An urgent proposal for the immediate use of melatonin as an adjuvant to anti- SARS-CoV-2 vaccination
Bibliographic record
Abstract
Competition among pharmaceutical companies to develop safe and effective vaccines against SARS-CoV-2 is high. However, based on the prior experience with the influenza vaccine, up to 50% in lack of effectiveness would be found among healthy adults receiving effective vaccines against SARS-CoV-2. There is growing evidence that insufficient sleep may potentially be a pervasive and prominent factor accounting for this variability. Individuals experiencing total or partial sleep loss exhibit markedly reduced antigen-specific antibodies as compared to healthy sleepers. Besides, pre-vaccination sleep quality is also an important contributing factor. Several meta-analyses and expert consensus reports support the view that the chronobiotic/hypnotic properties of melatonin are useful in patients with primary sleep disorders to decrease sleep onset latency and to increase total sleep time. Hence, the prescription of melatonin for at least 2 weeks prior to vaccination can be a useful approach to improve sleep quality and to ensure that the vaccination is performed at a moment of optimal sleep conditions. Moreover, melatonin enhances the immune response to vaccines by increasing peripheral blood CD4+ T cells and IgG-expressing B cells. Administration of exogenous melatonin could increase the potency of the immune response and the duration of the immunity induced by the vaccine. Besides, melatonin could also prevent adverse effects of the vaccination due to its antioxidant and immunomodulatory properties. Therefore, the administration of melatonin from 2 weeks to at least 4 weeks after vaccination may constitute an effective means to enhance the efficacy of vaccination against SARS-CoV-2.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".