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Record W2139236929 · doi:10.1093/brain/awp031

Clinical field-strength MRI of amyloid plaques induced by low-level cholesterol feeding in rabbits

2009· article· en· W2139236929 on OpenAlexafffund
John A. Ronald, Yuanxin Chen, Lisa M. Bernas, Hagen H. Kitzler, Kem A. Rogers, Robert A. Hegele, Brian K. Rutt

Bibliographic record

VenueBrain · 2009
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsRobarts Clinical TrialsWestern University
FundersNational Heart, Lung, and Blood InstituteCanadian Institutes of Health ResearchNational Institutes of HealthHeart and Stroke Foundation of Canada
KeywordsMagnetic resonance imagingPathologyImmunostainingAmyloid (mycology)Ex vivoIn vivoPittsburgh compound BMedicineAlzheimer's diseaseDiseaseImmunohistochemistryBiologyRadiology

Abstract

fetched live from OpenAlex

Two significant barriers have limited the development of effective treatment of Alzheimer's disease. First, for many cases the aetiology is unknown and likely multi-factorial. Among these factors, hypercholesterolemia is a known risk predictor and has been linked to the formation of beta-amyloid plaques, a pathological hallmark this disease. Second, standardized diagnostic tools are unable to definitively diagnose this disease prior to death; hence new diagnostic tools are urgently needed. Magnetic resonance imaging (MRI) using high field-strength scanners has shown promise for direct visualization of beta-amyloid plaques, allowing in vivo longitudinal tracking of disease progression in mouse models. Here, we present a new rabbit model for studying the relationship between cholesterol and Alzheimer's disease development and new tools for direct visualization of beta-amyloid plaques using clinical field-strength MRI. New Zealand white rabbits were fed either a low-level (0.125-0.25% w/w) cholesterol diet (n = 5) or normal chow (n = 4) for 27 months. High-resolution (66 x 66 x 100 microm(3); scan time = 96 min) ex vivo MRI of brains was performed using a 3-Tesla (T) MR scanner interfaced with customized gradient and radiofrequency coils. Beta-amyloid-42 immunostaining and Prussian blue iron staining were performed on brain sections and MR and histological images were manually registered. MRI revealed distinct signal voids throughout the brains of cholesterol-fed rabbits, whereas minimal voids were seen in control rabbit brains. These voids corresponded directly to small clusters of extracellular beta-amyloid-positive plaques, which were consistently identified as iron-loaded (the presumed source of MR contrast). Plaques were typically located in the hippocampus, parahippocampal gyrus, striatum, hypothalamus and thalamus. Quantitative analysis of the number of histologically positive beta-amyloid plaques (P < 0.0001) and MR-positive signal voids (P < 0.05) found in cholesterol-fed and control rabbit brains corroborated our qualitative observations. In conclusion, long-term, low-level cholesterol feeding was sufficient to promote the formation of extracellular beta-amyloid plaque formation in rabbits, supporting the integral role of cholesterol in the aetiology of Alzheimer's disease. We also present the first evidence that MRI is capable of detecting iron-associated beta-amyloid plaques in a rabbit model of Alzheimer's disease and have advanced the sensitivity of MRI for plaque detection to a new level, allowing clinical field-strength scanners to be employed. We believe extension of these technologies to an in vivo setting in rabbits is feasible and that our results support future work exploring the role of MRI as a leading imaging tool for this debilitating and life-threatening disease.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.377
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2009
Admission routes2
Has abstractyes

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