Ski inhibits Scleraxis function: Effect on collagen expression in cardiac myofibroblasts
Bibliographic record
Abstract
Elevated collagen deposition and increased tissue stiffness is the primary contributor to cardiac dysfunction following MI. Ski acts as a cellular brake to modulate phenoconversion of cardiac fibroblasts to myofibroblasts. Ski is associated with reduced collagen and removal of myofibroblasts from damaged heart tissues. Conversely, Scleraxis accelerates expression of collagens in cardiac myofibroblasts. We suggest that a balance exists between pro‐ and anti‐fibrotic pathways in the healthy heart and that Ski and Scleraxis are key mediators. Determine the role that Ski has on Scleraxis expression and the deposition of collagens by cardiac myofibroblasts during cardiac fibrosis. Cardiac fibroblasts were isolated from rat hearts and P1 cardiac myofibroblasts were infected at varying MOI's (10, 25, 50) with sh‐Ski or sh‐EGFP (50) control virus for 6 days. Cells were harvested for cell phenotyping and analysis of fibrillar collagens. A progressive increase in Scleraxis protein expression was noted with increasing sh‐Ski MOI from 10 to 50. This corresponded with a significant increase in mature collagen protein expression suggesting that Ski regulates Scleraxis ability to induce collagen synthesis by cardiac myofibroblasts in vitro. Ski inhibits Scleraxis function eg, to stimulate expression of collagens in cardiac myofibroblasts indicating the existence of a feedback loop between Ski and Scleraxis.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".