Three-Dimensional Topography of Synthetic Scaffolds Induces Elastin Synthesis by Human Coronary Artery Smooth Muscle Cells
Bibliographic record
Abstract
Due to the important structural and signaling roles of elastin in vascular stability, engineered human vascular tissues must incorporate elastin. However, despite considerable progress toward engineering of elastin-containing vascular tissues from animal cells, currently engineered vascular tissues using human cells largely lack elastin. In this study, we evaluated the effect of scaffold topography (two dimensional [2D] vs. three dimensional [3D]) on elastogenesis in adult human coronary artery smooth muscle cells (HCASMCs). We report that elastin gene expression by HCASMCs was increased by twofold after 4 days of culture in porous 3D polyurethane scaffolds. Transforming growth factor β1 (TGF-β1) further increased elastin gene expression in 3D cultures but not in 2D cultures. To evaluate if gene expression is translated into elastin synthesis, both 2D and 3D cultures were analyzed using Western blots. We show that only HCASMCs in 3D scaffolds produced elastin, suggesting that scaffold geometry itself is an important cue for elastogenesis. Moreover, TGF-β1 enhanced elastin synthesis in 3D, but had no effect on cells grown on 2D surfaces. TGF-β1, known to induce vascular smooth muscle cells (VSMC) differentiation, upregulated contractile VSMC marker proteins smooth muscle-α-actin and calponin in cells on 2D surfaces. Interestingly, in 3D scaffolds, TGF-β1 failed to upregulate these differentiation marker proteins for at least 7 days, but did so in cells cultured for 14 days, whereas elastin synthesis was not affected. To our knowledge this study is the first to successfully demonstrate that adult human VSMC can produce elastin when seeded on 3D scaffolds and to directly compare the effect of scaffold topography on elastin synthesis. Knowledge about the conditions required to regulate the phenotype of human VSMCs is paramount to engineer elastin-containing autologous human 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".