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 analyze the risk factors of gastrointestinal bleeding in patients with type A aortic dissection(TAAD)after Sun's operation.Methods The clinical data of 87 patients who underwent TAAD Sun's operation in our hospital from March 2021 to June 2022 were retrospectively analyzed.They were divided into the bleeding group and the non-bleeding group according to whether there was gastrointestinal bleeding after operation.The clinical data of patients in the two groups was compared and analyzed.The binary Logistic regression analysis was used to analyze the risk factors of gastrointestinal bleeding.The clinical predictor of postoperative gastrointestinal bleeding was analyzed by receiver operating characteristic(ROC)curve.Results In this study,there were 40 cases of postoperative gastrointestinal bleeding(the bleeding group)and 47 cases of non-bleeding(the non-bleeding group).Compared with the non-bleeding group,the bleeding group had a shorter onset time,a higher proportion of patients with hypertension history,a higher preoperative creatinine abnormality rate,more intraoperative blood loss,longer postoperative mechanical ventilation time,higher postoperative infection rate,and higher poor prognosis rate,with statistically significant differences(P<0.05).There was no statistically significant difference in the gender,age,gastrointestinal diseases history,smoking history,preoperative platelets,preoperative international normalized ratio(INR),preoperative alanine aminotransferase(ALT),preoperative aspartate aminotransferase(AST),preoperative γ-glutamyl transpeptidase(GGT),preoperative dissection involving abdominal aorta,operation time,intraoperative cardiopulmonary bypass time,intraoperative circulatory arrest time,intraoperative aortic occlusion time or intraoperative blood transfusion rate.Logistic regression analysis showed that hypertension history(OR=2.468,95%CI:0.862 to 7.067,P=0.037),preoperative creatinine>105 μmol/L(OR=3.970,95%CI:1.352 to 11.659,P=0.011),long postoperative mechanical ventilation time(OR=1.015,95%CI:0.094 to 1.018,P=0.041)and postoperative infection(OR=3.435,95%CI:0.991 to 11.900,P=0.012)were the independent risk factors for postoperative gastrointestinal bleeding in TAAD patients.ROC curve showed that the postoperative mechanical ventilation time exceeding 64 hours were the clinical predictor of postoperative gastrointestinal bleeding in TAAD patients.Conclusion The prognosis of TAAD patients with postoperative gastrointestinal bleeding after Sun's operation is poor.Hypertension history,preoperative acute renal insufficiency,long postoperative mechanical ventilation time and postoperative infection are closely related to postoperative gastrointestinal bleeding in TAAD patients after operation,which should be paid more attention to,and corresponding evaluation,early identification and early intervention should be made to improve the prognosis of patients.

AIM:To evaluate the efficacy,safety,and economy of camrelizumab(CAM)combined with platinum-containing chemotherapy(CT)for the first-line treatment of locally advanced/meta-static non-small cell lung cancer(NSCLC).METH-ODS:Chinese and English databases such as Pubmed,the Cochrane Library,China Knowledge Network,Wanfang Data,and other related web-sites were systematically searched.After literature screening,quality assessment,and data extraction of the literature according to the inclusion and ex-clusion criteria,two researchers conducted a rapid health technology assessment(HTA).RESULTS:A total of 7 systematic evaluations/Meta-analyses and 17 economics evaluations were included.In terms of effectiveness,compared to docetaxel che-motherapy,CAM+CT significantly prolonged the overall survival(OS),progression-free survival(PFS),and improved the objective remission rate(ORR)of mutation-negative patients with locally ad-vanced/metastatic NSCLC.Compared with CT and pembrolizumab(PEM),CAM+CT significantly pro-longed the PFS,and improved the ORR of mutation-negative patients with locally advanced/metastatic NSCLC.Subgroup analysis showed that CAM+CT significantly prolonged PFS in patients with PD-L1 ≥1％and PD-L1 ≥ 50％compared with CT.Compared with CT,CAM+CT significantly prolonged the OS and PFS of mutation-negative patients with locally advanced/metastatic squamous NSCLC.Compared with sintilimab(SIN),CAM+CT significantly pro-longed the PFS of mutation-negative patients with locally advanced/metastatic squamous NSCLC.Sub-group analysis showed that CAM+CT significantly prolonged OS in patients with PD-L1＜1％com-pared with CT.In terms of safety,CAM+CT was comparable in terms of the occurrence of all grades of adverse events,but the incidence of grade 3 or higher treatment-related adverse events was significantly increased compared with CT and PEM for mutation-negative locally advanced/meta-static NSCLC patients.CAM+CT was significantly in-creased the occurrence of all grades of adverse events compared with CT,but was comparable in terms of the occurrence of grade 3 or higher treat-ment-related adverse events.In terms of economy,CAM+CT has a cost-effectiveness advantage over CT for patients with mutation-negative advanced/metastatic squamous NSCLC.CAM+CT has a cost-effectiveness advantage over CT and PEM+CT;and CAM+CT does not have a cost-effectiveness ad-vantage over SIN+CT for patients with mutation-negative locally advanced/metastatic non-squa-mous NSCLC.CONCLUSION:CAM+CT has good ef-ficacy and cost-effectiveness for the first-line treat-ment of locally advanced/metastatic NSCLC,and the safety aspect is compared with CT,PEM or slightly worse.

The effect of porcine SIRT5 on replication of foot and mouth disease virus type O(FMDV-O)and the underlying regulatory mechanism were investigated.Western blot and RT-qPCR analyses were employed to monitor expression of endoge-nous SIRT5 in PK-15 cells infected with FMDV-O.Three pairs of SIRT5-specific siRNAs were synthesized.Changes to SIRT5 and FMDV-O protein and transcript levels,in addition to virus copy numbers,were measured by western blot and RT-qPCR analyses.PK-15 cells were transfected with a eukaryotic SIRT5 expression plasmid.Western blot and RT-qPCR analyses were used to explore the impact of SIRT5 overexpression on FMDV-O replication.Meanwhile,RT-qPCR analysis was used to detect the effect of SIRT5 overexpression on the mRNA expression levels of type I interferon-stimulated genes induced by SeV and FMDV-O.The results showed that expression of SIRT5 was up-regulated in PK-15 cells infected with FMDV-O and siRNA interfered with SIRT5 to inhibit FMDV-O replication.SIRT5 overexpression promoted FMDV-O replication.SIRT5 over-expression decreased mRNA expression levels of interferon-stimulated genes induced by SeV and FMDV-O.These results suggest that FMDV-O infection stimulated expression of SIRT5 in PK-15 cells,while SIRT5 promoted FMDV-O rep-lication by inhibiting production of type I interferon-stimula-ted genes.These findings provide a reference to further ex-plore the mechanism underlying the ability of porcine SIRT5 to promote FMDV-O replication.

Objective:To investigate the effect of GRIM-19 on the resistance of carcinoma cells to the chemotherapeutic agent docetaxel in the treatment of PCa.Methods:Using siRNA technology to interfere with the gene expression in PCa cells,we estab-lished a model of GRIM-19 overexpression/knockdown in PCa cells.We investigated the effect of different expression levels of GRIM-19 on docetaxel-induced death of the PCa cells by qPCR,Western blot and flow cytometry,and assessed the value of GRIM-19 in re-ducing the chemotherapy-resistance of PCa cells.Results:GRIM-19 was down-regulated in PCa tissues and cells.Knockout of GRIM-19 significantly decreased the expression of siGRIM19 in the PC-3 and LNCaP cells,and reduced their death rate when treated with docetaxel compared with the control group.The expressions of GRIM-19 mRNA and protein were remarkably upregulated after transfection with GRIM-19,and the overexpressed GRIM-19 promoted the death of the PC-3 and LNCaP cells treated with docetaxel in a dose-dependent manner.Flow cytometry analysis showed a lower apoptosis rate of PC-3-R cells than that of PC-3 cells at different time points of docetaxel-induction at different doses.Conclusion:GRIM-19 is a PCa suppressor gene with a significant facilitating effect on the apoptosis of PCa cells,and the overexpression of GRIM-19 promotes docetaxel-induced PCa cell death and improves the sensitivity of chemotherapy.

AIM To establish an LC-MS/MS method for the simultaneous content determination of forsythin,forsythoside A,chlorogenic acid,neochlorogenic acid,amygdalin,emodin,rhein and salidroside in Lianhua Qingwen Capsules.METHODS The analysis was performed on a 35℃thermostatic ACQUITY UPlC-HSS T3 column(100 mm×2.1 mm,1.8 μm),with the mobile phase comprising of 0.1%formic acid-acetonitrile flowing at 0.3 mL/min in a gradient elution manner,and electron spray ionization source was adopted in negative ion scanning with multiple reaction monitoring mode.RESULTS Eight constituents showed good linear relationships within their own ranges(r≥0.999 5),whose average recoveries were 99.20%-100.96%with the RSDs of 0.62%-1.23%.CONCLUSION This simple,sensitive and reliable method can be used for the quality control of Lianhua Qingwen capsules.