Circulating MicroRNAs and the Occurrence of Acute Myocardial Infarction in Chinese Populations
Bibliographic record
Abstract
BACKGROUND: Circulating microRNAs ( miRNAs) are emerging as novel disease biomarkers. We aimed to explore the association between circulating miRNAs and the occurrence of acute myocardial infarction (AMI) in Chinese populations. METHODS AND RESULTS: In the discovery stage, the plasma of 20 patients with AMI and 20 controls were pooled respectively and profiled by massively parallel sequencing. Seventy-seven miRNAs showed differential expression. Selected miRNAs were validated in 178 patients with AMI and 198 controls using quantitative reverse transcriptase polymerase chain reaction assays and further replicated in 150 patients with AMI and 150 controls. Results suggest that miR-320b and miR-125b levels were significantly lower in patients with AMI than in controls in both validation populations (P<0.0001). Lower levels of miR-320b and miR-125b were associated with increased occurrence of AMI (adjusted odds ratio, 4.71; 95% confidence interval, 2.96-7.48 and odds ratio, 4.27; 95% confidence interval, 2.84-6.41, respectively). Addition of the 2 miRNAs to traditional risk factors led to a significant improvement in the area under the curve from 0.822 (95% confidence interval, 0.787-0.856) to 0.871 (95% confidence interval, 0.842-0.900), with a net reclassification improvement of 20.45% (P<0.0001) and an integrated discrimination improvement of 0.16 (P<0.0001) for patients with AMI. A functional study showed that miR-320b and miR-125b could regulate the expression profiles of genes enriched in several signal transduction pathways critical for coronary heart disease in human vascular endothelial cells. CONCLUSIONS: The plasma levels of miR-320b and miR-125b were significantly lower in patients with AMI when compared with controls, and these miRNAs may be involved in the pathogenesis of coronary heart disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".