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Record W3117970031 · doi:10.1111/jpi.12713

Mass‐spectrometry analysis of the human pineal proteome during night and day and in autism

2020· article· en· W3117970031 on OpenAlexafffund
Guillaume Dumas, Hany Goubran‐Botros, Mariette Matondo, Cécile Pagan, Cyril Boulègue, Thibault Chaze, Julia Chamot‐Rooke, Erik Maronde, Thomas Bourgeron

Bibliographic record

VenueJournal of Pineal Research · 2020
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersFonds de Recherche du Québec - SantéInstitut de Valorisation des DonnéesAssistance Publique - Hôpitaux de ParisFondation de FranceInstitut PasteurInstitut National de la Santé et de la Recherche MédicaleCentre National de la Recherche ScientifiqueFondation OrangeAgence Nationale de la RechercheFondation Cognacq-JayUniversité Paris DiderotFondation FondaMentalOrange County Community Foundation
KeywordsMelatoninPineal glandProteomeProteomicsBiologyAutismCircadian rhythmInternal medicineEndocrinologyBioinformaticsBiochemistryMedicine

Abstract

fetched live from OpenAlex

The human pineal gland regulates day-night dynamics of multiple physiological processes, especially through the secretion of melatonin. Using mass-spectrometry-based proteomics and dedicated analysis tools, we identify proteins in the human pineal gland and analyze systematically their variation throughout the day and compare these changes in the pineal proteome between control specimens and donors diagnosed with autism. Results reveal diverse regulated clusters of proteins with, among others, catabolic carbohydrate process and cytoplasmic membrane-bounded vesicle-related proteins differing between day and night and/or control versus autism pineal glands. These data show novel and unexpected processes happening in the human pineal gland during the day/night rhythm as well as specific differences between autism donor pineal glands and those from controls.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.730
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.071
GPT teacher head0.355
Teacher spread0.285 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations4
Published2020
Admission routes2
Has abstractyes

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