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Record W2112682314 · doi:10.3899/jrheum.110981

High-dose Intravenous Immunoglobulin Treatment Increases Regulatory T Cells in Patients with Eosinophilic Granulomatosis with Polyangiitis

2012· article· en· W2112682314 on OpenAlexvenueno aff
Naomi Tsurikisawa, Hiroshi Saito, Chiyako Oshikata, Takahiro Tsuburai, Kazuo Akiyama

Bibliographic record

VenueThe Journal of Rheumatology · 2012
Typearticle
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGranulomatosis with polyangiitisIL-2 receptorEosinophilicImmunologyFOXP3VasculitisAntibodyInternal medicineMononeuritis MultiplexMicroscopic polyangiitisGastroenterologyMepolizumabEosinophilImmune systemPathologyT cellAsthmaDisease

Abstract

fetched live from OpenAlex

OBJECTIVE: We studied the effects of intravenous immunoglobulin (IVIG) treatment on clinical symptoms and regulatory T (Treg) cell frequency in patients with eosinophilic granulomatosis with polyangiitis (EGPA). METHODS: Twenty-two EGPA patients with severe mononeuritis multiplex or cardiac dysfunction received IVIG therapy combined with conventional therapy (corticosteroid, immunosuppressants, or both). As a control, 24 EGPA patients without severe vasculitic symptoms were treated with conventional therapy. Before, during, and after treatment, we determined percentages of Treg cells and other relevant cells in patients' peripheral blood. RESULTS: The frequency of CD25+ among CD4+ T cells was lower at onset in the study group than in controls but increased significantly after IVIG treatment, relative to controls. The frequency of CD25+ among CD4+ T cells correlated with the frequency of FOXP3+ among CD4+ T cells and interleukin 10 produced by CD25+CD4+ T cells. CONCLUSION: The increase in Treg cells seen with the combination of IVIG and conventional therapy may promote remission in EGPA.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.004
GPT teacher head0.194
Teacher spread0.190 · 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 designObservational
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

Citations74
Published2012
Admission routes1
Has abstractyes

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