Cross-reactive neuraminidase inhibition antibodies against H5N1 by consecutive influenza A imprinting cohorts of the past century: population-based serosurvey, British Columbia, Canada
Bibliographic record
Abstract
Abstract Background Avian influenza of the H5N1 subtype shares substantial relatedness in its neuraminidase (NA) surface protein with human influenza A H1N1 viruses of the past century. Understanding variation in pre-existing anti-N1 antibodies against H5N1 is critical to pandemic risk assessment and preparedness. Methods We used anonymized, residual sera collected equally from ten age groups spanning one to >80 years during an August 2024 cross-sectional serosurvey in British Columbia, Canada. We assessed NA inhibition antibody titres by enzyme-linked lectin assay against H5N1 (N=575), H1N1pdm09 (N=250) and H3N2 (N=205). We compared anti-NA titres by birth (imprinting) cohorts defined in relation to historic N1 and/or N2 exposure opportunities. Results Among participants with median age 32 (IQR: 15-62) years, 404 (70%) had cross-reactive anti-N1 titre >10 against H5N1, with 260 (45%), 182 (32%) and 98 (17%), having titres >40, >80 and >160, respectively. H5N1 titres were consistently lower but strongly associated with H1N1pdm09 (r=0.86; 95%CI:0.82-0.89). Geometric mean titres against H1N1pdm09 and H5N1 peaked among young adults born 1997-2003 (427.9, 100.8), declining to lows among young children born 2015-2023 (20.7, 6.8) and middle-aged adults born 1957-1967 (25.1, 10.7), increasing to similar secondary peak among older adults born pre-1947 (387.3, 81.0). Conclusions A substantial proportion of the population has pre-existing, cross-reactive anti-N1 antibodies against H5N1. We interpret variation by age and imprinting cohorts within a unifying hypothesis, emphasizing the role of historic influenza pandemics in expanding and refining the immune repertoire through heightened attack rates and shifts in immunological hierarchies. Our findings have implications for H5N1 and other zoonotic influenza risk assessment.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".