Animal model of fetal and neonatal immune thrombocytopenia: role of neonatal Fc receptor in the pathogenesis and therapy
Bibliographic record
Abstract
Fetal and neonatal immune thrombocytopenia (FNIT) is a severe bleeding disorder in which maternal antibodies cross the placenta and destroy fetal/neonatal platelets. It has been demonstrated that the neonatal Fc receptor (FcRn) regulates immunoglobulin G (IgG) homeostasis and plays an important role in transplacental IgG transport. However, the role of FcRn in the pathogenesis and therapy of FNIT has not been studied. Here, we developed an animal model of FNIT using combined β3 integrin-deficient and FcRn-deficient (β3(-/-)FcRn(-/-)) mice. We found that β3(-/-)FcRn(-/-) mice are immunoresponsive to β3(+/+)FcRn(-/-) platelets. The generated antibodies were β3 integrin specific and were maintained at levels that efficiently induced thrombocytopenia in adult β3(+/+)FcRn(-/-) mice. FNIT was observed when immunized β3(-/-)FcRn(+/+) females were bred with β3(+/+)FcRn(+/+) males, while no FNIT occurred in β3(-/-)FcRn(-/-) females bred with β3(+/+)FcRn(-/-) males, suggesting that FcRn is indispensable for the induction of FNIT. We further demonstrated that fetal FcRn was responsible for the transplacental transport of various IgG isotypes. We found that anti-FcRn antibody and intravenous IgG prevented FNIT, and that intravenous IgG ameliorated FNIT through both FcRn-dependent and -independent pathways. Our data suggest that targeting FcRn may be a potential therapy for human FNIT as well as other maternal pathogenic antibody-mediated diseases.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".