Journal of breath research最新文献

Δ⁹-tetrahydrocannabinol (THC), the main psychoactive compound in cannabis, and other drug molecules that have large molar masses, are often described as 'nonvolatile' and are presumed to be carried in exhaled breath aerosols. Large variabilities in THC concentrations in breath have been measured with devices that only collect aerosols; it is possible that neglecting the vapor phase could be responsible. Partitioning of compounds between vapor and aerosol phases is directly dependent on vapor pressure (p_sat), which itself is strongly dependent on temperature. We describep_satmeasurements for THC, cannabidiol (CBD), and cannabinol (CBN) using a gas-saturation apparatus. The measured values ofp_satfor 364 K to 424 K are 0.0459 Pa to 7.833 Pa for THC, 0.0826 Pa to 13.44 Pa for CBD, and 0.0199 Pa to 5.678 Pa for CBN. The combined standard (k= 1, 68% confidence) measurement uncertainty inp_satranges from 2.9% to 5.3% for CBD and CBN, and from 5.2% to 9.5% for THC. To obtain thep_satat human body and exhaled breath temperatures, we extrapolated the measurements for each cannabinoid with a thermodynamic correlation. Then a vapor-aerosol partitioning model was used to predict mole fractions of each cannabinoid in each phase of exhaled breath. All three cannabinoids were predicted to reside primarily in the vapor phase of exhaled breath. However, relatively small changes in temperature or aerosol concentration can significantly impact the predicted partitioning. This work illustrates the utility of low-uncertaintyp_satmeasurements for any drug, including those thought to be too low in volatility for vapor-phase sampling, and may extend the market for forensic drug tests and clinical diagnostic tests via breath analysis.

Halitosis, commonly known as bad breath, is a highly prevalent condition often associated with volatile sulfur compounds (VSCs) produced by oral anaerobic bacteria. While intraoral halitosis is the most frequent type, its psychosocial impact remains underexplored in terms of its correlation with objective diagnostic markers. This study investigated the association between VSC concentrations and halitosis-related quality of life using the Halitosis Associated Life-Quality Test (HALT). In this cross-sectional study, 40 adults self-reporting halitosis were assessed using OralChroma™, a portable gas chromatograph that quantifies hydrogen sulfide, methyl mercaptan, and dimethyl sulfide. Breath samples were collected at baseline and after an L-cysteine oral rinse challenge. Participants completed the HALT questionnaire to assess emotional, social, and functional impacts. Statistical analyses included descriptive metrics, Pearson's correlation, and multiple linear regression. Model assumptions were tested for validity. At baseline, 25% of participants had VSC levels above diagnostic thresholds; after L-cysteine administration, this proportion increased to 87.5%, indicating the presence of latent halitosis. Methyl mercaptan concentration before the challenge showed a significant correlation with HALT scores (r = 0.353; p = 0.025) and was the only significant predictor in the final regression model (β = 1.03; p = 0.025; R² = 0.125). Elevated HALT scores were also observed in participants without clinically confirmed halitosis, suggesting that self-perception and emotional distress play a central role in patient experience. These findings highlight the relevance of combining objective VSC measurements with validated, condition-specific quality-of-life instruments to better understand and manage halitosis. Methyl mercaptan may serve as a biochemical marker of halitosis severity and also as a potential mediator of its psychosocial consequences.

This study presents a computational method to identify volatile organic compound (VOC) artefacts introduced by breath sampling hardware. To exclude endogenous biological variability, ambient air was collected using two sampling devices working in the same experimental conditions: the Mistral end-tidal breath sampler and the ACTI-VOC PLUS pump, a low-emission reference system. VOCs were pre-concentrated on sorbent-packed thermal desorption (TD) tubes and analyzed by TD-gas chromatography-mass spectrometry (TD-GC-MS). Differential chromatograms obtained by subtracting ACTI-VOC signals from Mistral traces were processed using stationary wavelet transform (SWT) to selectively enhance high-frequency features indicative of artefactual emissions. Four new compounds not previously associated with Mistral sampling hardware were consistently detected in Mistral samples and were absent in ACTI-VOC pump controls: 1,3,5-trioxane, 1,3,5,7-tetroxane, (Acetyloxy)acetic acid, and N,N-dimethylformamide. These molecules are indicative of polymer degradation, acetal resin breakdown, and material off-gassing specific to the breath sampler.

Pediatric post-COVID condition (PPCC) presents as a heterogeneous disease with a broad spectrum of symptoms. This study aimed to identify distinct phenotypes of PPCC through an unbiased cluster analysis of exhaled metabolites, with the goal of identifying biomarkers to stratify patients. Exhaled breath samples were collected from children with physician-diagnosed PPCC. An unsupervised clustering approach was applied to the exhaled breath metabolites, and the resulting clusters were compared with clinical variables. Sparse partial least squares-discriminant analysis (sPLS-DA) was applied to find most discriminative metabolites between clusters. A total of 54 children were included and categorized into two clusters. Compared to Cluster 1 (n= 38), Cluster 2 (n= 16) consisted predominantly of older girls (69%) with a median age of 16 years and exhibited more severe PPCC-related outcomes, including higher PROMIS fatigue scores. Six volatile organic compounds (VOCs) were identified as biomarkers that effectively differentiated the two clusters. These VOCs, previously reported in the literature, highlight metabolic and inflammatory disruptions and demonstrated high discriminatory performance (area under the receiver operating characteristic curve (AUROCC) = 1). This study found two distinct phenotypes of PPCC, and identified six discriminating VOCs, underscoring the potential of VOCs as non-invasive biomarkers for disease stratification in PPCC. While it could be a building block towards a better understanding of the metabolic disruptions underlying PPCC, further research with larger patient cohorts is necessary to elucidate the mechanisms driving these differences.

The feasibility of using a novel, non-invasive breath-based diagnostic test for detecting Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) was evaluated in a real-world clinical setting. The study aimed to assess the device's performance, usability and integration into routine point-of-care pathways, while also obtaining preliminary diagnostic accuracy metrics to inform future validation studies. This was a cross-sectional study performed in a single National Health Service hospital. Participants in cohort A were recruited with prior knowledge of their PCR swab results. Cohort B were sequential participants in a prospective 'real-world' evaluation. 385 participants were recruited, with 214 participants achieving a suitable sample for analysis. Of 179 participants in cohort A, 60 (33.5%) had clinically suspected SARS-CoV-2 confirmed with a positive PCR swab, 31 (17.3%) had clinically suspected SARS-CoV-2, but a negative PCR swab, 22 (12.3%) were asymptomatic but with a positive PCR swab and 66 (36.9%) had a low clinical suspicion of SARS-CoV-2 and negative PCR swab. Across all participants with a suitable sample for analysis, the Exhalation Technology Limited (ETL) CoronaCheck® had a sensitivity of 99% and specificity of 96%. Cohen'sKscore confirmed an excellent agreement between a positive vs negative CoronaCheck outcome against a positive vs negative PCR result (k= 0.990, 99%, SE:0.10). The CoronaCheck demonstrated an excellent predictive value for a positive PCR result, with low variability (both cohorts: ROC = 0.98 (CI:94%-100%)). Most participants (88%) reported the CoronaCheck was easy to use, highlighting its potential utility in clinical practice. This study demonstrates it is feasible to use exhaled breath condensate and the ETL CoronaCheck® device to detect SARS-CoV-2 in a rapid point-of-care test.

Early detection of critical illness is essential for timely intervention and improved outcomes. Conventional diagnostic methods, such as laboratory tests and imaging, are invasive and often delayed. In recent years, non-invasive monitoring approaches, particularly exhaled breath analysis, have gained attention in critical care. Various analytical platforms, including gas chromatography-mass spectrometry, proton transfer reaction mass spectrometry, and electronic nose systems, have been employed to identify volatile organic compound (VOC) patterns associated with acute conditions. Elevated aldehydes and ketones have been reported in ventilator-associated pneumonia, hydrocarbons such as octane in acute respiratory distress syndrome, and acetone in acute heart failure. These findings highlight the value of VOC-based approaches for early disease recognition, pathogen identification, and dynamic monitoring at the bedside. Exhaled breath analysis represents a promising, non-invasive tool to complement conventional diagnostics in the intensive care unit, though challenges such as standardization and large-scale validation remain. This review focuses on the application of VOCs in the management of critically ill patients, with special emphasis on their diagnostic and monitoring potential.

Irritable bowel syndrome (IBS), a disorder of gut-brain interaction, is diagnosed using symptom-based Rome criteria. These criteria classify IBS patients into four subtypes in accordance to their stool patterns. However, whether this subtyping approach is based on true differences in the underlying biology of IBS patients, is unclear. Volatile organic compounds (VOCs) in the faecal headspace reflect both the gut microbial and host intestinal intraluminal processes and thereby may be used to study pathophysiological differences between IBS and its subtypes. We profiled faecal headspace VOCs in a cohort of 164 patients with IBS and 143 healthy controls using gas chromatography-mass spectrometry. Random forest models were employed to impute missing values and identify discriminatory VOCs to differentiate IBS patients from healthy controls. We corrected for faecal water content using partial least squares regression. Multivariate associations between the obtained volatile profiles and Rome III IBS subtypes were evaluated using regularized MANOVA. A total of 39 VOCs, including short-chain fatty acid esters, neurotransmitter-related metabolites, alcohols, and sulphides, were selected as significantly altered in patients with IBS. Our classification model achieved an area under the curve of 0.82 on both training and independent test sets, demonstrating robust separation between IBS patients and healthy individuals. However, VOC profiles did not associate to Rome III -based IBS subtypes. This study highlights the potential of faecal VOC profiling as a non-invasive tool for studying and characterizing IBS, yet they also reveal a disconnect between metabolic signatures and current stool-based subtypes. While the Rome criteria remain the clinical standard for diagnosis and subtyping of IBS, they offer limited insight into underlying disease mechanisms. Future research should focus on integrating VOC analysis with other omics approaches to refine IBS sub-classification into biologically relevant clusters, which may aid to improve personalized therapeutic strategies.

Interstitial lung disease (ILD) encompasses pulmonary disorders characterised by varying degrees of inflammation and/or fibrosis. The presence and extent of these pulmonary abnormalities on (high-resolution) computed tomography (CT) have consequences for diagnosis and treatment; however, inter-observer assessment varies. Analysis of exhaled volatile organic compounds (VOCs) through gas chromatography-mass spectrometry (GC-MS) offers a noninvasive approach to biomarker discovery and pathophysiology understanding. Our study aims to explore the ability of GC-MS-driven exhaled breath analysis to differentiate ILD patients with predominant fibrotic, inflammatory, or a combination of fibrotic and inflammatory pulmonary abnormalities in a training and an external validation cohort. In a multicentre cross-sectional study, patients diagnosed with ILD were recruited. After central review of chest CT scans by independent radiologists, patients were categorised as fibrotic, inflammatory or mixed phenotype group based on the percentage of chest CT scan abnormalities. Breath samples were collected and analysed via GC-MS. Significantly different VOC fragments between groups were selected and used to differentiate groups in the training cohort with sparse partial least squares discriminant analysis. Analyses were validated with patients from an external cohort. 53 patients were included, 21 patients in the fibrotic, 14 in the inflammatory and 18 in the mixed phenotype group. Area under the curve (AUCs) for discrimination between groups ranged from 0.89-1.00 in training cohorts. An attempt to confirm these findings in our external validation cohort resulted in AUCs of 0.63-0.84. Re-evaluation of the training model led to an AUC of 0.78-0.83. This study shows that GC-MS driven exhaled breath analysis towards differentiation of ILD phenotypes is challenging. Current findings emphasise the importance of predefined validation steps during the process of biomarker discovery.

Chronic obstructive pulmonary disease (COPD) is a complex, progressive inflammatory disorder characterized by airflow limitation and respiratory symptoms. Its heterogeneity is manifested at etiological, pathological and clinical levels, and leads to different phenotypes: chronic bronchitis, emphysema, asthma-COPD overlap, frequent exacerbator and eosinophilic phenotypes. COPD is also associated with systemic manifestations including cardiovascular diseases, muscle dysfunction, osteoporosis and mental-health issues, which require a comprehensive management approach. Key risk factors are tobacco smoke and air pollution, both of which induce oxidative stress and airway remodeling. Although there is still no definitive cure for COPD, an early diagnosis and a multidisciplinary treatment are essential to prevent or slow the disease progression and reduce the mortality rate. Molecular biomarkers, particularly those identified through metabolomics, show promise for early detection, phenotyping and precision therapies. Challenges in biomarker discovery include specimen variability and stability. Overall, metabolomics provides valuable insights into COPD's molecular pathways, supporting improved diagnosis, prognosis and tailored treatments. In this tutorial, we will explore metabolomics findings from different COPD matrices and their clinical implications for diagnosis, treatment and prognosis.

Background: Despite major progress in diagnosis and treatment, cardiovascular disease (CVD) continues to be the leading cause of death worldwide, responsible for roughly 19.8 million lives lost each year. A key challenge in preventive cardiology is still the early detection of those at elevated risk of serious heart complications. Aims: Assess the ability of the machine learning model to stratify CVD risk using exhaled breath analysis. Materials and methods: A single-center study involved 80 participants with vs. without stress-induced myocardial perfusion defect. All participants underwent a single resting breath sample collection in PTR-TOF-MS-1000, single blood sample intake, and stress computed tomography myocardial perfusion imaging with vasodilation test. Statistical analyses were performed using Statistica 12 (StatSoft, Inc., 2014), IBM SPSS Statistics v29.0.1.1 (IBM Corp., 2024). The threshold for statistical significance was p < 0.05. Machine learning models were developed using Google Colab with Python 3. Results: The gradient-boosting model demonstrated the best performance and was therefore selected for further evaluation. The model showed an AUC of 0.77 [95% CI; 0.4976 - 1.0000] to differentiate participants with low CVD risk, moderate risk 0.55 [95% CI; 0.3345 - 0.7875], and high risk 0.66 [95% CI; 0.3765 - 0.8661]. Conclusion: The gradient boosting machine learning model provides initial evidence that rest exhaled breath analysis can differentiate cardiovascular risk strata through identifiable concentration patterns of specific volatile organic compounds. However, substantial challenges remain regarding model performance and the confounding effects of class imbalance within a limited sample. .