Persistent and improved functional gain in mdx dystrophic mice after treatment with L‐arginine and deflazacort
Bibliographic record
Abstract
Although an increase in nitric oxide (NO) in muscle is reported to improve the outcome of deflazacort treatment for mdx mouse muscular dystrophy, the genetic homologue of Duchenne muscular dystrophy (DMD), the impact such treatment on the functional outcomes of the disease, including fiber susceptibility to exercise-induced injury, is not established. Experiments were designed to test whether treatment with deflazacort and L-arginine (a substrate for NO synthase, NOS) would change the extent of fiber injury induced by 24 h of voluntary exercise. The impact of exercise-related injury to induce a secondary regenerative response by muscle was also examined as corroborating evidence of muscle damage. Dystrophic mdx mice were treated for 3 wk with placebo, deflazacort, or deflazacort plus either L-arginine or N(G)-nitro-L-arginine methyl ester (a NOS inhibitor). Deflazacort, especially combined with L-arginine, spared quadriceps muscle from injury-induced regeneration (myf5 expression) compared with placebo treatment, despite an increase in membrane permeability immediately after exercise (assessed by Evans blue dye infiltration). Deflazacort alone prevented the typical progressive loss of function (measured as voluntary distance run over 24 h) that was observed 3 months later in placebo-treated mice. Therefore, combined deflazacort plus L-arginine treatment spared mdx dystrophic limb muscle from exercise-induced damage and the need for regeneration and induced a persistent functional improvement in distance run. Results suggest a potential new treatment option for improving the quality of life for boys with 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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".