Distinct atrial and ventricular microRNA changes in experimental heart failure
Bibliographic record
Abstract
Heart failure (HF) causes different forms of remodeling in left atrium (LA) vs left ventricle (LV), with fibrosis much more prominent in LA. MicroRNAs (miRs) are central regulators in cardiac remodeling. This study compared LA and LV miRNA changes during the development of experimental HF. Methods HF was induced in dogs by ventricular tachypacing (VTP, 240 bpm) for 0 (CTL), 12, 24 hrs, 1, 2 and 5 wks (n=5–8/group). MiR expression was assessed by microarray and qPCR. Results Microarrays showed greater changes in LA vs LV (Fig A). MiR‐29b, miR‐133a and miR‐30a decreased within 24 hrs; MiR‐21 and miR‐146b increases started at 1 wk only in LA (B, C). In contrast, miR‐214 and miR‐146a showed similar changes in LA and LV (D). MiR‐21, miR‐29b, miR‐133 and miR‐30 are implicated in extracellular‐matrix (ECM) remodeling. Collagen‐1/ ‐3, fibronectin 1 and fibrillin increased ~3.8–26‐fold*** to steady state at 1–2 wk, TIMP‐3 decreased from 24 hrs (~58%***) while MMP‐9 increased at 12 hrs (~9‐fold***) and remained elevated. Changes were much smaller in LV at all times, agreeing with atrial‐predominant fibrosis. miR‐214 and miR‐146 target I Ks and I Ca,L subunits, known to change in HF LA and LV. Conclusions VTP‐induced HF causes greater miRNA changes in LA vs LV. LA expression of miRs with effects on ECM synthesis, paralleled by related ECM expression changes, implicate miRNAs in HF related LA‐selective structural remodeling.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".