Calcification by Valve Interstitial Cells Is Regulated by the Stiffness of the Extracellular Matrix
Bibliographic record
Abstract
OBJECTIVE: Extensive remodeling of the valve ECM in calcific aortic valve sclerosis alters its mechanical properties, but little is known about the impact of matrix mechanics on the cells within the valve interstitium. In this study, the influence of matrix stiffness in modulating calcification by valve interstitial cells (VICs), and their differentiation to pathological phenotypes was assessed. METHODS AND RESULTS: Primary porcine aortic VICs were cultured in standard media or calcifying media on constrained type I fibrillar collagen gels. Matrix stiffness was altered by changing only the thickness of the gels. Calcification did not occur in standard media, regardless of matrix stiffness. However, when VICs were grown in calcifying media on relatively compliant matrices with stiffness similar to that of normal tissue, they readily formed calcified aggregates of viable cells that expressed osteoblast-related transcripts and proteins. In contrast, VICs cultured in calcifying media on stiffer matrices (similar to stenotic tissue) differentiated to myofibroblasts and formed calcified aggregates that contained apoptotic cells. Actin depolymerization reduced aggregation on stiff, but not compliant, matrices. TGF-beta1 potentiated aggregate formation on stiff matrices by enhancing alpha-smooth muscle actin expression and cellular contractility, but not on compliant matrices attributable to downregulation of TGF-beta receptor I. Cell contraction by VICs inhibited Akt activation and enhanced apoptosis-dependent calcification on stiff matrices. CONCLUSIONS: Differentiation of VICs to pathological phenotypes in response to biochemical cues is modulated by matrix stiffness. Although osteogenic or myofibrogenic differentiation of VICs can result in calcification, the processes are distinct.
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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".