Levetiracetam Protects Spinal Motor Neurons Against Glutamate-Induced Neurotoxicity in Culture
Bibliographic record
Abstract
Background: Levetiracetam is widely used in numerous patients with epileptic seizures. In an animal model of cerebral ischemia after the occlusion of the internal carotid artery, pre-treatment with levetiracetam could reduce the infarct size. However, little is known how this antiepileptic drug can act on motor neurons. The purpose of this study is to evaluate whether levetiracetam has neuroprotective effects on spinal motor neurons in glutamate-treated culture of neonatal rats. Methods: Postnatal organotypic spinal cord cultures were exposed to glutamate (10 -5 M) alone or glutamate (10 -5 M) plus levetiracetam (10 -5 , 10 - 6 and 10 -7 M). Cultures were treated for two weeks and morphological changes were examined. The number of surviving spinal motor neurons and the activities of choline acetyltransferase (ChAT) were measured. Results: Cultures treated with glutamate had significant reductions of the surviving motor neurons and the ChAT activities compared to non-glutamate-treated control culture. Cultures added both glutamate and levetiracetam showed significantly suppression of the neuronal loss and potentiation of the ChAT activities. Conclusions: The present study indicated that levetiracetam prolonged the survival and the function of spinal motor neurons against glutamate-induced neurotoxicity in culture. This drug may have a therapeutic potential for several diseases that kill or degenerate the spinal motor neurons, including spinal cord injury and amyotrophic lateral sclerosis. doi:10.4021/jnr89w
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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) | 0.002 | 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".