Bibliographic record
Abstract
Introduction Duchenne muscular dystrophy (DMD) is a lethal, X‐linked disorder associated with dystrophin deficiency that results in chronic inflammation, sarcolemma damage, and severe skeletal muscle degeneration. Effective drug therapy for reducing or delaying the skeletal muscle weakness and necrosis could be a great hope for individuals suffering from DMD. Here we hypothesized that the early treatment of mdx neonatal mice with L‐arginine could ameliorate muscular dystrophy. Methods Seven days old animals were treated IP daily with 800 mg/kg of L‐arginine (L‐arg) or saline (control group) for six weeks. The hind limb skeletal muscle, the Tibialis Anterior (TA) was investigated. The following parameters were evaluated: the force generated, muscle resistance to mechanical stress, the level of centronucleation, detection of utrophine by immonostaining and western blot, creatine kinase (CK) activity and, nitric oxide (NO) production. Results Our results show that: 1) TA weight and the percentage of centronucleation in L‐arg treated animals were significantly lower than in control animals despite the fact that body weights were not different; 2) L‐arg improved TA ability to resist injury caused by high‐stress contractions; 3) CK level was two time higher in control animals compared to L‐arg treated mice, however, this difference was not statistically significant; 4) NO production was significantly higher in L‐arg treated animals; 5) there was no evidence that the improvements observed in L‐arg treated mice were associated with utrophin upregulation. Conclusion Our data strengthen the usefulness of L‐Arginine as a powerful pharmacological tool in Duchenne muscular dystrophies. However, the improvements observed were not associated with utrophin upregulation. Support or Funding Information Muscular Dystrophy Association (MDA)Canadian Institutes of Health Research (CIHR)
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".