Exploring the role of pericardial miRNAs and exosomes in modulating cardiac fibrosis
Bibliographic record
Abstract
The potential of the pericardial space as a therapeutic delivery tool for cardiac fibrosis and heart failure (HF) treatment has yet to be elucidated. Recently, miRNAs and exosomes have been discovered to be present in human pericardial fluid (PF). Novel studies have shown characteristic human PF miRNA compositions associated with cardiac diseases and higher miRNA expressions in PF compared to peripheral blood. Five key studies found differentially expressed miRNAs in HF, angina pectoris, aortic stenosis, ventricular tachycardia, and congenital heart diseases with either atrial fibrillation or sinus rhythm. As miRNA-based therapeutics for cardiac fibrosis and HF showed promising results in several in vivo studies for multiple miRNAs, we hypothesize a potential role of miRNA-based therapeutics delivered through the pericardial cavity. This is underlined by the favorable results of the first phase 1b clinical trial in this emerging field. Presenting the first human miRNA antisense drug trial, inhibition of miR-132 by intravenous administration of a novel antisense oligonucleotide, CDR132L, established efficacy in reducing miR-132 in plasma samples in a dose-dependent manner. We screened the literature, provided an overview of the miRNAs and exosomes present in PF, and drew a connection to those miRNAs previously elucidated in cardiac fibrosis and HF. Further, we speculate about clinical implications and potential delivery methods.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".