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Record W1990868266 · doi:10.1182/blood-2003-01-0023

IVIg-mediated amelioration of murine ITP via FcγRIIB is independent of SHIP1, SHP-1, and Btk activity

2003· article· en· W1990868266 on OpenAlexaff
Andrew R. Crow, Seng Song, John Freedman, Cheryl D. Helgason, R. Keith Humphries, Katherine A. Siminovitch, Alan H. Lazarus

Bibliographic record

VenueBlood · 2003
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsOntario Institute for Cancer Research
Fundersnot available
KeywordsBruton's tyrosine kinaseFc receptorCell biologyReceptorSignal transductionProtein tyrosine phosphataseMonocyteChemistryImmunologySykTyrosine kinaseImmune systemBiologyBiochemistry

Abstract

fetched live from OpenAlex

It has been established that amelioration of murine immune thrombocytopenia purpura (ITP) by IVIg is dependent on the inhibitory receptor FcgammaRIIB. Co-cross-linking of the FcgammaRIIB with the B-cell receptor complex or with FcepsilonRI in mast cells results in cell inhibition, which is mediated by recruitment of the inositol phosphatase SHIP1 to the cytoplasmic tail of the FcgammaR. The FcgammaRIIB can also associate with protein tyrosine phosphatase SHP-1 as a potential secondary target of the receptor. Alternatively, homoaggregation of FcgammaRIIB can induce a proapoptotic state in B cells that is dependent on the presence of Bruton tyrosine kinase (Btk), a kinase also expressed in monocytes. We sought to determine if these signaling pathways may direct IVIg-mediated FcgammaRIIB-dependent regulation of in vivo monocyte function in a murine model of ITP in which IVIg functions in an FcgammaRIIB-dependent manner. We demonstrate that mice deficient in SHIP1, SHP-1, and Btk respond to the ameliorating effects of IVIg with the same kinetics as control mice. We conclude that IVIgmediated inhibitory pathways operating via monocyte FcgammaRIIB may involve a transmembrane signaling pathway different from that of B cells.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.365
Threshold uncertainty score0.380

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 head0.011
GPT teacher head0.244
Teacher spread0.233 · 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

Citations119
Published2003
Admission routes1
Has abstractyes

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