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Record W4281390206 · doi:10.1371/journal.pbio.3001637

Early rhythmicity in the fetal suprachiasmatic nuclei in response to maternal signals detected by omics approach

2022· article· en· W4281390206 on OpenAlexfundno aff
Philipp Greiner, Pavel Houdek, Martin Sládek, Alena Sumová

Bibliographic record

VenuePLoS Biology · 2022
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsnot available
FundersUniversity of TorontoGrantová Agentura České Republiky
KeywordsBiologySuprachiasmatic nucleusCircadian rhythmPeriod (music)TranscriptomeCLOCKRhythmHypothalamusFetusCircadian clockNeuroscienceEndocrinologyInternal medicineGeneGene expressionGeneticsPregnancy

Abstract

fetched live from OpenAlex

The suprachiasmatic nuclei (SCN) of the hypothalamus harbor the central clock of the circadian system, which gradually matures during the perinatal period. In this study, time-resolved transcriptomic and proteomic approaches were used to describe fetal SCN tissue-level rhythms before rhythms in clock gene expression develop. Pregnant rats were maintained in constant darkness and had intact SCN, or their SCN were lesioned and behavioral rhythm was imposed by temporal restriction of food availability. Model-selecting tools dryR and CompareRhythms identified sets of genes in the fetal SCN that were rhythmic in the absence of the fetal canonical clock. Subsets of rhythmically expressed genes were assigned to groups of fetuses from mothers with either intact or lesioned SCN, or both groups. Enrichment analysis for GO terms and signaling pathways revealed that neurodevelopment and cell-to-cell signaling were significantly enriched within the subsets of genes that were rhythmic in response to distinct maternal signals. The findings discovered a previously unexpected breadth of rhythmicity in the fetal SCN at a developmental stage when the canonical clock has not yet developed at the tissue level and thus likely represents responses to rhythmic maternal signals.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.028
GPT teacher head0.247
Teacher spread0.219 · 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

Citations22
Published2022
Admission routes1
Has abstractyes

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