Characterization of Alzheimer's disease‐like neuropathology in Duchenne's muscular dystrophy using the DBA/2J mdx mouse model
Bibliographic record
Abstract
Duchenne muscular dystrophy (DMD) is a progressive muscle wasting disorder caused by a mutation in the dystrophin gene. In addition to muscle pathology, some patients with DMD will exhibit cognitive impairments with severity being linked to age and type of genetic mutation. Likewise, some studies have shown that mdx mice display impairments in spatial memory compared with wild-type (WT) controls, while others have not observed any such effect. Most studies have utilized the traditional C57BL/10 (C57) mdx mouse, which exhibits a mild disease phenotype. Recently, the DBA/2J (D2) mdx mouse has emerged as a more severe and perhaps clinically relevant DMD model; however, studies examining cognitive function in these mice are limited. Thus, in this study we examined cognitive function in age-matched C57 and D2 mdx mice along with their respective WT controls. Our findings show that 8- to 12-week-old C57 mdx mice did not display any differences in exploration time when challenged with a novel object recognition test. Conversely, age-matched D2 mdx mice spent less time exploring objects in total as a well as less time exploring the novel object, suggestive of impaired recognition memory. Biochemical analyses of the D2 mdx brain revealed higher soluble amyloid precursor protein β (APPβ) and APP in the prefrontal cortex of mdx mice compared with WT, and lower soluble APPα in the hippocampus, suggestive of a shift towards amyloidogenesis and a similar pathogenesis to Alzheimer's disease. Furthermore, our study demonstrates the utility of the D2 mdx model in studying cognitive impairment.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".