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Record W2114772731 · doi:10.1096/fj.14-265686

Glucocorticoids entrain molecular clock components in human peripheral cells

2014· article· en· W2114772731 on OpenAlexafffund
Marc Cuesta, Nicolas Cermakian, Diane B. Boivin

Bibliographic record

VenueThe FASEB Journal · 2014
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
FundersCanadian Institutes of Health Research
KeywordsPeripheralNeuroscienceBiologyCell biologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

In humans, shift work induces a desynchronization between the circadian system and the outside world, which contributes to shift work-associated medical disorders. Using a simulated night shift experiment, we previously showed that 3 d of bright light at night fully synchronize the central clock to the inverted sleep schedule, whereas the peripheral clocks located in peripheral blood mononuclear cells (PBMCs) took longer to reset. This underlines the need for testing the effects of synchronizers on both the central and peripheral clocks. Glucocorticoids display circadian rhythms controlled by the central clock and are thought to act as synchronizers of rodent peripheral clocks. In the present study, we tested whether the human central and peripheral clocks were sensitive to exogenous glucocorticoids (Cortef) administered in the late afternoon. We showed that 20 mg Cortef taken orally acutely increased PER1 expression in PBMC peripheral clocks. After 6 d of Cortef administration, the phases of central markers were not affected, whereas those of PER2-3 and BMAL1 expression in PBMCs were shifted by ∼ 9.5-11.5 h. These results demonstrate, for the first time, that human peripheral clocks are entrained by glucocorticoids. Importantly, they suggest innovative interventions for shift workers and jet-lag travelers, combining synchronizing agents for the central and peripheral clocks.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.023
GPT teacher head0.246
Teacher spread0.223 · 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

Citations126
Published2014
Admission routes2
Has abstractyes

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