Fluorinated surface‐modifying macromolecules: Modulating adhesive protein and platelet interactions on a polyether‐urethane
Bibliographic record
Abstract
Polyether-urethanes (PEUs) have been the materials of choice for the manufacture of conventional blood-contacting devices. Nevertheless, biostability and blood compatibility are still among the principal limitations in their long-term application. Studies investigating the development of protective coatings for PEUs have shown that degradation can be reduced with the use of fluorinated surface-modifying macromolecules (SMMs). It has also been hypothesized that SMM-modified PEU surfaces may exhibit improved blood compatibility because other studies have shown a modulation in fibrinogen adsorption onto these surfaces. To determine the blood compatibility of a PEU-containing fluorinated SMMs, a series of in vitro experiments were designed to study the pattern of protein adsorption from plasma and then to assess the nature of platelet adhesion and activation on each substrate. Western blot analysis as well as single protein studies revealed that the dominant "adhesive proteins" [fibrinogen (Fg), fibronectin (Fnc), and vitronectin (Vnc)] were adsorbed on two of the SMM-containing PEUs in lower amounts relative to unmodified base. Platelet adhesion and activation data further highlighted the differences among the various substrates. It was shown that the unmodified base had a higher number of adhered platelets relative to the SMM-modified surfaces, and that of the SMM-containing substrates, which showed the lowest levels of adhesive proteins also, exhibited significantly lower platelet densities. Close morphological examination further revealed that platelets residing on these latter substrates were not appreciably activated. Based on the current evidence, it is believed that the fluorinated SMMs demonstrate good potential for the development of surfaces with minimal thrombogenic character in in vivo 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) | 0.000 | 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".