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Record W2033357192 · doi:10.1038/srep02498

Induction of Pax3 gene expression impedes cardiac differentiation

2013· article· en· W2033357192 on OpenAlexafffund
Qiao Li, Melanie Le May, Natascha Lacroix, Jihong Chen

Bibliographic record

VenueScientific Reports · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCongenital heart defects research
Canadian institutionsUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsPAX3MyogenesisBiologyCellular differentiationMesodermCell biologyInduced pluripotent stem cellStem cellGeneComputational biologyMyocyteGeneticsTranscription factorEmbryonic stem cell

Abstract

fetched live from OpenAlex

Cell-based therapies using pluripotent stem cells hold great promise as regenerative approaches to treat many types of diseases. Nevertheless many challenges remain and, perhaps foremost, is the issue of how to direct and enhance the specification and differentiation of a desired cell type for potential therapeutics. We have examined the molecular basis for the inverse correlation of cardiac and skeletal myogenesis in small molecule-enhanced stem cell differentiation. Our study shows that activation of premyogenic factor Pax3 coincides with inhibiting gene expression of early cardiac factor GATA4. Interestingly, the inhibitory effect of small molecules on cardiac differentiation depends on the function of Pax3, but not the mesoderm factor Meox1. Thus Pax3 is an inhibitor of cardiac differentiation in lineage specification. Our studies reveal the dual roles of Pax3 in stem cell fate determinations and provide new molecular insights into small molecule-enhanced lineage specification.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.012
GPT teacher head0.262
Teacher spread0.250 · 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 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

Citations8
Published2013
Admission routes2
Has abstractyes

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