β‐Catenin mediates TGF‐β1‐induced myofibroblast differentiation in a matrix stiffness‐dependent manner: implication to aortic valve sclerosis
Bibliographic record
Abstract
Introduction In aortic valve sclerosis, myofibroblast differentiation and activation of the TGF‐ß1 and ß‐catenin pathways are observed in the fibrosa, the stiffer layer of the leaflet. Objectives To investigate the roles of ß‐catenin and matrix stiffness in TGF‐ß1‐induced myofibroblast differentiation. Methods Sclerotic aortic valves were from pigs fed an atherogenic diet. Porcine aortic valve interstitial cells (VICs) were studied in vitro on collagen‐coated polyacrylamide gels with tunable stiffness. Results In sclerotic valves, myofibroblasts colocalized with TGF‐ß1, Wnt3a, ß‐catenin, and pSmad2/3 in the fibrosa, which was significantly stiffer than the ventricularis (P=0.006). TGF‐ß1 induced pSmad2/3‐dependent ß‐catenin nuclear translocation in VICs more readily on matrices with fibrosa‐like stiffness (P<0.05). Degrading ß‐catenin pharmacologically inhibited TGF‐ß1‐induced myofibroblast differentiation (P=0.04) without altering pSmad2/3 activity. Conversely, increasing ß‐catenin stability with Wnt3a alone did not induce differentiation (P=0.73). However, combining TGF‐ß1 and Wnt3a caused greater myofibroblast differentiation than TGF‐ß1 treatment alone (P=0.004). These novel results suggest that myofibroblast differentiation in valve sclerosis involves matrix stiffness‐dependent cross‐talk between TGF‐ß1 and Wnt signaling pathways. Support: HSFO, NSERC.
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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) | 0.003 | 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".