This paper analyses the high-density lipoprotein cholesterol(HDL-ch), low-density lipoprotein cholesterol (LDL-ch) and very low-density lipoprotein cholesterol (VLDL-ch) levels in 877 healthy persons, 328 atheletes, 261 patients with coronary heart disease(CHD)and apoplexy, and so on.The result achieved is very close to that reported in foreign literature. The results show that the ratio of LDL-ch/HDL-ch for prevention and treatment of atherosclerosis is more sensitive than testing of other lipids and portions of lipopro-teins.

Objective To construct a prognostic prediction model for hepatocellular carcinoma (HCC) based on immune and metabolism related genes, analyze the prognostic immune response of HCC patients, and screen potential drugs for HCC treatment through drug sensitivity analysis. Methods HCC expression profiling and clinical data were obtained from The Cancer Genome Atlas (TCGA) database, and a list of immune-related genes was obtained from the Immport database; the Perl language was used to extract metabolism-related pathway gene sets from the Molecular Signatures Database(MSig DB), and co-expression related genes were found through differential analysis and co-expression analysis; the univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO) regression analysis, and multivariate Cox regression analysis were used to screen prognosis-related genes and construct a risk prognosis model for HCC, and risk scores for all HCC samples were calculated. Using the median risk score as the critical value, the reliability of the prognostic model was evaluated through risk curves, Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, independent prognostic analysis, and Nomograms. The correlations between risk scores and pathway enrichment analysis as well as immune cell infiltration were analyzed. Drug sensitivity analysis was used to identify potential therapeutic drugs for HCC. Results Five immune and metabolic genes with independent prognostic value were obtained, and a prognostic model based on immune and metabolic genes was constructed. Survival analysis showed that in the total dataset, training group and validation group, the survival rate of the low-risk group was significantly higher than that ofthe high-risk group (P < 0.05). The areas under the ROC curves of the prognostic model for the training group at 1, 3 and 5 years were 0.780, 0.699 and 0.706 respectively. Cox regression analysis showed that grading and risk score could be used as independent prognostic factors for HCC (P < 0.05), with a concordance index of 0.734 (95%CI, 0.669 to 0.798), indicating good model performance. Immune cell infiltration results showed significant differences in resting NK cells, monocytes, M0 macrophages, and M1 macrophages between the high-risk and low-risk groups (P < 0.05). Drug sensitivity analysis screened 12 drugs that may have potential therapeutic effects in HCC patients (P < 0.01). Conclusion The prognostic model of HCC based on five immune and metabolic genes has good predictive performance, and can be used as a new indicator for prognosis evaluation; the 12 drugs screened out have potential efficacy for HCC.

Objective To screen mitochondria-related genes in Crohn's disease (CD) based on the Gene Expression Omnibus (GEO) database, construct an artificial neural network diagnostic model and evaluate its performance. Methods The CD-related datasets GSE186582 and GSE102133 were downloaded from the GEO database for differential expression genes (DEGs) screening. The intersection of DEGs and mitochondrial genes from the MitoCarta 3.0 database was obtained. Least absolute shrinkage and selection operator regression and random forest algorithms were used to identify CD-specific genes and construct an artificial neural network diagnostic model. The model was further validated by the validation set GSE95095, and the diagnostic performance was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve. The immune cell infiltration in CD was assessed by the CIBERSORT algorithm, and the relationship between biomarkers and infiltrated immune cells was investigated. Results A total of 551 DEGs were obtained, including 275 upregulated and 276 downregulated genes. There were 20 mitochondria-related genes associated with CD. A total of 9 mitochondria-related feature genes (SOD2, MTHFD2, BPHL, PXMP2, RMND1, AGXT2, MAOA, HMGCS2, MAOB) were screened by two machine learning algorithms. An artificial neural network diagnostic model was constructed by the selected feature genes. The values of AUC of the model in the training and validation groups were 0.956 and 0.736 respectively. Immune cell infiltration analysis showed that the feature genes were associated with resting memory CD4⁺ T cells, activated memory CD4⁺ T cells, activated dendritic cells, neutrophils, and CD8⁺ T cells. Conclusion The artificial neural network diagnostic model for CD based on 9 mitochondrial genes has good predictive performance.

At present, some "5+3" integration students have different levels of understanding and application problems in various stages, such as role transformation, professional knowledge and technology, communication ability and humanistic care ability, clinical thinking and evidence-based medicine concepts, clinical research thinking, learning and work attitude. This research will permeate and run through the training of "5+3" integrated students' diagnostic and therapeutic operation ability through the training of modern clinical thinking oriented by post competency, and integrate humanistic care, evidence-based medicine, learning attitude, working attitude, and attitude towards patients in the whole process to gradually complete the comprehensive training goal of clinical thinking oriented by post competency + diagnostic and therapeutic operation ability.

Objective To study the correlation between the changes of matrix metalloproteinase-9(MMP-9)and neutrophil/lymphocyte ratio(NLR)before and after the revascularization of acute ischemic stroke(AIS),so as to find biomarkers to predict the bleeding transformation risk of AIS patients.Methods From February 2022 to December 2022,161 patients admitted to the Stroke Center of Qujing Hospital Affiliated to Kunming Medical University with AIS werre divided in to the hemorrhagic transformation group and the non-hemorrhagic transfor-mation groupand treated with revascularization(intravenous thrombolysis,endovascular treatment,combined the intravenous thrombolysis with endovascular treatment).Among them,there were 46 cases in the hemorrhagic transformation group and 115 cases in the non hemorrhagic transformation group.And the general data,NLR value and MMP-9 before and after the revascularization were compared between the two groups.Results There was no statistical difference in general data between the two groups(all P>0.05)except for C-reactive protein in hemorrhagic transformation group and in non-hemorrhagic transformation group(P<0.001).The white blood cells,neutrophils,neutrophil percentage,neutrophil absolute value,lymphocyte absolute value,NLR and MMP-9 value in hemorrhagic transformation group were significantly higher than those in non-hemorrhagic transformation group before the treatment and there was a statistical significance(P<0.05).After revascularization,the indexes of blood routine and MMP-9 were significantly higher than those before the revascularization,and the increase in hemorrhagic transformation group was more obvious than that in non-hemorrhagic transformation group and there was a statistical significance(P<0.001),The ROC curve showed that the area under the curve(AUC)of NLR and MMP-9 predicting bleeding transformation after AIS revascularization were 0.74 and 0.90.Conclusion NLR,MMP-9 are associated with the risk of bleeding transformation in AIS patients after the revascularization and can they can be used as the predictive factors for bleeding transformation risk.