Asian patients with chronic hepatitis C (CHC) are known to have better virological responses to pegylated (Peg) IFN-based therapy than Western patients. Although IL28B gene polymorphisms may contribute to this difference, whether favorable hepatitis C virus (HCV) kinetics during treatment plays a role remains unclear. We enrolled 145 consecutive Taiwanese patients with CHC receiving Peg-IFN α-2a plus ribavirin for the study. Blood samples were taken more frequently at defined intervals in the first 3 d. Peg-IFN was administered at week 1. It was then administered weekly in combination with daily ribavirin for 24 or 48 wk. A mathematical model fitted to the observed HCV kinetics was constructed, which could interpret the transient HCV titer elevation after Peg-IFN treatment. The results demonstrated a comparable viral clearance rate (c = 3.45 ± 3.73) (day −1 , mean ± SD) but lower daily viral production rate (P = 10 6 -10 12 ) in our patients than those reported previously in Western patients. Of 110 patients with a sustained virological response (SVR), 47 (43%) had a transient elevation of viral titer within 12 h (proportion of 12 h/3 d: 44% in non-SVR vs. 70% in SVR; P = 0.029). Among 91 patients with available rs8099917 data, patients with the TT genotype had an early surge of viral titer after therapy and a higher SVR and viral clearance rate than those with the GT genotype. In conclusion, Taiwanese patients with CHC receiving Peg-IFN plus ribavirin therapy have a lower daily viral production rate than Western patients, and the rs8099917 TT genotype may contribute to the increased viral clearance rate and better virological responses in these patients. H epatitis C virus (HCV) infection is the major etiology of chronic liver disease, liver cirrhosis, and hepatocellular carcinoma (1, 2). According to the estimate from the World Health Organization, there are more than 180 million chronic HCV-infected persons worldwide (1-3); hence, effective treatment of chronic hepatitis C (CHC) is important. Nevertheless, the current standard of care for CHC using pegylated (Peg) IFN plus ribavirin is expensive, is effective in only a certain proportion of patients who have CHC, and has many unpleasant adverse effects (4, 5). Therefore, identifying predictive factors of therapeutical response in patients with CHC is important.Several factors have been linked to the therapeutical response of patients who have CHC, including viral factors (6-11), host factors (12-15), metabolic factors (16-18), histological factors (19), types of regimen (4), and duration of infection (20). Among these factors, viral kinetics following antiviral therapy has become widely accepted in both clinical trials and daily practice (21) and increasingly recognized as the most outweighing predictor of sustained virological response (SVR) to IFN-based therapy (22). Using mathematical models of hepatitis C viral kinetics may further clarify the mechanisms of antiviral therapy, the evolution of resistant viral strains, and the length of time necessary to er...

BackgroundCancer survival studies are commonly analyzed using survival-time prediction models for cancer prognosis. A number of different performance metrics are used to ascertain the concordance between the predicted risk score of each patient and the actual survival time, but these metrics can sometimes conflict. Alternatively, patients are sometimes divided into two classes according to a survival-time threshold, and binary classifiers are applied to predict each patient’s class. Although this approach has several drawbacks, it does provide natural performance metrics such as positive and negative predictive values to enable unambiguous assessments.MethodsWe compare the survival-time prediction and survival-time threshold approaches to analyzing cancer survival studies. We review and compare common performance metrics for the two approaches. We present new randomization tests and cross-validation methods to enable unambiguous statistical inferences for several performance metrics used with the survival-time prediction approach. We consider five survival prediction models consisting of one clinical model, two gene expression models, and two models from combinations of clinical and gene expression models.ResultsA public breast cancer dataset was used to compare several performance metrics using five prediction models. 1) For some prediction models, the hazard ratio from fitting a Cox proportional hazards model was significant, but the two-group comparison was insignificant, and vice versa. 2) The randomization test and cross-validation were generally consistent with the p-values obtained from the standard performance metrics. 3) Binary classifiers highly depended on how the risk groups were defined; a slight change of the survival threshold for assignment of classes led to very different prediction results.Conclusions1) Different performance metrics for evaluation of a survival prediction model may give different conclusions in its discriminatory ability. 2) Evaluation using a high-risk versus low-risk group comparison depends on the selected risk-score threshold; a plot of p-values from all possible thresholds can show the sensitivity of the threshold selection. 3) A randomization test of the significance of Somers’ rank correlation can be used for further evaluation of performance of a prediction model. 4) The cross-validated power of survival prediction models decreases as the training and test sets become less balanced.

A classification model is presented for rapid identification of Salmonella serotypes based on pulsed-field gel electrophoresis (PFGE) fingerprints. The classification model was developed using random forest and support vector machine algorithms and was then applied to a database of 45,923 PFGE patterns, randomly selected from all submissions to CDC PulseNet from 2005 to 2010. The patterns selected included the top 20 most frequent serotypes and 12 less frequent serotypes from various sources. The prediction accuracies for the 32 serotypes ranged from 68.8% to 99.9%, with an overall accuracy of 96.0% for the random forest classification, and ranged from 67.8% to 100.0%, with an overall accuracy of 96.1% for the support vector machine classification. The prediction system improves reliability and accuracy and provides a new tool for early and fast screening and source tracking of outbreak isolates. It is especially useful to get serotype information before the conventional methods are done. Additionally, this system also works well for isolates that are serotyped as "unknown" by conventional methods, and it is useful for a laboratory where standard serotyping is not available.