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Record W2993246221 · doi:10.1016/j.celrep.2019.11.010

Interleukin-27 Is Essential for Type 1 Diabetes Development and Sjögren Syndrome-like Inflammation

2019· article· en· W2993246221 on OpenAlexaff
Ashley E. Ciecko, Bardees M. Foda, Jennifer Barr, Sheela Ramanathan, Mark A. Atkinson, David Serreze, Aron M. Geurts, Scott M. Lieberman, Yi‐Guang Chen

Bibliographic record

VenueCell Reports · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiabetes and associated disorders
Canadian institutionsUniversité de Sherbrooke
FundersNational Center for Research ResourcesChildren’s Hospital of Wisconsin Research InstituteRoy J. and Lucille A. Carver College of Medicine, University of IowaNational Institute of Allergy and Infectious DiseasesNational Eye InstituteHolden Comprehensive Cancer Center, University of IowaComprehensive Cancer Center, City of HopeNational Cancer InstituteNational Institutes of HealthCenter for Outcomes Research and Evaluation, Yale School of MedicineNational Institute of Diabetes and Digestive and Kidney DiseasesUniversity of IowaAmerican Association of Immunologists
KeywordsNodPathogenesisInflammationNOD miceType 1 diabetesImmunologyInterleukinBiologyDiabetes mellitusType 2 diabetesMedicineCytokineEndocrinology

Abstract

fetched live from OpenAlex

strains are completely resistant to T1D. IL-27 from myeloid cells and IL-27 signaling in T cells are critical for T1D development. IL-27 directly alters the balance of regulatory T cells (Tregs) and T helper 1 (Th1) cells in pancreatic islets, which in turn modulates the diabetogenic activity of CD8 T cells. IL-27 also directly enhances the effector function of CD8 T cells within pancreatic islets. In addition to T1D, IL-27 signaling in T cells is also required for lacrimal and salivary gland inflammation in NOD mice. Our study reveals that IL-27 contributes to autoimmunity in NOD mice through multiple mechanisms and provides substantial evidence to support its pathogenic role in human T1D.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.274
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.005
GPT teacher head0.211
Teacher spread0.206 · 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 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

Citations55
Published2019
Admission routes1
Has abstractyes

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