Emerging epigenetic therapies of cardiac fibrosis and remodelling in heart failure: from basic mechanisms to early clinical development
Bibliographic record
Abstract
Cardiovascular diseases and specifically heart failure (HF) impact global health and impose a significant economic burden on society. Despite current advances in standard of care, the risks for death and readmission of HF patients remain unacceptably high and new therapeutic strategies to limit HF progression are highly sought. In disease settings, persistent mechanical or neurohormonal stress to the myocardium triggers maladaptive cardiac remodelling, which alters cardiac function and structure at both the molecular and cellular levels. The progression and magnitude of maladaptive cardiac remodelling ultimately leads to the development of HF. Classical therapies for HF are largely protein-based and mostly are targeted to ameliorate the dysregulation of neuroendocrine pathways and halt adverse remodelling. More recently, investigation of novel molecular targets and the application of cellular therapies, epigenetic modifications, and regulatory RNAs has uncovered promising new avenues to address HF. In this review, we summarize the current knowledge on novel cellular and epigenetic therapies and focus on two non-coding RNA-based strategies that reached the phase of early clinical development to counteract cardiac remodelling and HF. The current status of the development of translating those novel therapies to clinical practice, limitations, and future perspectives are additionally discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".