Role of melatonin, serotonin 2B, and serotonin 2C receptors in modulating the firing activity of rat dopamine neurons
Bibliographic record
Abstract
Melatonin has been widely used for the management of insomnia, but is devoid of antidepressant effect in the clinic. In contrast, agomelatine which is a potent melatonin receptor agonist is an effective antidepressant. It is, however, a potent serotonin 2B (5-HT(2B)) and serotonin 2C (5-HT(2C)) receptor antagonist as well. The present study was aimed at investigating the in vivo effects of repeated administration of melatonin (40 mg/kg/day), the 5-HT(2C) receptor antagonist SB 242084 (0.5 mg/kg/day), the selective 5-HT(2B) receptor antagonist LY 266097 (0.6 mg/kg/day) and their combination on ventral tegmental area (VTA) dopamine (DA), locus coeruleus (LC) norepinephrine (NE), and dorsal raphe nucleus (DRN) serotonin (5-HT) firing activity. Administration of melatonin twice daily increased the number of spontaneously active DA neurons but left the firing of NE neurons unaltered. Long-term administration of melatonin and SB 242084, by themselves, had no effect on the firing rate and burst parameters of 5-HT and DA neurons. Their combination, however, enhanced only the number of spontaneously active DA neurons, while leaving the firing of 5-HT neurons unchanged. The addition of LY 266097, which by itself is devoid of effect, to the previous regimen increased for DA neurons the number of bursts per minute and the percentage of spikes occurring in bursts. In conclusion, the combination of melatonin receptor activation as well as 5-HT(2C) receptor blockade resulted in a disinhibition of DA neurons. When 5-HT(2B) receptors were also blocked, the firing and the bursting activity of DA neurons were both enhanced, thus reproducing the effect of agomelatine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".