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Retrospective Assessment of the Antigenic Similarity of Egg-Propagated and Cell Culture-Propagated Reference Influenza Viruses as Compared with Circulating Viruses across Influenza Seasons 2002-2003 to 2017-2018.

Sankarasubramanian RajaramPirada SuphaphiphatJosephine van BoxmeerMendel HaagBrett LeavIke IheanachoKristin KistlerRaúl Ortiz de Lejarazu
Published in: International journal of environmental research and public health (2020)
Suboptimal vaccine effectiveness against seasonal influenza is a significant public health concern, partly explained by antigenic differences between vaccine viruses and viruses circulating in the environment. Haemagglutinin mutations within vaccine viruses acquired during serial passage in eggs have been identified as a source of antigenic variation between vaccine and circulating viruses. This study retrospectively compared the antigenic similarity of circulating influenza isolates with egg- and cell-propagated reference viruses to assess any observable trends over a 16-year period. Using annual and interim reports published by the Worldwide Influenza Centre, London, for the 2002-2003 to 2017-2018 influenza seasons, we assessed the proportions of circulating viruses which showed antigenic similarity to reference viruses by season. Egg-propagated reference viruses were well matched against circulating viruses for A/H1N1 and B/Yamagata. However, A/H3N2 and B/Victoria cell-propagated reference viruses appeared to be more antigenically similar to circulating A/H3N2 and B/Victoria viruses than egg-propagated reference viruses. These data support the possibility that A/H3N2 and B/Victoria viruses are relatively more prone to egg-adaptive mutation. Cell-propagated A/H3N2 and B/Victoria reference viruses were more antigenically similar to circulating A/H3N2 and B/Victoria viruses over a 16-year period than were egg-propagated reference viruses.
Keyphrases
  • public health
  • genetic diversity
  • randomized controlled trial
  • emergency department
  • systematic review
  • stem cells
  • cell therapy
  • machine learning
  • mesenchymal stem cells
  • deep learning
  • bone marrow
  • cross sectional