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Record W2995136651 · doi:10.1126/sciimmunol.aax8189

GCN2 drives macrophage and MDSC function and immunosuppression in the tumor microenvironment

2019· article· en· W2995136651 on OpenAlexafffund
Marie Jo Halaby, Kebria Hezaveh, Sara Lamorte, M. Teresa Ciudad, Andreas Kloetgen, Bethany MacLeod, Mengdi Guo, Ankur Chakravarthy, Tiago da Silva Medina, Stefano Ugel, Aristotelis Tsirigos, Vincenzo Bronte, David H. Munn, Trevor J. Pugh, Daniel D. De Carvalho, Marcus O. Butler, Pamela S. Ohashi, David G. Brooks, Tracy L. McGaha

Bibliographic record

VenueScience Immunology · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune cells in cancer
Canadian institutionsPrincess Margaret Cancer CentreOntario Institute for Cancer ResearchUniversity of TorontoUniversity Health Network
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Cancer InstituteNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health ResearchCanada First Research Excellence Fund
KeywordsImmunosuppressionTumor microenvironmentMacrophageFunction (biology)ImmunologyImmune systemMicrobiologyCancer researchBiologyCell biologyIn vitroBiochemistry

Abstract

fetched live from OpenAlex

by small interfering RNA knockdown reduced tumor growth. Last, analysis of patients with cutaneous melanoma showed that GCN2-dependent transcriptional signatures correlated with macrophage polarization, T cell infiltrates, and overall survival. Thus, these data reveal a previously unknown dependence of tumors on myeloid GCN2 signals for protection from immune attack.

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 score0.002
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.003
GPT teacher head0.205
Teacher spread0.201 · 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

Citations186
Published2019
Admission routes2
Has abstractyes

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