Literature DB >> 35091804

Differentiating Live Versus Dead Gram-Positive and Gram-Negative Bacteria With and Without Oxidative Stress Using Buoyant Mass Measurements.

Christina L Lewis1, Andre G Senecal1, Michael S Wiederoder2, Brian M Lewis3.   

Abstract

Expeditious and accurate determination of pathogenic bacteria cell viability is of great importance to public health for numerous areas including medical diagnostics, food safety, and environmental monitoring. In this work a cell buoyant mass classifier approach is presented to assess bacteria cell viability in real time. Buoyant mass measurements for live and dead Gram-positive and Gram-negative bacteria populations were acquired with a commercial suspended microchannel resonator, Archimedes, to generate receiver operating characteristic (ROC) curves. To quantitatively assess the difference in buoyant mass for live and dead bacteria populations, ROC curves were generated to demonstrate cell viability determination. The results are presented as a binary classifier with a decision boundary, above which cells are considered live and below which cells are considered dead. A decision threshold value is evaluated with consideration that a certain true positive rate (correct classification of a live cell) is maintained with an acceptable false positive rate. The potential for this approach to monitor cell viability in real time is significant, especially when considering multiple classifier dimensions such as buoyant mass and density. This classifier approach represents a next generation technique for rapid and label-free diagnostics based on cell feature measurements.
© 2022. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.

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Year:  2022        PMID: 35091804     DOI: 10.1007/s00284-022-02764-1

Source DB:  PubMed          Journal:  Curr Microbiol        ISSN: 0343-8651            Impact factor:   2.188


  18 in total

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Review 4.  Recent developments in the use of viability dyes and quantitative PCR in the food microbiology field.

Authors:  P Elizaquível; R Aznar; G Sánchez
Journal:  J Appl Microbiol       Date:  2013-11-01       Impact factor: 3.772

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Authors:  Sen Xu; Raj Mutharasan
Journal:  Anal Chem       Date:  2011-01-18       Impact factor: 6.986

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Journal:  J Agric Food Chem       Date:  2012-01-19       Impact factor: 5.279

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Journal:  Emerg Infect Dis       Date:  2011-01       Impact factor: 6.883

Review 8.  Rapid methods for the detection of foodborne bacterial pathogens: principles, applications, advantages and limitations.

Authors:  Jodi Woan-Fei Law; Nurul-Syakima Ab Mutalib; Kok-Gan Chan; Learn-Han Lee
Journal:  Front Microbiol       Date:  2015-01-12       Impact factor: 5.640

Review 9.  Advances and Challenges in Viability Detection of Foodborne Pathogens.

Authors:  Dexin Zeng; Zi Chen; Yuan Jiang; Feng Xue; Baoguang Li
Journal:  Front Microbiol       Date:  2016-11-22       Impact factor: 5.640

10.  Galleria mellonella: An Infection Model for Screening Compounds Against the Mycobacterium tuberculosis Complex.

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Journal:  Front Microbiol       Date:  2019-11-20       Impact factor: 5.640

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