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Capturing and Storing Exhaled Breath for Offline Analysis
Stephen J Fowler, Iain R White, 2019, independent scientific component part or a chapter in a monograph

Abstract: In this chapter we will summarize and discuss methods for the capture and storage of exhaled breath, prior to offline (and indirect online) analysis. We will detail and compare methods currently in use, including their applications, key strengths, and limitations. In synthesizing the best features of each technique, we will propose an ideal standardized breath sampling solution, and give a personal vision on the next steps to be taken in this exciting area of breath research.
Found in: ključnih besedah
Keywords: Breath analysis, Breath sampling, Offline analysis, Thermal desorption, Gas chromatography-mass spectrometry
Published: 22.07.2019; Views: 812; Downloads: 0
.pdf Fulltext (36,24 MB)

Exhaled breath metabolomics reveals a pathogen-specific response in a rat pneumonia model for two human pathogenic bacteria: a proof-of-concept study
Iain R White, Pouline M van Oort, 2019, original scientific article

Abstract: Volatile organic compounds in breath can reflect host and pathogen metabolism and might be used to diagnose pneumonia. We hypothesized that rats with Streptococcus pneumoniae (SP) or Pseudomonas aeruginosa (PA) pneumonia can be discriminated from uninfected controls by thermal desorption-gas chromatography-mass-spectrometry (TD-GC-MS) and selected ion flow tube-mass spectrometry (SIFT-MS) of exhaled breath. Male adult rats (n = 50) received an intratracheal inoculation of 1) 200 µl saline, or 2) 1 × 107 colony-forming units of SP or 3) 1 × 107 CFU of PA. Twenty-four hours later the rats were anaesthetized, tracheotomized, and mechanically ventilated. Exhaled breath was analyzed via TD-GC-MS and SIFT-MS. Area under the receiver operating characteristic curves (AUROCCs) and correct classification rate (CCRs) were calculated after leave-one-out cross-validation of sparse partial least squares-discriminant analysis. Analysis of GC-MS data showed an AUROCC (95% confidence interval) of 0.85 (0.73-0.96) and CCR of 94.6% for infected versus noninfected animals, AUROCC of 0.98 (0.94-1) and CCR of 99.9% for SP versus PA, 0.92 (0.83-1.00), CCR of 98.1% for SP versus controls and 0.97 (0.92-1.00), and CCR of 99.9% for PA versus controls. For these comparisons the SIFT-MS data showed AUROCCs of 0.54, 0.89, 0.63, and 0.79, respectively. Exhaled breath analysis discriminated between respiratory infection and no infection but with even better accuracy between specific pathogens. Future clinical studies should not only focus on the presence of respiratory infection but also on the discrimination between specific pathogens.
Found in: ključnih besedah
Summary of found: ...leave-one-out cross-validation of sparse partial least squares-discriminant analysis. Analysis of GC-MS data showed an AUROCC...
Keywords: biomarkers, exhaled breath analysis, infection, pneumonia
Published: 22.07.2019; Views: 749; Downloads: 0
.pdf Fulltext (320,58 KB)

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