Maternal anti-platelet β3 integrins impair angiogenesis and cause intracranial hemorrhage
Bibliographic record
Abstract
Fetal and neonatal alloimmune thrombocytopenia (FNAIT) is a life-threatening disease in which intracranial hemorrhage (ICH) is the major risk. Although thrombocytopenia, which is caused by maternal antibodies against β3 integrin and occasionally by maternal antibodies against other platelet antigens, such as glycoprotein GPIbα, has long been assumed to be the cause of bleeding, the mechanism of ICH has not 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-mediated, 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 signaling, and increased endothelial cell apoptosis, all of which were abrogated by maternal administration of intravenous immunoglobulin (IVIG). ICH and impairment of retinal angiogenesis were 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 that fetal hemostasis is distinct and that impairment of angiogenesis rather than thrombocytopenia likely causes FNAIT-associated ICH. Additionally, our results indicate that maternal IVIG therapy can effectively prevent this devastating disorder.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".