Antigenic Categorization of Contemporary H3N2 Swine Influenza Virus Isolates Using a High-Throughput Serum Neutralization Assay
Bibliographic record
Abstract
In vivo, neutralizing antibodies are critical for viral clearance. A high-throughput serum neutralization (HTSN) assay was developed to antigenically categorize Swine influenza virus (SIV) isolates. Uncategorized viruses were tested using a panel of antisera representing the H3N2 SIV subtypes and the results expressed as a serum neutralization ratio. Antisera were generated against contemporary isolates representing circulating H3N2 SIV subtypes (clusters I, III, IV). Reference viruses and the corresponding antisera were evaluated using traditional hemagglutination inhibition (HI) and the HTSN assays and good correlation (r = 0.84) was observed between the 2 tests. Categorical clustering of 40 recent (2008-2009) SIV isolates was assessed using the HTSN assay. The H3N2 SIV isolates with amino acid similarity >97% to the commonly used H3N2 cluster IV reference strain A/Swine/Ontario/33853/2005 (ON05) showed strong reactivity with cluster IV antisera. Isolates with <97% amino acid similarity to ON05 sporadically or completely failed to react with any antiserum. A cluster of 3 isolates with weak reaction with cluster III antiserum may be a potential emerging cluster of H3N2 with moderate genetic similarity to cluster II H3N2 (93% similarity). Potential uses of the HTSN assay include identification of broadly cross-reactive or antigenically distinct SIV isolates for use in vaccine virus selection or as part of surveillance efforts monitoring antigenic drift.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".