Peptide modified gold-coated polyurethanes as thrombin scavenging surfaces
Bibliographic record
Abstract
Thin layers of gold were deposited on polyurethane film and chemisorbed with three peptides having an N-terminal cysteine: Cys-Pro-Arg, Cys-(L)Phe-Pro-Arg, and Cys-(D)Phe-Pro-Arg. The ability of these surfaces to act as thrombin scavengers was evaluated. The peptides are related to the known thrombin inhibitor Phe-Pro-Arg chloromethyl ketone and were shown to have significant thrombin inhibitory activity in solution. Attachment of the peptides to gold was confirmed by water contact angle and X-ray photoelectron spectroscopy measurements. Thrombin adsorption from a buffer and plasma was investigated, and chromogenic substrate assays were carried out for thrombin activity on the surfaces and in the supernatant following adsorption. The data suggest that the peptide-modified surfaces are able to adsorb thrombin with high affinity from a buffer and that thrombin is taken up selectively from plasma. The Cys-(D)Phe-Pro-Arg modified surfaces showed particularly high affinity for thrombin. It was also found that the activity of thrombin adsorbed on the peptide surfaces was inhibited, and inhibition was greatest on the Cys-(D)Phe-Pro-Arg surface. We concluded that the peptide surfaces may have potential as antithrombogenic materials via their ability to scavenge and inhibit thrombin generated as a result of blood-material contact.
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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".