydro-climatic time series, error correction, etc.

Optimism as a predictor of the effects of laboratory-induced stress on fears and hope.

PubMed

Kimhi, Shaul; Eshel, Yohanan; Shahar, Eldad

2013-01-01

The objective of the current study is to explore optimism as a predictor of personal and collective fear, as well as hope, following laboratory-induced stress. Students (N = 107; 74 female, 33 male) were assigned randomly to either the experimental (stress--political violence video clip) or the control group (no-stress--nature video clip). Questionnaires of fear and hope were administered immediately after the experiment (Time 1) and 3 weeks later (Time 2). Structural equation modeling indicated the following: (a) Optimism significantly predicted both fear and hope in the stress group at Time 1, but not in the no-stress group. (b) Optimism predicted hope but not fear at Time 2 in the stress group. (c) Hope at Time 1 significantly predicted hope at Time 2, in both the stress and the no-stress groups. (d) Gender did not predict significantly fear at Time 1 in the stress group, despite a significant difference between genders. This study supports previous studies indicating that optimism plays an important role in people's coping with stress. However, based on our research the data raise the question of whether optimism, by itself, or environmental stress, by itself, may accurately predict stress response.

Sensitivity of NTCP parameter values against a change of dose calculation algorithm.

PubMed

Brink, Carsten; Berg, Martin; Nielsen, Morten

2007-09-01

Optimization of radiation treatment planning requires estimations of the normal tissue complication probability (NTCP). A number of models exist that estimate NTCP from a calculated dose distribution. Since different dose calculation algorithms use different approximations the dose distributions predicted for a given treatment will in general depend on the algorithm. The purpose of this work is to test whether the optimal NTCP parameter values change significantly when the dose calculation algorithm is changed. The treatment plans for 17 breast cancer patients have retrospectively been recalculated with a collapsed cone algorithm (CC) to compare the NTCP estimates for radiation pneumonitis with those obtained from the clinically used pencil beam algorithm (PB). For the PB calculations the NTCP parameters were taken from previously published values for three different models. For the CC calculations the parameters were fitted to give the same NTCP as for the PB calculations. This paper demonstrates that significant shifts of the NTCP parameter values are observed for three models, comparable in magnitude to the uncertainties of the published parameter values. Thus, it is important to quote the applied dose calculation algorithm when reporting estimates of NTCP parameters in order to ensure correct use of the models.

Sensitivity of NTCP parameter values against a change of dose calculation algorithm

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

Brink, Carsten; Berg, Martin; Nielsen, Morten

2007-09-15

Optimization of radiation treatment planning requires estimations of the normal tissue complication probability (NTCP). A number of models exist that estimate NTCP from a calculated dose distribution. Since different dose calculation algorithms use different approximations the dose distributions predicted for a given treatment will in general depend on the algorithm. The purpose of this work is to test whether the optimal NTCP parameter values change significantly when the dose calculation algorithm is changed. The treatment plans for 17 breast cancer patients have retrospectively been recalculated with a collapsed cone algorithm (CC) to compare the NTCP estimates for radiation pneumonitis withmore » those obtained from the clinically used pencil beam algorithm (PB). For the PB calculations the NTCP parameters were taken from previously published values for three different models. For the CC calculations the parameters were fitted to give the same NTCP as for the PB calculations. This paper demonstrates that significant shifts of the NTCP parameter values are observed for three models, comparable in magnitude to the uncertainties of the published parameter values. Thus, it is important to quote the applied dose calculation algorithm when reporting estimates of NTCP parameters in order to ensure correct use of the models.« less

Management of advanced prostate cancer in senior adults: the new landscape.

PubMed

Aapro, Matti S

2012-01-01

The landscape of treatment for advanced prostate cancer is continually evolving as new therapies are developed and guidelines are constantly updated. However, the management of older men with advanced disease is not optimal. Many men are denied chemotherapy based on their chronological age, not their health status. Androgen-deprivation therapy (ADT) remains the mainstay of first-line treatment of advanced disease. Once the disease becomes resistant to castration, docetaxel-based chemotherapy is the regulatory-approved standard of care, irrespective of age. The place of weekly docetaxel in patients with poor performance status and signs of frailty has to be further evaluated in clinical studies. New treatments are now available, or on the horizon, for disease that progresses during or after docetaxel therapy. Cabazitaxel and abiraterone have been shown to prolong survival, irrespective of age, and are already in clinical use having received regulatory approval. The optimal sequence for these two agents is still unknown, although there is some indication that in patients predicted to be poor responders to abiraterone (high Gleason score, progression during docetaxel therapy, rapid progression to castrate-resistant prostate cancer with ADT) cabazitaxel should be the preferred choice. Further advances are being investigated, with promising data reported from phase III trials.

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

Carlson, D.

The physical pattern of energy deposition and the enhanced relative biological effectiveness (RBE) of protons and carbon ions compared to photons offer unique and not fully understood or exploited opportunities to improve the efficacy of radiation therapy. Variations in RBE within a pristine or spread out Bragg peak and between particle types may be exploited to enhance cell killing in target regions without a corresponding increase in damage to normal tissue structures. In addition, the decreased sensitivity of hypoxic tumors to photon-based therapies may be partially overcome through the use of more densely ionizing radiations. These and other differences betweenmore » particle and photon beams may be used to generate biologically optimized treatments that reduce normal tissue complications. In this symposium, speakers will examine the impact of the RBE of charged particles on measurable biological endpoints, treatment plan optimization, and the prediction or retrospective assessment of treatment outcomes. In particular, an AAPM task group was formed to critically examine the evidence for a spatially-variant RBE in proton therapy. Current knowledge of proton RBE variation with respect to dose, biological endpoint, and physics parameters will be reviewed. Further, the clinical relevance of these variations will be discussed. Recent work focused on improving simulations of radiation physics and biological response in proton and carbon ion therapy will also be presented. Finally, relevant biology research and areas of research needs will be highlighted, including the dependence of RBE on genetic factors including status of DNA repair pathways, the sensitivity of cancer stem-like cells to charged particles, the role of charged particles in hypoxic tumors, and the importance of fractionation effects. In addition to the physical advantages of protons and more massive ions over photons, the future application of biologically optimized treatment plans and their potential to provide higher levels of local tumor control and improved normal tissue sparing will be discussed. Learning Objectives: To assess whether the current practice of a constant RBE of 1.1 should be revised or maintained in proton therapy and to evaluate the potential clinical consequences of delivering RBE-weighted dose distributions based on variable RBE To review current research on biological models used to predict the increased biological effectiveness of proton and carbon ions to help move towards a practical understanding and implementation of biological optimization in particle therapy To discuss potential differences in biological mechanisms between photons and charged particles (light and heavy ions) that could impact clinical cancer therapy H. Paganetti, NCI U19 CA21239D. Grosshans, Our research is supported by the NCIK. Held, Funding Support: National Cancer Institute of the National Institutes of Health, USA, under Award Number R21CA182259 and Federal Share of Program Income Earned by Massachusetts General Hospital on C06CA059267, Proton Therapy Research and Treatment Center.« less

Predicting asthma exacerbations in children.

PubMed

Forno, Erick; Celedón, Juan C

2012-01-01

This review critically assesses recently published literature on predicting asthma exacerbations in children, while also providing general recommendations for future research in this field. Current evidence suggests that every effort should be made to provide optimal treatment to achieve adequate asthma control, as this will significantly reduce the risk of severe disease exacerbations. Children who have had at least one asthma exacerbation in the previous year are at highest risk for subsequent exacerbations, regardless of disease severity and/or control. Although several tools and biomarkers to predict asthma exacerbations have been recently developed, these approaches need further validation and/or have only had partial success in identifying children at risk. Although considerable progress has been made, much remains to be done. Future studies should clearly differentiate severe asthma exacerbations due to inadequate asthma control from those occurring in children whose asthma is well controlled, utilize standardized definitions of asthma exacerbations, and use a systematic approach to identify the best predictors after accounting for the multiple dimensions of the problem. Our ability to correctly predict the development of severe asthma exacerbations in an individual child should improve in parallel with increased knowledge and/or understanding of the complex interactions among genetic, environmental (e.g. viral infections) and lifestyle (e.g. adherence to treatment) factors underlying these events.

New Paradigm for Translational Modeling to Predict Long‐term Tuberculosis Treatment Response

PubMed Central

Bartelink, IH; Zhang, N; Keizer, RJ; Strydom, N; Converse, PJ; Dooley, KE; Nuermberger, EL

2017-01-01

Abstract Disappointing results of recent tuberculosis chemotherapy trials suggest that knowledge gained from preclinical investigations was not utilized to maximal effect. A mouse‐to‐human translational pharmacokinetics (PKs) – pharmacodynamics (PDs) model built on a rich mouse database may improve clinical trial outcome predictions. The model included Mycobacterium tuberculosis growth function in mice, adaptive immune response effect on bacterial growth, relationships among moxifloxacin, rifapentine, and rifampin concentrations accelerating bacterial death, clinical PK data, species‐specific protein binding, drug‐drug interactions, and patient‐specific pathology. Simulations of recent trials testing 4‐month regimens predicted 65% (95% confidence interval [CI], 55–74) relapse‐free patients vs. 80% observed in the REMox‐TB trial, and 79% (95% CI, 72–87) vs. 82% observed in the Rifaquin trial. Simulation of 6‐month regimens predicted 97% (95% CI, 93–99) vs. 92% and 95% observed in 2RHZE/4RH control arms, and 100% predicted and observed in the 35 mg/kg rifampin arm of PanACEA MAMS. These results suggest that the model can inform regimen optimization and predict outcomes of ongoing trials. PMID:28561946

Impact of DOTS compared with DOTS-plus on multidrug resistant tuberculosis and tuberculosis deaths: decision analysis.

PubMed

Sterling, Timothy R; Lehmann, Harold P; Frieden, Thomas R

2003-03-15

This study sought to determine the impact of the World Health Organization's directly observed treatment strategy (DOTS) compared with that of DOTS-plus on tuberculosis deaths, mainly in the developing world. Decision analysis with Monte Carlo simulation of a Markov decision tree. People with smear positive pulmonary tuberculosis. Analyses modelled different levels of programme effectiveness of DOTS and DOTS-plus, and high (10%) and intermediate (3%) proportions of primary multidrug resistant tuberculosis, while accounting for exogenous reinfection. The cumulative number of tuberculosis deaths per 100 000 population over 10 years. The model predicted that under DOTS, 276 people would die from tuberculosis (24 multidrug resistant and 252 not multidrug resistant) over 10 years under optimal implementation in an area with 3% primary multidrug resistant tuberculosis. Optimal implementation of DOTS-plus would result in four (1.5%) fewer deaths. If implementation of DOTS-plus were to result in a decrease of just 5% in the effectiveness of DOTS, 16% more people would die with tuberculosis than under DOTS alone. In an area with 10% primary multidrug resistant tuberculosis, 10% fewer deaths would occur under optimal DOTS-plus than under optimal DOTS, but 16% more deaths would occur if implementation of DOTS-plus were to result in a 5% decrease in the effectiveness of DOTS CONCLUSIONS: Under optimal implementation, fewer tuberculosis deaths would occur under DOTS-plus than under DOTS. If, however, implementation of DOTS-plus were associated with even minimal decreases in the effectiveness of treatment, substantially more patients would die than under DOTS.

Surgical Treatment of Recurrent Endometrial Cancer: Time for a Paradigm Shift.

PubMed

Papadia, Andrea; Bellati, Filippo; Ditto, Antonino; Bogani, Giorgio; Gasparri, Maria Luisa; Di Donato, Violante; Martinelli, Fabio; Lorusso, Domenica; Benedetti-Panici, Pierluigi; Raspagliesi, Francesco

2015-12-01

Although surgery represents the cornerstone treatment of endometrial cancer at initial diagnosis, scarce data are available in recurrent setting. The purpose of this study was to review the outcome of surgery in these patients. Medical records of all patients undergoing surgery for recurrent endometrial cancer at NCI Milano between January 2003 and January 2014 were reviewed. Survival was determined from the time of surgery for recurrence to last follow-up. Survival was estimated using Kaplan-Meier methods. Differences in survival were analyzed using the log-rank test. The Fisher's exact test was used to compare optimal versus suboptimal cytoreduction against possible predictive factors. Sixty-four patients were identified. Median age was 66 years. Recurrences were multiple in 38 % of the cases. Optimal cytoreduction was achieved in 65.6 %. Median OR time was 165 min, median postoperative hemoglobin drop was 2.4 g/dl, and median length hospital stay was 5.5 days. Eleven patients developed postoperative complications, but only four required surgical management. Estimated 5-year progression-free survival (PFS) was 42 and 19 % in optimally and suboptimally cytoreduced patients, respectively. At multivariate analysis, only residual disease was associated with PFS. Estimated 5-year overall survival (OS) was 60 and 30 % in optimally and suboptimally cytoreduced patients, respectively. At multivariate analysis, residual disease and histotype were associated with OS. At multivariate analysis, only performance status was associated with optimal cytoreduction. Secondary cytoreduction in endometrial cancer is associated with long PFS and OS. The only factors associated with improved long-term outcome are the absence of residual disease at the end of surgical resection and histotype.

Predicting drug-disease interactions by semi-supervised graph cut algorithm and three-layer data integration.

PubMed

Wu, Guangsheng; Liu, Juan; Wang, Caihua

2017-12-28

Prediction of drug-disease interactions is promising for either drug repositioning or disease treatment fields. The discovery of novel drug-disease interactions, on one hand can help to find novel indictions for the approved drugs; on the other hand can provide new therapeutic approaches for the diseases. Recently, computational methods for finding drug-disease interactions have attracted lots of attention because of their far more higher efficiency and lower cost than the traditional wet experiment methods. However, they still face several challenges, such as the organization of the heterogeneous data, the performance of the model, and so on. In this work, we present to hierarchically integrate the heterogeneous data into three layers. The drug-drug and disease-disease similarities are first calculated separately in each layer, and then the similarities from three layers are linearly fused into comprehensive drug similarities and disease similarities, which can then be used to measure the similarities between two drug-disease pairs. We construct a novel weighted drug-disease pair network, where a node is a drug-disease pair with known or unknown treatment relation, an edge represents the node-node relation which is weighted with the similarity score between two pairs. Now that similar drug-disease pairs are supposed to show similar treatment patterns, we can find the optimal graph cut of the network. The drug-disease pair with unknown relation can then be considered to have similar treatment relation with that within the same cut. Therefore, we develop a semi-supervised graph cut algorithm, SSGC, to find the optimal graph cut, based on which we can identify the potential drug-disease treatment interactions. By comparing with three representative network-based methods, SSGC achieves the highest performances, in terms of both AUC score and the identification rates of true drug-disease pairs. The experiments with different integration strategies also demonstrate that considering several sources of data can improve the performances of the predictors. Further case studies on four diseases, the top-ranked drug-disease associations have been confirmed by KEGG, CTD database and the literature, illustrating the usefulness of SSGC. The proposed comprehensive similarity scores from multi-views and multiple layers and the graph-cut based algorithm can greatly improve the prediction performances of drug-disease associations.

Robust model predictive control for optimal continuous drug administration.

PubMed

Sopasakis, Pantelis; Patrinos, Panagiotis; Sarimveis, Haralambos

2014-10-01

In this paper the model predictive control (MPC) technology is used for tackling the optimal drug administration problem. The important advantage of MPC compared to other control technologies is that it explicitly takes into account the constraints of the system. In particular, for drug treatments of living organisms, MPC can guarantee satisfaction of the minimum toxic concentration (MTC) constraints. A whole-body physiologically-based pharmacokinetic (PBPK) model serves as the dynamic prediction model of the system after it is formulated as a discrete-time state-space model. Only plasma measurements are assumed to be measured on-line. The rest of the states (drug concentrations in other organs and tissues) are estimated in real time by designing an artificial observer. The complete system (observer and MPC controller) is able to drive the drug concentration to the desired levels at the organs of interest, while satisfying the imposed constraints, even in the presence of modelling errors, disturbances and noise. A case study on a PBPK model with 7 compartments, constraints on 5 tissues and a variable drug concentration set-point illustrates the efficiency of the methodology in drug dosing control applications. The proposed methodology is also tested in an uncertain setting and proves successful in presence of modelling errors and inaccurate measurements. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Pharmacokinetic optimization of class-selective histone deacetylase inhibitors and identification of associated candidate predictive biomarkers of hepatocellular carcinoma tumor response.

PubMed

Wong, Jason C; Tang, Guozhi; Wu, Xihan; Liang, Chungen; Zhang, Zhenshan; Guo, Lei; Peng, Zhenghong; Zhang, Weixing; Lin, Xianfeng; Wang, Zhanguo; Mei, Jianghua; Chen, Junli; Pan, Song; Zhang, Nan; Liu, Yongfu; Zhou, Mingwei; Feng, Lichun; Zhao, Weili; Li, Shijie; Zhang, Chao; Zhang, Meifang; Rong, Yiping; Jin, Tai-Guang; Zhang, Xiongwen; Ren, Shuang; Ji, Ying; Zhao, Rong; She, Jin; Ren, Yi; Xu, Chunping; Chen, Dawei; Cai, Jie; Shan, Song; Pan, Desi; Ning, Zhiqiang; Lu, Xianping; Chen, Taiping; He, Yun; Chen, Li

2012-10-25

Herein, we describe the pharmacokinetic optimization of a series of class-selective histone deacetylase (HDAC) inhibitors and the subsequent identification of candidate predictive biomarkers of hepatocellular carcinoma (HCC) tumor response for our clinical lead using patient-derived HCC tumor xenograft models. Through a combination of conformational constraint and scaffold hopping, we lowered the in vivo clearance (CL) and significantly improved the bioavailability (F) and exposure (AUC) of our HDAC inhibitors while maintaining selectivity toward the class I HDAC family with particular potency against HDAC1, resulting in clinical lead 5 (HDAC1 IC₅₀ = 60 nM, mouse CL = 39 mL/min/kg, mouse F = 100%, mouse AUC after single oral dose at 10 mg/kg = 6316 h·ng/mL). We then evaluated 5 in a biomarker discovery pilot study using patient-derived tumor xenograft models, wherein two out of the three models responded to treatment. By comparing tumor response status to compound tumor exposure, induction of acetylated histone H3, candidate gene expression changes, and promoter DNA methylation status from all three models at various time points, we identified preliminary candidate response prediction biomarkers that warrant further validation in a larger cohort of patient-derived tumor models and through confirmatory functional studies.

Influence of monitoring data selection for optimization of a steady state multimedia model on the magnitude and nature of the model prediction bias.

PubMed

Kim, Hee Seok; Lee, Dong Soo

2017-11-01

SimpleBox is an important multimedia model used to estimate the predicted environmental concentration for screening-level exposure assessment. The main objectives were (i) to quantitatively assess how the magnitude and nature of prediction bias of SimpleBox vary with the selection of observed concentration data set for optimization and (ii) to present the prediction performance of the optimized SimpleBox. The optimization was conducted using a total of 9604 observed multimedia data for 42 chemicals of four groups (i.e., polychlorinated dibenzo-p-dioxins/furans (PCDDs/Fs), polybrominated diphenyl ethers (PBDEs), phthalates, and polycyclic aromatic hydrocarbons (PAHs)). The model performance was assessed based on the magnitude and skewness of prediction bias. Monitoring data selection in terms of number of data and kind of chemicals plays a significant role in optimization of the model. The coverage of the physicochemical properties was found to be very important to reduce the prediction bias. This suggests that selection of observed data should be made such that the physicochemical property (such as vapor pressure, octanol-water partition coefficient, octanol-air partition coefficient, and Henry's law constant) range of the selected chemical groups be as wide as possible. With optimization, about 55%, 90%, and 98% of the total number of the observed concentration ratios were predicted within factors of three, 10, and 30, respectively, with negligible skewness. Copyright © 2017 Elsevier Ltd. All rights reserved.

Segmental osteotomies of the maxilla.

PubMed

Rosen, H M

1989-10-01

Multiple segment Le Fort I osteotomies provide the maxillofacial surgeon with the capabilities to treat complex dentofacial deformities existing in all three planes of space. Sagittal, vertical, and transverse maxillomandibular discrepancies as well as three-dimensional abnormalities within the maxillary arch can be corrected simultaneously. Accordingly, optimal aesthetic enhancement of the facial skeleton and a functional, healthy occlusion can be realized. What may be perceived as elaborate treatment plans are in reality conservative in terms of osseous stability and treatment time required. The close cooperation of an orthodontist well-versed in segmental orthodontics and orthognathic surgery is critical to the success of such surgery. With close attention to surgical detail, the complication rate inherent in such surgery can be minimized and the treatment goals achieved in a timely and predictable fashion.

Photosensitizer fluorescence emission during photodynamic therapy applied to dermatological diseases

NASA Astrophysics Data System (ADS)

Salas-García, I.; Fanjul-Vélez, F.; Ortega-Quijano, N.; Arce-Diego, J. L.

2011-09-01

Photodynamic Therapy (PDT) is an optical treatment modality that allows malignant tissue destruction. It is based on the administration of a photosensitizer and the posterior irradiation by an optical source. Photosensitizer molecules absorb the excitation light photons triggering a series of photochemical reactions in the presence of oxygen in the target tissue. During such interactions it is produced the de-excitation of the photosensitizer molecules in the singlet excited state which return to their minimum energy state by emitting fluorescence photons. These days, there are fixed clinical PDT protocols that make use of a particular optical dose and photosensitizer amount. However treatment response varies among patients and the type of pathology. In order to adjust an optimal dosimetry, the development of accurate predictive models plays an important role. The photosensitizer fluorescence can be used to estimate the degradation of the photoactive agent and as an implicit dosimetric measurement during treatment. However it is complex to know the fluorescence dependence with the depth in the tumor from observed fluorescence in the pathology surface. We present a first approach to predict the photosensitizer fluorescence dependence with depth during the PDT treatment applied to a skin disease commonly treated in the dermatological clinical practice. The obtained results permit us to know the photosensitizer temporal fluorescence evolution in different points of the tumor sample during the photochemical reactions involved in PDT with a predictive purpose related to the treatment evolution. The model presented also takes into account the distribution of a topical photosensitizer, the propagation of light in a biological media and the subsequent photochemical interactions between light and tissue. This implies that different parameters related with the photosensitizer distribution or the optical source characteristics could be adjusted to provide a specific treatment to a particular pathology.

WE-F-BRB-00: New Developments in Knowledge-Based Treatment Planning and Automation

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

NONE

2015-06-15

Advancements in informatics in radiotherapy are opening up opportunities to improve our ability to assess treatment plans. Models on individualizing patient dose constraints from prior patient data and shape relationships have been extensively researched and are now making their way into commercial products. New developments in knowledge based treatment planning involve understanding the impact of the radiation dosimetry on the patient. Akin to radiobiology models that have driven intensity modulated radiotherapy optimization, toxicity and outcome predictions based on treatment plans and prior patient experiences may be the next step in knowledge based planning. In order to realize these predictions, itmore » is necessary to understand how the clinical information can be captured, structured and organized with ontologies and databases designed for recall. Large databases containing radiation dosimetry and outcomes present the opportunity to evaluate treatment plans against predictions of toxicity and disease response. Such evaluations can be based on dose volume histogram or even the full 3-dimensional dose distribution and its relation to the critical anatomy. This session will provide an understanding of ontologies and standard terminologies used to capture clinical knowledge into structured databases; How data can be organized and accessed to utilize the knowledge in planning; and examples of research and clinical efforts to incorporate that clinical knowledge into planning for improved care for our patients. Learning Objectives: Understand the role of standard terminologies, ontologies and data organization in oncology Understand methods to capture clinical toxicity and outcomes in a clinical setting Understand opportunities to learn from clinical data and its application to treatment planning Todd McNutt receives funding from Philips, Elekta and Toshiba for some of the work presented.« less

WE-F-BRB-02: Setting the Stage for Incorporation of Toxicity Measures in Treatment Plan Assessments

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

Mayo, C.

2015-06-15

Advancements in informatics in radiotherapy are opening up opportunities to improve our ability to assess treatment plans. Models on individualizing patient dose constraints from prior patient data and shape relationships have been extensively researched and are now making their way into commercial products. New developments in knowledge based treatment planning involve understanding the impact of the radiation dosimetry on the patient. Akin to radiobiology models that have driven intensity modulated radiotherapy optimization, toxicity and outcome predictions based on treatment plans and prior patient experiences may be the next step in knowledge based planning. In order to realize these predictions, itmore » is necessary to understand how the clinical information can be captured, structured and organized with ontologies and databases designed for recall. Large databases containing radiation dosimetry and outcomes present the opportunity to evaluate treatment plans against predictions of toxicity and disease response. Such evaluations can be based on dose volume histogram or even the full 3-dimensional dose distribution and its relation to the critical anatomy. This session will provide an understanding of ontologies and standard terminologies used to capture clinical knowledge into structured databases; How data can be organized and accessed to utilize the knowledge in planning; and examples of research and clinical efforts to incorporate that clinical knowledge into planning for improved care for our patients. Learning Objectives: Understand the role of standard terminologies, ontologies and data organization in oncology Understand methods to capture clinical toxicity and outcomes in a clinical setting Understand opportunities to learn from clinical data and its application to treatment planning Todd McNutt receives funding from Philips, Elekta and Toshiba for some of the work presented.« less

Discriminate the response of Acute Myeloid Leukemia patients to treatment by using proteomics data and Answer Set Programming.

PubMed

Chebouba, Lokmane; Miannay, Bertrand; Boughaci, Dalila; Guziolowski, Carito

2018-03-08

During the last years, several approaches were applied on biomedical data to detect disease specific proteins and genes in order to better target drugs. It was shown that statistical and machine learning based methods use mainly clinical data and improve later their results by adding omics data. This work proposes a new method to discriminate the response of Acute Myeloid Leukemia (AML) patients to treatment. The proposed approach uses proteomics data and prior regulatory knowledge in the form of networks to predict cancer treatment outcomes by finding out the different Boolean networks specific to each type of response to drugs. To show its effectiveness we evaluate our method on a dataset from the DREAM 9 challenge. The results are encouraging and demonstrate the benefit of our approach to distinguish patient groups with different response to treatment. In particular each treatment response group is characterized by a predictive model in the form of a signaling Boolean network. This model describes regulatory mechanisms which are specific to each response group. The proteins in this model were selected from the complete dataset by imposing optimization constraints that maximize the difference in the logical response of the Boolean network associated to each group of patients given the omic dataset. This mechanistic and predictive model also allow us to classify new patients data into the two different patient response groups. We propose a new method to detect the most relevant proteins for understanding different patient responses upon treatments in order to better target drugs using a Prior Knowledge Network and proteomics data. The results are interesting and show the effectiveness of our method.

Transmission Dynamics of Visceral Leishmaniasis in the Indian Subcontinent – A Systematic Literature Review

PubMed Central

Boelaert, Marleen; Matlashewski, Greg; Mondal, Dinesh; Arana, Byron; Kroeger, Axel; Olliaro, Piero

2016-01-01

Background As Bangladesh, India and Nepal progress towards visceral leishmaniasis (VL) elimination, it is important to understand the role of asymptomatic Leishmania infection (ALI), VL treatment relapse and post kala-azar dermal leishmaniasis (PKDL) in transmission. Methodology/ Principal Finding We reviewed evidence systematically on ALI, relapse and PKDL. We searched multiple databases to include studies on burden, risk factors, biomarkers, natural history, and infectiveness of ALI, PKDL and relapse. After screening 292 papers, 98 were included covering the years 1942 through 2016. ALI, PKDL and relapse studies lacked a reference standard and appropriate biomarker. The prevalence of ALI was 4–17-fold that of VL. The risk of ALI was higher in VL case contacts. Most infections remained asymptomatic or resolved spontaneously. The proportion of ALI that progressed to VL disease within a year was 1.5–23%, and was higher amongst those with high antibody titres. The natural history of PKDL showed variability; 3.8–28.6% had no past history of VL treatment. The infectiveness of PKDL was 32–53%. The risk of VL relapse was higher with HIV co-infection. Modelling studies predicted a range of scenarios. One model predicted VL elimination was unlikely in the long term with early diagnosis. Another model estimated that ALI contributed to 82% of the overall transmission, VL to 10% and PKDL to 8%. Another model predicted that VL cases were the main driver for transmission. Different models predicted VL elimination if the sandfly density was reduced by 67% by killing the sandfly or by 79% by reducing their breeding sites, or with 4–6y of optimal IRS or 10y of sub-optimal IRS and only in low endemic setting. Conclusion/ Significance There is a need for xenodiagnostic and longitudinal studies to understand the potential of ALI and PKDL as reservoirs of infection. PMID:27490264

TU-AB-BRB-01: Coverage Evaluation and Probabilistic Treatment Planning as a Margin Alternative

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

Siebers, J.

The accepted clinical method to accommodate targeting uncertainties inherent in fractionated external beam radiation therapy is to utilize GTV-to-CTV and CTV-to-PTV margins during the planning process to design a PTV-conformal static dose distribution on the planning image set. Ideally, margins are selected to ensure a high (e.g. >95%) target coverage probability (CP) in spite of inherent inter- and intra-fractional positional variations, tissue motions, and initial contouring uncertainties. Robust optimization techniques, also known as probabilistic treatment planning techniques, explicitly incorporate the dosimetric consequences of targeting uncertainties by including CP evaluation into the planning optimization process along with coverage-based planning objectives. Themore » treatment planner no longer needs to use PTV and/or PRV margins; instead robust optimization utilizes probability distributions of the underlying uncertainties in conjunction with CP-evaluation for the underlying CTVs and OARs to design an optimal treated volume. This symposium will describe CP-evaluation methods as well as various robust planning techniques including use of probability-weighted dose distributions, probability-weighted objective functions, and coverage optimized planning. Methods to compute and display the effect of uncertainties on dose distributions will be presented. The use of robust planning to accommodate inter-fractional setup uncertainties, organ deformation, and contouring uncertainties will be examined as will its use to accommodate intra-fractional organ motion. Clinical examples will be used to inter-compare robust and margin-based planning, highlighting advantages of robust-plans in terms of target and normal tissue coverage. Robust-planning limitations as uncertainties approach zero and as the number of treatment fractions becomes small will be presented, as well as the factors limiting clinical implementation of robust planning. Learning Objectives: To understand robust-planning as a clinical alternative to using margin-based planning. To understand conceptual differences between uncertainty and predictable motion. To understand fundamental limitations of the PTV concept that probabilistic planning can overcome. To understand the major contributing factors to target and normal tissue coverage probability. To understand the similarities and differences of various robust planning techniques To understand the benefits and limitations of robust planning techniques.« less

Programming stress-induced altruistic death in engineered bacteria

PubMed Central

Tanouchi, Yu; Pai, Anand; Buchler, Nicolas E; You, Lingchong

2012-01-01

Programmed death is often associated with a bacterial stress response. This behavior appears paradoxical, as it offers no benefit to the individual. This paradox can be explained if the death is ‘altruistic': the killing of some cells can benefit the survivors through release of ‘public goods'. However, the conditions where bacterial programmed death becomes advantageous have not been unambiguously demonstrated experimentally. Here, we determined such conditions by engineering tunable, stress-induced altruistic death in the bacterium Escherichia coli. Using a mathematical model, we predicted the existence of an optimal programmed death rate that maximizes population growth under stress. We further predicted that altruistic death could generate the ‘Eagle effect', a counter-intuitive phenomenon where bacteria appear to grow better when treated with higher antibiotic concentrations. In support of these modeling insights, we experimentally demonstrated both the optimality in programmed death rate and the Eagle effect using our engineered system. Our findings fill a critical conceptual gap in the analysis of the evolution of bacterial programmed death, and have implications for a design of antibiotic treatment. PMID:23169002

Alginate coated chitosan nanogel for the controlled topical delivery of Silver sulfadiazine.

PubMed

El-Feky, Gina S; El-Banna, Sally T; El-Bahy, G S; Abdelrazek, E M; Kamal, Mustafa

2017-12-01

Burn wounds environment favors the growth of micro-organisms causing delay in wound healing. The traditional treatment with antimicrobial creams offer inaccurate doses. The aim of the present study is to formulate and evaluate different silver sulfadiazine loaded nanogel formulations. A factorial design experiment was used for the identification of critical process parameters and for the optimization of the respective process conditions. The prepared drug loaded nanogels were characterized for their particle size, zeta potential, entrapment efficiency and swelling index in order to demonstrate their physicochemical properties, in addition, FTIR, TEM, SEM and in vitro release were used for characterization. The release profile of all tested nanogels showed an initial burst followed by a slow and continuous release rate. An optimum nanogel formulation was predicted by the JMP ® software according to the stated prediction expressions and was composed of 0.4% sodium alginate (ALG) and 0.414% Silver sulfadiazine (SSD). The optimized formulation showed higher therapeutic efficacy in vivo when compared to market product. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prediction of different ovarian responses using anti-Müllerian hormone following a long agonist treatment protocol for IVF.

PubMed

Heidar, Z; Bakhtiyari, M; Mirzamoradi, M; Zadehmodarres, S; Sarfjoo, F S; Mansournia, M A

2015-09-01

The purpose of this study was to predict the poor and excessive ovarian response using anti-Müllerian hormone (AMH) levels following a long agonist protocol in IVF candidates. Through a prospective cohort study, the type of relationship and appropriate scale for AMH were determined using the fractional polynomial regression. To determine the effect of AMH on the outcomes of ovarian stimulation and different ovarian responses, the multi-nominal and negative binomial regression models were fitted using backward stepwise method. The ovarian response of study subject who entered a standard long-term treatment cycle with GnRH agonist was evaluated using prediction model, separately and in combined models with (ROC) curves. The use of standard long-term treatments with GnRH agonist led to positive pregnancy test results in 30% of treated patients. With each unit increase in the log of AMH, the odds ratio of having poor response compared to normal response decreases by 64% (OR 0.36, 95% CI 0.19-0.68). Also the results of negative binomial regression model indicated that for one unit increase in the log of AMH blood levels, the odds of releasing an oocyte increased 24% (OR 1.24, 95% CI 1.14-1.35). The optimal cut-off points of AMH for predicting excessive and poor ovarian responses were 3.4 and 1.2 ng/ml, respectively, with area under curves of 0.69 (0.60-0.77) and 0.76 (0.66-0.86), respectively. By considering the age of the patient undergoing infertility treatment as a variable affecting ovulation, use of AMH levels showed to be a good test to discriminate between different ovarian responses.

Immuno-oncology Clinical Trial Design: Limitations, Challenges, and Opportunities

PubMed Central

Baik, Christina S.; Rubin, Eric H.; Forde, Patrick M.; Mehnert, Janice M.; Collyar, Deborah; Butler, Marcus O.; Dixon, Erica L.; Chow, Laura Q.M.

2017-01-01

Recent advances in immuno-oncology and regulatory approvals have been rapid and paradigm shifting in many difficult-to-treat malignancies. Despite immune checkpoint inhibitor therapy becoming the standard of care across multiple tumor types, there are many unanswered questions that need to be addressed before this therapeutic modality can be fully harnessed. Areas of limitations include treatment of patients not sufficiently represented in clinical trials, uncertainty of the optimal treatment dosing and duration, and lack of understanding regarding long-term immune related toxicities and atypical tumor responses. Patients such as those with autoimmune disease, chronic viral infections, limited performance status, and brain metastases were often excluded from initial trials due to concerns of safety. However, limited data suggest that some of these patients can benefit from therapy with manageable toxicities; thus, future studies should incorporate these patients to clearly define safety and efficacy. There are still controversies regarding the optimal dosing strategy that can vary from weight-based to flat dosing, with undefined treatment duration. Further elucidation of the optimal dosing approach and evaluation of predictive biomarkers should be incorporated in the design of future trials. Finally, there are long-term immune-mediated toxicities, atypical tumor responses such as pseudoprogression and endpoints unique to immuno-oncology that are not adequately captured by traditional trial designs; thus, novel study designs are needed. In this article, we discuss in detail the above challenges and propose needed areas of research for exploration and incorporation in the next generation of immuno-oncology clinical trials. PMID:28864727

Machine Learning Approach to Optimizing Combined Stimulation and Medication Therapies for Parkinson's Disease.

PubMed

Shamir, Reuben R; Dolber, Trygve; Noecker, Angela M; Walter, Benjamin L; McIntyre, Cameron C

2015-01-01

Deep brain stimulation (DBS) of the subthalamic region is an established therapy for advanced Parkinson's disease (PD). However, patients often require time-intensive post-operative management to balance their coupled stimulation and medication treatments. Given the large and complex parameter space associated with this task, we propose that clinical decision support systems (CDSS) based on machine learning algorithms could assist in treatment optimization. Develop a proof-of-concept implementation of a CDSS that incorporates patient-specific details on both stimulation and medication. Clinical data from 10 patients, and 89 post-DBS surgery visits, were used to create a prototype CDSS. The system was designed to provide three key functions: (1) information retrieval; (2) visualization of treatment, and; (3) recommendation on expected effective stimulation and drug dosages, based on three machine learning methods that included support vector machines, Naïve Bayes, and random forest. Measures of medication dosages, time factors, and symptom-specific pre-operative response to levodopa were significantly correlated with post-operative outcomes (P < 0.05) and their effect on outcomes was of similar magnitude to that of DBS. Using those results, the combined machine learning algorithms were able to accurately predict 86% (12/14) of the motor improvement scores at one year after surgery. Using patient-specific details, an appropriately parameterized CDSS could help select theoretically optimal DBS parameter settings and medication dosages that have potential to improve the clinical management of PD patients. Copyright © 2015 Elsevier Inc. All rights reserved.

Proceedings of the 3rd Annual Albert Institute for Bladder Cancer Research Symposium.

PubMed

Flaig, Thomas W; Kamat, Ashish M; Hansel, Donna; Ingersoll, Molly A; Barton Grossman, H; Mendelsohn, Cathy; DeGraff, David; Liao, Joseph C; Taylor, John A

2017-07-27

The Third Annual Albert Institute Bladder Symposium was held on September 8-10th, 2016, in Denver Colorado. Participants discussed several critical topics in the field of bladder cancer: 1) Best practices for tissue analysis and use to optimize correlative studies, 2) Modeling bladder cancer to facilitate understanding and innovation, 3) Targeted therapies for bladder cancer, 4) Tumor phylogeny in bladder cancer, 5) New Innovations in bladder cancer diagnostics. Our understanding of and approach to treating urothelial carcinoma is undergoing rapid advancement. Preclinical models of bladder cancer have been leveraged to increase our basic and mechanistic understanding of the disease. With the approval of immune checkpoint inhibitors for the treatment of advanced urothelial carcinoma, the treatment approach for these patients has quickly changed. In this light, molecularly-defined subtypes of bladder cancer and appropriate pre-clinical models are now essential to the further advancement and appropriate application of these therapeutic improvements. The optimal collection and processing of clinical urothelial carcinoma tissues samples will also be critical in the development of predictive biomarkers for therapeutic selection. Technological advances in other areas including optimal imaging technologies and micro/nanotechnologies are being applied to bladder cancer, especially in the localized setting, and hold the potential for translational impact in the treatment of bladder cancer patients. Taken together, advances in several basic science and clinical areas are now converging in bladder cancer. These developments hold the promise of shaping and improving the clinical care of those with the disease.

Experimental design for the formulation and optimization of novel cross-linked oilispheres developed for in vitro site-specific release of Mentha piperita oil.

PubMed

Sibanda, Wilbert; Pillay, Viness; Danckwerts, Michael P; Viljoen, Alvaro M; van Vuuren, Sandy; Khan, Riaz A

2004-03-12

A Plackett-Burman design was employed to develop and optimize a novel crosslinked calcium-aluminum-alginate-pectinate oilisphere complex as a potential system for the in vitro site-specific release of Mentha piperita, an essential oil used for the treatment of irritable bowel syndrome. The physicochemical and textural properties (dependent variables) of this complex were found to be highly sensitive to changes in the concentration of the polymers (0%-1.5% wt/vol), crosslinkers (0%-4% wt/vol), and crosslinking reaction times (0.5-6 hours) (independent variables). Particle size analysis indicated both unimodal and bimodal populations with the highest frequency of 2 mm oilispheres. Oil encapsulation ranged from 6 to 35 mg/100 mg oilispheres. Gravimetric changes of the crosslinked matrix indicated significant ion sequestration and loss in an exponential manner, while matrix erosion followed Higuchi's cube root law. Among the various measured responses, the total fracture energy was the most suitable optimization objective (R2 = 0.88, Durbin-Watson Index = 1.21%, Coefficient of Variation (CV) = 33.21%). The Lagrangian technique produced no significant differences (P > .05) between the experimental and predicted total fracture energy values (0.0150 vs 0.0107 J). Artificial Neural Networks, as an alternative predictive tool of the total fracture energy, was highly accurate (final mean square error of optimal network epoch approximately 0.02). Fused-coated optimized oilispheres produced a 4-hour lag phase followed by zero-order kinetics (n > 0.99), whereby analysis of release data indicated that diffusion (Fickian constant k1 = 0.74 vs relaxation constant k2 = 0.02) was the predominant release mechanism.

Multiple sclerosis: individualized disease susceptibility and therapy response.

PubMed

Pravica, Vera; Markovic, Milos; Cupic, Maja; Savic, Emina; Popadic, Dusan; Drulovic, Jelena; Mostarica-Stojkovic, Marija

2013-02-01

Multiple sclerosis (MS) is a heterogeneous disease in which diverse genetic, pathological and clinical backgrounds lead to variable therapy response. Accordingly, MS care should be tailored to address disease traits unique to each person. At the core of personalized management is the emergence of new knowledge, enabling optimized treatment and disease-modifying therapies. This overview analyzes the promise of genetic and nongenetic biomarkers in advancing decision-making algorithms to assist diagnosis or in predicting the disease course and therapy response in any given MS patient.

An Effective Solution to Discover Synergistic Drugs for Anti-Cerebral Ischemia from Traditional Chinese Medicinal Formulae

PubMed Central

Lu, Peng; Chen, Chang; Fu, Meihong; Fang, Jing; Gao, Jian; Zhu, Li; Liang, Rixin; Shen, Xin; Yang, Hongjun

2013-01-01

Recently, the pharmaceutical industry has shifted to pursuing combination therapies that comprise more than one active ingredient. Interestingly, combination therapies have been used for more than 2500 years in traditional Chinese medicine (TCM). Understanding optimal proportions and synergistic mechanisms of multi-component drugs are critical for developing novel strategies to combat complex diseases. A new multi-objective optimization algorithm based on least angle regression-partial least squares was proposed to construct the predictive model to evaluate the synergistic effect of the three components of a novel combination drug Yi-qi-jie-du formula (YJ), which came from clinical TCM prescription for the treatment of encephalopathy. Optimal proportion of the three components, ginsenosides (G), berberine (B) and jasminoidin (J) was determined via particle swarm optimum. Furthermore, the combination mechanisms were interpreted using PLS VIP and principal components analysis. The results showed that YJ had optimal proportion 3(G): 2(B): 0.5(J), and it yielded synergy in the treatment of rats impaired by middle cerebral artery occlusion induced focal cerebral ischemia. YJ with optimal proportion had good pharmacological effects on acute ischemic stroke. The mechanisms study demonstrated that the combination of G, B and J could exhibit the strongest synergistic effect. J might play an indispensable role in the formula, especially when combined with B for the acute stage of stroke. All these data in this study suggested that in the treatment of acute ischemic stroke, besides restoring blood supply and protecting easily damaged cells in the area of the ischemic penumbra as early as possible, we should pay more attention to the removal of the toxic metabolites at the same time. Mathematical system modeling may be an essential tool for the analysis of the complex pharmacological effects of multi-component drug. The powerful mathematical analysis method could greatly improve the efficiency in finding new combination drug from TCM. PMID:24236065

Recent Results on "Approximations to Optimal Alarm Systems for Anomaly Detection"

NASA Technical Reports Server (NTRS)

Martin, Rodney Alexander

2009-01-01

An optimal alarm system and its approximations may use Kalman filtering for univariate linear dynamic systems driven by Gaussian noise to provide a layer of predictive capability. Predicted Kalman filter future process values and a fixed critical threshold can be used to construct a candidate level-crossing event over a predetermined prediction window. An optimal alarm system can be designed to elicit the fewest false alarms for a fixed detection probability in this particular scenario.

Optimization of the moving-bed biofilm sequencing batch reactor (MBSBR) to control aeration time by kinetic computational modeling: Simulated sugar-industry wastewater treatment.

PubMed

Faridnasr, Maryam; Ghanbari, Bastam; Sassani, Ardavan

2016-05-01

A novel approach was applied for optimization of a moving-bed biofilm sequencing batch reactor (MBSBR) to treat sugar-industry wastewater (BOD5=500-2500 and COD=750-3750 mg/L) at 2-4 h of cycle time (CT). Although the experimental data showed that MBSBR reached high BOD5 and COD removal performances, it failed to achieve the standard limits at the mentioned CTs. Thus, optimization of the reactor was rendered by kinetic computational modeling and using statistical error indicator normalized root mean square error (NRMSE). The results of NRMSE revealed that Stover-Kincannon (error=6.40%) and Grau (error=6.15%) models provide better fits to the experimental data and may be used for CT optimization in the reactor. The models predicted required CTs of 4.5, 6.5, 7 and 7.5 h for effluent standardization of 500, 1000, 1500 and 2500 mg/L influent BOD5 concentrations, respectively. Similar pattern of the experimental data also confirmed these findings. Copyright © 2016 Elsevier Ltd. All rights reserved.

Vibrational self-consistent field theory using optimized curvilinear coordinates.

PubMed

Bulik, Ireneusz W; Frisch, Michael J; Vaccaro, Patrick H

2017-07-28

A vibrational SCF model is presented in which the functions forming the single-mode functions in the product wavefunction are expressed in terms of internal coordinates and the coordinates used for each mode are optimized variationally. This model involves no approximations to the kinetic energy operator and does not require a Taylor-series expansion of the potential. The non-linear optimization of coordinates is found to give much better product wavefunctions than the limited variations considered in most previous applications of SCF methods to vibrational problems. The approach is tested using published potential energy surfaces for water, ammonia, and formaldehyde. Variational flexibility allowed in the current ansätze results in excellent zero-point energies expressed through single-product states and accurate fundamental transition frequencies realized by short configuration-interaction expansions. Fully variational optimization of single-product states for excited vibrational levels also is discussed. The highlighted methodology constitutes an excellent starting point for more sophisticated treatments, as the bulk characteristics of many-mode coupling are accounted for efficiently in terms of compact wavefunctions (as evident from the accurate prediction of transition frequencies).

Optimization of global model composed of radial basis functions using the term-ranking approach

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

Cai, Peng; Tao, Chao, E-mail: taochao@nju.edu.cn; Liu, Xiao-Jun

2014-03-15

A term-ranking method is put forward to optimize the global model composed of radial basis functions to improve the predictability of the model. The effectiveness of the proposed method is examined by numerical simulation and experimental data. Numerical simulations indicate that this method can significantly lengthen the prediction time and decrease the Bayesian information criterion of the model. The application to real voice signal shows that the optimized global model can capture more predictable component in chaos-like voice data and simultaneously reduce the predictable component (periodic pitch) in the residual signal.

Using string invariants for prediction searching for optimal parameters

NASA Astrophysics Data System (ADS)

Bundzel, Marek; Kasanický, Tomáš; Pinčák, Richard

2016-02-01

We have developed a novel prediction method based on string invariants. The method does not require learning but a small set of parameters must be set to achieve optimal performance. We have implemented an evolutionary algorithm for the parametric optimization. We have tested the performance of the method on artificial and real world data and compared the performance to statistical methods and to a number of artificial intelligence methods. We have used data and the results of a prediction competition as a benchmark. The results show that the method performs well in single step prediction but the method's performance for multiple step prediction needs to be improved. The method works well for a wide range of parameters.

Counteracting Obstacles with Optimistic Predictions

ERIC Educational Resources Information Center

Zhang, Ying; Fishbach, Ayelet

2010-01-01

This research tested for counteractive optimism: a self-control strategy of generating optimistic predictions of future goal attainment in order to overcome anticipated obstacles in goal pursuit. In support of the counteractive optimism model, participants in 5 studies predicted better performance, more time invested in goal activities, and lower…

Predictive optimal control of sewer networks using CORAL tool: application to Riera Blanca catchment in Barcelona.

PubMed

Puig, V; Cembrano, G; Romera, J; Quevedo, J; Aznar, B; Ramón, G; Cabot, J

2009-01-01

This paper deals with the global control of the Riera Blanca catchment in the Barcelona sewer network using a predictive optimal control approach. This catchment has been modelled using a conceptual modelling approach based on decomposing the catchments in subcatchments and representing them as virtual tanks. This conceptual modelling approach allows real-time model calibration and control of the sewer network. The global control problem of the Riera Blanca catchment is solved using a optimal/predictive control algorithm. To implement the predictive optimal control of the Riera Blanca catchment, a software tool named CORAL is used. The on-line control is simulated by interfacing CORAL with a high fidelity simulator of sewer networks (MOUSE). CORAL interchanges readings from the limnimeters and gate commands with MOUSE as if it was connected with the real SCADA system. Finally, the global control results obtained using the predictive optimal control are presented and compared against the results obtained using current local control system. The results obtained using the global control are very satisfactory compared to those obtained using the local control.

Construct measurement quality improves predictive accuracy in violence risk assessment: an illustration using the personality assessment inventory.

PubMed

Hendry, Melissa C; Douglas, Kevin S; Winter, Elizabeth A; Edens, John F

2013-01-01

Much of the risk assessment literature has focused on the predictive validity of risk assessment tools. However, these tools often comprise a list of risk factors that are themselves complex constructs, and focusing on the quality of measurement of individual risk factors may improve the predictive validity of the tools. The present study illustrates this concern using the Antisocial Features and Aggression scales of the Personality Assessment Inventory (Morey, 1991). In a sample of 1,545 prison inmates and offenders undergoing treatment for substance abuse (85% male), we evaluated (a) the factorial validity of the ANT and AGG scales, (b) the utility of original ANT and AGG scales and newly derived ANT and AGG scales for predicting antisocial outcomes (recidivism and institutional infractions), and (c) whether items with a stronger relationship to the underlying constructs (higher factor loadings) were in turn more strongly related to antisocial outcomes. Confirmatory factor analyses (CFAs) indicated that ANT and AGG items were not structured optimally in these data in terms of correspondence to the subscale structure identified in the PAI manual. Exploratory factor analyses were conducted on a random split-half of the sample to derive optimized alternative factor structures, and cross-validated in the second split-half using CFA. Four-factor models emerged for both the ANT and AGG scales, and, as predicted, the size of item factor loadings was associated with the strength with which items were associated with institutional infractions and community recidivism. This suggests that the quality by which a construct is measured is associated with its predictive strength. Implications for risk assessment are discussed. Copyright © 2013 John Wiley & Sons, Ltd.

Optimizing radiotherapy protocols using computer automata to model tumour cell death as a function of oxygen diffusion processes.

PubMed

Paul-Gilloteaux, Perrine; Potiron, Vincent; Delpon, Grégory; Supiot, Stéphane; Chiavassa, Sophie; Paris, François; Costes, Sylvain V

2017-05-23

The concept of hypofractionation is gaining momentum in radiation oncology centres, enabled by recent advances in radiotherapy apparatus. The gain of efficacy of this innovative treatment must be defined. We present a computer model based on translational murine data for in silico testing and optimization of various radiotherapy protocols with respect to tumour resistance and the microenvironment heterogeneity. This model combines automata approaches with image processing algorithms to simulate the cellular response of tumours exposed to ionizing radiation, modelling the alteration of oxygen permeabilization in blood vessels against repeated doses, and introducing mitotic catastrophe (as opposed to arbitrary delayed cell-death) as a means of modelling radiation-induced cell death. Published data describing cell death in vitro as well as tumour oxygenation in vivo are used to inform parameters. Our model is validated by comparing simulations to in vivo data obtained from the radiation treatment of mice transplanted with human prostate tumours. We then predict the efficacy of untested hypofractionation protocols, hypothesizing that tumour control can be optimized by adjusting daily radiation dosage as a function of the degree of hypoxia in the tumour environment. Further biological refinement of this tool will permit the rapid development of more sophisticated strategies for radiotherapy.

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

NASA Astrophysics Data System (ADS)

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

2016-06-01

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

The impact of different dose response parameters on biologically optimized IMRT in breast cancer

NASA Astrophysics Data System (ADS)

Costa Ferreira, Brigida; Mavroidis, Panayiotis; Adamus-Górka, Magdalena; Svensson, Roger; Lind, Bengt K.

2008-05-01

The full potential of biologically optimized radiation therapy can only be maximized with the prediction of individual patient radiosensitivity prior to treatment. Unfortunately, the available biological parameters, derived from clinical trials, reflect an average radiosensitivity of the examined populations. In the present study, a breast cancer patient of stage I II with positive lymph nodes was chosen in order to analyse the effect of the variation of individual radiosensitivity on the optimal dose distribution. Thus, deviations from the average biological parameters, describing tumour, heart and lung response, were introduced covering the range of patient radiosensitivity reported in the literature. Two treatment configurations of three and seven biologically optimized intensity-modulated beams were employed. The different dose distributions were analysed using biological and physical parameters such as the complication-free tumour control probability (P+), the biologically effective uniform dose (\\bar{\\bar{D}} ), dose volume histograms, mean doses, standard deviations, maximum and minimum doses. In the three-beam plan, the difference in P+ between the optimal dose distribution (when the individual patient radiosensitivity is known) and the reference dose distribution, which is optimal for the average patient biology, ranges up to 13.9% when varying the radiosensitivity of the target volume, up to 0.9% when varying the radiosensitivity of the heart and up to 1.3% when varying the radiosensitivity of the lung. Similarly, in the seven-beam plan, the differences in P+ are up to 13.1% for the target, up to 1.6% for the heart and up to 0.9% for the left lung. When the radiosensitivity of the most important tissues in breast cancer radiation therapy was simultaneously changed, the maximum gain in outcome was as high as 7.7%. The impact of the dose response uncertainties on the treatment outcome was clinically insignificant for the majority of the simulated patients. However, the jump from generalized to individualized radiation therapy may significantly increase the therapeutic window for patients with extreme radio sensitivity or radioresistance, provided that these are identified. Even for radiosensitive patients a simple treatment technique is sufficient to maximize the outcome, since no significant benefits were obtained with a more complex technique using seven intensity-modulated beams portals.

[Research on Energy Distribution During Osteoarthritis Treatment Using Shock Wave Lithotripsy].

PubMed

Zhang, Shinian; Wang, Xiaofeng; Zhang, Dong

2015-04-01

Extracorporeal shock wave treatment is capable of providing a non-surgical and effective treatment modality for patients suffering from osteoarthritis. The major objective of current works is to investigate how the shock wave (SW) field would change if a bony structure exists in the path of the acoustic wave. Firstly, a model of finite element method (FEM) was developed based on Comsol software in the present study. Then, high-speed photography experiments were performed to record cavitation bubbles with the presence of mimic bone. On the basis of comparing experimental with simulated results, the effectiveness of FEM model could be verified. Finally, the energy distribution during extracorporeal shock wave treatment was predicted. The results showed that the shock wave field was deflected with the presence of bony structure and varying deflection angles could be observed as the bone shifted up in the z-direction relative to shock wave geometric focus. Combining MRI/CT scans to FEM modeling is helpful for better standardizing the treatment dosage and optimizing treatment protocols in the clinic.

Parent training for young Norwegian children with ODD and CD problems: predictors and mediators of treatment outcome.

PubMed

Fossum, Sturla; Mørch, Willy-Tore; Handegård, Bjørn H; Drugli, May B; Larsson, Bo

2009-04-01

Participants were 121 children, aged 4-8 years referred for conduct problems, and their mothers. A parent training intervention was implemented in two outpatient clinics in Norway. Treatment responders were defined as children scoring below a cut-off on the Eyberg Child Behavior Inventory, a score below an optimal cut-off for children in day-care and school as reported by teachers, in addition to a 30% reduction or greater in observed negative parenting. Self-reported parenting practices were explored as potential mediators. The results of logistic regression analyses showed that high levels of maternal stress, clinical levels of ADHD, and being a girl predicted a poorer outcome in conduct problems at home, while pretreatment clinical levels of ADHD predicted a poorer outcome as perceived by the teachers. Harsh and inconsistent parental disciplining emerged as significant partial mediators of changes in conduct problems, highlighting the importance of altering parenting practices to modify young children's conduct problems.

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

Ausborn, Natalie L.; Le, Quynh Thu; Bradley, Jeffrey D.

Therapeutic decisions in non-small cell lung cancer (NSCLC) have been mainly based on disease stage, performance status, and co-morbidities, and rarely on histological or molecular classification. Rather than applying broad treatments to unselected patients that may result in survival increase of only weeks to months, research efforts should be, and are being, focused on identifying predictive markers for molecularly targeted therapy and determining genomic signatures that predict survival and response to specific therapies. The availability of such targeted biologics requires their use to be matched to tumors of corresponding molecular vulnerability for maximum efficacy. Molecular markers such as epidermal growthmore » factor receptor (EGFR), K-ras, vascular endothelial growth factor (VEGF), mammalian target of rapamycin (mTOR), and anaplastic lymphoma kinase (ALK) represent potential parameters guide treatment decisions. Ultimately, identifying patients who will respond to specific therapies will allow optimal efficacy with minimal toxicity, which will result in more judicious and effective application of expensive targeted therapy as the new paradigm of personalized medicine develops.« less

Are We Ready to Use ESR1 Mutations in Clinical Practice?

PubMed

Jeselsohn, Rinath

2017-10-01

The recurrent ligand-binding domain ESR1 mutations are an important mechanism of endocrine resistance in estrogen receptor-positive (ER+) metastatic breast cancer. These mutations evolve under the selective pressure of endocrine treatments and are rarely found in treatment-naïve ER+ breast cancers. Preclinical studies showed that these mutations lead to ligand-independent activity facilitating resistance to aromatase inhibitors and relative resistance to tamoxifen and fulvestrant. Retrospective analyses of ESR1 mutations in baseline plasma circulating tumor DNA from clinical trials suggest that these mutations are prognostic of poor overall survival and predictive of resistance to aromatase inhibitors in metastatic disease. Larger datasets and prospective studies to confirm these results are lacking. In addition, response to other standard treatments for metastatic breast cancer in the presence of the ESR1 mutations is unknown, and studies to determine the optimal treatment combinations for patients with ESR1 mutations are also needed.

The Paradox of Equipoise: The Principle That Drives and Limits Therapeutic Discoveries in Clinical Research

PubMed Central

Djulbegovic, Benjamin

2009-01-01

Background Progress in clinical medicine relies on the willingness of patients to take part in experimental clinical trials, particularly randomized controlled trials (RCTs). Before agreeing to enroll in clinical trials, patients require guarantees that they will not knowingly be harmed and will have the best possible chances of receiving the most favorable treatments. This guarantee is provided by the acknowledgment of uncertainty (equipoise), which removes ethical dilemmas and makes it easier for patients to enroll in clinical trials. Methods Since the design of clinical trials is mostly affected by clinical equipoise, the “clinical equipoise hypothesis” has been postulated. If the uncertainty requirement holds, this means that investigators cannot predict what they are going to discover in any individual trial that they undertake. In some instances, new treatments will be superior to standard treatments, while in others, standard treatments will be superior to experimental treatments, and in still others, no difference will be detected between new and standard treatments. It is hypothesized that there must be a relationship between the overall pattern of treatment successes and the uncertainties that RCTs are designed to address. Results An analysis of published trials shows that the results cannot be predicted at the level of individual trials. However, the results also indicate that the overall pattern of discovery of treatment success across a series of trials is predictable and is consistent with clinical equipoise hypothesis. The analysis shows that we can discover no more than 25% to 50% of successful treatments when they are tested in RCTs. The analysis also indicates that this discovery rate is optimal in helping to preserve the clinical trial system; a high discovery rate (eg, a 90% to 100% probability of success) is neither feasible nor desirable since under these circumstances, neither the patient nor the researcher has an interest in randomization. This in turn would halt the RCT system as we know it. Conclusions The “principle or law of clinical discovery” described herein predicts the efficiency of the current system of RCTs at generating discoveries of new treatments. The principle is derived from the requirement for uncertainty or equipoise as a precondition for RCTs, the precept that paradoxically drives discoveries of new treatments while limiting the proportion and rate of new therapeutic discoveries. PMID:19910921

Method of predicting the mean lung dose based on a patient's anatomy and dose-volume histograms

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

Zawadzka, Anna, E-mail: a.zawadzka@zfm.coi.pl; Nesteruk, Marta; Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich

The aim of this study was to propose a method to predict the minimum achievable mean lung dose (MLD) and corresponding dosimetric parameters for organs-at-risk (OAR) based on individual patient anatomy. For each patient, the dose for 36 equidistant individual multileaf collimator shaped fields in the treatment planning system (TPS) was calculated. Based on these dose matrices, the MLD for each patient was predicted by the homemade DosePredictor software in which the solution of linear equations was implemented. The software prediction results were validated based on 3D conformal radiotherapy (3D-CRT) and volumetric modulated arc therapy (VMAT) plans previously prepared formore » 16 patients with stage III non–small-cell lung cancer (NSCLC). For each patient, dosimetric parameters derived from plans and the results calculated by DosePredictor were compared. The MLD, the maximum dose to the spinal cord (D{sub max} {sub cord}) and the mean esophageal dose (MED) were analyzed. There was a strong correlation between the MLD calculated by the DosePredictor and those obtained in treatment plans regardless of the technique used. The correlation coefficient was 0.96 for both 3D-CRT and VMAT techniques. In a similar manner, MED correlations of 0.98 and 0.96 were obtained for 3D-CRT and VMAT plans, respectively. The maximum dose to the spinal cord was not predicted very well. The correlation coefficient was 0.30 and 0.61 for 3D-CRT and VMAT, respectively. The presented method allows us to predict the minimum MLD and corresponding dosimetric parameters to OARs without the necessity of plan preparation. The method can serve as a guide during the treatment planning process, for example, as initial constraints in VMAT optimization. It allows the probability of lung pneumonitis to be predicted.« less

Is Optimism Real?

ERIC Educational Resources Information Center

Simmons, Joseph P.; Massey, Cade

2012-01-01

Is optimism real, or are optimistic forecasts just cheap talk? To help answer this question, we investigated whether optimistic predictions persist in the face of large incentives to be accurate. We asked National Football League football fans to predict the winner of a single game. Roughly half (the partisans) predicted a game involving their…

A prediction model for early death in non-small cell lung cancer patients following curative-intent chemoradiotherapy.

PubMed

Jochems, Arthur; El-Naqa, Issam; Kessler, Marc; Mayo, Charles S; Jolly, Shruti; Matuszak, Martha; Faivre-Finn, Corinne; Price, Gareth; Holloway, Lois; Vinod, Shalini; Field, Matthew; Barakat, Mohamed Samir; Thwaites, David; de Ruysscher, Dirk; Dekker, Andre; Lambin, Philippe

2018-02-01

Early death after a treatment can be seen as a therapeutic failure. Accurate prediction of patients at risk for early mortality is crucial to avoid unnecessary harm and reducing costs. The goal of our work is two-fold: first, to evaluate the performance of a previously published model for early death in our cohorts. Second, to develop a prognostic model for early death prediction following radiotherapy. Patients with NSCLC treated with chemoradiotherapy or radiotherapy alone were included in this study. Four different cohorts from different countries were available for this work (N = 1540). The previous model used age, gender, performance status, tumor stage, income deprivation, no previous treatment given (yes/no) and body mass index to make predictions. A random forest model was developed by learning on the Maastro cohort (N = 698). The new model used performance status, age, gender, T and N stage, total tumor volume (cc), total tumor dose (Gy) and chemotherapy timing (none, sequential, concurrent) to make predictions. Death within 4 months of receiving the first radiotherapy fraction was used as the outcome. Early death rates ranged from 6 to 11% within the four cohorts. The previous model performed with AUC values ranging from 0.54 to 0.64 on the validation cohorts. Our newly developed model had improved AUC values ranging from 0.62 to 0.71 on the validation cohorts. Using advanced machine learning methods and informative variables, prognostic models for early mortality can be developed. Development of accurate prognostic tools for early mortality is important to inform patients about treatment options and optimize care.

Lower NIH stroke scale scores are required to accurately predict a good prognosis in posterior circulation stroke.

PubMed

Inoa, Violiza; Aron, Abraham W; Staff, Ilene; Fortunato, Gilbert; Sansing, Lauren H

2014-01-01

The NIH stroke scale (NIHSS) is an indispensable tool that aids in the determination of acute stroke prognosis and decision making. Patients with posterior circulation (PC) strokes often present with lower NIHSS scores, which may result in the withholding of thrombolytic treatment from these patients. However, whether these lower initial NIHSS scores predict better long-term prognoses is uncertain. We aimed to assess the utility of the NIHSS at presentation for predicting the functional outcome at 3 months in anterior circulation (AC) versus PC strokes. This was a retrospective analysis of a large prospectively collected database of adults with acute ischemic stroke. Univariate and multivariate analyses were conducted to identify factors associated with outcome. Additional analyses were performed to determine the receiver operating characteristic (ROC) curves for NIHSS scores and outcomes in AC and PC infarctions. Both the optimal cutoffs for maximal diagnostic accuracy and the cutoffs to obtain >80% sensitivity for poor outcomes were determined in AC and PC strokes. The analysis included 1,197 patients with AC stroke and 372 with PC stroke. The median initial NIHSS score for patients with AC strokes was 7 and for PC strokes it was 2. The majority (71%) of PC stroke patients had baseline NIHSS scores ≤4, and 15% of these 'minor' stroke patients had a poor outcome at 3 months. ROC analysis identified that the optimal NIHSS cutoff for outcome prediction after infarction in the AC was 8 and for infarction in the PC it was 4. To achieve >80% sensitivity for detecting patients with a subsequent poor outcome, the NIHSS cutoff for infarctions in the AC was 4 and for infarctions in the PC it was 2. The NIHSS cutoff that most accurately predicts outcomes is 4 points higher in AC compared to PC infarctions. There is potential for poor outcomes in patients with PC strokes and low NIHSS scores, suggesting that thrombolytic treatment should not be withheld from these patients based solely on the NIHSS. © 2014 S. Karger AG, Basel. © 2014 S. Karger AG, Basel.

Evaluation of the prediction precision capability of partial least squares regression approach for analysis of high alloy steel by laser induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Sarkar, Arnab; Karki, Vijay; Aggarwal, Suresh K.; Maurya, Gulab S.; Kumar, Rohit; Rai, Awadhesh K.; Mao, Xianglei; Russo, Richard E.

2015-06-01

Laser induced breakdown spectroscopy (LIBS) was applied for elemental characterization of high alloy steel using partial least squares regression (PLSR) with an objective to evaluate the analytical performance of this multivariate approach. The optimization of the number of principle components for minimizing error in PLSR algorithm was investigated. The effect of different pre-treatment procedures on the raw spectral data before PLSR analysis was evaluated based on several statistical (standard error of prediction, percentage relative error of prediction etc.) parameters. The pre-treatment with "NORM" parameter gave the optimum statistical results. The analytical performance of PLSR model improved by increasing the number of laser pulses accumulated per spectrum as well as by truncating the spectrum to appropriate wavelength region. It was found that the statistical benefit of truncating the spectrum can also be accomplished by increasing the number of laser pulses per accumulation without spectral truncation. The constituents (Co and Mo) present in hundreds of ppm were determined with relative precision of 4-9% (2σ), whereas the major constituents Cr and Ni (present at a few percent levels) were determined with a relative precision of ~ 2%(2σ).

Lifestyle and precision diabetes medicine: will genomics help optimise the prediction, prevention and treatment of type 2 diabetes through lifestyle therapy?

PubMed

Franks, Paul W; Poveda, Alaitz

2017-05-01

Precision diabetes medicine, the optimisation of therapy using patient-level biomarker data, has stimulated enormous interest throughout society as it provides hope of more effective, less costly and safer ways of preventing, treating, and perhaps even curing the disease. While precision diabetes medicine is often framed in the context of pharmacotherapy, using biomarkers to personalise lifestyle recommendations, intended to lower type 2 diabetes risk or to slow progression, is also conceivable. There are at least four ways in which this might work: (1) by helping to predict a person's susceptibility to adverse lifestyle exposures; (2) by facilitating the stratification of type 2 diabetes into subclasses, some of which may be prevented or treated optimally with specific lifestyle interventions; (3) by aiding the discovery of prognostic biomarkers that help guide timing and intensity of lifestyle interventions; (4) by predicting treatment response. In this review we overview the rationale for precision diabetes medicine, specifically as it relates to lifestyle; we also scrutinise existing evidence, discuss the barriers germane to research in this field and consider how this work is likely to proceed.

Optimizing finite element predictions of local subchondral bone structural stiffness using neural network-derived density-modulus relationships for proximal tibial subchondral cortical and trabecular bone.

PubMed

Nazemi, S Majid; Amini, Morteza; Kontulainen, Saija A; Milner, Jaques S; Holdsworth, David W; Masri, Bassam A; Wilson, David R; Johnston, James D

2017-01-01

Quantitative computed tomography based subject-specific finite element modeling has potential to clarify the role of subchondral bone alterations in knee osteoarthritis initiation, progression, and pain. However, it is unclear what density-modulus equation(s) should be applied with subchondral cortical and subchondral trabecular bone when constructing finite element models of the tibia. Using a novel approach applying neural networks, optimization, and back-calculation against in situ experimental testing results, the objective of this study was to identify subchondral-specific equations that optimized finite element predictions of local structural stiffness at the proximal tibial subchondral surface. Thirteen proximal tibial compartments were imaged via quantitative computed tomography. Imaged bone mineral density was converted to elastic moduli using multiple density-modulus equations (93 total variations) then mapped to corresponding finite element models. For each variation, root mean squared error was calculated between finite element prediction and in situ measured stiffness at 47 indentation sites. Resulting errors were used to train an artificial neural network, which provided an unlimited number of model variations, with corresponding error, for predicting stiffness at the subchondral bone surface. Nelder-Mead optimization was used to identify optimum density-modulus equations for predicting stiffness. Finite element modeling predicted 81% of experimental stiffness variance (with 10.5% error) using optimized equations for subchondral cortical and trabecular bone differentiated with a 0.5g/cm 3 density. In comparison with published density-modulus relationships, optimized equations offered improved predictions of local subchondral structural stiffness. Further research is needed with anisotropy inclusion, a smaller voxel size and de-blurring algorithms to improve predictions. Copyright © 2016 Elsevier Ltd. All rights reserved.

Pilot-scale treatment of atrazine production wastewater by UV/O3/ultrasound: Factor effects and system optimization.

PubMed

Jing, Liang; Chen, Bing; Wen, Diya; Zheng, Jisi; Zhang, Baiyu

2017-12-01

This study shed light on removing atrazine from pesticide production wastewater using a pilot-scale UV/O 3 /ultrasound flow-through system. A significant quadratic polynomial prediction model with an adjusted R 2 of 0.90 was obtained from central composite design with response surface methodology. The optimal atrazine removal rate (97.68%) was obtained at the conditions of 75 W UV power, 10.75 g h -1 O 3 flow rate and 142.5 W ultrasound power. A Monte Carlo simulation aided artificial neural networks model was further developed to quantify the importance of O 3 flow rate (40%), UV power (30%) and ultrasound power (30%). Their individual and interaction effects were also discussed in terms of reaction kinetics. UV and ultrasound could both enhance the decomposition of O 3 and promote hydroxyl radical (OH·) formation. Nonetheless, the dose of O 3 was the dominant factor and must be optimized because excess O 3 can react with OH·, thereby reducing the rate of atrazine degradation. The presence of other organic compounds in the background matrix appreciably inhibited the degradation of atrazine, while the effects of Cl - , CO 3 2- and HCO 3 - were comparatively negligible. It was concluded that the optimization of system performance using response surface methodology and neural networks would be beneficial for scaling up the treatment by UV/O 3 /ultrasound at industrial level. Copyright © 2017 Elsevier Ltd. All rights reserved.

Treatment of systematic errors in land data assimilation systems

NASA Astrophysics Data System (ADS)

Crow, W. T.; Yilmaz, M.

2012-12-01

Data assimilation systems are generally designed to minimize the influence of random error on the estimation of system states. Yet, experience with land data assimilation systems has also revealed the presence of large systematic differences between model-derived and remotely-sensed estimates of land surface states. Such differences are commonly resolved prior to data assimilation through implementation of a pre-processing rescaling step whereby observations are scaled (or non-linearly transformed) to somehow "match" comparable predictions made by an assimilation model. While the rationale for removing systematic differences in means (i.e., bias) between models and observations is well-established, relatively little theoretical guidance is currently available to determine the appropriate treatment of higher-order moments during rescaling. This talk presents a simple analytical argument to define an optimal linear-rescaling strategy for observations prior to their assimilation into a land surface model. While a technique based on triple collocation theory is shown to replicate this optimal strategy, commonly-applied rescaling techniques (e.g., so called "least-squares regression" and "variance matching" approaches) are shown to represent only sub-optimal approximations to it. Since the triple collocation approach is likely infeasible in many real-world circumstances, general advice for deciding between various feasible (yet sub-optimal) rescaling approaches will be presented with an emphasis of the implications of this work for the case of directly assimilating satellite radiances. While the bulk of the analysis will deal with linear rescaling techniques, its extension to nonlinear cases will also be discussed.

Predictive models for water sources with high susceptibility for bromine-containing disinfection by-product formation: implications for water treatment.

PubMed

Watson, Kalinda; Farré, Maria José; Birt, James; McGree, James; Knight, Nicole

2015-02-01

This study examines a matrix of synthetic water samples designed to include conditions that favour brominated disinfection by-product (Br-DBP) formation, in order to provide predictive models suitable for high Br-DBP forming waters such as salinity-impacted waters. Br-DBPs are known to be more toxic than their chlorinated analogues, in general, and their formation may be favoured by routine water treatment practices such as coagulation/flocculation under specific conditions; therefore, circumstances surrounding their formation must be understood. The chosen factors were bromide concentration, mineral alkalinity, bromide to dissolved organic carbon (Br/DOC) ratio and Suwannee River natural organic matter concentration. The relationships between these parameters and DBP formation were evaluated by response surface modelling of data generated using a face-centred central composite experimental design. Predictive models for ten brominated and/or chlorinated DBPs are presented, as well as models for total trihalomethanes (tTHMs) and total dihaloacetonitriles (tDHANs), and bromide substitution factors for the THMs and DHANs classes. The relationships described revealed that increasing alkalinity and increasing Br/DOC ratio were associated with increasing bromination of THMs and DHANs, suggesting that DOC lowering treatment methods that do not also remove bromide such as enhanced coagulation may create optimal conditions for Br-DBP formation in waters in which bromide is present.

Associations of early childhood adversities with mental disorders, psychological functioning, and suitability for psychotherapy in adulthood.

PubMed

Heinonen, Erkki; Knekt, Paul; Härkänen, Tommi; Virtala, Esa; Lindfors, Olavi

2018-06-01

Childhood adversities frequently precede adulthood depression and anxiety. Yet, how they impact needed treatment duration, type or focus in these common disorders, is unclear. For developing more individualized and precise interventions, we investigated whether specific early adversities associate with patients' distinct psychiatric problems, psychological vulnerabilities, and suitability for psychotherapy. A total of 221 depressed and anxious adult outpatients (excluding psychotic, severe personality, bipolar, and substance abuse disorders) referred from community, student, occupational, and private healthcare services filled the Childhood Family Atmosphere Questionnaire (CFAQ). They also filled self-reports on interpersonal behavior and problems, perceived competence, dispositional optimism, sense of coherence, defenses, and psychiatric history. Clinicians assessed the patients' symptomatology, personality, object relations, cognitive performance, and psychotherapy suitability. Regression analyses were conducted. Childhood adversities predicted both worse current psychological functioning (e.g., interpersonal problems), and better clinician-rated capacities for benefiting from psychotherapy (e.g. self-reflection, capacity for interaction). Parental problems had the most numerous negative associations to psychological functioning. Best capacities for psychotherapy were predicted by recollected family unhappiness. Associations with psychiatric criteria were, however, largely non-significant. In conclusion, for psychosocial treatment planning, patients' early adversities may indicate both vulnerability and resources. As childhood adversities are frequent among treatment-seekers, further studies examining how early adversities predict psychotherapy outcome are needed. Copyright © 2018. Published by Elsevier B.V.

Crosswalk between DSM-IV Dependence and DSM-5 Substance Use Disorders for Opioids, Cannabis, Cocaine and Alcohol

PubMed Central

Compton, Wilson M.; Dawson, Deborah A.; Goldstein, Risë B.; Grant, Bridget F.

2013-01-01

Background Ascertaining agreement between DSM-IV and DSM-5 is important to determine the applicability of treatments for DSM-IV conditions to persons diagnosed according to the proposed DSM-5. Methods Data from a nationally representative sample of US adults were used to compare concordance of past-year DSM-IV Opioid, Cannabis, Cocaine and Alcohol Dependence with past-year DSM-5 disorders at thresholds of 3+, 4+ 5+ and 6+ positive DSM-5 criteria among past-year users of opioids (n=264), cannabis (n=1,622), cocaine (n=271) and alcohol (n=23,013). Substance-specific 2×2 tables yielded overall concordance (kappa), sensitivity, specificity, positive predictive values (PPV) and negative predictive values (NPV). Results For DSM-IV Alcohol, Cocaine and Opioid Dependence, optimal concordance occurred when 4+ DSM-5 criteria were endorsed, corresponding to the threshold for moderate DSM-5 Alcohol, Cocaine and Opioid Use Disorders. Maximal concordance of DSM-IV Cannabis Dependence and DSM-5 Cannabis Use Disorder occurred when 6+ criteria were endorsed, corresponding to the threshold for severe DSM-5 Cannabis Use Disorder. At these optimal thresholds, sensitivity, specificity, PPV and NPV generally exceeded 85% (>75% for cannabis). Conclusions Overall, excellent correspondence of DSM-IV Dependence with DSM-5 Substance Use Disorders was documented in this general population sample of alcohol, cannabis, cocaine and opioid users. Applicability of treatments tested for DSM-IV Dependence is supported by these results for those with a DSM-5 Alcohol, Cocaine or Opioid Use Disorder of at least moderate severity or Severe Cannabis Use Disorder. Further research is needed to provide evidence for applicability of treatments for persons with milder substance use disorders. PMID:23642316

Conditional power and predictive power based on right censored data with supplementary auxiliary information.

PubMed

Sun, Libo; Wan, Ying

2018-04-22

Conditional power and predictive power provide estimates of the probability of success at the end of the trial based on the information from the interim analysis. The observed value of the time to event endpoint at the interim analysis could be biased for the true treatment effect due to early censoring, leading to a biased estimate of conditional power and predictive power. In such cases, the estimates and inference for this right censored primary endpoint are enhanced by incorporating a fully observed auxiliary variable. We assume a bivariate normal distribution of the transformed primary variable and a correlated auxiliary variable. Simulation studies are conducted that not only shows enhanced conditional power and predictive power but also can provide the framework for a more efficient futility interim analysis in terms of an improved accuracy in estimator, a smaller inflation in type II error and an optimal timing for such analysis. We also illustrated the new approach by a real clinical trial example. Copyright © 2018 John Wiley & Sons, Ltd.

Predictive factor and antihypertensive usage of tyrosine kinase inhibitor-induced hypertension in kidney cancer patients

PubMed Central

IZUMI, KOUJI; ITAI, SHINGO; TAKAHASHI, YOSHIKO; MAOLAKE, AERKEN; NAMIKI, MIKIO

2014-01-01

Hypertension (HT) is the common adverse event associated with vascular endothelial growth factor receptor-tyrosine kinase inhibitors (VEGFR-TKI). The present study was performed to identify the predictive factors of TKI-induced HT and to determine the classes of antihypertensive agents (AHTA) that demonstrate optimal efficacy against this type of HT. The charts of 50 cases of patients that had received VEGFR-TKI treatment were retrospectively examined. The association between patient background and TKI-induced HT, and the effect of administering AHTA were analyzed. High systolic blood pressure at baseline was identified to be a predictive factor for HT. In addition, there was no difference observed between calcium channel blockers (CCBs) and angiotensin receptor II blockers (ARBs) as first-line AHTA for the control of HT. The findings of the present study may aid with predicting the onset of TKI-induced HT, as well as for its management via the primary use of either CCBs or ARBs. PMID:24959266

Multiple scattering theory for total skin electron beam design.

PubMed

Antolak, J A; Hogstrom, K R

1998-06-01

The purpose of this manuscript is to describe a method for designing a broad beam of electrons suitable for total skin electron irradiation (TSEI). A theoretical model of a TSEI beam from a linear accelerator with a dual scattering system has been developed. The model uses Fermi-Eyges theory to predict the planar fluence of the electron beam after it has passed through various materials between the source and the treatment plane, which includes scattering foils, monitor chamber, air, and a plastic diffusing plate. Unique to this model is its accounting for removal of the tails of the electron beam profile as it passes through the primary x-ray jaws. A method for calculating the planar fluence profile for an obliquely incident beam is also described. Off-axis beam profiles and percentage depth doses are measured with ion chambers, film, and thermoluminescent dosimeters (TLD). The measured data show that the theoretical model can accurately predict beam energy and planar fluence of the electron beam at normal and oblique incidence. The agreement at oblique angles is not quite as good but is sufficiently accurate to be of predictive value when deciding on the optimal angles for the clinical TSEI beams. The advantage of our calculational approach for designing a TSEI beam is that many different beam configurations can be tested without having to perform time-consuming measurements. Suboptimal configurations can be quickly dismissed, and the predicted optimal solution should be very close to satisfying the clinical specifications.

Bioethanol production optimization: a thermodynamic analysis.

PubMed

Alvarez, Víctor H; Rivera, Elmer Ccopa; Costa, Aline C; Filho, Rubens Maciel; Wolf Maciel, Maria Regina; Aznar, Martín

2008-03-01

In this work, the phase equilibrium of binary mixtures for bioethanol production by continuous extractive process was studied. The process is composed of four interlinked units: fermentor, centrifuge, cell treatment unit, and flash vessel (ethanol-congener separation unit). A proposal for modeling the vapor-liquid equilibrium in binary mixtures found in the flash vessel has been considered. This approach uses the Predictive Soave-Redlich-Kwong equation of state, with original and modified molecular parameters. The congeners considered were acetic acid, acetaldehyde, furfural, methanol, and 1-pentanol. The results show that the introduction of new molecular parameters r and q in the UNIFAC model gives more accurate predictions for the concentration of the congener in the gas phase for binary and ternary systems.

Modeling polyvinyl chloride Plasma Modification by Neural Networks

NASA Astrophysics Data System (ADS)

Wang, Changquan

2018-03-01

Neural networks model were constructed to analyze the connection between dielectric barrier discharge parameters and surface properties of material. The experiment data were generated from polyvinyl chloride plasma modification by using uniform design. Discharge voltage, discharge gas gap and treatment time were as neural network input layer parameters. The measured values of contact angle were as the output layer parameters. A nonlinear mathematical model of the surface modification for polyvinyl chloride was developed based upon the neural networks. The optimum model parameters were obtained by the simulation evaluation and error analysis. The results of the optimal model show that the predicted value is very close to the actual test value. The prediction model obtained here are useful for discharge plasma surface modification analysis.

Heat transfer simulation and retort program adjustment for thermal processing of wheat based Haleem in semi-rigid aluminum containers.

PubMed

Vatankhah, Hamed; Zamindar, Nafiseh; Shahedi Baghekhandan, Mohammad

2015-10-01

A mixed computational strategy was used to simulate and optimize the thermal processing of Haleem, an ancient eastern food, in semi-rigid aluminum containers. Average temperature values of the experiments showed no significant difference (α = 0.05) in contrast to the predicted temperatures at the same positions. According to the model, the slowest heating zone was located in geometrical center of the container. The container geometrical center F0 was estimated to be 23.8 min. A 19 min processing time interval decrease in holding time of the treatment was estimated to optimize the heating operation since the preferred F0 of some starch or meat based fluid foods is about 4.8-7.5 min.

Optimization of palm fruit sterilization by microwave irradiation using response surface methodology

NASA Astrophysics Data System (ADS)

Sarah, M.; Madinah, I.; Salamah, S.

2018-02-01

This study reported optimization of palm fruit sterilization process by microwave irradiation. The results of fractional factorial experiments showed no significant external factors affecting temperature of microwave sterilization (MS). Response surface methodology (RSM) was employed and model equation of MS of palm fruit was built. Response surface plots and their corresponding contour plots were analyzed as well as solving model equation. The optimum process parameters for lipase reduction were obtained from MS of 1 kg palm fruit at microwave power of 486 Watt and heating time of 14 minutes. The experimental results showed reduction of lipase activity in the present work under MS treatment. The adequacy of the model equation for predicting the optimum response value was verified by validation data (P>0.15).

A 3D correction method for predicting the readings of a PinPoint chamber on the CyberKnife® M6™ machine

NASA Astrophysics Data System (ADS)

Zhang, Yongqian; Brandner, Edward; Ozhasoglu, Cihat; Lalonde, Ron; Heron, Dwight E.; Saiful Huq, M.

2018-02-01

The use of small fields in radiation therapy techniques has increased substantially in particular in stereotactic radiosurgery (SRS) and stereotactic body radiation therapy (SBRT). However, as field size reduces further still, the response of the detector changes more rapidly with field size, and the effects of measurement uncertainties become increasingly significant due to the lack of lateral charged particle equilibrium, spectral changes as a function of field size, detector choice, and subsequent perturbations of the charged particle fluence. This work presents a novel 3D dose volume-to-point correction method to predict the readings of a 0.015 cc PinPoint chamber (PTW 31014) for both small static-fields and composite-field dosimetry formed by fixed cones on the CyberKnife® M6™ machine. A 3D correction matrix is introduced to link the 3D dose distribution to the response of the PinPoint chamber in water. The parameters of the correction matrix are determined by modeling its 3D dose response in circular fields created using the 12 fixed cones (5 mm-60 mm) on a CyberKnife® M6™ machine. A penalized least-square optimization problem is defined by fitting the calculated detector reading to the experimental measurement data to generate the optimal correction matrix; the simulated annealing algorithm is used to solve the inverse optimization problem. All the experimental measurements are acquired for every 2 mm chamber shift in the horizontal planes for each field size. The 3D dose distributions for the measurements are calculated using the Monte Carlo calculation with the MultiPlan® treatment planning system (Accuray Inc., Sunnyvale, CA, USA). The performance evaluation of the 3D conversion matrix is carried out by comparing the predictions of the output factors (OFs), off-axis ratios (OARs) and percentage depth dose (PDD) data to the experimental measurement data. The discrepancy of the measurement and the prediction data for composite fields is also performed for clinical SRS plans. The optimization algorithm used for generating the optimal correction factors is stable, and the resulting correction factors were smooth in the spatial domain. The measurement and prediction of OFs agree closely with percentage differences of less than 1.9% for all the 12 cones. The discrepancies between the prediction and the measurement PDD readings at 50 mm and 80 mm depth are 1.7% and 1.9%, respectively. The percentage differences of OARs between measurement and prediction data are less than 2% in the low dose gradient region, and 2%/1 mm discrepancies are observed within the high dose gradient regions. The differences between the measurement and prediction data for all the CyberKnife based SRS plans are less than 1%. These results demonstrate the existence and efficiency of the novel 3D correction method for small field dosimetry. The 3D correction matrix links the 3D dose distribution and the reading of the PinPoint chamber. The comparison between the predicted reading and the measurement data for static small fields (OFs, OARs and PDDs) yield discrepancies within 2% for low dose gradient regions and 2%/1 mm for high dose gradient regions; the discrepancies between the predicted and the measurement data are less than 1% for all the SRS plans. The 3D correction method provides an access to evaluate the clinical measurement data and can be applied to non-standard composite fields intensity modulated radiation therapy point dose verification.

Systems modeling accurately predicts responses to genotoxic agents and their synergism with BCL-2 inhibitors in triple negative breast cancer cells.

PubMed

Lucantoni, Federico; Lindner, Andreas U; O'Donovan, Norma; Düssmann, Heiko; Prehn, Jochen H M

2018-01-19

Triple negative breast cancer (TNBC) is an aggressive form of breast cancer which accounts for 15-20% of this disease and is currently treated with genotoxic chemotherapy. The BCL2 (B-cell lymphoma 2) family of proteins controls the process of mitochondrial outer membrane permeabilization (MOMP), which is required for the activation of the mitochondrial apoptosis pathway in response to genotoxic agents. We previously developed a deterministic systems model of BCL2 protein interactions, DR_MOMP that calculates the sensitivity of cells to undergo mitochondrial apoptosis. Here we determined whether DR_MOMP predicts responses of TNBC cells to genotoxic agents and the re-sensitization of resistant cells by BCL2 inhibitors. Using absolute protein levels of BAX, BAK, BCL2, BCL(X)L and MCL1 as input for DR_MOMP, we found a strong correlation between model predictions and responses of a panel of TNBC cells to 24 and 48 h cisplatin (R 2  = 0.96 and 0.95, respectively) and paclitaxel treatments (R 2  = 0.94 and 0.95, respectively). This outperformed single protein correlations (best performer BCL(X)L with R 2 of 0.69 and 0.50 for cisplatin and paclitaxel treatments, respectively) and BCL2 proteins ratio (R 2 of 0.50 for cisplatin and 0.49 for paclitaxel). Next we performed synergy studies using the BCL2 selective antagonist Venetoclax /ABT199, the BCL(X)L selective antagonist WEHI-539, or the MCL1 selective antagonist A-1210477 in combination with cisplatin. In silico predictions by DR_MOMP revealed substantial differences in treatment responses of BCL(X)L, BCL2 or MCL1 inhibitors combinations with cisplatin that were successfully validated in cell lines. Our findings provide evidence that DR_MOMP predicts responses of TNBC cells to genotoxic therapy, and can aid in the choice of the optimal BCL2 protein antagonist for combination treatments of resistant cells.

A clinical nomogram to predict the successful shock wave lithotripsy of renal and ureteral calculi.

PubMed

Wiesenthal, Joshua D; Ghiculete, Daniela; Ray, A Andrew; Honey, R John D'A; Pace, Kenneth T

2011-08-01

Although shock wave lithotripsy is dependent on patient and stone related factors, there are few reliable algorithms predictive of its success. In this study we develop a comprehensive nomogram to predict renal and ureteral stone shock wave lithotripsy outcomes. During a 5-year period data from patients treated at our lithotripsy unit were reviewed. Analysis was restricted to patients with a solitary renal or ureteral calculus 20 mm or less. Demographic, stone, patient, treatment and 3-month followup data were collected from a prospective database. All patients were treated using the Philips Lithotron® lithotripter. A total of 422 patients (69.7% male) were analyzed. Mean stone size was 52.3±39.3 mm2 for ureteral stones and 78.9±77.3 mm2 for renal stones, with 95 (43.6%) of the renal stones located in the lower pole. The single treatment success rates for ureteral and renal stones were 60.3% and 70.2%, respectively. On univariate analysis predictors of shock wave lithotripsy success, regardless of stone location, were age (p=0.01), body mass index (p=0.01), stone size (p<0.01), mean stone density (p<0.01) and skin to stone distance (p<0.01). By multivariate logistic regression for renal calculi, age, stone area and skin to stone distance were significant predictors with an AUC of 0.75. For ureteral calculi predictive factors included body mass index and stone size (AUC 0.70). Patient and stone parameters have been identified to create a nomogram that predicts shock wave lithotripsy outcomes using the Lithotron lithotripter, which can facilitate optimal treatment based decisions and provide patients with more accurate single treatment success rates for shock wave lithotripsy tailored to patient specific situations. Copyright © 2011 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

Symptom Presentation and Prescription of Sleep Medications for Veterans With Posttraumatic Stress Disorder.

PubMed

Greenbaum, Mark A; Neylan, Thomas C; Rosen, Craig S

2017-02-01

This study tested whether sleep medications prescribed to veterans diagnosed with posttraumatic stress disorder (PTSD) are being targeted to patients who report more severe insomnia or nightmares. Secondary analysis of survey and pharmacy data was conducted in samples of veterans from two periods: from 2006 to 2008 and from 2009 to 2013. Logistic regression tested associations between self-reported insomnia and nightmare severity, and being prescribed trazodone, prazosin, zolpidem, and benzodiazepines, controlling for PTSD severity and other covariates. In both samples, insomnia severity independently predicted trazodone receipt, and nightmare severity independently predicted prazosin receipt. In the later study, insomnia severity predicted receipt of zolpidem. Veterans in the later sample were more likely to receive trazodone, prazosin, and non-benzodiazepine hypnotics, and less likely to receive benzodiazepines than those in the earlier sample. Further research is needed to evaluate and optimize pharmacological and psychosocial treatments for sleep problems among veterans with PTSD.

Optimizing the Anti-VEGF Treatment Strategy for Neovascular Age-Related Macular Degeneration: From Clinical Trials to Real-Life Requirements.

PubMed

Mantel, Irmela

2015-06-01

This Perspective discusses the pertinence of variable dosing regimens with anti-vascular endothelial growth factor (VEGF) for neovascular age-related macular degeneration (nAMD) with regard to real-life requirements. After the initial pivotal trials of anti-VEGF therapy, the variable dosing regimens pro re nata (PRN), Treat-and-Extend, and Observe-and-Plan, a recently introduced regimen, aimed to optimize the anti-VEGF treatment strategy for nAMD. The PRN regimen showed good visual results but requires monthly monitoring visits and can therefore be difficult to implement. Moreover, application of the PRN regimen revealed inferior results in real-life circumstances due to problems with resource allocation. The Treat-and-Extend regimen uses an interval based approach and has become widely accepted for its ease of preplanning and the reduced number of office visits required. The parallel development of the Observe-and-Plan regimen demonstrated that the future need for retreatment (interval) could be reliably predicted. Studies investigating the observe-and-plan regimen also showed that this could be used in individualized fixed treatment plans, allowing for dramatically reduced clinical burden and good outcomes, thus meeting the real life requirements. This progressive development of variable dosing regimens is a response to the real-life circumstances of limited human, technical, and financial resources. This includes an individualized treatment approach, optimization of the number of retreatments, a minimal number of monitoring visits, and ease of planning ahead. The Observe-and-Plan regimen achieves this goal with good functional results. Translational Relevance: This perspective reviews the process from the pivotal clinical trials to the development of treatment regimens which are adjusted to real life requirements. The article discusses this translational process which- although not the classical interpretation of translation from fundamental to clinical research, but a subsequent process after the pivotal clinical trials - represents an important translational step from the clinical proof of efficacy to optimization in terms of patients' and clinics' needs. The related scientific procedure includes the exploration of the concept, evaluation of security, and finally proof of efficacy.

Estimation of an optimal chemotherapy utilisation rate for cancer: setting an evidence-based benchmark for quality cancer care.

PubMed

Jacob, S A; Ng, W L; Do, V

2015-02-01

There is wide variation in the proportion of newly diagnosed cancer patients who receive chemotherapy, indicating the need for a benchmark rate of chemotherapy utilisation. This study describes an evidence-based model that estimates the proportion of new cancer patients in whom chemotherapy is indicated at least once (defined as the optimal chemotherapy utilisation rate). The optimal chemotherapy utilisation rate can act as a benchmark for measuring and improving the quality of care. Models of optimal chemotherapy utilisation were constructed for each cancer site based on indications for chemotherapy identified from evidence-based treatment guidelines. Data on the proportion of patient- and tumour-related attributes for which chemotherapy was indicated were obtained, using population-based data where possible. Treatment indications and epidemiological data were merged to calculate the optimal chemotherapy utilisation rate. Monte Carlo simulations and sensitivity analyses were used to assess the effect of controversial chemotherapy indications and variations in epidemiological data on our model. Chemotherapy is indicated at least once in 49.1% (95% confidence interval 48.8-49.6%) of all new cancer patients in Australia. The optimal chemotherapy utilisation rates for individual tumour sites ranged from a low of 13% in thyroid cancers to a high of 94% in myeloma. The optimal chemotherapy utilisation rate can serve as a benchmark for planning chemotherapy services on a population basis. The model can be used to evaluate service delivery by comparing the benchmark rate with patterns of care data. The overall estimate for other countries can be obtained by substituting the relevant distribution of cancer types. It can also be used to predict future chemotherapy workload and can be easily modified to take into account future changes in cancer incidence, presentation stage or chemotherapy indications. Copyright © 2014 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Mechanical testing and finite element analysis of orthodontic teardrop loop.

PubMed

Coimbra, Maria Elisa Rodrigues; Penedo, Norman Duque; de Gouvêa, Jayme Pereira; Elias, Carlos Nelson; de Souza Araújo, Mônica Tirre; Coelho, Paulo Guilherme

2008-02-01

Understanding how teeth move in response to mechanical loads is an important aspect of orthodontic treatment. Treatment planning should include consideration of the appliances that will meet the desired loading of the teeth to result in optimized treatment outcomes. The purpose of this study was to evaluate the use of computer simulation to predict the force and the torsion obtained after the activation of tear drop loops of 3 heights. Seventy-five retraction loops were divided into 3 groups according to height (6, 7, and 8 mm). The loops were subjected to tensile load through displacements of 0.5, 1.0, 1.5, and 2.0 mm, and the resulting forces and torques were recorded. The loops were designed in AutoCAD software(2005; Autodesk Systems, Alpharetta, GA), and finite element analysis was performed with Ansys software(version 7.0; Swanson Analysis System, Canonsburg, PA). Statistical analysis of the mechanical experiment results was obtained by ANOVA and the Tukey post-hoc test (P < .01). The correlation test and the paired t test (P < .05) were used to compare the computer simulation with the mechanical experiment. The computer simulation accurately predicted the experimentally determined mechanical behavior of tear drop loops of different heights and should be considered an alternative for designing orthodontic appliances before treatment.

Parental influence on children with attention-deficit/hyperactivity disorder: II. Results of a pilot intervention training parents as friendship coaches for children.

PubMed

Mikami, Amori Yee; Lerner, Matthew D; Griggs, Marissa Swaim; McGrath, Alison; Calhoun, Casey D

2010-08-01

We report findings from a pilot intervention that trained parents to be "friendship coaches" for their children with Attention-Deficit/Hyperactivity Disorder (ADHD). Parents of 62 children with ADHD (ages 6-10; 68% male) were randomly assigned to receive the parental friendship coaching (PFC) intervention, or to be in a no-treatment control group. Families of 62 children without ADHD were included as normative comparisons. PFC was administered in eight, 90-minute sessions to parents; there was no child treatment component. Parents were taught to arrange a social context in which their children were optimally likely to develop good peer relationships. Receipt of PFC predicted improvements in children's social skills and friendship quality on playdates as reported by parents, and peer acceptance and rejection as reported by teachers unaware of treatment status. PFC also predicted increases in observed parental facilitation and corrective feedback, and reductions in criticism during the child's peer interaction, which mediated the improvements in children's peer relationships. However, no effects for PFC were found on the number of playdates hosted or on teacher report of child social skills. Findings lend initial support to a treatment model that targets parental behaviors to address children's peer problems.

Optimal Prediction in the Retina and Natural Motion Statistics

NASA Astrophysics Data System (ADS)

Salisbury, Jared M.; Palmer, Stephanie E.

2016-03-01

Almost all behaviors involve making predictions. Whether an organism is trying to catch prey, avoid predators, or simply move through a complex environment, the organism uses the data it collects through its senses to guide its actions by extracting from these data information about the future state of the world. A key aspect of the prediction problem is that not all features of the past sensory input have predictive power, and representing all features of the external sensory world is prohibitively costly both due to space and metabolic constraints. This leads to the hypothesis that neural systems are optimized for prediction. Here we describe theoretical and computational efforts to define and quantify the efficient representation of the predictive information by the brain. Another important feature of the prediction problem is that the physics of the world is diverse enough to contain a wide range of possible statistical ensembles, yet not all inputs are probable. Thus, the brain might not be a generalized predictive machine; it might have evolved to specifically solve the prediction problems most common in the natural environment. This paper summarizes recent results on predictive coding and optimal predictive information in the retina and suggests approaches for quantifying prediction in response to natural motion. Basic statistics of natural movies reveal that general patterns of spatiotemporal correlation are present across a wide range of scenes, though individual differences in motion type may be important for optimal processing of motion in a given ecological niche.

The Potential Benefits of Advanced Casing Treatment for Noise Attenuation in Utra-High Bypass Ratio Turbofan Engines

NASA Technical Reports Server (NTRS)

Elliott, David

2007-01-01

In order to increase stall margin in a high-bypass ratio turbofan engine, an advanced casing treatment was developed that extracted a small amount of flow from the casing behind the fan and injected it back in front of the fan. Several different configurations of this casing treatment were designed by varying the distance of the extraction and injection points, as well as varying the amount of flow. These casing treatments were tested on a 55.9 cm (22 in.) scale model of the Pratt & Whitney Advanced Ducted Propulsor in the NASA Glenn 9 by 15 Low Speed Wind Tunnel. While all of the casing treatment configurations showed the expected increase in stall margin, a few of the designs showed a potential noise benefit for certain engine speeds. This paper will show the casing treatments and the results of the testing as well as propose further research in this area. With better prediction and design techniques, future casing treatment configurations could be developed that may result in an optimized casing treatment that could conceivably reduce the noise further.

Assessing treatment response in triple-negative breast cancer from quantitative image analysis in perfusion magnetic resonance imaging.

PubMed

Banerjee, Imon; Malladi, Sadhika; Lee, Daniela; Depeursinge, Adrien; Telli, Melinda; Lipson, Jafi; Golden, Daniel; Rubin, Daniel L

2018-01-01

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is sensitive but not specific to determining treatment response in early stage triple-negative breast cancer (TNBC) patients. We propose an efficient computerized technique for assessing treatment response, specifically the residual tumor (RT) status and pathological complete response (pCR), in response to neoadjuvant chemotherapy. The proposed approach is based on Riesz wavelet analysis of pharmacokinetic maps derived from noninvasive DCE-MRI scans, obtained before and after treatment. We compared the performance of Riesz features with the traditional gray level co-occurrence matrices and a comprehensive characterization of the lesion that includes a wide range of quantitative features (e.g., shape and boundary). We investigated a set of predictive models ([Formula: see text]) incorporating distinct combinations of quantitative characterizations and statistical models at different time points of the treatment and some area under the receiver operating characteristic curve (AUC) values we reported are above 0.8. The most efficient models are based on first-order statistics and Riesz wavelets, which predicted RT with an AUC value of 0.85 and pCR with an AUC value of 0.83, improving results reported in a previous study by [Formula: see text]. Our findings suggest that Riesz texture analysis of TNBC lesions can be considered a potential framework for optimizing TNBC patient care.

Less than Optimal Parenting Strategies Predict Maternal Low-Level Depression beyond that of Child Transgressions

ERIC Educational Resources Information Center

Lagace-Seguin, Daniel G.; d'Entremont, Marc-Robert L.

2006-01-01

The relationship between less than optimal parenting styles, child transgressions and maternal depression were examined. It was predicted that variations in parenting styles would predict maternal depression over and above child transgressions. The present study involved approximately 68 children, their mothers and their preschool teachers.…

A prospective observational study to evaluate the effect of social and personality factors on continuous positive airway pressure (CPAP) compliance in obstructive sleep apnoea syndrome.

PubMed

Gulati, Atul; Ali, Masood; Davies, Mike; Quinnell, Tim; Smith, Ian

2017-03-22

Compliance with CPAP treatment for OSAS is not reliably predicted by the severity of symptoms or physiological variables. We examined a range of factors which could be measured before CPAP initiation to look for predictors of compliance. This was a prospective cohort-study of CPAP treatment for OSAS, recording; socio-economic status, education, type D personality and clinician's prediction of compliance. We recruited 265 subjects, of whom 221 were still using CPAP at 6 months; median age 53 years, M: F, 3.4:1, ESS 15 and pre-treatment ODI 21/h. Median compliance at 6 months was 5.6 (3.4- 7.1) hours/night with 73.3% of subjects using CPAP ≥4 h/night. No association was found between compliance and different socio-economic classes for people in work, type D personality, education level, sex, age, baseline ESS or ODI. The clinician's initial impression could separate groups of good and poor compliers but had little predictive value for individual patients. Compared to subjects who were working, those who were long term unemployed had a lower CPAP usage and were more likely to use CPAP < 4 h a night (OR 4.6; p value 0.011). A high Beck Depression Index and self-reported anxiety also predicted poor compliance. In our practice there is no significant association between CPAP compliance with socio-economic status, education or personality type. Long term unemployed or depressed individuals may need more intensive support to gain the optimal benefit from CPAP.

Long-term prognosis of patients with life-threatening ventricular arrhythmias induced by coronary artery spasm.

PubMed

Rodríguez-Mañero, Moisés; Oloriz, Teresa; le Polain de Waroux, Jean-Benoit; Burri, Haran; Kreidieh, Bahij; de Asmundis, Carlos; Arias, Miguel A; Arbelo, Elena; Díaz Fernández, Brais; Fernández-Armenta, Juan; Basterra, Nuria; Izquierdo, María Teresa; Díaz-Infante, Ernesto; Ballesteros, Gabriel; Carrillo López, Andrés; García-Bolao, Ignacio; Benezet-Mazuecos, Juan; Expósito-García, Victor; Larraitz-Gaztañaga; Martínez-Sande, Jose Luis; García-Seara, Javier; González-Juanatey, Jose Ramón; Peinado, Rafael

2018-05-01

Coronary artery spasm (CAS) is associated with ventricular arrhythmias (VA). Much controversy remains regarding the best therapeutic interventions for this specific patient subset. We aimed to evaluate the clinical outcomes of patients with a history of life-threatening VA due to CAS with various medical interventions, as well as the need for ICD placement in the setting of optimal medical therapy. A multicentre European retrospective survey of patients with VA in the setting of CAS was aggregated and relevant clinical and demographic data was analysed. Forty-nine appropriate patients were identified: 43 (87.8%) presented with VF and 6 (12.2%) with rapid VT. ICD implantation was performed in 44 (89.8%). During follow-up [59 (17-117) months], appropriate ICD shocks were documented in 12. In 8/12 (66.6%) no more ICD therapies were recorded after optimizing calcium channel blocker (CCB) therapy. SCD occurred in one patient without ICD. Treatment with beta-blockers was predictive of appropriate device discharge. Conversely, non-dihydropyridine CCB therapy was significantly protective against VAs. Patients with life-threatening VAs secondary to CAS are at particularly high-risk for recurrence, especially when insufficient medical therapy is administered. Non-dihydropyridine CCBs are capable of suppressing episodes, whereas beta-blocker treatment is predictive of VAs. Ultimately, in spite of medical intervention, some patients exhibited arrhythmogenic events in the long-term, suggesting that ICD implantation may still be indicated for all.

Dual purpose recovered coagulant from drinking water treatment residuals for adjustment of initial pH and coagulation aid in electrocoagulation process.

PubMed

Jung, Kyung-Won; Ahn, Kyu-Hong

2016-01-01

The present study is focused on the application of recovered coagulant (RC) by acidification from drinking water treatment residuals for both adjusting the initial pH and aiding coagulant in electrocoagulation. To do this, real cotton textile wastewater was used as a target pollutant, and decolorization and chemical oxygen demand (COD) removal efficiency were monitored. A preliminary test indicated that a stainless steel electrode combined with RC significantly accelerated decolorization and COD removal efficiencies, by about 52% and 56%, respectively, even at an operating time of 5 min. A single electrocoagulation system meanwhile requires at least 40 min to attain the similar removal performances. Subsequently, the interactive effect of three independent variables (applied voltage, initial pH, and reaction time) on the response variables (decolorization and COD removal) was evaluated, and these parameters were statistically optimized using the response surface methodology. Analysis of variance showed a high coefficient of determination values (decolorization, R(2) = 0.9925 and COD removal, R(2) = 0.9973) and satisfactory prediction second-order polynomial quadratic regression models. Average decolorization and COD removal of 89.52% and 94.14%, respectively, were achieved, corresponding to 97.8% and 98.1% of the predicted values under statistically optimized conditions. The results suggest that the RC effectively played a dual role of both adjusting the initial pH and aiding coagulant in the electrocoagulation process.

Economic Model Predictive Control of Bihormonal Artificial Pancreas System Based on Switching Control and Dynamic R-parameter.

PubMed

Tang, Fengna; Wang, Youqing

2017-11-01

Blood glucose (BG) regulation is a long-term task for people with diabetes. In recent years, more and more researchers have attempted to achieve automated regulation of BG using automatic control algorithms, called the artificial pancreas (AP) system. In clinical practice, it is equally important to guarantee the treatment effect and reduce the treatment costs. The main motivation of this study is to reduce the cure burden. The dynamic R-parameter economic model predictive control (R-EMPC) is chosen to regulate the delivery rates of exogenous hormones (insulin and glucagon). It uses particle swarm optimization (PSO) to optimize the economic cost function and the switching logic between insulin delivery and glucagon delivery is designed based on switching control theory. The proposed method is first tested on the standard subject; the result is compared with the switching PID and the switching MPC. The effect of the dynamic R-parameter on improving the control performance is illustrated by comparing the results of the EMPC and the R-EMPC. Finally, the robustness tests on meal change (size and timing), hormone sensitivity (insulin and glucagon), and subject variability are performed. All results show that the proposed method can improve the control performance and reduce the economic costs. The simulation results verify the effectiveness of the proposed algorithm on improving the tracking performance, enhancing robustness, and reducing economic costs. The method proposed in this study owns great worth in practical application.

In vitro assessment of eye irritancy using the Reconstructed Human Corneal Epithelial SkinEthic HCE model: application to 435 substances from consumer products industry.

PubMed

Cotovio, José; Grandidier, Marie-Hélène; Lelièvre, Damien; Bremond, Christelle; Amsellem, Carolle; Maloug, Saber; Ovigne, Jean-Marc; Loisel-Joubert, Sophie; Lee, Aline Van Der; Minondo, Anne-Marie; Capallere, Christophe; Bertino, Béatrice; Alépée, Nathalie; Tinois-Tessonneaud, Estelle; de Fraissinette, Anne De Brugerolle; Meunier, Jean-Roch; Leclaire, Jacques

2010-03-01

The 7th amendment of the EU Cosmetics Directive led to the ban of eye irritation testing for cosmetic ingredients in animals, effective from March 11th 2009. Over the last 20years, many efforts have been made to find reliable and relevant alternative methods. The SkinEthic HCE model was used to evaluate the in vitro eye irritancy potential of substances from a cosmetic industry portfolio. An optimized protocol based on a specific 1-h treatment and a 16-h post-treatment incubation period was first assessed on a set of 102 substances. The prediction model (PM) based on a 50% viability cut-off, allowed to draw up two classes (Irritants and Non-Irritants), with good associated sensitivity (86.2%) and specificity (83.5%). To check the robustness of the method, the evaluated set was expanded up to 435 substances. Final performances maintained a high level and were characterized by an overall accuracy value > 82% when using EU or GHS classification rules. Results showed that the SkinEthic HCE test method is a promising in vitro tool for the prediction of eye irritancy. Optimization datasets were shared with the COLIPA Eye Irritation Project Team and ECVAM experts, and reviewed as part of an ongoing progression to enter an ECVAM prospective validation study for eye irritation. Copyright (c) 2009 Elsevier Ltd. All rights reserved.

Optimising the inactivation of grape juice spoilage organisms by pulse electric fields.

PubMed

Marsellés-Fontanet, A Robert; Puig, Anna; Olmos, Paola; Mínguez-Sanz, Santiago; Martín-Belloso, Olga

2009-04-15

The effect of some pulsed electric field (PEF) processing parameters (electric field strength, pulse frequency and treatment time), on a mixture of microorganisms (Kloeckera apiculata, Saccharomyces cerevisiae, Lactobacillus plantarum, Lactobacillus hilgardii and Gluconobacter oxydans) typically present in grape juice and wine were evaluated. An experimental design based on response surface methodology (RSM) was used and results were also compared with those of a factorially designed experiment. The relationship between the levels of inactivation of microorganisms and the energy applied to the grape juice was analysed. Yeast and bacteria were inactivated by the PEF treatments, with reductions that ranged from 2.24 to 3.94 log units. All PEF parameters affected microbial inactivation. Optimal inactivation of the mixture of spoilage microorganisms was predicted by the RSM models at 35.0 kV cm(-1) with 303 Hz pulse width for 1 ms. Inactivation was greater for yeasts than for bacteria, as was predicted by the RSM. The maximum efficacy of the PEF treatment for inactivation of microorganisms in grape juice was observed around 1500 MJ L(-1) for all the microorganisms investigated. The RSM could be used in the fruit juice industry to optimise the inactivation of spoilage microorganisms by PEF.

Optimization of conventional water treatment plant using dynamic programming.

PubMed

Mostafa, Khezri Seyed; Bahareh, Ghafari; Elahe, Dadvar; Pegah, Dadras

2015-12-01

In this research, the mathematical models, indicating the capability of various units, such as rapid mixing, coagulation and flocculation, sedimentation, and the rapid sand filtration are used. Moreover, cost functions were used for the formulation of conventional water and wastewater treatment plant by applying Clark's formula (Clark, 1982). Also, by applying dynamic programming algorithm, it is easy to design a conventional treatment system with minimal cost. The application of the model for a case reduced the annual cost. This reduction was approximately in the range of 4.5-9.5% considering variable limitations. Sensitivity analysis and prediction of system's feedbacks were performed for different alterations in proportion from parameters optimized amounts. The results indicated (1) that the objective function is more sensitive to design flow rate (Q), (2) the variations in the alum dosage (A), and (3) the sand filter head loss (H). Increasing the inflow by 20%, the total annual cost would increase to about 12.6%, while 20% reduction in inflow leads to 15.2% decrease in the total annual cost. Similarly, 20% increase in alum dosage causes 7.1% increase in the total annual cost, while 20% decrease results in 7.9% decrease in the total annual cost. Furthermore, the pressure decrease causes 2.95 and 3.39% increase and decrease in total annual cost of treatment plants. © The Author(s) 2013.

Integrating prediction, provenance, and optimization into high energy workflows

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

Schram, M.; Bansal, V.; Friese, R. D.

We propose a novel approach for efficient execution of workflows on distributed resources. The key components of this framework include: performance modeling to quantitatively predict workflow component behavior; optimization-based scheduling such as choosing an optimal subset of resources to meet demand and assignment of tasks to resources; distributed I/O optimizations such as prefetching; and provenance methods for collecting performance data. In preliminary results, these techniques improve throughput on a small Belle II workflow by 20%.

Expected treatment dose construction and adaptive inverse planning optimization: Implementation for offline head and neck cancer adaptive radiotherapy

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

Yan Di; Liang Jian

Purpose: To construct expected treatment dose for adaptive inverse planning optimization, and evaluate it on head and neck (h and n) cancer adaptive treatment modification. Methods: Adaptive inverse planning engine was developed and integrated in our in-house adaptive treatment control system. The adaptive inverse planning engine includes an expected treatment dose constructed using the daily cone beam (CB) CT images in its objective and constrains. Feasibility of the adaptive inverse planning optimization was evaluated retrospectively using daily CBCT images obtained from the image guided IMRT treatment of 19 h and n cancer patients. Adaptive treatment modification strategies with respect tomore » the time and the number of adaptive inverse planning optimization during the treatment course were evaluated using the cumulative treatment dose in organs of interest constructed using all daily CBCT images. Results: Expected treatment dose was constructed to include both the delivered dose, to date, and the estimated dose for the remaining treatment during the adaptive treatment course. It was used in treatment evaluation, as well as in constructing the objective and constraints for adaptive inverse planning optimization. The optimization engine is feasible to perform planning optimization based on preassigned treatment modification schedule. Compared to the conventional IMRT, the adaptive treatment for h and n cancer illustrated clear dose-volume improvement for all critical normal organs. The dose-volume reductions of right and left parotid glands, spine cord, brain stem and mandible were (17 {+-} 6)%, (14 {+-} 6)%, (11 {+-} 6)%, (12 {+-} 8)%, and (5 {+-} 3)% respectively with the single adaptive modification performed after the second treatment week; (24 {+-} 6)%, (22 {+-} 8)%, (21 {+-} 5)%, (19 {+-} 8)%, and (10 {+-} 6)% with three weekly modifications; and (28 {+-} 5)%, (25 {+-} 9)%, (26 {+-} 5)%, (24 {+-} 8)%, and (15 {+-} 9)% with five weekly modifications. Conclusions: Adaptive treatment modification can be implemented including the expected treatment dose in the adaptive inverse planning optimization. The retrospective evaluation results demonstrate that utilizing the weekly adaptive inverse planning optimization, the dose distribution of h and n cancer treatment can be largely improved.« less

Virtual surgical planning for treatment of severe mandibular retrognathia with collapsed occlusion using contemporary surgical and prosthodontic protocols.

PubMed

Dhima, Matilda; Salinas, Thomas J; Rieck, Kevin L

2013-11-01

To meet functional and esthetic needs in an older adult for treatment of complex skeletal and dentoalveolar deformities using contemporary surgical and prosthodontic protocols. An older adult with dentoalveolar complex and skeletal deformity (mandibular retrognathia) was treated by a combination of virtual planning and current surgical and prosthodontic protocols. Treatment planning steps and sequencing are presented. Skeletal, soft tissue, and dental harmonies were attained without biological or mechanical complications. Definitive oral rehabilitation was completed with a maxillary complete denture and a mandibular metal ceramic fixed implant-retained prosthesis. A surgical and prosthodontic team approach in combination with technologic advances can predictably optimize esthetic and functional outcomes for patients with complex skeletal and dentoalveolar deformities. Copyright © 2013 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria

PubMed Central

Farasat, Iman; Kushwaha, Manish; Collens, Jason; Easterbrook, Michael; Guido, Matthew; Salis, Howard M

2014-01-01

Developing predictive models of multi-protein genetic systems to understand and optimize their behavior remains a combinatorial challenge, particularly when measurement throughput is limited. We developed a computational approach to build predictive models and identify optimal sequences and expression levels, while circumventing combinatorial explosion. Maximally informative genetic system variants were first designed by the RBS Library Calculator, an algorithm to design sequences for efficiently searching a multi-protein expression space across a > 10,000-fold range with tailored search parameters and well-predicted translation rates. We validated the algorithm's predictions by characterizing 646 genetic system variants, encoded in plasmids and genomes, expressed in six gram-positive and gram-negative bacterial hosts. We then combined the search algorithm with system-level kinetic modeling, requiring the construction and characterization of 73 variants to build a sequence-expression-activity map (SEAMAP) for a biosynthesis pathway. Using model predictions, we designed and characterized 47 additional pathway variants to navigate its activity space, find optimal expression regions with desired activity response curves, and relieve rate-limiting steps in metabolism. Creating sequence-expression-activity maps accelerates the optimization of many protein systems and allows previous measurements to quantitatively inform future designs. PMID:24952589

Implementation of Chaotic Gaussian Particle Swarm Optimization for Optimize Learning-to-Rank Software Defect Prediction Model Construction

NASA Astrophysics Data System (ADS)

Buchari, M. A.; Mardiyanto, S.; Hendradjaya, B.

2018-03-01

Finding the existence of software defect as early as possible is the purpose of research about software defect prediction. Software defect prediction activity is required to not only state the existence of defects, but also to be able to give a list of priorities which modules require a more intensive test. Therefore, the allocation of test resources can be managed efficiently. Learning to rank is one of the approach that can provide defect module ranking data for the purposes of software testing. In this study, we propose a meta-heuristic chaotic Gaussian particle swarm optimization to improve the accuracy of learning to rank software defect prediction approach. We have used 11 public benchmark data sets as experimental data. Our overall results has demonstrated that the prediction models construct using Chaotic Gaussian Particle Swarm Optimization gets better accuracy on 5 data sets, ties in 5 data sets and gets worse in 1 data sets. Thus, we conclude that the application of Chaotic Gaussian Particle Swarm Optimization in Learning-to-Rank approach can improve the accuracy of the defect module ranking in data sets that have high-dimensional features.

WE-F-BRB-01: The Power of Ontologies and Standardized Terminologies for Capturing Clinical Knowledge

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

Gabriel, P.

2015-06-15

Advancements in informatics in radiotherapy are opening up opportunities to improve our ability to assess treatment plans. Models on individualizing patient dose constraints from prior patient data and shape relationships have been extensively researched and are now making their way into commercial products. New developments in knowledge based treatment planning involve understanding the impact of the radiation dosimetry on the patient. Akin to radiobiology models that have driven intensity modulated radiotherapy optimization, toxicity and outcome predictions based on treatment plans and prior patient experiences may be the next step in knowledge based planning. In order to realize these predictions, itmore » is necessary to understand how the clinical information can be captured, structured and organized with ontologies and databases designed for recall. Large databases containing radiation dosimetry and outcomes present the opportunity to evaluate treatment plans against predictions of toxicity and disease response. Such evaluations can be based on dose volume histogram or even the full 3-dimensional dose distribution and its relation to the critical anatomy. This session will provide an understanding of ontologies and standard terminologies used to capture clinical knowledge into structured databases; How data can be organized and accessed to utilize the knowledge in planning; and examples of research and clinical efforts to incorporate that clinical knowledge into planning for improved care for our patients. Learning Objectives: Understand the role of standard terminologies, ontologies and data organization in oncology Understand methods to capture clinical toxicity and outcomes in a clinical setting Understand opportunities to learn from clinical data and its application to treatment planning Todd McNutt receives funding from Philips, Elekta and Toshiba for some of the work presented.« less