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 assess the risk of aristolochic acid (AA)-associated cancer in patients with AA nephropathy (AAN). METHODS: A retrospective study was conducted on patients diagnosed with AAN at Peking University First Hospital from January 1997 to December 2014. Long-term surveillance and follow-up data were analyzed to investigate the influence of different factors on the prevalence of cancer. The primary endpoint was the incidence of liver cancer, and the secondary endpoint was the incidence of urinary cancer during 1 year after taking AA-containing medication to 2014. RESULTS: A total of 337 patients diagnosed with AAN were included in this study. From the initiation of taking AA to the termination of follow-up, 39 patients were diagnosed with cancer. No cases of liver cancer were observed throughout the entire follow-up period, with urinary cancer being the predominant type (34/39, 87.17%). Logistic regression analysis showed that age, follow-up period, and diabetes were potential risk factors, however, the dosage of the drug was not significantly associated with urinary cancer. CONCLUSIONS No cases of liver cancer were observed at the end of follow-up. However, a high prevalence of urinary cancer was observed in AAN patients. Establishing a direct causality between AA and HCC is challenging.
Humans ; Retrospective Studies ; Incidence ; Carcinoma, Hepatocellular ; Liver Neoplasms/epidemiology* ; Kidney Diseases/chemically induced* ; Aristolochic Acids/adverse effects*

Objective To explore the effect of scutellarin on lipopolysaccharide(LPS)induced neuroinflammation in BV-2 microglia cells.Methods BV-2 microglia were cultured and randomly divided into 6 groups:control group(Ctrl),cyclic GMP-AMP synthetase(cGAS)inhibitor RU320521 group(RU.521 group),LPS group,LPS+RU.521 group,LPS+scutellarin pretreatment group(LPS+S)and LPS+S+RU.521 group.The expressions of cGAS,stimulator of interferon gene(STING),nuclear factor kappa B(NF-κB),phosphorylated NF-κB(p-NF-κB),neuroinflammatory factors PYD domains-containing protein 3(NLRP3)and tumor necrosis factor α(TNF-α)in BV-2 microglia were detected by Western blotting and immunofluorescent double staining(n= 3).Results Western blotting and immunofluorescent double staining showed that compared with the control group,the expression of cGAS,STING,p-NF-κB,NLRP3 and TNF-α in BV-2 microglia increased significantly after LPS induction(P<0.05),while the expression of cGAS,STING,p-NF-κB,NLRP3 and TNF-α in LPS+S group were significantly lower than those in LPS group(P<0.05).Treatment with cGAS pathway inhibitor RU.521 showed similar effects as the pre-treatment group with scutellarin.In addition,the change of NF-κB in each group was not statistically significant(P>0.05).Conclusion Scutellarin inhibits the neuroinflammation mediated by BV-2 microglia cells,which may be related to cGAS-STING signaling pathway.

italic>Salmonella has emerged as a promising tumor-targeting strategy in recent years due to its good tumor targeting ability and certain safety. In order to further optimize its therapeutic effect, scientists have tried to modify Salmonella, including its attenuation and drug loading. This paper summarizes the mechanism and research progress of Salmonella-mediated targeted tumor therapy, and introduces the strategies and related progress of its modification and optimization. At the same time, the advantages, current challenges and future development directions of Salmonella-mediated tumor therapy are summarized.

We aimed to identify the infectious source of a case of melioidosis,to provide evidence for the prevention and control of melioidosis in Shanxi Province,China.The patient developed repeated fever,fatigue,diarrhea,and other symptoms after being caught in the rain while traveling in Hainan Province.The blood culture was positive,and the bacterial strain was i-dentified as Burkholderia thayensis and sent to the provincial Center for Disease Control and Prevention for further evaluation.MALDI-TOF MS and biochemical identification were used to identify the strain,whole genome sequencing was performed after nucleic acid extraction,MLST type and drug-resistance genes were analyzed,and a phylogenetic tree was constructed.The iso-lated strain was identified as Burkholderia pseudomallei by MALDI-TOF MS and biochemistry,and the MLST type was 366.The whole gene sequencing analysis indicated a close evolutionary relationship with the three isolates in Hainan Province,with high homology.This case of melioidosis was indeed imported from Hainan Province,according to molecular tracing analysis and epidemiological investigation,thus suggesting that medical institutions and disease control departments should strengthen understanding of melioidosis,and improve the diagnosis and treatment ability.

Objective:To evaluate the efficacy and safety of traditional Chinese medicine(TCM)in the treatment of male im-mune infertility(MII)by meta-analysis.Methods:We retrieved randomized controlled trial(RCT)on the treatment of male im-mune infertility with traditional Chinese medicine from the databases of WanFang,Chinese Biomedical Literature,Cochrane Library,Weipu,PubMed and CNKI,and performed methodological quality assessment of the RCTs identified and statistical analysis and evalua-tion of the publication bias using the RevMan5.4 software.Results:Totally,25 RCTs(2 563 cases)were included in this study.Compared with Western medicine alone in the treatment of MII,TCM achieved a significantly higher total effectiveness rate(OR=6.35,95% CI:4.96-8.13,P<0.000 01),negative conversion rate of seminal plasma anti-sperm antibodies(OR=4.52,95% CI:2.72-7.51,P<0.000 01),negative rate of serum anti-sperm antibodies(OR=2.98,95% CI:2.23-3.96,P<0.000 01),sperm concentration(MD=15.56,95% CI:11.32-19.79,P<0.000 01),grade a sperm motility(MD=3.85,95% CI:1.91-5.79,P=0.000 01),grade a+b sperm motility(MD=13.77,95% CI:7.06-20.48,P<0.000 1),sperm viability(MD=10.32,95% CI:6.78-13.86,P<0.000 01)and pregnancy rate(OR=3.53,95% CI:2.68-4.63,P<0.000 01),but a lower rate of adverse reactions(OR=0.06,95% CI:0.01-0.23,P<0.000 01).There was no statistically significant difference in the percentage of morphologically abnormal sperm between TCM and Western medicine alone in the treatment of MII(MD=-7.53,95% CI:-15.50-0.44,P=0.06).Conclusion:TCM has a definite effectiveness and high safe in the treatment of male immune infertility.