ObjectiveTo investigate the repair effects of Zhengan Xifengtang on intestinal mucosal barrier in spontaneously hypertensive rats （SHRs） through the pathways of farnesoid X receptor （FXR）-myosin light chain kinase （MLCK）/phosphorylated myosin light chain （p-MLC） and its potential mechanism. MethodsForty 10-week-old male SHRs were randomly divided into model group， amlodipine besylate group （0.5 mg·kg^-1·d^-1）， and high， middle and low dose groups of Zhengan Xifengtang （34.5， 17.25， 8.625 g·kg^-1·d^-1）； eight age-matched male Wistar-Kyoto （WKY） rats of the same origin served as the normal control group. Drug intervention was carried out for 8 weeks， and the blood pressure of the rats was measured regularly. After the intervention， the rats were euthanized to collect tissue samples. Hematoxylin and eosin （HE） staining and Alcian blue-periodic acid-Schiff （AB-PAS） staining were used to evaluate the pathological morphology and the number of goblet cells in colonic mucosa. Enzyme-linked immunosorbent assay （ELISA） was employed to measure plasma lipopolysaccharide （LPS） and diamine oxidase （DAO） levels in each group of rats. Immunofluorescence （IF） assay was used to detect the relative fluorescence intensity of FXR and p-MLC in colonic tissue. Western blot was used to detect the zonula occludens-1 （ZO-1）， Occludin， Claudin-1， junctional adhesion molecule-1 （JAM-1）， mucin-2 （MUC2）， FXR， MLCK， and p-MLC protein expression in colon tissue. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was used to detect mRNA expression levels of ZO-1， Occludin， Claudin-1， JAM-1， MUC2， FXR， MLCK， and MLC in colon tissue. ResultsCompared with the normal group， the blood pressure in the model group increased significantly （P<0.01）， the colonic mucosal structure was seriously damaged， and the number of goblet cells decreased significantly. The contents of LPS and DAO increased significantly （P<0.01）， the relative fluorescence intensity of FXR in colon decreased， while that of p-MLC increased （P<0.01）. The protein and mRNA expressions of ZO-1， Claudin-1， JAM-1， Occludin， MUC2 and FXR decreased significantly （P<0.01）， whereas the protein expression of MLCK and p-MLC increased significantly （P<0.01）. Additionally， the mRNA expression of MLCK and MLC increased significantly （P<0.01）. Compared with the model group， the Zhengan Xifengtang groups at various dosages and the amlodipine besylate group showed significantly reduced blood pressure （P<0.01）. The damage of colonic mucosa structure was alleviated， the infiltration of inflammatory cells decreased， and the number of goblet cells increased. LPS and DAO levels were decreased （P<0.05， P<0.01）. In the colon， the relative fluorescence intensity of FXR increased， while that of p-MLC decreased （P<0.05， P<0.01）. The protein and mRNA expression levels of ZO-1， Claudin-1， JAM-1， Occludin， and FXR increased significantly （P<0.05， P<0.01）， while the protein expression of MLCK and p-MLC decreased significantly （P<0.05， P<0.01）. Furthermore， the mRNA expression of MLCK and MLC decreased significantly （P<0.05， P<0.01）， and the protein expression of MUC2 increased significantly （P<0.05， P<0.01）. The expression of MUC2 mRNA in each dose group of Zhengan Xifengtang increased significantly （P<0.05， P<0.01）. ConclusionZhengan Xifengtang may regulate the FXR-MLCK/p-MLC signaling pathway and enhance the expression of tight junction protein， thereby reducing intestinal mucosal permeability， promoting mucosal barrier repair， decreasing LPS release into the blood， and ultimately lowering blood pressure.

ObjectiveTo investigate the repair effects of Zhengan Xifengtang on intestinal mucosal barrier in spontaneously hypertensive rats （SHRs） through the pathways of farnesoid X receptor （FXR）-myosin light chain kinase （MLCK）/phosphorylated myosin light chain （p-MLC） and its potential mechanism. MethodsForty 10-week-old male SHRs were randomly divided into model group， amlodipine besylate group （0.5 mg·kg^-1·d^-1）， and high， middle and low dose groups of Zhengan Xifengtang （34.5， 17.25， 8.625 g·kg^-1·d^-1）； eight age-matched male Wistar-Kyoto （WKY） rats of the same origin served as the normal control group. Drug intervention was carried out for 8 weeks， and the blood pressure of the rats was measured regularly. After the intervention， the rats were euthanized to collect tissue samples. Hematoxylin and eosin （HE） staining and Alcian blue-periodic acid-Schiff （AB-PAS） staining were used to evaluate the pathological morphology and the number of goblet cells in colonic mucosa. Enzyme-linked immunosorbent assay （ELISA） was employed to measure plasma lipopolysaccharide （LPS） and diamine oxidase （DAO） levels in each group of rats. Immunofluorescence （IF） assay was used to detect the relative fluorescence intensity of FXR and p-MLC in colonic tissue. Western blot was used to detect the zonula occludens-1 （ZO-1）， Occludin， Claudin-1， junctional adhesion molecule-1 （JAM-1）， mucin-2 （MUC2）， FXR， MLCK， and p-MLC protein expression in colon tissue. Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） was used to detect mRNA expression levels of ZO-1， Occludin， Claudin-1， JAM-1， MUC2， FXR， MLCK， and MLC in colon tissue. ResultsCompared with the normal group， the blood pressure in the model group increased significantly （P<0.01）， the colonic mucosal structure was seriously damaged， and the number of goblet cells decreased significantly. The contents of LPS and DAO increased significantly （P<0.01）， the relative fluorescence intensity of FXR in colon decreased， while that of p-MLC increased （P<0.01）. The protein and mRNA expressions of ZO-1， Claudin-1， JAM-1， Occludin， MUC2 and FXR decreased significantly （P<0.01）， whereas the protein expression of MLCK and p-MLC increased significantly （P<0.01）. Additionally， the mRNA expression of MLCK and MLC increased significantly （P<0.01）. Compared with the model group， the Zhengan Xifengtang groups at various dosages and the amlodipine besylate group showed significantly reduced blood pressure （P<0.01）. The damage of colonic mucosa structure was alleviated， the infiltration of inflammatory cells decreased， and the number of goblet cells increased. LPS and DAO levels were decreased （P<0.05， P<0.01）. In the colon， the relative fluorescence intensity of FXR increased， while that of p-MLC decreased （P<0.05， P<0.01）. The protein and mRNA expression levels of ZO-1， Claudin-1， JAM-1， Occludin， and FXR increased significantly （P<0.05， P<0.01）， while the protein expression of MLCK and p-MLC decreased significantly （P<0.05， P<0.01）. Furthermore， the mRNA expression of MLCK and MLC decreased significantly （P<0.05， P<0.01）， and the protein expression of MUC2 increased significantly （P<0.05， P<0.01）. The expression of MUC2 mRNA in each dose group of Zhengan Xifengtang increased significantly （P<0.05， P<0.01）. ConclusionZhengan Xifengtang may regulate the FXR-MLCK/p-MLC signaling pathway and enhance the expression of tight junction protein， thereby reducing intestinal mucosal permeability， promoting mucosal barrier repair， decreasing LPS release into the blood， and ultimately lowering blood pressure.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective To investigate the role of the neutrophil-to-lymphocyte ratio(NLR)in predicting the in-hospital mortality risk of patients with acute aortic dissection(AAD)in multicenter hospitals.Methods A multicenter retrospective cohort study was conducted.Clinical data were collected from 2642 AAD patients who were hospitalized in five teaching hospitals:Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology,Henan Provincial People's Hospital,Fuwai Central China Cardiovascular Hospital,the Third Affiliated Hospital of Xinxiang Medical University,and the Second Affiliated Hospital of Chongqing Medical University between August 2010 and December 2021.According to the quartiles of serum NLRlevels,the patients were divided into four groups:first quartile(Q1,n=660),second quartile(Q2,n=661),third quartile(Q3,n=661),and fourth quartile(Q4,n=660).The clinical characteristics and biochemical indicators of each group were compared.Partial correlation analysis was used to assess the relationship between NLR and cardiovascular parameters.Restricted cubic splines,Kaplan-Meier survival analysis,and Cox regression models were employed to evaluate the association between NLR levels and in-hospital mortality risk in AAD patients.Results The median age of all patients was 54[interquartile range(IQR):46-63]years,including 2096 males and 546 females.Compared with Q1-Q3 groups,patients inQ4group had a lower incidence of smoking history and diabetes history,and were more likely to have DeBakey type Ⅰ AAD(P＜0.05).Additionally,the levels of aspartate aminotransferase,high-density lipoprotein cholesterol,creatinine,and D-dimer in Q4 group were higher,while the levels of triglycerides and C-reactive protein(CRP)were lower(P＜0.01).The results of partial correlation analysis showed that the plasma NLR level was positively correlated with D-dimer(r=0.43,P＜0.01)and creatinine(r=0.16,P＜0.01).The restricted cubic spline function in the Cox model revealed a significant non-linear relationship between the plasma NLR level and clinical outcomes in AAD patients(P＜0.01).Kaplan-Meier survival analysis indicated that patients in Q4 group had the highest in-hospital mortality rate compared with Q1-Q3 groups(P＜0.0001).Furthermore,multivariate Cox regression analysis demonstrated that compared with Q1 group,the hazard ratio(HR)of NLR in Q4 group was 1.77(95％CI 1.33-2.37,P＜0.001),which was an independent risk factor for the primary endpoint events.Conclusion A higher plasma NLR level is significantly associated with the occurrence of cardiovascular events in AAD patients,and this association remains significant even after adjusting for potential confounding factors such as the multicenter visiting hospitals.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective To find the relationship between phthalates(PAEs)metabolite concentrations and body mass index(BMI)in infertile patients.Methods From December 2018 to January 2021,120 infertile pa-tients were selected from the Reproductive Medicine Centre of the 988th Hospital of the Joint Logistics Support Force of the Chinese People's Liberation Army and the Reproductive Medicine Centre of the Henan Provincial People's Hospital,while 60 patients with no history of infertility and normal weight were selected from the Medical Check-up Centre as the normal group.According to BMI index,the infertility patients were divided into the control group(BMI＜28 kg/m2),obesity group(obesity,BMI≥28 kg/m2).The obesity group was divided into BHI(BMI≥32 kg/m2)and BH2(BMI≥28 kg/m2,＜32 kg/m2).The basic information of each group was collected.Serum level of PAEs metabolites diethyl phthalate(DEP),dibutyl phthalate(DBP)and di-isooctyl phthalate(DEHP)was detected using liquid chromatography/mass spectrometry(HPLC).The rela-tionship between PAEs metabolite concentration and body mass index(BMI)were analyzed by Spearman's analysis.Results The serum level of DEP,DBP and DEHP was significantly higher in obese group than in the control group(P＜0.05).The serum level of DEP,DBP and DEHP was significantly higher in the control group than in the normal group(P＜0.05);The metabolites level of PAEs was higher in patients with BHI than in the patients with BH2(P＜0.05).Spearman's analysis showed that the BMI of the patients in the obese group of infertility was positively correlated with DEP,DBP and DEHP content(P＜0.05).Conclusions Obesity and PAEs metabolite concentration in infertility patients are related,the higher the degree of obesity,the higher the content of related metabolites.

Objective To explore the clinical characteristics and influencing factors in type 2 diabetes mellitus(T2DM)patients with metabolic-associated fatty liver disease(MAFLD)who are at moderate-to-high risk of metabolic-associated fatty liver fibrosis.Methods A retrospective analysis was conducted on the clinical data of 495 T2DM patients with MAFLD who were treated in the Department of Endocrinology,the Seventh People's Hospital Affiliated to Shanghai University of Traditional Chinese Medicine,from August 2022 to May 2024.According to the fibrosis-4(FIB-4)index,the patients were divided into two groups:low risk group for metabolic-associated fatty liver fibrosis(n=311)and moderate-to-high risk group for metabolic-associated fatty liver fibrosis(n=184).Differences in clinical characteristics and laboratory test results between the two groups were compared.Univariate and multivariate binary logistic regression analyses were used to screen the influencing factors of moderate-to-high risk of metabolic-associated fatty liver fibrosis in T2DM patients with MAFLD.Receiver operating characteristic(ROC)curves and area under the curve(AUC)were employed to evaluate the predictive value of these factors for moderate-to-high risk of metabolic-associated fatty liver fibrosis in T2DM patients with MAFLD.Results Compared with low risk group,moderate-to-high risk group had significantly higher age,proportions of patients with a history of hypertension and coronary heart disease,as well as higher levels of aspartate aminotransferase(AST),blood urea nitrogen(BUN),and creatinine(Cr)(P<0.05).In contrast,moderate-to-high group had a lower proportion of male patients,and lower levels of platelet count(PLT),total cholesterol(TC),triglycerides(TG),low-density lipoprotein cholesterol(LDL-C),glycated hemoglobin(HbA1c),free triiodothyronine(FT3),free thyroxine(FT4),and thyroid feedback quantile-based index(TFQI)(P<0.05).Univariate binary logistic regression analysis showed that male(P=0.020),HbA1c(P=0.014),BUN(P<0.001),Cr(P<0.001),TC(P=0.001),LDL-C(P<0.001),FT3(P<0.001),FT4(P<0.001),and TFQI(P=0.039)were influencing factors for moderate-to-high risk of metabolic-associated fatty liver fibrosis in T2DM patients with MAFLD.Multivariate binary logistic regression analysis revealed that BUN(OR=1.165,95%CI 1.006-1.348,P=0.042)and Cr(OR=1.020,95%CI 1.005-1.036,P=0.008)were independent risk factors for moderate-to-high risk of metabolic-associated fatty liver fibrosis in T2DM patients with MAFLD,while male(OR=0.574,95%CI 0.339-0.972,P=0.039),LDL-C(OR=0.659,95%CI 0.483-0.898,P=0.008),FT3(OR=0.590,95%CI 0.404-0.864,P=0.007),and FT4(OR=0.863,95%CI 0.762-0.977,P=0.020)were independent protective factors.ROC curve analysis showed that the AUC of the combined 6 influencing factors for predicting moderate-to-high risk of metabolic-associated fatty liver fibrosis in T2DM patients with MAFLD was 0.728(95%CI 0.682-0.774),with a sensitivity of 0.620 and a specificity of 0.759.Conclusions Gender,BUN,Cr,LDL-C,FT3,and FT4 are independent influencing factors for moderate-to-high risk of metabolic-associated fatty liver fibrosis in T2DM patients with MAFLD.Monitoring and early intervention for the above abnormal biochemical indices are beneficial in delaying the occurrence and development of liver fibrosis in T2DM patients with MAFLD.