Polyurethane biomaterials for fabricating 3D porous scaffolds and supporting vascular cells
Bibliographic record
Abstract
Successful tissue engineering of vascular grafts largely depends on synthetic scaffolds that support the survival, proliferation, and differentiation of seeded cells. To investigate the utility of polyurethanes for vascular tissue engineering, three-dimensional porous polyurethane scaffolds with highly interconnected pore structures were fabricated by a pressure differential/particulate leaching technique. Ammonium chloride and paraffin porogens were prepared to fabricate the scaffolds. Grinding of ammonium chloride resulted in particulates with uniform particle sizes but irregular shapes. Paraffin particulates made by a dispersion method, on the other hand, had spherical shapes and uniform particle sizes. Polyurethane scaffolds fabricated from these particulates had open faced, highly interconnected channels that could allow cellular infiltration and nutrient delivery. Human coronary artery smooth muscle and endothelial cell interactions with polyurethane surfaces revealed these biomaterials to maintain the contractile phenotype of human coronary artery smooth muscle cells and the formation of endothelial monolayers. During longer culture times, surface modification with cell adhesive extracellular matrix (ECM) protein promoted vascular cell proliferation, maintenance of the differentiated phenotype and endothelial monolayer integrity. Our results suggest that these polyurethanes, in conjunction with cell adhesive ECM proteins, could also support vascular cells in three-dimensional bioreactor-based culture conditions.
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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".