Interfacial interactions of apolipoprotein AI and high density lipoprotein: Overlooked phenomena in blood‐material contact
Bibliographic record
Abstract
Apolipoprotein AI (apo AI) is the major protein component of high density lipoprotein (HDL), and represents ∼1% of the total protein content of plasma. In previous work, apo AI was identified as a major component of the protein layers adsorbed from plasma to biomaterials having a wide range of surface properties. Notwithstanding such indications of the major contribution of lipoprotein interactions, these phenomena have been largely overlooked in the blood-contacting biomaterials area. In this communication, detailed quantitative data on the adsorption of apo AI to typical "biomedical" segmented polyurethane (PU) are reported. Using radiolabeled apo AI, adsorption levels from buffer and plasma were found to be ∼0.5 and ∼0.2 μg/cm(2), respectively. Albumin adsorption from plasma was comparable at about ∼0.17 μg/cm(2). Since, it is unknown how much of the adsorbed apo AI is associated with HDL versus how much is in the free state, the corresponding molar quantities cannot be determined with certainty. However, if it is assumed that all of the adsorbed apo AI is free, the molar quantities adsorbed from plasma were in the ratio apo AI:albumin = 2.77, compared with 0.00063 in plasma. Immunoblot data showed similar trends with respect both to the variation in adsorbed quantity with plasma concentration and to the relative adsorbed quantities of apo AI and albumin. These data show unequivocally the very strong surface activity of apo AI and suggest that a key future focus for blood compatibility research should be lipoprotein interactions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".