Colchicine reduces atherosclerotic plaque vulnerability in rabbits
Bibliographic record
Abstract
The anti-inflammatory agent colchicine is gaining interest as a treatment for coronary artery disease. However, the effects of colchicine in atherosclerotic animal models are mostly unknown. This study aimed to evaluate colchicine in a rabbit model of atherosclerosis. Twenty-two rabbits were fed a 0.5% cholesterol-enriched diet for 10 weeks and then randomized to receive either oral saline (n=11) or colchicine (350 μg/kg/day; n=11) for 6 weeks, with 0.2% cholesterol-diet during the treatment period. We performed intravascular ultrasound imaging (at start and end of treatment) and histology analyses of the descending thoracic aorta. Leucocyte activation was assessed in vitro on blood samples obtained during treatment. Colchicine prevented positive aortic vascular remodelling (p=0.029 vs placebo). This effect was even more marked at high plasma cholesterol level (third quartile of plasma cholesterol, p=0.020). At high cholesterol level, both atherosclerotic plaque and media areas on histomorphology were reduced by colchicine compared to placebo (p=0.031 and p=0.039, respectively). Plaque fibrosis and macrophage area were reduced by colchicine (Masson's trichrome stain: p=0.038; RAM-11: p=0.026). The plaque vulnerability index, assessed by histology, was reduced by colchicine (p=0.040). Elastin/type I collagen ratio in media was significantly higher with colchicine compared to placebo (p=0.013). At a high level of plasma cholesterol, in vitro LPS challenge revealed a decrease in monocyte activation following treatment with colchicine (p<0.001) and no change in the placebo group (p=0.353). Colchicine decreases plaque vulnerability with reductions in plaque inflammation, medial fibrosis, outward vascular remodelling and ex vivo monocyte activation.
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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) | 0.001 | 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".