To explore the inhibitory effect of ginkgolide B (GB) on MH7A human fibroblast-like synoviocytes (FLS) and its potential mechanism. Firstly, 20 μg/L tumor necrosis factor-α (TNF-α) was pretreated with MH7A to establish a cell model of arthritis. After incubation of MH7A cells with various concentrations of GB, CCK-8 assay, Transwell assay, and flow cytometry (FCM) were separately used to detect cell viability, cell invasion, and cell apoptosis rate and cell cycle; Real-time quantitative PCR and Western blot assay were performed to detect the apoptosis- and cycle-related gene transcriptions and protein expressions, respectively. The results showed that compared with the control group, GB dose- and time-dependently suppressed cell viability to a greater extent; GB significantly reduced cell invasive ability and increased cell apoptosis rate and proportion of G0/G1 phase in MH7A cells, along with increased transcription levels of Bcl-2-associated X protein (Bax) and p21 mRNA and decreased transcription levels of Bcl-2, myeloid cell leukemia 1(Mcl-1), protein kinase B (PKB; AKT), IP3K, Cyclin D1 and cyclin-dependent kinase 4 (CDK4) mRNA; GB remarkably increased expression levels of Bax, p21, and cleaved-Caspase 3 protein and decreased expression levels of Bcl-2, Mcl-1, p-AKT, p-PI3K, Cyclin D1, and CDK4 protein, with decreased ratios of p-PI3K/PI3K, p-AKT/AKT, and Bcl-2/Bax. In conclusion, GB blocks the G1-to-S cell cycle transition, suppresses cell viability and cell invasion and induces cell apoptosis of MH7A human RA-FLS via suppressing the PI3K/AKT signaling pathway.

OBJECTIVE To evaluate the efficacy， safety and economy of inclisiran in the treatment of atherosclerotic cardiovascular disease with hypercholesterolemia. METHODS A rapid health technology assessment （HTA） approach was employed. HTA reports， systematic reviews（SR）/meta-analyses， and pharmacoeconomic studies related to inclisiran were systematically identified through comprehensive searches of Chinese and English databases， including PubMed， Embase， the Cochrane Library， CNKI and Wanfang database， supplemented by HTA institutional repositories. The search timeframe spanned from database inception to April 2025. The results of the studies were descriptively analysed and summarized through literature screening， data extraction and literature quality assessment. RESULTS The final analysis included 22 studies， comprising one HTA report， 15 SR/meta-analyses， and 6 pharmacoeconomic evaluations. Regarding therapeutic efficacy， compared with control group， inclisiran could significantly reduce the levels of low-density lipoprotein cholesterol， proprotein convertase subtilisin/kexin type 9， total cholesterol， triacylglycerol， apolipoprotein B， and lipoprotein（a）， increase the level of high-density lipoprotein cholesterol， and reduce the risk of adverse cardiovascular events. In terms of safety， the inclisiran group showed no significant difference compared with the control group in the risk of total adverse events， serious adverse events， or non-serious adverse events； however， an increased incidence of injection site reactions was observed， most of which were mild. In terms of cost-effectiveness， there were discrepancies in research conclusions both domestically and internationally. More studies indicated that inclisiran did not demonstrate cost-effectiveness advantage and would require an appropriate price reduction to meet cost-effectiveness criteria. CONCLUSIONS Inclisiran demonstrates favorable efficacy and acceptable safety in treating atherosclerotic cardiovascular disease with hypercholesterolemia， though its economic profile requires improvement.

Objective: To establish a prediction model for high-risk cardiovascular disease (CVD) among residents aged 35 to 75 years, so as to provide the basis for improving CVD prevention and control measures. Methods: Permanent residents aged 35 to 75 years were selected from Dongcheng District, Beijing Municipality using the stratified random sampling method from 2018 to 2023. Demographic information, lifestyle, waist circumference and blood biochemical indicators were collected through questionnaire surveys, physical examinations and laboratory tests. Influencing factors for high-risk CVD among residents aged 35 to 75 years were identified using a multivariable logistic regression model, and a prediction model for high-risk CVD was established. The predictive effect was evaluated using the receiver operating characteristic (ROC) curve. Results: A total of 6 968 individuals were surveyed, including 2 821 males (40.49%) and 4 147 females (59.51%), and had a mean age of (59.92±9.33) years. There were 1 155 high-risk CVD population, with a detection rate of 16.58%. Multivariable logistic regression analysis showed that gender, age, smoking, central obesity, systolic blood pressure, fasting blood glucose, triglyceride and low-density lipoprotein cholesterol were influencing factors for high-risk CVD among residents aged 35 to 75 years (all P<0.05). The area under the ROC curve of the established prediction model was 0.849 (95%CI: 0.834-0.863), with a sensitivity of 0.693 and a specificity of 0.863, indicating good discrimination. Conclusion The model constructed by eight factors including demographic characteristics, lifestyle and blood biochemical indicators has good predictive value for high-risk CVD among residents aged 35 to 75 years.

OBJECTIVE:The main clinical manifestations of knee osteoarthritis are pain,swelling,stiffness,and limited activity,which have a serious impact on the life of patients.Exercise therapy can effectively improve the related symptoms of patients with knee osteoarthritis.This paper uses the method of network meta-analysis to compare the efficacy of different exercise types in the treatment of knee osteoarthritis. METHODS:CNKI,WanFang,PubMed,Embase,Cochrane Library,Web of Science,Scopus,Ebsco,SinoMed,and UpToDate were searched with Chinese search terms"knee osteoarthritis,exercise therapy"and English search terms"knee osteoarthritis,exercise".Randomized controlled trials on the application of different exercise types in patients with knee osteoarthritis from October 2013 to October 2023 were collected.The outcome measures included visual analog scale,Western Ontario and McMaster Universities Osteoarthritis Index score,Timed Up and Go test,and 36-item short form health survey.Literature quality analysis was performed using the Cochrane Manual recommended tool for risk assessment of bias in randomized controlled trials.Two researchers independently completed the data collection,collation,extraction and analysis.RevMan 5.4 and Stata 18.0 software were used to analyze and plot the obtained data. RESULTS:A total of 29 articles with acceptable quality were included,involving 1 633 patients with knee osteoarthritis.The studies involved four types of exercise:aerobic training,strength training,flexibility/skill training,and mindfulness relaxation training.(1)The results of network meta-analysis showed that compared with routine care/health education,aerobic training could significantly improve pain symptoms(SMD=-3.26,95%CI:-6.33 to-0.19,P<0.05);strength training(SMD=-0.79,95%CI:-1.34 to-0.23,P<0.05)and mindfulness relaxation training(SMD=-0.79,95%CI:-1.23 to-0.34,P<0.05)could significantly improve the function of patients.Aerobic training(SMD=-1.37,95%CI:-2.24 to-0.51,P<0.05)and mindfulness relaxation training(SMD=-0.41,95%CI:-0.80 to-0.02,P<0.05)could significantly improve the functional mobility of patients.Mindfulness relaxation training(SMD=0.70,95%CI:0.21-1.18,P<0.05)and strength training(SMD=0.42,95%CI:0.03-0.81,P<0.05)could significantly improve the quality of life of patients.(2)The cumulative probability ranking results were as follows:pain:aerobic training(86.6%)>flexibility/skill training(60.1%)>strength training(56.8%)>mindfulness relaxation training(34.7%)>routine care/health education(11.7%);Knee function:strength training(73.7%)>mindfulness relaxation training(73.1%)>flexibility/skill training(56.1%)>aerobic training(39.9%)>usual care/health education(7.6%);Functional mobility:aerobic training(94.7%)>mindfulness relaxation training(65.5%)>strength training(45.1%)>flexibility/skill training(41.6%)>routine care/health education(3.2%);Quality of life:mindfulness relaxation training(91.3%)>strength training(68.0%)>flexibility/skill training(44.3%)>aerobic training(34.0%)>usual care/health education(12.3%). CONCLUSION:(1)Exercise therapy is effective in the treatment of knee osteoarthritis,among which aerobic training has the best effect on relieving pain and improving functional mobility.Strength training and mindfulness relaxation training has the best effect on improving patients'function.Mindfulness relaxation training has the best effect on improving the quality of life of patients.(2)Limited by the quality and quantity of the included literature,more high-quality studies are needed to verify it.

Immobilized enzyme-based enzyme electrode biosensors, characterized by high sensitivity and efficiency, strong specificity, and compact size, demonstrate broad application prospects in life science research, disease diagnosis and monitoring, etc. Immobilization of enzyme is a critical step in determining the performance (stability, sensitivity, and reproducibility) of the biosensors. Random immobilization (physical adsorption, covalent cross-linking, etc.) can easily bring about problems, such as decreased enzyme activity and relatively unstable immobilization. Whereas, directional immobilization utilizing amino acid residue mutation, affinity peptide fusion, or nucleotide-specific binding to restrict the orientation of the enzymes provides new possibilities to solve the problems caused by random immobilization. In this paper, the principles, advantages and disadvantages and the application progress of enzyme electrode biosensors of different directional immobilization strategies for enzyme molecular sensing elements by specific amino acids (lysine, histidine, cysteine, unnatural amino acid) with functional groups introduced based on site-specific mutation, affinity peptides (gold binding peptides, carbon binding peptides, carbohydrate binding domains) fused through genetic engineering, and specific binding between nucleotides and target enzymes (proteins) were reviewed, and the application fields, advantages and limitations of various immobilized enzyme interface characterization techniques were discussed, hoping to provide theoretical and technical guidance for the creation of high-performance enzyme sensing elements and the manufacture of enzyme electrode sensors.

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.