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Record W3004546115 · doi:10.1016/j.isci.2020.100891

Toll-Interacting Protein Regulates Immune Cell Infiltration and Promotes Colitis-Associated Cancer

2020· article· en· W3004546115 on OpenAlexaff
Christina Begka, Céline Pattaroni, Catherine Mooser, Stéphane Nancey, Kathy D. McCoy, Dominique Velin, Michel H. Maillard

Bibliographic record

VenueiScience · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsUniversity of Calgary
FundersNovartis FoundationUniversité de LausanneAbbVie
KeywordsFOXP3ColitisCancer researchCD8AzoxymethaneImmune systemImmunologyBiologyCancerColorectal cancer

Abstract

fetched live from OpenAlex

Expression of Toll-interacting protein (Tollip), a potent TLR modulator, decreases in patients with inflammatory bowel diseases (IBD), whereas Tollip −/− mice are susceptible to colitis. Tollip expression was shown to be reduced in sporadic adenoma. In contrast, we found variable Tollip expression in patients with colitis-associated adenomas. In Tollip −/− mice challenged to develop colitis-associated cancer (CAC), tumor formation was significantly reduced owing to decreased mucosal proliferative and apoptotic indexes. This protection was associated with blunt inflammatory responses without significant changes in microbial composition. mRNA expression of Cd62l and Ccr5 homing receptors was reduced in colons of untreated Tollip −/− mice, whereas CD62L + CD8 + T cells accumulated in the periphery. In Tollip-deficient adenomas Ctla-4 mRNA expression and tumor-infiltrating CD4 + Foxp3 + regulatory T cell (Treg) were decreased. Our data show that protection from CAC in Tollip-deficient mice is associated with defects in lymphocyte accumulation and composition in colitis-associated adenomas.

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 score0.003
Threshold uncertainty score0.009

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.237
Teacher spread0.224 · 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

Citations20
Published2020
Admission routes1
Has abstractyes

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