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Record W2116353116 · doi:10.1093/carcin/bgs284

Focal adhesion kinase is required for β-catenin-induced mobilization of epidermal stem cells

2012· article· en· W2116353116 on OpenAlexaff
Rachel A. Ridgway, Bryan Serrels, Susan Mason, Andrew Kinnaird, Morwenna Muir, Hitesh Patel, William J. Muller, Owen J. Sansom, Valerie G. Brunton

Bibliographic record

VenueCarcinogenesis · 2012
Typearticle
Languageen
FieldMedicine
TopicHair Growth and Disorders
Canadian institutionsMcGill University
FundersCancer Research UK
KeywordsFocal adhesionPTK2Stem cellCell biologyWnt signaling pathwayCancer researchTyrosine kinaseProto-oncogene tyrosine-protein kinase SrcCateninBiologyHair follicleSignal transductionChemistryProtein kinase CMitogen-activated protein kinase kinase

Abstract

fetched live from OpenAlex

Focal adhesion kinase (FAK) is a non-receptor tyrosine kinase that integrates signals downstream of integrin and growth factor activation. Previously, we have shown that skin-specific loss of fak prevents chemically induced skin carcinogenesis in mice following phorbol ester treatment. In this study, we show that skin-specific deletion of fak prevents mobilization of stem cells within the bulge region of the hair follicle, which are the precursors of papillomas following phorbol ester treatment. We also show that phorbol ester treatment results in activation of-catenin within the skin and that FAK is required for β-catenin-induced stem cell mobilization. In addition, inhibition of Src kinase activity, a major binding partner of FAK also prevents stem cell mobilization. We show that FAK is required for the nuclear localization of β-catenin in the skin following phorbol ester treatment and the transcriptional activation of the β-catenin target gene c-Myc. This provides the first evidence of cross-talk between integrin and Wnt signalling pathways in the control of epidermal stem cells and the early events associated with skin carcinogenesis.

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 score0.073
Threshold uncertainty score0.615

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 head0.058
GPT teacher head0.290
Teacher spread0.232 · 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

Citations47
Published2012
Admission routes1
Has abstractyes

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