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April 2015

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Subject:
From:
"Diane St. Germain" <[log in to unmask]>
Reply To:
Diane St. Germain
Date:
Thu, 16 Apr 2015 20:54:41 +0000
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Dissertation Defense Announcement
To:  The George Mason University Community

Candidate: Nazly Shafagati

Program: PhD in Biosciences



Date:   Friday April 24, 2015

Time:   11:00 am

Place:  George Mason University
             Prince William Campus<http://www.gmu.edu/resources/welcome/Directions-to-GMU.html>

             Bull Run Hall, Room 258



Title: "Enhanced Detection of Influenza with Nanotrap Particles"
Committee Chair: Dr. Kylene Kehn-Hall
Committee Members:  Dr. Aarthi Narayanan, Dr. Alessandra Luchini, Dr. Barney Bishop
A copy of the dissertation is available in the Mercer Library.  All are invited to attend the defense.
ABSTRACT:
The Influenza virus is a leading cause of respiratory disease in the United States each year.  The virus normally causes mild to moderate disease, however, hospitalization and death can occur in many cases.  While there are several methodologies that are used for detection, problems such as decreased sensitivity and high false-negatives may arise. There is a crucial need for a fast, yet highly specific detection method.  Nanotrap particles work to enrich whole virus and can be coupled to various downstream assays. Here, we demonstrate how Nanotrap particles with acrylic acid baits can be used to concentrate virus from high sample volumes and enhance detection up to 6-fold when coupled to plaque assays and qRT-PCR methodologies.  The acrylic acid Nanotrap particles can concentrate virus from nasal fluid swab specimens and nasal aspirates. Importantly, the Nanotrap particles stabilize and protect the virus from degradation over extended periods of time and elevated temperatures.  Lastly, in a co-infection scenario, other pathogens such as Coronavirus and Streptococcus pneumoniae do not interfere with capture of Influenza virus. These results collectively demonstrate that Nanotrap particles are an important tool that can easily be integrated into various detection methodologies.
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