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Label-Free Phenotypic Detection, Dia...

Trexler, Micaela.

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Label-Free Phenotypic Detection, Diagnosis, and Characterization of Influenza Virus Using AFM, Raman Spectroscopy (TERS), and Machine Learning Classification.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Label-Free Phenotypic Detection, Diagnosis, and Characterization of Influenza Virus Using AFM, Raman Spectroscopy (TERS), and Machine Learning Classification./
作者:	Trexler, Micaela.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	105 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-12, Section: B.
Contained By:	Dissertations Abstracts International82-12B.
標題:	Biomedical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28150307
ISBN:	9798738640582

Label-Free Phenotypic Detection, Diagnosis, and Characterization of Influenza Virus Using AFM, Raman Spectroscopy (TERS), and Machine Learning Classification.
Trexler, Micaela.

Label-Free Phenotypic Detection, Diagnosis, and Characterization of Influenza Virus Using AFM, Raman Spectroscopy (TERS), and Machine Learning Classification. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 105 p.

Source: Dissertations Abstracts International, Volume: 82-12, Section: B.

Thesis (Ph.D.)--Wayne State University, 2021.

This item must not be sold to any third party vendors.

Influenza virus mutates quickly and unpredictably creating emerging pathogenic strains that are difficult to detect, diagnose, and characterize. Each year there are millions of flu cases and tens of thousands of deaths in the United States alone. Conventional tools to study and characterize virus, such as next generation sequencing, genome amplification (RT-PCR), and serological antibody testing, are not adequately suited to rapidly mutating pathogens like Influenza virus where the success of infection heavily depends on the phenotypic expression of surface glycoproteins. This limits the ability to assess pathogenicity of newly emerging strains. Phenotypic identification of the serotype expression of rapidly mutating Influenza surface glycoproteins, hemagglutinin and neuraminidase, would be a valuable tool in diagnosing and characterizing the pathogenicity of the virus. However, agnostic phenotypic diagnostic tools do not yet exist. Bridging the gap between genome and pathogenic expression remains a challenge.Using sialic acid as a universal Influenza virus binding receptor, we have developed and characterized a novel virus avidin-biotin complex-based capture coating that may be used to further create new diagnostic and characterization techniques of viable whole Influenza virus. As a proof of concept demonstration, we have used our novel capture coating to immobilize live virus for Atomic Force Microscopy imaging (AFM). Using AFM we were able to confirm and profile the novel capture coating and virus, including capture efficiency, as well as provide new topographical insight on an individual Influenza A H3N2 virion.Furthermore, we demonstrate the ability of machine learning models to accurately classify Tip Enhanced Raman Spectroscopy (TERS) and general Raman spectroscopy data on individual Influenza virions. We created a binary classification model that differentiates Influenza A H1N1, A H3N2, B Victoria, and B Yamagata from background with up to 100% accuracy. In addition, we demonstrate a multiclass model that differentiates between the four serotypes of Influenza virus with 95% accuracy. Our results highlight the wider potential of sub-virion resolution AFM to locate virus and Raman spectroscopy to diagnose viable virus at the species and serotype level without labels, dyes, or loss of viral envelope integrity.

ISBN: 9798738640582Subjects--Topical Terms:

535387
Biomedical engineering.
Subjects--Index Terms:

Biomimicry

Label-Free Phenotypic Detection, Diagnosis, and Characterization of Influenza Virus Using AFM, Raman Spectroscopy (TERS), and Machine Learning Classification.
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