Acute myocardial infarction (AMI) is a prevalent cardiovascular disease associated with high morbidity and mortality, posing a significant threat to human health. Therefore, early diagnosis of AMI has become a focal point of research. MiR-208 is specifically expressed in the heart and is involved in the regulation of cardiomyocyte hypertrophy, cardiac fibrosis, and other myocardial gene expressions. It is expected to be applied in the clinical detection of AMI due to its release by damaged myocardial cells within 3 hours of AMI. In this study, we developed a denatured bubble-mediated reverse transcription-accelerated strand exchange amplification (RT-ASEA) method to detect the early biomarker miR-208a of AMI. The novel approach allowed rapid amplification of miR-208a in 15 minutes, with good performance in terms of repeatability (CV < 6%), determination limit (1 × 10 0 pmol L -1 ), and linearity ( R 2 = 0.9690). Based on the analysis of 42 clinical samples, a strong correlation was observed between the Ct value of miR-208a detected by the RT-ASEA method and the cTnI concentration, considered the gold standard for diagnosis of AMI. The research suggested that the RT-ASEA method could be applied to distinguish between AMI and healthy groups. The area under the receiver operating characteristic curve (AUC) was 0.9976, with a sensitivity of 96% and a specificity of 100%. Optimized RT-ASEA is a reliable and efficient method for miRNA detection. Furthermore, this study provides crucial data support for the development of miR-208a as an early biomarker for AMI, which is of great significance in life and health.
Keyphrases
- acute myocardial infarction
- cell proliferation
- left ventricular
- long non coding rna
- percutaneous coronary intervention
- long noncoding rna
- cardiovascular disease
- human health
- loop mediated isothermal amplification
- risk assessment
- healthcare
- heart failure
- label free
- public health
- transcription factor
- coronary artery disease
- mental health
- acute coronary syndrome
- induced apoptosis
- computed tomography
- genome wide
- climate change
- electronic health record
- magnetic resonance
- deep learning
- image quality
- dual energy