Inhibition of platelet phagocytosis as an in vitro predictor for therapeutic potential of RBC antibodies in murine ITP
Bibliographic record
Abstract
Polyclonal anti-D is a first-line therapy for immune thrombocytopenia (ITP). Monoclonal antibodies are desirable alternatives, but none have yet proven successful despite their ability to opsonize erythrocytes (or red blood cells, RBCs) and cause anemia. Here, we examined 12 murine erythrocyte-specific antibodies of different specificity and subtypes and found that 8 of these antibodies could induce anemia in antigen-positive mice. Of these 8 antibodies, only 5 ameliorated ITP. All antibodies were examined for their in vitro ability to support macrophage-mediated phagocytosis of erythrocytes. Antibodies which supported erythrocyte phagocytosis in vitro successfully ameliorated ITP in vivo. To examine the ability of each antibody to inhibit phagocytosis of platelets, the antibodies were used to sensitize erythrocytes in vitro and these were added to a platelet phagocytosis assay. Antibodies that inhibited platelet phagocytosis in vitro also all ameliorated ITP in vivo. We conclude that inducing anemia is not a sufficient condition for amelioration of ITP but that the antibody's ability to prevent platelet phagocytosis in vitro predicted its ability to ameliorate ITP. We suggest that inhibition of in vitro platelet phagocytosis may prove to be a valuable tool for determining which erythrocyte antibodies would likely be candidates for clinical use in ITP.
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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 | 0.001 | 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".