Objective To investigate the improvement effect of microsurgical varicocelectomy with vein ligation on scrotal pain and the related factors affecting the improvement of postoperative pain.Methods A retrospective analysis was conducted on the clinical data of 110 patients who underwent microsurgical inguinal varicocelectomy in Central Theater Command General Hospital from November 2021 to June 2023.The degree of pain before and after the operation was evaluated by Numerical Rating Scale(NRS).The pain relief rate,the occurrence of postoperative complications and the overall satisfaction of patients were evaluated.The semen and hormone levels of the patients before and after the operation were analyzed.Univariate and multivariate linear regression analyses were performed to investigate the relationship between clinical variables and postoperative 7-day NRS score.Results The average number of ligated veins during the operation was(6.60±3.76);postoperative satisfaction of patients was(4.23±1.08)points;complications occurred in 7 patients after the operation;after 3 months of follow-up,the overall pain relief rate was 92.73%(102/110).Compared with those before the operation,the NRS score of the patients significantly decreased 7 days after the operation,and the levels of total sperm motility,prolactin and testosterone increased 3 months after the operation,while the levels of serum follicle-stimulating hormone(FSH)and luteinizing hormone(LH)decreased,the differences were statistically significant(P<0.05).The univariate and multivariate linear regression analyses showed that the Dubin-Amelar classification of varicocele(P=0.045),operation time(P=0.049),and number of ligated veins(P=0.024)were significantly correlated with the change of postoperative 7-day NRS score.Patients with 1 to 4,5 to 10,and>10 intraoperative ligation veins were analyzed,and pain relief rates 3 months after the operation were 77.78%(28/36),100%(58/58),and 100%(16/16),respectively,with statistically significant difference(χ2=17.732,P<0.001).Conclusion Microsurgical varicocelectomy can effectively relieve scrotal pain and improve semen quality.Moreover,the relief of postoperative pain is related to the Dubin-Amelar classification of varicocele,the sufficient number of ligated spermatic veins,and the surgical experience of the surgeon.

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:To investigate the prevalence of sexual drug use in men who have sex with men (MSM) in Chengdu and analyze the relationship between depression and sexual drug use.Methods:A total of 1 277 MSM were recruited between November 2021 and May 2022. Questionnaire was used to collect information about their demographic characteristics, depression status and sexual drug use behavior. Univariate and multivariate logistic regression models were used to analyze the relationship between depression and sexual drug use.Results:In the 1 277 MSM, assessment identified 503 mild depression cases (39.4%), 196 moderate depression cases (15.3%) and 171 severe depression cases (13.4%) and 444 MSM (34.8%) reported sexual drug use in the past 6 months. Multivariate logistic regression models showed that compared with non-depression, mild depression (a OR=1.67,95% CI:1.24-2.23), moderate depression (a OR=1.50,95% CI: 1.02-2.19) and severe depression (a OR=1.56,95% CI:1.04-2.32) were positively associated with sexual drug use. Conclusions:The prevalence of depression and sexual drug use were high in MSM in Chengdu. There was a positive correlation between depression and sexual drug use. It is necessary to pay close attention to depression and sexual drug use and conduct targeted intervention in MSM.

Aim To study the protective effect of the silibinin derivative Sil-1 on acute myocardial ischemia in SD rats and its mechanism of action.Methods Af-ter 18 hours of oxygen-glucose deprivation and treat-ment of H9c2 cells,the protective effect of Sil-1 on rat cardiomyocytes was examined.SD rats were treated 30 minutes before surgery,followed by 24 h ligation of the left anterior descending coronary artery.The cardiopro-tective effects of Sil-1 and its mechanisms for improving myocardial ischemic injury were investigated using pro-teomics technology.Results In vitro,compared with the control group,the activity of H9c2 cells in the mod-el group showed reduced cell viability,increased dead cells,elevated ROS and higher levels of LDH and in-flammatory cytokines TNF-α,IL-1β and IL-6 in the culture medium.Sil-1 could improve the above condi-tions to different degrees.In vivo,compared with the control group,rats in the model group showed signifi-cantly higher T waves on electrocardiogram,significant ischemic areas in the heart section,disorganized ar-rangement of cardiomyocytes,increased inflammatory factor infiltration and elevated CK,CK-MB,LDH and inflammatory factors TNF-α,IL-6 and IL-1β.Besides,NF-κB phosphorylation levels in myocardial tissue in-creased.Sil-1 improved the above conditions to varying degrees.The results of proteomics showed that 90 pro-teins were found between the control vs model group and the Sil-1 vs model group,and KEGG enrichment a-nalysis showed that MAPK,chemokines,VEGF and other signaling pathways were abundant.Western blot results showed that Sil-1 blocked the phosphorylation of ERK,JNK and p38 MAPK.Conclusions Sil-1 inhib-its the MAPK pathway by blocking the phosphorylation of JNK,ERK,and p38 MAPK,and achieves a protec-tive effect on rats with acute myocardial infarction.

Objective:To investigate the prevalence of sexual drug use in men who have sex with men (MSM) in Chengdu and analyze the relationship between depression and sexual drug use.Methods:A total of 1 277 MSM were recruited between November 2021 and May 2022. Questionnaire was used to collect information about their demographic characteristics, depression status and sexual drug use behavior. Univariate and multivariate logistic regression models were used to analyze the relationship between depression and sexual drug use.Results:In the 1 277 MSM, assessment identified 503 mild depression cases (39.4%), 196 moderate depression cases (15.3%) and 171 severe depression cases (13.4%) and 444 MSM (34.8%) reported sexual drug use in the past 6 months. Multivariate logistic regression models showed that compared with non-depression, mild depression (a OR=1.67,95% CI:1.24-2.23), moderate depression (a OR=1.50,95% CI: 1.02-2.19) and severe depression (a OR=1.56,95% CI:1.04-2.32) were positively associated with sexual drug use. Conclusions:The prevalence of depression and sexual drug use were high in MSM in Chengdu. There was a positive correlation between depression and sexual drug use. It is necessary to pay close attention to depression and sexual drug use and conduct targeted intervention in MSM.

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 characteristics of gut microbiota changes in patients with acute myeloid leukemia (AML) undergoing induction chemotherapy and to explore the relationship between infectious complications and gut microbiota. METHODS: Fecal samples were collected from 37 newly diagnosed AML patients at four time points: before induction chemotherapy, during chemotherapy, during the neutropenic phase, and during the recovery phase. Metagenomic sequencing was used to analyze the dynamic changes in gut microbiota. Correlation analyses were conducted to assess the relationship between changes in gut microbiota and the occurrence of infectious complications. RESULTS: During chemotherapy, the gut microbiota α-diversity (Shannon index) of AML patients exhibited significant fluctuations. Specifically, the diversity decreased significantly during induction chemotherapy, further declined during the neutropenic phase (P < 0.05, compared to baseline), and gradually recovered during the recovery phase, though not fully returning to baseline levels.The abundances of beneficial bacteria, such as Firmicutes and Bacteroidetes, gradually decreased during chemotherapy, whereas the abundances of opportunistic pathogens, including Enterococcus, Klebsiella, and Escherichia coli, progressively increased.Analysis of the dynamic changes in gut microbiota of seven patients with bloodstream infections revealed that the bloodstream infection pathogens could be detected in the gut microbiota of the corresponding patients, with their abundance gradually increasing during the course of infection. This finding suggests that bloodstream infections may be associated with opportunistic pathogens originating from the gut microbiota.Compared to non-infected patients, the baseline samples of infected patients showed a significantly lower relative abundance of Bacteroidetes (P < 0.05). Regression analysis indicated that Bacteroidetes abundance is an independent predictive factor for infectious complications (P < 0.05, OR =13.143). CONCLUSION During induction chemotherapy in AML patients, gut microbiota α-diversity fluctuates significantly, and the abundance of opportunistic pathogens increase, which may be associated with bloodstream infections. Patients with lower baseline Bacteroidetes abundance are more prone to infections, and its abundance can serve as an independent predictor of infectious complications.
Humans ; Gastrointestinal Microbiome ; Leukemia, Myeloid, Acute/microbiology* ; Induction Chemotherapy ; Feces/microbiology* ; Male ; Female ; Middle Aged

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.