Differentiation of Adipose-Derived Stem Cells into Vascular Smooth Muscle Cells for Tissue Engineering Applications
Bibliographic record
Abstract
Synthetic grafts have been developed for vascular bypass surgery, however, the risks of thrombosis and neointimal hyperplasia still limit their use. Tissue engineering with the use of adipose-derived stem cells (ASCs) has shown promise in addressing these limitations. Here we further characterized and optimized the ASC differentiation into smooth muscle cells (VSMCs) induced by TGF-β and BMP-4. TGF-β and BMP-4 induced a time-dependent expression of SMC markers in ASC. Shortening the differentiation period from 7 to 4 days did not impair the functional property of contraction in these cells. Stability of the process was demonstrated by switching cells to regular growth media for up to 14 days. The role of IGFBP7, a downstream effector of TGF-β, was also examined. Finally, topographic and surface patterning of a substrate is recognized as a powerful tool for regulating cell differentiation. Here we provide evidence that a non-woven PET structure does not affect the differentiation of ASC. Taken together, our results indicate that VSMCs differentiated from ASCs are a suitable candidate to populate a PET-based vascular scaffolds. By employing an autologous source of cells we provide a novel alternative to address major issues that reduces long-term patency of currently vascular grafts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".