Biomimetic coating of cross‐linked gelatin to improve mechanical and biological properties of electrospun PET: A promising approach for small caliber vascular graft applications
Bibliographic record
Abstract
Electrospun PET (ePET) is a promising material for small caliber vascular graft applications owing to its tunable mechanical properties, biocompatibility, and nanofibrous structure that mimic the morphology of natural extracellular matrix. However, the inherent inertness of PET impairs the adhesion and proliferation of endothelial cells on the inner surface of ePET tubular grafts, hindering the formation of a functional endothelium. Gelatin coatings, owing to their ability to promote endothelialization, are a valuable approach to overcome the limitations of ePET. Herein, a novel process for the deposition of stable biomimetic coatings of gelatin on ePET tubular grafts is proposed. Electrospun PET was first aminated by plasma treatment and then coated with a gelatin hydrogel cross-linked in situ by a Michael-type addition reaction. Amination provided a superhydrophilic behavior to the ePET surface, allowing easy gelatin interpenetration along the wall thickness of the tubular structure, and the obtainment of thin coatings that maintained the morphology of ePET fibers. Gelatin coating was stable at long term in a physiological-like environment, noncytotoxic and promoted in vitro cell adhesion and proliferation. Noteworthy, the mechanical properties of gelatin-coated ePET tubular grafts were improved in terms of elastic modulus, compliance, and elastic recoil, finally better matching the characteristics of native blood vessels. Altogether, the proposed coating technique successfully combines the advantages of ePET nanofibrous structure with cross-linked gelatin biological cues and mechanical reinforcement, and emerges as a promising strategy for the development of biocompatible small caliber vascular grafts with superior biomimetic and mechanical properties. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 105A: 2405-2415, 2017.
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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) | 0.001 | 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".