BackgroundStudents in secondary vocational health school are at the age of puberty and prone to depressive symptoms. Peer victimization and social media addiction are found to be crucial in influencing the development of depression， and positive mental health has been proven to alleviate depressive symptoms， whereas there remains a striking lack of research on the mediating role of positive mental health and social media addiction in the relationship between peer victimization and depressive symptoms among secondary vocational health school students. ObjectiveTo explore the relationship between peer victimization and depressive symptoms and investigate the mediating role of positive mental health and social media addiction， so as to provide references for the prevention of depression among secondary vocational health school students. MethodsFrom October to December 2020， a cluster sampling framework was utilized to recruit 7 307 students from a secondary vocational health school in Luzhou City， Sichuan Province. Assessments were performed using Multidimensional Peer Victimization Scale （MPVS）， Warwick-Edinburgh Mental Well-being Scale （WEMWBS）， Bergen Social Media Addiction Scale （BSMAS） and Patient Health Questionnaire Depression Scale-9 item （PHQ-9）. Spearman correlation analysis was calculated to determine correlations between scores of scales， Process 4.0 was employed to test the mediation effect， and the bias-corrected Bootstrap procedure was used to test the significance of the mediation effect. ResultsA total of 7 044 （96.40%） valid questionnaires were collected. And 4 391（62.34%）students were found to have depressive symptoms. Correlation analysis revealed that PHQ-9 score was positively correlated with BSMAS score and MPVS score （r=0.404， 0.506， P<0.01）. WEMWBS score was negatively correlated with PHQ-9 score， BSMAS score and MPVS score （r=-0.587， -0.259， -0.358， P<0.01）. BSMAS score was positively correlated with MPVS score （r=0.328， P<0.01）. Positive mental health played a mediating role in the relationship between peer victimization and depressive symptoms， with an indirect effect value of 0.130 （95% CI： 0.119~0.141）， accounting for 30.81% of the total effect. Social media addiction also mediated the relationship between peer victimization and depressive symptoms， with an indirect effect value of 0.052 （95% CI： 0.045~0.059）， accounting for 12.34% of the total effect. Positive mental health and social media addiction exhibited a chained mediation effect on the relationship between peer victimization and depressive symptoms， with an indirect effect value of 0.012 （95% CI： 0.010~0.014） and accounting for 2.84% of the total effect. ConclusionPeer victimization can affect the presence of depressive symptoms among secondary vocational health school students both directly and indirectly through either separate or chained mediation of positive mental health and social media addiction.

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: 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 analyze the risk factors of gastrointestinal bleeding in patients with type A aortic dissection(TAAD)after Sun's operation.Methods The clinical data of 87 patients who underwent TAAD Sun's operation in our hospital from March 2021 to June 2022 were retrospectively analyzed.They were divided into the bleeding group and the non-bleeding group according to whether there was gastrointestinal bleeding after operation.The clinical data of patients in the two groups was compared and analyzed.The binary Logistic regression analysis was used to analyze the risk factors of gastrointestinal bleeding.The clinical predictor of postoperative gastrointestinal bleeding was analyzed by receiver operating characteristic(ROC)curve.Results In this study,there were 40 cases of postoperative gastrointestinal bleeding(the bleeding group)and 47 cases of non-bleeding(the non-bleeding group).Compared with the non-bleeding group,the bleeding group had a shorter onset time,a higher proportion of patients with hypertension history,a higher preoperative creatinine abnormality rate,more intraoperative blood loss,longer postoperative mechanical ventilation time,higher postoperative infection rate,and higher poor prognosis rate,with statistically significant differences(P<0.05).There was no statistically significant difference in the gender,age,gastrointestinal diseases history,smoking history,preoperative platelets,preoperative international normalized ratio(INR),preoperative alanine aminotransferase(ALT),preoperative aspartate aminotransferase(AST),preoperative γ-glutamyl transpeptidase(GGT),preoperative dissection involving abdominal aorta,operation time,intraoperative cardiopulmonary bypass time,intraoperative circulatory arrest time,intraoperative aortic occlusion time or intraoperative blood transfusion rate.Logistic regression analysis showed that hypertension history(OR=2.468,95%CI:0.862 to 7.067,P=0.037),preoperative creatinine>105 μmol/L(OR=3.970,95%CI:1.352 to 11.659,P=0.011),long postoperative mechanical ventilation time(OR=1.015,95%CI:0.094 to 1.018,P=0.041)and postoperative infection(OR=3.435,95%CI:0.991 to 11.900,P=0.012)were the independent risk factors for postoperative gastrointestinal bleeding in TAAD patients.ROC curve showed that the postoperative mechanical ventilation time exceeding 64 hours were the clinical predictor of postoperative gastrointestinal bleeding in TAAD patients.Conclusion The prognosis of TAAD patients with postoperative gastrointestinal bleeding after Sun's operation is poor.Hypertension history,preoperative acute renal insufficiency,long postoperative mechanical ventilation time and postoperative infection are closely related to postoperative gastrointestinal bleeding in TAAD patients after operation,which should be paid more attention to,and corresponding evaluation,early identification and early intervention should be made to improve the prognosis of patients.

Objective:To compare the short-term effect and adverse reaction of venetoclax(VEN)combined with azacitidine(AZA)versus"7+3"regimen in newly diagnosed elder patients with acute myeloid leukemia(AML).Methods:From January 2021 to January 2022,the clinical data of seventy-nine newly diagnosed elder patients with AML at the Second Hospital of Shanxi Medical University and the Shanxi Bethune Hospital were retrospectively analyzed,including VEN+AZA group(41 cases)and"7+3"group(38 cases).The propensity score matching(PSM)method was used to balance confounding factors,then response,overall survival(OS),progression-free survival(PFS)and adverse reactions between the two groups were compared.Results:The ORR of VEN+AZA group and"7+3"group was 68％and 84％,respectively,and the CRc was 64％and 72％,respectively,the differents were not statistically significant(P＞0.05).In the VEN+AZA group,there were 5 non-remission(NR)patients,4 with chromosome 7 abnormality(7q-/-7),and 1 with ETV6 gene mutation.Median followed-up time between the two groups was 8 months and 12 months,respectively,and the 6-months OS was 84％vs 92％(P=0.389),while 6-months PFS was 84％vs 92％(P=0.258).The main hematological adverse reactions in two groups were stage Ⅲ-Ⅳmyelosuppression,and the incidence rate was not statistically different(P＞0.05).The median time of neutrophil recovery in two groups was 27(11-70)d,25(14-61)d(P=0.161),and platelet recovery was 27(11-75)d,25(16-50)d(P=0.270),respectively.The infection rate of VEN+AZA group was lower than that of"7+3"group(56％vs 88％,P=0.012).The rate of lung infections of two groups was 36％and 64％,respectively,the difference was statistically significant(P=0.048).Conclusion:The short-term effect of VEN+AZA group and"7+3"regimens in eldrly AML patients are similar,but the VEN+AZA regimen had a lower incidence of infection.The presence of chromosome 7 abnormality(7q-/-7)may be a poor prognostic factor for elderly AML patients treated with VEN+AZA.

Objective:To investigate the effect of CD8+CD28-T cells on acute graft-versus-host disease(aGVHD)after haploidentical hematopoietic stem cell transplantation(haplo-HSCT).Methods:The relationship between absolute count of CD8+CD28-T cells and aGVHD in 60 patients with malignant hematological diseases was retrospectively analyzed after haplo-HSCT,and the differences in the incidence rate of chronic graft-versus host disease(cGVHD),infection and prognosis between different CD8+CD28-T absolute cells count groups were compared.Results:aGVHD occurred in 40 of 60 patients after haplo-HSCT,with an incidence rate of 66.67％.The median occurrence time of aGVHD was 32.5(20-100)days.At 30 days after the transplantation,the absolute count of CD8+CD28-T cells of aGVHD group was significantly lower than that of non-aGVHD group(P=0.03).Thus the absolute count of CD8+CD28-T cells at 30 days after transplantation can be used to predict the occurrence of aGVHD to some extent.At 30 days after transplantation,the incidence rate of aGVHD in the low cell count group(CD8+CD28-T cells absolute count＜0.06/μl)was significantly higher than that in the high cell count group(CD8+CD28-T cells absolute count ≥0.06/μl,P=0.011).Multivariate Cox regression analysis further confirmed that the absolute count of CD8+CD28-T cells at 30 days after transplantation was an independent risk factor for aGVHD,and the risk of aGVHD in the low cell count group was 2.222 times higher than that in the high cell count group(P=0.015).The incidence of cGVHD,fungal infection,EBV infection and CMV infection were not significantly different between the two groups with different CD8+CD28-T cells absolute count.The overall survival,non-recurrent mortality and relapse rates were not significantly different between different CD8+CD28-T cells absolute count groups.Conclusion:Patients with delayed CD8+CD28-T cells reconstitution after haplo-HSCT are more likely to develop aGVHD,and the absolute count of CD8+CD28-T cells can be used to predict the incidence of aGVHD to some extent.The absolute count of CD8+CD28-T cells after haplo-HSCT was not associated with cGVHD,fungal infection,EBV infection,and CMV infection,and was also not significantly associated with the prognosis after transplantation.

Objective:To study the prevalence and association factors of depressive and anxiety disorders in the hypertensive population.Methods:Using the database obtained from the 2013 China Chronic Disease and Risk Factor Surveillance and the 2013-2015 China Mental Health Survey,4 861 hypertensive residents were used as study subjects.And using the Diagnostic and Statistical Manual of Mental Disorders,Fourth Edition(DSM-Ⅳ)as diagnostic criterion for depressive and anxiety disorders,the 12-month prevalence was calculated.Multifactorial lo-gistic regression models were used to explore the association factors of hypertension comorbid depressive and anxie-ty disorders.Results:The 12-month prevalence rates of depressive disorders and anxiety disorders were 4.1％and 5.0％in 4 861 hypertensive residents.Chinese Han[OR(95％CI):2.00(1.01-3.93)],lack of sleep[OR(95％CI):1.82(1.34-2.48)],having myocardial infarction[OR(95％CI):2.35(1.18～4.67)]and stroke in the past year[OR(95％CI):2.10(1.19-3.72)],and chronic obstructive pulmonary disease[OR(95％CI):2.11(1.11-4.05)]were risk factors of hypertension comorbid depressive disorder.Hypertensive people with controlled blood pressure[OR(95％CI):2.01(1.30-3.13)]had a higher risk of co-morbid depressive disorder than those with blood pressure above the normal range on this measurement.Chinese Han[OR(95％CI):2.51(1.32-4.80)],Southwest China[OR(95％CI):1.64(1.02-2.63)],and lack of sleep[OR(95％CI):1.45(1.09-1.93)]were risk factors of hypertension comorbid anxiety disorder.Former but current non-smoking[OR(95％CI):0.48(0.23-0.99)]was a protective factor of hypertension comorbid anxiety disorder.Conclusion:The 12-month prevalence of anxiety disorder was higher than that of depressive disorder in this hypertensive population.Both Han and sleep deprived hypertensive people had a higher risk of comorbid depressive and anxiety disorders.