Aortic Regurgitation Dramatically Alters the Distribution of Atherosclerotic Lesions and Enhances Atherogenesis in Mice
Bibliographic record
Abstract
OBJECTIVE: Hemodynamics plays a critical role in atherogenesis, but the association between flow pattern and preferential localization of lesion is not fully understood. We developed a mouse model of aortic valve regurgitation (AR) to change the aortic flow pattern and observed the effects on plaque formation. METHODS AND RESULTS: High-frequency Doppler ultrasound imaging of 10 untreated C57BL/6J mice and 6 sham-treated low-density lipoprotein receptor-deficient (Ldlr(-/-)) mice revealed consistent antegrade blood flow throughout the aorta and oscillatory flow only along the lesser curvature of the aortic arch. Catheter-induced AR in 7 Ldlr(-/-) mice produced various degrees of diastolic retrograde flow throughout the aorta. After the mice were fed a cholesterol-enriched diet for 6 weeks, the burden of atherosclerotic lesions was increased 6-fold, with the naturally plaque-resistant descending aorta becoming susceptible. The AR severity correlated positively with the lesion burden in the descending thoracic and abdominal aorta but negatively with the lesions in the ascending aorta and aortic arch. CONCLUSIONS: This AR model is valuable for elucidating the relationship between hemodynamics and predisposition of the artery wall to atherosclerosis, because of the significant alterations in local flow patterns and the conversion of large regions in the descending aorta from lesion resistant to lesion prone.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".