Co-stimulation with LPS or Poly I:C markedly enhances the anti-platelet immune response and severity of fetal and neonatal alloimmune thrombocytopenia
Bibliographic record
Abstract
Fetal and neonatal alloimmune thrombocytopenia (FNAIT) is a life-threatening bleeding disorder caused by maternal antibodies against fetal/neonatal platelets. FNAIT is also linked with miscarriages, although the incidence and mechanisms of fetal death have not been well studied. IntegrinαIIbβ3 (GPIIbIIIa) and the GPIbα complex are major glycoproteins expressed on platelets and are also major antigens targeted in autoimmune thrombocytopenia (ITP), but reported cases of anti-GPIb-mediated FNAIT are rare. Bacterial and viral infections have been causally linked with the pathogenesis of immune-mediated thrombocytopenia (ITP); however, it is unknown whether these infections contribute to the severity of FNAIT. Here, immune responses against platelet antigens were examined by transfusing wild-type (WT) mouse platelets into β3-/- or GPIbα-/- mice. To mimic bacterial or viral infections, lipopolysaccharide (LPS) or polyinosinic:polycytidylic acid (Poly I:C) were injected intraperitoneally following platelet transfusions. The FNAIT model was established by breeding the immunised female mice with WT male mice. We demonstrated for the first time that the platelet GPIbα has lower immunogenicity compared to β3 integrin. Interestingly, co-stimulation with LPS or Poly I:C markedly enhanced the immune response against platelet GPIbα and caused severe pathology of FNAIT (i.e. miscarriages). LPS or Poly I:C also enhanced the immune response against platelet β3 integrin. Our data suggest that bacterial and viral infections facilitate the anti-platelet GPIbα response, which may lead to a severe non-classical FNAIT (i.e. miscarriage but not neonatal bleeding) that has not been adequately reported in humans.
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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) | 0.001 | 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".