Anti–platelet factor 4 antibody class and subclass in patients with vaccine-induced immune thrombocytopenia and thrombosis
Bibliographic record
Abstract
Background Vaccine-induced immune thrombocytopenia and thrombosis (VITT) is a rare complication of adenoviral vector–based SARS-CoV-2 vaccines caused by platelet-activating anti–platelet factor 4 (PF4, CXCL4) antibodies. Despite similarities to heparin-induced thrombocytopenia (HIT), the humoral characteristics of VITT antibodies remain largely unknown. Objectives In this study, we described the distribution of antibody classes and subclasses in patients with VITT and compared the findings with those in published reports from patients with HIT. Methods We studied acute samples from patients diagnosed with VITT ( n = 31) from Canada between March and July 2021. We quantified anti-PF4 antibody class and subclass distributions using an in-house anti-PF4 enzyme immunoassay. We then compared our results with clinical severity, such as time of symptom onset, platelet counts, and thrombosis. Results All VITT patients ( n = 31) had anti-PF4 immunoglobulin G (IgG) antibodies. Of those, 16 (51.6%) also had immunoglobulin M and 5 (16.1%) also had immunoglobulin A. For anti-PF4 IgG subclasses, of the 31 VITT patients with IgG anti-PF4, 28 (90.3%) had IgG1, 20 (64.5%) had IgG2, 4 (12.9%) had IgG3, and 1 (3.2%) had IgG4. No significant correlations were observed between acute-phase clinical characteristics of VITT and different antibody distributions. Conclusion Anti-PF4 antibodies in VITT patients were predominantly IgG, particularly IgG1 and IgG2. Compared with published data on HIT, VITT antibodies were more often IgG2 and less frequently immunoglobulin A. The role of IgG1 and IgG2 anti-PF4 antibodies in VITT pathogenesis remains unknown, but our findings can improve our understanding of VITT immunology including its clinical presentations and aid in designing monoclonal antibodies to study anti-PF4 disorders further.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".