Diverse origins and activation of fibroblasts in cardiac fibrosis
Bibliographic record
Abstract
Cardiac fibroblasts (cFBs) have emerged as a heterogenous cell population. Fibroblasts are considered the main cell source for synthesis of the extracellular matrix (ECM) and as such a dysregulation in cFB function, activity, or viability can lead to disrupted ECM structure or fibrosis. Fibrosis can be initiated in response to different injuries and stimuli, and can be reparative (beneficial) or reactive (damaging). FBs need to be activated to myofibroblasts (MyoFBs) which have augmented capacity in synthesizing ECM proteins, causing fibrosis. In addition to the resident FBs in the myocardium, a number of other cells (pericytes, fibrocytes, mesenchymal, and hematopoietic cells) can transform into MyoFBs, further driving the fibrotic response. Multiple molecules including hormones, cytokines, and growth factors stimulate this process leading to generation of activated MyoFBs. Contribution of different cell types to cFBs and MyoFBs can result in an exponential increase in the number of MyoFBs and an accelerated pro-fibrotic response. Given the diversity of the cell sources, and the array of interconnected signalling pathways that lead to formation of MyoFBs and subsequently fibrosis, identifying a single target to limit the fibrotic response in the myocardium has been challenging. This review article will delineate the importance and relevance of fibroblast heterogeneity in mediating fibrosis in different models of heart failure and will highlight important signalling pathways implicated in myofibroblast activation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".