Smooth Muscle α-Actin and Calponin Expression and Extracellular Matrix Production of Human Coronary Artery Smooth Muscle Cells in 3D Scaffolds
Bibliographic record
Abstract
For a tissue-engineered coronary artery substitute to be a viable clinical option in the treatment of vascular diseases, it is necessary to use tissue-specific human cells. Coronary artery smooth muscle cells are the main resident cells in the tunica media of arteries. In this work, we examined the behavior and differentiation state of human coronary artery smooth muscle cells (HCASMCs) when cultured on 3D polyurethane scaffolds to fabricate hybrid vascular tissues. As the mechanical strength of the scaffold is an important element in engineered hybrid vascular substitutes, porous 3D polyurethane scaffolds fabricated using paraffin spheres and ammonium chloride particles were tested for their mechanical properties both in tension and in compression. The use of ammonium chloride particles as porogen generated scaffolds with superior mechanical properties, which are suitable for vascular tissue engineering. When seeded on uncoated, fibronectin-coated, and Matrigel-coated scaffolds, HCASMCs were well spread and started producing collagen as judged by histochemical analysis but appeared to lack elastin production. Fibronectin coating appeared to promote the infiltration of HCASMCs into the scaffold better than Matrigel coating but did not appear to affect the expression of collagen and elastin. Western blot analyses after successful cell recovery from the scaffolds indicated that HCASMCs, after culturing for 4 and 7 days, expressed similar amounts of smooth muscle alpha-actin and calponin regardless of extracellular matrix coating. Taken together, our data showed that the behavior and differentiation phenotype of HCASMCs can be analyzed after culture in 3D polyurethane scaffolds to establish appropriate conditions that will favor the fabrication of hybrid-engineered vascular substitutes.
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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".