Current concepts in the epigenetic regulation of cardiac fibrosis
Bibliographic record
Abstract
Cardiac fibrosis is a significant driver of congestive heart failure, a syndrome that continues to affect a growing patient population globally. Cardiac fibrosis results from a constellation of complex processes at the transcription, receptor, and signaling axes levels. Various mediators and signaling cascades, such as the transformation growth factor-beta pathway, have been implicated in the pathophysiology of cardiac tissue fibrosis. Our understanding of these markers and pathways has improved in recent years as more advanced technologies and assays have been developed, allowing for better delineation of the crosstalk between specific factors. There is mounting evidence suggesting that epigenetic modulation plays a pivotal role in the progression of cardiac fibrosis. Transcriptional regulation of key pro- and antifibrotic pathways can accentuate or blunt the rate and extent of fibrosis at the tissue level. Exosomes, micro-RNAs, and long noncoding RNAs all belong to factors that can impact the epigenetic signature in cardiac fibrosis. Herein, we comprehensively review the latest literature about exosomes, their contents, and cardiac fibrosis. In doing so, we highlight the specific transcriptional factors with pro- or antifibrotic properties. We also assimilate the data supporting these mediators' potential utility as diagnostic or prognostic biomarkers. Finally, we offer insight into where further work can be done to fill existing gaps to translate preclinical findings better and improve clinical outcomes.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".