Low-Dose Docetaxel Is Effective in Reducing Atherogenic Lipids and Atherosclerosis
Bibliographic record
Abstract
High-density lipoprotein (HDL) particles form during cellular cholesterol removal, positioning HDL biogenesis as a potential strategy to combat atherosclerosis. We identified desmocollin 1 (DSC1) as a negative regulator of HDL biogenesis and discovered that docetaxel (DTX) effectively inhibits DSC1 activity. This study assessed the efficacy of DTX in reducing atherosclerosis in ApoE−/− mice. After two weeks on a high-fat diet, mice were divided into baseline, vehicle-treated, and DTX-treated groups. Baseline mice were sacrificed at the end of the two weeks, while the other groups received a vehicle or DTX (1 μg/μL) via subcutaneously implanted osmotic pumps delivering 0.15 μL/h for six weeks, with the high-fat diet continued. The controlled drug delivery system maintained stable DTX blood concentrations (2.7–4.3 nM) over six weeks without hematologic toxicity. DTX treatment significantly reduced circulating atherogenic lipids, including triglycerides, non-esterified fatty acids, low-density lipoprotein cholesterol, and total cholesterol, while increasing the HDL cholesterol/total cholesterol ratio. These improvements were associated with significant reductions in atherosclerotic lesions in the aortic sinus and arch. Notably, these effects occurred without altering circulating inflammatory cytokine levels. These results demonstrate that DTX effectively reduces dyslipidemia-induced atherosclerosis. Its HDL-biogenic and anti-atherosclerotic effects establish DTX as a promising candidate for developing HDL-directed therapies for 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".