GSK3 inhibition improves skeletal muscle function and whole-body metabolism in the severe DBA/2J <i>mdx</i> mouse model
Bibliographic record
Abstract
Introductory paragraph Duchenne muscular dystrophy (DMD) is a severe X-linked muscle wasting disorder that affects 1 in 5,000 males worldwide 1 . It is caused by the absence of functional dystrophin, which compromises muscle integrity, leading to progressive muscle wasting and weakness 2 . Glucocorticoids are the standard of care for patients with DMD as they delay the loss of ambulation by an average of 3 years 3 ; however, they are also associated with adverse effects such as insulin resistance and increased risk of type 2 diabetes 4 . Thus, alternative therapeutic options should be explored. Here, we show that treating the DBA/2J mdx mouse with the glycogen synthase kinase 3 (GSK3) inhibitor, tideglusib, improved skeletal muscle function and insulin sensitivity, while also attenuating the hypermetabolic phenotype previously observed in these mice 5 . Furthermore, treating mdx mice with the GSK3 inhibitor, lithium, augmented the benefits of voluntary wheel running on insulin sensitivity and skeletal muscle function despite running half of the total distance compared to control-treated mdx mice. This is important given that some patients with DMD may not be able to engage in adequate amounts of physical activity. Thus, GSK3 inhibition alone or in combination with exercise can enhance skeletal muscle function and insulin sensitivity in mdx mice.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".