Scleraxis: a novel target for anti‐fibrotic therapy?
Bibliographic record
Abstract
Therapeutic approaches to managing cardiac fibrosis are lacking. Our objective is to examine the role of the transcription factor scleraxis (Scx) in cardiac type I collagen production, and to evaluate its potential for anti-fibrotic therapy development. We noted Scx expression in various collagen-producing cell types, including cardiac fibroblasts. Scx expression increases concomitant with collagen production, e.g. in response to TGF-β1 in vitro and in cardiac infarct scar or in pressure-overloaded myocardium in vivo. We have identified sites in the human collagen Iα2 (COLIα2) gene promoter mediating transactivation by scleraxis, and have shown that Scx is sufficient to induce ColIα2 expression in cardiac fibroblasts. Scx expression is regulated by the pro-fibrotic Smad signalling pathway downstream of TGF-β1, which regulates COLIα2 expression additively with Scx. A dominant-negative Scx mutant abrogated TGF-β1-mediated ColIα2 gene expression, suggesting that Scx activity is required for collagen expression in agreement with our finding of reduced cardiac collagen gene expression in Scx null mice. Scx thus plays a central and critical role in collagen expression in the heart, and targeting Scx activity or expression may provide a novel approach in the development of new anti-fibrotic therapies. Supported by the Canadian Institutes of Health Research & the St. Boniface Hospital Research Foundation.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".