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Record W2944462489 · doi:10.1002/jlb.3mr1217-497r

The role of type 2 innate lymphoid cells in eosinophilic asthma

2019· review· en· W2944462489 on OpenAlexaff
Brittany Salter, Michael Aw, Roma Sehmi

Bibliographic record

VenueJournal of Leukocyte Biology · 2019
Typereview
Languageen
FieldImmunology and Microbiology
TopicIL-33, ST2, and ILC Pathways
Canadian institutionsMcMaster University
Fundersnot available
KeywordsInnate lymphoid cellEosinophilicImmunologyEosinophiliaAsthmaBiologyInterleukin 5AllergyImmune systemInnate immune systemPulmonary EosinophiliaDiseaseMedicineEosinophilInterleukinCytokinePathology

Abstract

fetched live from OpenAlex

Abstract Eosinophilic asthma has conventionally been proposed to be a T helper 2 driven disease but emerging evidence supports a central role of type 2 innate lymphoid cells (ILC2s). These are non-T, non-B cells that lack antigen specificity and produce more IL-5 and IL-13 than CD4+ T lymphocytes, on a cell per cell basis, in vitro. Although it is clear that ILC2s and CD4+ T cells work in concert with each other to drive type 2 immune responses, kinetic studies in allergic asthma suggest that ILC2s may act locally within the airways to “initiate” eosinophilic responses, whereas CD4+ T cells act locally and systemically to “perpetuate” eosinophilic inflammatory responses. Importantly, ILC2s are increased within the airways of severe asthmatics, with the greatest number of IL-5+IL-13+ILC2s being detected in sputum from severe asthmatics with uncontrolled eosinophilia despite high-dose steroid therapy. Although the precise relationship between ILC2s and steroid sensitivity in asthma remains unclear, controlling the activation of ILC2s within the airways may provide an effective therapeutic target for eosinophilic inflammation in airways diseases.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.003

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 head0.027
GPT teacher head0.287
Teacher spread0.259 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations28
Published2019
Admission routes1
Has abstractyes

Explore more

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