Identification of A/H5N1 influenza viruses using a single gene diagnostic microarray
| Autor: | Michael B. Townsend, Daniela M. Dankbar, James A. Smagala, Kathy L. Rowlen, Chad L. Moore, Robert D. Kuchta, Martin Mehlmann, Catherine B. Smith, Nancy J. Cox, Erica D. Dawson |
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| Rok vydání: | 2006 |
| Předmět: |
Time Factors
Microarray Orthomyxoviridae Nigeria Computational biology medicine.disease_cause Sensitivity and Specificity Virus Analytical Chemistry medicine False positive paradox Animals Humans Oligonucleotide Array Sequence Analysis biology Influenza A Virus H5N1 Subtype Chemistry virus diseases biology.organism_classification Influenza A virus subtype H5N1 Subtyping Kazakhstan mChip Molecular Diagnostic Techniques Vietnam Indonesia DNA microarray |
| Zdroj: | Analytical chemistry. 79(1) |
| ISSN: | 0003-2700 |
| Popis: | In previous work, a simple diagnostic DNA microarray that targeted only the matrix gene segment of influenza A (MChip) was developed and evaluated with patient samples. In this work, the analytical utility of the MChip for detection and subtyping of an emerging virus was evaluated with a diverse set of A/H5N1 influenza viruses. A total of 43 different highly pathogenic A/H5N1 viral isolates that were collected from diverse geographic locations, including Vietnam, Nigeria, Indonesia, and Kazakhstan, representing human, feline, and a variety of avian infections spanning the time period 2003-2006 were used in this study. A probabilistic artificial neural network was developed for automated microarray image interpretation through pattern recognition. The microarray assay and subsequent subtype assignment by the artificial neural network resulted in correct identification of 24 "unknown" A/H5N1 positive samples with no false positives. Analysis of a data set composed of A/H5N1, A/H3N2, and A/H1N1 positive samples and negative controls resulted in a clinical sensitivity of 97% and a clinical specificity of 100%. |
| Databáze: | OpenAIRE |
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