Journal of breath research最新文献

Breath and fecal VOCs, among others, represent a new and encouraging clinical practice for the differential diagnosis of CRC. The purpose of our research was to identify VOCs present in exhaled air and feces of 20 HVs and 15 CRC patients. For collection of gas phase released from feces, emission microchambers were applied. Sorption tubes were used to enrich analytes for both breath and fecal samples. TD technique combined with GC-MS was used at the separation and identification step. The combination of statistical methods was used to evaluate the ability of VOCs to classify control group and CRC patients. Heptanoic acid, acetone, 2,6,10-trimethyldodecane, n-hexane, skatole, and dimethyl trisulfide are observed in elevated amounts in the patients group. The performance of diagnostic models on the tested data set was above 90%. This study is the first attempt to document the using of TD-GC-MS to analyze both breath and fecal samples to search for volatile biomarkers of CRC. A full evaluation of the results described herein requires further studies involving a larger number of samples. Moreover, it is particularly important to understand the metabolic pathways of substances postulated as tumor biomarkers.

SARS-CoV-2 is expected to cause metabolic alterations due to viral replication and the host immune response resulting in increase of cytokine secretion and cytolytic activity. The present prospective observational study is addressed at exploring the potentialities of breath analysis in discrimination between patients with a documented previous history of symptomatic SARS-CoV-2 infection and, at the moment of the enrollment, exhibiting a negative nasopharyngeal swab and acquired immunity (post-COVID) and healthy subjects with no evidence of previous SARS-CoV-2 infection (no-COVID). The main purpose is to understand if traces of metabolic alterations induced during the acute phase of the infection are still detectable after negativization, in the form of a characteristic volatile organic compound (VOC) pattern. An overall number of 60 volunteers aged between 25 and 70 years were enrolled in the study (post-COVID: n.30; no-COVID: n. 30), according to well-determined criteria. Breath and ambient air samples were collected by means of an automated sampling system (Mistral) and analyzed by thermal desorption-gas chromatography-mass spectrometry (TD-GC/MS). Statistical tests (Wilcoxon/Kruskal-Wallis test) and multivariate data analysis (principal component analysis (PCA), linear discriminant analysis) were performed on data sets. Among all compounds detected (76 VOCs in 90% of breath samples), 5 VOCs (1-propanol, isopropanol, 2-(2-butoxyethoxy)ethanol, propanal and 4-(1,1-dimethylpropyl)phenol) showed abundances in breath samples collected from post-COVID subjects significantly different with respect to those collected from no-COVID group (Wilcoxon/Kruskal-Wallis test,p-values <0.05). Although not completely satisfactory separation between the groups was obtained, variables showing significant differences between the two groups and higher loadings for PCA are recognized biomarkers of COVID-19, according to previous studies in literature. Therefore, based on the outcomes obtained, traces of metabolic alterations induced by SARS-CoV-2 infection are still detectable after negativization. This evidence raises questions about the eligibility of post-COVID subjects in observational studies addressed at the detection of COVID-19. (Ethical Committee Registration number: 120/AG/11).

Exhaled breath research has been hindered by a lack of standardization in collection and analysis methodologies. Recently, the Respiration Collector forIn VitroAnalysis (ReCIVA) sampling device has illustrated the potential to provide a consistent and convenient method for exhaled breath collection onto adsorbent media. However, the significant costs, compared to exhaled breath bags, associated with the standardized collector is believed to be the reason for limited widespread use by researchers in the exhaled breath field. For example, in addition to the sampling hardware, a single-use disposable silicon mask affixed with a filter is required for each exhaled breath collection. To reduce the financial burden, streamline device upkeep, reduce waste material, and ease the logistical burden associated with the single use masks, it is hypothesized that the consumable masks and filters could be sterilized by autoclaving for reuse. The masks were contaminated, autoclaved, and then tested for any surviving pathogens with spore strip standards and by measuring the optical density of cultures. The compound background collected when using the ReCIVA with new masks was compared to that collected with repeatedly autoclaved masks via thermal desorption gas chromatography mass spectrometry (TD-GC-MS). The capacity to block particulate matter of new filters was tested against that of autoclaved filters by introducing an aerosol and comparing pre-filter and post-filter particle counts. Finally, breath samplings were conducted with new masks and autoclaved masks to test for changes in measurements by TD-GC-MS of exogenous and endogenous compounds. The data illustrate the autoclave cycle sterilizes masks spiked with saliva to background levels (p= 0.2527). The results indicate that background levels of siloxane compounds are increased as masks are repetitively autoclaved. The data show that mask filters have significant breakthrough of 1μm particles after five repetitive autoclaving cycles compared to new filters (p= 0.0219). Finally, exhaled breath results utilizing a peppermint ingestion protocol indicate two compounds associated with peppermint, menthone and 1-Methyl-4-(1-methylethyl)-cyclohexanol, and an endogenous exhaled breath compound, isoprene, show no significant difference if sampled with a new mask or a mask autoclaved five times (p> 0.1063). Collectively, the data indicate that ReCIVA masks and filters can be sterilized via autoclave and reused. The results suggest ReCIVA mask and filter reuse should be limited to three times to limit potentially problematic background contaminants and filter dysfunction.

Prolonged exposure to hyperbaric hyperoxia can lead to pulmonary oxygen toxicity (PO₂tox). PO₂tox is a mission limiting factor for special operations forces divers using closed-circuit rebreathing apparatus and a potential side effect for patients undergoing hyperbaric oxygen (HBO) treatment. In this study, we aim to determine if there is a specific breath profile of compounds in exhaled breath condensate (EBC) that is indicative of the early stages of pulmonary hyperoxic stress/PO₂tox. Using a double-blind, randomized 'sham' controlled, cross-over design 14 U.S. Navy trained diver volunteers breathed two different gas mixtures at an ambient pressure of 2 ATA (33 fsw, 10 msw) for 6.5 h. One test gas consisted of 100% O₂(HBO) and the other was a gas mixture containing 30.6% O₂with the balance N₂(Nitrox). The high O₂stress dive (HBO) and low O₂stress dive (Nitrox) were separated by at least seven days and were conducted dry and at rest inside a hyperbaric chamber. EBC samples were taken immediately before and after each dive and subsequently underwent a targeted and untargeted metabolomics analysis using liquid chromatography coupled to mass spectrometry (LC-MS). Following the HBO dive, 10 out of 14 subjects reported symptoms of the early stages of PO₂tox and one subject terminated the dive early due to severe symptoms of PO₂tox. No symptoms of PO₂tox were reported following the nitrox dive. A partial least-squares discriminant analysis of the normalized (relative to pre-dive) untargeted data gave good classification abilities between the HBO and nitrox EBC with an AUC of 0.99 (±2%) and sensitivity and specificity of 0.93 (±10%) and 0.94 (±10%), respectively. The resulting classifications identified specific biomarkers that included human metabolites and lipids and their derivatives from different metabolic pathways that may explain metabolomic changes resulting from prolonged HBO exposure.

Volatile organic compounds (VOCs) originating from human metabolic activities can be detected in, for example, breath, urine, feces, and blood. Thus, attention has been given to identifying VOCs from the above matrices. Studies identifying and measuring human blood VOCs are limited to those focusing on monitoring specific pollutants, or blood storage and/or decomposition. However, a comprehensive characterization of VOCs in human blood collected for routine diagnostic testing is lacking. In this pilot study, 72 blood-derived plasma samples were obtained from apparently healthy adult participants. VOCs were extracted from plasma using solid-phase microextraction and analyzed using comprehensive two-dimensional gas chromatography tandem time-of-flight mass spectrometry. Chromatographic data were aligned, and putative compound identities were assigned via spectral library comparison. All statistical analysis, including contaminant removal, data normalization, and transformation were performed usingR. We identified 401 features which we called the pan volatilome of human plasma. Of the 401 features, 34 were present in all the samples with less than 15% variance (core molecules), 210 were present in ⩾10% but <100% of the samples (accessory molecules), and 157 were present in less than 10% of the samples (rare molecules). The core molecules, consisting of aliphatic, aromatic, and carbonyl compounds were validated using 25 additional samples. The validation accuracy was 99.9%. Of the 34 core molecules, 2 molecules (octan-2-one and 4-methyl heptane) have been identified from the plasma samples for the first time. Overall, our pilot study establishes the methodology of profiling VOCs in human plasma and will serve as a resource for blood-derived VOCs that can complement future biomarker studies using different matrices with more heterogeneous cohorts.

Analysis of volatile organic compounds (VOCs) in breath specimens has potential for point of care (POC) screening due to ease of sample collection. While the electronic nose (e-nose) is a standard VOC measure across a wide range of industries, it has not been adopted for POC screening in healthcare. One limitation of the e-nose is the absence of mathematical models of data analysis that yield easily interpreted findings at POC. The purposes of this review were to (1) examine the sensitivity/specificity results from studies that analyzed breath smellprints using the Cyranose 320, a widely used commercial e-nose, and (2) determine whether linear or nonlinear mathematical models are superior for analyzing Cyranose 320 breath smellprints. This systematic review was conducted according to the guidelines of the Preferred Reporting Items for Systematic Review and Meta-Analyses using keywords related to e-nose and breath. Twenty-two articles met the eligibility criteria. Two studies used a linear model while the rest used nonlinear models. The two studies that used a linear model had a smaller range for mean of sensitivity and higher mean (71.0%-96.0%;M= 83.5%) compared to the studies that used nonlinear models (46.9%-100%;M= 77.0%). Additionally, studies that used linear models had a smaller range for mean of specificity and higher mean (83.0%-91.5%;M= 87.2%) compared to studies that used nonlinear models (56.9%-94.0%;M= 76.9%). Linear models achieved smaller ranges for means of sensitivity and specificity compared to nonlinear models supporting additional investigations of their use for POC testing. Because our findings were derived from studies of heterogenous medical conditions, it is not known if they generalize to specific diagnoses.

An accurate cannabis breathalyzer based on quantitation of the psychoactive cannabinoid Δ⁹-tetrahydrocannabinol (THC) could be an important tool for deterring impaired driving. Such a device does not exist. Simply translating what is known about alcohol breathalyzers is insufficient because ethanol is detected as a vapor. THC has extremely low volatility and is hypothesized to be carried in breath by aerosol particles formed from lung surfactant. Exhaled breath aerosols can be recovered from electrostatic filter devices, but consistent quantitative results across multiple studies have not been demonstrated. We used a simple-to-use impaction filter device to collect breath aerosols from participants before and after they smoked a legal market cannabis flower containing ∼25% Δ⁹-tetrahydrocannabinolic acid. Breath collection occurred at an intake session (baseline-intake) and four weeks later in a federally-compliant mobile laboratory 15 min before (baseline-experimental) and 1 h after cannabis use (post-use). Cannabis use was in the participant's residence. Participants were asked to follow a breathing maneuver designed to increase aerosol production. Breath extracts were analyzed by liquid chromatography with tandem mass spectrometry with multiple reaction monitoring of two transitions for analytes and their deuterated internal standards. Over more than 1 yr, 42 breath samples from 18 participants were collected and analyzed in six batches. THC was quantified in 31% of baseline-intake, 36% of baseline-experimental, and 80% of 1 h post-use breath extracts. The quantities observed 1 h post-use are compared to those reported in six other pilot studies that sampled breath at known intervals following cannabis use and are discussed with respect to participant characteristics and breath sampling protocols. Larger studies with verified abstinence and more post-use timepoints are necessary to generate statistically significant data to develop meaningful cannabis breathalyzer technology.

Exhaled breath and gastric-endoluminal gas (volatile products of diseased tissues) contain a large number of volatile organic compounds, which are valuable for early diagnosis of upper gastrointestinal (UGI) cancer. In this study, exhaled breath and gastric-endoluminal gas of patients with UGI cancer and benign disease were analyzed by gas chromatography-mass spectrometry (GC-MS) and ultraviolet photoionization time-of-flight mass spectrometry (UVP-TOFMS) to construct UGI cancer diagnostic models. Breath samples of 116 UGI cancer and 77 benign disease subjects and gastric-endoluminal gas samples of 114 UGI cancer and 76 benign disease subjects were collected. Machine learning (ML) algorithms were used to construct UGI cancer diagnostic models. Classification models based on exhaled breath for distinguishing UGI cancer from the benign group have area under the curve (AUC) of receiver operating characteristic curve values of 0.959 and 0.994 corresponding to GC-MS and UVP-TOFMS analysis, respectively. The AUC values of models based on gastric-endoluminal gas for UGI cancer and benign group classification are 0.935 and 0.929 corresponding to GC-MS and UVP-TOFMS analysis, respectively. This work indicates that volatolomics analysis of exhaled breath and gastric-endoluminal diseased tissues have great potential in early screening of UGI cancer. Moreover, gastric-endoluminal gas can be a means of gas biopsy to provide auxiliary information for the examination of tissue lesions during gastroscopy.

The first and most crucial step in breath research is adequate sampling, which plays a pivotal role in quality assurance of breath datasets. In particular, the emissions or uptake of volatile organic compounds (VOCs) by sampling interface materials present a risk of disrupting breath gas samples. This study investigated emissions and uptake by three interface components, namely a silicon facemask, a reusable 3D-printed mouthpiece adapter, and a pulmonary function test filter compatible with the commercial Respiration Collector forIn-VitroAnalysis (ReCIVA) breath sampling device. Emissions were examined before and after (hydro-)thermal treatment of the components, and uptake was assessed by exposing each material to 12 representative breath VOCs comprising alcohols, aldehydes, ketones, carboxylic acids, terpenes, sulphurous and nitrogenous compounds at different target concentration ranges (∼10 ppb_Vand ∼100 ppb_V). Chemical analyses of VOCs were performed using proton transfer reaction-time-of-flight-mass spectrometry (PTR-TOFMS) with supporting analyses via thermal desorption comprehensive two-dimensional gas chromatography-TOFMS (TD-GC×GC-TOFMS). The filter exhibited the lowest overall emissions compared to the mask or adapter, which both had equivalently high emissions (albeit for different compounds). Treatment of the materials reduced the total VOC emissions by 62% in the mask, 89% in the filter and 99% in the adapter. Uptakes of compounds were lowest for the adapter and most pronounced in the mask. In particular, 1-butanol, acetone, 2-butanone, 1,8-cineole and dimethyl sulphide showed negligible uptake across all materials, whereas ethanol, nonanal, acetic acid, butanoic acid, limonene and indole exhibited marked losses. Knowledge of emissions and/or uptake by sampling components is key to reducing the likelihood of erroneous data interpretation, ultimately expediting progress in the field of breath test development.

PM_2.5is a well-known airborne hazard to cause various diseases. Evidence suggests that air pollution exposure contributes to the occurrence of pulmonary nodules. Pulmonary nodules detected on the computed tomography scans can be malignant or progress to malignant during follow-up. But the evidence of the association between PM_2.5exposure and pulmonary nodules was limited. To examine potential associations of exposures to PM_2.5and its major chemical constituents with the prevalence of pulmonary nodules. A total of 16 865 participants were investigated from eight physical examination centers in China from 2014 to 2017. The daily concentrations of PM_2.5and its five components were estimated by high-resolution and high-quality spatiotemporal datasets of ground-level air pollutants in China. The logistic regression and the quantile-based g-computation models were used to assess the single and mixture impact of air pollutant PM_2.5and its components on the risk of pulmonary nodules, respectively. Each 1 mg m^-3increase in PM_2.5(OR 1.011 (95% CI: 1.007-1.014)) was positively associated with pulmonary nodules. Among five PM_2.5components, in single-pollutant effect models, every 1μg m^-3increase in organic matter (OM), black carbon (BC), and NO₃^-elevated the risk of pulmonary nodule prevalence by 1.040 (95% CI: 1.025-1.055), 1.314 (95% CI: 1.209-1.407) and 1.021 (95% CI: 1.007-1.035) fold, respectively. In mixture-pollutant effect models, the joint effect of every quintile increase in PM_2.5components was 1.076 (95% CI: 1.023-1.133) fold. Notably, NO₃^-BC and OM contributed higher risks of pulmonary nodules than other PM_2.5components. And the NO₃^-particles were identified to have the highest contribution. The impacts of PM_2.5components on pulmonary nodules were consistent across gender and age.These findings provide important evidence for the positive correlation between exposure to PM_2.5and pulmonary nodules in China and identify that NO₃^-particles have the highest contribution to the risk.