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Object <p>The differentiation of acute myocardial infarction (AMI) has long been a challenging problem in clinical diagnosis and forensic identification. Recent studies have shown that microRNAs (miRNAs) in exosomes are involved in the development and progression of AMI. results indicated that plasma exosomal miRNAs can be considered as novel biomarkers for early AMI recognition.</p> Method <p>In this study, exosomal miRNAs in plasma associated with the pathogenesis of AMI was explored and AMI identification model based on these miRNAs were established using machine learning technology.</p> Result <p>Following the analysis of differentially expressed miRNAs in plasma-derived exosomes, the expression levels of 36 miRNAs increase with the passage of time, including miR-3473, miR-504, miR-490-5p, miR-218a-2-3p, and miR-760-3p, showed an increasing trend over time in the plasma exosomes of AMI rats. Based on machine learning techniques, miR-3473, miR-504, miR-490-5p, miR-218a-2-3p were used to construct a model for recognizing early AMI. The precision of the AMI identification model reached 0.955.</p> Conclusion <p>The results indicated that plasma exosomal miRNAs can be considered as novel biomarkers for early AMI recognition.</p>

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Differential diagnosis of acute myocardial infarction based on plasma Exosomal MicroRNA

  • 期刊论文

  • 发表日期：

    2025年8月25日

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  • Peng Zhou,
  • Jia Zhang,
  • Xiangjun Wu,
  • Leilei Zhang,
  • Qinlai Liu,
  • Ruotong Xiong,
  • Yujie Wang,
  • Min Li,
  • Ran Wei,
  • Xiaoqun Xu,
  • Deping Meng,
  • Chunjiang Yu,
  • Jiangwei Yan,
  • Chen Fang

摘要

Object

The differentiation of acute myocardial infarction (AMI) has long been a challenging problem in clinical diagnosis and forensic identification. Recent studies have shown that microRNAs (miRNAs) in exosomes are involved in the development and progression of AMI. results indicated that plasma exosomal miRNAs can be considered as novel biomarkers for early AMI recognition.

Method

In this study, exosomal miRNAs in plasma associated with the pathogenesis of AMI was explored and AMI identification model based on these miRNAs were established using machine learning technology.

Result

Following the analysis of differentially expressed miRNAs in plasma-derived exosomes, the expression levels of 36 miRNAs increase with the passage of time, including miR-3473, miR-504, miR-490-5p, miR-218a-2-3p, and miR-760-3p, showed an increasing trend over time in the plasma exosomes of AMI rats. Based on machine learning techniques, miR-3473, miR-504, miR-490-5p, miR-218a-2-3p were used to construct a model for recognizing early AMI. The precision of the AMI identification model reached 0.955.

Conclusion

The results indicated that plasma exosomal miRNAs can be considered as novel biomarkers for early AMI recognition.