N-Acetylcysteine Prevents Retrograde Motor Neuron Death after Neonatal Peripheral Nerve Injury
Bibliographic record
Abstract
BACKGROUND: Neuronal death may be an overlooked and unaddressed component of disability following neonatal nerve injuries, such as obstetric brachial plexus injury. N-acetylcysteine and acetyl-L-carnitine improve survival of neurons after adult nerve injury, but it is unknown whether they improve survival after neonatal injury, when neurons are most susceptible to retrograde neuronal death. The authors' objective was to examine whether N-acetylcysteine or acetyl-L-carnitine treatment improves survival of neonatal motor or sensory neurons in a rat model of neonatal nerve injury. METHODS: Rat pups received either a sciatic nerve crush or transection injury at postnatal day 3 and were then randomized to receive either intraperitoneal vehicle (5% dextrose), N-acetylcysteine (750 mg/kg), or acetyl-L-carnitine (300 mg/kg) once or twice daily. Four weeks after injury, surviving neurons were retrograde-labeled with 4% Fluoro-Gold. The lumbar spinal cord and L4/L5 dorsal root ganglia were then harvested and sectioned to count surviving motor and sensory neurons. RESULTS: Transection and crush injuries resulted in significant motor and sensory neuron loss, with transection injury resulting in significantly less neuron survival. High-dose N-acetylcysteine (750 mg/kg twice daily) significantly increased motor neuron survival after neonatal sciatic nerve crush and transection injury. Neither N-acetylcysteine nor acetyl-L-carnitine treatment improved sensory neuron survival. CONCLUSIONS: Proximal neonatal nerve injuries, such as obstetric brachial plexus injury, produce significant retrograde neuronal death after injury. High-dose N-acetylcysteine significantly increases motor neuron survival, which may improve functional outcomes after obstetrical brachial plexus injury.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".