A systematic review of <scp>micro‐RNAs</scp> in aortic stenosis and cardiac fibrosis
Bibliographic record
Abstract
Aortic stenosis (AS) is the commonest valve lesion requiring surgery in the Western world. The presence of myocardial fibrosis is associated with mortality even after valve replacement. MicroRNAs could serve as biomarkers of fibrosis and risk stratify patients for earlier intervention. This study aimed to systematically review reports of micro-RNA (miR) associated with fibrosis in AS and identify potential biomarkers. We searched EMBASE, Medline, and Web of Science up to May 2020. Studies that reported on the role of miRs in AS and cardiac fibrosis were included. Study quality was assessed using the Newcastle-Ottawa scale. Of 4230 reports screened, 25 were included. All studies were of low to moderate quality. MiRs were analyzed in myocardial tissue (n = 10), aortic valve tissue (n = 5), plasma (n = 5), and serum (n = 5). A total of 365 miRs were reported, of which only a few were reported in more than one paper (3 in the myocardium, 5 in the aortic valve, and 1 in plasma). miR-21 was upregulated in plasma and myocardial tissue. MiR-19b was downregulated in the myocardium. Papers reporting myocardial miR-1 contradicted each other, and miR-133a was associated with increased left ventricular mass regression post-surgery. In the aortic valve, miRs-665, 602 and 939 were downregulated, and miRs-193b and 214 were upregulated. The data on miR in fibrosis in AS is scarce and of low to moderate quality. Further studies are needed to identify novel miRs as biomarkers, especially at an earlier asymptomatic phase of the disease.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".