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.

Major depressive disorder(MDD)is a prevalent con-dition associated with substantial impairment and low remission rates.Traditional antidepressants demonstrate delayed effects,low cure rate,and inadequate therapeutic effectiveness for man-aging treatment-resistant depression(TRD).Several studies have shown that ketamine,a non-selective N-methyl-D-aspartate receptor(NMDAR)antagonist,can produce rapid and sustained antidepressant effects.Ketamine has demonstrated efficacy for reducing suicidality in TRD patients.However,the pharmaco-logical mechanism for ketamine's antidepressant effects remains incompletely understood.Previous research suggests that the an-tidepressant effects of ketamine may involve the monoaminergic,glutamatergic and dopaminergic systems.This paper provides an overview of the pharmacological mechanism for ketamine's anti-depressant effects and discuss the potential directions for future research.

Veno-arterial extracorporeal membrane oxygenation is an effective method to reduce perioperative adverse events such as cardiogenic shock in patients undergoing complex high-risk indicated percutaneous coronary intervention.Femoral artery and femoral vein are the main routes for conventional veno-arterial extracorporeal membrane oxygenation in China,while the cases of extracorporeal membrane oxygenation insertion via axillary artery are relatively rare.However,the axillary artery intubation veno-arterial extracorporeal membrane oxygenation assisted mode has been regarded as one of the routine clinical paths for the treatment of critically ill patients in foreign countries.This paper reports a case of an elderly male patient who underwent high risk and complex percutaneous coronary interventional therapy by right axillary artery implantation with extracorporeal membrane oxygenation assisted circulation due to the difficulty of femoral artery approach.In order to provide reference for the selection of clinical extracorporeal membrane oxygenation technique route.

Objective:To study the reversal effect of NVP-BEZ235 on doxorubicin resistance in Burkitt lymphoma RAJI cell line.Methods:The doxorubicin-resistant cell line was induced by treating RAJI cells with a concentration gradient of doxorubicin.The levels of Pgp,p-AKT,and p-mTOR in cells were detected by Western blot.Cell viability was detected by MTT assay.IC50 was computed by SPSS.Results:The doxorubicin-resistant Burkitt lymphoma cell line,RAJI/DOX,was established successfully.The expression of Pgp and the phosphorylation levels of AKT and mTOR in RAJI/DOX cell line were both higher than those in RAJI cell line.NVP-BEZ235 downregulated the phosphorylation levels of AKT and mTOR in RAJI/DOX cell line.NVP-BEZ235 inhibited the proliferation of RAJI/DOX cell line,and the effect was obvious when it was cooperated with doxorubicin.Conclusion:The constitutive activation of PI3K/AKT/mTOR pathway of RAJI/DOX cell line was more serious than RAJI cell line.NVP-BEZ235 reversed doxorubicin resistance of RAJI/DOX cell line by inhibiting the PI3K/AKT/mTOR signal pathway.

Objective:To investigate the efficacy of different doses of bortezomib combined with chemotherapy for multiple myeloma (MM).Methods:A prospective case series study was performed. A total of 81 MM patients at Leshan People's Hospital from February 2022 to May 2023 were collected as study subjects. According to the random number table method, patients were divided into high-dose bortezomib group (39 cases treated with 1.6 mg/m 2 bortezomib combined with dexamethasone and thalidomide) and low-dose bortezomib group (42 cases treated with 1.3 mg/m 2 bortezomib combined with dexamethasone and thalidomide). The clinical efficacy after 4 courses of treatment, adverse reactions, C-reactive protein (CRP), β 2 microglobulin (β 2-MG) and serum creatinine levels before and after treatment, survival and prognosis of patients in both groups were compared. Results:There were 29 males and 10 females in the high-dose bortezomib group and the age was (59±5) years; there were 31 males and 11 females in the low-dose bortezomib group and the age was (59±6) years. The differences in the general data of both groups were statistically significant (all P > 0.05). The overall effectiveness rate was 87.2% (34/39) and 80.9% (34/42), respectively in the high-dose bortezomib group and the low-dose bortezomib group, and the difference was not statistically significant of both groups ( χ2 = 0.58, P = 0.446). The incidence rate of adverse reactions was 30.8% (12/39), 19.0% (8/39), respectively in the high-dose bortezomib group and the low-dose bortezomib group, and the difference was not statistically significant of both groups ( χ2 = 1.49, P = 0.222). Before treatment, there were no statistically significant differences in the levels of CRP, β 2-MG and serum creatinine between the 2 groups (all P > 0.05); after treatment, there were statistically significant differences in the levels of CRP [(23.6±2.2) g/L vs. (31.5±3.6) g/L)], β 2-MG [(2 317±63) μg/L vs. (4 212±114) μg/L] and serum creatinine [(70±5) μmol/L vs. (79±7) μmol/L] in the high-dose bortezomib group and the low-dose bortezomib group ( t value was 4.28, 18.29, 4.00, all P<0.05); and the levels of above 3 indicators after treatment were lower than those before treatment of both groups (all P < 0.05). The mortality rate was 10.3% (4/39) and 14.3% (6/42), respectively in the high-dose bortezomib group and the low-dose bortezomib group 1-year follow-up after treatment, and the difference was not statistically significant ( χ2 = 0.30, P = 0.582). Conclusions:The efficacy and safety of high-dose bortezomib combined with chemotherapy are comparable to those of low-dose bortezomib combined with chemotherapy in treatment of MM, while the former could improve renal function and inflammatory status of MM patients.

Relevant literature on TCM diagnostic and treatment equipment from January 1994 to May 2023 was collected with three Chinese databases,namely,China National Knowledge Infrastructure,Wanfang and Wipu.CiteSpace software was used for the analyses of the trend of annual publication volume,co-occurrence of publication institutions,co-occurrence of keywords,cluster and burst and the generation of corresponding knowledge graphs.It's pointed out TCM diagnostic and treatment equipment had problems in low publication volume and collaboration between research institutions,hotspots of Chinese medicine diagnosis and treatment,diagnosis and treatment equipment,diagnosis device,Chinese medicine diagnosis,diagnosis and treatment technology and traditional Chinese medicine,and research frontiers of artificial intelligence,medical alliance,curriculum design and innovation and entrepreneurship.References were provided for relevant research in China.[Chinese Medical Equipment Journal,2024,45(3):76-80]