Early identification of aortic valve sclerosis using iron oxide enhanced MRI
Bibliographic record
Abstract
PURPOSE: To test the ability of MION-47 enhanced MRI to identify tissue macrophage infiltration in a rabbit model of aortic valve sclerosis (AVS). MATERIALS AND METHODS: The aortic valves of control and cholesterol-fed New Zealand White rabbits were imaged in vivo pre- and 48 h post-intravenous administration of MION-47 using a 1.5 Tesla (T) MR clinical scanner and a CINE fSPGR sequence. MION-47 aortic valve cusps were imaged ex vivo on a 3.0T whole-body MR system with a custom gradient insert coil and a three-dimensional (3D) FIESTA sequence and compared with aortic valve cusps from control and cholesterol-fed contrast-free rabbits. Histopathological analysis was performed to determine the site of iron oxide uptake. RESULTS: MION-47 enhanced the visibility of both control and cholesterol-fed rabbit valves in in vivo images. Ex vivo image analysis confirmed the presence of significant signal voids in contrast-administered aortic valves. Signal voids were not observed in contrast-free valve cusps. In MION-47 administered rabbits, histopathological analysis revealed iron staining not only in fibrosal macrophages of cholesterol-fed valves but also in myofibroblasts from control and cholesterol-fed valves. CONCLUSION: Although iron oxide labeling of macrophage infiltration in AVS has the potential to detect the disease process early, a macrophage-specific iron compound rather than passive targeting may be required.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".