Systematic analysis on the mechanism of Zhizi-Bopi decoction against hepatitis B via network pharmacology and molecular docking.

PURPOSE: Zhizi-Bopi decoction (ZZBPD) is a classic herbal formula with wide clinical applications in treating liver diseases including hepatitis B. However, the mechanism needs to be elucidated. METHODS: Chemical components of ZZBPD were identified by ultra-high-performance liquid chromatography coupled with time-of-flight mass spectrometry (UHPLC-TOF-MS). Then we used network pharmacology to identify their potential targets. Network construction, coupled with protein-protein interaction and enrichment analysis was used to identify representative components and core targets. Finally, molecular docking simulation was conducted to further refine the drug-target interaction. RESULTS: One hundred and forty-eight active compounds were identified in ZZBPD, targeting 779 genes/proteins, among which 174 were related to hepatitis B. ZZBPD mainly influences the progression of hepatitis B through the hepatitis B pathway (hsa05161) via core anti-HBV targets (AKT1, PIK3CA, PIK3R1, SRC, TNF, MAPK1, and MAPK3). Enrichment analysis indicated that ZZBPD can also potentially regulate lipid metabolism and enhance cell survival. Molecular docking suggested that the representative active compounds can bind to the core anti-HBV targets with high affinity. CONCLUSION: The potential molecular mechanisms of ZZBPD in hepatitis B treatment were identified using network pharmacology and molecular docking approaches. The results serve as an important basis for the modernization of ZZBPD.

Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021.

Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the VAscular Lesions DetectiOn and Segmentation (Where is VALDO?) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1-EPVS, 9 for Task 2-Microbleeds and 6 for Task 3-Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1-EPVS and Task 2-Microbleeds and not practically useful results yet for Task 3-Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level.

Machine learning: A non-invasive prediction method for gastric cancer based on a survey of lifestyle behaviors.

Gastric cancer remains an enormous threat to human health. It is extremely significant to make a clear diagnosis and timely treatment of gastrointestinal tumors. The traditional diagnosis method (endoscope, surgery, and pathological tissue extraction) of gastric cancer is usually invasive, expensive, and time-consuming. The machine learning method is fast and low-cost, which breaks through the limitations of the traditional methods as we can apply the machine learning method to diagnose gastric cancer. This work aims to construct a cheap, non-invasive, rapid, and high-precision gastric cancer diagnostic model using personal behavioral lifestyles and non-invasive characteristics. A retrospective study was implemented on 3,630 participants. The developed models (extreme gradient boosting, decision tree, random forest, and logistic regression) were evaluated by cross-validation and the generalization ability in our test set. We found that the model developed using fingerprints based on the extreme gradient boosting (XGBoost) algorithm produced better results compared with the other models. The overall accuracy of which test set was 85.7%, AUC was 89.6%, sensitivity 78.7%, specificity 76.9%, and positive predictive values 73.8%, verifying that the proposed model has significant medical value and good application prospects.