Development of aortic valve sclerosis in a rabbit model of atherosclerosis: an immunohistochemical and histological study.
Bibliographic record
Abstract
BACKGROUND AND AIM OF THE STUDY: It has been suggested that aortic valve sclerosis (AVS) is an atherosclerotic disease process that can proceed to aortic stenosis. The absence of reports studying an animal model of the early stages of this disease has precluded the development of preventive therapeutic strategies. A cholesterol-fed (0.25% cholesterol in chow) rabbit model of atherosclerosis that is characterized by a moderate level of hypercholesterolemia was studied to determine its efficacy as a model of early AVS. Cellular, structural and morphological changes in the aortic valves of these rabbits were studied. METHODS: Twenty rabbits were assigned randomly to four experimental groups: Group 1 received normal chow for 40 weeks; group 2 received 0.25% cholesterol-supplemented chow for 20 weeks; group 3 received 0.25% cholesterol-supplemented chow for 40 weeks; and group 4 received 0.25% cholesterol-supplemented chow for 20 weeks followed by normal chow for an additional 20 weeks. The aortas and aortic valves were analyzed using immunohistochemical and histological methods to detect cellular and structural components of the developing lesions. RESULTS: All rabbits in groups 2, 3 and 4 developed atherosclerotic lesions in their aortas. Aortic valves from these animals demonstrated thickening, lipid deposition, a change in collagen content and organization, a reorganization of elastin, and the presence of both macrophage infiltrate and osteopontin. CONCLUSION: These findings were consistent with the suggestion of a link between atherosclerosis and AVS. Results were also similar to changes reported in human sclerotic aortic valves, suggesting the suitability of this rabbit model of atherosclerosis as a model for AVS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| 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 | 0.001 |
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".