Natural influenza infection produces a greater diversity of humoral responses than vaccination in immunosuppressed transplant recipients
Bibliographic record
Abstract
The humoral immune response to influenza virus infection is complex and may be different compared to the antibody response elicited by vaccination. We analyzed the breadth of IgG and IgA responses in solid organ transplant (SOT) recipients to a diverse collection of 86 influenza antigens elicited by natural influenza A virus (IAV) infection or by vaccination. Antibody levels were quantified using a custom antigen microarray. A total of 120 patients were included: 80 IAV infected (40 A/H1N1 and 40 A/H3N2) and 40 vaccinated. Based on hierarchical clustering analysis, infection with either H1N1 or H3N2 virus showed a more diverse antibody response compared to vaccination. Similarly, H1N1-infected individuals showed a significant IgG response to 27.9% of array antigens and H3N2-infected patients to 43.0% of antigens, whereas vaccination elicited a less broad immune response (7.0% of antigens). Immune responses were not exclusively targeting influenza hemagglutinin (HA) proteins but were also directed against conserved influenza antigens. Serum IgA responses followed a similar profile. This study provides novel data on the breadth of antibody responses to influenza. We also found that the diversity of response is greater in influenza-infected rather than vaccinated patients, providing a potential mechanistic rationale for suboptimal vaccine efficacy in this population.
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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 | 0.000 | 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 | 0.001 |
| 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".