Typing and Subtyping Influenza Virus Using DNA Microarrays and Multiplex Reverse Transcriptase PCR
Bibliographic record
Abstract
A model DNA microarray has been prepared and shown to facilitate typing and subtyping of human influenza A and B viruses. Reverse transcriptase PCR was used to prepare cDNAs encoding approximately 500-bp influenza virus gene fragments, which were then cloned, sequenced, reamplified, and spotted to form a glass-bound microarray. These target DNAs included multiple fragments of the hemagglutinin, neuraminidase, and matrix protein genes. Cy3- or Cy5-labeled fluorescent probes were then hybridized to these target DNAs, and the arrays were scanned to determine the probe binding site(s). The hybridization pattern agreed perfectly with the known grid location of each target, and the signal-to-background ratio varied from 5 to 30. No cross-hybridization could be detected beyond that expected from the limited degree of sequence overlap between different probes and targets. At least 100 to 150 bp of homology was required for hybridization under the conditions used in this study. Combinations of Cy3- and Cy5-labeled DNAs can also be hybridized to the same chip, permitting further differentiation of amplified molecules in complex mixtures. In a more realistic test of the technology, several sets of multiplex PCR primers that collectively target influenza A and B virus strains were identified and were used to type and subtype several previously unsequenced influenza virus isolates. The results show that DNA microarray technology provides a useful supplement to PCR-based diagnostic methods.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".