Application of computer tongue image analysis technology in the diagnosis of NAFLD.

Nonalcoholic fatty liver disease (NAFLD), a leading cause of chronic hepatic disease, can progress to liver fibrosis, cirrhosis, and hepatocellular carcinoma. Therefore, it is extremely important to explore early diagnosis and screening methods. In this study, we developed models based on computer tongue image analysis technology to observe the tongue characteristics of 1778 participants (831 cases of NAFLD and 947 cases of non-NAFLD). Combining quantitative tongue image features, basic information, and serological indexes, including the hepatic steatosis index (HSI) and fatty liver index (FLI), we utilized machine learning methods, including Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Adaptive Boosting Algorithm (AdaBoost), Naïve Bayes, and Neural Network for NAFLD diagnosis. The best fusion model for diagnosing NAFLD by Logistic Regression, which contained the tongue image parameters, waist circumference, BMI, GGT, TG, and ALT/AST, achieved an AUC of 0.897 (95% CI, 0.882-0.911), an accuracy of 81.70% with a sensitivity of 77.62% and a specificity of 85.22%; in addition, the positive likelihood ratio and negative likelihood ratio were 5.25 and 0.26, respectively. The application of computer intelligent tongue diagnosis technology can improve the accuracy of NAFLD diagnosis and may provide a convenient technical reference for the establishment of early screening methods for NAFLD, which is worth further research and verification.

Discovery of hCES2A inhibitors from Glycyrrhiza inflata via combination of docking-based virtual screening and fluorescence-based inhibition assays.

Human carboxylesterase 2 (hCES2A) is a key target to ameliorate the intestinal toxicity triggered by irinotecan that causes severe diarrhea in 50%-80% of patients receiving this anticancer agent. Herbal medicines are frequently used for the prevention and treatment of the intestinal toxicity of irinotecan, but it is very hard to find strong hCES2A inhibitors from herbal medicines in an efficient way. Herein, an integrated strategy via combination of chemical profiling, docking-based virtual screening and fluorescence-based high-throughput inhibitor screening assays was utilized. Following the screening of a total of 73 herbal products, licorice (the dried root of Glycyrrhiza species) was found with the most potent hCES2A inhibition activity. Further investigation revealed that the chalcones and several flavonols in licorice displayed strong hCES2A inhibition activities, while isoliquiritigenin, echinatin, naringenin, gancaonin I and glycycoumarin exhibited moderate inhibition of hCES2A. Inhibition kinetic analysis demonstrated that licochalcone A, licochalcone C, licochalcone D and isolicoflavonol potently inhibited hCES2A-mediated fluorescein diacetate hydrolysis in a reversible and mixed inhibition manner, with Ki values less than 1.0 µM. Further investigations demonstrated that licochalcone C, the most potent hCES2A inhibitor identified from licorice, dose-dependently inhibited intracellular hCES2A in living HepG2 cells. In summary, this study proposed an integrated strategy to find hCES2A inhibitors from herbal medicines, and our findings suggested that the chalcones and isolicoflavonol in licorice were the key ingredients responsible for hCES2A inhibition, which would be very helpful to develop new herbal remedies or drugs for ameliorating hCES2A-associated drug toxicity.