Objective:To study the earlier application of the breathing volumetric exerciser to improve the lung function and prevent the respiratory complications of the postoperative patient of OPCAB.Methods:80 patients were randomly divided into following groups:group A,with breathing exercise;group B, control group.Results:Compared with group B the maximal inspiratory capacity in group A increased significantly(P

Objective:To study the effect of aprotinin on blood coagulation and fibrinolysis system.Methods:60 cases of esophagectomy were randomly divided into aprotinin group(A) and control group(B), and the results of Coagulate Start Time(CST),Most Coagulate Extent (MCE),Whole Fibrinolytic Time (WFT) were detected in different time(30 minute before operation and 2h,24h,48h,72h after operation).Results:Compared with group B,CST in 2h,24h after operation was reduced significantly in group A( P

Objective To explore the relationship between MRI signal intensity in pallidum and levels of total bilirubin in neonatal hy-perbilirubinemia. Methods From July, 2014 to October, 2015, sixty neonates were divided into three groups according to the levels of total serum bilirubin (TSB), that were group I (TSB 17.1~34.2μmol/L, n=16), group II (TSB>34.2~340μmol/L, n=34), and group III (TSB>340μmol/L, n=10). They were screened with 3.0 T MRI, and the T1WI signal intensity of bilateral pallidum was measured. Results The bi-lateral signal intensity was higher in group III than in group II and group I. There was positive correlation between signal intensity and TSB levels. Conclusion The MRI signal intensity in pallidum may help for diagnosis of neonatal bilirubin encephalopathy.

Objective:To explore the causal association of glucose-lipid metabolism and obesity indicators with myocardial infarction by a two-sample Mendelian randomization analysis.Methods:Single nucleotide polymorphisms (SNPs) related to phenotypes were obtained from genome-wide association study databases. The body mass index (BMI) and glycated hemoglobin dataset includes 99 998 samples and 8 126 035 SNPs; the waist-to-hip ratio dataset includes 224 459 samples and 2 562 516 SNPs; the waist circumference and hip circumference dataset includes 462 166 samples and 9 851 867 SNPs; the fasting glucose dataset includes approximately 12 million SNPs; the low-density lipoprotein cholesterol (LDL-C) dataset includes 201 678 samples and 12 321 875 SNPs; the high-density lipoprotein cholesterol (HDL-C), and triglycerides dataset includes 156 109 samples and 15 784 414 SNPs; and the body fat percentage, whole-body fat mass, trunk fat percentage, and trunk fat mass dataset includes 454 588 samples and 9 851 867 SNPs. This study primarily used inverse-variance weighted method to analyze the associations between various exposure factors and outcomes. Heterogeneity among SNPs was assessed using Cochran′s Q test, and horizontal pleiotropy of SNPs was examined using the MR-Egger method. Additionally, a multivariable MR approach was used to adjust for BMI, further validating associations between exposure factors and the risk of myocardial infarction. Results:Higher BMI ( OR=1.070, 95% CI: 1.041-1.100), waist-to-hip ratio ( OR=1.366, 95% CI: 1.113-1.677), LDL-C ( OR=1.638, 95% CI: 1.488-1.803), triglycerides ( OR=1.445, 95% CI: 1.300-1.606), waist circumference ( OR=1.841, 95% CI: 1.650-2.055), hip circumference ( OR=1.247, 95% CI: 1.132-1.372), body fat percentage ( OR=1.795, 95% CI: 1.568-2.055), whole-body fat mass ( OR=1.519, 95% CI: 1.381-1.670), trunk fat percentage ( OR=1.538, 95% CI: 1.374-1.723), and trunk fat mass ( OR=1.421, 95% CI: 1.294-1.561), as well as lower HDL-C ( OR=0.799, 95% CI: 0.729-0.875), have causal effects on myocardial infarction (all P<0.05). After adjusting for BMI, hip circumference, trunk fat percentage, and trunk fat mass were no longer associated with myocardial infarction. However, waist-to-hip ratio ( OR=1.457, 95% CI: 1.132-1.877), fasting glucose ( OR=1.191, 95% CI: 1.024-1.383), glycated hemoglobin ( OR=1.129, 95% CI: 1.034-1.233), LDL-C ( OR=1.592, 95% CI: 1.314-1.929), triglycerides ( OR=1.410, 95% CI: 1.279-1.553), waist circumference ( OR=1.922, 95% CI: 1.448-2.551), body fat percentage ( OR=1.421, 95% CI: 1.072-1.884), and whole-body fat mass ( OR=1.295, 95% CI: 1.031-1.626) remained positively associated with myocardial infarction, while HDL-C ( OR=0.809, 95% CI: 0.729-0.897) remained negatively associated. Conclusions:Abdominal obesity and dysregulation of glucose-lipid metabolism are risk factors for myocardial infarction. Screening for glucose-lipid metabolism (fasting glucose, HDL-C, LDL-C, triglycerides) and obesity-related indicators (waist circumference, waist-to-hip ratio, body fat percentage, and whole-body fat mass) is of great importance for the primary prevention of myocardial infarction.