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Record W2121867299 · doi:10.7554/elife.06253

Oxyntomodulin regulates resetting of the liver circadian clock by food

2015· article· en· W2121867299 on OpenAlexafffund
Dominic Landgraf, Anthony H. Tsang, Alexei Leliavski, Christiane E. Koch, Johanna L. Barclay, Daniel J. Drucker, Henrik Oster

Bibliographic record

VenueeLife · 2015
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of TorontoMount Sinai Hospital
FundersCanadian Institutes of Health ResearchNovo NordiskCanada Research ChairsVolkswagen FoundationDeutsche Forschungsgemeinschaft
KeywordsPER1Suprachiasmatic nucleusCircadian rhythmCLOCKBiologyCircadian clockLight effects on circadian rhythmEndocrinologyInternal medicineBacterial circadian rhythmsCell biologyMedicine

Abstract

fetched live from OpenAlex

Circadian clocks coordinate 24-hr rhythms of behavior and physiology. In mammals, a master clock residing in the suprachiasmatic nucleus (SCN) is reset by the light-dark cycle, while timed food intake is a potent synchronizer of peripheral clocks such as the liver. Alterations in food intake rhythms can uncouple peripheral clocks from the SCN, resulting in internal desynchrony, which promotes obesity and metabolic disorders. Pancreas-derived hormones such as insulin and glucagon have been implicated in signaling mealtime to peripheral clocks. In this study, we identify a novel, more direct pathway of food-driven liver clock resetting involving oxyntomodulin (OXM). In mice, food intake stimulates OXM secretion from the gut, which resets liver transcription rhythms via induction of the core clock genes Per1 and 2. Inhibition of OXM signaling blocks food-mediated resetting of hepatocyte clocks. These data reveal a direct link between gastric filling with food and circadian rhythm phasing in metabolic tissues.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.000
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 head0.044
GPT teacher head0.246
Teacher spread0.202 · 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

Citations103
Published2015
Admission routes2
Has abstractyes

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