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Record W2009685791 · doi:10.1371/journal.pone.0027097

Gingival Fibroblasts Display Reduced Adhesion and Spreading on Extracellular Matrix: A Possible Basis for Scarless Tissue Repair?

2011· article· en· W2009685791 on OpenAlexafffund
Feng Guo, David E. Carter, Anuradha Mukhopadhyay, Andrew Leask

Bibliographic record

VenuePLoS ONE · 2011
Typearticle
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsRobarts Clinical TrialsWestern University
FundersCanadian Arthritis NetworkCanadian Institutes of Health ResearchArthritis Society
KeywordsFibronectinConnective tissueExtracellular matrixFocal adhesionIntegrinFibrosisFibroblastChemistryWound healingCell biologyPathologyAdhesionMedicineBiologyImmunologyCellSignal transductionBiochemistryIn vitro

Abstract

fetched live from OpenAlex

Unlike skin, oral gingiva do not scar in response to injury. The basis of this difference is likely to be revealed by comparing the responses of dermal and gingival fibroblasts to fibrogenic stimuli. Previously, we showed that, compared to dermal fibroblasts, gingival fibroblasts are less responsive to the potent pro-fibrotic cytokine TGFβ, due to a reduced production of endothelin-1 (ET-1). In this report, we show that, compared to dermal fibroblasts, human gingival fibroblasts show reduced expression of pro-adhesive mRNAs and proteins including integrins α2 and α4 and focal adhesion kinase (FAK). Consistent with these observations, gingival fibroblasts are less able to adhere to and spread on both fibronectin and type I collagen. Moreover, the enhanced production of ET-1 mRNA and protein in dermal fibroblasts is reduced by the FAK/src inhibitor PP2. Given our previous observations suggesting that fibrotic fibroblasts display elevated adhesive properties, our data suggest that scarring potential may be based, at least in part, on differences in adhesive properties among fibroblasts resident in connective tissue. Controlling adhesive properties may be of benefit in controlling scarring in response to tissue injury.

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 score0.037
Threshold uncertainty score0.824

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 head0.077
GPT teacher head0.305
Teacher spread0.228 · 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

Citations53
Published2011
Admission routes2
Has abstractyes

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