Early administration of L‐arginine in <i>mdx</i> neonatal mice delays the onset of muscular dystrophy in tibialis anterior (TA) muscle
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it