MRI of early‐ and late‐stage arterial remodeling in a low‐level cholesterol‐fed rabbit model of atherosclerosis
Bibliographic record
Abstract
PURPOSE: To monitor early- and late-stage arterial remodeling following low-level cholesterol (CH) feeding in rabbits using a standardized MRI protocol. MATERIALS AND METHODS: New Zealand White rabbits were fed a CH diet (0.25% w/w) (n = 15) or normal chow (n = 6) and imaged either at 0, 2, 6, 8, and 11 months ("early-stage") or 12, 14, 16, 18, and 20 months ("late-stage"). T2-weighted fast-spin-echo images ( approximately 200 microm in-plane resolution) of aortic lesions were collected using either a 1.5 or 3.0T MR scanner interfaced with a customized surface RF coil. Luminal (LA), outer vessel wall boundary (OVBA), and vessel wall areas (VWA) were assessed. RESULTS: Among CH-fed animals in the early-stage group, increased VWA associated with decreased OVBA and a more pronounced decrease in LA was first detectable at 8 months. These changes became more evident between 8 and 11 months. In the late-stage group, lesions continued to grow in response to CH-feeding, as VWA significantly increased at regular 2-month intervals. Beyond 16 months, signal intensity differences (reflecting increased lesion complexity) within the vessel wall were noted. CONCLUSION: This often-overlooked rabbit model combined with customized MR technology holds tremendous promise for studying the natural progression, regression, and remodeling of atherosclerotic lesions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".