Multiplex Assay for Simultaneously Typing and Subtyping Influenza Viruses by Use of an Electronic Microarray
Bibliographic record
Abstract
We report on the use of an electronic microarray to simultaneously type influenza A and B viruses and to distinguish influenza A virus subtypes H1N1 and H3N2 from the potentially pandemic avian virus subtype H5N1. The assay targets seven genes: the H1, H3, H5, N1, and N2 genes of influenza A virus; the matrix protein M1 gene of influenza A virus; and the nonstructural protein (NS) gene of influenza B virus. By combining a two-step reverse transcription-multiplex PCR with typing and subtyping on the electronic microarray, the assay achieved an analytical sensitivity of 10(2) to 10(3) copies of transcripts per reaction for each of the genes. The assay correctly typed and subtyped 15 different influenza virus isolates, including two influenza B virus, five A/H1N1, six A/H3N2, and two A/H5N1 isolates. In addition, the assay correctly identified 8 out of 10 diluted, archived avian influenza virus specimens with complete typing and subtyping information and 2 specimens with partial subtyping information. In a study of 146 human clinical specimens that had previously been shown to be positive for influenza virus or another respiratory virus, the assay showed a clinical sensitivity of 96% and a clinical specificity of 100%. The assay is a rapid, accurate, user-friendly method for simultaneously typing and subtyping 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".