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Differential calcineurin/NFATc1‐c3 signaling in the regulation of the skeletal muscle fiber phenotype

2010· article· en· W76562646 on OpenAlexafffund
Ewa M. Kulig, Mathieu St‐Louis, Joe K. Eibl, Grace K. Pavlath, Robin N. Michel

Bibliographic record

VenueThe FASEB Journal · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSignaling Pathways in Disease
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsNFATGene isoformBiologyPhenotypeTranscription factorSkeletal muscleCalcineurinCell biologyGene expressionTransgeneRegulation of gene expressionGeneGeneticsInternal medicineEndocrinologyTransplantationMedicine

Abstract

fetched live from OpenAlex

Involvement of Cn/NFAT signaling in the regulation of the skeletal muscle fiber phenotype is recognized, however, the role of the various Cn‐regulated NFAT isoforms in this process remains rudimentary. We thus assessed the expression, localization and function of NFAT isoforms in muscle fibers of wild type or transgenic NFATc2 or NFATc3 deficient mice under normal weightbearing or functional compensatory work overload conditions. Immunohistochemical analyses indicated that NFAT isoforms have distinct roles in the regulation of muscle remodeling associated with compensatory growth. Semi‐quantitative RT‐PCR did not reveal compensatory up‐regulation of surviving NFAT isoform mRNA levels in transgenic mice. On the other hand, western blotting and immunofluorescence data showed NFAT protein localization to be differentially regulated during compensatory growth. Moreover, gene array analyses showed distinct gene expression patterns across conditions. Taken together, these results show that members of the NFAT family of transcription factors have distinct roles in the expression of the muscle fiber phenotype and a limited capacity to substitute for one another during conditions of rapid fiber growth. Supported by NSERC, CIHR and CRC to RNM. EMK is a CIHR scholar.

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 score0.070
Threshold uncertainty score0.258

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 science0.0010.000
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 head0.011
GPT teacher head0.238
Teacher spread0.227 · 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

Citations1
Published2010
Admission routes2
Has abstractyes

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