Objective To study the correlation between smoking and levels of endothelial microparticles(EMPs)in patients with acute myocardial infarction(AMI).Methods A total of 74 patients with AMI who were admittedto Xinjiang Uighur Autonomous Region People's Hospital from January 2020 to December 2020 were selected to be included in AMI group,and 72 patients with non-AMI chest tightness/pain were included in the non-AMI group.The general information of the patients in the two groups was collected,and the pe-ripheral venous bloodof the two groups was extracted.The EMPs levels and other clinical biochemical indexes were detected by flow cytometry.Results The proportion of males,WBC,proportion of smokers,and EMPslevel in patients in the AMI group were higher than those in the non-AMI group,and the difference was statistically significant(P＜0.05).The results of univariate logistic analysis showed that gender,WBC,EMPs,and smoking were risk factors for the development of AMI,and the difference was statistically signifi-cant(P＜0.05).Further multifactorial logistic regression results showed that high levels of elevated EMPs and smoking were independently associated with the occurrence of AMI.The receiver operating characteristic(ROC)curve was drawn to analyze the diagnostic value of EMPs in predicting AMI,and the area under the curve(AUC)between EMPs and AMI was 0.877(95％CI:0.824-0.929,P＜0.001).Logistic regression with EMPs as the dependent variable showed that low high-density lipoprotein(HDL)levels and smoking were independent risk factors for increased levels of EMPs.Conclusion High levels of EMPs and smoking were in-dependently associated with the development of AMI,and smoking-induced release of EMPs may be one of the reasons why patients are more likely to develop AMI.

Objective:To explore the risk factors of in hospital death in patients with acute circulatory failure, and to further construct the prediction model.Methods:This study retrospectively analysed clinical data of 224 shock patients admitted to Xinjiang Uygur Autonomous Region People’s Hospital from January 2014 to January 2023, and patients were eligible for shock diagnosis according to the expert consensus of emergency clinical practice in China for acute circulatory failure. Including age, gender, admission diagnosis and other basic information, as well as platelet, lactic acid, lymphocyte count, NK cell count, CD4, CD8 and other indicators completed within 24 hours of admission.They were divided into survival group and death group according to the condition at discharge.Variables with P<0.1 in the univariate analysis were included in the LASSO regression model to screen out the most important predictors of hospital death in ACF patients, and the prediction model was constructed by Logistic regression.The model differentiation was evaluated by receiver operator characteristic (ROC) curve and area under the curve (AUC). Hosmer-Lemeshow test was used to evaluate the calibration degree of the prediction model. Finally, clinical decision curve analysis (DCA) was used to test the clinical benefit and application value of the model. Results:A total of 224 ACF patients, 113 survived and 111 died. The results of the univariate analysis showed statistically significant differences between the two groups in age, mental status, type of shock, respiratory rate, APACHE score, lymphocyte count, lactate, CD4, CD8 and qsofa ( P<0.05).The Logistic regression prediction model was constructed according to the 4 predictors and outcome variables selected by LASSO method,in which increased delirium, coma, respiratory rate and APACHE score were risk factors and increased CD4 was a protective factor.The above indicators were used to construct a line graph model for predicting in-hospital death in ACF patients, with a probability cut-off value of 0.4404 for predicting in-hospital death, corresponding to a total line graph score of approximately 136.This model had an AUC of 0.830 (0.764-0.895), a sensitivity of 81.25% and a specificity of 68.83%.The Hosmer-Lemshow test for the modelling set showed χ 2=712 and P=0.624, suggesting good accuracy of the model predictions.The assessment of the DCA analysis showed that the net benefit of the model was significantly higher than the two extreme conditions and had good clinical applicability. Conclusions:Mental status, respiratory rate, APACHE score as risk factors for in-hospital mortality in patients with acute circulatory failure and CD4 as a protective factor. The predictive model constructed from this may predict the risk of in-hospital death in patients and has certain clinical application value.