The maternal immune response to fetal platelet GPIbα causes frequent miscarriage in mice that can be prevented by intravenous IgG and anti-FcRn therapies
Bibliographic record
Abstract
Fetal and neonatal immune thrombocytopenia (FNIT) is a severe bleeding disorder caused by maternal antibody-mediated destruction of fetal/neonatal platelets. It is the most common cause of severe thrombocytopenia in neonates, but the frequency of FNIT-related miscarriage is unknown, and the mechanism(s) underlying fetal mortality have not been explored. Furthermore, although platelet αIIbβ3 integrin and GPIbα are the major antibody targets in immune thrombocytopenia, the reported incidence of anti-GPIbα-mediated FNIT is rare. Here, we developed mouse models of FNIT mediated by antibodies specific for GPIbα and β3 integrin and compared their pathogenesis. We found, unexpectedly, that miscarriage occurred in the majority of pregnancies in our model of anti-GPIbα-mediated FNIT, which was far more frequent than in anti-β3-mediated FNIT. Dams with anti-GPIbα antibodies exhibited extensive fibrin deposition and apoptosis/necrosis in their placentas, which severely impaired placental function. Furthermore, anti-GPIbα (but not anti-β3) antiserum activated platelets and enhanced fibrin formation in vitro and thrombus formation in vivo. Importantly, treatment with either intravenous IgG or a monoclonal antibody specific for the neonatal Fc receptor efficiently prevented anti-GPIbα-mediated FNIT. Thus, the maternal immune response to fetal GPIbα causes what we believe to be a previously unidentified, nonclassical FNIT (i.e., spontaneous miscarriage but not neonatal bleeding) in mice. These results suggest that a similar pathology may have masked the severity and frequency of human anti-GPIbα-mediated FNIT, but also point to possible therapeutic interventions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".