The split-virus influenza vaccine activates Fcγ receptors instead of Toll-like receptors (VAC2P.930)
Bibliographic record
Abstract
Abstract Seasonal influenza vaccination is the most common medical procedure targeting the immune system and yet the extent to which influenza vaccination activates innate immunity in humans is not fully understood. Currently, the most prevalent formulations of the vaccine consist of degraded or “split” viral particles often prepared without any adjuvants. We sought to determine whether the unadjuvanted split influenza vaccine activates innate immune receptors—specifically Toll-like receptors. A mass-cytometry (CyTOF) based proteomic profiling platform was developed and used to compare signaling pathway activation and cytokine production between the split influenza vaccine and a prototypical TLR response in human whole-blood (ex vivo). This analysis revealed that the split vaccine rapidly and potently activates multiple immune cell types but yields a proteomic signature distinct from TLR activation. Importantly, vaccine induced activity was dependent upon the presence of human sera indicating that a serum factor was necessary for vaccine-dependent immune activation. We found this serum factor to be human antibodies specific for influenza proteins and therefore immediate immune activation by the split vaccine is immune-complex dependent. These studies demonstrate that influenza vaccine splitting inactivates any microbial adjuvants endogenous to influenza but potentially elicits a potent immune modulator by facilitating the rapid formation of immune complexes.
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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) | 0.002 | 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".