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Anti‐idiotype interactions are not required for the protective effect of intravenous immunoglobulin in a murine model of passively transferred immune thrombocytopenia

2000· article· en· W2007104080 on OpenAlexaff
Alan H. Lazarus, S. Song, John Freedman, Andrew R. Crow

Bibliographic record

VenueTransfusion Medicine · 2000
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsAntibodyImmunologyIdiotypeMedicineAutoantibodyImmune systemThrombocytopenic purpuraImmune thrombocytopeniaAutoimmunityAutoimmune diseaseMonoclonal antibody

Abstract

fetched live from OpenAlex

The use of intravenous immunoglobulin (IVIg) as a treatment for autoimmune disease was first realized in the reversal of thrombocytopenia in Immune Thrombocytopenic Purpura (ITP) and, based upon this finding, it is currently used to treat an increasing number of autoimmune states and to prevent graft rejection. Previous reports, as well as much recent literature, have implied that the reactivity of variable region‐reactive (anti‐idiotypic) antibodies predominantly accounts for the beneficial effects of IVIg. Anti‐idiotypic antibodies reactive with endogenous Ig, autoantibodies, and those anti‐idiotype antibodies that modulate immune function have been implicated. Herein, we demonstrate that IVIg ameliorates thrombocytopenia in a passively transferred model of ITP (P‐ITP) and that anti‐idiotype antibodies present in IVIg ameliorates thrombocytopenia in a passively transferred model of ITP (P‐ITP) and that anti‐idiotype antibodies present in IVIg are not required for its protective effect. Both IVIg and an anti‐idiotype‐depleted IVIg protected equally against thrombocytopenia. As well, immunocompromised (SCID) mice, which lack endogenous IgM/G/A/D necessary for any idiotype‐anti–idiotype interactions, were protected by IVIg against thrombocytopenia to the same degree as normal mice.

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 score0.181
Threshold uncertainty score0.650

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 head0.024
GPT teacher head0.295
Teacher spread0.271 · 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

Citations0
Published2000
Admission routes1
Has abstractyes

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