Characterization of Porcine Aortic Valvular Interstitial Cell ‘Calcified’ Nodules
Bibliographic record
Abstract
Valve interstitial cells populate aortic valve cusps and have been implicated in aortic valve calcification. Here we investigate a common in vitro model for aortic valve calcification by characterizing nodule formation in porcine aortic valve interstitial cells (PAVICs) cultured in osteogenic (OST) medium supplemented with transforming growth factor beta 1 (TGF-β1). Using a combination of materials science and biological techniques, we investigate the relevance of PAVICs nodules in modeling the mineralised material produced in calcified aortic valve disease. PAVICs were grown in OST medium supplemented with TGF-β1 (OST+TGF-β1) or basal (CTL) medium for up to 21 days. Murine calvarial osteoblasts (MOBs) were grown in OST medium for 28 days as a known mineralizing model for comparison. PAVICs grown in OST+TGF-β1 produced nodular structures staining positive for calcium content; however, micro-Raman spectroscopy allowed live, noninvasive imaging that showed an absence of mineralized material, which was readily identified in nodules formed by MOBs and has been identified in human valves. Gene expression analysis, immunostaining, and transmission electron microscopy imaging revealed that PAVICs grown in OST+TGF-β1 medium produced abundant extracellular matrix via the upregulation of the gene for Type I Collagen. PAVICs, nevertheless, did not appear to further transdifferentiate to osteoblasts. Our results demonstrate that 'calcified' nodules formed from PAVICs grown in OST+TGF-β1 medium do not mineralize after 21 days in culture, but rather they express a myofibroblast-like phenotype and produce a collagen-rich extracellular matrix. This study clarifies further the role of PAVICs as a model of calcification of the human aortic valve.
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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".