Melatonin, Melatonin Receptors and Sleep: Moving Beyond Traditional Views
Bibliographic record
Abstract
Sleep, constituting approximately one-third of the human lifespan, is a crucial physiological process essential for physical and mental well-being. Normal sleep consists of an orderly progression through wakefulness, non-rapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep, all of which are tightly regulated. Melatonin, often referred to as the "hormone of sleep," plays a pivotal role as a regulator of the sleep/wake cycle and exerts its effects through high-affinity G-protein coupled receptors known as MT1 and MT2. Selective modulation of these receptors presents a promising therapeutic avenue for sleep disorders. This review examines research on the multifaceted role of melatonin in sleep regulation, focusing on selective ligands targeting MT1 and MT2 receptors, as well as studies involving MT1 and MT2 knockout mice. Contrary to common beliefs, growing evidence suggests that melatonin, through MT1 and MT2 receptors, might not only influence circadian aspects of sleep but likely, also modulate the homeostatic process of sleep and sleep architecture, or could be the molecule linking the homeostatic and circadian regulation of sleep. Furthermore, the distinct brain localization of MT1 and MT2 receptors, with MT1 receptors primarily regulating REM sleep and MT2 receptors regulating NREM sleep, is discussed. Collectively, sleep regulation extends beyond the circulating levels and circadian peak of melatonin; it also critically involves the expression, molecular activation, and regulatory functions of MT1 and MT2 receptors across various brain regions and nuclei involved in the regulation of sleep. This research underscores the importance of ongoing investigation into the selective roles of MT1 and MT2 receptors in sleep. Such research efforts are expected to pave the way for the development of targeted MT1 or MT2 receptors ligands, thereby optimizing therapeutic interventions for sleep disorders.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| 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".