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Gram-negative and -positive bacteria differentiation in blood culture samples by headspace volatile compound analysis

Overview of attention for article published in Journal of Biological Research, March 2016
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Title
Gram-negative and -positive bacteria differentiation in blood culture samples by headspace volatile compound analysis
Published in
Journal of Biological Research, March 2016
DOI 10.1186/s40709-016-0040-0
Pubmed ID
Authors

Michael E. Dolch, Silke Janitza, Anne-Laure Boulesteix, Carola Graßmann-Lichtenauer, Siegfried Praun, Wolfgang Denzer, Gustav Schelling, Sören Schubert

Abstract

Identification of microorganisms in positive blood cultures still relies on standard techniques such as Gram staining followed by culturing with definite microorganism identification. Alternatively, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry or the analysis of headspace volatile compound (VC) composition produced by cultures can help to differentiate between microorganisms under experimental conditions. This study assessed the efficacy of volatile compound based microorganism differentiation into Gram-negatives and -positives in unselected positive blood culture samples from patients. Headspace gas samples of positive blood culture samples were transferred to sterilized, sealed, and evacuated 20 ml glass vials and stored at -30 °C until batch analysis. Headspace gas VC content analysis was carried out via an auto sampler connected to an ion-molecule reaction mass spectrometer (IMR-MS). Measurements covered a mass range from 16 to 135 u including CO2, H2, N2, and O2. Prediction rules for microorganism identification based on VC composition were derived using a training data set and evaluated using a validation data set within a random split validation procedure. One-hundred-fifty-two aerobic samples growing 27 Gram-negatives, 106 Gram-positives, and 19 fungi and 130 anaerobic samples growing 37 Gram-negatives, 91 Gram-positives, and two fungi were analysed. In anaerobic samples, ten discriminators were identified by the random forest method allowing for bacteria differentiation into Gram-negative and -positive (error rate: 16.7 % in validation data set). For aerobic samples the error rate was not better than random. In anaerobic blood culture samples of patients IMR-MS based headspace VC composition analysis facilitates bacteria differentiation into Gram-negative and -positive.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 30%
Researcher 5 19%
Student > Bachelor 2 7%
Professor 2 7%
Other 1 4%
Other 2 7%
Unknown 7 26%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 19%
Nursing and Health Professions 2 7%
Biochemistry, Genetics and Molecular Biology 2 7%
Engineering 2 7%
Medicine and Dentistry 2 7%
Other 5 19%
Unknown 9 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 August 2016.
All research outputs
#21,011,157
of 25,806,080 outputs
Outputs from Journal of Biological Research
#56
of 78 outputs
Outputs of similar age
#235,090
of 316,304 outputs
Outputs of similar age from Journal of Biological Research
#5
of 5 outputs
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So far Altmetric has tracked 78 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.8. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
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