Background: Influenza A viruses (IAVs) have always remain a serious concern for the global economy and public health. A rapid, specific and sensitive detection method is always needed to control the influenza in its early stages by timely intervention of therapy and early clinical management.

Objectives: To develop RT-LAMP assays for detection of influenza A viruses, their further subtyping into seasonal (H1N1, H3N2) and novel pandemic H1N1 viruses and to evaluate clinical applicability of optimized RT-LAMP assays on patients' samples.

Study Design: In this study, we optimized RT-LAMP assay to detect IAVs by using primers against matrix gene and subtyping of IAVs was done by using primers against hemagglutinin gene. Optimized RT-LAMP assays were applied on clinical samples from patients having influenza like illness and results were compared with conventional one-step RT-PCR and real-time RT-PCR.

Results: RT-LAMP assays successfully detected and differentiated IAVs into H1N1, H3N2 and pdm09/H1N1 subtypes. One hundred and sixty seven clinical swab samples from influenza suspected patients were taken and tested with RT-LAMP assay, detecting 30 (17.9%) samples positive for Influenza A virus. Out of 30 samples, 21, 7 and 2 were found positive for pdm09/H1N1, H3N2 and seasonal H1 respectively. Conventional one-step RT-PCR detected a total of 27 (16.2%) samples for influenza A and further subtyping showed 20 and 7 samples positive for pdm09/H1N1 and H3N2 virus respectively whereas none was found positive for seasonal H1N1. RT-LAMP assay demonstrated higher sensitivity (93.8%) than conventional RT-PCR (84.4%) for influenza A viruses detection in clinical samples.

Conclusions: RT-LAMP assay is rapid, sensitive, specific and cost effective method for detection of influenza A viruses than conventional one-step RT-PCR and it can serve as a good alternate for diagnosis and surveillance studies during influenza outbreaks in resource-limited setups of developing countries.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7113880PMC
http://dx.doi.org/10.1016/j.jviromet.2017.12.005DOI Listing

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