Regression of atherosclerosis: lessons learned from genetically modified mouse models
Bibliographic record
Abstract
PURPOSE OF REVIEW: Regression, or reversal, of atherosclerosis has become an important clinical objective. The development of consistent models of murine atherosclerosis regression has accelerated this field of research. The purpose of this review is to highlight recent mouse studies that reveal molecular mechanisms as well as therapeutics targeted for regression. RECENT FINDINGS: Atherosclerosis regression does not involve the same mechanisms as progression in reverse order. Distinct molecular processes within the plaque characterize regression. These processes remained elusive until the advent of murine regression models including aortic transplant, the Reversa mouse, gene complementation and dietary intervention. Studies revealed that depletion of plaque macrophages is a quintessential characteristic of regression, driven by reduced monocyte recruitment into plaques, increased egress of macrophages from plaques and reduced macrophage proliferation. In addition, regression results in polarization of remaining plaque macrophages towards an anti-inflammatory phenotype, smaller necrotic cores and promotion of an organized fibrous cap. Furthermore, type 1 diabetes hinders plaque regression, and several therapeutic interventions show promise in slowing plaque progression or inducing regression. SUMMARY: Mouse models of atherosclerosis regression have accelerated our understanding of the molecular mechanisms governing lesion resolution. These insights will be valuable in identifying therapeutic targets aimed at atherosclerosis regression.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".