Toward endothelialization via vascular endothelial growth factor immobilization on cell‐repelling functional polyurethanes
Bibliographic record
Abstract
We screened a family of nonspecific cell-repelling polyurethanes (PUs) whose backbones are attached with epoxy group-terminated polyethylene glycol (PEG) side chains. Water incubation of the PU films (with 9.2-31.1 wt % PEG) caused a surface enrichment of PEG chains where vascular endothelial growth factor (VEGF) was grafted by forming secondary amine linkages between VEGF molecules and the PEG spacer. These linkages are still ionizable similar to original primary amines in VEGF, thereby retaining the original charge distribution on VEGF macromolecules. This charge conservation together with PEG steric repulsion helped to preserve VEGF conformation and bioactivity. The PU substrates with suitable hard segments contents and VEGF surface densities can selectively induce endothelial cells (ECs) adhesion and proliferation toward endothelialization. Moreover, the PU substrates, even grafted with fibrinogen (Fg), cannot trigger platelet adhesion and deformation, suggesting an inactive conformation of the grafted Fg. Thus enough antithrombogenicity of the PU substrates could be expected before full endothelialization. These PU materials might be applied onto the lumens of vascular grafts, potentially stimulating luminal endothelialization in vivo. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater 107B: 965-977, 2019.
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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) | 0.000 | 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".