ArticleCardiology2023
Differentiation of Atrial Fibrillation and Atrial Fibrillation-Associated Ischemic Stroke Based on Serum Exosome miRNA-Seq.
Article in Cardiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deciphering the Role of microRNAs: Unveiling Clinical Biomarkers and Therapeutic Avenues in Atrial Fibrillation and Associated Stroke-A Systematic Review.International journal of molecular sciences · 2024Pooled it
- Article
- miRNA101a secreted by EATMs regulates atrial fibrillation through the PDGF-mediated PI3K/AKT pathway.Frontiers in pharmacology · 2026Article
- Microarray-based analysis reveals a novel role of the miRNA-613/SNAI2/CXCR4 axis in atrial fibrillation.PloS one · 2025Article
- Review
- Integrative identification of hub genes in development of atrial fibrillation related stroke.PloS one · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAtrial fibrillation (AF) is the most common cardiac arrhythmia in the general population, and stroke is the most severe complication of AF. Exosomal miRNAs have been reported to be candidates as biomarkers for cardiovascular diseases, including AF and stroke. This study aimed to identify differentially expressed miRNAs (DEMs) in serum exosomes of AF and AF-associated ischemic stroke (AF-IS) patients and evaluate their potential in distinguishing AF and AF-IS patients.
methodsSerum exosomes were isolated from 8 healthy individuals with sinus rhythm (SR controls), 8 AF patients, and 8 AF-IS patients. miRNA-seq was performed to identify DEMs, and qRT-PCR analysis was performed to confirm the sequencing results. A support vector machine (SVM) model was developed using Python to distinguish AF and AF-IS patients.
results68 and 86 DEMs were identified in serum exosomes of AF patients compared to AF-IS patients and SR controls, respectively. Levels of miR-641 and miR-30e-5p were found significantly higher in AF-IS patients. The SVM model achieved an accuracy of 100%, with an area under curve of 1.
conclusionsThe results indicated that miRNA expression profiles of serum exosomes in AF patients were distinct from those in AF-IS patients, and based on the distinction, AF and AF-IS patients can be distinguished.
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