Stereological estimation of neuron number and plaque load in the hippocampal region of a transgenic rat model of <scp>A</scp>lzheimer's disease
Bibliographic record
Abstract
The main hallmarks of Alzheimer's disease (AD) are senile plaques, neurofibrillary tangles and neuronal death. The McGill-R-Thy1-APP rat is one of the few transgenic rat models of AD that displays progressive amyloid pathology. This study aimed to further characterise this rat model, focusing on the pathological changes in the hippocampal formation and the parahippocampal region. These structures, that are important for episodic memory and spatial navigation, are affected in the early stages of the disease. This study used unbiased stereology to investigate possible neuronal loss in the CA1, subiculum and entorhinal cortex of 18-month-old homozygous McGill-R-Thy1-APP rats, and also quantified the plaque load in all the areas of the hippocampal formation and parahippocampal region from 9 to 18 months old. A significant reduction of neurons at 18 months was only seen in the subiculum. The first plaque pathology was seen at 9 months in the subiculum. Although the quantified plaque load was variable between animals, the pattern of spatiotemporal progression was similar for all animals. The spread of plaque pathology mainly affected anatomically connected regions. Overall, the plaque pathology observed in the transgenic rats was similar to the early phases of amyloid beta (Aβ)-deposition described in human patients. The findings here thus indicate that the McGill-R-Thy1-APP rat could be a good model of the Aβ pathology in AD, but less so with respect to neuron loss.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".