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.

The development of a sensitive and rapid diagnostic test is needed for early detection of avian influenza (AI) H7 subtype. In this study, novel monoclonal antibodies (mAbs) against influenza A H7N9 recombinant hemagglutinin (rHA)1 were developed and applied to a Europium nanoparticle–based rapid fluorescent immunochromatographic strip test (FICT) to improve the sensitivity of the rapid diagnostic system. Two antibodies (2F4 and 6D7) exhibited H7 subtype specificity in a dot-FICT assay by optimization of the conjugate and the pH of the lysis buffer. The subtype specificity was confirmed by an immunofluorescence assay and Western blot analysis. The limit of detection of the FICT employing novel mAbs 31 ng/mL for H7N9 rHA1 and 40 hemagglutination units/mL for H7 subtype virus. Sensitivity was improved 25-fold using Europium as confirmed by comparison of colloidal gold-based rapid diagnostic kit using the 2F4 and 6D7 mAbs.

Currently, the point of care testing (POCT) is not fully developed for subtype-specific avian influenza virus detection. In this study, an H5N1 hemaglutinin 1 (HA1) epitope (P0: KPNDAINF) and three modified peptides (P1: KPNTAINF, P2: KPNGAINF, P3: KPNDAINDAINF) were evaluated as POCT elements for rapid detection of avian influenza virus. Based on modeling predictions by Autodock Vina, binding affinity varied depending on alteration of one amino acid in these peptides. The binding energy of P2 indicated its potential for a strong interaction with HA. Fluorescence-linked immunosorbent assay experimentally demonstrated the interaction between these peptides and virus. The four peptides interacted with HA1 of H5N3 with different binding affinities with P2 showing the strongest binding affinity. When P0 and P2 peptides were used in rapid fluorescent immunochromatographic test (FICT) as detection elements, the inter-assay coefficients of variation (CV) indicated that P2-linked FICT was more acceptable than the P0-linked FICT in the presence of human specimens. Antibody pair-linked FICT was influenced by clinical samples more than the P2-linked FICT assay, which showed a 4-fold improvement in the detection limit of H5N3 and maintained H5 subtype-specificity. Compared to the rapid diagnostic test (RDT) which is not specific for influenza subtypes, P2-linked FICT could increase virus detection. In conclusion, results of this study suggest that HA epitope-derived peptides can be used as alternatives to antibodies for a rapid fluorescent diagnostic assay to detect avian influenza virus.

Zika virus (ZIKV) has been identified as a cause of adverse outcomes of pregnancy, including microcephaly and other congenital diseases. Most people infected with ZIKV do not show any symptoms. Development of a method to discriminate dengue virus (DENV) and ZIKV infections has been challenging, and efficient assays for patient management are limited, attributable to high levels of cross-reactivity among co-circulating Flaviviruses. Thus, there is an urgent need for a specific high-throughput diagnostic assay to discriminate ZIKV infections from other Flavivirus infections.Methods: A novel epitope peptide of the ZIKV envelope protein was predicted using three immune epitope database analysis tools and then further modified. A molecular docking study was conducted using three-dimensional structures of the ZIKV envelope and peptide. Experimentally, interactions between the selected peptides and virus were assessed via a fluorescence-linked sandwich immunosorbent assay (FLISA), and performance of peptide-linked sandwich FLISA was evaluated in virus-spiked human serum and urine.Results: The Z_10.8 peptide (KRAVVSCAEA) was predicted to be a suitable detector, with a higher binding affinity than other candidates based on four criteria (binding affinity, root mean square deviation, position of amine residue of lysine at the N-terminus, and interactive site) in a docking study. Z_10.8 was significantly more efficient at detecting ZIKV than the other two peptides, as shown in the direct FLISA (P < 0.001). Further, the equilibrium dissociation constant (Kd) for the Z_10.8 peptide was 706.0 ± 177.9 (mean ± SD, nM), with specificity to discriminate ZIKV from DENV. The limit of detection for the sandwich FLISA was calculated as 1×104 tissue culture infective dose (TCID)50/mL. The presence of serum or urine did not interfere with the performance of the Z_10.8-linked sandwich FLISA.Conclusion: Four criteria are suggested for the development of an in silico modeled peptide aptamer; this computerized peptide aptamer discriminated ZIKV from DENV via immunoassay.

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