Melatonin as an Add-On Treatment of COVID-19 Infection: Current Status
Bibliographic record
Abstract
This brief review was written to provide a perspective on the flurry of reports suggesting that melatonin can be an important add-on therapy for COVID-19. Despite the passage of more than 60 years since its discovery and much evidence representing the contrary, there has been great reluctance to conceive melatonin as anything other than a hormone. Many other body chemicals are known to have multiple roles. Melatonin was first shown to be a hormone derived from the pineal gland, to be actively synthesized there only at night, and to act on targets directly or via the G-protein-coupled receptors (GPCRs) superfamily. It is of note that over 40 years ago, it was also established that melatonin is present, synthesized locally, and acts within the gastrointestinal tract. A wider distribution was then found, including the retina and multiple body tissues. In addition, melatonin is now known to have non-hormonal actions, acting as a free radical scavenger, an antioxidant, and as modulating immunity, dampening down innate tissue responses to invaders while boosting the production of antibodies against them. These actions make it a potentially excellent weapon against infection by the SARS-CoV-2 virus. Early published results support that thesis. Recently, a randomized controlled study reported that low doses of melatonin significantly improved symptoms in hospitalized COVID-19 patients, leading to more rapid discharge with no side effects, while significantly decreasing levels of CRP, proinflammatory cytokines, and modulating dysregulated genes governing cellular and humoral immunity. It is now critical that these trials be repeated, with dose-response studies conducted and safety proven. Numerous randomized controlled trials are ongoing, which should complete those objectives while also allowing for a more thorough evaluation of the mechanisms of action and possible applications to other severe diseases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".