Humanization of the mdx Mouse Phenotype for Duchenne Muscular Dystrophy Modeling: A Metabolic Perspective
Bibliographic record
Abstract
Duchenne muscular dystrophy (DMD) is a severe form of muscular dystrophy (MD) that is characterized by early muscle wasting and lethal cardiorespiratory failure. While the mdx mouse is the most common model of DMD, it fails to replicate the severe loss of muscle mass and other complications observed in patients, in part due to the multiple rescue pathways found in mice. This led to several attempts at improving DMD animal models by interfering with these rescue pathways through double transgenic approaches, resulting in more severe phenotypes with mixed relevance to the human pathology. As a growing body of literature depicts DMD as a multi-system metabolic disease, improvements in mdx-based modeling of DMD may be achieved by modulating whole-body metabolism instead of muscle homeostasis. This review provides an overview of the established dual-transgenic approaches that exacerbate the mild mdx phenotype by primarily interfering with muscle homeostasis and highlights how advances in DMD modeling coincide with inducing whole-body metabolic changes. We focus on the DBA2/J strain-based D2.mdx mouse with heightened transforming growth factor (TGF)-β signaling and the dyslipidemic mdx/apolipoprotein E (mdx/ApoE) knock-out (KO) mouse, and summarize how these novel models emulate the metabolic changes observed in DMD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".