Fc-independent Phagocytosis: Implications for IVIG and other Therapies in Immune-mediated Thrombocytopenia
Bibliographic record
Abstract
Phagocytes were first described by Dr. Metchnikoff in 1873. The roles of phagocytes in innate and adaptive immunity have been well established to date, although the molecular mechanisms involved in initiating phagocytosis (through Fc or other receptors) remain to be further explored. Phagocytes in the reticuloendothelial system, particularly macrophages, have been implicated in the clearance of senescent blood cells. The destruction of these cells may be primarily mediated via an Fc-independent pathway. Fc-independent phagocytosis may also play an important role in platelet clearance, including in autoimmune thrombocytopenia (ITP), and in clearance of platelet-rich emboli detached from sites of vascular injury. In ITP, the two major platelet auto-antigens have been located on glycoprotein (GP)IIbIIIa and the GPIb complex. It has been demonstrated that anti-GPIb antibodies, in contrast to anti-GPIIbIIIa, can induce thrombocytopenia in an Fc-independent manner. We further demonstrated in an animal model that intravenous IgG (IVIG) is unable to ameliorate thrombocytopenia caused by most anti-GPIb antibodies, despite its efficacy in anti- GPIIbIIIa-mediated thrombocytopenia. Our data was supported by subsequent retrospective studies with ITP patients by several independent groups. Most recently, we found that anti-GPIb-mediated ITP was also resistant to steroid therapy and that platelet activation and apoptosis induced by anti-GPIb antibodies may be involved in the Fc-independent platelet clearance. Therefore, identification of antibody specificity in patients, e.g. anti-GPIIbIIIa (Fc-dependent) versus anti-GPIb (Fc-independent), may be important for therapies against ITP, as well as other immune-mediated thrombocytopenias.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".