Vascular Smooth Muscle Cells as a Valvular Interstitial Cell Surrogate in Heart Valve Tissue Engineering
Bibliographic record
Abstract
BACKGROUND: Vascular smooth muscle cells (VSMCs) are a potential autologous cell source for aortic valve tissue engineering, but have a phenotype that differs from that of valvular interstitial cells in vivo. We hypothesized that combining basic fibroblast growth factor (bFGF), epidermal growth factor (EGF), or platelet-derived growth factor (PDGF) with transforming growth factor beta-1 (TGF-beta1) would achieve a valvular interstitial cell-like phenotype of VSMCs. METHODS: VSMC phenotype was assessed by immunofluorescence, proliferation was measured by the tetrazolium reduction (MTT) assay, and extracellular matrix gene expression was determined by real-time polymerase chain reaction. RESULTS: Combinations of growth factors that included PDGF showed the greatest increases in proliferation. Immunofluorescence for alpha-smooth muscle actin demonstrated an inverse correlation between proliferation and a myofibroblast-like phenotype, while combinations of TGF-beta1+ EGF+bFGF (TEF) and TGF-beta1+EGF+PDGF (TEP) induced the greatest change of alpha-smooth muscle actin expression compared to untreated controls. Finally, TEP treatment showed an increase in versican, fibronectin, and type I collagen mRNA expression, while decreasing matrix metalloproteinase 1 expression. CONCLUSIONS: Combination of TGF-beta1 with EGF and PDGF induces VSMC proliferation and expression of extracellular matrix constituents found in the aortic valve. In vitro preconditioning of VSMCs provides a potentially viable surrogate cell source for developing a valve graft.
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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 | 0.001 | 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 | 0.001 |
| 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".