Platelet physiology and immunology: pathogenesis and treatment of classical and non-classical fetal and neonatal alloimmune thrombocytopenia
Bibliographic record
Abstract
Abstract: Fetal and neonatal alloimmune thrombocytopenia (FNAIT) is a devastating disorder affecting approximately 0.5–1.5/1,000 live neonates. It occurs due to maternal immune responses against paternally inherited human platelet antigens (HPAs), resulting in low platelet counts, severe bleeding (e.g., intracranial hemorrhage; ICH), intrauterine growth restriction (IUGR), and miscarriage. There are currently 37 known HPAs; and 15 HPAs are located on the integrin β3 subunit. Fetomaternal incompatibilities in this protein have been most frequently reported, in which HPA-1 system accounts for more than 75% of FNAIT cases. The data from our animal models and from human anti-HPA-1a demonstrate that maternal anti-β3 antibodies have an anti-angiogenic effect, and that anti-angiogenesis, not thrombocytopenia, may be the key cause of ICH, suggesting that fetal platelet transfusion may have limited clinical benefits. Anti-β3 antibodies may also damage antigen positive trophoblasts via natural killer (NK) cells, causing placental dysfunction and miscarriage. Notably, anti-GPIbα alloantibody (e.g., anti-HPA-2) may induce platelet cell-based thrombin generation and thrombosis in placenta, leading to miscarriage. The consequences of anti-angiogenesis and pathology in placenta have not been adequately explored but may cause symptoms beyond thrombocytopenia and bleeding disorders, termed non-classical FNAIT. Meanwhile, maternal intravenous IgG (IVIG) transfusion is likely able to block pathogenic antibody transport across placenta, and ameliorate thrombocytopenia in fetal reticuloendothelial system (RES), although it is currently unclear whether IVIG has equal efficacy for all anti-HPAs or other platelet antigens such as CD36. Further research is required to define standard treatment protocols and explore new treatment options, such as anti-HPA-1a prophylaxis, anti-neonatal Fc receptor (FcRn) and anti-NK therapies. In this review, we summarize the current state of literature comprising platelet versatilities and hemostasis, and integrate new discoveries related to FNAIT etiological factors in order to develop better diagnostic and therapeutic strategies against this life-threatening disease.
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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 | 0.000 | 0.000 |
| 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".