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FluChip‐8G Insight: HA and NA subtyping of potentially pandemic influenza A viruses in a single assay
Author(s) -
Toth Evan,
Dawson Erica D.,
Taylor Amber W.,
Stoughton Robert S.,
Blair Rebecca H.,
Johnson James E.,
Slinskey Amelia,
Fessler Ryan,
Smith Catherine B.,
Talbot Sarah,
Rowlen Kathy
Publication year - 2020
Publication title -
influenza and other respiratory viruses
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.743
H-Index - 57
eISSN - 1750-2659
pISSN - 1750-2640
DOI - 10.1111/irv.12683
Subject(s) - subtyping , influenza a virus , pandemic influenza , biology , pandemic , computational biology , virology , microarray , identification (biology) , virus , covid-19 , gene , medicine , genetics , computer science , infectious disease (medical specialty) , disease , gene expression , ecology , programming language
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.

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