Monoclonal IgG Specific for the CD44 Antigen Mimics the Action of IVIg in the Amelioration of ITP.
Bibliographic record
Abstract
Abstract Previous work in our laboratory has shown that both IVIg and “anti-D like” erythrocyte-reactive antibodies ameliorate immune thrombocytopenia (ITP) in a murine model. However, they appear to function through different mechanisms: IVIg is dependent upon the inhibitory Fc receptor, FcγRIIB in the amelioration of ITP, but the anti-erythrocyte antibodies function independently of FcγRIIB expression. We have also demonstrated that anti-erythrocyte antibodies down-modulate the expression of the activating Fc receptor, FcγRIIIA in splenic macrophages during the amelioration of ITP. A monoclonal antibody to CD44, cell surface protein involved in cell homing, cancer progression and inflammation, can also ameliorate murine ITP. Elucidation of the mechanisms of action of anti-CD44 antibodies in the amelioration of ITP would be critical to the successful application of this potential therapy. In this report, two monoclonal antibodies against CD44, KM114 and IM7 were studied in C57BL/6 and FcγRIIB−/− mice to assess their therapeutic activity in treating ITP. Both KM114 and IM7 bound well to splenic cells from all mice tested. Neither KM114 nor IM7 demonstrated any in vitro anti-idiotypic activity which could neutralize the binding of the thrombocytopenia-inducing anti-platelet antibody. At the dose that protected against thrombocytopenia, KM114 did not mediate RES blockade. In contrast, IM7, which did not at all protect against thrombocytopenia, blocked RES. Since FcγRIIB has been documented to play a key role in the function of IVIg in the amelioration of ITP, we questioned the significance of it in the function of KM114. Surprisingly, KM114 was not able to ameliorate ITP in mice genetically deficient in FcγRIIB. These results suggest that, like IVIg, monoclonal antibody against CD44 mediates amelioration of murine thrombocytopenia in a manner dependent upon the inhibitory FcγRIIB rather than via RES blockade.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".