Objective Through 34 clinical studies, the influencing factors of coronary heart disease(AD) incidence in postmenopausal women were identified, and a risk prediction model for coronary heart disease in postmenopausal women was constructed based on the above influencing factors, while verifying its predictive efficacy. Methods Retrieve 4 foreign electronic databases(Cochrane Library, Embase, PubMed, Web of Science) and 4 Chinese electronic databases(CNKI, Wanfang Database, China Biomedical Literature Database, and VIP Database) through computer retrieval, and collect literature from the public development table established until December 2022. Using RevMan5.4 software and meta-analysis method, frequency statistics and analysis were conducted on the influencing factors included in the literature 5 or more times, to calculate the comprehensive risk OR of the relevant factors affecting the onset of CAD in menopausal women. Statistically significant(P<0.05) factors were included in the construction of a logistic regression prediction model and external validation was conducted; Using the clinical data of 303 menopausal female patients admitted to the Department of Cardiology at Ruikang Hospital Affiliated to Guangxi University of Traditional Chinese Medicine from September 2017 to July 2022, a column chart model was drawn and validated. Results Meta analysis showed that 10 risk factors(X1～X10), including diabetes, hypertension, smoking, body mass index(BMI), family history of coronary heart disease, Dyslipidemia, total cholesterol(TC), low-density lipoprotein(LDL), mean platelet volume(MPV), and platelet distribution width(PDW), were independent risk factors for CAD in menopausal women(P<0.05), Constructing a risk prediction model for CAD in menopausal women: Logit(P)=-0.26 + 0.61X1+0.65X2+0.88X3+0.7X4+0.87X5+1.26X6+0.56X7+0.27X8+0.94X9+1.13X10. The model has been verified to have certain accuracy(AUC=0.630), and the column chart model has a high accuracy(AUC=0.732). After calibration test and decision curve analysis, it indicates that the model has high clinical application value. Conclusion This prediction model has good accuracy and can provide evidence-based basis for early clinical screening of the risk of CAD in menopausal women.

Objective Using bioinformatics methods to analyze the expression of prognostic autophagy related genes in pancancer and construct a prognostic model for renal clear cell carcinoma(KIRC) to accurately assess the prognosis of patients.Methods The autophagy related genes related to prognosis were screened from the GEPIA database, and the obtained genes were used to calculate their autophagy scores in various cancers using ssGSEA to quantify their expression in cancer. Univariate and multivariate Cox regression analysis screened the correlation between autophagy scores and prognosis in patients with KIRC. The edgeR package analyzed autophagy genes differentially expressed in patients with KIRC, and then conducted stepwise, LASSO, and multivariate Cox regression analysis to construct a risk prognosis model. The survival curve, and subject work characteristic curve(ROC) were used to evaluate the predictive value of the model. CIBERSORT calculates the infiltration of immune cells between high-risk and high-risk groups, and uses the CellMiner database to screen sensitive drugs for model genes. Results The expression of autophagy related genes related to prognosis varies among various cancers, with the highest expression in KIRC, and autophagy scores are closely related to prognosis. Four prognostic model genes for KIRC were selected through stepwise regression analysis and LASSO analysis. Multivariate Cox regression analysis showed that the high expression of BIRC5 and EIF4EBP1 was a risk factor affecting the prognosis of KIRC [HR(95%CI)=1.021(1.010-1.033), 1.003(1.001-1.005)], while the high expression of BAG1 and BNIP3 was a protective factor affecting the prognosis of KIRC [HR(95%CI)=0.963(0.942-0.985), 0.997(0.994-0.999)]. Model gene risk score, M stage, and N stage can be independent influencing factors for the prognosis of KIRC(P<0.05). The ROC curve showed that the area under the curve(AUC) of the model gene predicting the 1, 3, and 5 year survival rates of KIRC patients were 0.743, 0.740, and 0.699, respectively. There are differences in the infiltration of multiple immune cells in patients with high and low risk. Compared with the low risk group, the proportion of CD8+T cells, auxiliary follicular T cells, regulatory T cells, and M0 macrophages in the high risk group significantly increased, while the proportion of resting killer cells, monocytes, and resting mast cells significantly decreased. Model genes are associated with the sensitivity of various cancer cell lines to drugs, especially in renal cancer cell lines, with a correlation coefficient of more than 0.9.Conclusions Autophagy related gene analysis can provide independent and reliable biomarkers and treatment targets for patients with KIRC, and provide new ideas for exploring personalized treatment of KIRC.

Objective To analyze the relationship between serum levels of nuclear factor E2 related factor 2(Nrf2), heme oxygenase-1(HO-1) and pregnancy outcome in patients with gestational diabetes(GDM).Methods One hundred and thirty-two GDM patients admitted to the obstetrics department of Xinjiang Uygur Autonomous Region People's Hospital from January 2021 to May 2022 were selected as the GDM group. According to pregnancy outcomes, they were divided into 41 poor outcome subgroups and 91 good outcome subgroups. Additionally, 64 healthy pregnant women who underwent prenatal examinations at the same time were selected as the healthy control group. Enzyme-linked immunosorbent assay was used to detect serum Nrf2 and HO-1 levels. Pearson correlation analysis of the relationship between serum Nrf2 and HO-1 levels in GDM patients, multivariate logistic regression analysis of the influencing factors of poor pregnancy outcomes in GDM patients, and subject work characteristic curve(ROC) analysis of the value of serum Nrf2 and HO-1 in predicting poor pregnancy outcomes in GDM patients.Results Compared with the healthy control group, the serum Nrf2 and HO-1 levels in the GDM group increased(t/P=10.834/<0.001, 14.359/<0.001). There is a positive correlation between serum Nrf2 and HO-1 levels in GDM patients(r/P=0.721/<0.001). The incidence of adverse pregnancy outcomes in 132 GDM patients was 31.06%(41/132), and the serum HbA1c, HOMA-IR, Nrf2, and HO-1 levels in the poor outcome subgroup were higher than those in the good outcome subgroup [t(Z)/P=3.111/0.002, 5.220/<0.001, 5.555/<0.001, 5.517/<0.001]. Elevated HbA1c, HOMA-IR, Nrf2, and HO-1 are independent risk factors for poor pregnancy outcomes in GDM patients [OR(95%CI)=2.599(1.330-5.079), 2.383(1.481-3.833), 1.085(1.041-1.130), and 1.077(1.037-1.120)]. The AUC of poor pregnancy outcomes in GDM patients predicted by serum Nrf2 and HO-1 levels alone and in combination were 0.770, 0.769, and 0.886, respectively. The AUC predicted by the combination of the two was higher than that predicted separately(Z/P=2.861/0.004, 2.936/0.003). Conclusion The levels of serum Nrf2 and HO-1 are independently correlated with poor pregnancy outcomes in GDM patients. The combination of the two has high value in predicting poor pregnancy outcomes in GDM patients and may become an auxiliary predictor of poor pregnancy outcomes in GDM patients.

Objective To explore the role of overexpression of C1q/tumor necrosis factor related protein-6(CTRP6) in alleviating cerebral ischemia-reperfusion injury in diabetes mice by regulating nuclear factor E2 related factor 2/heme oxygenase-1(Nrf2/HO-1) pathway.Methods The experiment was conducted at the People's Hospital of Wuhan University from September 2021 to June 2022. Eighteen clean grade male C57BL/6 mice were selected and randomly divided into sham surgery group(Sham), cerebral ischemia-reperfusion group(IR), and cerebral ischemia-reperfusion+CTRP6 overexpression group(IR+CTRP6) using a random number table method, with 6 mice in each group. In IR group and IR+CTRP6 group, STZ 50 mg/kg was intraperitoneally injected for 5 consecutive days to establish the mouse model of diabetes. In IR+CTRP6 group, adeno-associated virus(AAV)-CTRP6 was injected into the ventricles of the brain three weeks after the establishment of the diabetes model. After another three weeks, the two groups used the suture method to prepare the mouse model of cerebral ischemia reperfusion injury. Sham group was only operated without any treatment. After 24 hours of reperfusion, the area of cerebral infarction was measured by TTC. Western blot detection of Nrf2, HO-1, NQO-1, p-NF-κ B/NF-κ B. P-IKK/IKK protein levels. WST-1 method for detecting SOD, visible light method for detecting CAT, TBA method for detecting MDA, ELISA method for detecting IL-1 β, TNF-α, MCP-1 level; Colorimetric detection of Caspase-3 and Caspase-9 activity; In situ apoptosis fluorescence assay was used to detect cell apoptosis.Results Compared with the Sham group, the IR group showed a decrease in Nrf2, HO-1, NQO-1, SOD, CAT(P<0.01), p-NF-κ B/NF-κ B、p-IKK/IKK、MDA、IL-1 β、 TNF-α、 MCP-1, Caspase-3, Caspase-9, and TUNEL positive cells increased(P<0.01). Compared with the IR group, the IR+CTRP6 group showed an increase in Nrf2, HO-1, NQO-1, SOD, CAT(P<0.01), cerebral infarction area, p-NF-κB/NF-κB, p-IKK/IKK、MDA、IL-1 β, TNF-α, MCP-1, Caspase-3, Caspase-9, and TUNEL positive cells all decreased(P<0.01).Conclusion CTRP6 alleviates cerebral ischemia-reperfusion injury in diabetes mice by regulating oxidative stress, inflammatory response and apoptosis.

Report 2 cases of children with Cohen syndrome caused by VPS13B gene mutation and review the literature.