CCNP Award Paper: Unveiling the role of melatonin MT <sub>2</sub> receptors in sleep, anxiety and other neuropsychiatric diseases: a novel target in psychopharmacology
Bibliographic record
Abstract
BACKGROUND: Melatonin (MLT) is a pleiotropic neurohormone controlling many physiological processes and whose dysfunction may contribute to several different diseases, such as neurodegenerative diseases, circadian and mood disorders, insomnia, type 2 diabetes and pain. Melatonin is synthesized by the pineal gland during the night and acts through 2 G-protein coupled receptors (GPCRs), MT1 (MEL1a) and MT2 (MEL1b). Although a bulk of research has examined the physiopathological effects of MLT, few studies have investigated the selective role played by MT1 and MT2 receptors. Here we have reviewed current knowledge about the implications of MT2 receptors in brain functions. METHODS: We searched PubMed, Web of Science, Scopus, Google Scholar and articles' reference lists for studies on MT2 receptor ligands in sleep, anxiety, neuropsychiatric diseases and psychopharmacology, including genetic studies on the MTNR1B gene, which encodes the melatonin MT2 receptor. RESULTS: These studies demonstrate that MT2 receptors are involved in the pathophysiology and pharmacology of sleep disorders, anxiety, depression, Alzheimer disease and pain and that selective MT2 receptor agonists show hypnotic and anxiolytic properties. LIMITATIONS: Studies examining the role of MT2 receptors in psychopharmacology are still limited. CONCLUSION: The development of novel selective MT2 receptor ligands, together with further preclinical in vivo studies, may clarify the role of this receptor in brain function and psychopharmacology. The superfamily of GPCRs has proven to be among the most successful drug targets and, consequently, MT2 receptors have great potential for pioneer drug discovery in the treatment of mental diseases for which limited therapeutic targets are currently available.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.123 | 0.024 |
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".