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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.

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.

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