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Record W2004029940 · doi:10.1002/eji.200324303

Natural killer T cells contribute to airway eosinophilic inflammation induced by ragweed through enhanced IL‐4 and eotaxin production

2004· article· en· W2004029940 on OpenAlexaff
Laura Bilenki, Jie Yang, Yijun Fan, Shuhe Wang, Xi Yang

Bibliographic record

VenueEuropean Journal of Immunology · 2004
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsImmunologyEotaxinNatural killer T cellRagweedEosinophiliaBiologyCD1EosinophilImmune systemCytokineInflammationChemokineAllergyT cellAsthma

Abstract

fetched live from OpenAlex

Although NKT cells have been found to be capable of modulating immune responses in several model systems, the role of NKT cells in allergy remains unclear. Using CD1 gene knockout (KO) mice, which lack NKT cells, we examined the function of NKT cells in the development of allergic inflammation induced by a common airborne human allergen, ragweed. The data showed that airway eosinophilia and mucus overproduction induced by ragweed were significantly reduced in CD1 KO mice, which was correlated with significantly lower allergen-driven IL-4 production and lower eotaxin responses in the airways of CD1 KO mice. Moreover, both ragweed-specific and total serum IgE levels in CD1 KO mice were significantly lower than those in control BALB/c mice. The reduced allergic reaction in CD1 KO mice is not due to intrinsic deficiency because they showed normal levels of immune cells and function. In addition, in vivo stimulation of NKT cells using their natural ligand, alpha-galactosylceramide, enhanced ragweed-induced airway eosinophilia, IL-4, and eotaxin production in control, but not CD1 KO mice. These data provide in vivo evidence for the involvement of NKT cells in the allergic mechanisms responsible for allergen-driven cytokine and chemokine production and airway inflammation.

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 score0.043
Threshold uncertainty score0.480

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 head0.008
GPT teacher head0.233
Teacher spread0.225 · 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

Citations75
Published2004
Admission routes1
Has abstractyes

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