Plasma and Myocardial miRNomes Similarities and Differences during Cardiac Remodelling and Reverse Remodelling in a Murine Model of Heart Failure with Preserved Ejection Fraction
Bibliographic record
Abstract
Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome characterised by multiple risk factors touching various organs outside the heart. Using a murine HFpEF model, we studied cardiac reverse remodelling (RR) after stopping the causing metabolic-hypertensive stress (MHS; Angiotensin II [AngII] and a high-fat diet [HFD]) after 28 days and introducing voluntary exercise (VE) for four more weeks. We measured the effects of MHS and RR on the plasma and myocardial microRNA (miR) profile (miRNome) to characterise better cardiac and non-cardiac responses to HFpEF-inducing risk factors and their reversibility. AngII alone, the HFD or the MHS caused cardiac hypertrophy (CH), left ventricular (LV) concentric remodelling and left atrial enlargement in females. Only AngII and the MHS, but not HFD, did in males. After RR, CH, LV concentric remodelling and atrial enlargement were normalised. Among the 25 most abundant circulating miRs, 10 were modulated by MHS. Plasma miRNomes from AngII, HFD or MHS mice shared 31 common significantly modulated miRs (24 upregulated and 7 downregulated), suggesting that the response of organs producing the bulk of those circulating miRs was similar even for seemingly different stress. In the LV, 19 out of 25 most expressed miRs were modulated. RR restored normality for the plasma miRNome but not for the LV miRNome, which remained mostly unchanged. Our results suggest that abnormalities persist in the myocardium of the HFpEF mice and that the normalisation of circulatory markers may be falsely reassuring after recovery.
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 | 0.000 | 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) | 0.001 | 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".