Background: Acute ischemic stroke (AIS) occurs due to brain ischemia as a result of thrombosis of a cerebral blood vessel. It is a common cerebral blood circulation disorder worldwide and an important cause of death and disability.Objective: This study aims to establish a prediction model of multiple single category indicators and a joint model, through which to plot multiple receiver operating characteristic curves and compare area under curve of the models so as to predict the occurrence of AIS, explore the pathogenesis of AIS, and provide reference for clinical diagnosis and treatment of AIS.Methods: A retrospective clinical study was conducted in a Level A tertiary hospital in Sichuan Province, China. The patients participated in this study were over 18 years of age and suffered from acute ischemic stroke. They were hospitalized in department of neurology from October 1, 2019 to September 30, 2022, and underwent coronary artery computed tomographic arteriography (CTA) and blood biomarker detection. We collected demographic information, CTA data and blood biomarker detection values of all these patients. Conclusion:Through analyzing the clinical data of high-risk groups, this study provides guidance for the prevention and treatment of AIS, and promote further research.

Background:The peripheral blood gene expression profile of patients with coronary artery disease (CAD) has not been fully resolved. The aim of this study was to further analyze the peripheral blood transcriptome information of CAD patients and to uncover key genes and regulatory mechanisms in the pathogenesis and disease progression of CAD. Methods:The Gene Expression Omnibus (GEO) database was applied to screen out differentially expressed genes (DEGs) in the peripheral blood of CAD patients, and the DEGs were subjected to Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA). The core genes were screened by GO, KEGG, and GSEA, and the gene-gene interaction (GGI) and protein-protein interaction (PPI) networks of DEGs were constructed. The GeneCards database was used to obtain CAD-related genes, and the GEO dataset was used to obtain intersecting genes. The intersecting genes were analyzed for bioenrichment and prediction of potential therapeutic agents, and predictive models were constructed for the intersecting genes. Finally, immune infiltrating cells from the GEO dataset were analyzed.Results: A total of 79 DEGs were screened in the peripheral blood of CAD patients, of which three were autophagy-related genes. Biological enrichment analysis showed that the DEGs were associated with metabolic pathways, and vascular smooth muscle contraction and were mainly involved the MAPK signaling pathway, metabolic pathways, and the PI3K-Akt signaling pathway. The S100A8, ENTPD1, and MMP9 further screened were screened. A total of 11 CAD crossover genes and 75 potential therapeutic agents were obtained, and the column line graph prediction models constructed for S100A8, HSPB1, F5, MMP9, and PDE9A had good predictive power. There were significant differences in immune cells in CAD patients compared to healthy individuals, especially in T cells regulatory (Tregs) and B cells naïve. Conclusions:The peripheral blood of CAD patients screened by the GEO dataset was significantly different from that of the healthy population, and the DEGs and intersecting genes were involved in numerous key biological processes that may be involved in the development and progression of CAD and could serve as its regulatory sites and therapeutic drug targets.