Decreased tissue factor expression with increased CD11b upregulation on elastin-based biomaterial coatings
Bibliographic record
Abstract
Elastin-like polypeptide (ELP) coatings have been shown to have non-thrombogenic properties both in vitro and in vivo. In this work, we expand our understanding of this phenomenon by investigating the interaction of these coatings with leukocytes. Citrated whole blood was exposed to a shear rate of 300 s−1 for 2 hours at 37 °C on ELP1- and ELP4-coated polyethylene terephthalate (Mylar™) surfaces in a cone and plate device. Scanning electron microscopy and flow cytometry were used to measure leukocyte activation and platelet–leukocyte aggregation in response to the ELP1 and ELP4 coatings on the surface and in the bulk, respectively. Surface analysis showed little leukocyte activity on the surface of uncoated positive controls. Both the tissue factor (TF) expression (indicative of leukocyte activation) and CD61 expression (indicative of platelet–leukocyte aggregates), in the bulk were decreased by 40% and 20%, respectively, with the ELP coating of Mylar™, while a two- to three-fold increase in CD11b upregulation (indicative of leukocyte activation) for ELP1 and ELP4 was determined. Two of three bulk markers indicated that ELP-coated Mylar™ decreased the leukocyte response compared to the uncoated Mylar™, while the third, CD11b, indicated an increase in leukocyte response to the ELP coatings.
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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".