Antiplatelet antibody‐induced thrombocytopenia does not correlate with megakaryocyte abnormalities in murine immune thrombocytopenia
Bibliographic record
Abstract
Immune thrombocytopenia (ITP) is an autoimmune bleeding disorder characterized by increased peripheral immune platelet destruction and megakaryocyte defects in the bone marrow. Although ITP was originally thought to be primarily due to antibody-mediated autoimmunity, it is now clear that T cells also play a significant role in the disease. However, the exact interplay between platelet destruction, megakaryocyte dysfunction and the elements of both humoral and cell-mediated immunity in ITP remains incompletely defined. While most studies have focused on immune platelet destruction in the spleen, an additional possibility is that the antiplatelet antibodies can also destroy bone marrow megakaryocytes. To address this, we negated the effects of T cells by utilizing an in vivo passive ITP model where BALB/c mice were administered various anti-αIIb, anti-β3 or anti-GPIb antibodies or antisera and platelet counts and bone marrow megakaryocytes were enumerated. Our results show that after 24 hours, all the different antiplatelet antibodies/sera induced variable degrees of thrombocytopenia in recipient mice. Compared with naïve control mice, however, histological examination of the bone marrow revealed that only 2 antibody preparations (mouse-anti-mouse β3 sera and an anti- αIIb monoclonal antibody (MWReg30) could affect bone marrow megakaryocyte counts. Our study shows that while most antiplatelet antibodies induce acute thrombocytopenia, the majority of them do not affect the number of megakaryocytes in the bone marrow. This suggests that other mechanisms may be responsible for megakaryocyte abnormalities seen during immune thrombocytopenia.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".