Insights in ChAdOx1 nCoV-19 vaccine-induced immune thrombotic thrombocytopenia
Bibliographic record
Abstract
SARS-CoV-2 vaccine ChAdOx1 nCoV-19 (AstraZeneca) causes a thromboembolic complication termed vaccine-induced immune thrombotic thrombocytopenia (VITT). Using biophysical techniques, mouse models, and analysis of VITT patient samples, we identified determinants of this vaccine-induced adverse reaction. Super-resolution microscopy visualized vaccine components forming antigenic complexes with platelet factor 4 (PF4) on platelet surfaces to which anti-PF4 antibodies obtained from VITT patients bound. PF4/vaccine complex formation was charge-driven and increased by addition of DNA. Proteomics identified substantial amounts of virus production-derived T-REx HEK293 proteins in the ethylenediaminetetraacetic acid (EDTA)-containing vaccine. Injected vaccine increased vascular leakage in mice, leading to systemic dissemination of vaccine components known to stimulate immune responses. Together, PF4/vaccine complex formation and the vaccine-stimulated proinflammatory milieu trigger a pronounced B-cell response that results in the formation of high-avidity anti-PF4 antibodies in VITT patients. The resulting high-titer anti-PF4 antibodies potently activated platelets in the presence of PF4 or DNA and polyphosphate polyanions. Anti-PF4 VITT patient antibodies also stimulated neutrophils to release neutrophil extracellular traps (NETs) in a platelet PF4-dependent manner. Biomarkers of procoagulant NETs were elevated in VITT patient serum, and NETs were visualized in abundance by immunohistochemistry in cerebral vein thrombi obtained from VITT patients. Together, vaccine-induced PF4/adenovirus aggregates and proinflammatory reactions stimulate pathologic anti-PF4 antibody production that drives thrombosis in VITT. The data support a 2-step mechanism underlying VITT that resembles the pathogenesis of (autoimmune) heparin-induced thrombocytopenia.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".