Objective To identify the N6-methyladenosine (m6A)-related characteristic genes analyzed by gene clustering and immune cell infiltration in myocardial ischemia-reperfusion injury (MI/RI) after cardiopulmonary bypass through machine learning. Methods The differential genes associated with m6A methylation were screened by the dataset GSE132176 in GEO, the samples of the dataset were clustered based on the differential gene expression profile, and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the differential genes of the m6A cluster after clustering were performed to determine the gene function of the m6A cluster. R software was used to determine the better models in machine learning of support vector machine (SVM) model and random forest (RF) model, which were used to screen m6A-related characteristic genes in MI/RI, and construct characteristic gene nomogram to predict the incidence of disease. R software was used to analyze the correlation between characteristic genes and immune cells, and the online website was used to build a characteristic gene regulatory network. Results In this dataset, a total of 5 m6A-related differential genes were screened, and the gene expression profiles were divided into two clusters for cluster analysis. The enrichment analysis of m6A clusters showed that these genes were mainly involved in regulating monocytes differentiation, response to lipopolysaccharides, response to bacteria-derived molecules, cellular response to decreased oxygen levels, DNA transcription factor binding, DNA-binding transcription activator activity, RNA polymerase Ⅱ specificity, NOD-like receptor signaling pathway, fluid shear stress and atherosclerosis, tumor necrosis factor signaling pathway, interleukin-17 signaling pathway. The RF model was determined by R software as the better model, which determined that METTL3, YTHDF1, RBM15B and METTL14 were characteristic genes of MI/RI, and mast cells, type 1 helper lymphocytes (Th1), type 17 helper lymphocytes (Th17), and macrophages were found to be associated with MI/RI after cardiopulmonary bypass in immune cell infiltration. Conclusion The four characteristic genes METTL3, YTHDF1, RBM15B and METTL14 are obtained by machine learning, while cluster analysis and immune cell infiltration analysis can better reveal the pathophysiological process of MI/RI.
ObjectiveTo develop a standardized venous thromboembolism (VTE) prevention program for burn patients and verify its safety and effectiveness by comparing with traditional thrombus prophylaxis.MethodsAll burn patients admitted and met selection criteria betweem April 2017 and September 2018 were included. Patients between January 2018 and September 2018 were included as the interventional group to implement standardized VTE prevention programs, while patients between April 2017 and December 2017 were included as the control group and traditional active and passive exercises were used to prevent VTE. There was no significant difference in the age, gender, ethnic group, marriage, education, occupation, type and site of the injury, burn area, operation time, and hospital stay between the two groups (P>0.05), which was comparable. The incidence of VTE, number of cases of tissue or organ hemorrhage, survival rate of skin grafting, and time of wound healing were compared.ResultsThe incidence of VTE was obviously lower in the interventional group (1.56%, 1/64) than in the control group (10.17%, 6/59) (χ2=−2.05, P=0.04). No bleeding occurred in any tissue or organ in the two groups. The survival rate of skin grafting and the time of wound healing were 89.06% (57/64) and (11.78±3.08) days respectively in the interventional group and 91.53% (54/59) and (11.66±2.30) days respectively in the control group; and the differences between the two groups were not statistically significant (χ2=0.21, P=0.65; t=−0.22, P=0.83).ConclusionThe standardized VTE prevention program can effectively prevent the occurrence of VTE, and its safety is relatively high.