Low Doses of Innate Defence Regulator Peptide, IDR-1018, Enhances HDL-Mediated Cholesterol Efflux from Smooth Muscle Cells and Macrophages
Bibliographic record
Abstract
Cardiovascular diseases due to atherosclerosis are the leading cause of death worldwide. In recent years, novel therapeutics designed to enhance high-density lipoprotein (HDL) activity as a treatment for atherosclerosis have been explored. Of particular interest is the use of amphipathic α-helical peptides to mimic the action of the main HDL protein, apolipoprotein (Apo)AI. Immunomodulatory peptides share many physical and functional properties with ApoAI and its mimetics; we therefore hypothesized that they too might be capable of enhancing cholesterol efflux. The aim of this study was to determine whether a potent immunomodulatory peptide, innate defence regulator (IDR)-1018, could promote cholesterol efflux from cells. Here, we report that IDR-1018 induced a dose-dependent increase in ApoAI on the cell surface and ATP-binding cassette transporter A1 (ABCA1) protein levels. Functional assays revealed that low doses of IDR-1018 improved HDL-mediated suppression of intracellular cholesteryl ester accumulation in macrophages and enhanced HDL-mediated cellular cholesterol efflux from smooth muscle cells and macrophages. Based on these results we propose that natural and synthetic immunomodulatory peptides like IDR-1018 represent a large new group of peptides that could be developed for the treatment of atherosclerosis.
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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 | 0.001 |
| 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".