Proliferation and extracellular matrix protein expression in vascular smooth muscle cells cultured for aortic valve tissue engineering
Bibliographic record
Abstract
OBJECTIVES: Vascular smooth muscle cells are a potential autologous cell source for aortic valve tissue engineering. We hypothesized that combining basic fibroblast growth factor (bFGF), epidermal growth factor (EGF) and platelet derived growth factor (PDGF) with transforming growth factor beta‐1 (TGF‐β1) treatment would allow temporal control of rat aortic smooth muscle cell (RASMC) proliferation and ECM production. METHODS: Growth factor (alone or in combination with TGF‐β1) ability to induce myofibrolast‐like phenotype in RASMCs in monolayer culture was assessed. Cell proliferation was measured by MTT assay following treatment with growth factor combinations. Total RNA was isolated to assess ECM gene expression by real‐time PCR. RESULTS: Combinations of growth factors which included PDGF showed the greatest increases in proliferation. Immunofluorescence for alpha‐smooth muscle actin (α‐SMA) in conditioned cultures demonstrated an inverse correlation between proliferation and myofibroblast phenotype, with the combination of TGF‐β1+bFGF+EGF showing the greatest α‐SMA expression. Finally, TGF‐β1+EGF+PDGF treatment showed a significant increase in versican, fibronectin and type I collagen mRNA expression. CONCLUSION: A combination of TGF‐β1+EGF+PDGF, can increase RASMC proliferation and induce ECM gene expression profiles similar to those of native aortic valve interstitial cells.
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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".