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Record W4386465535 · doi:10.3390/biom13091347

Loss of dlx5a/dlx6a Locus Alters Non-Canonical Wnt Signaling and Meckel’s Cartilage Morphology

2023· article· en· W4386465535 on OpenAlexafffund
Emily Yu, Vishal Saxena, Sofia Perin, Marc Ekker

Bibliographic record

VenueBiomolecules · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDevelopmental Biology and Gene Regulation
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Ottawa
KeywordsZebrafishWnt signaling pathwayNeural crestBiologyMorphogenesisPhenotypeCell biologyLocus (genetics)MutantGeneticsCraniofacialAnatomyGeneSignal transduction

Abstract

fetched live from OpenAlex

The dlx genes encode transcription factors that establish a proximal–distal polarity within neural crest cells to bestow a regional identity during craniofacial development. The expression regions of dlx paralogs are overlapping yet distinct within the zebrafish pharyngeal arches and may also be involved in progressive morphologic changes and organization of chondrocytes of the face. However, how each dlx paralog of dlx1a, dlx2a, dlx5a and dlx6a affects craniofacial development is still largely unknown. We report here that the average lengths of the Meckel’s, palatoquadrate and ceratohyal cartilages in different dlx mutants were altered. Mutants for dlx5a−/− and dlx5i6−/−, where the entire dlx5a/dlx6a locus was deleted, have the shortest lengths for all three structures at 5 days post fertilization (dpf). This phenotype was also observed in 14 dpf larvae. Loss of dlx5i6 also resulted in increased proliferation of neural crest cells and expression of chondrogenic markers. Additionally, altered expression and function of non-canonical Wnt signaling were observed in these mutants suggesting a novel interaction between dlx5i6 locus and non-canonical Wnt pathway regulating ventral cartilage morphogenesis.

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 score0.006
Threshold uncertainty score0.516

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 integrity0.0000.000
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 head0.009
GPT teacher head0.252
Teacher spread0.243 · 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

Citations5
Published2023
Admission routes2
Has abstractyes

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