1. Exhaled volatile organic compounds and respiratory disease : recent progress and future outlookMaria Chiara Magnano, Waqar Ahmed, Ran Wang, Martina Bergant Marušič, Stephen J. Fowler, Iain R. White, 2024, review article Abstract: The theoretical basis of eVOCs as biomarkers for respiratory disease diagnosis is described, followed by a review of the potential biomarkers that have been proposed as targets from in vitro studies. The utility of these targets is then discussed based on comparison with results from clinical breath studies. The current status of breath research is summarised for various diseases, with emphasis placed on quantitative and targeted studies. Potential for bias highlights several important concepts related to standardization, including practices adopted for compound identification, correction for background inspired VOC levels and computation of mixing ratios. The compiled results underline the need for targeted studies across different analytical platforms to understand how sampling and analytical factors impact eVOC quantification. The impact of environmental VOCs as confounders in breath analysis is discussed alongside the potential that eVOCs have as biomarkers of air pollution exposure and future perspectives on clinical breath sampling are provided.
Keywords: breath analysis, disease diagnosis, exhaled volatile organic compounds, respiratory disease, environmental exposure analysis, breath analysis
Published in RUNG: 06.05.2024; Views: 2492; Downloads: 9
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3. Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultationMohanachandran Nair Sindhu Swapna, RAJ VIMAL, RENJINI A, SREEJYOTHI S, SANKARARMAN S, 2020, original scientific article Abstract: The development of novel digital auscultation techniques has become highly significant in the context
of the outburst of the pandemic COVID 19. The present work reports the spectral, nonlinear time series,
fractal, and complexity analysis of vesicular (VB) and bronchial (BB) breath signals. The analysis is carried
out with 37 breath sound signals. The spectral analysis brings out the signatures of VB and BB through
the power spectral density plot and wavelet scalogram. The dynamics of airflow through the respiratory tract during VB and BB are investigated using the nonlinear time series and complexity analyses in
terms of the phase portrait, fractal dimension, Hurst exponent, and sample entropy. The higher degree
of chaoticity in BB relative to VB is unwrapped through the maximal Lyapunov exponent. The principal
component analysis helps in classifying VB and BB sound signals through the feature extraction from the
power spectral density data. The method proposed in the present work is simple, cost-effective, and sensitive, with a far-reaching potential of addressing and diagnosing the current issue of COVID 19 through
lung auscultation.
Keywords: Breath sound analysis, Fractal dimension, Nonlinear time series analysis, Sample entropy, Hurst exponent, Principal component analysis
Published in RUNG: 28.06.2022; Views: 3147; Downloads: 0
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4. Untargeted molecular analysis of exhaled breath as a diagnostic test for ventilator-associated lower respiratory tract infections (BreathDx)Pouline M. van Oort, Tamara M. E. Nijsen, Iain R. White, Hugo Knobel, Timothy Felton, Nicholas J. W. Rattray, Oluwasola Lawal, Murtaza Bulut, Waqar Ahmed, Antonio Artigas, 2021, other scientific articles Abstract: Patients suspected of ventilator-associated lower respiratory tract infections (VA-LRTIs) commonly receive broad-spectrum antimicrobial therapy unnecessarily. We tested whether exhaled breath analysis can discriminate between patients suspected of VA-LRTI with confirmed infection, from patients with negative cultures. Breath from 108 patients suspected of VA-LRTI was analysed by gas chromatography-mass spectrometry. The breath test had a sensitivity of 98% at a specificity of 49%, confirmed with a second analytical method. The breath test had a negative predictive value of 96% and excluded pneumonia in half of the patients with negative cultures. Trial registration number: UKCRN ID number 19086, registered May 2015.
Keywords: ventilator-associated pneumonia, breath analysis, volatile organic compounds, metabolomics, intensive care, hospital acquired infections
Published in RUNG: 07.09.2021; Views: 5841; Downloads: 0
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5. Capturing and Storing Exhaled Breath for Offline AnalysisIain R. White, Stephen J. Fowler, 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.
Keywords: Breath analysis, Breath sampling, Offline analysis, Thermal desorption, Gas chromatography-mass spectrometry
Published in RUNG: 22.07.2019; Views: 5068; Downloads: 0
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6. Exhaled breath metabolomics reveals a pathogen-specific response in a rat pneumonia model for two human pathogenic bacteria: a proof-of-concept studyPouline M van Oort, Iain R. White, 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.
Keywords: biomarkers, exhaled breath analysis, infection, pneumonia
Published in RUNG: 22.07.2019; Views: 4569; Downloads: 0
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