Novel factors that activate and deactivate cardiac fibroblasts: A new perspective for treatment of cardiac fibrosis
Bibliographic record
Abstract
Abstract Heart disease with attendant cardiac fibrosis kills more patients in developed countries than any other disease, including cancer. We highlight the recent literature on factors that activate and also deactivate cardiac fibroblasts. Activation of cardiac fibroblasts results in myofibroblasts phenotype which incorporates aSMA to stress fibres, express ED‐A fibronectin, elevated PDGFRα and are hypersecretory ECM components. These cells facilitate both acute wound healing (infarct site) and chronic cardiac fibrosis. Quiescent fibroblasts are associated with normal myocardial tissue and provide relatively slow turnover of the ECM. Deactivation of activated myofibroblasts is a much less studied phenomenon. In this context, SKI is a known negative regulator of TGFb 1 /Smad signalling, and thus may share functional similarity to PPARγ activation. The discovery of SKI's potent anti‐fibrotic role, and its ability to deactivate and/or myofibroblasts is featured and contrasted with PPARγ. While myofibroblasts are typically recruited from pools of potential precursor cells in a variety of organs, the importance of activation of resident cardiac fibroblasts has been recently emphasised. Myofibroblasts deposit ECM components at an elevated rate and contribute to both systolic and diastolic dysfunction with attendant cardiac fibrosis. A major knowledge gap exists as to specific proteins that may signal for fibroblast deactivation. As SKI may be a functionally pluripotent protein, we suggest that it serves as a scaffold to proteins other than R‐Smads and associated Smad signal proteins, and thus its anti‐fibrotic effects may extend beyond binding R‐Smads. While cardiac fibrosis is causal to heart failure, the treatment of cardiac fibrosis is hampered by the lack of availability of effective pharmacological anti‐fibrotic agents. The current review will provide an overview of work highlighting novel factors which cause fibroblast activation and deactivation to underscore putative therapeutic avenues for improving disease outcomes in cardiac patients with fibrosed hearts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".