Introduction of a New Model for In Vitro Volatile Organic Compound Detection from Co-Culture of Lung Epithelial Cell Line with Bacteria Commonly Associated with Pulmonary Infections

Fenn, Dominic; Ahmed, Waqar; Boer, A. Tuip-de; Lilien, Thijs A.; Brinkman, Paul; Neerincx, Anne H.; Fowler, SJ; Schultz, M.J.; Zee, Anke H. Maitland‐van der; Bos, Linda

doi:10.1164/ajrccm-conference.2022.205.1_meetingabstracts.a3446

Cited by 2 publications

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“…The vials were then sealed for headspace sampling using PTFE crimp top-caps and crimping tool (Markes International, Cincinnati, Ohio, USA). All vials were incubated for 24 hours in purpose built HiSorb agitator: T37C, RPM 200 (Markes International, Cincinnati, Ohio, USA) (23).…”

Section: Induction Of Oxidative Stress In Epitheliummentioning

confidence: 99%

Validation of volatile metabolites of pulmonary oxidative injury: a bench to bedside study

et al. 2022

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BackgroundChanges in exhaled volatile organic compounds (VOCs) can be used to discriminate between respiratory diseases, and increased concentrations of hydrocarbons are commonly linked to oxidative stress. However, the VOCs identified are inconsistent between studies, and translational studies are lacking.MethodsIn this bench-to-bedside study, we captured VOCs in the headspace of A549 epithelial cells after exposure to hydrogen peroxide (H2O2), to induce oxidative stress, using high-capacity polydimethylsiloxane sorbent fibres. Exposed and unexposed cells were compared using targeted and untargeted analysis. Breath samples of invasively ventilated ICU patients (n=489) were collected on sorbent tubes and associated with the inspired oxygen fraction (FiO2) to reflect pulmonary oxidative stress. Headspace samples and breath samples were analysed using gas-chromatography and mass-spectrometry.ResultsIn the cell, headspace octane concentration was decreased after oxidative stress (p=0.0013), while the other VOCs were not affected. 2-ethyl-1-hexanol, showed an increased concentration in the headspace of cells undergoing oxidative stress in untargeted analysis (p=0.00014). None of the VOCs that were linked to oxidative stress showed a significant correlation with FiO2 (Rsrange: −0.015 to −0.065) or discriminated between patients with FiO2≥0.6 or below (AUC range: 0.48 to 0.55).ConclusionDespite a comprehensive translational approach, validation of known and novel volatile biomarkers of oxidative stress was not possible in patients at risk of pulmonary oxidative injury. The inconsistencies observed highlight the difficulties faced in VOC biomarker validation and that caution is warranted in the interpretation of the pathophysiological origin of discovered exhaled breath biomarkers.

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Section: Induction Of Oxidative Stress In Epitheliummentioning

confidence: 99%

Validation of volatile metabolites of pulmonary oxidative injury: a bench to bedside study

et al. 2022

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“…Some potential changes in expiratory metabolism occurred due to differences in wearing duration, quality of masks, and filtering effectiveness of masks. Some possible explanations including the fact that our exhalation components frequently contain germs [10,8,9,11,19]. Because of the masks' barrier, some germs begin to gather around the masks and then continue to move through the body with breath in and out.…”

Section: Resultsmentioning

confidence: 99%

Metabolic changes of VOCs in human caused by wearing masks: a preliminary study based on electronic nose

Yang

Xiang

Meng

et al. 2022

International Conference on Biomedical and Intelligent Systems (IC-BIS 2022)

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Wearing masks has been generally recommended to reduce the spreading of COVID-19. However, little is known about its effects on metabolic VOC changes in human body. To explore how the duration of wearing masks influences VOC metabolism in the human body, the essay used a self-developed electronic nose to analyse exhaled breath samples from 10 healthy individuals in this study. Firstly, polytetrafluoroethylene sampling bags are used to collect breath samples after volunteers wearing masks for 1h, 2h, 3h, 4h, and 5h. Secondly, data pre-processing, including baseline calibration and normalization are carried out. Thirdly, the study used LDA for dimensionality reduction on the original data to extract 4 features. Fourthly, differences in the length of time of wearing masks are analysed. Then, 4 algorithms were applied for cluster analysis based on extracted features. Moreover, 3 supervised classification algorithms were used to recognize the duration of wearing masks. Finally, multi-dimensional linear regression is used to study the possibility of predicting the duration of wearing masks based on breath signals acquired through electronic noses. As a result, the first feature extracted by LDA significantly differs from each other in the duration of wearing masks (p<0.05). Cluster analysis results show that the optimal internal parameters Adjusted Rand Index, Adjusted Mutual Information, Homogeneity and V-measure reach 80.2%, 81.5%, 83.5% and 83.7% respectively. Using 5-fold cross-validation on the K nearest neighbour classification model, the best accuracy of recognizing durations of wearing a mask reaches 88%. R-square of multi-dimensional linear regression reaches 92.5%, which shows excellent fitting performance. It can be concluded that the VOC metabolism of the human may change with the duration of wearing masks. Further, “breath prints” obtained by electronic nose may have the potential to predict the effective time and even the quality of masks.

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Introduction of a New Model for In Vitro Volatile Organic Compound Detection from Co-Culture of Lung Epithelial Cell Line with Bacteria Commonly Associated with Pulmonary Infections

Cited by 2 publications

References 0 publications

Validation of volatile metabolites of pulmonary oxidative injury: a bench to bedside study

Validation of volatile metabolites of pulmonary oxidative injury: a bench to bedside study

Metabolic changes of VOCs in human caused by wearing masks: a preliminary study based on electronic nose

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