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Record W2114649991 · doi:10.4049/jimmunol.0902796

IL-23 Receptor Regulates Unconventional IL-17–Producing T Cells That Control Bacterial Infections

2010· article· en· W2114649991 on OpenAlexaff
Lorena Riol‐Blanco, Vanja Lazarevic, Amit Awasthi, Meike Mitsdoerffer, Brian Wilson, Andy Croxford, Ari Waisman, Vijay K. Kuchroo, Laurie H. Glimcher, Mohamed Oukka

Bibliographic record

VenueThe Journal of Immunology · 2010
Typearticle
Languageen
FieldImmunology and Microbiology
TopicPsoriasis: Treatment and Pathogenesis
Canadian institutionsChildren’s Health Research Institute
FundersNational Institute of Neurological Disorders and StrokeNational Institute of Allergy and Infectious DiseasesU.S. Public Health ServiceNational Institutes of HealthDeutsche ForschungsgemeinschaftEllison Medical FoundationMultiple Sclerosis SocietyNational Multiple Sclerosis Society
KeywordsIntracellularEffectorIntracellular parasiteBiologyImmune systemCell biologyInterleukin 17InflammationMicrobiologyImmunology

Abstract

fetched live from OpenAlex

IL-23 plays an important role in autoimmune tissue inflammation and induces the generation of not fully characterized effector cells that mediate protection against pathogens. In this paper, we established the essential role of IL-23R in the host response against intracellular pathogens. IL-23 was critical for the expansion or maintenance of gammadelta and double negative (DN) alphabeta T cells. These cells were rapidly recruited to the site of infection and produced large amounts of IL-17, IFN-gamma, and TNF-alpha. Notably, DN T cells transferred into L. monocytogenes-infected RAG2(-/-) mice prevented bacterial growth, confirming their protective role against intracellular pathogens. Our results show that IL-23 regulates the function of IL-17-producing gammadelta and DN T cells, two essential components of the early protective immune response directed against intracellular pathogens.

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 score0.001
Threshold uncertainty score0.005

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.216
Teacher spread0.205 · 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

Citations111
Published2010
Admission routes1
Has abstractyes

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