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Record W2015410269 · doi:10.1096/fj.12-208033

Th17‐associated cytokines promote human airway smooth muscle cell proliferation

2012· article· en· W2015410269 on OpenAlexafffund
Ying Chang, Laila Al‐Alwan, Paul‐André Risse, Andrew J. Halayko, James G. Martin, Carolyn J. Baglole, David H. Eidelman, Qutayba Hamid

Bibliographic record

VenueThe FASEB Journal · 2012
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsMcGill UniversityUniversity of ManitobaChristie (Canada)
FundersCanadian Institutes of Health ResearchSean Costello Memorial Fund for Research on Bipolar Disorder
KeywordsAirwayCell growthCell biologyImmunologyMedicineBiologyAnesthesiaGenetics

Abstract

fetched live from OpenAlex

Increased airway smooth muscle (ASM) mass is a hallmark of airway remodeling in severe asthma. Th17-associated cytokines, particularly IL-17A, IL-17F, and IL-22, have been postulated to play a role in the pathogenesis of asthma. To investigate the in vitro effect of Th17 cytokines on the proliferation and survival of airway smooth muscle cells (ASMCs), human ASMCs from asthmatic and nonasthmatic subjects were incubated with IL-17A, IL-17F, or IL-22. The aforementioned cytokines demonstrated an ability to promote proliferation and survival of ASMCs from asthmatic and nonasthmatic subjects, which were mediated by selective activation of their corresponding receptors on ASMCs, including IL-17RA, IL-17RC, or IL-22R1, respectively. IL-17A and IL-17F-induced proliferation of ASMCs was dependent on ERK1/2 MAPK pathway, while IL-22-induced proliferation involved both ERK1/2 MAPK and NF-κB pathways. The involvement of signaling pathways was further confirmed by the inhibition of proliferation by knockdown of ERK1/2 MAPK or NF-κB p65 expression with pathway-specific siRNA. Together, our results show that Th17-associated cytokines promote proliferation and reduce the apoptotic rate of human ASMCs, raising the possibility that Th17 cytokines may contribute to increasing airway smooth muscle mass and airway remodeling in asthma.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.019
GPT teacher head0.267
Teacher spread0.249 · 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

Citations141
Published2012
Admission routes2
Has abstractyes

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