Cellular and molecular mechanisms driving cardiac tissue fibrosis: On the precipice of personalized and precision medicine
Bibliographic record
Abstract
Cardiac fibrosis is a significant contributor to heart failure, a condition that continues to affect a growing number of patients worldwide. Various cardiovascular comorbidities can exacerbate cardiac fibrosis. While fibroblasts are believed to be the primary cell type underlying fibrosis, recent and emerging data suggest that other cell types can also potentiate or expedite fibrotic processes. Over the past few decades, clinicians have developed therapeutics that can blunt the development and progression of cardiac fibrosis. While these strategies have yielded positive results, overall clinical outcomes for patients suffering from heart failure continue to be dire. Herein, we overview the molecular and cellular mechanisms underlying cardiac tissue fibrosis. To do so, we establish the known mechanisms that drive fibrosis in the heart, outline the diagnostic tools available, and summarize the treatment options used in contemporary clinical practice. Finally, we underscore the critical role the immune microenvironment plays in the pathogenesis of cardiac fibrosis.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".