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ABSTRACT: Background
Global influenza surveillance in humans and animals is a critical component of pandemic preparedness. The FluChip-8G Insight assay was developed to subtype both seasonal and potentially pandemic influenza viruses in a single assay with a same day result. FluChip-8G Insight uses whole gene segment RT-PCR-based amplification to provide robustness against genetic drift and subsequent microarray detection with artificial neural network-based data interpretation.Objectives
The objective of this study was to verify and validate the performance of the FluChip-8G Insight assay for the detection and positive identification of human and animal origin non-seasonal influenza A specimens.Methods
We evaluated the ability of the FluChip-8G Insight technology to type and HA and NA subtype a sample set consisting of 297 results from 180 unique non-seasonal influenza A strains (49 unique subtypes).Results
FluChip-8G Insight demonstrated a positive percent agreement ?93% for 5 targeted HA and 5 targeted NA subtypes except for H9 (88%), and negative percent agreement exceeding 95% for all targeted subtypes.Conclusions
The FluChip-8G Insight neural network-based algorithm used for virus identification performed well over a data set of 297 naïve sample results, and can be easily updated to improve performance on emerging strains without changing the underlying assay chemistry.
SUBMITTER: Toth E
PROVIDER: S-EPMC6928037 | biostudies-literature | 2020 Jan
REPOSITORIES: biostudies-literature
Toth Evan E Dawson Erica D ED Taylor Amber W AW Stoughton Robert S RS Blair Rebecca H RH Johnson James E JE Slinskey Amelia A Fessler Ryan R Smith Catherine B CB Talbot Sarah S Rowlen Kathy K
Influenza and other respiratory viruses 20191013 1
<h4>Background</h4>Global influenza surveillance in humans and animals is a critical component of pandemic preparedness. The FluChip-8G Insight assay was developed to subtype both seasonal and potentially pandemic influenza viruses in a single assay with a same day result. FluChip-8G Insight uses whole gene segment RT-PCR-based amplification to provide robustness against genetic drift and subsequent microarray detection with artificial neural network-based data interpretation.<h4>Objectives</h4> ...[more]