Objective To investigate the effects of emodin on the triglyceride metabolism and oxidative stress in steatosis in HepG2 cells and possible underlying mechanisms.Methods The appropriate concentration of emodin on HepG2 cells were detected by methyl thiazolyl tetrazolium (MTT) assay.HepG2 cells were induced to fat overaccumulation by 1 mmol/L free fatty acids (FFA) (oleate∶ palmitate =2∶1).The model group exposed to 10 μmol/L,20 μmol/L,40 μmol/L emodin.The intracellular lipid accumulation was documented by Oil Red O staining and the content of triglyceride and total cholesterol was observed.Reactive oxygen species (ROS) was determined by flow cytometry.Western blotting was performed to analyze the protein levels of adenosine monophosphate-activated protein kinase (AMPK),phosphorylated AMPK,and sterol regulatory element-binding protein 1 (SREBP-1).Results Emodin reduced lipid accumulation and triglycerides (TG) content (P ＜ 0.05).At the same time,it significantly reduced ROS production (P ＜ 0.05).Moreover,the levels of AMPK and p-AMPK protein were significantly upregulated,and SREBP-1 protein was significantly downregulated with the treatment of emodin (P ＜ 0.01).Conclusions This study has demonstrated that emodin can reduce fatty degeneration induced by FFAs in hepatocytes,and this effect may be partially mediated by the AMPK/SREBP-1 pathway.

Purpose/Significance The paper discusses the application of artificial intelligence technology to the key entity recognition ofunstructured text data in the electronic medical records of lymphedema patients.Method/Process It expounds the solution of model fine-tuning training under the background of sample scarcity,a total of 594 patients admitted to the department of lymphatic surgery of Beijing Shijitan Hospital,Capital Medical University are selected as the research objects.The prediction layer of the GlobalPointer model is fine-tuned according to 15 key entity categories labeled by clinicians,nested and non-nested key entities are identified with its glob-al pointer.The accuracy of the experimental results and the feasibility of clinical application are analyzed.Result/Conclusion After fine-tuning,the average accuracy rate,recall rate and Macro_F1 ofthe model are 0.795,0.641 and 0.697,respectively,which lay a foundation for accurate mining of lymphedema EMR data.