Insights into the antigenic advancement of influenza A(H3N2) viruses, 2011–2018
Bibliographic record
Abstract
Influenza A(H3N2) viruses evade human immunity primarily by acquiring antigenic changes in the haemagglutinin (HA). HA receptor-binding features of contemporary A(H3N2) viruses hinder traditional antigenic characterization using haemagglutination inhibition and promote selection of HA mutants. Thus, alternative approaches are needed to reliably assess antigenic relatedness between circulating viruses and vaccines. We developed a high content imaging-based neutralization test (HINT) to reduce antigenic mischaracterization resulting from virus adaptation to cell culture. Ferret reference antisera were raised using clinical specimens containing viruses representing recent vaccine strains. Analysis of viruses circulating during 2011-2018 showed that gain of an N158-linked glycosylation in HA was a molecular determinant of antigenic distancing between A/Hong Kong/4801/2014-like (clade 3C.2a) and A/Texas/50/2012-like viruses (clade 3C.1), while multiple evolutionary HA F193S substitution were linked to antigenic distancing from A/Switzerland/97152963/2013-like (clade 3C.3a) and further antigenic distancing from A/Texas/50/2012-like viruses. Additionally, a few viruses carrying HA T135K and/or I192T showed reduced neutralization by A/Hong Kong/4801/2014-like antiserum. Notably, this technique elucidated the antigenic characteristics of clinical specimens, enabling direct characterization of viruses produced in vivo, and eliminating in vitro culture, which rapidly alters the genotype/phenotype. HINT is a valuable new antigenic analysis tool for vaccine strain selection.
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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".