Effects of Oxidation on Structural Stability and Remodeling of Human Very Low Density Lipoprotein
Bibliographic record
Abstract
Very low density lipoproteins (VLDL) are triglyceride-rich precursors of low-density lipoproteins (LDL) and a risk factor for atherosclerosis. The effects of oxidation on VLDL metabolism may be pro- or antiatherogenic. To understand the underlying biophysical basis, we determined the effects of copper (that preferentially oxidizes lipids) and hypochlorite (that preferentially oxidizes proteins) on the heat-induced VLDL remodeling. This remodeling involves VLDL fusion, rupture, and fission of apoE-containing high-density lipoprotein- (HDL-) like particles; HDL with similar size, density, and protein composition are formed upon VLDL remodeling by lipoprotein lipase, a key enzyme in triglyceride metabolism. Circular dichroism, turbidity, and electron microscopy show that mild oxidation promotes VLDL fusion and rupture, while advanced oxidation hampers these reactions. VLDL destabilization upon moderate oxidation results, in part, from the exchangeable apolipoprotein modifications, including proteolysis and limited cross-linking. VLDL stabilization against fusion and rupture upon advanced oxidation probably results from massive protein cross-linking on the particle surface. Electron microscopy and gel electrophoresis reveal that oxidation promotes fission of apoE-containing HDL-size particles; hydrolysis of apolar core lipids probably contributes to this effect. Copper and hypochlorite have similar effects on VLDL remodeling, suggesting that these effects may be produced by other oxidants. In summary, moderate oxidation that encompasses in vivo conditions destabilizes VLDL and promotes fission of HDL-size particles. Consequently, mild oxidation may be synergistic with lipoprotein lipase reaction and, hence, may help to accelerate VLDL metabolism.
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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".