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Record W2789956951 · doi:10.1038/s41598-018-20608-6

Type 2 Innate Lymphocytes Actuate Immunity Against Tumours and Limit Cancer Metastasis

2018· article· en· W2789956951 on OpenAlexafffund
Iryna Saranchova, Jeffrey S. Han, Rysa Zaman, Hitesh Arora, Hui Huang, Franz Fenninger, Kyung Bok Choi, Lonna Munro, Cheryl G. Pfeifer, Ian Welch, Fumio Takei, Wilfred A. Jefferies

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicIL-33, ST2, and ILC Pathways
Canadian institutionsBC Cancer AgencyUniversity of British Columbia HospitalCanada's Michael Smith Genome Sciences CentreVancouver Coastal HealthVancouver Coastal Health Research InstituteUniversity of British Columbia
FundersCanadian Institutes of Health ResearchÖsterreichischen Akademie der Wissenschaften
KeywordsMetastasisImmune systemAcquired immune systemInnate immune systemImmunityBiologyInnate lymphoid cellCytolysisImmunologyCancerCancer researchCytotoxic T cell

Abstract

fetched live from OpenAlex

Type 2 innate lymphoid cells (ILC2) potentiate immune responses, however, their role in mediating adaptive immunity in cancer has not been assessed. Here, we report that mice genetically lacking ILC2s have significantly increased tumour growth rates and conspicuously higher frequency of circulating tumour cells (CTCs) and resulting metastasis to distal organs. Our data support the model that IL-33 dependent tumour-infiltrating ILC2s are mobilized from the lungs and other tissues through chemoattraction to enter tumours, and subsequently mediate tumour immune-surveillance by cooperating with dendritic cells to promote adaptive cytolytic T cell responses. We conclude that ILC2s play a fundamental, yet hitherto undescribed role in enhancing anti-cancer immunity and controlling tumour metastasis.

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.261
Teacher spread0.237 · 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

Citations93
Published2018
Admission routes2
Has abstractyes

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Same venueScientific ReportsSame topicIL-33, ST2, and ILC PathwaysFrench-language works237,207