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 elucidate the clinical significance of high expression levels of endonuclease meiosis 1(EME1)in the prognosis of hepatocellular carcinoma(HCC).Methods The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO)databases were used to screen and analyze differential gene expression between HCC and non-tumor tissues.A retrospective collection of liver tissue samples from 80 HCC patients who underwent hepatectomy in the Fifth Medical Center of Chinese PLA General Hospital between January 2010 and December 2014 was performed.Immunohistochemistry analysis was employed to detect the EME1 expression levels.Survival analysis was then conducted to assess the impact of EME1 expression on 5-year postoperative survival rate of HCC patients.Additionally,gene enrichment analysis was applied to predict the function of EME1 in HCC.Results A total of 371 HCC tissue samples and 50 non-tumor liver tissue samples from TCGA database were analyzed,revealing significantly higher EME1 expression in HCC tissues.Microarray analysis of 107 samples within the GEO database(70 HCC tissues and 37 non-tumor tissues)confirmed that EME1 mRNA expression was markedly elevated in HCC tissues compared with non-tumor tissues(P<0.05).The 5-year overall survival(OS)rate was notably lower in high EME1 expression group than that in low expression group(44.1%vs.53.0%,P<0.05).Semi-quantitative immunohistochemistry analysis demonstrated that patients with high EME1 expression had a significantly lower OS rate than those with low EME1 expression(32.8%vs.45.0%,P<0.05).Multivariate COX regression analysis identified that high EME1 expression(HR=2.234,95%CI 1.073-4.649,P=0.032)and advanced China liver caner(CNLC)staging(HR=4.317,95%CI 1.799-10.359,P=0.001)were independent risk factors for the 5-year OS of post-operation patients with HCC.Conclusion Elevated EME1 expression in HCC tissues correlates with an adverse prognosis of HCC and suggests that EME1 could serve as a potential therapeutic target for HCC.

Objective To analyze the stresses in implanted titanium solid and bone trabecular prosthesis hip replacements.Methods A femur model was built inversely based on Mimics software,and optimized using Geomagic software,and then materialized by SolidWorks software.The osteotomized femur was assembled with the metal femoral stem to form a model,and then the model was imported into ABAQUS for finite element calculation.The upper femur was divided into four regions in different states of integration:medial proximal point(small trochanter region),lateral proximal region(large trochanter region),proximal point of the femoral stem(region around the mid-portion of the styloid process)and distal region(around the end of the styloid process and distal portion).Calculations were carried out over the femoral stresses before and after implantation of titanium solid and trabecular prostheses under gait and stair-climbing loads and the interfacial stresses when the region was unintegrated.The type of deformation at the bone interface was analyzed by means of a stress ellipsoid.Results At the small trochanter region,the stress shielding rates of the trabecular prosthesis under gait and stair climbing loads were reduced by 20.5％and 14.7％compared to the titanium solid prosthesis,respectively.In case of different integration states of the titanium solid prosthesis,the interface tensile stresses under the gait and stair climbing loads were up to 10.842 MPa and 12.900 MPa,and the shear stresses reached 7.050 MPa and 6.805 MPa,respectively;in case of different integration states of the trabecular prosthesis,the interface tensile stresses under the gait and stair climbing loads were up to 3.858 MPa and 4.389 MPa,and the shear stresses reached 4.156 MPa and 3.854 MPa,respectively.Under the 2 different loads,the inboard shear stress ellipsoid of the interface opened toward the sides and the bone interface showed tensile deformation;the outboard shear stress ellipsoid of the interface opened up and down and had compressive deformation.Conclusion After total hip arthroplasty,the overall performance of the trabecular prosthesis is better than that of the titanium solid prosthesis.The unintegrated edges of the prosthesis-bone interface are susceptible to stress concentrations and distortion which may result in occurrence of failures.[Chinese Medical Equipment Journal,2024,45(3):29-35]

This study was performed to explore the effects of berberine(BBR)and berbamine(BA)on the apoptosis and activity of eosinophils,and to provide new ideas for the treatment of eosinophil-related diseases.Anticoagulated whole blood was taken from hospitalized patients,and eosinophils were purified by magnetic bead sorting technology,and the cell purification rate was compared by using flow cytometry.The purified eosinophils were cultured and stimulated with different concentrations of BBR and BA,and the apoptosis of eosinophils was observed using immunofluorescence microscopy.Flow cytometry was used to detect the apoptosis rate,reactive oxygen species(ROS)release,mitochondrial membrane potential,and CD11b,CD62L and CD63 molecular expression of eosinophils.Data showed that 10 μmol/L BBR displayed significant anti-apoptosis effect on eosinophils at 18 h,24 h,48 h,however,100 μmol/L and 150 μmol/L BBR displayed significant apoptosis-promoting effects on eosinophils at 24 h,48 h,especially in the early stage.BA at 5 μmol/L,15 μmol/L and 25 μmol/L showed significant apoptosis-promoting effects on eosinophils at 24 h,48 h,especially in the late stage.In eosinophils,BA stimulated the production of ROS and the decrease of mitochondrial membrane potential in a concentration-dependent manner,reaching the maximum effect at 25 μmol/L,while BBR stimulation did not change mitochondria and ROS.Pretreatment with 25 μmol/L BA significantly inhibited the increase of CD11b expression and the decrease of CD62L expression in eosinophils after eotaxin-2 stimulation.In conclusion,berberine has a bidirectional regulatory effect on the quantity of eosinophils,while berbamine promotes eosinophil apoptosis and inhibits eosinophil activation.Both compounds hold significant potential for regulating the progression of eosinophil-associated diseases,and their regulatory mechanisms need further investigation.

Objective:To explore the expressions of zinc homeostasis-related proteins,G protein-coupled receptor 39(GPR39)and ANO1 mRNA in the sperm of patients with asthenozoospermia(AS),and analyze their correlation with sperm motility.Methods:We collected semen samples from 82 male subjects with PR+NP＜40％,PR＜32％and sperm concentration＞15 × 106/ml(the AS group,n=40)or PR+NP≥40％,PR≥32％and sperm concentration＞15 × 106/ml(the normal control group,n=42).We analyzed the routine semen parameters and measured the zinc content in the seminal plasma using the computer-assisted sperm analysis system,detected the expressions of zinc transporters(ZIP13,ZIP8 and ZNT10),metallothioneins(MT1G,MT1 and MTF),GPR39,and calcium-dependent chloride channel protein(ANO1)in the sperm by real-time quantitative PCR(RT qPCR),examined free zinc distribution in the sperm by laser confocal microscopy,and determined the expressions of GPR39 and MT1 proteins in the sperm by immunofluorescence staining,followed by Spearman rank correlation analysis of their correlation with semen parameters.Results:There was no statistically significant difference in the zinc concentration in the seminal plasma between the AS and normal control groups(P＞0.05).Compared with the controls,the AS patients showed a significantly reduced free zinc level(P＜0.05),relative expressions of MT1G,MTF,ZIP13,GPR39 and ANO1 mRNA(P＜0.05),and that of the GPR39 protein in the AS group(P＜0.05).No statistically significant differences were observed in the relative expression levels of ZIP8,ZNT10 and MT1 mRNA between the two groups(P＞0.05).The relative expression levels of GPR39,ANO1,MT1G and MTF mRNA were positively correlated with sperm motility and the percentage of progressively motile sperm(P＜0.05).Conclusion:The expressions of zinc homeostasis proteins(MT1G,MTF and ZIP13),GPR39 and ANO1 mRNA are downregulated in the sperm of asthenozoospermia pa-tients,and positively correlated with sperm motility.