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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".