Evaluation of the bulk platelet response and fibrinogen interaction to elastin‐like polypeptide coatings
Bibliographic record
Abstract
In this work, we expand on our understanding of the thrombogenicity of coatings prepared with three different recombinant elastin-like polypeptides (ELPs). The bulk platelet response of the ELP coatings was characterized following whole blood contact under physiological shear flow (300 s(-1) ) using flow cytometry. Prolonged exposure to shear flow (1-h) indicated that materials coated with the longer ELP coatings (ELP2 and ELP4) had less bulk platelet activation and microparticle formation than materials coated with the shorter ELP1. Quartz crystal microbalance with dissipation (QCM-D) was used to monitor the binding of the platelet membrane receptor GPIIb/IIIa to ELP-adsorbed fibrinogen (Fg) surfaces. Compared to the shorter ELPs, a lower amount of Fg adsorbed to the ELP4 coated material and ELP4 appeared to form a softer, more structurally flexible coating layer. When Fg was adsorbed to the ELP coated surface it demonstrated an altered binding for GPIIb/IIIa that was inhibited in the presence of an AGDV-containing peptide but not an RGD-containing peptide. Conversely, on the shorter ELP coatings, binding of GPIIb/IIIa to an adsorbed Fg layer was partially inhibited in the presence of an RGD-containing peptide. These results indicate that both the quantity and conformational state of Fg varies when adsorbed to surfaces coated with ELPs of varying sequence length, which may be mediating their platelet response. Collectively, the findings reinforce the applicability of the ELPs as potential thromboresistant coatings, especially with the use of the longer polypeptide-ELP4.
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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".