Sleep well. Untangling the role of melatonin MT1 and MT2 receptors in sleep
Bibliographic record
Abstract
The pharmacological potential of targeting selectively melatonin MT1 or MT2 receptors has not yet been exploited in medicine. Research using selective MT1/MT2 receptor ligands and MT1/MT2 receptor knockout mice has indicated that the activation of MT2 receptors selectively increases non-rapid eye movement (NREM) sleep whereas MT1 receptors seem mostly implicated in the regulation of REM sleep. Moreover, MT1 knockout mice show an increase in NREM sleep, while MT2 knockout a decrease, suggesting an opposite role of these two receptors. A recent paper in mice by Sharma et al (J Pineal Res, 2018, e12498) found that MT1 but not MT2 receptors are expressed on orexin neurons in the perifornical lateral hypothalamus (PFH). Moreover, after injecting melatonin or luzindole into the mouse PFH, the authors suggest that melatonin promotes NREM sleep because activates PFH MT1 receptors, which in turn inhibit orexin neurons that are important in promoting arousal and maintaining wakefulness. In this commentary, we have critically commented on some of these findings on the bases of previous literature. In addition, we highlighted the fact that no conclusions could be drawn on the melatonin receptor subtype mediating the effects of melatonin on sleep because the authors used the non-selective MT1/MT2 receptors antagonist luzindole. More solid research should further characterize the pharmacological function of these two melatonin receptors in sleep.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| 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".