Regression of aortic valve stenosis by ApoA‐I mimetic peptide infusions in rabbits
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Aortic valve stenosis (AVS) is the most common valvular heart disease, and standard curative therapy remains open heart surgical valve replacement. The aim of our experimental study was to determine if apolipoprotein A-I (ApoA-I) mimetic peptide infusions could induce regression of AVS. EXPERIMENTAL APPROACH: Fifteen New Zealand White male rabbits received a cholesterol-enriched diet and vitamin D(2) until significant AVS was detected by echocardiography. The enriched diet was then stopped to mimic cholesterol-lowering therapy and animals were allocated randomly to receive saline (control group, n=8) or an ApoA-I mimetic peptide (treated group, n=7), three times per week for 2 weeks. Serial echocardiograms and post mortem valve histology were performed. KEY RESULTS: Aortic valve area increased significantly by 25% in the treated group after 14 days of treatment (P=0.012). Likewise, aortic valve thickness decreased by 21% in the treated group, whereas it was unchanged in controls (P=0.0006). Histological analysis revealed that the extent of lesions at the base of valve leaflets and sinuses of Valsalva was smaller in the treated group compared with controls (P=0.032). The treatment also reduced calcification, as revealed by the loss of the positive relationship observed in the control group (r=0.87, P=0.004) between calcification area and aortic valve thickness. CONCLUSIONS AND IMPLICATIONS: Infusions of ApoA-I mimetic peptide lead to regression of experimental AVS. These positive results justify the further testing of high-density lipoprotein (HDL)-based therapies in patients with valvular aortic stenosis. Regression of aortic stenosis, if achieved safely, could transform the clinical treatment of this disease.
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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 | 0.000 |
| 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".