Abstract 385: Ultracentrifugation and Depletion Methods of High-density Lipoprotein Isolation Yield Particles That Are Functionally Distinct in Some of Their Vasoprotective Functions
Bibliographic record
Abstract
The evaluation of the functional capabilities of high-density lipoproteins (HDL) has been shown to be a better predictor of several cardiometabolic diseases than the traditional marker of plasma HDL cholesterol. An important, yet often unconsidered, factor in clinically evaluating HDL function is the method used to purify HDL from plasma. It has been shown that the method of HDL isolation can impact the composition of the HDL produced yet the effect of these compositional changes on HDL function has been explored very little. The evaluation of isolation method is especially important when studying novel HDL functions, such as the vasoprotective functions relevant to Alzheimer’s disease (AD) recently discovered on brain vascular cells and in 3-dimensional brain vessel models. We compared the high-throughput HDL isolation method of polyethylene glycol (PEG) precipitation to the more time-consuming but purer method of sequential density gradient ultracentrifugation (UC) in several of these novel, brain-relevant vasoprotective functions. HDL isolated from young, healthy human plasma by PEG precipitation and UC were equal in their capacity for effluxing cholesterol from macrophages, suppressing TNFα-induced inflammation in brain endothelial cells, and preventing the pathological accumulation of the AD protein amyloid beta (Aβ) in 3D vessel models. However, only HDL isolated by UC, and not PEG precipitation, could induce nitric oxide production and inhibit Aβ-induced inflammation in brain endothelial cells. These findings highlight the importance of HDL isolation method in the composition and function of the HDL produced. Furthermore, that HDL isolation method affected some but not all HDL functions shows that HDL may act on brain vascular cells to protect against AD through more than one pathway.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".