Hippocampal Melatonin Receptors Modulate Seizure Threshold
Bibliographic record
Abstract
PURPOSE: The pineal hormone melatonin has been shown to enhance hippocampal excitability. We therefore investigated whether inactivation of hippocampal melatonin receptors affects behavioral seizures. METHODS: Intrahippocampal infusions were performed in rats to study the effect of different melatonin receptor antagonists on behavioral activity, EEG, and seizure susceptibility. Experiments were conducted at 2 times of the day that coincided with the peak and trough of the daily melatonin rhythm. RESULTS: Local infusion of the Mel(1b) receptor antagonist 4-phenyl-2-propionamidotetralin (4-P-PDOT) into the hippocampus, but not the overlying neocortex, significantly increased seizure latency and in some cases provided complete protection against seizure development. In addition, 4-P-PDOT suppressed open field activity and hippocampal EEG amplitude. The mixed Mel(1a)/Mel(1b) receptor antagonist luzindole also increased seizure latency but to a lesser degree than 4-P-PDOT. The behavioral effects of Mel(1b) receptor inhibition were comparable to those of the gamma-aminobutyric acid (GABA)(A) receptor agonist muscimol and were observed during the dark phase (2400-0200 h) but not the light phase (1200-1400 h) of the daily photocycle. The anticonvulsant effect of intrahippocampal infusion of 4P-P-DOT was blocked by coadministration of the GABA(A) antagonist bicuculline. CONCLUSIONS: Our results suggest that nocturnal activation of hippocampal Mel(1b) receptors depresses GABA(A) receptor function in the hippocampus and enhances seizure susceptibility.
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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".