Nitric Oxide (NO) and Convulsions Induced by Pentylenetetrazol
Bibliographic record
Abstract
Data about the role of nitric oxide (NO) in epileptogenesis are contradictory. It is found to exert both proconvulsant and anticonvulsant effects. In an attempt to elucidate the role of NO in seizures, male Wistar rats were treated intraperitoneally by pentylenetetrazol (PTZ) (60, 80, and 100 mg/kg) and by a nitric oxide synthase antagonist, N-omega-nitro-L-arginine-methyl-ester (L-NAME) (10, 40, and 70 mg/kg), applied before PTZ. The time to onset and incidence of forelimb dystonia (FLD), generalized clonic convulsions (GCC), clonic-tonic convulsions (CTC), and mortality were recorded. The most successful convulsive response and mortality prevention were found in PTZ (80 mg/kg)-treated groups, where L-NAME (70 mg/kg) decreased the incidence by 29, 50, 67 (p = 0.052), and 50%, respectively, and significantly prolonged the time to onset, except that for mortality. Unexpectedly, L-NAME (40 mg/kg) increased incidence of GCC and mortality by 16%, similar to L-NAME (10 mg/kg) in PTZ (60 mg/kg)-treated groups, where GCC, CTC, and mortality increased by 14, 14, and 28%, respectively. Convulsive latency was prolonged in some PTZ (100 mg/kg) + L-NAME (40 and 70 mg/kg)-treated groups. In the experimental model and protocol used, it is concluded that (1) the effects of NO are L-NAME- and PTZ-dose dependent; (2) clonic-tonic convulsions are more strongly influenced by NO than limbic, probably because of PTZ limbic structure overstimulation; (3) L-NAME decreases the incidence of CTC and prolongs FLD, GCC, and CTC times to onset, indicating that NO acts as a proconvulsant; and (3) increased GCC, CTC, and mortality that suggests an anticonvulsant effect of NO needs further investigation.
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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 | 0.000 |
| 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".