ABSTRACT
Background@#Nonalcoholic fatty liver disease (NAFLD) increases the risk of hepatocellular carcinoma, which is currently the leading cause of obesity-related cancer deaths in middle-aged men. @*Methods@#Probiotics with lipid-lowering function were screened from the fecal microbiota of healthy adults. Polysaccharide from different sources was screened for improving insulin resistance. The combination of probiotics and Salvia miltiorrhiza polysaccharide (LBM) was investigated for alleviating hepatic steatosis. @*Results@#First, Bifidobacterium bifidum V (BbV) and Lactobacillus plantarum X (LpX) were obtained from the fecal microbiota of healthy adults. Second, to improve insulin resistance, a Salvia miltiorrhiza Bunge polysaccharide showing good performance in reducing insulin resistance was obtained. The liver total cholesterol (TC) and total triglyceride (TG) levels and the serum levels of free fatty acid, alanine transaminase, aspartate transaminase, low density lipoprotein cholesterol, TG, and TC can be significantly reduced through supplementation with LpX-BbV (LB) in NAFLD mice. Interestingly, the function of the probiotic LB can be enhanced by S. miltiorrhiza Bunge polysaccharide. Furthermore, the gut microbiota was modulated by LpX-BbV+S. miltiorrhiza Bunge polysaccharide (LBM). The lipopolysaccharide concentration of the LBM group was decreased by 73.6% compared to the NAFLD group. Ultimately, the mRNA concentrations of the proinflammatory cytokines (tumor necrosis factor α, interleukin 1β [IL-1β], and IL-6) decreased with LB and LBM treatment. @*Conclusion@#The results of this this study indicate that the LBM combination can be used as a therapeutic for ameliorating NAFLD via modulating the gut microbiota and improving insulin resistance.
ABSTRACT
Objective: By using Youden index, to improve the performance of the hepatic fibrosis diagnostic models, and to solve the problem of unbalanced diagnostic sensitivity when there is a big difference in the sample size of two groups. Methods: Two hepatitis B virus (HBV) datasets available on GitHub were selected, including 482 HBV infected subjects recruited from Shuguang Hospital in affiliation with Shanghai University of Traditional Chinese Medicine (train set) and 86 HBV infected subjects from Xiamen Hospital of Traditional Chinese Medicine (validation set). By using the two datasets, linear discriminant analysis model, random forest model, gradient boosting model and decision tree model were established, based on four clinical parameters (age, glutamic-oxaloacetic transaminase, glutamic-pyruvic transaminase, and platelet count) of patients, for the diagnosis of early and advanced hepatic fibrosis as well as the diagnosis of hepatic fibrosis and cirrhosis. Youden index was used to adjust the threshold value and the classification result of each diagnostic model. The diagnostic performances of each machine learning model and fibrosis index based on the 4 factor (FIB-4) were evaluated by accuracy, the area under the receiver operating characteristic curve (AUC) and sensitivity. Results: The intergroup sensitivity imbalance occurred in all machine learning models. After using Youden index, the difference of intergroup sensitivity was greatly reduced, and the total accuracy and AUC values of machine learning models were generally higher than those of FIB-4 index. Conclusion:The improved diagnostic models based on Youden index can reduce the difference of intergroup sensitivity and improve the comprehensive performance of the diagnostic models of hepatic fibrosis.