IC‐P‐048: LONGITUDINAL FOLLOW‐UP OF AMYLOIDOSIS AND GLUCOSE HYPOMETABOLISM IN A TRANSGENIC RAT MODEL OF ALZHEIMER'S DISEASE
Bibliographic record
Abstract
Biomarkers of amyloid and neurodegeneration are both ubiquitous in the field of Alzheimer's Disease research. However, suggesting a direct causal relationship between those two pathological features is still problematic considering other factors present in the disease, such as neurofibrillary tangles. We present here imaging data of a transgenic rat model exclusively expressing amyloidosis. We aimed to quantify brain fibrillary amyloid accumulation as well as neurodegeneration in the McGill-R-Thy1-APP using the amyloid imaging agent [18 F]NAV4694 and [18 F]FDG, respectively. We hypothesize an age-dependent [18 F]NAV4694 binding increase in Tg rats concordant with a decrease of [18 F]FDG uptake. [18 F]NAV4694 PET (precursor provided by Navidea Biopharmaceuticals Inc.) was acquired at 10 and 16 months in 12 transgenic McGill-R-Thy1-APP rats and 13 wildtype Wistar rats. BP ND maps were obtained from 60 minute dynamic acquisitions following [18 F]NAV4694 tail-vein injection, using a Simplified Reference Tissue Method with the cerebellum as a reference region. [18 F]FDG images were acquired for 20 minutes starting 50 minutes post-injection, and SUVr parametric maps were generated using the pons as reference. Voxel-level age effects were estimated using a repeated measure general linear model. Compared to the 10-month baseline, 16 months-old rats showed [18 F]NAV4694 binding increases of up to 32% in the frontal cortex [t(11) = 3.23), p = 0.008] as well as in the dorsal hippocampus [t(11) = 3.94, p = 0.002], concomitant with a decrease in [18 F]FDG uptake of up to 17% in the basal forebrain [t(11) = 3.49, p = 0.005], the dorsal hippocampus [t(11) = 2.33), p = 0.04] and the cingulate cortex [t(11) = 2.87, p = 0.01] (see Fig1).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".