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Record W2095613868 · doi:10.1007/s00381-010-1278-8

Measurement of melatonin in body fluids: Standards, protocols and procedures

2010· article· en· W2095613868 on OpenAlexaff
Eduardo Alves de Almeida, Paolo Di Mascio, Tatsuo Harumi, D. Warren Spence, Adam Moscovitch, Rüdiger Hardeland, Daniel P. Cardinali, Gregory M. Brown, Seithikurippu R. Pandi‐Perumal

Bibliographic record

VenueChild s Nervous System · 2010
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsCentre for Addiction and Mental HealthToronto Sleep InstituteBrock University
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa do Estado de São PauloConsejo Nacional de Investigaciones Científicas y TécnicasCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorJohn Simon Guggenheim Memorial FoundationL'Oreal USA
KeywordsMelatoninCircadian rhythmDark therapyMedicinePineal glandPinealectomyPsychologyBioinformaticsInternal medicineBiology

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: The circadian rhythm of melatonin in saliva or plasma, or of the melatonin metabolite 6-sulfatoxymelatonin (a6MTs) in urine, is a defining feature of suprachiasmatic nucleus (SCN) function, the body's endogenous oscillatory pacemaker. The primary objective of this review is to ascertain the clinical benefits and limitations of current methodologies employed for detection and quantification of melatonin in biological fluids and tissues. DATA IDENTIFICATION: A search of the English-language literature (Medline) and a systematic review of published articles were carried out. STUDY SELECTION: Articles that specified both the methodology for quantifying melatonin and indicated the clinical purpose were chosen for inclusion in the review. DATA EXTRACTION: The authors critically evaluated the methodological issues associated with various tools and techniques (e.g. standards, protocols, and procedures). RESULTS OF DATA SYNTHESIS: Melatonin measurements are useful for evaluating problems related to the onset or offset of sleep and for assessing phase delays or advances of rhythms in entrained individuals. They have also become an important tool for psychiatric diagnosis, their use being recommended for phase typing in patients suffering from sleep and mood disorders. Additionally, there has been a continuous interest in the use of melatonin as a marker for neoplasms of the pineal region. Melatonin decreases such as found with aging are or post pinealectomy can cause alterations in the sleep/wake cycle. The development of sensitive and selective methods for the precise detection of melatonin in tissues and fluids has increasingly been shown to have direct relevance for clinical decision making. CONCLUSIONS: Due to melatonin's low concentration, as well as the coexistence of numerous other compounds in the blood, the routine determination of melatonin has been an analytical challenge. The available evidence indicates however that these challenges can be overcome and consequently that evaluation of melatonin's presence and activity can be an accessible and useful tool for clinical diagnosis.

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)0.011
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.011
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.010
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.004
Science and technology studies0.0040.004
Scholarly communication0.0030.002
Open science0.0050.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0080.008

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 head0.015
GPT teacher head0.259
Teacher spread0.244 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations151
Published2010
Admission routes1
Has abstractno

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