Early administration of L‐arginine in <i>mdx</i> neonatal mice delays the onset of muscular dystrophy in tibialis anterior (TA) muscle
Bibliographic record
Abstract
Abstract Duchenne muscular dystrophy (DMD) is a genetic disorder that results in the absence of dystrophin, a cytoskeletal protein. Individuals with this disease experience progressive muscle destruction, which leads to muscle weakness. Studies have been conducted to find solutions for the relief of individuals with this disease, several of which have shown that utrophin, a protein closely related to dystrophin, when overexpressed in mdx neonatal mice (the murine model of DMD), is able to prevent the progressive muscle destruction observed in the absence of dystrophin. Furthermore, recent studies have shown that L‐arginine induces utrophin upregulation in adult mdx mice. We hypothesized that L‐arginine treatment also induces utrophin upregulation to prevent the development of muscle weakness in neonatal mdx mice. Hence, L‐arginine should also prevent progressive muscle destruction via utrophin upregulation in mdx neonatal mice. Mdx neonatal mice were injected intraperitoneally daily with 800 mg/kg of L‐arginine for 6 weeks, whereas control mice were injected with a physiological saline. The following experiments were performed on the tibialis anterior (TA) muscle: muscle contractility and resistance to mechanical stress; central nucleation and peripheral nucleation, utrophin, and creatine kinase quantification as well as a nitric oxide (NO) assay. Our findings show that early administration of L‐arginine in mdx neonatal mice prevents the destruction of the tibialis anterior (TA) muscle. However, this improvement was related to nitric oxide (NO) production rather than the expected utrophin upregulation.
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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 | 0.000 |
| 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) | 0.002 | 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".