Monoclonal IgG can ameliorate immune thrombocytopenia in a murine model of ITP: an alternative to IVIG
Bibliographic record
Abstract
Intravenous immunoglobulin (IVIG) is used to treat immune thrombocytopenia resulting from a variety of autoimmune and nonautoimmune diseases such as idiopathic thrombocytopenic purpura (ITP), heparin-induced thrombocytopenia, and posttransfusion purpura. IVIG is a limited resource and although considered safe, may nevertheless carry some risk of transferring disease. Its high cost makes monoclonal antibodies, capable of mimicking the clinical effects of IVIG, highly desirable. We show here, using a murine model of ITP, that selected monoclonal antibodies can protect against thrombocytopenia. SCID mice were pretreated with 1 of 21 monoclonal antibodies before induction of thrombocytopenia by antiplatelet antibody. Four antibodies reacted with the CD24 antigen on erythrocytes. Two antibodies were of the IgM class, and although one IgM antibody caused a minimal degree of anemia (P <.05), neither antibody ameliorated immune thrombocytopenia. One of 2 anti-CD24 antibodies of the IgG class ameliorated immune thrombocytopenia and blocked reticuloendothelial system function at the same doses that protected against thrombocytopenia. Some antibodies reactive with other circulating cell types also protected against immune-mediated thrombocytopenia while no antibody without a distinct target antigen in the mice was protective. Protective monoclonal antibodies significantly prevented thrombocytopenia at down to a 1000-fold lower dose (200 microg/kg) as compared with standard IVIG treatment (2 g/kg). It is concluded that monoclonal IgG with specificity for a circulating cellular target antigen may provide an alternative therapeutic approach to treating immune thrombocytopenia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".