Regulation of valvular interstitial cell phenotype by matrix mechanics involves alpha‐smooth muscle actin
Bibliographic record
Abstract
Introduction: Remodeling of the extracellular matrix (ECM) is a hallmark of valvular calcification. Remodeling may alter the mechanical properties of the ECM and consequently regulate the fate of valve interstitial cells (VICs) to a pathologic phenotype. Objectives: To determine if matrix stiffness regulates calcification by VICs in vitro, and to investigate the role of alpha‐smooth muscle actin (SMA) in mechanically‐regulated cell responses. Methods: Primary porcine aortic VICs were cultured on topographically identical stiff and compliant Type I collagen matrices. We compared the morphology, proliferation, and differentiation of VICs by (immuno)staining, RT‐PCR, and biochemical analyses. Results: Compared with VICs grown on stiff substrates, VICs grown on compliant substrates proliferated more rapidly and formed more bone‐like nodules (P<0.05) that displayed alkaline phosphatase activity, calcium accumulation, bone gene expression, and viable cells. VICs on stiff substrates expressed abundant filamentous SMA in stress fibres, while those on compliant substrates did not. Disruption of SMA stress fibres with Swinholide A had no effect on VICs on compliant matrices, but further reduced the number of bone‐like nodules on stiff matrices. Conclusion: This study demonstrates for the first time that mechanical cues from the matrix, mediated in part by SMA, determine VIC phenotype.
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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) | 0.001 | 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".