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A guide for the use of the ferret model for influenza virus infection

tetano

Editor, Senior Moderator
Am J Pathol. 2019 Oct 22. pii: S0002-9440(19)30769-2. doi: 10.1016/j.ajpath.2019.09.017. [Epub ahead of print] [h=1]A guide for the use of the ferret model for influenza virus infection.[/h]
Belser JA[SUP]1[/SUP], Eckert AM[SUP]2[/SUP], Huynh T[SUP]3[/SUP], Gary JM[SUP]3[/SUP], Ritter JM[SUP]3[/SUP], Tumpey TM[SUP]4[/SUP], Maines TR[SUP]4[/SUP].
[h=3]Author information[/h] 1 Influenza Division, National Center for Immunization and Respiratory Diseases,. Electronic address: jax6@cdc.gov. 2 Division of Communication Services, Office of the Associate Director for Communication. 3 Division of High-Consequence Pathogens and Pathology, Centers for Disease Control and Prevention, Atlanta, Georgia 30333. 4 Influenza Division, National Center for Immunization and Respiratory Diseases.

[h=3]Abstract[/h] As influenza viruses continue to jump species barriers to cause human infection, assessments of disease severity and viral replication kinetics in vivo provide crucial information for public health professionals. The ferret model is a valuable resource for evaluating influenza virus pathogenicity; thus, understanding the most effective sample collection and usage techniques, as well as the full spectrum of attainable data following experimental inoculation in this species, is paramount. This is especially true for scheduled necropsy of virus-infected ferrets, a standard component in evaluation of influenza virus pathogenicity, as necropsy findings can provide important information regarding disease severity and pathogenicity that is not otherwise available from the live animal. In this review, we describe the range of influenza viruses assessed in ferrets, the measures of experimental disease severity in this model, and optimal sample collection during necropsy of virus-infected ferrets. Collectively, this information is critical for assessing systemic involvement following influenza virus infection in mammals.
Copyright ? 2019. Published by Elsevier Inc.


PMID: 31654637 DOI: 10.1016/j.ajpath.2019.09.017
 
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