Nonfouling biomaterials based on polyethylene oxide‐containing amphiphilic triblock copolymers as surface modifying additives: Adsorption of proteins from human plasma to copolymer/polyurethane blends
Bibliographic record
Abstract
Three polyethylene oxide-polyurethane-polyethylene oxide (PEO-PU-PEO) block copolymers of variable PEO block size (MW 550, 2000, and 5000) were used to modify the surface of a conventional segmented polyurethane (PU) with the objective of inhibiting interactions with proteins. The surface-active copolymers were blended with the PU by solution methods. Protein adsorption from human plasma to the modified materials was investigated using radiolabeling and immunoblotting methods. From the radiolabeling experiments, it was found that fibrinogen adsorption from plasma to all of the modified surfaces was much lower than to the unmodified PU matrix. For blends of low copolymer content, resistance to adsorption was greatest on the copolymer 1 (PEO550)-modified materials, and increased with increasing copolymer content for all three blend types. At high copolymer content inhibition of adsorption was very strong and independent of PEO block size. The immunoblotting experiments showed that on materials of high copolymer content (20 wt %), the proteins investigated (fibrinogen, albumin, complement C3, and apolipoprotein A-I) were undetectable. At low copolymer content (< or = 5 wt %), the blends of copolymer 1, with the shortest PEO block, exhibited greater protein resistance than those of copolymers 2 and 3 (PEO blocks of MW 2000 and 5000, respectively), and resistance decreased with decreasing protein size. Evidence of complement activation was seen for the blends of low copolymer content. Adsorption of C3 and complement activation decreased with increasing content of the copolymers. It was concluded that surface density of PEO is more important than chain length for protein resistance in contact with plasma.
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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) | 0.001 | 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".