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Diagnostic Evasion of Highly-Resistant Microorganisms: A Critical Factor in Nosocomial Outbreaks

Overview of attention for article published in Frontiers in Microbiology, November 2017
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Title
Diagnostic Evasion of Highly-Resistant Microorganisms: A Critical Factor in Nosocomial Outbreaks
Published in
Frontiers in Microbiology, November 2017
DOI 10.3389/fmicb.2017.02128
Pubmed ID
Authors

Xuewei Zhou, Alexander W. Friedrich, Erik Bathoorn

Abstract

Highly resistant microorganisms (HRMOs) may evade screening strategies used in routine diagnostics. Bacteria that have evolved to evade diagnostic tests may have a selective advantage in the nosocomial environment. Evasion of resistance detection can result from the following mechanisms: low-level expression of resistance genes not resulting in detectable resistance, slow growing variants, mimicry of wild-type-resistance, and resistance mechanisms that are only detected if induced by antibiotic pressure. We reviewed reports on hospital outbreaks in the Netherlands over the past 5 years. Remarkably, many outbreaks including major nation-wide outbreaks were caused by microorganisms able to evade resistance detection by diagnostic screening tests. We describe various examples of diagnostic evasion by several HRMOs and discuss this in a broad and international perspective. The epidemiology of hospital-associated bacteria may strongly be affected by diagnostic screening strategies. This may result in an increasing reservoir of resistance genes in hospital populations that is unnoticed. The resistance elements may horizontally transfer to hosts with systems for high-level expression, resulting in a clinically significant resistance problem. We advise to communicate the identification of HRMOs that evade diagnostics within national and regional networks. Such signaling networks may prevent inter-hospital outbreaks, and allow collaborative development of adapted diagnostic tests.

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Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 25%
Researcher 3 15%
Student > Ph. D. Student 3 15%
Professor 2 10%
Student > Bachelor 1 5%
Other 4 20%
Unknown 2 10%
Readers by discipline Count As %
Immunology and Microbiology 4 20%
Medicine and Dentistry 4 20%
Agricultural and Biological Sciences 4 20%
Biochemistry, Genetics and Molecular Biology 2 10%
Environmental Science 1 5%
Other 2 10%
Unknown 3 15%