Assessment of cross-reactive neutralizing antibodies induction against H5N1 clade 2.3.4.4b by prior seasonal influenza immunization in retail workers
Bibliographic record
Abstract
ABSTRACT Highly pathogenic avian influenza (HPAI) H5N1 has been a global concern since its emergence in 1997, causing widespread outbreaks in birds and sporadic human infections. The clade 2.3.4.4b H5N1 virus has rapidly expanded across continents, infecting numerous mammalian species. In 2024, it was detected in dairy cattle for the first time in the U.S., along with human cases following exposure. In Canada, the first human case of this avian influenza was reported in a critically ill adolescent in late 2024. No human-to-human transmission has been documented, but concerns persist regarding mutations associated with enhanced virulence and human adaptation. Although seasonal influenza vaccines are not directed against H5N1, studies suggest that pre-existing immunity from prior infections or vaccinations may provide partial protection against severe H5N1 infections through cross-reactive immune response. Given the ongoing circulation of avian influenza and the rise in human infections, this study evaluated the effectiveness of neutralizing antibodies developed against seasonal influenza viruses and their cross-reactivity with recent H5N1 strains. Serum samples from 194 retail sector workers in Quebec, collected between late 2021 and 2022, were analyzed using a microneutralization assay. While strong neutralizing activity was found against seasonal influenza viruses, no neutralizing antibodies were detected against H5N1 strains in either vaccinated or unvaccinated individuals. These findings emphasize the need to evaluate cross-reactive antibodies against the neuraminidase protein of H5N1, assess cellular immune responses potentially linked to protection against severe HPAI H5N1 infections and targeted vaccine strategies against recently emerged H5N1 influenza viruses.
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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) | 0.001 | 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".