Objective:To analyze associations of sedentary behavior and physical activity with dyslipidemia among residents in Wuhai city.Methods: Data about social demographic characteristics, life style, health status and other covariate required for analysis in this study was obtained from a cross-sectional study on a total of 11 497 18-79 years old residents in Wuhai City by questionnaire, body mea-surement and laboratory examination.In this study, sedentary behavior and physical activity were evaluated using international physical activity questionnaire long version (IPAQ).IPAQ is widely used all over the world, and its reliability and validity have been tested in Chinese population.2016 Chinese Guideline for the Management of Dyslipidemia in Adults was used to define dyslipidemia in this study.Results: According to IPAQ scoring protocol, 124 participants were excluded as a result of reporting more than 960 min of physical activity per day.50.58% of 11 373 participants included in the analysis reported more than 4 hours of sedentary behavior per day in this study, thus 49.42% participants reported no more than 4 hours of sedentary behavior per day;the proportions of these 11 373 participants who reached Low level physical activity, Moderate level physical activity and high level physical activity were 23.43%, 37.29% and 39.28% respectively;and the detection ratios of new cases and prevalent cases of dyslipidemia in Wuhai City were 20.46% and 16.13% respectively.After controlling for confounders in this study, we found out that sedentary behavior increased the risk of new cases of dyslipidemia in women (OR=1.17, 95% CI: 1.00-1.36), and increased the risk of prevalent cases of dyslipidemia in both men (OR=1.21, 95% CI: 1.02-1.44) and women (OR=1.24, 95% CI: 1.04-1.48);as for association of physical activity with dyslipidemia, association was found between high level physical activity and prevalent cases of dyslipidemia in men in this study (OR=0.78, 95% CI: 0.62-0.98), suggested that high level physical activity may help to reduce the risk of prevalent cases of dyslipidemia in men.Conclusion: Our results from this cross-sectional study in Wuhai City suggested that sedentary behavior increased the risk of dyslipidemia;by contrast, physical activity may help to reduce the risk of dyslipidemia.

【Objective】 To predict the risk factors of intraoperative blood transfusion by establishing a random forest algorithm prediction model, and to evaluate its prediction performance in clinical. 【Methods】 A total of 48 176 patients who underwent surgery from January 2014 to December 2017 in the First Medical Center of the Chinese People′s Liberation Army General Hospital were collected and divided into a blood transfusion group(n=5 035) and a non-transfusion group(n=43 141) according to whether blood was transfused or not during the operation, and the age, gender, weight, blood routine, coagulation test indicators, surgical grade, number of operations and anesthesia methods, and preoperative blood transfusion history between the two groups were compared and analyzed. All cases were randomly divided into training set(n=33 723) and the test set(n=14 453), using the sklearn function package in the computer programming language(Python V 3.9.0) to introduce the random forest algorithm, with 2 groups of different factors incorporated into the random forest algorithm to build the model, and the model was evaluated using the operating curve(ROC). 【Results】 1) There were statistically significant differences between the blood transfusion group and the non-transfusion group in terms of gender, age, blood routine, coagulation function, surgical grade, and preoperative blood transfusion history(P<0.05); 2) Except for the distribution of blood types, there was no statistically significant difference in other indicators between the test set and the training set(P>0.05); 3) In the established intraoperative blood model, the blood routine, coagulation function and general anesthesia had a great influence, with the cumulative importance > " 0.90" ; 4) The ROC analysis showed that the area under the ROC curve of the random forest model was 0.91 and 0.82 in the training set and the test set, which demonstrated a good predictive ability. 【Conclusion】 The intraoperative blood, using prediction model based on random forest method, can predict intraoperative blood use and blood transfusion risk factors.