Interactions of Apolipoproteins AI, AII, B and HDL, LDL, VLDL with Polyurethane and Polyurethane-PEO Surfaces
Bibliographic record
Abstract
The lipoproteins (HDL, LDL, VLDL) are important components of blood present in high concentration. Surprisingly, their role in blood-biomaterial interactions has been largely ignored. In previous work apolipoprotein AI (the main protein component of HDL) was identified as a major constituent of protein layers adsorbed from plasma to biomaterials having a wide range of surface properties, and quantitative data on the adsorption of apo AI to a biomedical grade polyurethane were reported. In the present communication quantitative data on the adsorption of apo AI, apo AII and apoB (the latter being a constituent of LDL and VLDL), as well as the lipoprotein particles themselves (HDL, LDL, VLDL), to a biomedical segmented polyurethane (PU) with and without an additive containing poly(ethylene oxide) (material referred to as PEO) are reported. Using radiolabeled apo AI, apo AII, and apoB, adsorption levels on PU from buffer at a protein concentration of 50 μg/mL were found to be 0.34, 0.40, and 0.14 μg/cm(2) (12, 23, and 0.25 nmol/cm(2)) respectively. Adsorption to the PEO surface was <0.02 μg/cm(2) for all three apolipoproteins demonstrating the strong protein resistance of this material. In contrast to the apolipoproteins, significant amounts of the lipoproteins were found to adsorb to the PEO as well as to the PU surface. X-ray photoelectron spectra, following exposure of the surfaces to the lipoproteins, showed a strong phosphorus signal, confirming that adsorption had occurred. It therefore appears that a PEO-containing surface that is resistant to apolipoproteins may be less resistant to the corresponding lipoproteins.
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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 | 0.001 |
| 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".