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 effects of epithelial transformation sequence 2(ECT2)and p33ING1 on the metastatic activity of esophageal squamous cell carcinoma(ESCC)cells.Methods The expressions of ECT2 and p33ING1 in esophageal squamous cell carcinoma tissues and adjacent tissues were detected by immunohistochemistry and Western blotting.Human esophageal squamous carcinoma cell line KYSE140 cells were divided into 4 groups:blank group,negative control(pcDNA 3.1 NC)group,overexpression group(pcDNA 3.1 ECT2)and inhibited expression group(si ECT2).MTT assay and cell colony formation assay were used to study the proliferation and growth ability of cells,Transwell assay and scratch assay used to study the invasion and migration ability of cells,and flow cytometry used to detect apoptosis and cell cycle,Western blotting used to detect the effect of ECT2 on p33ING1 protein.Results ECT2 expression increased and p33ING1 expression decreased in esophageal squamous cell carcinoma tissues.Overexpression of ECT2 significantly increased the growth,colony formation,migration and invasion abilities of KYSE140 cells,and decreased the apoptosis rate and p33ING1 expression of KYSE140 cells.In addition,inhibition of ECT2 expression could reverse the above changes.Conclusion The high expression of ECT2 can promote the growth and metastasis of esophageal squamous cell carcinoma KYSE140 cells and inhibit their apoptosis.The mechanism may be related to the inhibition of p33ING1 expression by ECT2.

Objective:To evaluate the value of 18F-FDG PET metabolic parameters in predicting histopathological grade of soft tissue sarcoma (STS). Methods:From December 2012 to December 2021, 51 patients (26 males, 25 females, age range: 32-84 years) who underwent 18F-FDG PET/CT imaging before treatment and confirmed STS pathologically in the First Affiliated Hospital of Dalian Medical University were retrospectively collected. 18F-FDG PET metabolic parameters SUV max, metabolic tumor volume (MTV), total lesion glycolysis (TLG) and intertumoral FDG uptake heterogeneity (IFH) were measured. Kruskal-Wallis rank sum test was used to analyze the differences in metabolic parameters among different groups and Spearman rank correlation analysis was used to analyze the correlation of each metabolic parameter and histological grade. Logistic regression was used to screen and construct the prediction model for high-grade STS. ROC curve was plotted and Delong test was used to analyze the differences among AUCs. Results:The metabolic parameters SUV max, MTV, TLG and IFH were significantly different among French Federation of Cancer Centers Sarcoma Group (FNCLCC)Ⅰ( n=8), Ⅱ( n=10) and Ⅲ ( n=33) grade groups ( H values: 16.24, 10.52, 19.29 and 16.99, all P<0.05), and each metabolic parameter was positively correlated with histological grade ( rs values: 0.58, 0.45, 0.52, and 0.62, all P<0.05). Multivariate logistic regression analysis showed that SUV max(odds ratio ( OR)=1.27, 95% CI: 1.06-1.51, P=0.009) and IFH ( OR=6.83, 95% CI: 1.44-32.27, P=0.015) were independent risk indicators for high-grade STS. The prediction model constructed by combining SUV max and IFH had better diagnostic efficacy for differentiating high-grade STS with the AUC of 0.93, and the sensitivity of 93.9%(31/33) and the specificity of 16/18, respectively. The AUC of prediction model was significant different from SUV max, MTV, TLG and IFH (AUCs: 0.81, 0.78, 0.86 and 0.85; z values: 2.69, 2.53, 1.94 and 1.97, all P<0.05). Conclusions:The metabolic parameters SUV max, MTV, TLG and IFH are valuable predictors for histological grade of STS. The combination of SUV max and IFH may be a more meaningful method than using each of the above metabolic parameters alone.

Following the principles of evidence-based medicine,in accordance with the structure and drafting rules of standardized documents,based on literature research,according to the characteristics of chronic cough in children and issues that need to form a consensus,the TCM Guidelines for Diagnosis and Treatment of Chronic Cough in Children was formulated based on the Delphi method,expert discussion meetings,and public solicitation of opinions.The guideline includes scope of application,terms and definitions,eti-ology and diagnosis,auxiliary examination,treatment,prevention and care.The aim is to clarify the optimal treatment plan of Chinese medicine in the diagnosis and treatment of this disease,and to provide guidance for improving the clinical diagnosis and treatment of chronic cough in children with Chinese medicine.

As interstitial collagen,type Ⅲ collagen(Col Ⅲ)does not express in normal glomeruli.However,in Col Ⅲ nephropathy,a large amount of Col Ⅲ deposit in the mesangial and subendothelial area of the glomeruli.IgA nephropathy with Col Ⅲ deposition was extremely rare.In this article,we reported two cases of such disease.After treating with immunosuppressive agents or traditional Chinese medicine decoction,the renal function of the two patients remained stable and the urinary protein levels reduced significantly.

Aim To investigate the molecular mecha-nisms of the Wenyang Huazhuo Tongluo formula in in-hibiting endothelial-to-mesenchymal transition(En-doMT)of pulmonary microvascular endothelial cells and improving pulmonary microvascular injury in sys-temic sclerosis(SSc).Methods Pulmonary micro-vascular endothelial cells were cultured with serum from SSc patients to establish SSc pulmonary microvas-cular endothelial cells.A hypoxia model was estab-lished in SSc pulmonary microvascular endothelial cells using liquid paraffin sealing,and the cells were treated with the Wenyang Huazhuo Tongluo formula or HIF-1a inhibitor KC7F2.Western blot was used to detect the protein expression levels of VE-cadherin,CD31,vimen-tin,HIF-1α,Foxm1,smad3,Tie-1,and vWF.ELISA was used to measure the concentrations of E-selectin and ICAM-1 in cell culture medium.The luciferase re-porter gene system was used to detect the promoter ac-tivity of the Foxm1 gene.Results Compared to the control group,the levels of VE-cadherin,CD31,HIF-1α,Foxm1,smad3,Tie-1,and vWF significantly de-creased under hypoxic condition,while the levels of vi-mentin,E-selectin,and ICAM-1 significantly in-creased.In addition,the cell morphology exhibited a distinct"spindle-like"myoblast morphology.Treat-ment with the Wenyang Huazhuo Tongluo formula or KC7F2 reversed these changes in protein expression levels and cell morphology induced by hypoxia.Con-clusion The Wenyang Huazhuo Tongluo formula im-proves pulmonary microvascular injury in SSc by inhib-iting the HIF-1a/Foxm1/smad3 pathway-mediated En-doMT of pulmonary microvascular endothelial cells.