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Record W2912765323 · doi:10.1002/path.5241

The innate immune architecture of lung tumors and its implication in disease progression

2019· review· en· W2912765323 on OpenAlexafffund
Simon Milette, Pierre Fiset, Logan A. Walsh, Jonathan Spicer, Daniela F. Quail

Bibliographic record

VenueThe Journal of Pathology · 2019
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune cells in cancer
Canadian institutionsMcGill Genome CentreMcGill UniversityMcGill University Health Centre
FundersCanadian Institutes of Health ResearchPfizer CanadaSusan G. KomenMerck CanadaMcGill UniversityMcGill University Health CentreAstraZeneca CanadaCancer Research SocietyCanada Research ChairsAstraZenecaMcMaster UniversityPfizer
KeywordsContext (archaeology)Immune systemLung cancerImmunologyLungInnate immune systemHomeostasisDiseaseMedicineCancerMalignancyBiologyPathologyInternal medicine

Abstract

fetched live from OpenAlex

Lung malignancies are the leading cause of cancer-related mortality. By virtue of its unique physiological function, the lung microenvironment is highly dynamic and constantly subjected to mechanical, chemical and pathogenic stimuli. Thus, the airways rely on highly organized innate defense mechanisms to rapidly protect against pathogens and maintain pulmonary homeostasis. However, in the context of lung malignancy, these defenses often provide collateral inflammatory insults that can foster tumor progression. This review summarizes the interactions between cancer cells, recruited immune cells and tissue-resident cell subpopulations, such as airway epithelial cells and alveolar macrophages, during homeostasis and disease. Furthermore, we examine the role of the lung immune landscape in response to current therapeutic interventions for cancer. Given the prevalence of lung malignancies, we propose that consideration of lung physiology as a whole is necessary to understand and treat these lethal diseases. Copyright © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.328
Teacher spread0.310 · 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 designOther design
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

Citations52
Published2019
Admission routes2
Has abstractyes

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