P2–377: Enhanced motor performance and motor learning in 3xTg‐AD mice in two tests: A longitudinal study
Bibliographic record
Abstract
People with Alzheimer's disease (AD) often develop motor deficits (Hebert, et al., 2010. Amer. J. Alz. Dis & Other Dementias, 25, 425–431). Mouse models of AD also develop motor deficits, and because cognitive tests are based on motor performance, motor dysfunction can confound measures of cognition. We examined age-related changes in motor performance and motor learning in the 3xTg-AD mouse model of AD, which has three transgenes, amyloid precursor protein (APPswe), presenilin (PS1M1461), and tau (Tau301L). To study the effect of maternal genotype, we cross-fostered pups into mixed genotype litters, with foster mothers. In a longitudinal study, we tested motor co-ordination and learning in male and female 3xTg-AD and wildtype controls (B6129S/F2) at 2, 6, 12 and 18 months of age on the Rotarod (6 trials/ day for 5 days) and on a cued visual platform swimming task (4 trials on one day). The 3xTg-AD mice had a longer latency to fall from the Rotarod and swam significantly faster than the wildtype mice at all ages. There were no sex differences and no effect of maternal genotype on either test. These results indicate that the 3xTg-AD mice have better motor performance than the wildtype mice from 2 until at least 18 months of age. This paradoxical enhancement of motor performance in the 3xTg-AD mice may be related to the background genes of the mice, as they are bred separately from wildtype controls; to the effect of the transgenes on motor function; or to the effect of differences in motivation between the strains.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".