IC‐P1‐049: Direct visualization of β‐amyloid plaques in hypercholesterolemic rabbits using clinical field‐strength magnetic resonance imaging
Bibliographic record
Abstract
Definitive diagnosis of Alzheimer's disease (AD) requires postmortem pathologic demonstration of beta-amyloid (Aβ) plaques. The ability to non-invasively image Aβ plaque burden would markedly improve the diagnosis and treatment of AD patients. This has been accomplished in several transgenic mouse models using animal-dedicated high-field (7 or 9.4 T) MRI scanners. However, to determine the translational potential of MR techniques, it is important to visualize plaques using clinical field-strength (3T) scanners in larger animal models. Rabbits fed high (2%) cholesterol (CH) diets form Aβ plaques, but plaque progression is hard to track due to a high mortality rate (liver failure). Here we show that low-level CH-feeding in rabbits resulted in a low mortality rate whilst still promoting the formation of Aβ plaques. In addition, we developed a state-of-the-art clinical field MR technique allowing direct visualization of these plaques. Rabbits were fed a low (0.25%) CH (n=5) or normal (n=4) diet for 27 months. Ex vivo MRI of half brains was performed on a 3T MR scanner interfaced with customized gradient and RF coils. 66x66x100 μm3 MR images were acquired in 96 minutes. Aβ-42 immunostaining and Prussian blue iron staining were performed on matched brain sections. MR images revealed distinct signal voids throughout the brains of the CH-fed animals, located primarily in the hippocampus and adjacent cortex, striatum and thalamus (Fig. 1A). Voids correlated directly to small clusters of Aβ-42-positive plaques, which were also consistently identified as iron-loaded (the presumed source of MR contrast) (Fig. 2). Minimal voids and plaques were seen in control brains (Fig. 1B). Our findings combine for the first time a large animal model of AD with clinical field MRI, and demonstrate direct visualization of Aβ plaques. The low CH diet used resulted in both significant Aβ plaque burden and a survivable model, allowing treatment effects after disease establishment to be assessed in the future. Extension of these technologies to an in vivo setting is practical, and should allow the study of AD pathogenesis in animals over time. These exciting results also hint at the promise of clinical MRI-based detection of Aβ plaques in humans.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".