Anticonvulsant effect of minocycline on pentylenetetrazole-induced seizure in mice: involvement of nitric oxide and <i>N</i>-methyl-<scp>d</scp>-aspartate receptor
Bibliographic record
Abstract
Anticonvulsant effects of minocycline have been explored recently. This study was designed to examine the anticonvulsant effect of acute administration of minocycline on pentylenetetrazole-induced seizures in mouse considering the possible role of the nitric oxide/N-methyl-d-aspartate (NMDA) pathway. We induced seizure using intravenous administration of pentylenetetrazole. Our results showed that acute administration of minocycline increased the seizure threshold. Furthermore, co-administration of subeffective doses of the nonselective nitric oxide synthase (NOS) inhibitor NG-l-arginine methyl ester (10 mg/kg) and the neuronal NOS inhibitor 7-nitroindazole (40 mg/kg) enhanced the anticonvulsant effect of subeffective doses of minocycline (40 mg/kg). We found that inducible NOS inhibitor aminoguanidine (100 mg/kg) had no effect on the antiseizure effect of minocycline. Moreover, l-arginine (60 mg/kg), as a NOS substrate, reduced the anticonvulsant effect of minocycline. We also demonstrated that pretreatment with the NMDA receptor antagonists ketamine (0.5 mg/kg) and MK-801 (0.05 mg/kg) increased the anticonvulsant effect of subeffective doses of minocycline. Results showed that minocycline significantly decreased the hippocampal nitrite level. Furthermore, co-administration of a neuronal NOS inhibitor like NMDA receptor antagonists augmented the effect of minocycline on the hippocampal nitrite level. In conclusion, we revealed that anticonvulsant effect of minocycline might be, at least in part, due to a decline in constitutive hippocampal nitric oxide activity as well as inhibition of NMDA receptors.
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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) | 0.000 | 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 | 0.001 |
| 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".