Journal of breath research最新文献

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. .

To evaluate the therapeutic efficacy of probiotics in managing halitosis and to determine the optimal intervention strategies. An extensive search was conducted in PubMed, Web of Science, Cochrane Library, and Embase up to December 2024, focusing on studies evaluating probiotic interventions for halitosis. Sensitivity and subgroup analyses were undertaken to assess the robustness of the results and to explore potential sources of heterogeneity. All analyses were performed through Review Manager 5.4 and STATA 15.0. Of the 194 records initially identified, 10 studies met the predefined criteria. The pooled results demonstrated a significant reduction in volatile sulfur compounds (VSCs) levels in the probiotic group compared to controls (SMD = - 1.01, 95% CI [-1.93, -0.09],P= 0.03). Likewise, the organoleptic test (OLT) scores showed a marked improvement in the probiotic group (RR = 1.31, 95% CI [1.22, 1.41],P< 0.00001). Nevertheless, no substantial differences were observed between groups in oral health-related quality of life (SMD = 0.21, 95% CI [-0.06, 0.49],P= 0.12), subjective oral health status (SMD = - 0.04, 95% CI [-0.35, 0.28],I²= 0%,p= 0.74), depression (SMD = 0.03, 95% CI [-0.29, 0.35],I²= 0%,p= 0.85), self-esteem (SMD = - 0.07, 95% CI [-0.39, 0.25],I²= 0%,p= 0.67), OLT scores (SMD = - 0.24, 95% CI [-0.64, 0.16],I²= 0%,p= 0.24), or plaque index (SMD = - 0.06, 95% CI [-0.57, 0.46],I²= 0%,p= 0.82). The findings suggest that probiotic therapy, when combined with conventional treatments, may be more effective in enhancing OLT scores and reducing VSC levels in individuals with halitosis than using probiotic alone. Nonetheless, potential publication bias, limited sample sizes, and heterogeneity among the included clinical trials may affect the reliability of these conclusions.

Silicosis is a prevalent chronic occupational disease, causing incurable damage to the lungs. The conventional methods for diagnosing silicosis are costly and complex. Noninvasive biomarker studies based on exhalation metabolomics have potential in the early diagnosis of silicosis, but existing studies remain scarce and especially lack the biomarkers for staging diagnosis. Exhaled breath from 74 healthy controls and 112 patients, including 28 stage Ⅰ silicosis patients (SILs), 22 stage Ⅱ SILs, 52 stage ࡲ SILs and 10 coal workers' pneumoconiosis patients, were detected using solid-phase microextraction incorporating gas chromatography-mass spectrometry for the identification of the volatile metabolites. The univariate statistical analysis and orthogonal partial least squares-discriminant analysis were employed to screen potential biomarkers of SILs, with diagnostic performance assessed with the receiver-operating characteristic (ROC) curve and decision tree model. Fourteen volatile metabolites were found to distinguish different stages of SILs from healthy controls, and 8 metabolites differentiating stage Ⅰ and ࡲ as well as 3 metabolites distinguishing stage Ⅱ and ࡲ. The ROC analysis of silicosis based on the biomarkers exhibited an area under curve (AUC) of more than 0.9, with the largest AUC of 0.986 in stage Ⅰ SILs and healthy controls. Mechanistic exploration showed that these biomarkers were associated with inflammatory response, oxidative stress, and fibrosis in silicosis, respectively. This study preliminarily screened out the biomarkers of exhaled breath for different stages of SILs, and explored the metabolic pathways of biomarkers, which can provide basic data for the early, staging and specific diagnosis of SILs.

Several investigations have identified volatile organic compounds (VOCs) as potential biomarkers for the detection and identification of microbial contamination of metabolically active mammalian cell cultures. In this study, we showed that emitted VOCs discriminate between uncontaminated mesenchymal stromal cells (MSCs) and those contaminated with the bacteriumStaphylococcus epidermidisor fungusAspergillus Fumigatusseparately,in vitro, using a methodology based on an adapted cell culture and thermal desorption-gas chromatography-mass spectrometry. In addition, we elucidated a set of discriminatory volatile compounds from the MSC cultures and media alone across a time series experiment. Partial least squares-discriminant analysis-variable importance in projection confirmed putative identifications of 18, 16, and 26 VOCs that showed relevant changes in a bacterial, fungal, and universal pathogen model, respectively, with an accuracy of 100% in the fungal model. Among these metabolites, octane, 2,5,6-trimethyl- overlapped between the three groups. Furthermore, a total of 15 VOCs were found most relevant to cell culture expansion over three days based on cluster analysis. This novel study goes a step further in identifying distinct VOC signatures of MSCs contaminated withS. epidermidisorA. fumigatus, and in monitoring MSCs proliferation over time. This pilot study shows preliminary results that indicate that VOC headspace analysis could serve as a suitable, rapid, non-invasive, and non-destructive tool for the metabolic and growth monitoring of MSCs in a dynamic cell culture bioreactor system.

Background.Increased exhaled breath acetone (EBA) concentrations might reflect impaired myocardial energetics and haemodynamic stress. We investigated the relation of EBA and cardiac structure, function, and exercise capacity in patients with or at risk of heart failure (HF).Methods.We enrolled outpatients with HF and reduced (<50%, HFrEF) or preserved (>50%, HFpEF) left ventricular ejection fraction (LVEF) and subjects with cardiovascular risk factors and/or structural heart disease without established HF. All participants underwent clinical and laboratory evaluation, resting transthoracic echocardiography, and a combined cardiopulmonary-echocardiographic stress test with EBA monitoring at rest (EBA_rest) and during exercise (EBA_ex).Results.Patients with HFpEF (n= 62) were older and more often female than those at risk of HF (n= 50) or with HFrEF (n= 41). EBA_rest(1.5, interquartile range (IQR) 1.0-3.1 vs 0.9, IQR 0.7-1.2 mcg l^-1) and EBA_ex(2.4, IQR 1.5-4.4 vs 1.1, IQR 0.9-2.1 mcg l^-1; allp< 0.0001) were significantly higher in patients with HF compared to others. Among HF patients, those in the highest EBA_resttertile had lower LVEF, greater echocardiographic signs of congestion, higher NT-proBNP levels, and lower peak oxygen consumption, indicating impaired exercise capacity. In multivariate regression, NT-proBNP (p= 0.0004) and the slope of minute ventilation to carbon dioxide production (p= 0.0013) were independent predictors of EBA_rest(adjustedR²= 0.458).Conclusions.EBA concentrations are higher in patients with HF compared to those without, regardless of LVEF, and are associated with markers of disease severity. Further studies are needed to determine whether EBA measurement can aid in HF diagnosis and management.

Atmospheric microplastics and nanoplastics (MPs/NPs) have become an increasing concern, with health impacts that remain insufficiently characterised and measured. Indoor airborne MP/NPs have raised greater alarm due to their origins in routine domestic activities and sources such as synthetic textiles, bed linen, face masks, electric dryers, and household laundry, posing a high inhalation risk that can lead to serious health consequences. These particles can enter the human body through various routes, with inhalation being the main pathway for both short- and long-term health effects. Additionally, they are engulfed by epithelial and immune cells, causing multiple pathological effects on the lungs, which can subsequently lead to or contribute to various disease entities. This narrative review thoroughly explores potential cellular, genetic, and immunological mechanisms by which MP/NPs impact the respiratory system, emphasising immune mediators and cytosolic pathways involved, and linking these mechanisms to various pulmonary diseases.