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Abstract: Objectives: Serum samples from patients with chronic hepatitis C were subjected to metabolomics analysis to clarify the pretreatment characteristics of their metabolites and also changes in specific metabolites resulting from antiviral therapy with pegylated interferon plus ribavirin (PegIFN/RBV). Materials/Methods: The serum levels of low-molecular-weight metabolites in the twenty patients before and 24weeks after completion of PegIFN/RBV therapy were analyzed using capillary electrophoresis and liquid chromatography–mass spectrometry. Results: Ten patients showed a non-virological response (NVR) and 10 achieved a sustained virological response (SVR) with eradication of viremia. The pretreatment levels of tryptophan were significantly higher in the patients of SVR than in those of NVR (p=0.010). The area under the curve (AUC) value of tryptophan calculated from the receiver operating characteristic (ROC) curve for discriminating SVR from NVR was 0.84 (95% confidential interval, 0.66–1.02, p=0.010). The ROC curve of multiple logistic regression model incorporating the pretreatment levels of tryptophan and γ-glutamate-arginine showed that the AUC value was highly significant (AUC=0.92, 95% confidential interval, 0.79–1.05, p=0.002). Twenty four weeks after completion of treatment, the levels of γ-glutamyl dipeptides, glutamic acid, 5-oxoproline, glucosamine and methionine sulfoxide were decreased, whereas those of 5-methoxy-3-indoleacetate, glutamine, kynurenine and lysine were increased significantly (p<0.05) in both the NVR and SVR patients. Conclusions: The pretreatment serum levels of certain metabolites including tryptophan are associated with the response to PegIFN/RBV therapy. PegIFN/RBV therapy can ameliorate the oxidative stress responsible for glutathione metabolism. [Copyright &y& Elsevier]

Reliable prediction of landslide occurrence is important for hazard risk reduction and prevention. Taking Guixi in northeast Jiangxi as an example, this research aimed to conduct such a landslide risk assessment using a multiple logistic regression (MLR) algorithm. Field-investigated landslides and non-landslide sites were converted into polygons. We randomly generated 50,000 sampling points to intersect these polygons and the intersected points were divided into two parts, a training set (TS) and a validation set (VT) in a ratio of 7 to 3. Thirteen geo-environmental factors, including elevation, slope, and distance from roads were employed as hazard-causative factors, which were intersected by the TS to create the random point (RP)-based dataset. The next step was to compute the certainty factor (CF) of each factor to constitute a CF-based dataset. MLR was applied to the two datasets for landslide risk modeling. The probability of landslides was then calculated in each pixel, and risk maps were produced. The overall accuracy of these two models versus VS was 91.5% and 90.4% with a Kappa coefficient of 0.814 and 0.782, respectively. The RP-based MLR modeling achieved more reliable predictions and its risk map seems more plausible for providing technical support for implementing disaster prevention measures in Guixi. [ABSTRACT FROM AUTHOR]