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Record W2941388405 · doi:10.1089/jir.2018.0166

Poly(I:C)-Mediated Death of Human Prostate Cancer Cell Lines Is Induced by Interleukin-27 Treatment

2019· article· en· W2941388405 on OpenAlexafffund
Olena Kourko, Robin Smyth, Daniela Cino, Kyle Seaver, Carlene Petes, So Young Eo, Sameh Basta, Katrina Gee

Bibliographic record

VenueJournal of Interferon & Cytokine Research · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
Topicinterferon and immune responses
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaProstate Cancer Fight FoundationProstate Cancer Foundation
KeywordsDU145Programmed cell deathCancer researchProstate cancerCytokineCancer cellCell cultureImmune systemBiologyImmunogenic cell deathCancerApoptosisImmunologyChemistryLNCaPBiochemistry

Abstract

fetched live from OpenAlex

Interleukin (IL)-27 is a promising anti-cancer cytokine with therapeutic potential. Exhibiting overlapping properties with type I and II interferons (IFNs), IL-27 impacts cancer cell viability and immune cell activity. Known to modulate toll-like receptor (TLR) expression, we investigated whether IL-27 affected TLR-mediated death in cancer cells. Using DU145 and PC3 cell lines as models of prostate cancer, we investigated whether IL-27 and IFN-γ affect TLR3-mediated cell death. Our results demonstrate that when IL-27 or IFN-γ is added with polyinosinic-polycytidylic acid [poly(I:C)], type I IFN (IFN-I) expression increases concurrently with cell death. IL-27 and IFN-γ enhanced TLR3 expression, suggesting a mechanism for sensitization to cell death. Further, PC3 cells were more sensitive to IL-27/poly(I:C)-induced cell death compared with DU145 cells. This correlated with higher production of IFN-β and inducible protein-10 versus IL-6 in response to treatment of PC3 cells compared with DU145. Taken together, this study demonstrates a potential role for IL-27 in the treatment of prostate cancer.

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 categoriesInsufficient payload (model declined to judge)
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 score0.024
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.053
GPT teacher head0.371
Teacher spread0.318 · 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.

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

Citations17
Published2019
Admission routes2
Has abstractyes

Explore more

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