Highly Pathogenic Avian Influenza A(H5N1) Viruses at the Animal-Human Interface in Vietnam, 2003-2010.

Mutation and reassortment of highly pathogenic avian influenza A(H5N1) viruses at the animal-human interface remain a major concern for emergence of viruses with pandemic potential. To understand the relationship of H5N1 viruses circulating in poultry and those isolated from humans, comprehensive phylogenetic and molecular analyses of viruses collected from both hosts in Vietnam between 2003 and 2010 were performed. We examined the temporal and spatial distribution of human cases relative to H5N1 poultry outbreaks and characterized the genetic lineages and amino acid substitutions in each gene segment identified in humans relative to closely related viruses from avian hosts. Six hemagglutinin clades and 8 genotypes were identified in humans, all of which were initially identified in poultry. Several amino acid mutations throughout the genomes of viruses isolated from humans were identified, indicating the potential for poultry viruses infecting humans to rapidly acquire molecular markers associated with mammalian adaptation and antiviral resistance.

Smartphone-Based Fluorescent Diagnostic System for Highly Pathogenic H5N1 Viruses.

Field diagnostic tools for avian influenza (AI) are indispensable for the prevention and controlled management of highly pathogenic AI-related diseases. More accurate, faster and networked on-site monitoring is demanded to detect such AI viruses with high sensitivity as well as to maintain up-to-date information about their geographical transmission. In this work, we assessed the clinical and field-level performance of a smartphone-based fluorescent diagnostic device with an efficient reflective light collection module using a coumarin-derived dendrimer-based fluorescent lateral flow immunoassay. By application of an optimized bioconjugate, a smartphone-based diagnostic device had a two-fold higher detectability as compared to that of the table-top fluorescence strip reader for three different AI subtypes (H5N3, H7N1, and H9N2). Additionally, in a clinical study of H5N1-confirmed patients, the smartphone-based diagnostic device showed a sensitivity of 96.55% (28/29) [95% confidence interval (CI): 82.24 to 99.91] and a specificity of 98.55% (68/69) (95% CI: 92.19 to 99.96). The measurement results from the distributed individual smartphones were wirelessly transmitted via short messaging service and collected by a centralized database system for further information processing and data mining. Smartphone-based diagnosis provided highly sensitive measurement results for H5N1 detection within 15 minutes. Because of its high sensitivity, portability and automatic reporting feature, the proposed device will enable agile identification of patients and efficient control of AI dissemination.