Motor deficits in the McGill-R-Thy1-APP transgenic rat model of Alzheimer’s Disease
Bibliographic record
Abstract
The McGill-R-Thy1-APP rat is a transgenic model of Alzheimer’s Disease (AD) which expresses APP with two mutations found in cases of familial AD, resulting in the development of amyloid pathology and cognitive deficits. Motor deficits are common symptoms of AD, emerging early in the disease, and are correlated with AD neuropathology and cognitive symptoms. This study evaluated heterozygous and homozygous McGill-R-Thy1-APP rats and their wildtype littermates for spontaneous alternation and locomotion in the T and Y mazes, and motor behaviour on an accelerating rotarod. Because rats often jumped off the rotarod, the maximum latency to fall across trials was examined. We found no genotype or sex effects in spontaneous alternation in either maze, nor a significant correlation of spontaneous alternation behaviour between the mazes. Female rats travelled greater distances than male rats in both mazes. While there was no genotype effect in the T maze on distance travelled, in the Y maze the hemizygous rats travelled shorter distances than the wildtype rats, while the homozygous rats travelled greater distances. There was a significant correlation between the distances travelled in each maze. Both heterozygous and homozygous rats performed worse than their wildtype littermates on the rotarod, while heavier rats performed worse than lighter rats, and female rats performed worse than male rats once their differences in weights were accounted for. These findings support the continued use of these rats as a model of AD and highlight the need to consider the possible confounding effect motor impairments have on other behavioural tests.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".