Development of a compliant and cytocompatible micro‐fibrous polyethylene terephthalate vascular scaffold
Bibliographic record
Abstract
Bioengineering approaches have been intensively applied to create small diameter vascular grafts using artificial materials. However, a fully successful, high performing and anti-thrombogenic structure has not been achieved yet. In this study, we have designed and fabricated a novel non-woven fibrous vascular graft with biomechanical properties closely resembling those of native vessels. Vascular cell growth, preservation of cell phenotype, retention of vasoactive properties, as well as the effect of gelatin coating on the cellular interaction with the scaffolds under static and shear stress conditions were investigated. The non-woven fibrous scaffolds were made from melt blown polyethylene terephthalate fiber webs stacked by means of a consolidation technique. The scaffold variables were fiber diameter distribution and the number of consolidated web stacks. SEM analysis confirmed various fiber diameter and pore size ranges corresponding to the different conditions. The scaffolds showed burst pressure values of ∼1500 mmHg and compliance (8.4 ± 1.0 × 10(-2) % mmHg(-1) ) very similar to those of native arteries (∼8 × 10(-2) % mmHg(-1) ). The structure with the smallest fiber diameter range (1-5 μm) and pore size range (1-20 μm) was the most suitable for the growth of human brain endothelial cells and aortic smooth muscle cells. The cells maintained their specific cell phenotype, expressed collagen and elastin and produced cAMP in response to α-calcitonin gene-related peptide. However, under shear stress conditions (0.9 dyne cm(-2) ), only 30% of the cells were retained in both uncoated and gelatin-coated scaffolds indicating the need for improving the cell retention capacity of these structures, which is our future research direction. This study indicates that the biomechanical and biocompatible properties of this novel vascular scaffold are promising for the development of a vascular graft with similar characteristics to those of native vessels.
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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 | 0.001 | 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 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".