• Journal of Microbiology and Biotechnology
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• v.28 no.10
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• pp.1683-1690
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• 2018

Accurate and rapid diagnosis of influenza infection is essential to enable early antiviral treatment and reduce the mortality associated with seasonal and epidemic infections. Immunochromatography is one of the most common methods used for the diagnosis of seasonal human influenza; however, it is less effective in diagnosing pandemic influenza virus. Currently, rapid diagnostic kits for pandemic influenza virus rely on the detection of nucleoprotein (NP) or hemagglutinin (HA). NP detection shows higher specificity and is more sensitive than HA detection. In this study, we time-dependently screened expression conditions, and herein report optimal conditions for the expression of recombinant nucleoprotein (rNP), which was 48 h after infection. In addition, we report the use of the expressed rNP in a rapid influenza diagnostic test (SGT i-flex Influenza A&B Test). We constructed expression vectors that synthesized rNP (antigen) of influenza A and B in insect cells (Sf9 cells), employed the purified rNP to the immunoassay test kit, and clearly distinguished NPs of influenza A and influenza B using this rapid influenza diagnostic kit. This approach may improve the development of rapid test kits for influenza using NP.

• Journal of Veterinary Science
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• v.21 no.2
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• pp.24.1-24.11
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• 2020

The pandemic of avian influenza viruses (AIVs) in Asia has caused enormous economic loss in poultry industry and human health threat, especially clade 2.3.4.4 H5 and H7 subtypes in recent years. The endemic chicken H6 virus in Taiwan has also brought about human and dog infections. Since wild waterfowls is the major AIV reservoir, it is important to monitor the diversified subtypes in wildfowl flocks in early stage to prevent viral reassortment and transmission. To develop a more efficient and sensitive approach is a key issue in epidemic control. In this study, we integrate multiplex reverse transcription recombinase polymerase amplification (RT-RPA) and capillary electrophoresis (CE) for high-throughput detection and differentiation of AIVs in wild waterfowls in Taiwan. Four viral genes were detected simultaneously, including nucleoprotein (NP) gene of all AIVs, hemagglutinin (HA) gene of clade 2.3.4.4 H5, H6 and H7 subtypes. The detection limit of the developed detection system could achieve as low as one copy number for each of the four viral gene targets. Sixty wild waterfowl field samples were tested and all of the four gene signals were unambiguously identified within 6 h, including the initial sample processing and the final CE data analysis. The results indicated that multiplex RT-RPA combined with CE was an excellent alternative for instant simultaneous AIV detection and subtype differentiation. The high efficiency and sensitivity of the proposed method could greatly assist in wild bird monitoring and epidemic control of poultry.

• Journal of the Korean Data and Information Science Society
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• v.27 no.4
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• pp.1075-1081
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• 2016

The big data analysis has received much attention from the researchers working in various fields because the big data has a great potential in detecting or predicting future events such as epidemic outbreaks and changes in stock prices. Reflecting the current popularity of big data analysis, many authors have proposed methods tracking influenza epidemics based on internet-based information. The recently proposed 'autoregressive model using Google (ARGO) model' (Yang et al., 2015) is one of those influenza tracking models that harness search queries from Google as well as the reports from the Centers for Disease Control (CDC), and appears to outperform the existing method such as 'Google Flu Trends (GFT)'. Although the ARGO predicts well the outbreaks of influenza, this study demonstrates that a classical seasonal autoregressive integrated moving average (SARIMA) model can outperform the ARGO. The SARIMA model incorporates more accurate seasonality of the past influenza activities and takes less input variables into account. Our findings show that the SARIMA model is a functional tool for monitoring influenza epidemics.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.31-39
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• 2014

Seasonal influenza epidemics cause 3 to 5 millions severe illness and 250,000 to 500,000 deaths worldwide each year. To prepare better controls on severe influenza epidemics, many studies have been proposed to achieve near real-time surveillance of the spread of influenza. Korea CDC publishes clinical data of influenza epidemics on a weekly basis typically with a 1-2-week reporting lag. To provide faster detection of epidemics, recently approaches using unofficial data such as news reports, social media, and search queries are suggested. Collection of such data is cheap in cost and is realized in near real-time. This research aims to develop regression models for early detecting the outbreak of the seasonal influenza epidemics in Korea with keyword query information provided from the Naver (Korean representative portal site) trend services for PC and mobile device. We selected 20 key words likely to have strong correlations with influenza-like illness (ILI) based on literature review and proposed a logistic regression model and a multiple regression model to predict the outbreak of ILI. With respect of model fitness, the multiple regression model shows better results than logistic regression model. Also we find that a mobile-based regression model is better than PC-based regression model in estimating ILI percentages.

• Journal of Microbiology and Biotechnology
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• v.24 no.5
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• pp.704-713
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• 2014

The highly pathogenic avian influenza A (HPAI) viruses of the H5N1 subtype infect poultry and have also been spreading to humans. Although new antiviral drugs and vaccinations can be effective, rapid detection would be more efficient to control the outbreak of infections. In this study, a phage-display library was applied to select antibody fragments for HPAI strain A/Hubei/1/2010. As a result, three clones were selected and sequenced. A hemagglutinin inhibition assay of the three scFvs revealed that none exhibited hemagglutination inhibition activity towards the H5N1 virus, yet they showed a higher binding affinity for several HPAI H5N1 strains compared with other influenza viruses. An ELISA confirmed that the HA protein was the target of the scFvs, and the results of a protein structure simulation showed that all the selected scFvs bound to the HA2 subunit of the HA protein. In conclusion, the three selected scFVs could be useful for developing a specific detection tool for the surveillance of HPAI epidemic strains.

• Clinical and Experimental Pediatrics
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• v.55 no.12
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• pp.470-473
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• 2012

Purpose: The purpose of this prospective case-control study was to survey the detection rate of respiratory viruses in children with Kawasaki disease (KD) by using multiplex reverse transcriptase-polymerase chain reaction (RT-PCR), and to investigate the clinical implications of the prevalence of respiratory viruses during the acute phase of KD. Methods: RT-PCR assays were carried out to screen for the presence of respiratory syncytial virus A and B, adenovirus, rhinovirus, parainfluenza viruses 1 to 4, influenza virus A and B, metapneumovirus, bocavirus, coronavirus OC43/229E and NL63, and enterovirus in nasopharyngeal secretions of 55 KD patients and 78 control subjects. Results: Virus detection rates in KD patients and control subjects were 32.7% and 30.8%, respectively (P=0.811). However, there was no significant association between the presence of any of the 15 viruses and the incidence of KD. Comparisons between the 18 patients with positive RT-PCR results and the other 37 KD patients revealed no significant differences in terms of clinical findings (including the prevalence of incomplete presentation of the disease) and coronary artery diameter. Conclusion: A positive RT-PCR for currently epidemic respiratory viruses should not be used as an evidence against the diagnosis of KD. These viruses were not associated with the incomplete presentation of KD and coronary artery dilatation.