Bibliographic record
Abstract
Immune thrombocytopenia (ITP) is an autoimmune disease characterized by a low platelet count in the absence of secondary or other explanations of thrombocytopenia. Platelet destruction in ITP is thought to be caused predominantly by pathogenic factors present in circulation. In particular, platelets are understood to be targets of immunoglobulin G (IgG) anti-platelet autoantibodies, which may then engage Fc gamma receptors (FcγRs) on macrophages in the spleen, triggering clearance of platelets by phagocytosis. However, direct demonstrations of splenic macrophage phagocytosis of autoantibody opsonized platelets are lacking. In addition, it is unclear which specific FcγRs are utilized in the phagocytosis of platelets. There is also the potential of other humoral factors to mediate or augment macrophage phagocytosis of platelets which remains insufficiently characterized. In this thesis, I focus on the mechanisms of macrophage phagocytosis of IgG opsonized platelets, the utilization of FcγRs in phagocytosis, and potential strategies to prevent this process in ITP. In the first study, I characterize the mechanism of anti-CD44 antibodies, a potential ITP therapeutic able to ameliorate passive IgG-mediated ITP in mice. I demonstrate that anti-CD44 inhibits macrophage FcγRs through an apparent blockade-type mechanism via the Fc region of the anti-CD44 antibody, preventing macrophage phagocytosis of platelets and explaining amelioration of passive IgG-mediated murine ITP. In the second study, I demonstrate that splenic macrophages from ITP patients utilize FcγRI and FcγRIII in the phagocytosis of platelets opsonized with ITP serum positive for IgG anti-platelet autoantibodies. I suggest that the individual or combined blockade of FcγRI and FcγRIII may be an effective therapeutic strategy for ITP. In the third study, I evaluate the ability of serum from ITP patients as a source of ITP humoral thrombocytopenic factors to mediate macrophage phagocytosis of platelets. I demonstrate that 40% of ITP sera mediated phagocytosis greater than the normal human serum mean plus two standard deviations, while an additional 35% mediated phagocytosis greater than one standard deviation. I further characterized ITP sera for IgG anti-platelet autoantibodies and serum pentraxins to evaluate their contribution to macrophage phagocytosis.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".