Breath analysis: translation into clinical practice

Brodrick, Emma; Davies, Antony N.; Neill, Paul; Hanna, Louise; Williams, Edgar M.

doi:10.1088/1752-7155/9/2/027109

Cited by 21 publications

(11 citation statements)

References 50 publications

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“…Furthermore, other diseases such as Alzheimer and Parkinson disease (27 ) and chemotherapyinduced neutropenia (28 ) have shown the capabilities of breath analysis for a more global investigation of the overall metabolome. Recent advances have aimed to take advantage of these benefits for the potential translation of ion mobility methods into the clinical laboratory (29 ). Although hyphenated IMS techniques offer substantially more information to be collected in each analysis, the interpretation of data can be a significant bottleneck.…”

Section: Applications In Analysis Of Vocs Rapid Disease Diagnosis By mentioning

confidence: 99%

Ion Mobility in Clinical Analysis: Current Progress and Future Perspectives

et al. 2016

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BACKGROUND Ion mobility spectrometry (IMS) is a rapid separation tool that can be coupled with several sampling/ionization methods, other separation techniques (e.g., chromatography), and various detectors (e.g., mass spectrometry). This technique has become increasingly used in the last 2 decades for applications ranging from illicit drug and chemical warfare agent detection to structural characterization of biological macromolecules such as proteins. Because of its rapid speed of analysis, IMS has recently been investigated for its potential use in clinical laboratories. CONTENT This review article first provides a brief introduction to ion mobility operating principles and instrumentation. Several current applications will then be detailed, including investigation of rapid ambient sampling from exhaled breath and other volatile compounds and mass spectrometric imaging for localization of target compounds. Additionally, current ion mobility research in relevant fields (i.e., metabolomics) will be discussed as it pertains to potential future application in clinical settings. SUMMARY This review article provides the authors' perspective on the future of ion mobility implementation in the clinical setting, with a focus on ambient sampling methods that allow IMS to be used as a “bedside” standalone technique for rapid disease screening and methods for improving the analysis of complex biological samples such as blood plasma and urine.

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Section: Applications In Analysis Of Vocs Rapid Disease Diagnosis By mentioning

confidence: 99%

Ion Mobility in Clinical Analysis: Current Progress and Future Perspectives

et al. 2016

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“…The first dataset (dataset A) consists of 120 samples and 333 spectral variables selected from multicapillary column—ion mobility spectrometry measurements with a previously developed data analysis strategy . A total of 120 breath samples are collected from 67 lung cancer subjects and 53 age‐matched healthy subjects . The second dataset (dataset B) consists of 96 samples and 101 lipid levels measured at the Netherlands Metabolomics Centre with the UPLC‐MS lipidomics platform.…”

Section: Weight Randomization Test In 2cvmentioning

confidence: 99%

“…49,50 A total of 120 breath samples are collected from 67 lung cancer subjects and 53 age-matched healthy subjects. 51 The second dataset (dataset B) consists of 96 samples and 101 lipid levels measured at the Netherlands Metabolomics Centre with the UPLC-MS lipidomics platform. Further details are described in Szymańska et al 52 A total of 96 samples are serum samples collected from healthy subjects before the start of a nutritional intervention study.…”

Section: Datamentioning

confidence: 99%

Weight randomization test for the selection of the number of components in PLS models

Tran

Szymańska²,

Gerretzen

et al. 2017

Journal of Chemometrics

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The selection of the optimal number of components remains a difficult but essential task in partial least squares (PLS). Randomization tests have the advantage of being automatic and they make use of the entire dataset, in contrary with the widely used cross‐validation approaches. Partial least squares modeling may include component(s) with a large amount of irrelevant data variation, and this might affect the model, depending on the assigned y‐loading (which is the regression coefficient in the latent domain). This has recently been indicated by us in the basic sequence framework with respect to the underlying theory of the PLS algorithm and presented to the chemometrics society. We will show in this work that this irrelevant data variation is the root cause of the difficulty in current methods for selecting the optimal number of components. For randomization tests, PLS models with nonsignificant components may result in false positive tests because of the incorrect assumption that “the components enter the model in a natural order”. In this work, we introduce a new randomization test, weight randomization test, selection of the optimal number of components in PLS in light of the underlying theory of the PLS algorithm. In the proposed method the null distribution is well characterized and efficiently determined taking into account a newly defined model quality metric: the number of consecutive non‐significant components (CNC). We illustrate the effectiveness of weight randomization test in optimization of preprocessing as well as in classification models, where results are compared with the double cross‐validation procedure for the latter. This is an important step towards the full automation of PLS model development and routine updates.

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“…In these cases, the targeted methods would fail to detect these compounds and products, which could interfere with sample integrity or lead to a false assessment of health state. Therefore, a combination of targeted and non-targeted work could be important both for protecting health and in other applications of clinical practice, such as pulmonary testing and hypoxia, forensic science, and national security [7,8,9]. This has traditionally required separate instrumental methods as described below, and depending upon sampling schemes, could even require additional sample collections.…”

Section: Introductionmentioning

confidence: 99%

Calibration and performance of synchronous SIM/scan mode for simultaneous targeted and discovery (non-targeted) analysis of exhaled breath samples from firefighters

Wallace

Pleil

Menteşe

et al. 2017

Journal of Chromatography A

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Traditionally, gas chromatography – mass spectrometry (GC/MS) analysis has used a targeted approach called selected ion monitoring (SIM) to quantify specific compounds that may have adverse health effects. Due to method limitations and the constraints of preparing duplicate samples, the information that could be obtained from separately collecting the full scan chromatogram of the sample has often been sacrificed. However, a hybrid technique called synchronous SIM/scan mode that switches back and forth between the two acquisition methods has become available from equipment manufacturers that maintains the accuracy and sensitivity of SIM for targeted analysis while also providing the full scan chromatogram for discovery of non-target compounds. We have explored the value and performance of this new technology using calibration data and real-world breath samples from a joint EPA/NIOSH collaboration that assessed the safety of firefighters’ protective gear during controlled structural burns. Collecting field samples is costly and must be performed strategically to ensure that time points and replicates are accurate and representative of the intended population. This is especially difficult, if not impossible, to accomplish with firefighters who are working under volatile conditions. The synchronous SIM/scan method decreases the number of field samples that need to be collected by half and reduces error in trying to recreate time points since a breath sample from a single sorbent tube can be used to collect both the SIM and scan data simultaneously. This work demonstrates the performance of the technology using calibration data. As a practical demonstration of the method, we investigate thirty-six firefighter breath samples, document organic compounds of interest, and identify additional non-target compounds.

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Breath analysis: translation into clinical practice

Cited by 21 publications

References 50 publications

Ion Mobility in Clinical Analysis: Current Progress and Future Perspectives

Ion Mobility in Clinical Analysis: Current Progress and Future Perspectives

Weight randomization test for the selection of the number of components in PLS models

Calibration and performance of synchronous SIM/scan mode for simultaneous targeted and discovery (non-targeted) analysis of exhaled breath samples from firefighters

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