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Record W2038403078 · doi:10.1074/jbc.m111.261677

Fibronectin and β-Catenin Act in a Regulatory Loop in Dermal Fibroblasts to Modulate Cutaneous Healing

2011· article· en· W2038403078 on OpenAlexafffund
Kirsten A. Bielefeld, Saeid Amini‐Nik, Heather Whetstone, Raymond Poon, Andrew Youn, Jian Wang, Benjamin A. Alman

Bibliographic record

VenueJournal of Biological Chemistry · 2011
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaSchool of Medicine, Stanford UniversityCanadian Institutes of Health ResearchHospital for Sick ChildrenUniversity of Toronto
KeywordsFibronectinExtracellular matrixCell biologyWound healingFibroblastCateninIntegrinBeta-cateninChemistrySignal transductionBiologyWnt signaling pathwayCellImmunologyBiochemistryIn vitro

Abstract

fetched live from OpenAlex

β-Catenin is an important regulator of dermal fibroblasts during cutaneous wound repair. However, the factors that modulate β-catenin activity in this process are not completely understood. We investigated the role of the extracellular matrix in regulating β-catenin and found an increase in β-catenin-mediated Tcf-dependent transcriptional activity in fibroblasts exposed to various extracellular matrix components. This occurs through an integrin-mediated GSK3β-dependent pathway. The physiologic role of this mechanism was demonstrated during wound repair in extra domain A-fibronectin-deficient mice, which exhibited decreased β-catenin-mediated signaling during the proliferative phase of healing. Extra domain A-fibronectin-deficient mice have wounds that fail at a lower tensile strength and contain fewer fibroblasts compared with wild type mice. This phenotype was rescued by genetic or pharmacologic activation of β-catenin signaling. Because fibronectin is a transcriptional target of β-catenin, this suggests the existence of a feedback loop between these two molecules that regulates dermal fibroblast cell behavior during wound repair.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.349

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 head0.043
GPT teacher head0.280
Teacher spread0.237 · 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 designObservational
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

Citations69
Published2011
Admission routes2
Has abstractyes

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