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Record W2113917537 · doi:10.1002/dvdy.20165

Evidence for the involvement of neurotrophins in muscle transdifferentiation and acetylcholine receptor transformation in the esophagus of <i>Myf5</i><sup>−/−</sup><i>:MyoD</i><sup>−/−</sup> and <i>NT‐3</i><sup>−/−</sup> embryos

2004· article· en· W2113917537 on OpenAlexafffund
Boris Kablar

Bibliographic record

VenueDevelopmental Dynamics · 2004
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaHospital for Sick Children
KeywordsMyoDMYF5BiologyTransdifferentiationEndocrinologyInternal medicineAcetylcholine receptorNeurotrophinMyogeninCell biologySkeletal muscleReceptorStem cellGeneticsMyogenesis

Abstract

fetched live from OpenAlex

The primary aim of our study was to determine whether the esophageal innervation (i.e., vagal and enteric) and the skeletal muscle-secreted neurotrophins have a role in smooth-to-skeletal muscle transdifferentiation and in the muscarinic-to-nicotinic acetylcholine receptor type transition. To that end, we used genetically engineered embryos and immunohistochemistry. We found that, in the absence of Myf5 and MyoD, the esophageal muscle cells failed to develop the striated phenotype of acetylcholine receptors. In addition, the development of vagal and enteric innervation was delayed in Myf5(-/-):MyoD(-/-) and NT-3(-/-) mutants, but it was reestablished 2 days before the end of gestation. The smooth muscle cells in the esophagus appeared to be a distinct subpopulation of cells and their ability to transdifferentiate was based on their competence to express neurotrophins and their receptors. Finally, our data suggest a role for NT-3 in the esophageal muscle transdifferentiation.

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 score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.041
GPT teacher head0.265
Teacher spread0.224 · 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

Citations13
Published2004
Admission routes2
Has abstractyes

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