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 clinical efficacy of Yiqi Huayu Decoction(mainly composed of Astragali Radix,Dioscoreae Rhizoma,Poria,fried Euryales Semen,Ecliptae Herba,Rosae Laevigatae Fructus,charred Crataegi Fructus,Ligustri Lucidi Fructus,Salviae Miltiorrhizae Radix et Rhizoma,and Leonuri Herba)combined with Calcium Dobesilate in the treatment of diabetic nephropathy(DN)with qi deficiency and blood stasis syndrome,and to observe the effect of the therapy on vascular endothelial growth factor(VEGF)and insulin-like growth factor 1(IGF-1).Methods Ninety patients with DN of qi deficiency and blood stasis type were randomly divided into an observation group and a control group,with 45 patients in each group.All patients received basic hypoglycemic therapy and treatment for controlling blood pressure and regulating lipid metabolism disorders.Moreover,the patients in the control group were given Calcium Dobesilate orally,and the patients in the observation group were given Yiqi Huayu Decoction combined with Calcium Dobesilate.The course of treatment lasted for 3 months.The changes of traditional Chinese medicine(TCM)syndrome scores,renal function parameters and serum VEGF and IGF-1 levels in the two groups of patients were observed before and after the treatment,and the clinical efficacy of the two groups was evaluated after treatment.Results(1)After 3 months of treatment,the total effective rate of the observation group was 91.11%(41/45),and that of the control group was 75.56%(34/45).The intergroup comparison(tested by chi-square test)showed that the therapeutic effect of the observation group was significantly superior to that of the control group(P＜0.05).(2)After one month and 3 months of treatment,the TCM syndrome scores of both groups were significantly lower than those before treatment(P＜0.05),and the scores after 3 months of treatment in the two groups were significantly lower than those after one month of treatment(P＜0.05).The intergroup comparison showed that the reduction of TCM syndrome scores of the observation group was significantly superior to that of the control group after one month and 3 months of treatment(P＜0.01).(3)After treatment,the levels of renal function parameters such as serum creatinine(Scr),blood urea nitrogen(BUN),and glomerular filtration rate(GFR)in the two groups of patients were significantly improved compared with those before treatment(P＜0.05),and the observation group's effect on the improvement of all renal function parameters was significantly superior to that of the control group(P＜0.01).(4)After treatment,the serum VEGF and IGF-1 levels in the two groups of patients were significantly lower than those before treatment(P＜0.05),and the observation group's effect on the decrease of serum VEGF and IGF-1 levels was significantly superior to that of the control group(P＜0.01).(5)In the course of treatment,no significant adverse reactions occurred in the two groups of patients,with a high degree of safety.Conclusion Yiqi Huayu Decoction combined with Calcium Dobesilate exerts certain therapeutic effect in treating DN patients with qi deficiency and blood stasis syndrome.The combined therapy can effectively down-regulate the serum levels of VEGF and IGF-1,significantly improve the renal function,and alleviate the clinical symptoms of the patients,with a high degree of safety.

Objective To explore the efficacy and safety of recombinant human anti-severe acute respiratory syn-drome coronavirus 2(anti-SARS-CoV-2)monoclonal antibody injection(F61 injection)in the treatment of patients with coronavirus disease 2019(COVID-19)combined with renal damage.Methods Patients with COVID-19 and renal damage who visited the PLA General Hospital from January to February 2023 were selected.Subjects were randomly divided into two groups.Control group was treated with conventional anti-COVID-19 therapy,while trial group was treated with conventional anti-COVID-19 therapy combined with F61 injection.A 15-day follow-up was conducted after drug administration.Clinical symptoms,laboratory tests,electrocardiogram,and chest CT of pa-tients were performed to analyze the efficacy and safety of F61 injection.Results Twelve subjects(7 in trial group and 5 in control group)were included in study.Neither group had any clinical progression or death cases.The ave-rage time for negative conversion of nucleic acid of SARS-CoV-2 in control group and trial group were 3.2 days and 1.57 days(P=0.046),respectively.The scores of COVID-19 related target symptom in the trial group on the 3rd and 5th day after medication were both lower than those of the control group(both P＜0.05).According to the clinical staging and World Health Organization 10-point graded disease progression scale,both groups of subjects improved but didn't show statistical differences(P＞0.05).For safety,trial group didn't present any infusion-re-lated adverse event.Subjects in both groups demonstrated varying degrees of elevated blood glucose,elevated urine glucose,elevated urobilinogen,positive urine casts,and cardiac arrhythmia,but the differences were not statistica-lly significant(all P＞0.05).Conclusion F61 injection has initially demonstrated safety and clinical benefit in trea-ting patients with COVID-19 combined with renal damage.As the domestically produced drug,it has good clinical accessibility and may provide more options for clinical practice.

Aim To investigate the regulatory mecha-nism of butin on the proliferation,migration,cycle blockage and pyroptosis related inflammatory factors in human fibroblast-like synoviocytes of rheumatoid arthri-tis(HFLS-RA).Methods Cell proliferation,migra-tion and invasion were studied using cell migration and invasion assays.Cell cycle was detected by flow cytom-etry,and the expression of the pyroptosis-associated in-flammatory factors IL-1β,IL-18,caspase-1 and caspase-3 was detected by ELISA,RT-qPCR and West-ern blot.Results Migration and invasion experiments showed that the cell proliferation rate of the butin group was lower than that of the blank control group(P＜0.05).Cell cycle analysis demonstrated that in the G0/G1 phase,the DNA expression was elevated in the medium and high-dose groups of butin(P＜0.05),while in the G2 and S phases,the DNA expression was reduced in the medium and high-dose groups of butin(P＜0.05).The results of ELISA,RT-qPCR and Western blot assay revealed that the expression of IL-1β,IL-1 8,caspase-1,and caspase-3 decreased in the butin group compared with the IL-1β+caspase-3 in-hibitor group(P＜0.05).Conclusions Butin inhib-its HFLS-RA proliferation by inhibiting the synthesis of inflammatory vesicles by caspase-1 in the pyroptosis pathway,thereby reducing the production and release of inflammatory factors such as IL-1β and IL-18 down-stream of the pathway,and also inhibits HFLS-RA pro-liferation by exerting a significant blocking effect in the G1 phase,which may be one of the potential mecha-nisms of butin in the treatment of RA.

Aim To investigate the impact of Cinobu-facini on the proliferation,invasion,and apoptosis of HepG2 cells and the underlying mechanism.Methods The proliferation of HepG2 cells was assessed using the CCK-8 method following treatment with Cinobufaci-ni.The invasion capability of HepG2 cells was evalua-ted through Transwell assay after exposure to Cinobufa-cini.The apoptosis rates of HepG2 cells post Cinobufa-cini intervention were measured using flow cytometry,and the expression levels of VEGF in the culture medi-um of HepG2 cells were determined using enzyme-linked immunoassay.Furthermore,qRT-PCR and Western blot analyses were conducted to assess the im-pact of Cinobufacini on mRNA and protein expression levels related to the CXCL5/FOXD1/VEGF pathway.The interaction between CXCL5 and FOXD1 was inves-tigated via co-immunoprecipitation.Results Cinobufa-cini treatment led to a gradual decrease in HepG2 cell viability in a dose-dependent manner compared to the control group(P＜0.05).Moreover,Cinobufacini sig-nificantly suppressed HepG2 cell invasion(P＜0.05)while enhancing cell apoptosis(P＜0.05).Notably,Cinobufacini exhibited inhibitory effects on the CX-CL5/FOXD1/VEGF pathway,as evidenced by re-duced expression of related mRNA and proteins(P＜0.05).FOXD1 was identified as the binding site of CXCL5.Overexpression of CXCL5 resulted in in-creased proliferation and VEGF secretion by HepG2 cells(P＜0.05),and increased expression of FOXD1 and VEGF(P＜0.05).However,Cinobufacini inter-vention effectively inhibited liver cancer cell prolifera-tion and invasion(P＜0.05),promoted apoptosis(P＜0.05),reduced VEGF secretion by HepG2 cells(P＜0.05),and downregulated the expression of CXCL5 and FOXD1 in HepG2 cells(P＜0.05);but com-pared with the unexpressed group of Cinobufacini,its ability to inhibit cell activity was weakened(P＜0.05),and its ability to inhibit the expression of CX-CL5,FOXD1,and VEGF was weakened(P＜0.05).Conclusion Cinobufacini may inhibit HepG2 cell pro-liferation and invasion and promote HepG2 cell apopto-sis by regulating the CXCL5/FOXD1/VEGF pathway.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.