Fibrinogen adsorption and platelet lysis characterization of fluorinated surface‐modified polyetherurethanes
Bibliographic record
Abstract
A polyetherurethane (PU) was modified using fluorinated surface-modifying macromolecules (SMMs). A double radiolabel method was used simultaneously to measure the number of adhered platelets ((51)Cr) and the quantity of adsorbed Fg ((125)I), in a cone-and-plate instrument. The objectives were to determine if adsorbed Fg levels correlated to platelet adhesion on the surfaces, and to assess if any reductions in platelet adhesion for the SMM-treated surfaces resulted from surface-induced platelet lysis, rather than changes directly related to lower platelet activation and attachment on the novel surfaces. Platelet lysis was determined from lactate dehydrogenase (LDH) and unbound (51)Cr released into plasma isolated from whole blood exposed to test materials. The corresponding Fg adsorption, evaluated under the same platelet adhesion conditions, did not account for the reduced platelet adhesion on the treated surfaces. LDH and (51)Cr platelet release were very low and indicated no statistically significant differences between the materials. It was therefore concluded that platelet lysis did not contribute to the reduction in platelet adhesion characteristic observed on the SMM-treated surfaces. More importantly, the work emphasizes that the platelet activation cannot be inferred to by assessing the quantity of fibrinogen as is commonly done in the literature. The finding suggests a much more complex mechanism of action for the SMM surface modifiers. On-going work is investigating other Fg parameters such as protein binding affinity and protein conformational state in order to establish the mechanism by which the fluorinated surface modifiers may be reducing platelet adhesion via intermediary changes in initial protein adsorption.
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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".