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Record W2100307125 · doi:10.1084/jem.20091373

Identification of a novel antigen-presenting cell population modulating antiinfluenza type 2 immunity

2010· article· en· W2100307125 on OpenAlexafffund
Jae‐Kwang Yoo, Carole L. Galligan, Carl Virtanen, Eleanor N. Fish

Bibliographic record

VenueThe Journal of Experimental Medicine · 2010
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health Research
KeywordsIdentification (biology)ImmunityAntigenBiologyPopulationImmunologyCell mediated immunityComputational biologyImmune systemMedicine

Abstract

fetched live from OpenAlex

Antiinfluenza type 2 (T2) immunity contributes to both immunopathology and immunoprotection, yet the underlying mechanisms modulating T2 immunity remain ill defined. We describe a novel mouse antigen (Ag)-presenting cell (APC), designated late-activator APC (LAPC). After pulmonary influenza A (H1N1) virus infection, LAPCs enter the lungs, capture viral Ag, and subsequently migrate to the draining lymph node (DLN) and spleen, with delayed kinetics relative to dendritic cells (DCs). In the DLN, influenza virus-activated LAPCs present Ag and selectively induce T helper type 2 (Th2) effector cell polarization by cell-cell contact-mediated modulation of GATA-3 expression. In adoptive transfer experiments, influenza virus-activated LAPCs augmented Th2 effector T cell responses in the DLN, increased production of circulating antiinfluenza immunoglobulin, and increased levels of T2 cytokines in bronchoalveolar lavage fluid in recipient influenza virus-infected mice. LAPC-recipient mice exhibited exacerbated pulmonary pathology, with delayed viral clearance and enhanced pulmonary eosinophilia. Collectively, our results identify and highlight the importance of LAPCs as immunomodulators of T2 immunity during influenza A virus infection.

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)0.000
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 score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.024
GPT teacher head0.317
Teacher spread0.293 · 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

Citations32
Published2010
Admission routes2
Has abstractyes

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