MicroRNAs in atrial fibrillation target genes in structural remodelling
Bibliographic record
Abstract
We aim to elucidate how miRNAs regulate the mRNA signature of atrial fibrillation (AF), to gain mechanistic insight and identify candidate targets for future therapies. We present combined miRNA-mRNA sequencing using atrial tissues of patient without AF (n = 22), with paroxysmal AF (n = 22) and with persistent AF (n = 20). mRNA sequencing previously uncovered upregulated epithelial to mesenchymal transition, endothelial cell proliferation and extracellular matrix remodelling involving glycoproteins and proteoglycans in AF. MiRNA co-sequencing discovered miRNAs regulating the mRNA expression changes. Key downregulated miRNAs included miR-135b-5p, miR-138-5p, miR-200a-3p, miR-200b-3p and miR-31-5p and key upregulated miRNAs were miR-144-3p, miR-15b-3p, miR-182-5p miR-18b-5p, miR-4306 and miR-206. MiRNA expression levels were negatively correlated with the expression levels of a multitude of predicted target genes. Downregulated miRNAs associated with increased gene expression are involved in upregulated epithelial and endothelial cell migration and glycosaminoglycan biosynthesis. In vitro inhibition of miR-135b-5p and miR-138-5p validated an effect of miRNAs on multiple predicted targets. Altogether, the discovered miRNAs may be explored in further functional studies as potential targets for anti-fibrotic therapies in AF.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".