EP80317, a selective ligand of the CD36 scavenger receptor, reduces 111In‐labeled macrophages trafficking to atherosclerotic lesions in apoE‐deficient mice
Bibliographic record
Abstract
Our recent studies have shown that long‐term (12 weeks) treatment with growth hormone‐releasing peptides (GHRPs), as ligands of the CD36 scavenger receptor, shows striking anti‐atherosclerotic effects in apoE‐deficient mice (apoE−/−) mice fed a high fat diet. Synthetic GHRPs such as hexarelin (Hex), in addition to binding CD36 on macrophages, also bind to the ghrelin receptor (GHS‐R1a). In order to assess the relative contribution of these receptors to fatty streak formation, apoE−/− mice have been treated with ghrelin, the endogenous GHS‐R1a ligand or with EP80317, a selective CD36 ligand. Long‐term treatment with ghrelin failed to modulate the development of aortic lesions whereas EP80317 was associated with a 51% reduction of lesions. Importantly, the effects of GHRPs were shown to be CD36‐dependent, no anti‐atherosclerotic effects were observed in apoE/CD36 double‐deficient mice. GHRP‐treated mice received 111In‐labeled macrophages and the aortic accumulation of labeled cells was assessed by radioactivity count of the aortic tree 48 hours later. Ghrelin failed to modulate 111In‐labeled macrophages accumulation within lesions whereas treatment with EP 80317 was associated with a 31 ± 6 % reduction of macrophages trafficking to atherosclerotic lesions, suggesting a potential role of EP80317 in modulating the inflammatory component of atherosclerosis. Our results suggest that GHRPs might be prototypes for a novel class of anti‐atherosclerotic agents.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".