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 study the in vitro and in vivo antitumor effects of the polysaccharide of Alocasia cucullata (PAC) and the underlying mechanism. METHODS: B16F10 and 4T1 cells were cultured with PAC of 40 µg/mL, and PAC was withdrawn after 40 days of administration. The cell viability was detected by cell counting kit-8. The expression of Bcl-2 and Caspase-3 proteins were detected by Western blot and the expressions of ERK1/2 mRNA were detected by quantitative real-time polymerase chain reaction (qRT-PCR). A mouse melanoma model was established to study the effect of PAC during long-time administration. Mice were divided into 3 treatment groups: control group treated with saline water, positive control group (LNT group) treated with lentinan at 100 mg/(kg·d), and PAC group treated with PAC at 120 mg/(kg·d). The pathological changes of tumor tissues were observed by hematoxylin-eosin staining. The apoptosis of tumor tissues was detected by TUNEL staining. Bcl-2 and Caspase-3 protein expressions were detected by immunohistochemistry, and the expressions of ERK1/2, JNK1 and p38 mRNA were detected by qRT-PCR. RESULTS: In vitro, no strong inhibitory effects of PAC were found in various tumor cells after 48 or 72 h of administration. Interestingly however, after 40 days of cultivation under PAC, an inhibitory effect on B16F10 cells was found. Correspondingly, the long-time administration of PAC led to downregulation of Bcl-2 protein (P<0.05), up-regulation of Caspase-3 protein (P<0.05) and ERK1 mRNA (P<0.05) in B16F10 cells. The above results were verified by in vivo experiments. In addition, viability of B16F10 cells under long-time administration culture in vitro decreased after drug withdrawal, and similar results were also observed in 4T1 cells. CONCLUSIONS Long-time administration of PAC can significantly inhibit viability and promote apoptosis of tumor cells, and had obvious antitumor effect in tumor-bearing mice.
Mice ; Animals ; Alocasia/metabolism* ; MAP Kinase Signaling System ; Caspase 3/metabolism* ; Apoptosis ; RNA, Messenger/metabolism*

OBJECTIVE:There are many kinds of biological agents for the treatment of rheumatoid arthritis in clinic,but the differences in therapeutic efficacy and safety are still unclear.The purpose of this study is to compare the differences in effectiveness and safety of different biological agents for the treatment of rheumatoid arthritis. METHODS:CNKI,VIP,WanFang,China Biomedical Literature System,PubMed,Cochrane Library,Web of Science,and Embase databases were searched to collect the randomized controlled trials on biological agents for rheumatoid arthritis that meet the requirements from inception to October 1,2022.The literature was selected by EndNote software,and the quality of the included literature was evaluated by RevMan 5.3 software.The software Stata 14.2 was used for direct meta-analysis and network meta-analysis of ACR20(American College of Rheumatology 20%response),ACR50(American College of Rheumatology 50%response),ACR70(American College of Rheumatology 70%response),erythrocyte sedimentation rate,and adverse reactions. RESULTS:Totally 39 articles were included,including 5 low-risk articles,4 high-risk articles,and the remaining 30 articles contained unknown risk bias,with a total of 13 treatment measures.The results of network meta-analysis:(1)In ACR20,infliximab combined with methotrexate(OR=5.54,95%CI:1.33-23.01,P<0.05),abatacept+methotrexate tablets(OR=3.21,95%CI:1.13-9.10,P<0.05),and tocilizumab(OR=2.95,95%CI:1.61-5.44,P<0.05)were better than methotrexate tablets.The probabilistic ranking of ACR20 was:infliximab+methotrexate tablets>abatacept+methotrexate tablets>tocilizumab>certlizumab>etanercept+methotrexate tablets.(2)In the aspect of ACR50,etanercept combined with methotrexate tablets(OR=4.04,95%CI:2.13-7.66,P<0.05),infliximab combined with methotrexate tablets(OR=4.79,95%CI:1.19-19.26,P<0.05),and tocilizumab combined with methotrexate tablets(OR=3.54,95%CI:1.36-9.22,P<0.05)had better therapeutic effects than methotrexate tablets.The probabilistic ranking of ACR50 was:etanercept+methotrexate tablets>infliximab+methotrexate tablets>tocilizumab+methotrexate tablets>tocilizumab>certlizumab+methotrexate tablets.(3)In terms of ACR70,the therapeutic effects of infliximab combined with methotrexate tablets(OR=8.00,95%CI:2.31-27.69,P<0.05),etanercept combined with methotrexate tablets(OR=4.26,95%CI:2.51-7.21,P<0.05),and tocilizumab combined with methotrexate tablets(OR=3.51,95%CI:1.82-6.80,P<0.05)were better than methotrexate tablets.The probabilistic ranking of ACR70 was infliximab+methotrexate tablets>etanercept+methotrexate tablets>tocilizumab+methotrexate tablets>certlizumab>adalimumab+methotrexate tablets.(4)In erythrocyte sedimentation rate,etanercept combined with methotrexate tablets(SMD=-9.23,95%CI:-16.55 to-1.92,P<0.05)was better than etanercept and methotrexate tablets(SMD=14.59,95%CI:7.28-21.91,P<0.05).The probabilistic ranking of erythrocyte sedimentation rate was etanercept+methotrexate tablets>infliximab+methotrexate tablets>etanercept>adalimumab+methotrexate tablets>methotrexate tablets.(5)In terms of adverse reactions,placebo(OR=0.62,95%CI:0.39-0.99,P<0.05)was better than infliximab and certlizumab(OR=0.44,95%CI:0.25-0.78,P<0.05).The probabilistic ranking of adverse reactions was placebo>infliximab>etanercept+methotrexate tablets>certlizumab>etanercept. CONCLUSION:Based on evidence from 39 randomized controlled trials,infliximab combined with methotrexate tablets(highly recommended)can be the first choice in clinic,and etanercept combined with methotrexate tablets(highly recommended)can be the second choice in terms of good effectiveness and safety.

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 evaluate and compare the success rate and short-term clinical prognosis of transfemoral transcatheter aortic valve replacement(TF-TAVR)for patients with pure aortic regurgitation(PAR)of different annulus sizes.Methods This study is a single center retrospective study,selecting symptomatic PAR patients who received TF-TAVR treatment at Zhongshan Hospital Fudan University from September 2019 to September 2023.Based on preoperative CT results,all patients were divided into three groups:Group A(aortic annulus circumference＜80 mm),Group B(80 mm≤aortic annulus circumference＜85 mm),and Group C(aortic annulus circumference≥ 85 mm).The primary endpoint was success rate and 30d all-cause mortality,while the secondary endpoint was TAVR related complications.Results A total of 61 PAR patients were included in this study,including 27 in Group A,21 in Group B,and 13 in Group C.The overall success rate is 82.0％,and the 30 d all-cause mortality rate is 3.3％.The success rate of Group C patients was significantly lower(P=0.012),with higher rates of conversion to surgery and valve-in-valve implantation(P=0.022 and P=0.040).In terms of secondary endpoint events,there were no significant differences among the three groups in major bleeding events,major vascular complications,stroke,myocardial infarction,newly developed atrial fibrillation,implantation of new pacemakers,coronary artery occlusion,and postoperative moderate to severe perivalvular leakage(all P＞0.05).Conclusions The circumference of the aortic valve annulus is a key factor affecting the success rate of TF-TAVR in PAR,and PAR patients with an aortic valve annulus circumference less than 85mm may be more suitable for TF-TAVR.

Objective To investigate the clinical characteristics and prognosis of pneumococcal meningitis(PM),and drug sensitivity of Streptococcus pneumoniae(SP)isolates in Chinese children.Methods A retrospective analysis was conducted on clinical information,laboratory data,and microbiological data of 160 hospitalized children under 15 years old with PM from January 2019 to December 2020 in 33 tertiary hospitals across the country.Results Among the 160 children with PM,there were 103 males and 57 females.The age ranged from 15 days to 15 years,with 109 cases(68.1% )aged 3 months to under 3 years.SP strains were isolated from 95 cases(59.4% )in cerebrospinal fluid cultures and from 57 cases(35.6% )in blood cultures.The positive rates of SP detection by cerebrospinal fluid metagenomic next-generation sequencing and cerebrospinal fluid SP antigen testing were 40% (35/87)and 27% (21/78),respectively.Fifty-five cases(34.4% )had one or more risk factors for purulent meningitis,113 cases(70.6% )had one or more extra-cranial infectious foci,and 18 cases(11.3% )had underlying diseases.The most common clinical symptoms were fever(147 cases,91.9% ),followed by lethargy(98 cases,61.3% )and vomiting(61 cases,38.1% ).Sixty-nine cases(43.1% )experienced intracranial complications during hospitalization,with subdural effusion and/or empyema being the most common complication[43 cases(26.9% )],followed by hydrocephalus in 24 cases(15.0% ),brain abscess in 23 cases(14.4% ),and cerebral hemorrhage in 8 cases(5.0% ).Subdural effusion and/or empyema and hydrocephalus mainly occurred in children under 1 year old,with rates of 91% (39/43)and 83% (20/24),respectively.SP strains exhibited complete sensitivity to vancomycin(100% ,75/75),linezolid(100% ,56/56),and meropenem(100% ,6/6).High sensitivity rates were also observed for levofloxacin(81% ,22/27),moxifloxacin(82% ,14/17),rifampicin(96% ,25/26),and chloramphenicol(91% ,21/23).However,low sensitivity rates were found for penicillin(16% ,11/68)and clindamycin(6% ,1/17),and SP strains were completely resistant to erythromycin(100% ,31/31).The rates of discharge with cure and improvement were 22.5% (36/160)and 66.2% (106/160),respectively,while 18 cases(11.3% )had adverse outcomes.Conclusions Pediatric PM is more common in children aged 3 months to under 3 years.Intracranial complications are more frequently observed in children under 1 year old.Fever is the most common clinical manifestation of PM,and subdural effusion/emphysema and hydrocephalus are the most frequent complications.Non-culture detection methods for cerebrospinal fluid can improve pathogen detection rates.Adverse outcomes can be noted in more than 10% of PM cases.SP strains are high sensitivity to vancomycin,linezolid,meropenem,levofloxacin,moxifloxacin,rifampicin,and chloramphenicol.[Chinese Journal of Contemporary Pediatrics,2024,26(2):131-138]