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Record W2059088759 · doi:10.1073/pnas.0409103102

Requirement for serum response factor for skeletal muscle growth and maturation revealed by tissue-specific gene deletion in mice

2005· article· en· W2059088759 on OpenAlexfundno aff
Shijie Li, Michael P. Czubryt, John McAnally, Rhonda Bassel‐Duby, James A. Richardson, Franziska F. Wiebel, Alfred Nordheim, Eric N. Olson

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchNational Institutes of HealthWelch FoundationDeutsche ForschungsgemeinschaftMuscular Dystrophy AssociationHeart and Stroke Foundation of Canada
KeywordsMyocardinSerum response factorSkeletal muscleBiologyTranscription factorCell biologyMyosinMyocyteEndocrinologyInternal medicineGeneGeneticsMedicine

Abstract

fetched live from OpenAlex

Serum response factor (SRF) controls the transcription of muscle genes by recruiting a variety of partner proteins, including members of the myocardin family of transcriptional coactivators. Mice lacking SRF fail to form mesoderm and die before gastrulation, precluding an analysis of the roles of SRF in muscle tissues. To investigate the functions of SRF in skeletal muscle development, we conditionally deleted the Srf gene in mice by skeletal muscle-specific expression of Cre recombinase. In mice lacking skeletal muscle SRF expression, muscle fibers formed, but failed to undergo hypertrophic growth after birth. Consequently, mutant mice died during the perinatal period from severe skeletal muscle hypoplasia. The myopathic phenotype of these mutant mice resembled that of mice expressing a dominant negative mutant of a myocardin family member in skeletal muscle. These findings reveal an essential role for the partnership of SRF and myocardin-related transcription factors in the control of skeletal muscle growth and maturation in vivo.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.176

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 science0.0000.000
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 head0.026
GPT teacher head0.299
Teacher spread0.273 · 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

Citations291
Published2005
Admission routes1
Has abstractyes

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