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 construct and apply drug utilization evaluation （DUE） criteria for low-dose rivaroxaban in atherosclerotic cardiovascular disease （ASCVD） based on the dual pathway inhibition （DPI） antithrombotic therapy scheme， to promote clinical rational drug use. METHODS Based on the instructions and relevant guidelines of low-dose rivaroxaban （2.5 mg， bid）， the Delphi method was used to establish the DUE criteria for low-dose rivaroxaban used in ASCVD. Weighted technique for order preference by similarity to an ideal solution method was used to determine the relative weights of each evaluation index， and the rationality of the filing medical records of discharged patients using low-dose rivaroxaban for ASCVD at Anqing Municipal Hospital from February 2024 to January 2025 was evaluated. RESULTS The established DUE criteria included 3 primary indicators （medication indications， medication process， medication results） and 11 secondary indicators （such as indications， contraindications， etc.）. The higher weighted secondary indicators being contraindications （0.117 9） and indications （0.112 1）. A total of 265 medical records were included for evaluation. The evaluation results showed that 192 cases （72.45%） had reasonable medical records， 69 cases （26.04%） had basic reasonable medical records， and 4 cases （1.51%） had unreasonable medical records； unreasonable types mainly included inappropriate combination therapy， inappropriate usage and dosage， inappropriate post- medication monitoring， and inappropriate drug switching， etc. CONCLUSIONS This study establishes a DUE criteria for low-dose rivaroxaban in ASCVD based on the DPI antithrombotic treatment regimen， and the evaluation results are intuitive， reliable， and quantifiable. The use of low-dose rivaroxaban in ASCVD patients in our hospital is relatively reasonable， but further management needs to be strengthened.

Objective To investigate the clinical efficacy and safety of single-port thoracoscopic surgery for elderly patients with non-small cell lung cancer(NSCLC).Methods The clinical data of 93 patients with NSCLC who underwent thoracoscopic lobectomy or segmentectomy was collected,the patients were divided into uniportal operation group(40 cases,received single-port thoracoscopic surgery)and single-operation port operation group(53 cases,received single-operation port thoracoscopic surgery)according to the operation methods.The operation time,the amount of blood loss,the number of lymph node dissection,chest drainage volume 3 days after surgery,duration of indwelling drainage tube,postoperative hospital stay,visual analogue scale(VAS)score of postoperative pain,and incidence of postoperative complications of patients between the two groups were compared.The cumulative survival rate between the two groups was compared.Results The operation were successfully completed in both groups.There was no statistically significant difference in terms of operation time,the amount of blood loss,the number of lymph node dissection,chest drainage volume 3 days after surgery,duration of indwelling drainage tube,or postoperative hospital stay of patients between the two groups(P>0.05).There was significant difference in VAS score of postoperative pain of patients between the two groups(P<0.05).There was no early death within 1 months after surgery in both groups.There was no significant difference in the incidence of complications between the two groups(P>0.05).After 4 to 30 months of follow-up,there was no significant difference in the cumulative survival rate between the two groups(P>0.05).Conclusion Single-port thoracoscopic lobectomy or segmentectomy for elderly patients with NSCLC has high safety and feasibility,with less trauma,faster recovery and less postoperative pain.

Objective:We aimed to develop a novel visualized model based on nomogram to predict postoperative overall survival.Methods:This was a multicenter, retrospective, observational cohort study, including participants with histologically confirmed gastric and colorectal cancer who underwent radical surgery from 11 medical centers in China from August 1, 2015 to June 30, 2018. Baseline characteristics, histopathological data and nutritional status, as assessed using Nutrition Risk Screening 2002 (NRS 2002) score and the scored Patient-Generated Subjective Global Assessment, were collected. The least absolute shrinkage and selection operator regression and Cox regression were used to identify variables to be included in the predictive model. Internal and external validations were performed.Results:There were 681 and 127 patients in the training and validation cohorts, respectively. A total of 188 deaths were observed over a median follow-up period of 59 (range: 58 to 60) months. Two independent predictors of NRS 2002 and Tumor-Node-Metastasis (TNM) stage were identified and incorporated into the prediction nomogram model together with the factor of age. The model's concordance index for 1-, 3- and 5-year overall survival was 0.696, 0.724, and 0.738 in the training cohort and 0.801, 0.812, and 0.793 in the validation cohort, respectively.Conclusions:In this study, a new nomogram prediction model based on NRS 2002 score was developed and validated for predicting the overall postoperative survival of patients with gastric colorectal cancer. This model has good differentiation, calibration and clinical practicability in predicting the long-term survival rate of patients with gastrointestinal cancer after radical surgery.

Objective:To gain a deep understanding of the current cognition and training needs of nursing undergraduates regarding their emergency response teamwork skills, and to provide reference for the development of courses on emergency response teamwork among nursing undergraduates.Methods:From September to October 2023, purposive sampling was used to select 15 senior nursing undergraduates from Peking Union Medical College, Beijing University of Chinese Medicine, and Beijing City University as subjects for semi-structured interviews. Colazizzi 7-step analysis method was used to summarize and extract themes.Results:Three themes were extracted, including insufficient cognition and skill in emergency response, lack of emergency response teamwork cultivation, and the need for systematic and comprehensive training courses.Conclusions:Universities, hospitals, and other training institutions should work together to develop a systematic emergency response teamwork training course for nursing undergraduates, to cultivate the skills of nursing undergraduates and reserve talents for high-quality emergency response nursing teams.

In order to establish the isolation,culture and identification method of cow bone marrow-derived macrophages,three different media(RPMI-1640,DMEM,DMEM/F12)were added with 20％fetal bovine serum(FBS),2.4％chlorine-streptomycin,1.2％glutamine(Gln),and M-CSF(20 ng/mL),respectively,to induce the monocytes extracted from the bone marrow of dairy cows to become macrophages.The induced M0 macrophages were polarized into M1-type macrophages by adding lipopolysaccharide(LPS).The morphology of macrophages was observed by optical mi-croscope at day 1,4 and 7,and the differences of differentiated macrophages between the three media were compared.The effects of prostaglandin D2(PGD2)-DP2 receptor pathway on the secre-tion of cytokines(IL-6,TNF-α)induced by Escherichia coli and phagocytosis of macrophages were also investigated.The results showed that the morphological changes of cells cultured in the medium of RPMI-1640 were the most obvious and the number was large.A large number of char-acteristic markers of mononuclear macrophages were detected(M0 markers:CD1 1b,CD14;M1 markers:CD11b,CD80)expression,M0 and M1 macrophage purity were 79.9％and 93.5％,re-spectively.COX-2 and H-PGDS gene expressions were significantly increased in E.coli group com-pared with the blank control group.The secretion of PGD2also increased significantly(P＜0.000 1).DP2 receptor inhibitors(CAY10471,CAY10595)could significantly inhibit the secretion of E.coli in-duced pro-inflammatory cytokines(IL-6,TNF-α)and significantly enhance the killing effect of macrophages on E.coli.The above results showed that the induced cells had the characteristic mor-phology and immunophenotype of macrophages.E.coli can induce the production of PGD2 in mac-rophages,and the PGD2-DP2 pathway regulates the secretion of cytokines in E.coli infected macro-phages.