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Record W2416476141 · doi:10.1002/dvdy.24421

Raf kinase inhibitor protein1 is a myogenic inhibitor with conserved function in avians and mammals

2016· article· en· W2416476141 on OpenAlexaff
Gary R. Coulton, Yanwen Hou, Samantha Mirczuk, Steven P. Allen

Bibliographic record

VenueDevelopmental Dynamics · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMelanoma and MAPK Pathways
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsMyogenesisBiologyC2C12MAPK/ERK pathwayCell biologyKinasePhosphorylationMEK inhibitorMyocyteSignal transductionActivator (genetics)Molecular biologyReceptorGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: Raf Kinase Inhibitor Protein1 (RKIP) is a tumor suppressor that is present in several adult tissues. It functions as an inhibitor of both Raf/Mek/Erk and NFĸB signaling when unphosphorylated, but following phosphorylation the ability to inhibit Raf/Mek/Erk signaling is lost and RKIP becomes an activator of G-protein coupled receptor signaling. In neonates and adults, RKIP is known to be expressed in muscle; however, its physiological function is currently unknown. RESULTS: In this study, we show by in situ hybridization and immunofluorescence that RKIP is also expressed in developing chick embryonic muscle, and mouse C2C12 myoblasts. Furthermore, we demonstrate that, in these systems, it functions as an inhibitor of myogenesis: increased levels of RKIP suppress myotube differentiation whereas decreasing RKIP promotes differentiation. Additionally, we show that the ability of RKIP to inhibit myogenesis is dependent upon its phosphorylation state as only the nonphosphorylated form of RKIP suppresses myogenesis. CONCLUSIONS: This study, therefore, clearly demonstrates that RKIP has conserved functions as a myogenic inhibitor in both mammalian and avian muscle. Developmental Dynamics 245:902-912, 2016. © 2016 Wiley Periodicals, Inc.

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 score0.011
Threshold uncertainty score0.557

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 head0.006
GPT teacher head0.183
Teacher spread0.177 · 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
Published2016
Admission routes1
Has abstractyes

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