A Framework to Predict Gastric Cancer Based on Tongue Features and Deep Learning.

Gastric cancer has become a global health issue, severely disrupting daily life. Early detection in gastric cancer patients and immediate treatment contribute significantly to the protection of human health. However, routine gastric cancer examinations carry the risk of complications and are time-consuming. We proposed a framework to predict gastric cancer non-invasively and conveniently. A total of 703 tongue images were acquired using a bespoke tongue image capture instrument, then a dataset containing subjects with and without gastric cancer was created. As the images acquired by this instrument contain non-tongue areas, the Deeplabv3+ network was applied for tongue segmentation to reduce the interference in feature extraction. Nine tongue features were extracted, relationships between tongue features and gastric cancer were explored by using statistical methods and deep learning, finally a prediction framework for gastric cancer was designed. The experimental results showed that the proposed framework had a strong detection ability, with an accuracy of 93.6%. The gastric cancer prediction framework created by combining statistical methods and deep learning proposes a scheme for exploring the relationships between gastric cancer and tongue features. This framework contributes to the effective early diagnosis of patients with gastric cancer.

Network pharmacology analysis of Chaihu Lizhong Tang treating non-alcoholic fatty liver disease.

BACKGROUND: In this study, the network pharmacological methods were used to predict the target of active components of Chaihu Lizhong Tang (CHLZT) in the treatment of non-alcoholic fatty liver disease (NAFLD). METHOD: The active components of "CHLZT", their targets, and NAFLD related targets were screened by multiple databases, and the potential targets of "CHLZT" in the treatment of NAFLD were predicted. The active component-target network of "CHLZT" was constructed by Cytoscape software. The potential target of "CHLZT" for the treatment of NAFLD constructed protein-protein interaction (PPI) network in the Search Tool for the Retrieval of Interacting Genes Database (STRING). The hub genes of "CHLZT" in the treatment of NAFLD were screened by network topological parameters, and the results were verified by molecular docking. "ClusterProfiler" in R was used for Gene Ontology (GO) analysis and KEGG pathway enrichment analysis. RESULTS: OB ≥ 30 % and DL ≥ 0.18 were selected as the screening criteria of active components. A total of 83 active components and 456 targets were selected. Based on the evaluation of topological parameters of degree network, five hub genes for interaction with "CHLZT" therapy for NAFLD were screened, that is, AKT1, ALB, IL6, EGFR, and CASP3. The results of molecular docking showed that the active components in "CHLZT" had a good binding ability with the key targets. The enrichment analysis results showed that the treatment of NAFLD with "CHLZT" mainly involved in cofactor binding, protease binding, AGE-RAGE signaling pathway in diabetic complications, and IL-17 signaling pathway, which mediated the potential mechanism of "CHLZT" intervention in NAFLD. CONCLUSION: The molecular mechanism of "CHLZT" in the treatment of NAFLD indicated the synergistic features of multi-component, multi-target, and multi-pathway of traditional Chinese medicine, which provided an important scientific basis for further elucidating the mechanism of "CHLZT" in the treatment of NAFLD.

The Chinese medicine Chai Hu Li Zhong Tang protects against non-alcoholic fatty liver disease by activating AMPKα.

An effective treatment for non-alcoholic fatty liver disease (NAFLD) is urgently needed. In the present study, we investigated whether the Chinese medicine Chai Hu Li Zhong Tang (CHLZT) could protect against the development of NAFLD. Rats in an animal model of NAFLD were treated with CHLZT, and their serum levels of cholesterol (TC), triglycerides (TG), high density lipoprotein-cholesterol (HDL-C), low density lipoprotein-cholesterol (LDL-C), aspartate aminotransferase (AST), and alanine aminotransferase (ALT) were detected with an automatic biochemical analyzer. A cellular model of NAFLD was also established by culturing HepG2 cells in a medium that contained a long chain fat emulsion. Those cells were treated with CHLZT that contained serum from rats. After treatment, the levels of adenylate-activated protein kinase (AMPK) α (AMPKα), p-AMPKα, acetyl coenzyme A carboxylase (ACC) α (ACCα), pACCα, PPARγ, and SREBP-2 were detected. The AMPK agonist, acadesine (AICAR), was used as a positive control compound. Our results showed that CHLZT or AICAR significantly decreased the serum levels of TG, TC, LDL-C, AST, ALT, and insulin in NAFLD rats, and significantly increased their serum HDL-C levels. Treatments with CHLZT or AICAR significantly decreased the numbers of lipid droplets in NAFLD liver tissues and HepG2 cells. CHLZT and AICAR increased the levels of p-AMPKα and PPARγ in the NAFLD liver tissues and HepG2 cells, but decreased the levels of ACC-α, p-ACC-α, SREBP-2, and 3-hydroxyl-3-methylglutaryl-coenzyme A reductase (HMGR). CHLZT protects against NAFLD by activating AMPKα, and also by inhibiting ACC activity, down-regulating SREBP2 and HMGR, and up-regulating PPAR-γ. Our results suggest that CHLZT might be useful for treating NAFLD in the clinic.