Longitudinal assessment of [<sup>18</sup>F]FDG‐PET in the TgF344‐AD rat
Bibliographic record
Abstract
Abstract Background The development and characterization of biomarkers abnormalities in Alzheimer’s disease (AD) animal models is particularly important for the investigation of pathophysiological mechanisms and drug development. With this is mind, here, we aimed at characterizing the brain glucose metabolism, indexed by [18F]FDG‐microPET, of the TgF344‐AD rat, a model harboring human APP/PS1 mutations. We hypothesized early hypometabolism ‐ due to astrocyte reactivity and microglial activation ‐ followed by hypometabolism in later stages – due to neurodegeneration. Method Hemizygous TgF344‐AD rats and their sex‐matched wild type littermates (n=16) were evaluated in three time points: 3, 6 and 9 months of age. Rats underwent [18F]FDG‐microPET for brain glucose metabolism analysis (SUVr, using the pons as reference region). Locomotor activity and spatial memory were assessed using Open‐Field and and Y‐maze tests, respectively. Result TgF344‐AD animals showed no differences in [18F]FDG‐microPET analysis in any of the ages analyzed (Figure A). On the other hand, a decline on their performance in the Y‐maze task was identified at the 9 months time point (p = 0.0132), suggesting a decline in their spatial memory at this age (Figure B). No differences were identified on the parameters analyzed in the Open Field task. Conclusion This study is the first to investigate the in vivo brain glucose metabolism in the TgF344‐AD rat model. Our preliminary results suggest that, in comparison to age and sex‐matched littermates, this APP/PS1 rat model does not present relevant changes in the brain glucose metabolism at early stages, even in the presence of detectable cognitive decline.
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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) | 0.000 | 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 | 0.000 | 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".