Toward a prophylaxis against fetal and neonatal alloimmune thrombocytopenia: induction of antibody‐mediated immune suppression and prevention of severe clinical complications in a murine model
Bibliographic record
Abstract
BACKGROUND: Fetal and neonatal alloimmune thrombocytopenia (FNAIT) is a severe bleeding disorder caused by maternal antibody-mediated destruction of fetal or neonatal platelets (PLTs). Results from our recent large screening study suggest that the pathophysiology of FNAIT is more similar to hemolytic disease of the fetus and newborn (HDFN) than previously thought. Immunization against HPA-1a might therefore be preventable by a prophylactic regimen of inducing antibody-mediated immune suppression (AMIS), which has been documented to be a useful prophylaxis against HDFN. This preclinical proof-of-concept study investigated whether passive administration of anti-β3 integrin could induce AMIS and thereby prevent clinical complications of FNAIT. STUDY DESIGN AND METHODS: A murine model of FNAIT using β3 integrin (GPIIIa)-deficient (β3-/-) mice was employed for this study. AMIS in β3-/- mice was induced by intravenous administration of human anti-HPA-1a immunoglobulin G or murine anti-β3 antisera given as prophylaxis after transfusion of HPA-1a-positive human PLTs or murine wild-type PLTs, respectively. RESULTS: AMIS against both human and murine PLT antigens was induced using this prophylactic approach, reducing the amount of maternal PLT antibodies by up to 90%. Neonatal PLT counts were significantly increased and pregnancy outcome was improved in a dose-dependent manner. The incidence of intracranial hemorrhage, miscarriage, and dead-born pups in mice receiving high-dose prophylaxis was reduced to that of normal controls. We also observed that the severity of thrombocytopenia inversely correlated with birth weight. CONCLUSION: This work conceptually proves that prophylactic administration of PLT antibodies induces AMIS and prevents poor pregnancy outcome in FNAIT.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".