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Record W2285127602 · doi:10.1111/cei.12735

Pathogenic T helper type 17 cells contribute to type 1 diabetes independently of interleukin-22

2015· article· en· W2285127602 on OpenAlexafffund
Stacey Bellemore, Enayat Nikoopour, Olga Krougly, Edwin Lee‐Chan, Lynette A. Fouser, Bhagirath Singh

Bibliographic record

VenueClinical & Experimental Immunology · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiabetes and associated disorders
Canadian institutionsWestern University
FundersCanadian Institutes of Health ResearchPfizer
KeywordsNOD miceInterleukin 21ImmunologyAdoptive cell transferNodInterleukin 17Interleukin 12BiologyInterleukin 22Interleukin 3Pancreatic isletsInterleukin 4InterleukinCytotoxic T cellT cellCytokineAntibodyImmune systemEndocrinologyAutoimmune diseaseDiabetes mellitusIn vitroIslet

Abstract

fetched live from OpenAlex

We have shown that pathogenic T helper type 17 (Th17) cells differentiated from naive CD4(+) T cells of BDC2·5 T cell receptor transgenic non-obese diabetic (NOD) mice by interleukin (IL)-23 plus IL-6 produce IL-17, IL-22 and induce type 1 diabetes (T1D). Neutralizing interferon (IFN)-γ during the polarization process leads to a significant increase in IL-22 production by these Th17 cells. We also isolated IL-22-producing Th17 cells from the pancreas of wild-type diabetic NOD mice. IL-27 also blocked IL-22 production from diabetogenic Th17 cells. To determine the functional role of IL-22 produced by pathogenic Th17 cells in T1D we neutralized IL-22 in vivo by using anti-IL-22 monoclonal antibody. We found that blocking IL-22 did not alter significantly adoptive transfer of disease by pathogenic Th17 cells. Therefore, IL-22 is not required for T1D pathogenesis. The IL-22Rα receptor for IL-22 however, increased in the pancreas of NOD mice during disease progression and based upon our and other studies we suggest that IL-22 may have a regenerative and protective role in the pancreatic islets.

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 score0.039
Threshold uncertainty score0.758

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 head0.025
GPT teacher head0.329
Teacher spread0.304 · 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

Citations34
Published2015
Admission routes2
Has abstractyes

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