Improvement of aortic valve stenosis by <scp>ApoA</scp>‐<scp>I</scp> mimetic therapy is associated with decreased aortic root and valve remodelling in mice
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: We have shown that infusions of apolipoprotein A-I (ApoA-I) mimetic peptide induced regression of aortic valve stenosis (AVS) in rabbits. This study aimed at determining the effects of ApoA-I mimetic therapy in mice with calcific or fibrotic AVS. EXPERIMENTAL APPROACH: Apolipoprotein E-deficient (ApoE(-/-) ) mice and mice with Werner progeria gene deletion (Wrn(Δhel/Δhel) ) received high-fat diets for 20 weeks. After developing AVS, mice were randomized to receive saline (placebo group) or ApoA-I mimetic peptide infusions (ApoA-I treated groups, 100 mg·kg(-1) for ApoE(-/-) mice; 50 mg·kg(-1) for Wrn mice), three times per week for 4 weeks. We evaluated effects on AVS using serial echocardiograms and valve histology. KEY RESULTS: Aortic valve area (AVA) increased in both ApoE(-/-) and Wrn mice treated with the ApoA-I mimetic compared with placebo. Maximal sinus wall thickness was lower in ApoA-I treated ApoE(-/-) mice. The type I/III collagen ratio was lower in the sinus wall of ApoA-I treated ApoE(-/-) mice compared with placebo. Total collagen content was reduced in aortic valves of ApoA-I treated Wrn mice. Our 3D computer model and numerical simulations confirmed that the reduction in aortic root wall thickness resulted in improved AVA. CONCLUSIONS AND IMPLICATIONS: ApoA-I mimetic treatment reduced AVS by decreasing remodelling and fibrosis of the aortic root and valve in mice.
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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 | 0.001 |
| 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".