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Pathogens
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Technologies for Detection of Babesia microti: Advances and Challenges

Authors: Scott Meredith; Miranda Oakley; Sanjai Kumar;

Technologies for Detection of Babesia microti: Advances and Challenges

Abstract

The biology of intraerythrocytic Babesia parasites presents unique challenges for the diagnosis of human babesiosis. Antibody-based assays are highly sensitive but fail to detect early stage Babesia infections prior to seroconversion (window period) and cannot distinguish between an active infection and a previously resolved infection. On the other hand, nucleic acid-based tests (NAT) may lack the sensitivity to detect window cases when parasite burden is below detection limits and asymptomatic low-grade infections. Recent technological advances have improved the sensitivity, specificity and high throughput of NAT and the antibody-based detection of Babesia. Some of these advances include genomics approaches for the identification of novel high-copy-number targets for NAT and immunodominant antigens for superior antigen and antibody-based assays for Babesia. Future advances would also rely on next generation sequencing and CRISPR technology to improve Babesia detection. This review article will discuss the historical perspective and current status of technologies for the detection of Babesia microti, the most common Babesia species causing human babesiosis in the United States, and their implications for early diagnosis of acute babesiosis, blood safety and surveillance studies to monitor areas of expansion and emergence and spread of Babesia species and their genetic variants in the United States and globally.

Keywords

next generation sequencing, multiplex detection, R, nucleic acid tests, Medicine, Review, antibody-based assays, <i>Babesia microti</i>

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
23
Top 10%
Top 10%
Top 10%
Green
gold