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Record W1906040587 · doi:10.4049/jimmunol.167.2.1039

Functional Expression of IL-12 Receptor by Human Eosinophils: IL-12 Promotes Eosinophil Apoptosis

2001· article· en· W1906040587 on OpenAlexaff
Esra Nutku, Abdelilah S. Gounni, Fadi Aris, Bruce Mazer, Qutayba Hamid

Bibliographic record

VenueThe Journal of Immunology · 2001
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsMcGill UniversityChristie (Canada)
Fundersnot available
KeywordsEosinophilApoptosisInterleukin 5Propidium iodideBiologyEosinophil cationic proteinEosinophiliaImmunologyReceptorCell biologyMolecular biologyInterleukinChemistryCytokineProgrammed cell deathBiochemistry

Abstract

fetched live from OpenAlex

In murine models of allergic inflammation, IL-12 has been shown to decrease tissue eosinophilia, but the underlying mechanisms are not known. We evaluated the expression of IL-12R and the effect of IL-12 on eosinophil survival. In situ hybridization demonstrated the presence of mRNA and immunoreactivity for IL-12Rbeta1 and -beta2 subunits in human peripheral blood eosinophils. Surface expression of IL-12Rbeta1 and -beta2 subunits on freshly isolated human eosinophils was optimally expressed after incubation with PMA. To determine the functional significance of IL-12R studies, we studied cell viability and apoptosis. Morphological analysis and propidium iodide staining for cell cycle demonstrated that recombinant human IL-12 increased in vitro human eosinophil apoptosis in a dose-dependent manner. Addition of IL-5 together with IL-12 abrogated eosinophil apoptosis, suggesting that IL-12 and IL-5 have antagonistic effects. Our findings provide evidence for a novel role for IL-12 in regulating eosinophil function by increasing eosinophil apoptosis.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0020.001

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 head0.018
GPT teacher head0.233
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), 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

Citations34
Published2001
Admission routes1
Has abstractyes

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