Cell adhesion peptide modification of gold-coated polyurethanes for vascular endothelial cell adhesion
Bibliographic record
Abstract
Gold-coated polyurethanes were chemisorbed with three cell-adhesion peptides having an N-terminal cysteine: cys-arg-gly-asp (CRGD), cys-arg-glu-asp-val (CREDV), and the cyclic peptide cys-cys-arg-arg-gly-asp-try-leu-cys (CCRRGDWLC). The peptides were selected based on their presumed preferential interactions with the cell-surface integrins on vascular endothelial cells. The ability of the surfaces to support the preferential adhesion of human vascular endothelial cells was studied by comparing in vitro adhesion results for these cells with those from mouse 3T3 fibroblasts. Surface modification with the peptides was confirmed by water-contact angles and XPS. Surface morphology was determined by AFM and SEM. In vitro cell-culture studies in conjunction with plasma-protein adsorption and immunoblotting were performed on the various modified surfaces. The data suggest that peptide-modified surfaces have significant potential for supporting cell adhesion. Little or no cell adhesion was noted on gold- or cysteine-modified control surfaces. Human vascular endothelial cells showed the greatest adhesion to the CCRRGDWLC-modified surfaces, and the 3T3 fibroblasts adhered best to the CREDV-modified surfaces. Protein adsorption studies suggest that the preferential adsorption of the cell-adhesive proteins fibronectin and vitronectin is not likely mediating the differences noted. It is concluded that the cell-adhesive peptide-modified gold-coated polymers have significant potential for further development both as model substrates for fundamental studies and for use in biomaterials applications.
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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".