Maternal Anti-Platelet β3 Integrin Antibodies Impair Angiogenesis and Cause Intracranial Hemorrhage in Fetal and Neonatal Alloimmune Thrombocytopenia
Bibliographic record
Abstract
Abstract Fetal and neonatal alloimmune thrombocytopenia (FNAIT) is a life-threatening disease in which intracranial hemorrhage (ICH) is the major risk. Although thrombocytopenia caused by maternal antibodies against β3 integrin and occasionally against other platelet antigens (e.g. GPIbα) has long been assumed to be the cause of bleeding, the mechanism of ICH has never been adequately explored. Utilizing murine models of FNAIT and a high frequency ultrasound imaging system, we found that ICH only occurred in fetuses and neonates with anti-β3 integrin- but not anti-GPIbα-mediated FNAIT, despite similar thrombocytopenia in both groups. Only anti-β3 integrin-mediated FNAIT reduced brain and retina vessel density, impaired angiogenic signalling, and increased endothelial cell apoptosis; which were abrogated by maternal administration of intravenous immunoglobulin (IVIG). ICH and impairment of retinal angiogenesis was further reproduced in neonates by injection of anti-β3 integrin- but not anti-GPIbα-antisera. Utilizing cultured human endothelial cells, we found that cell proliferation, network formation, and Akt phosphorylation were inhibited only by murine anti-β3 integrin-antisera and human anti-HPA-1a IgG purified from mothers with FNAIT children. Our data suggest fetal hemostasis is unique in that impairment of angiogenesis rather than thrombocytopenia is likely the cause of ICH; importantly maternal IVIG therapy can effectively prevent this devastating disorder. Disclosures No relevant conflicts of interest to declare.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".