T-cell responses following Natural Influenza Infection or Vaccination in Solid Organ Transplant Recipients
Bibliographic record
Abstract
Abstract Little is known about cell-mediated immune responses to natural influenza infection in solid organ transplant (SOT) patients. The aim of our study was to evaluate the CD4+ and CD8+ responses to influenza A and B infection in a cohort of SOT patients. We collected peripheral blood mononuclear cells at influenza diagnosis and four weeks later from 31 SOT patients during the 2017–2018 influenza season. Infection-elicited influenza-specific CD4+ and CD8+ T-cell responses were measured using flow cytometry and intracellular cytokine staining and compared to responses following influenza vaccine in SOT patients. Natural infection was associated with a significant increase in CD4+ T-cell responses. For example, polyfunctional cells increased from 21 to 782 and from 193 to 1436 cells per 106 CD4+ T-cells among influenza A/H3N2 and B-infected patients (p = 0.006 and 0.004 respectively). Moreover, infection-elicited CD4+ responses were superior than vaccine-elicited responses for influenza A/H1N1 (931 vs 1; p = 0.026), A/H3N2 (647 vs 1; p = 0.041) and B (619 vs 1; p = 0.004). Natural influenza infection triggers a significant increase in CD4+ T-cell responses in SOT patients. Infection elicits significantly stronger CD4+ responses compared to the influenza vaccine and thereby likely elicits better protection against reinfection.
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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 | 0.000 |
| 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".