Genetic characterization of seasonal influenza <scp>A</scp> (<scp>H</scp>3<scp>N</scp>2) viruses in <scp>O</scp>ntario during 2010–2011 influenza season: high prevalence of mutations at antigenic sites
Bibliographic record
Abstract
BACKGROUND: The direct effect of antigenic site mutations in influenza viruses on antigenic drift and vaccine effectiveness is poorly understood. OBJECTIVE: To investigate the genetic and antigenic characteristics of human influenza A (H3N2) viruses circulating in Ontario during the early 2010-2011 winter season. STUDY DESIGN: We sequenced the hemagglutinin (HA) and neuraminidase (NA) genes from 41 A(H3N2) viruses detected in nasopharyngeal specimens. Strain typing was performed by hemagglutination inhibition (HI) assay. Molecular and phylogenetic tree analyses were conducted. RESULTS: HA and NA genes showed high similarity to the 2010-2011 vaccine strain, A/Perth/16/2009 (H3N2)-like virus (97·7-98·5% and 98·7-99·5% amino acid (AA) identity, respectively). Compared to A/Perth/16/2009 strain, HA gene mutations were documented at 28 different AA positions across all five H3 antigenic sites, with a range of 5-11 mutations in individual viruses. Thirty-six (88%) viruses had 8 AA substitutions in common; none of these had reduced HI titer. Among Ontario isolates, 11 antigenic site AAs were positively selected with an increase in glycosylation sites. CONCLUSION: The presence of antigenic site mutations with high frequency among 2010-2011 influenza H3N2 isolates confirms ongoing adaptive H3N2 evolution. These may represent early phylogenetic changes that could cause antigenic drift with further mutations. Clinical relevance of antigenic site mutations not causing drift in HI assays is unknown and requires further investigation. In addition, viral sequencing information will assist with vaccine strain planning and may facilitate early detection of vaccine escape.
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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 | 0.001 |
| 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) | 0.000 | 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".