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Record W2339748828 · doi:10.4049/jimmunol.1502276

Production of Proinflammatory Cytokines by Monocytes in Liver-Transplanted Recipients with De Novo Autoimmune Hepatitis Is Enhanced and Induces TH1-like Regulatory T Cells

2016· article· en· W2339748828 on OpenAlexaff
Adam S. Arterbery, Awo D. Osafo-Addo, Yaron Avitzur, Maria Ciarleglio, Yanhong Deng, Steven Lobritto, Mercedes Martínez, David A. Hafler, Markus Kleinewietfeld, Udeme D. Ekong

Bibliographic record

VenueThe Journal of Immunology · 2016
Typearticle
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsUniversity of Toronto
FundersNational Center for Advancing Translational SciencesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Cancer Institute
KeywordsProinflammatory cytokineImmunologyFOXP3MonocytePathogenesisBiologyMedicineInflammationImmune system

Abstract

fetched live from OpenAlex

A subset of human regulatory T cells (Tregs) can secrete IFN-γ or IL-17, and thus share features of TH1 or TH17 effector cells and lose suppressive function. The main factors driving this differentiation of Tregs toward a proinflammatory phenotype include IL-12 for TH1-like and IL-6 for TH17-type Tregs. In this study we show that Tregs of patients with de novo autoimmune hepatitis (dAIH) display increased frequencies of proinflammatory IFN-γ and IL-17 cytokines. Irrespective of a fully demethylated FOXP3 locus, Tregs of subjects with dAIH are functionally impaired. In line with the observed Treg phenotype, we detected the presence of two dominant cytokines (IL-12 and IL-6) clustering with CD68(+) monocyte/macrophage cells in livers of subjects with dAIH, and isolated monocytes of subjects with dAIH secrete high levels of proinflammatory IL-12 and IL-6, suggesting that this inflammatory milieu is key for functional impairment of Tregs. Importantly, the blockade of IFN-γ partially restores suppressive function of Tregs of subjects with dAIH, indicating that monocyte/macrophage-derived triggers might play a central role in Treg dysfunction and pathogenesis of dAIH.

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 score0.445
Threshold uncertainty score0.255

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 head0.212
Teacher spread0.204 · 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

Citations67
Published2016
Admission routes1
Has abstractyes

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