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 investigate the epidemiological characteristics of cross-county imported dengue fever cases within Yunnan province in 2023, so as to provide insights into formulation of preventive and control measures for intra-provincial spread of dengue fever. Methods All data pertaining cross-county imported dengue fever cases within Yunnan Province in 2023 were collected, and the temporal, regional and population distributions of the cases were descriptively analyzed. Results A total of 1 664 intra-provincial cross-county imported dengue fever cases were reported in 95 counties (cities, districts) cross 16 profectures (cities) in Yunnan Province in 2023, accounting for 12.34% of total cases in the province. Cross-county imported dengue fever cases were predominantly reported during the period between August and October (1 516 cases, 91.11% of total cases), and peaked in September (659 cases), with a single-day peak on October 8 (36 cases). During the period from September 4 to 10, five counties (cities) with local dengue fever epidemics, including Jinghong City of Xishuangbanna Dai Autonomous Prefecture, Gengma Dai and Wa Autonomous County of Lincang City, Ruili City of Dehong Dai and Jingpo Autonomous Prefecture, Mengla Coun ty of Xishuangbanna Dai Autonomous Prefecture, and Zhenkang County of Lincang City, exported 165 cross-county imported dengue fever cases to the rest of the province. Among the 1 644 intra-provincial cross-county imported dengue fever cases, the male to female ratio was 1.40∶1.00, and 1 329 cases were at ages of 15 to 55 years (79.87%), with farmers as the predominant occupation (886 cases, 53.25%). The top 5 counties (cities/districts) reporting the highest number of intra-provincial cross-county imported dengue fever cases included Simao District (266 cases) and Lancang Lahu Autonomous County (118 cases) of Pu’er City, Mengla County (91 cases) and Menghai County (91 cases) of Xishuangbanna Dai Autonomous Prefecture, and Mangshi City (73 cases) of Dehong Dai and Jingpo Autonomous Prefecture, which accounting for 38.40% of total imported cases. These intra-provincial cross-county imported dengue fever cases originated from 7 counties (cities/districts) in 4 prefectures (cities), including 1 261 cases (76.70%) from Jinghong City of Xishuangbanna Dai Autonomous Prefecture, 224 cases (13.63%) from Ruili City of Dehong Dai and Jingpo Autonomous Prefecture, 103 cases (6.27%) from Gengma Dai and Wa Autonomous County of Lincang City, 31 cases (1.89%) from Mengla County of Xishuangbanna Dai Autonomous Prefecture, 30 cases (1.82%) from Zhenkang County of Lincang City, 10 cases (0.61%) from Cangyuan Wa Autonomous County of Lincang City, and 5 cases (0.30%) from Mohan-Boten Economic Cooperation Zone of Kunming City. In addition, local dengue fever epidemics following intra-provincial cross-county importation of dengue fevers cases in Simao District, Jinggu Dai and Yi Autonomous County, Mangshi City, Longchuan County, and Cangyuan Wa Autonomous County. Conclusions Farmers and students are high-risk populations for intra-provincial cross-county imported dengue fever cases in Yunnan Province, and health education pertaining personal protection against dengue fever should be strengthened among these high-risk populations by governments at all levels. There is a high risk of local out-break of dengue fever following continuous introduction of intra-provincial cross-county imported cases. Standardized management of intra-provincial cross-county imported dengue fever cases should be reinforced to reduce the risk of local epidemics.

Vascular calcification is a highly regulated process of ectopic calcification in cardiovascular system while no effective intervention can be clinically performed up to date.As vascular calcification undergoes a common regulatory mechanism within bone formation,bone morphogenetic protein 7(BMP-7)main-tains contractile phenotype of vascular smooth muscle cells and further inhibits vascular calcification via promoting the process of osteoblast differentiation,reducing ectopic calcification pressure by increasing bone formation and reducing bone resorption.This work systematically reviews the role of BMP-7 in vascular calcifi-cation and the possible mechanism,and their current clinical application as well.The current proceedings may help develope early diagnostic strategy and therapeutic treatment with BMP-7 as a new molecular marker and potential drug target.The expec-tation could achieve early prevention and intervention of vascular calcification and improve poor prognosis on patients.

Aim To investigate the protective effect of fisetin on testis and sperm of rats with oligoasthenosper-mia and to explore its mechanism.Methods The rat model of oligoasthenospermia was established.The rats were randomly divided into the blank group,model group,low-,medium-,and high-dose fisetin treat-ment groups,and LKB1 agonist group,with 10 rats in each group.ELISA was used to detect the levels of FSH,LH,T,E2 and PRL.Flow cytometry was used to detect sperm cell apoptosis.HE staining was used to detect testicular tissue damage.Transmission electron microscopy was used to detect the ultrastructure of sperm cells.qRT-PCR and Western blot were used to detect the mRNA and protein expression of LKB1,AMPK,mTOR,and p70S6K.Results Compared with the blank group,the levels of FSH,LH,PRL,T and other hormones in the model group and LKB1 ago-nist group were significantly reduced,and sperm cell apoptosis and testicular injury were severe.The ex-pressions of LKB1 and p-AMPK/AMPK were signifi-cantly up-regulated,while the expressions of mTOR and p-p70S6K/p70S6K were significantly down-regula-ted(P＜0.05).Compared with the model group,af-ter different doses of fisetin treatment,the number of apoptotic sperm cells was significantly reduced,the levels of FSH,LH,PRL,T and other hormones markedly increased,the expression of LKB1 and p-AMPK/AMPK was significantly down-regulated,and the expression of mTOR and p-p70S6K/p70S6K was evidently up-regulated(P＜0.05).Conclusion Fi-setin is effective in the treatment of oligoasthenospermia rats,which may be related to LKB1-AMPK-mTOR-p70S6K signaling pathway.

Aim To investigate the effect of discoidin domain receptor 1(DDR1)on high glucose induced endothelial cell dysfunction and the underlying mecha-nism.Methods Human umbilical vein endothelial cells(HUVECs)were cultured in vitro and divided in-to the control group and high glucose induction group(HG).HUVECs were treated with 33 mmol·L-1 D-glucose for 48 hours to construct endothelial dysfunc-tion.Pyroptosis was detected using propidium iodide staining(PI);lactate dehydrogenase(LDH)and IL-1β,IL-18 levels were determined using enzyme linked immunosorbent assay(ELISA);the expression of DDR1 and NF-κB/NLRP3 signaling pathway proteins and pyroptosis related proteinses were detected using Western blot.Subsequently,the experiment was divid-ed into the control group,HG group,HG+DDR1 NC group,and HG+DDR1 siRNA group.The effect of high glucose on the proliferation and migration of HU-VECs was observed after transfection with DDR1 siR-NA for 24 hours;ELISA was used to detect the endo-thelial nitric oxide synthase(eNOS),vascular cell ad-hesion molecule-1(VCAM-1),intercellular adhesion molecule-1(ICAM-1),as well as LDH,IL-1β,IL-18 levels;PI was employed to detect pyroptosis;Western blot was applied to detect DDR1 and NF-κB/NLRP3 signaling pathway proteins and pyroptosis related pro-teins.Results Compared with the control group,HG group decreased eNOS content,increased VCAM-1 and ICAM-1 contents,decreased cell viability and migration ability,and significantly increased the expressions of DDR1,p-NF-κB,NLRP3 and pyroptosis related pro-teins.The levels of LDH,IL-1β,IL-18 and the rate of pyroptosis significantly increased(P＜0.05).Com-pared with HG group,DDR1 siRNA could promote the secretion of eNOS,decrease the levels of VCAM-1,ICAM-1,LDH,IL-1β and IL-1 8,increase cell viability and migration ability,reduce the expression of p-NF-κB,NLRP3 and pyroptosis related proteins,and inhibit high glucose-induced pyroptosis of HUVECs(P＜0.05).Conclusions Gene silencing DDR1 can im-prove vascular endothelial cell dysfunction induced by high glucose,and the mechanism is related to the inhi-bition of NF-κB/NLRP3 signaling pathway mediated pyroptosis.

Objective To investigate the distribution and antimicrobial resistance profiles of the common pathogens isolated from urine from 2015 to 2021 in the CHINET Antimicrobial Resistance Surveillance Program.Methods The bacterial strains were isolated from urine and identified routinely in 51 hospitals across China in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Antimicrobial susceptibility was determined by Kirby-Bauer method,automatic microbiological analysis system and E-test according to the unified protocol.Results A total of 261 893 nonduplicate strains were isolated from urine specimen from 2015 to 2021,of which gram-positive bacteria accounted for 23.8％(62 219/261 893),and gram-negative bacteria 76.2％(199 674/261 893).The most common species were E.coli(46.7％),E.faecium(10.4％),K.pneumoniae(9.8％),E.faecalis(8.7％),P.mirabilis(3.5％),P.aeruginosa(3.4％),SS.agalactiae(2.6％),and E.cloacae(2.1％).The strains were more frequently isolated from inpatients versus outpatients and emergency patients,from females versus males,and from adults versus children.The prevalence of ESBLs-producing strains in E.coli,K.pneumoniae and P.mirabilis was 53.2％,52.8％and 37.0％,respectively.The prevalence of carbapenem-resistant strains in E.coli,K.pneumoniae,P.aeruginosa and A.baumannii was 1.7％,18.5％,16.4％,and 40.3％,respectively.Lower than 10％of the E.faecalis isolates were resistant to ampicillin,nitrofurantoin,linezolid,vancomycin,teicoplanin and fosfomycin.More than 90％of the E.faecium isolates were ressitant to ampicillin,levofloxacin and erythromycin.The percentage of strains resistant to vancomycin,linezolid or teicoplanin was＜2％.The E.coli,K.pneumoniae,P.aeruginosa and A.baumannii strains isolated from ICU inpatients showed significantly higher resistance rates than the corresponding strains isolated from outpatients and non-ICU inpatients.Conclusions E.coli,Enterococcus and K.pneumoniae are the most common pathogens in urinary tract infection.The bacterial species and antimicrobial resistance of urinary isolates vary with different populations.More attention should be paid to antimicrobial resistance surveillance and reduce the irrational use of antimicrobial agents.