Melatonin and its analogs in insomnia and depression
Bibliographic record
Abstract
Benzodiazepine sedative-hypnotic drugs are widely used for the treatment of insomnia. Nevertheless, their adverse effects, such as next-day hangover, dependence and impairment of memory, make them unsuitable for long-term treatment. Melatonin has been used for improving sleep in patients with insomnia mainly because it does not cause hangover or show any addictive potential. However, there is a lack of consistency on its therapeutic value (partly because of its short half-life and the small quantities of melatonin employed). Thus, attention has been focused either on the development of more potent melatonin analogs with prolonged effects or on the design of slow release melatonin preparations. The MT(1) and MT(2) melatonergic receptor ramelteon was effective in increasing total sleep time and sleep efficiency, as well as in reducing sleep latency, in insomnia patients. The melatonergic antidepressant agomelatine, displaying potent MT(1) and MT(2) melatonergic agonism and relatively weak serotonin 5HT(2C) receptor antagonism, was found effective in the treatment of depressed patients. However, long-term safety studies are lacking for both melatonin agonists, particularly considering the pharmacological activity of their metabolites. In view of the higher binding affinities, longest half-life and relative higher potencies of the different melatonin agonists, studies using 2 or 3mg/day of melatonin are probably unsuitable to give appropriate comparison of the effects of the natural compound. Hence, clinical trials employing melatonin doses in the range of 50-100mg/day are warranted before the relative merits of the melatonin analogs versus melatonin can be settled.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".