Anti‐idiotype interactions are not required for the protective effect of intravenous immunoglobulin in a murine model of passively transferred immune thrombocytopenia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".