MétaCan
Menu
Back to cohort

MyoD–<i>lacZ</i> transgenes are early markers in the neural retina, but MyoD function appears to be inhibited in the developing retinal cells

2004· article· en· W2051293044 on OpenAlexafffund
Boris Kablar

Bibliographic record

VenueInternational Journal of Developmental Neuroscience · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaHospital for Sick Children
KeywordsMyoDRetinaRetinalBiologyFunction (biology)TransgeneNeuroscienceCell biologyGeneticsMyogenesisGeneBiochemistry

Abstract

fetched live from OpenAlex

Recent findings suggest that eye and skeletal muscle development in vertebrates share the same regulatory network. In that network, Pax3 gene is apparently activated through Dach/Eya/Six feedback loop to mediate MyoD-driven myogenesis. The purpose of this study was to investigate previously reported MyoD-lacZ expression in the developing mouse neural retina and to gain insight into the potential role of MyoD in the embryonic retinal cells. The analysis of MD6.0-lacZ and 258/-2.5lacZ transgenic embryos revealed that the retinal temporal expression pattern of the two transgenes resembled their expression pattern in the MyoD-dependent precursor muscle cells. However, MyoD transcripts and protein could not be found in the sites of MyoD-lacZ retinal expression. Furthermore, our immunohistochemical analysis suggests the existence of diverse factors (e.g., Pax6 and Chx10) within the retinal cells that differentially and inappropriately activate the two transgenes. Finally, the retinal phenotype observed in Pax7-/- knock-out mice suggests a role for Pax7 in photoreceptor cell differentiation, retinal lamination and in the etiopathology of retinoblastoma. Taken together, our data suggest that the MyoD gene evolved a different mechanism to achieve its down-regulation within the retina than that of the Myf5 gene.

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 score0.915
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.017
GPT teacher head0.251
Teacher spread0.234 · 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
Published2004
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Developmental NeuroscienceSame topicMuscle Physiology and DisordersFrench-language works237,207