A novel murine model of fetal and neonatal alloimmune thrombocytopenia: response to intravenous IgG therapy
Bibliographic record
Abstract
Fetal and neonatal alloimmune thrombo cytopenia (FNAITP) is a life-threatening bleeding disorder caused by maternal antibodies directed against fetal platelet antigens. The immunoreactive epitopes in FNAITP are primarily located in the extracellular regions of the platelet glycoprotein IIIa (beta3 integrin). Here we have established a novel animal model of FNAITP using beta3 integrin-deficient (beta3-/-) mice. We demonstrated first that these mice are immunoresponsive to beta3 integrin; beta3-/- mice transfused with wild-type platelets generated specific anti-beta3 antibodies which were able to induce thrombocytopenia in wild-type mice. Subsequently, beta3-/- female mice (both naive and immunized) were bred with wild-type male mice to recapitulate the features of FNAITP. The titer of generated maternal antibodies correlated with the severity of FNAITP. High titer maternal anti-beta3 anti-bodies caused severe fetal thrombocytopenia, intracranial hemorrhage, and even miscarriage. Furthermore, maternal administration of intravenous immunoglobulin G (IgG) ameliorated FNAITP and down-regulated pathogenic antibodies in both the maternal and fetal circulations.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".