Platelet Activating Immune Complexes Identified in COVID-19 Associated Coagulopathy
Bibliographic record
Abstract
Abstract Thrombosis is a prominent feature of coronavirus disease 2019 (COVID-19) and often occurs in patients who are critically ill; however, the mechanism is unclear. This COVID-19 associated coagulopathy (CAC) shares features with heparin-induced thrombocytopenia (HIT), including mild thrombocytopenia and thrombosis. We thus tested 10 CAC patients for anti-PF4/heparin antibodies and functional platelet activation. HIT was excluded in all samples based on anti-PF4/heparin antibody and serotonin release assay results. Of note, 6 CAC patients demonstrated platelet activation by the serotonin release assay that was inhibited by FcγRIIA receptor blockade, confirming an IgG-specific immune complex (IC)-mediated reaction. Platelet activation was independent of heparin, but inhibitable by both therapeutic and high dose heparin. All 6 samples were positive for IgG-specific antibodies targeting the receptor binding domain (RBD) or the spike protein of the SARS-CoV-2 virus. These samples were additionally characterized by significant endothelial activation, shown by increased von Willebrand factor antigen and activity. ADAMTS13 activity was not severely reduced, and ADAMTS13 inhibitors were not present, ruling out thrombotic thrombocytopenic purpura. Our study thus identifies platelet-activating ICs as a mechanism that contributes to CAC thrombosis. Scientific Category Thrombosis and Hemostasis Key Points Patients with COVID-19 thrombosis have immune complexes that activate platelets through FcγRIIA signalling Patients with COVID-19 thrombosis demonstrate increased VWF antigen and activity that is not related to severe ADAMTS13 reduction
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".