MétaCan
Menu
Back to cohort
Record W3017272456 · doi:10.1126/sciimmunol.aav3942

IFN-III is selectively produced by cDC1 and predicts good clinical outcome in breast cancer

2020· article· en· W3017272456 on OpenAlexfundno aff
Margaux Hubert, Élisa Gobbini, Coline Couillault, Thien‐Phong Vu Manh, Justine Berthet, Céline Rodriguez, Vincent Ollion, Janice Kielbassa, Christophe Sajous, Isabelle Treilleux, Olivier Trédan, Bertrand Dubois, Marc Dalod, Nathalie Bendriss‐Vermare, Christophe Caux, Jenny Valladeau‐Guilemond

Bibliographic record

VenueScience Immunology · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsnot available
FundersCentre Léon BérardInstitut national de la recherche scientifiqueRégion Auvergne-Rhône-AlpesLigue Contre le CancerLabEx DEvweCANFondation ARC pour la Recherche sur le CancerAgence Nationale de la RechercheEuropean Research CouncilAgence Nationale de Recherches sur le Sida et les Hépatites ViralesEuropean Society for Medical OncologyInstitut National Du CancerInstitut National de la Santé et de la Recherche MédicaleUniversité Claude Bernard Lyon 1Université de Lyon
KeywordsImmune systemImmunotherapyCancer researchChemokineTumor microenvironmentDendritic cellCCR4BiologyInterferonImmunologyCytotoxic T cellMedicineChemokine receptor

Abstract

fetched live from OpenAlex

1 microenvironment through increased production of IL-12p70, IFN-γ, and cytotoxic lymphocyte-recruiting chemokines. Last, we showed that engagement of TLR3 is a therapeutic strategy to induce IFN-III production by tumor-associated cDC1. These data provide insight into potential IFN- or cDC1-targeting antitumor therapies.

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: Observational · Consensus signal: Observational
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
Bibliometrics0.0010.000
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 head0.026
GPT teacher head0.320
Teacher spread0.294 · 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 designObservational
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

Citations142
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueScience ImmunologySame topicImmunotherapy and Immune ResponsesFrench-language works237,207