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Record W3091806737 · doi:10.32794/mr11250079

Low melatonin as a contributor to SARS-CoV-2 disease

2020· article· en· W3091806737 on OpenAlexaff
Seithikurippu R. Pandi‐Perumal, Daniel P. Cardinali, Rüssel J. Reiter, Gregory M. Brown

Bibliographic record

VenueMelatonin Research · 2020
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsMelatoninPineal glandCytokine stormImmune systemReactive oxygen speciesOxidative phosphorylationDiseaseAntioxidantPandemicCytokineBiologyImmunologyMedicineCoronavirus disease 2019 (COVID-19)Internal medicineInfectious disease (medical specialty)EndocrinologyCell biologyBiochemistry

Abstract

fetched live from OpenAlex

That the pineal gland is a source of melatonin is widely known; however, by comparison, few know of the much larger pool of extrapineal melatonin. That pool is widely distributed in all animals, including those that do not have a pineal gland, e.g., insects. Extrapineal melatonin is not released into the blood but is used locally to function as an antioxidant, anti-inflammatory agent, etc. A major site of action of peripherally-produced melatonin is the mitochondria where it neutralizes reactive oxygen species (ROS) that are generated during oxidative phosphorylation. Its role also includes major actions as an immune modulator reducing overreactions to foreign agents while simultaneously boosting immune processes. During a pandemic such as coronavirus disease 2019 (COVID-19), caused by the virus SARS-CoV-2, melatonin is capable of suppressing the damage inflicted by the cytokine storm. The implications of melatonin in susceptibility and treatment of COVID-19 disease are discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.140
GPT teacher head0.396
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2020
Admission routes1
Has abstractyes

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