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Record W2036472115 · doi:10.1164/rccm.200305-706oc

Type 2 Cytokines in the Pathogenesis of Sustained Airway Dysfunction and Airway Remodeling in Mice

2004· article· en· W2036472115 on OpenAlexaff
Richard Leigh, Russ Ellis, Jennifer Wattie, Jeremy A. Hirota, Klaus I. Matthaei, Paul S. Foster, Paul M. O’Byrne, Mark D. Inman

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2004
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAirwayMedicineImmunologyAllergenInflammationAllergyAnesthesia

Abstract

fetched live from OpenAlex

The mechanisms underlying airway hyperresponsiveness remain unclear, although airway inflammation and remodeling likely play important roles. We have observed sustained airway hyperreactivity and airway remodeling occurring in mice after chronic allergen exposure and persisting beyond resolution of allergen-induced inflammation. The aim of this study was to delineate mechanisms involved in allergen-induced airway hyperreactivity and airway remodeling and to examine evidence for a causal association between airway remodeling and sustained airway hyperreactivity. Wild-type (WT) and interleukin (IL)-4-, IL-5-, and IL-13-deficient (-/-) mice were sensitized and studied 4 weeks after chronic allergen exposure. By measuring airway responsiveness and airway morphometry, we demonstrated that WT mice developed sustained airway hyperreactivity and aspects of airway remodeling after chronic allergen exposure. Both IL-4(-/-) and IL-13(-/-) mice were protected from developing sustained airway hyperreactivity and aspects of airway remodeling. In contrast, IL-5(-/-) mice developed sustained airway hyperreactivity and aspects of airway remodeling similar to that seen in WT mice. Our results confirm that IL-4 and IL-13, but not IL-5, are critical for the development of sustained airway hyperreactivity and airway remodeling after allergen exposure.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.538
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.014
GPT teacher head0.294
Teacher spread0.280 · 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

Citations162
Published2004
Admission routes1
Has abstractyes

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