Journal of breath research最新文献

Human studies provide valuable information on components or analytes recovered from exhaled breath, but there are limitations due to inter-individual and intra-individual variation. Future development and implementation of breath tests based on aerosol analysis require a clear understanding of how human factors interact with device geometry to influence particle transport and deposition. The computational fluid and particle dynamics (CFPD) algorithm combines (i) the Eulerian approach to fluid dynamics and (ii) the Lagrangian approach to single particle transport and deposition to predict how particles are carried in fluids and deposited on surfaces. In this work, we developed a 3D multiscale CFPD model to provide insight into human factors that could be important to control or measure during sampling. We designed the model to characterize the local transport, spatial distribution, and deposition of polydisperse particles in a single impaction filter of a commercial aerosol collection device. We highlight the use of decoupling numerical strategies to simultaneously quantify the influence of filter geometry, fluid flowrate, and particle size. Our numerical models showed the remarkable effect of flowrate on aerosol dynamics. Specifically, aerosol mass deposition, spatial distribution, and deposition mechanisms inside the filter. This work as well as future studies on the effect of filter geometry and human factors on aerosol collection will guide the development, standardization, and validation of breath sampling protocols for current and emerging breath tests for forensic and clinical applications.

Nitric oxide has different roles in asthma as both an endogenous modulator of airway function and a pro-inflammatory mediator. Fractional exhaled nitric oxide (FeNO) is a reliable, quantitative, non-invasive, simple, and safe biomarker for assessing airways inflammation in asthma. Previous genome-wide and genetic association studies have shown that different genes and single nucleotide polymorphisms (SNPs) are linked to FeNO. We aimed at identifying SNPs in candidate genes or gene regions that are associated with FeNO in asthma. We evaluated 264 asthma cases (median age 42.8 years, female 47.7%) who had been identified in the general adult population within the Gene Environment Interactions in Respiratory Diseases survey in Verona (Italy; 2008-2010). Two hundred and twenty-one tag-SNPs, which are representative of 50 candidate genes, were genotyped by a custom GoldenGate Genotyping Assay. A two-step association analysis was performed without assuming ana priorigenetic model: step (1) a machine learning technique [gradient boosting machine (GBM)] was used to select the 15 SNPs with the highest variable importance measure; step (2) the GBM-selected SNPs were jointly tested in a linear regression model with natural log-transformed FeNO as the normally distributed outcome and with age, sex, and the SNPs as covariates. We replicated our results within an independent sample of 296 patients from the European Community Respiratory Health Survey III. We found that SNP rs987314 in family with sequence similarity 13 member A (FAM13A) and SNP rs3218258 in interleukin 2 receptor subunit beta (IL2RB) gene regions are significantly associated with FeNO in adult subjects with asthma. These genes are involved in different mechanisms that affect smooth muscle constriction and endothelial barrier function responses (FAM13A), or in immune response processes (IL2RB). Our findings contribute to the current knowledge on FeNO in asthma by identifying two novel SNPs associated with this biomarker of airways inflammation.

It can be a clinical challenge to distinguish inflammation from infection in critically ill patients. Therefore, valid and conclusive surrogate markers for infections are desired. Nitric oxide (NO) might be that marker since concentrations of exhaled NO have shown to change in the presence of various diseases. This observational, prospective, single-center feasibility study aimed to investigate if fractional exhaled NO (FeNO) can be measured in intubated patients with or without infection, pneumonia and septic shock in a standardized, reliable setting. 20 intubated patients in the intensive care unit (ICU) were included for analysis. FeNO mean values were measured in the endotracheal tube via the suction channel using a chemiluminescence based analyzer. We developed a pragmatic method to measure FeNO repeatedly and reliably in intubated patients using a chemiluminescence based analyzer. We found a median of 0.98 (0.59-1.44) FeNO mean (ppb) in exhaled breath from all 20 intubated patient. Intubated patient with suspected infection had a significantly lower median FeNO mean compared with the intubated patients without suspected infection. Similarly did patients with septic shock demonstrate a significantly lower median FeNO mean than without septic shock. We found no statistical difference in median FeNO mean for intubated patients with pneumonia. It was feasible to measure FeNO in intubated patients in the ICU. Our results indicate decreased levels of FeNO in infected intubated patients in the ICU. The study was not powered to provide firm conclusions, so larger trials are needed to confirm the results and to prove FeNO as a useful biomarker for distinguishment between infection and inflammation in the ICU.

Pleural mesothelioma (PM) is an aggressive cancer of the serosal lining of the thoracic cavity, predominantly caused by asbestos exposure. Due to nonspecific symptoms, PM is characterized by an advanced-stage diagnosis, resulting in a dismal prognosis. However, early diagnosis improves patient outcome. Currently, no diagnostic biomarkers or screening tools are available. Therefore, exhaled breath was explored as this can easily be obtained and contains volatile organic compounds, which are considered biomarkers for multiple (patho)physiological processes. A breath test, which differentiates asbestos-exposed (AEx) individuals from PM patients with 87% accuracy, was developed. However, before being implemented as a screening tool, the clinical utility of the test must be determined. Occupational AEx individuals underwent annual breath tests using multicapillary column/ion mobility spectrometry. A baseline breath test was taken and their individual risk of PM was estimated. PM patients were included as controls. In total, 112 AEx individuals and six PM patients were included in the first of four screening rounds. All six PM patients were correctly classified as having mesothelioma (100% sensitivity) and out of 112 AEx individuals 78 were classified by the breath-based model as PM patients (30% specificity). Given the large false positive outcome, the breath test will be repeated annually for three more consecutive years to adhere to the 'test, re-test' principle and improve the false positivity rate. A low-dose computed tomography scan in those with two consecutive positive tests will correlate test positives with radiological findings and the possible growth of a pleural tumor. Finally, the evaluation of the clinical value of a breath-based prediction model may lead to the initiation of a screening program for early detection of PM in Aex individuals, which is currently lacking. This clinical study received approval from the Antwerp University Hospital Ethics Committee (B300201837007).

Therapeutic drug monitoring (TDM) of medications with a narrow therapeutic window is a common clinical practice to minimize toxic effects and maximize clinical outcomes. Routine analyses rely on the quantification of systemic blood concentrations of drugs. Alternative matrices such as exhaled breath are appealing because of their inherent non-invasive nature. This is especially the case for pediatric patients. We have recently showcased the possibility of predicting systemic concentrations of valproic acid (VPA), an anti-seizure medication by real-time breath analysis in two real clinical settings. This approach, however, comes with the limitation of the patients having to physically exhale into the mass spectrometer. This restricts the possibility of sampling from patients not capable or available to exhale into the mass spectrometer located on the hospital premises. In this work, we developed an alternative method to overcome this limitation by collecting the breath samples in customized bags and subsequently analyzing them by secondary electrospray ionization coupled to high-resolution mass spectrometry (SESI-HRMS). A total ofn= 40 patients (mean ± SD, 11.5 ± 3.5 y.o.) diagnosed with epilepsy and taking VPA were included in this study. The patients underwent three measurements: (i) serum concentrations of total and free VPA, (ii) real-time breath analysis and (iii) off-line analysis of exhaled breath collected in bags. The agreement between the real-time and the off-line breath analysis methods was evaluated using Lin's concordance correlation coefficient (CCC). CCC was computed for ten mass spectral predictors of VPA concentrations. Lin's CCC was >0.6 for all VPA-associated features, except for two low-signal intensity isotopic peaks. Finally, free and total serum VPA concentrations were predicted by cross validating the off-line data set. Support vector machine algorithms provided the most accurate predictions with a root mean square error of cross validation of 29.0 ± 7.4 mg l^-1and 3.9 ± 1.4 mg l^-1for total and free VPA (mean ± SD), respectively. As a secondary analysis, we explored whether exhaled metabolites previously associated with side-effects and response to medication could be rendered by the off-line analysis method. We found that five features associated with side effects showed a CCC > 0.6, whereas none of the drug response-associated peaks reached this cut-off. We conclude that the clinically relevant free fraction of VPA can be predicted by this combination of off-line breath collection with rapid SESI-HRMS analysis. This opens new possibilities for breath based TDM in clinical settings.

The nasal dominance (ND) determination is crucial for nasal synchronized ventilator, optimum nasal drug delivery, identifying brain hemispheric dominance, nasal airway obstruction surgery, mindfulness breathing, and for possible markers of a conscious state. Given these wider applications of ND, it is interesting to understand the patterns of ND with varying temperature and respiration rates. In this paper, we propose a method which measures peak-to-peak temperature oscillations (difference between end-expiratory and end-inspiratory temperature) for the left and right nostrils during nasal breathing. These nostril-specific temperature oscillations are further used to calculate the nasal dominance index (NDI), nasal laterality ratio (NLR), inter-nostril correlation, and mean of peak-to-peak temperature oscillation for inspiratory and expiratory phase at (1) different ambient temperatures of 18 °C, 28 °C, and 38 °C and (2) at three different respiration rate of 6 bpm, 12 bpm, and 18 bpm. The peak-to-peak temperature (T_pp) oscillation range (averaged across participants;n= 8) for the left and right nostril were 3.80 ± 0.57 °C and 2.34 ± 0.61 °C, 2.03 ± 0.20 °C and 1.40 ± 0.26 °C, and 0.20 ± 0.02 °C and 0.29 ± 0.03 °C at the ambient temperature of 18 °C, 28 °C, and 38 °C respectively (averaged across participants and respiration rates). The NDI and NLR averaged across participants and three different respiration rates were 35.67 ± 5.53 and 2.03 ± 1.12; 8.36 ± 10.61 and 2.49 ± 3.69; and -25.04 ± 14.50 and 0.82 ± 0.54 at the ambient temperature of 18 °C, 28 °C, and 38 °C respectively. The Shapiro-Wilk test, and non-parametric Friedman test showed a significant effect of ambient temperature conditions on both NDI and NLR. No significant effect of respiration rate condition was observed on both NDI and NLR. The findings of the proposed study indicate the importance of ambient temperature while determining ND during the diagnosis of breathing disorders such as septum deviation, nasal polyps, nosebleeds, rhinitis, and nasal fractions, and in the intensive care unit for nasal synchronized ventilator.

Volatile organic compounds (VOCs) have shown promise as potential biomarkers in idiopathic pulmonary fibrosis. Measuring VOCs in the headspace ofin vitromodels of lung fibrosis may offer a method of determining the origin of those detected in exhaled breath. The aim of this study was to determine the VOCs associated with two lung cell lines (A549 and MRC-5 cells) and changes associated with stimulation of cells with the pro-fibrotic cytokine, transforming growth factor (TGF)-β1. A dynamic headspace sampling method was used to sample the headspace of A549 cells and MRC-5 cells. These were compared to media control samples and to each other to identify VOCs which discriminated between cell lines. Cells were then stimulated with the TGF-β1 and samples were compared between stimulated and unstimulated cells. Samples were analysed using thermal desorption-gas chromatography-mass spectrometry and supervised analysis was performed using sparse partial least squares-discriminant analysis (sPLS-DA). Supervised analysis revealed differential VOC profiles unique to each of the cell lines and from the media control samples. Significant changes in VOC profiles were induced by stimulation of cell lines with TGF-β1. In particular, several terpenoids (isopinocarveol, sativene and 3-carene) were increased in stimulated cells compared to unstimulated cells. VOC profiles differ between lung cell lines and alter in response to pro-fibrotic stimulation. Increased abundance of terpenoids in the headspace of stimulated cells may reflect TGF-β1 cell signalling activity and metabolic reprogramming. This may offer a potential biomarker target in exhaled breath in IPF.

In order to prevent long-term immunity-related complications after lung transplantation, close monitoring of immunosuppressant levels using therapeutic drug monitoring (TDM) is paramount. Novel electronic nose (eNose) technology may be a non-invasive alternative to the current invasive procedures for TDM. We investigated the diagnostic and categorization capacity of eNose breathprints for Tacrolimus trough blood plasma levels (TAC_trough) in lung transplant recipients (LTRs). We performed eNose measurements in stable LTR attending the outpatient clinic. We evaluated (1) the correlation between eNose measurements and TAC_trough, (2) the diagnostic capacity of eNose technology for TAC_trough, and (3) the accuracy of eNose technology for categorization of TAC_troughinto three clinically relevant categories (low: <7µg ml^-1, medium: 7-10µg ml^-1, and high: >10µg ml^-1). A total of 186 measurements from 86 LTR were included. There was a weak but statistically significant correlation (r= 0.21,p= 0.004) between the eNose measurements and TAC_trough. The root mean squared error of prediction for the diagnostic capacity was 3.186 in the training and 3.131 in the validation set. The accuracy of categorization ranged between 45%-63% for the training set and 52%-69% in the validation set. There is a weak correlation between eNose breathprints and TAC_troughin LTR. However, the diagnostic as well as categorization capacity for TAC_troughusing eNose breathprints is too inaccurate to be applicable in TDM.

Analyzing exhaled breath samples, especially using a highly sensitive method such as MCC/IMS (multi-capillary column/ion mobility spectrometry), may also detect analytes that are derived from exogenous production. In this regard, there is a discussion about the optimal interpretation of exhaled breath, either by considering volatile organic compounds (VOCs) only in exhaled breath or by additionally considering the composition of room air and calculating the alveolar gradients. However, there are no data on whether the composition and concentration of VOCs in room air are identical to those in truly inhaled air directly before analyzing the exhaled breath. The current study aimed to determine whether the VOCs in room air, which are usually used for the calculation of alveolar gradients, are identical to the VOCs in truly inhaled air. For the measurement of inhaled air and room air, two IMS, each coupled with an MCC that provided a pre-separation of the VOCs, were used in parallel. One device was used for sampling room air and the other for sampling inhaled air. Each device was coupled with a newly invented system that cleaned room air and provided a clean carrier gas, whereas formerly synthetic air had to be used as a carrier gas. In this pilot study, a healthy volunteer underwent three subsequent runs of sampling of inhaled air and simultaneous sampling and analysis of room air. Three of the selected 11 peaks (P4-unknown, P5-1-Butanol, and P9-Furan, 2-methyl-) had significantly higher intensities during inspiration than in room air, and four peaks (P1-1-Propanamine, N-(phenylmethylene), P2-2-Nonanone, P3-Benzene, 1,2,4-trimethyl-, and P11-Acetyl valeryl) had higher intensities in room air. Furthermore, four peaks (P6-Benzaldehyde, P7-Pentane, 2-methyl-, P8-Acetone, and P10-2-Propanamine) showed inconsistent differences in peak intensities between inhaled air and room air. To the best of our knowledge, this is the first study to compare simultaneous sampling of room air and inhaled air using MCC/IMS. The simultaneous measurement of inhaled air and room air showed that using room air for the calculation of alveolar gradients in breath analysis resulted in different alveolar gradient values than those obtained by measuring truly inhaled air.

Solid-phase sorption is widely used for the analysis of gaseous specimens as it allows at the same time to preconcentrate target analytes and store samples for relatively long periods. The addition of internal standards (ISs) in the analytical workflow can greatly reduce the variability of the analyses and improve the reliability of the protocols. In this work, we describe the development and testing of a portable system for the reliable production of gaseous mixture of⁸D-Toluene in a 1L Silonite canister as well as its reproducible loading into solid-phase sorbing tools as ISs. The portable system was tested using needle trap microextraction, solid-phase extraction, and thin-film microextraction techniques commonly employed for the analysis of gaseous samples. Even though our specific interest is in breath analysis, the system can also be used for the collection of any kind of gaseous specimen. A microcontroller allows the fine control of the sampling flow by a digital mass flow controller. Flow rate and sample volume could be set either through a rotary encoder mounted onto the control board or through a dedicated android app. The variability of the airflow is in the range 5-200 ml min^-1and it is lower than 1%, whereas the variability of the IS (⁸D-Toluene) concentration dispensed over time by the loader measured by selected-ion flow-tube mass spectrometry (MS) is <3%. This combination resulted in intra- and inter-day precision of the amount loaded in the sorbent tools lower than 15%. No carry-over was detected in the loader after the delivery of the⁸D-Toluene measured by gas chromatography-MS. The⁸D-Toluene concentration in the canister was stable for up to three weeks at room temperature.