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Record W2125028785 · doi:10.1182/blood-2006-05-023796

A role for IL-1 receptor antagonist or other cytokines in the acute therapeutic effects of IVIg?

2006· article· en· W2125028785 on OpenAlexaff
Andrew R. Crow, Seng Song, John W. Semple, John Freedman, Alan H. Lazarus

Bibliographic record

VenueBlood · 2006
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsCanadian Blood ServicesUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsReceptor antagonistImmunologyReceptorMedicineMechanism of actionInterleukinImmune systemInterleukin 1 receptor antagonistProinflammatory cytokineCytokineBiologyAntagonistInflammationIn vitroInternal medicine

Abstract

fetched live from OpenAlex

The exact mechanism of action of IVIg in the amelioration of immune thrombocytopenic purpura (ITP) is still unclear. Studies have suggested that IVIg may function through the regulation of cytokines, including interleukin-1 receptor antagonist (IL-1Ra), an inhibitor of phagocytosis. Using a mouse model relevant to ITP, we confirm an increase in mouse serum levels of IL-1Ra after exposure to IVIg, yet a recombinant IL-1Ra did not ameliorate thrombocytopenia. IVIg has also been shown to affect the expression of other regulatory cytokines. We have also recently established that IVIg specifically targets activating FcgammaRs on CD11c+ dendritic cells (DCs) as its primary mechanism of action in the amelioration of murine ITP. Herein, we show that IVIg functions therapeutically in mice lacking specific cytokines or their receptors that can potentially affect DC/macrophage function (IL-1 receptor, IL-4, IL-10, IL-12beta, TNF-alpha, IFN-gamma receptor, MIP-1alpha). This suggests that while IVIg may mediate the release of a variety of cytokines, the cytokines tested do not directly participate in the mechanism of IVIg action.

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 score0.071
Threshold uncertainty score0.214

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 head0.008
GPT teacher head0.257
Teacher spread0.249 · 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

Citations84
Published2006
Admission routes1
Has abstractyes

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