Regulation of Lipid Droplet Cholesterol Efflux From Macrophage Foam Cells
Bibliographic record
Abstract
Cholesterol efflux from macrophages is the first and potentially most important step in reverse cholesterol transport, a process especially relevant to atherosclerosis and to the regression of atherosclerotic plaques. Increasingly, lipid droplet (LD) cholesteryl ester (CE) hydrolysis is being recognized as a rate-limiting step in cholesterol efflux. The traditional view on macrophage CE hydrolysis is that this pathway is entirely dependent on the action of neutral hydrolases, and numerous candidate CE hydrolases have been proposed to play a role in lipid hydrolysis in macrophages and atherogenesis. Although the exact identity of macrophage-specific CE hydrolases remains to be clarified, a common point to all of these studies is that enhancing LD-associated CE hydrolysis increases cholesterol efflux and is antiatherogenic. Understanding how cholesterol is mobilized from LDs offers new steps for modulating cholesterol efflux, and recently a role for autophagy and lysosomal acid lipase in macrophage lipolysis has emerged. Autophagy and lysosomal acid lipase thus represent novel therapeutic targets to enhance macrophage reverse cholesterol transport. This review discusses our current understanding of the relationship between macrophage LDs and atherosclerosis and presents recent insights into the mechanisms for LD CE hydrolysis in macrophage foam cells.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".