Objective:To systematically evaluate the bias risk and applicability of short-term mortality risk prediction models within 30 days after stroke, providing a basis for selecting or developing standardized risk prediction models.Methods:Research on short-term mortality risk prediction models within 30 days after stroke was electronically retrieved from China National Knowledge Infrastructure, WanFang Data, VIP, and China Biomedical Database, PubMed, Web of Science, Embase, Cochrane Library and CINAHL. The search period was from database establishment to December 5, 2023. Two researchers independently conducted literature screening and quality evaluation.Results:Twelve studies were included, and a total of 31 models were internally validated, with 7 models undergoing external validation based on internal validation. 26 models reported discriminative power, and 18 models reported calibration methods. The most frequent predictors of modeling were age, hypertension, atrial fibrillation, diabetes and admission Glasgow Coma Scale score. Due to methodological problems such as insufficient sample size, improper handling of missing variables, and inadequate reporting of modeling information, all included studies were rated as high risk of bias.Conclusions:The research on short-term mortality risk prediction models for stroke patients is still in the development stage. Although it has good applicability, the risk of bias is relatively high. Future research should be designed and reported based on prediction model risk of bias assessment tool (PROBAST) and transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) to avoid common problems summarized in this study and reduce the risk of bias.

Objective To identify the biomarkers for early rheumatoid arthritis(RA)diagnosis and explore the possible immune regulatory mechanisms.Methods The differentially expressed genesin RA were screened and functionally annotated using the limma,RRA,batch correction,and clusterProfiler.The protein-protein interaction network was retrieved from the STRING database,and Cytoscape 3.8.0 and GeneMANIA were used to select the key genes and predicting their interaction mechanisms.ROC curves was used to validate the accuracy of diagnostic models based on the key genes.The disease-specific immune cells were selected via machine learning,and their correlation with the key genes were analyzed using Corrplot package.Biological functions of the key genes were explored using GSEA method.The expression of STAT1 was investigated in the synovial tissue of rats with collagen-induced arthritis(CIA).Results We identified 9 core key genes in RA(CD3G,CD8A,SYK,LCK,IL2RG,STAT1,CCR5,ITGB2,and ITGAL),which regulate synovial inflammation primarily through cytokines-related pathways.ROC curve analysis showed a high predictive accuracy of the 9 core genes,among which STAT1 had the highest AUC(0.909).Correlation analysis revealed strong correlations of CD3G,ITGAL,LCK,CD8A,and STAT1 with disease-specific immune cells,and STAT1 showed the strongest correlation with M1-type macrophages(R=0.68,P=2.9e-08).The synovial tissues of the ankle joints of CIA rats showed high expressions of STAT1 and p-STAT1 with significant differential expression of STAT1 between the nucleus and the cytoplasm of the synovial fibroblasts.The protein expressions of p-STAT1 and STAT1 in the cell nuclei were significantly reduced after treatment.Conclusion CD3G,CD8A,SYK,LCK,IL2RG,STAT1,CCR5,ITGB2,and ITGAL may serve as biomarkers for early diagnosis of RA.Gene-immune cell pathways such as CD3G/CD8A/LCK-γδ T cells,ITGAL-Tfh cells,and STAT1-M1-type macrophages may be closely related with the development of RA.

Objective To identify the biomarkers for early rheumatoid arthritis(RA)diagnosis and explore the possible immune regulatory mechanisms.Methods The differentially expressed genesin RA were screened and functionally annotated using the limma,RRA,batch correction,and clusterProfiler.The protein-protein interaction network was retrieved from the STRING database,and Cytoscape 3.8.0 and GeneMANIA were used to select the key genes and predicting their interaction mechanisms.ROC curves was used to validate the accuracy of diagnostic models based on the key genes.The disease-specific immune cells were selected via machine learning,and their correlation with the key genes were analyzed using Corrplot package.Biological functions of the key genes were explored using GSEA method.The expression of STAT1 was investigated in the synovial tissue of rats with collagen-induced arthritis(CIA).Results We identified 9 core key genes in RA(CD3G,CD8A,SYK,LCK,IL2RG,STAT1,CCR5,ITGB2,and ITGAL),which regulate synovial inflammation primarily through cytokines-related pathways.ROC curve analysis showed a high predictive accuracy of the 9 core genes,among which STAT1 had the highest AUC(0.909).Correlation analysis revealed strong correlations of CD3G,ITGAL,LCK,CD8A,and STAT1 with disease-specific immune cells,and STAT1 showed the strongest correlation with M1-type macrophages(R=0.68,P=2.9e-08).The synovial tissues of the ankle joints of CIA rats showed high expressions of STAT1 and p-STAT1 with significant differential expression of STAT1 between the nucleus and the cytoplasm of the synovial fibroblasts.The protein expressions of p-STAT1 and STAT1 in the cell nuclei were significantly reduced after treatment.Conclusion CD3G,CD8A,SYK,LCK,IL2RG,STAT1,CCR5,ITGB2,and ITGAL may serve as biomarkers for early diagnosis of RA.Gene-immune cell pathways such as CD3G/CD8A/LCK-γδ T cells,ITGAL-Tfh cells,and STAT1-M1-type macrophages may be closely related with the development of RA.