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Detection and characterization of type B influenza virus from influenza-like illness cases during the 2017–2018 winter influenza season in Beijing, China

  • Dong Zhu
  • Chonghou Lok
  • Shuang Chao
  • Lingling Chen
  • Runqing Li
  • Zhipeng Zhao
  • Jingxiao Dong
  • Kun QinEmail author
  • Xiuying ZhaoEmail author
Original Article
  • 11 Downloads

Abstract

In the winter of 2017-2018, there was significant influenza activity in China, resulting in unprecedented usage of influenza rapid antigen tests (IRAT) and neuraminidase inhibitors (NAIs). The aim of this study was to characterize the most prevalent influenza virus type in a clinical setting with respect to diagnosis and concomitant NAI treatment. From Dec 2017 to Jan 2018, 3257 patients with influenza-like illness (ILI) were screened using IRAT. We summarized and compared the results with the last influenza season. Subtyping of influenza B viruses and identification of NAI drug resistance mutations were carried out by sequencing the HA and NA genes and aligning these with genetic isotypes. The performance of IRAT and RT-PCR was compared. Screening results indicated that influenza B virus was the leading cause of this influenza epidemic, with children being more susceptible to infection than adults. Phylogenetic analysis revealed that the prevailing influenza B virus belonged to the Yamagata lineage and were genetically similar to strains isolated from North America in the same influenza season. Cross-continental spread of influenza/B/Yamagata occurred. NAI resistance mutations were not identified in the 18 samples analyzed. The current antiviral protocol was still effective for influenza B control. RT-PCR positivity was significantly higher than that of IRAT (P = 0.004). IRAT and RT-PCR had a consistency rate of 86.9%, with the consistency rates of the positive and negative cases being 54.3% and 97.3%, respectively. Clinicians should be alert to the possibility of obtaining false negative results when using IRAT, and RT-PCR is recommended to improve the accuracy of pathogen detection.

Notes

Acknowledgements

We thank our colleagues at the pediatric and respiratory departments for assistance with sample collection. We also appreciate Yihuan Dong at the Affiliated High School of Peking University for her help with RNA extraction and PCR analysis.

Funding

This work was supported by Beijing Municipal Science and Technology Commission Program of China (Grant no. Z181100001718148).

Compliance with ethical standards

Conflict of interest

The authors declare no potential conflicts of interest.

Ethical approval

The study was approved by the IRB in Beijing Tsinghua Chang-gung Hospital.

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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  • Dong Zhu
    • 1
  • Chonghou Lok
    • 1
  • Shuang Chao
    • 1
  • Lingling Chen
    • 2
  • Runqing Li
    • 1
  • Zhipeng Zhao
    • 1
  • Jingxiao Dong
    • 1
  • Kun Qin
    • 2
    Email author
  • Xiuying Zhao
    • 1
    Email author
  1. 1.School of Clinical Medicine, Beijing Tsinghua Chang-gung HospitalTsinghua UniversityBeijingPeople’s Republic of China
  2. 2.Key Laboratory for Medical Virology, National Institute for Viral Disease Control and Prevention, China CDCNational Health and Family Planning CommissionBeijingPeople’s Republic of China

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