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 effect of TBL1XR1 mutation on cell biological characteristics of diffuse large B-cell lymphoma (DLBCL). METHODS: The TBL1XR1 overexpression vector was constructed and DNA sequencing was performed to determine the mutation status. The effect of TBL1XR1 mutation on apoptosis of DLBCL cell line was detected by flow cytometry and TUNEL fluorescence assay; CCK-8 assay was used to detect the effect of TBL1XR1 mutation on cell proliferation; Transwell assay was used to detect the effect of TBL1XR1 mutation on cell migration and invasion; Western blot was used to detect the effect of TBL1XR1 mutation on the expression level of epithelial-mesenchymal transition (EMT) related proteins. RESULTS: The TBL1XR1 overexpression plasmid was successfully constructed. The in vitro experimental results showed that TBL1XR1 mutation had no significant effect on apoptosis of DLBCL cells. Compared with the control group, TBL1XR1 mutation enhanced cell proliferation, migration and invasion of DLBCL cells. TBL1XR1 gene mutation significantly increased the expression of N-cadherin protein, while the expression of E-cadherin protein decreased. CONCLUSION TBL1XR1 mutation plays a role in promoting tumor cell proliferation, migration and invasion in DLBCL. TBL1XR1 could be considered as a potential target for DLBCL therapy in future research.
Humans ; Lymphoma, Large B-Cell, Diffuse/pathology* ; Cell Proliferation ; Mutation ; Receptors, Cytoplasmic and Nuclear/genetics* ; Apoptosis ; Cell Line, Tumor ; Epithelial-Mesenchymal Transition ; Cell Movement ; Repressor Proteins/genetics* ; Nuclear Proteins/genetics* ; Cadherins/metabolism*

Objective To establish and evaluate a nomogram prediction model for spontaneous peritonitis in HBV-related primary liver cancer.Methods A retrospective study was conducted on 1298 patients with HBV-related primary liver cancer hospitalized in the Kunming Third People's Hospital from January 2012 to December 2022.General data and serological indicators were collected,and patients were divided into infection group(n=262)and control group(n=1036)based on the occurrence of spontaneous peritonitis.Univariate and LASSO regression analyses were used to screen variables,followed by binary logistic regression to analyze the influencing factors of spontaneous peritonitis in HBV-related primary liver cancer patients,leading to the establishment of a nomogram prediction model.Finally,the Hosmer-lemeshow(H-L)goodness of fit test,receiver operating characteristic(ROC)curve,calibration curve,decision curve analysis(DCA)and clinical impact curve(CIC)were utilized to evaluate the fit degree,accuracy,calibration,and clinical practicability of the nomogram prediction model.Results Single factor analysis revealed significant differences between infection group and control group in portal vein cancer thrombus(PVTT),Child-Pugh grade,China Liver Cancer Staging(CNLC)stage,alcohol consumption history,smoking history,white blood cell count(WBC),neutrophil count(NE),hemoglobin(Hb),fibrinogen(FIB),abnormal prothrombin(PIVKA-Ⅱ),aspartate aminotransferase(AST),alanine aminotransferase(ALT),total protein(TP),prealbumin(PA),γ-glutamyltransferase(GGT),alkaline phosphatase(ALP),cholinesterase(CHE),total bile acid(TBA),total cholesterol(TC),low density lipoprotein(LDL),creatinine(Cr),HBV DNA,CD3+T cells count,CD4+T cells count,CD8+T cells count,CD4+T cells/CD8+T cells ratio,procalcitonin(PCT),serum amyloid A(SAA),interleukin-6(IL-6),high-sensitivity C-reactive protein(hs-CRP),alpha-fetoprotein(AFP),and IL-4(P＜0.05).LASSO regression analysis identified 5 variables:Child-Pugh grade,PVTT,WBC,CHE and hs-CRP.Binary logistic regression analysis indicated that Child-Pugh grade(Grade B:OR=5.780,95％CI 3.271-10.213,P＜0.001;Grade C:OR=14.818,95％CI 7.697-28.526,P＜0.001),PVTT(OR=2.893,95％CI 2.037-4.108,P＜0.001),WBC(OR=1.088,95％CI 1.031-1.148,P=0.002),and hs-CRP(OR=1.005,95％CI 1.001-1.010,P=0.026)were the independent risk factors of spontaneous peritonitis in HBV-related primary liver cancer patients.Using these 4 variables,a nomogram prediction model was constructed and evaluated.The P-value of the H-L goodness of fit test was 0.760.Moreover,the area under ROC curve(AUC)was 0.866,with a sensitivity of 0.870 and a specificity of 0.716.The average absolute error of the calibration curve is 0.022.DCA and CIC analyses demonstrated that the nomogram prediction model possessed some clinical utility.Conclusion The nomogram prediction model for spontaneous peritonitis in HBV-related primary liver cancer patients,constructed using Child-Pugh grade,PVTT,WBC and hs-CRP,exhibits a high fitting degree and accuracy,with the prediction probability highly consistent with the actual occurrence probability,and possesses certain clinical practicability.

Objective To evaluate the efficacy,safety,and cost-effectiveness of berberine-based quadruple therapy vs.the clarithromycin-based quadruple therapy for Helicobacter pylori(H.pylori)eradication in treatment-na?ve patients.Methods This was a single-center,prospective,open-label randomized controlled trial.A total of 404 treatment-naive patients with H.pylori infection who visited the Outpatient Department of Gastroenterology,the First Medical Center of Chinese PLA General Hospital from September 2021 to May 2024 were enrolled.The patients were randomly assigned in a 1:1 ratio to two groups:berberine quadruple therapy group(berberine+amoxicillin+esomeprazole+colloidal bismuth pectin;n=202)and clarithromycin quadruple therapy group(clarithromycin+amoxicillin+esomeprazole+colloidal bismuth pectin;n=202).Both groups received a 14-day treatment course.The H.pylori eradication rate,incidence of adverse reactions,medication compliance,and treatment costs were compared between the two groups.Results By intention-to-treat(ITT)analysis,eradication rate did not differ significantly between the two groups[89.1%(180/202)in berberine quadruple therapy group vs.89.6%(181/202)in clarithromycin quadruple therapy group,P=0.872].The per-protocol(PP)analysis also showed no significant difference in the eradication rate between the two groups[90.4%(179/198)vs.91.3%(178/195),P=0.763].The incidence of adverse reactions in berberine quadruple therapy group was significantly lower than that in clarithromycin quadruple therapy group[18.2%(36/198)vs.38.5%(75/195),P<0.001].Specifically,the incidence of taste disturbance in berberine quadruple therapy group was significantly lower than that in clarithromycin quadruple therapy group(3.0%vs.15.4%,P<0.001).There was no statistically significant difference in medication compliance between the two groups[98.5%(195/198)in berberine quadruple therapy group vs.97.9%(191/195)in clarithromycin quadruple therapy group,P=0.688].The fixed direct medical cost per patient was significantly lower in berberine quadruple therapy group than that in clarithromycin quadruple therapy group(402.08 yuan vs.693.94 yuan).Conclusions The berberine-based quadruple therapy is as effective as traditional clarithromycin-based quadruple therapy for eradicating H.pylori,with the advantages of a lower incidence of adverse reactions and lower cost.It represents a safe,effective,and economical treatment option worthy of further promotion and application.

ObjectiveTo improve the quality standard of Yuanhu Zhitong oral liquid in order to strengthen the quality control of this oral liquid. MethodThin layer chromatography（TLC） was used for the qualitative identification of Corydalis Rhizoma and Angelicae Dahuricae Radix in Yuanhu Zhitong oral liquid by taking tetrahydropalmatine， corydaline reference substances and Corydalis Rhizoma reference medicinal materials as reference， and cyclohexane-trichloromethane-methanol（5∶3∶0.5） as developing solvent， Corydalis Rhizoma was identified using GF254 glass thin layer plate under ultraviolet light（365 nm）. And taking petroleum ether（60-90 ℃） -ether-formic acid（10∶10∶1） as developing solvent， Angelicae Dahuricae Radix was identified using a silica gel G TLC plate under ultraviolet light（305 nm）. High performance liquid chromatography（HPLC） was performed on a Waters XSelect HSS T3 column（4.6 mm×250 mm， 5 μm） with acetonitrile（A）-0.1% glacial acetic acid solution（adjusted pH to 6.1 by triethylamine）（B） as the mobile phase for gradient elution（0-10 min， 20%-30%A； 10-25 min， 30%-40%A； 25-40 min， 40%-50%A； 40-60 min， 50%-60%A）， the detection wavelength was set at 280 nm， then the fingerprint of Yuanhu Zhitong oral liquid was established， and the contents of tetrahydropalmatine and corydaline were determined. ResultIn the thin layer chromatograms， the corresponding spots of Yuanhu Zhitong oral liquid， the reference substances and reference medicinal materials were clear， with good separation and strong specificity. A total of 12 common peaks were identified in 10 batches of Yuanhu Zhitong oral liquid samples， and the peaks of berberine hydrochloride， dehydrocorydaline， glaucine， tetrahydropalmatine and corydaline. The similarities between the 10 batches of samples and the control fingerprint were all >0.90. The results of determination showed that the concentrations of corydaline and tetrahydropalmatine had good linearity with paek area in the range of 0.038 6-0.193 0， 0.034 0-0.170 0 g·L^-1， respectively. The methodological investigation was qualified， and the contents of corydaline and tetrahydropalmatine in 10 batches of Yuanhu Zhitong oral liquid samples were 0.077 5-0.142 9、0.126 1-0.178 2 g·L^-1， respectively. ConclusionThe established TLC， fingerprint and determination are simple， specific and reproducible， which can be used to improve the quality control standard of Yuanhu Zhitong oral liquid.