Influenza virus H1N1 activates platelets through FcγRIIA signaling and thrombin generation
Bibliographic record
Abstract
Platelets play crucial functions in hemostasis and the prevention of bleeding. During H1N1 influenza A virus infection, platelets display activation markers. The platelet activation triggers during H1N1 infection remain elusive. We observed that H1N1 induces surface receptor activation, lipid mediator synthesis, and release of microparticles from platelets. These activation processes require the presence of serum/plasma, pointing to the contribution of soluble factor(s). Considering that immune complexes in the H1N1 pandemic were reported to play a pathogenic role, we assessed their contribution in H1N1-induced platelet activation. In influenza-immunized subjects, we observed that the virus scaffolds with immunoglobulin G (IgG) to form immune complexes that promote platelet activation. Mechanistically, this activation occurs through stimulation of low-affinity type 2 receptor for Fc portion of IgG (FcγRIIA), a receptor for immune complexes, independently of thrombin. Using a combination of in vitro and in vivo approaches, we found that the antibodies from H3N2-immunized mice activate transgenic mouse platelets that express FcγRIIA when put in the presence of H1N1, suggesting that cross-reacting influenza antibodies suffice. Alternatively, H1N1 can activate platelets via thrombin formation, independently of complement and FcγRIIA. These observations identify both the adaptive immune response and the innate response against pathogens as 2 intertwined processes that activate platelets during influenza infections.
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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 | 0.000 | 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 | 0.000 |
| 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".