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Record W2899337283 · doi:10.1210/en.2018-00649

Immune Response to Extracellular Vesicles From Human Islets of Langerhans in Patients With Type 1 Diabetes

2018· article· en· W2899337283 on OpenAlexafffund
Alissa K. Rutman, Sarita Negi, Marco Gasparrini, Craig Hasilo, Jean Tchervenkov, Steven Paraskevas

Bibliographic record

VenueEndocrinology · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsMcGill University Health CentreCanadian Natural Resources
FundersMcGill University Health CentreCanadian Donation and Transplantation Research Program
KeywordsImmune systemIsletPeripheral blood mononuclear cellProinflammatory cytokineT cellImmunologyBiologyAutoantibodyCytokineEndocrinologyAntibodyInternal medicineInflammationMedicineDiabetes mellitusBiochemistryIn vitro

Abstract

fetched live from OpenAlex

The autoimmune response that characterizes type 1 diabetes (T1D) has no clear cause. Extracellular vesicles (EVs) play an important role in triggering the immune response in other contexts. Here, we propose a model by which EVs isolated from human islets stimulate proinflammatory immune responses and lead to peripheral blood mononuclear cell (PBMC) activation. We show that human islet EVs are internalized by monocytes and B cells and lead to an increase in T-helper 1, 2, and 17 cytokine expression, as well as T and B cell proliferation. Importantly, we demonstrate memory T and B cell activation by EVs selectively in PBMCs of patients with T1D. Additionally, human islet EVs induce an increase in antibodies against glutamic acid decarboxylase 65 (GAD65) in T1D PBMCs. Furthermore, pretreatment of T1D PBMCs with ibrutinib, an inhibitor of Bruton tyrosine kinase, dampens EV-induced memory B cell activation and GAD65 antibody production. Collectively, our findings indicate a role for human islet EVs in mediating activation of B and T cells and GAD65 autoantibody production.

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 score0.226
Threshold uncertainty score0.580

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 head0.007
GPT teacher head0.237
Teacher spread0.230 · 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

Citations50
Published2018
Admission routes2
Has abstractyes

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