Humoral and Cell-Mediated Immunity to Pandemic H1N1 Influenza in a Canadian Cohort One Year Post-Pandemic: Implications for Vaccination
Bibliographic record
Abstract
We evaluated a cohort of Canadian donors for T cell and antibody responses against influenza A/California/7/2009 (pH1N1) at 8-10 months after the 2nd pandemic wave by flow cytometry and microneutralization assays. Memory CD8 T cell responses to pH1N1 were detectable in 58% (61/105) of donors. These responses were largely due to cross-reactive CD8 T cell epitopes as, for those donors tested, similar recall responses were obtained to A/California 2009 and A/PR8 1934 H1N1 Hviruses. Longitudinal analysis of a single infected individual showed only a small and transient increase in neutralizing antibody levels, but a robust CD8 T cell response that rose rapidly post symptom onset, peaking at 3 weeks, followed by a gradual decline to the baseline levels seen in a seroprevalence cohort post-pandemic. The magnitude of the influenza-specific CD8 T cell memory response at one year post-pandemic was similar in cases and controls as well as in vaccinated and unvaccinated donors, suggesting that any T cell boosting from infection was transient. Pandemic H1-specific antibodies were only detectable in approximately half of vaccinated donors. However, those who were vaccinated within a few months following infection had the highest persisting antibody titers, suggesting that vaccination shortly after influenza infection can boost or sustain antibody levels. For the most part the circulating influenza-specific T cell and serum antibody levels in the population at one year post-pandemic were not different between cases and controls, suggesting that natural infection does not lead to higher long term T cell and antibody responses in donors with pre-existing immunity to influenza. However, based on the responses of one longitudinal donor, it is possible for a small population of pre-existing cross-reactive memory CD8 T cells to expand rapidly following infection and this response may aid in viral clearance and contribute to a lessening of disease severity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".