Objective:To observe the protective effect of Yulangsan polysaccharide ( YLSP) on liver injury induced by cyclophos-phamide(CTX) in mice. Methods:Liver injury induced by CTX in mice was used as the animal model and the mice were randomly di-vided into the normal group, CTX model group, biphenyldicarboxylate ( BPDC) group, YLSP group respectively with high, medium and low dose. Except the normal group, the other groups were injected with CTX, i. p. , for 7 days to make the model. Then the ani-mals in the YLSP groups were intragastrically administered with YLSP for 7 days. The activities of alanine aminotransferase( ALT) , as-partate aminotransferase( AST) in serum, malondialdehyde( MDA) , superoxide dismutase( SOD) , glutathione( GSH) and glutathione peroxidase ( GSH-Px) in liver tissue were investigated. Hematoxylin and eosin ( HE) stain was used to study the changes in hepatic tissue of the pathological mice. Results:Compared with the model group, YLSP could obviously reduce the activities of ALT, AST and the content of MDA, and increase the content of GSH, SOD and GSH-Px (P<0. 05 or P<0. 01). HE staining showed that YLSP had significant protective effect on liver injury induced by CTX. Conclusion:YLSP has protective effect on liver injury induced by CTX.

Objective: To assess the quality for meta-analysis on prevention and treatment of coronary artery disease (CAD) in China.
Methods: We systemically searched 4 Chinese databases of VIP, CNKI, CBM and Wan Fang for their meta-analysis on CAD prevention and treatment from 1987-01 to 2013-10. According to inclusion and exclusion criteria, 2 researchers independently screened and cross-checked all the literatures. The qualities of methodology and report were evaluated by R-AMSTAR and PRISMA scales.
Results: A total of 201 literatures were enrolled for our study. The average score of methodology quality was (24.65±3.97), no literature met all required items, and the major problems were as lack of“a priori design”, insufifcient and bias of data selection combining inappropriate data synthesis. The average score of report quality was (17.20 ± 2.90), no literature met all 27 required items, and the major problems were as incomplete report of abstract, objective, protocol and registration, incomplete data collection/analysis, using and publishing bias information, incomplete quality assessment.
Conclusion: Both of methodology and report of meta-analysis for CAD prevention and treatment have quality problems at different levels, further improvement should be expected.

Objective To establish an early prediction model for the diagnosis of severe acute pancreatitis based on the improved machine learning models,and to analyze its clinical value.Methods A case-control study was conducted on 352 patients with acute pancreatitis admitted to the Gastroenterology and Hepatobiliary Surgery Departments of the Army Medical Center of PLA and Emergency and Critical Care Medicine Department of No.945 Hospital of Joint Logistics Support Force of PLA from January 2014 to August 2023.According to the severity of the disease,the patients were divided into the severe group(n=88)and the non-severe group(n=264).The RUSBoost model and improved Archimead optimization algorithm was used to analyze 39 routine laboratory biochemical indicators within 48 h after admission to construct an early diagnosis and prediction model for severe acute pancreatitis.The task of feature screening and hyperparameter optimization was completed simultaneously.The ReliefF algorithm feature importance rank and multivariate logistic analysis were used to analyze the value of the selected features.Results In the training set,the area under curve(AUC)of the improved machine learning model was 0.922.In the testing set,the AUC of the improved machine learning model reached 0.888.The 4 key features of predicting severe acute pancreatitis based on the improved Archimedes optimization algorithm were C-reactive protein,blood chlorine,blood magnesium and fibrinogen level,which were consistent with the results of ReliefF algorithm feature importance ranking and multivariate logistic analysis.Conclusion The application of improved machine learning model analyzing the laboratory examination results can help to early predict the occurrence of severe acute pancreatitis.