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Record W4313500369 · doi:10.1038/s41467-022-35775-4

Molecular versatility during pluripotency progression

2023· review· en· W4313500369 on OpenAlexaff
Giacomo Furlan, Aurélia Huyghe, Noémie Combémorel, Fabrice Lavial

Bibliographic record

VenueNature Communications · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of Toronto
FundersInstitut National Du CancerFondation Bettencourt SchuellerCentre National de la Recherche ScientifiqueLigue Contre le CancerCentre Léon BérardFondation pour la Recherche MédicaleInstitut National de la Santé et de la Recherche MédicaleFondation ARC pour la Recherche sur le CancerAgence Nationale de la Recherche
KeywordsHomeobox protein NANOGBiologyEpigeneticsEnhancerWnt signaling pathwayEmbryonic stem cellTranscription factorComputational biologyCell biologyStem cellContext (archaeology)GeneticsInduced pluripotent stem cellGeneSignal transduction

Abstract

fetched live from OpenAlex

A challenge during development is to ensure lineage segregation while preserving plasticity. Using pluripotency progression as a paradigm, we review how developmental transitions are coordinated by redeployments, rather than global resettings, of cellular components. We highlight how changes in response to extrinsic cues (FGF, WNT, Activin/Nodal, Netrin-1), context- and stoichiometry-dependent action of transcription factors (Oct4, Nanog) and reconfigurations of epigenetic regulators (enhancers, promoters, TrxG, PRC) may confer robustness to naïve to primed pluripotency transition. We propose the notion of Molecular Versatility to regroup mechanisms by which molecules are repurposed to exert different, sometimes opposite, functions in close stem cell configurations.

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)0.000
metaresearch head score (Gemma)0.000
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: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.048
GPT teacher head0.401
Teacher spread0.353 · 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
GenreReview

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

Citations23
Published2023
Admission routes1
Has abstractyes

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Same venueNature CommunicationsSame topicPluripotent Stem Cells ResearchFrench-language works237,207