Fibroblast mechanosensing, SKI and Hippo signaling and the cardiac fibroblast phenotype: Looking beyond TGF-β
Bibliographic record
Abstract
Cardiac fibroblast activation to hyper-synthetic myofibroblasts following a pathological stimulus or in response to a substrate with increased stiffness may be a key tipping point for the evolution of cardiac fibrosis. Cardiac fibrosis per se is associated with progressive loss of heart pump function and is a primary contributor to heart failure. While TGF-β is a common cytokine stimulus associated with fibroblast activation, a druggable target to quell this driver of fibrosis has remained an elusive therapeutic goal due to its ubiquitous use by different cell types and also in the signaling complexity associated with SMADs and other effector pathways. More recently, mechanical stimulus of fibroblastic cells has been revealed as a major point of activation; this includes cardiac fibroblasts. Further, the complexity of TGF-β signaling has been offset by the discovery of members of the SKI family of proteins and their inherent anti-fibrotic properties. In this respect, SKI is a protein that may bind a number of TGF-β associated proteins including SMADs, as well as signaling proteins from other pathways, including Hippo. As SKI is also known to directly deactivate cardiac myofibroblasts to fibroblasts, this mode of action is a putative candidate for further study into the amelioration of cardiac fibrosis. Herein we provide a synthesis of this topic and highlight novel candidate pathways to explore in the treatment 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.003 | 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".