Journal of Breath Research

Occupational lung diseases, such as silicosis, are a significant global health concern, especially with increasing exposure to engineered stone dust. Early detection of silicosis is helpful for preventing disease progression, but existing diagnostic methods, including x-rays, computed tomography scans, and spirometry, often detect the disease only at late stages. This study investigates a rapid, non-invasive diagnostic approach using atmospheric pressure chemical ionization-mass spectrometry (APCI-MS) to analyze volatile organic compounds (VOCs) in exhaled breath from 31 silicosis patients and 60 healthy controls. Six different interpretable machine learning (ML) models with Shapley additive explanations (SHAP) were applied to classify these samples and determine VOC features that contribute the most significantly to model accuracy. The extreme gradient boosting classifier demonstrated the highest performance, achieving an area under the receiver-operator characteristic curve of 0.933 with the top ten SHAP features. The m/z 442 feature, potentially corresponding to leukotriene-E3, emerged as a significant predictor for silicosis. The VOC sampling and measurement process takes less than five minutes per sample, highlighting its potential suitability for large-scale population screening. Moreover, the ML models are interpretable through SHAP, providing insights into the features contributing to the model's predictions. This study suggests that APCI-MS breath analysis could enable early and non-invasive diagnosis of silicosis, helping to improve disease outcomes.

In recent decades, two facts have changed the opinion of researchers about the function of acetone in humans. Firstly, it has turned out that acetone cannot be regarded as simply a waste product of metabolism, because there are several pathways in which acetone is produced or broken down. Secondly, methods have emerged making possible its detection in exhaled breath, thereby offering an attractive alternative to investigation of blood and urine samples. From a clinical point of view the measurement of breath acetone levels is important, but there are limitations to its wide application. These limitations can be divided into two classes, technical and biological limits. The technical limits include the storage of samples, detection threshold, standardization of clinical settings, and the price of instruments. When considering the biological ranges of acetone, personal factors such as race, age, gender, weight, food consumption, medication, illicit drugs, and even profession/class have to be taken into account to use concentration information for disorders. In some diseases such as diabetes mellitus and lung cancer, as well as in nutrition-related behavior such as starvation and ketogenic diet, breath acetone has been extensively examined. At the same time, there is a lack of investigations in other cases in which ketosis is also evident, such as in alcoholism or an inborn error of metabolism. In summary, the detection of acetone in exhaled breath is a useful and promising tool for diagnosis and it can be used as a marker to follow the effectiveness of treatments in some disorders. However, further endeavors are needed for clarification of the exact distribution of acetone in different body compartments and evaluation of its complex role in humans, especially in those cases in which a ketotic state also occurs.

Volatolomics (or volatilomics), the study of volatile organic compounds, has emerged as a significant branch of metabolomics due to its potential for non-invasive diagnostics and disease monitoring. However, the analysis of high-resolution data from mass spectrometry and gas sensor array-based instruments remains challenging. The careful consideration of experimental design, data collection, and processing strategies is essential to enhance the quality of results obtained from subsequent analyses. This comprehensive guide provides an in-depth exploration of volatolomics data analysis, highlighting the essential steps, such as data cleaning, pretreatment, and the application of statistical and machine learning techniques, including dimensionality reduction, clustering, classification, and variable selection. The choice of these methodologies, along with data handling practices, such as missing data imputation, outlier detection, model validation, and data integration, is crucial for identifying meaningful metabolites and drawing accurate diagnostic conclusions. By offering researchers the tools and knowledge to navigate the complexities of volatolomics data analysis, this guide emphasizes the importance of understanding the strengths and limitations of each method. Such informed decision-making enhances the reliability of findings, ultimately advancing the field and improving the understanding of metabolic processes in health and disease

Chronic obstructive pulmonary disease (COPD) exacerbations significantly contribute to disease progression, hospitalizations, and decreased quality of life. Early detection of exacerbations through non-invasive methods, such as exhaled volatile organic compounds (VOCs), could enable timely interventions. This study aimed to identify and validate candidate VOC biomarkers that are associated with exacerbations and stable phases of COPD, and could contribute to the development of a breath-based monitoring device. A systematic review was conducted to identify VOCs associated with COPD and exacerbations. VOCs were selected as candidate biomarkers if they were reported in at least two studies by different research groups. These VOCs were then validated using longitudinal exhaled breath data from the TEXACOLD study, where exhaled breath samples were collected at baseline, during exacerbation, and at follow-up in 14 COPD patients. Sparse partial least squares-discriminant analysis was applied to differentiate between samples collected during exacerbation and those at stable phases. Diagnostic accuracy was assessed using receiver operating characteristic (ROC) curves. The systematic review identified nine candidate VOCs. Three were excluded from validation because their dataset overlapped with one used in one of the included review studies. Validation confirmed the discriminatory power of a composite model of these six VOCs, achieving an area under the ROC curve of 0.98, a diagnostic accuracy of 94.3% and a sensitivity of 0.97 and a specificity of 0.93. This study demonstrates that exhaled VOCs can differentiate between exacerbations and stable phases in COPD patients. The validated biomarkers hold promise for future clinical applications, particularly in the development of a non-invasive, breath-based monitoring device for early detection and management of COPD exacerbations, potentially reducing hospitalizations and improving patient outcomes.

Breath volatile organic compounds (VOCs) are increasingly under consideration as biomarkers of respiratory disease. Although numerous studies have identified VOCs that distinguish patient groups, a lack of standardisation among published studies has impeded translation into clinical diagnostics. Standardised breath collection protocols have been proposed for adults and children aged >4 years, but optimal methods for collecting breath from younger children remain to be determined. The aim of this study was to assess the feasibility and acceptability of breath sampling among a young paediatric cohort. A total of 61 children (age 6 months–12 years) were recruited prospectively to observational studies of chronic cough at two study sites. Mixed expiratory breath was collected into 1 l Tedlar Bags using either a drinking straw, mouthpiece, or mask. After concentrating onto thermal desorption tubes, the breath was analysed using two-dimensional gas chromatography coupled with time-of-flight mass spectrometry. Breath collection via a mouthpiece was highly feasible for children aged >2 years. Mask-based collection was required for younger children but was poorly tolerated. Drinking straw-based collections were unsuitable for some children aged <4 years due to challenges maintaining a sufficient seal. At least 700 ml of breath was sampled from 72.6% of children. The number of peaks per sample, total peak area per sample, and composition of breath VOCs were all consistent with successful breath sampling. The high feasibility of breath collection via a mouthpiece in our study suggests established protocols designed for children aged over 4 years can be used with confidence for children from as young as 2 years of age.

We introduce a novel method for efficient collection of analytes of low volatility from human breath, liquid secondary adsorption (LSA), and the application of this method to drug detection with mass spectrometry. Cannabis legalization has occurred in many jurisdictions, creating a need for a simple method for detection of recency of use. Most existing breath sampling methods rely on a time consuming and complex process of adsorption of the analyte of interest, and still often result in low collection efficiencies. The pilot study shows the capability of a breath capture technique and mass spectrometry add on analysis device (Cannabix Breath Analysis System) to easily collect breath samples in the field and rapidly analyze them without complex sample preparation. The study also shows correlation between the breath data collected with this method and blood Δ⁹-tetrahydrocannabinol (THC) levels.

Respiratory failure (RF) has a high mortality rate and poor prognosis, making the development of novel non-invasive biomarkers crucial. Hypoxia promotes lipolysis, increasing free fatty acid (FFA) and ketones. Exhaled breath acetone (EBA), a volatile component of ketone bodies, may be linked to the presence and severity of RF. In this study, 156 patients were enrolled and categorized based on arterial blood gas analysis into RF group (N = 74) and control group (N = 82). The EBA was compared between the two groups. RF patients were classified by PaO₂/FiO₂ (P/F): high P/F (200 ⩽ P/F < 300 mmHg; N = 42) and low P/F (P/F < 200 mmHg; N = 32), and subsequently EBA was compared. Multivariate and multiple-model logistic regression analyses were employed to investigate the impacts of EBA on the RF. Additionally, receiver operator characteristic curve was utilized to evaluate the diagnostic efficacy of EBA. The RF group presented a significantly higher EBA [1.61 (0.98–2.57) vs 1.24 (0.86–1.69) ppm, P= 0.001], compared to the control group. The EBA within the low P/F group was higher than within the high P/F group [2.43 (1.57–3.23) vs 1.37 (0.91–1.83) ppm, P < 0.001]. EBA was conspicuously negatively correlated with PaO₂/FiO₂, and positively correlated with beta-hydroxybutyrate and FFA. Logistic regression analyses demonstrated that EBA was correlated with the presence and severity of RF. The area under curve of EBA in the diagnosis of RF and low P/F were 0.651 (95% CI: 0.564–0.738, P = 0.001) and 0.763 (95% CI: 0.652–0.875, P < 0.001). EBA can serve as a valuable predictor for the presence and severity of RF.

Lung cancer is one of the common malignancies with high mortality rate and a poor prognosis. Most lung cancer cases are diagnosed at an advanced stage either due to limited resources of infrastructure, trained human resources, or delay in clinical suspicion. Low-dose computed tomography has emerged as a screening tool for lung cancer detection but this may not be a feasible option for most developing countries. Electronic nose is a unique non-invasive device that has been developed for lung cancer diagnosis and monitoring response by exhaled breath analysis of volatile organic compounds. The breath-print have been shown to differ not only among lung cancer and other respiratory diseases, but also between various types of lung cancer. Hence, we postulate that the breath-print analysis by electronic nose could be a potential biomarker for the early detection of lung cancer along with monitoring treatment response in a resource-limited setting. In this review, we have consolidated the current published literature suggesting the use of an electronic nose in the diagnosis and monitoring treatment response of lung cancer.

Pulmonary function tests (PFTs) are the gold standard for diagnosing of Chronic obstructive pulmonary disease (COPD). Given its limitation in some scenarios, it is imperative to develop new high-throughput screening methods for biomarkers in diagnosing COPD. This study aims to explore the feasibility of screening novel diagnostic biomarkers based on salivary metabolomics for the limited availability of PFTs and difficulties in implementation at primary care facilities. Participants were recruited from the outpatient department of West China Hospital. Saliva samples were collected to analyze the metabolites through the UPLC-Q-Exactive Orbitrap-MS platform. The raw data from the mass spectrometer was preprocessed with R software after peak extraction. The Wilcoxon rank sum test, Fold change analysis, PCA and orthogonal partial least squares - discriminant analysis were used to identify potential biomarkers. The receiver operating characteristic curve was used to assess the diagnostic efficacy of the predictive model generated by potential biomarkers. Saliva samples were collected from 66 patients with COPD and 55 healthy volunteers. Significant differences in the salivary metabolome between COPD patients and healthy controls were identified, with 261 differential metabolites recognized, 16 of which were considered as potential biomarker. The diagnostic model generated by these 16 biomarkers can successfully distinguish COPD patients from healthy people. Salivary metabolomic profiling is likely to emerge as a promising method for screening potential diagnostic biomarkers of COPD. Further prospective studies with large sample size are needed to verify the predictive value of these biomarkers in COPD diagnosis.

Trial registration

The study is registered with the China Clinical Trial Registry (www.chictr.org.cn/searchprojEN.html) on 26 September 2022, registration number: ChiCTR2200064091.

The measurement of exhaled carbon monoxide (eCO) is relevant to understanding normal physiology and disease states but has been limited by deficiencies in valid sampling protocols, accurate and feasible measurement methods, and the understanding of normal physiological variation. The purposes of this study were (1) to compare the three collection methods for eCO and (2) to gain a better understanding of patterns of normal variation by obtaining repeated daily and weekly measurements. We compared three techniques to sample eCO: continuous breathing (ConB), breath-holding (BrH), and short rebreathing (SrB). We used a Carbolyzer mBA-2000 instrument that involves an electrochemical method to quantify CO, with the final value corrected for ambient CO. In Phase I, we compared ConB with BrH in 10 healthy non-smokers (5 male, five female). On day 1, the eCO was determined from 07:30 to 17:00 (11 samples), and the first four morning time points were repeated on days 7, 14, and 21. ConB had a lower eCO than BrH, and eCO₂ was frequently below the threshold of 4.6% compatible with inadequate alveolar sampling. The eCO measured by the ConB and BrH methods increased during the day and showed week-to-week variability. In Phase II, we compared the BrH and SrB techniques by collecting prebreakfast samples weekly for four weeks in 30 healthy non-smokers (15 male,15 female). Comparing the SrB vs. the BrH method, SrB was the easier for the participants to perform, generated higher eCO (∼ 0.5 ppm), and produced higher eCO2 levels (5.2% ± 0.3 vs. 5.0% ± 0.2); Importantly, Phase II study revealed that week-to-week changes in prebreakfast fasting eCO for individual participants were ⩾1.0 ppm in ∼ 37%. This variability complicates the interpretation of the relationship between small changes in eCO and the underlying physiological or disease states.

The identification and quantitation of volatile organic compounds (VOCs) in exhaled human breath has attracted considerable interest due to its potential application in medical diagnostics, environmental exposure assessment, and forensic applications. Secondary electrospray ionization-mass spectrometry (SESI-MS) is a method capable of detecting thousands of VOCs. Nevertheless, most studies using SESI-MS for breath analysis have relied primarily on MS¹ measurements for identifications and quantification, which are susceptible to misassignments and errors. In this study, we targeted several endogenous compounds (C5 to C10 aldehydes, limonene and pyridine), known to occur in breath. These compounds were measured and quantified in exhaled breath from 12 volunteers over several days using three different acquisition methods: full scan, targeted selected ion monitoring and parallel reaction monitoring. These methods were used for identification and quantification by comparing with measurements of external standards. High-abundance features such as limonene and pyridine were successfully identified and quantified in exhaled human breath with all three methods, with MS² measurements supporting identification, albeit with limitations to separate between limonene and α-/β-pinene. For low-abundance features, the study highlights the challenges of false assignments in SESI-MS, even with MS² measurements. This was demonstrated in the case of aldehydes, which could not be reliably separated from isomeric ketones present in breath, leading to incorrect quantification.

Respiratory failure (RF) has a high mortality rate and poor prognosis, making the development of novel non-invasive biomarkers crucial. Hypoxia promotes lipolysis, increasing free fatty acid (FFA) and ketones. Exhaled breath acetone (EBA), a volatile component of ketone bodies, may be linked to the presence and severity of RF. In this study, 156 patients were enrolled and categorized based on arterial blood gas analysis into RF group (N = 74) and control group (N = 82). The EBA was compared between the two groups. RF patients were classified by PaO₂/FiO₂ (P/F): high P/F (200 ⩽ P/F < 300 mmHg; N = 42) and low P/F (P/F < 200 mmHg; N = 32), and subsequently EBA was compared. Multivariate and multiple-model logistic regression analyses were employed to investigate the impacts of EBA on the RF. Additionally, receiver operator characteristic curve was utilized to evaluate the diagnostic efficacy of EBA. The RF group presented a significantly higher EBA [1.61 (0.98–2.57) vs 1.24 (0.86–1.69) ppm, P= 0.001], compared to the control group. The EBA within the low P/F group was higher than within the high P/F group [2.43 (1.57–3.23) vs 1.37 (0.91–1.83) ppm, P < 0.001]. EBA was conspicuously negatively correlated with PaO₂/FiO₂, and positively correlated with beta-hydroxybutyrate and FFA. Logistic regression analyses demonstrated that EBA was correlated with the presence and severity of RF. The area under curve of EBA in the diagnosis of RF and low P/F were 0.651 (95% CI: 0.564–0.738, P = 0.001) and 0.763 (95% CI: 0.652–0.875, P < 0.001). EBA can serve as a valuable predictor for the presence and severity of RF.

Early prediction of cancer is crucial for effective treatment decisions. Stomach cancer is one of the worst malignancies in the world because it does not reveal the growth in symptoms. In recent years, non-invasive diagnostic methods, particularly exhaled breath analysis, have attracted interest in detecting stomach cancer. This review discusses invasive and non-invasive diagnostic methods for stomach cancer, with a special emphasis on breath analysis and electronic nose (e-nose) technology. Various analytical methods have been used to analyze volatile organic compounds (VOCs) associated with stomach cancer. Gas chromatography–mass Spectrometry is one of the most widely used techniques. These techniques enable the detection and analysis of VOCs, offering a promising route for early stomach cancer diagnosis. The e-nose system has been introduced as a cost-effective and portable alternative for VOC detection in stomach cancer to overcome the challenges associated with conventional methods. This review discusses the advantages and disadvantages of the e-nose system. This review recommends that e-nose sensors, combined with advanced pattern recognition techniques, be utilized to enable rapid and reliable diagnosis of stomach cancer.

We introduce a novel method for efficient collection of analytes of low volatility from human breath, liquid secondary adsorption (LSA), and the application of this method to drug detection with mass spectrometry. Cannabis legalization has occurred in many jurisdictions, creating a need for a simple method for detection of recency of use. Most existing breath sampling methods rely on a time consuming and complex process of adsorption of the analyte of interest, and still often result in low collection efficiencies. The pilot study shows the capability of a breath capture technique and mass spectrometry add on analysis device (Cannabix Breath Analysis System) to easily collect breath samples in the field and rapidly analyze them without complex sample preparation. The study also shows correlation between the breath data collected with this method and blood Δ⁹-tetrahydrocannabinol (THC) levels.

Occupational lung diseases, such as silicosis, are a significant global health concern, especially with increasing exposure to engineered stone dust. Early detection of silicosis is helpful for preventing disease progression, but existing diagnostic methods, including x-rays, computed tomography scans, and spirometry, often detect the disease only at late stages. This study investigates a rapid, non-invasive diagnostic approach using atmospheric pressure chemical ionization-mass spectrometry (APCI-MS) to analyze volatile organic compounds (VOCs) in exhaled breath from 31 silicosis patients and 60 healthy controls. Six different interpretable machine learning (ML) models with Shapley additive explanations (SHAP) were applied to classify these samples and determine VOC features that contribute the most significantly to model accuracy. The extreme gradient boosting classifier demonstrated the highest performance, achieving an area under the receiver-operator characteristic curve of 0.933 with the top ten SHAP features. The m/z 442 feature, potentially corresponding to leukotriene-E3, emerged as a significant predictor for silicosis. The VOC sampling and measurement process takes less than five minutes per sample, highlighting its potential suitability for large-scale population screening. Moreover, the ML models are interpretable through SHAP, providing insights into the features contributing to the model's predictions. This study suggests that APCI-MS breath analysis could enable early and non-invasive diagnosis of silicosis, helping to improve disease outcomes.

Early prediction of cancer is crucial for effective treatment decisions. Stomach cancer is one of the worst malignancies in the world because it does not reveal the growth in symptoms. In recent years, non-invasive diagnostic methods, particularly exhaled breath analysis, have attracted interest in detecting stomach cancer. This review discusses invasive and non-invasive diagnostic methods for stomach cancer, with a special emphasis on breath analysis and electronic nose (e-nose) technology. Various analytical methods have been used to analyze volatile organic compounds (VOCs) associated with stomach cancer. Gas chromatography–mass Spectrometry is one of the most widely used techniques. These techniques enable the detection and analysis of VOCs, offering a promising route for early stomach cancer diagnosis. The e-nose system has been introduced as a cost-effective and portable alternative for VOC detection in stomach cancer to overcome the challenges associated with conventional methods. This review discusses the advantages and disadvantages of the e-nose system. This review recommends that e-nose sensors, combined with advanced pattern recognition techniques, be utilized to enable rapid and reliable diagnosis of stomach cancer.

Halitosis has a multifactorial etiology being of interest by different health areas. The aim of this study was to perform a bibliometric and altmetric analyzes of the top 100 most-cited papers on halitosis to provide a comprehensive view of their scientific and alternative metrics. This would give perspectives on citation dynamics and online attention of the research outputs. A search strategy was designed, tested and applied in the Web of Science database on August 1st, 2023. The 100 most-cited papers were selected by two reviewers. Data on title, year of publication, number of citations, authorship, journal title, study design, halitosis etiology and subject/field of the study or pathogenesis of halitosis were extracted from each paper. Altmetric attention score (AAS) for each paper was registered. Papers were published between 1972 and 2019. Most cited papers were non-systematic reviews (28%). USA was the country with the greatest number of publications (20%). Journals with the greater number of citations were related to dentistry. The altmetric analysis did not show correlation with the citation count but showed a few papers with elevated AAS and a good diffusion in social media. The level of evidence of the study design did not influence the citation number. This can indicate the need for citing studies with more robust designs in order to provide better scientific evidence of citations in epidemiology, etiology, diagnosis and treatment. Databases showed positive correlation among citation counts, but no correlation with the online attention.

Volatolomics (or volatilomics), the study of volatile organic compounds, has emerged as a significant branch of metabolomics due to its potential for non-invasive diagnostics and disease monitoring. However, the analysis of high-resolution data from mass spectrometry and gas sensor array-based instruments remains challenging. The careful consideration of experimental design, data collection, and processing strategies is essential to enhance the quality of results obtained from subsequent analyses. This comprehensive guide provides an in-depth exploration of volatolomics data analysis, highlighting the essential steps, such as data cleaning, pretreatment, and the application of statistical and machine learning techniques, including dimensionality reduction, clustering, classification, and variable selection. The choice of these methodologies, along with data handling practices, such as missing data imputation, outlier detection, model validation, and data integration, is crucial for identifying meaningful metabolites and drawing accurate diagnostic conclusions. By offering researchers the tools and knowledge to navigate the complexities of volatolomics data analysis, this guide emphasizes the importance of understanding the strengths and limitations of each method. Such informed decision-making enhances the reliability of findings, ultimately advancing the field and improving the understanding of metabolic processes in health and disease

Exhaled breath condensate (EBC) is used as a promising noninvasive diagnostic tool in the field of respiratory medicine. EBC is achieved by cooling exhaled air, which contains aerosolized particles and volatile compounds present in the breath. This method provides useful information on the biochemical and inflammatory state of the airways. In respiratory diseases such as asthma, chronic obstructive pulmonary disease and cystic fibrosis, EBC analysis can reveal elevated levels of biomarkers such as hydrogen peroxide, nitric oxide and various cytokines, which correlate with oxidative stress and inflammation. Furthermore, the presence of certain volatile organic compounds in EBC has been linked to specific respiratory conditions, potentially serving as disease-specific fingerprints. The noninvasive nature of EBC sampling makes it particularly useful for repeated measures and for use in vulnerable populations, including children and the elderly. Despite its potential, the standardization of collection methods, analytical techniques and interpretation of results currently limits its use in clinical practice. Nonetheless, EBC holds significant promise for improving the diagnosis, monitoring and therapy of respiratory diseases. In this tutorial we will present the latest advances in EBC research in airway diseases and future prospects for clinical applications of EBC analysis, including the application of the Omic sciences for its analysis.

Breath biomarkers are substances found in exhaled breath that can be used for non-invasive diagnosis and monitoring of medical conditions, including kidney disease. Detection techniques include mass spectrometry (MS), gas chromatography (GC), and electrochemical sensors. Biosensors, such as GC-MS or electronic nose (e-nose) devices, can be used to detect volatile organic compounds (VOCs) in exhaled breath associated with metabolic changes in the body, including the kidneys. E-nose devices could provide an early indication of potential kidney problems through the detection of VOCs associated with kidney dysfunction. This review discusses the sources of breath biomarkers for monitoring renal disease during dialysis and different biosensor approaches for detecting exhaled breath biomarkers. The future of using various types of biosensor-based real-time breathing diagnosis for renal failure is also discussed.

Introduction and Background:&#xD;Lung cancer, the third leading cause of death in England, is challenging to diagnose early. Traditional methods are costly, time-consuming and uncomfortable. Exhaled breath condensate (EBC) analysis with the Inflammacheck® device offers a non-invasive alternative, employing advanced analytics like t-distributed Stochastic Neighbor Embedding (t-SNE), Bhattacharyya distances and network maps to differentiate respiratory conditions.&#xD;&#xD;Methods:&#xD;The VICTORY study recruited participants (age ≥16) with physician-confirmed respiratory conditions (asthma, COPD, bronchiectasis, interstitial lung disease, lung cancer, pneumonia or a breathing pattern disorder) from inpatient and outpatient settings at a single NHS university hospital. EBC was collected using the Inflammacheck® device, to assess seven parameters: H2O2 levels, peak CO2 percentage, peak breath humidity, peak breath temperature, exhalation flow rate, exhalation duration and sample collection time. After standardisation of EBC data, t-SNE was employed, Bhattacharyya distances calculated on tSNE components, network maps generated, and hierarchical clustering performed to illustrate the distinct classifications of the respiratory conditions based on the EBC parameters.&#xD;&#xD;Results:&#xD;The study included 282 participants. Multinomial logistic regression revealed elevated exhaled H₂O₂ increased the odds of pneumonia (25.7-fold) and lung cancer (3.6-fold). t-SNE analysis showed distinct disease clusters, with Bhattacharyya distances for lung cancer and pneumonia demonstrating good separability from other conditions. Hierarchical clustering confirmed clear group distinctions, as visualised in heatmaps and dendrograms.&#xD;&#xD;Conclusions:&#xD;The integration of advanced dimensionality reduction techniques (t-SNE), combined with Bhattacharyya distance-based network mapping to interpret the EBC results facilitated discrimination between respiratory diseases. These methods were chosen over standard machine-learning classifiers due to their ability to provide intuitive, interpretable visualisations of complex data relationships, complementing their strong discriminatory power. Harnessing these analytical tools facilitated disease discrimination, particularly for lung cancer and pneumonia, suggesting promise as a diagnostic aid, paving the way for improved clinical decision-making and patient care.

Asthma and Chronic Obstructive Pulmonary Disease (COPD) have many common clinical characteristics, thus making reliable differentiation between these two challenging. The goal of this study is to determine the clinical value of exhaled breath condensate derived miRNAs to discriminate between Asthma and COPD. This cross-sectional study included 65 subjects each with asthma (mean/SD age: 39/13 years, Male n/%: 27/42%), COPD (mean/SD age: 61/9 years, Male n/%: 53/81%) and healthy controls (mean/SD age: 34.4/12 years, Male n/%: 50/77%). Exhaled breath condensate (EBC) was collected using R-tubes and 40 EBC samples from each group were used for miRNA profiling. Profiling data was curated and the most highly expressed miRNAs were shortlisted for further validation. Selected microRNAs were subsequently validated using quantitative-PCR in an independent set of 25 subjects from both disease groups. A total of 103 miRNAs were significantly upregulated in the EBC of asthma patients and 97 miRNAs were upregulated in the EBC of COPD patients compared to control group. However, miR-512-3p was downregulated and miR-517c was upregulated in COPD compared with asthma. The top unique miRNAs were shortlisted for further validation. Of these, miR-375 was upregulated in Asthma, while miR-297, miR-367 and miR-539 were upregulated in COPD compared with healthy controls. Further, miR-512-3p was down-regulated and miR-517c was upregulated in COPD compared with Asthma. The comparison exhibited excellent discriminatory power with 100% differential expression of miR-512-3p and miR-517c secreted by respiratory cells, they could be quantitated in EBC samples and used to differentiate between Asthma and COPD.

Mycobacterium tuberculosis is a deadly infectious agent that infects over 10 million people every year. Early detection of Mycobacterium tuberculosis infection is essential for effective treatment and reduction of emerging drug resistance. However, current diagnostic methods are limited by lengthy procedures, invasive sampling or low sensitivity. Especially in the case of HIV co-infection, pediatric patients, extrapulmonary tuberculosis (TB) and drug-resistant TB, obtaining adequate samples and detecting and treating TB is challenging. Breath analysis is an alternative tool for TB diagnosis that can potentially overcome the limitations associated with conventional techniques. Nevertheless, TB breath tests are still in their infancy. This review provides an overview of recent advances in breath analysis for TB detection. We discuss the different biomarkers found for TB detection in exhaled breath and their strengths and limitations for the disease diagnostics. We conclude that breath analysis could be a promising TB diagnosis tool, calling for standardization of breath collection and validation of data obtained with various analysis techniques to ensure both sensitivity and specificity required in practice.

Online breath analysis provides a non-invasive method for monitoring drug concentrations. Ciprofol, a novel intravenous anesthetic, shows potential for real-time monitoring. However, the impact of changes in cardiac output (CO) on ciprofol concentration in exhaled breath (Ce-cipro) remains unclear. This study aims to evaluate the effect of CO changes on Ce-cipro monitoring during anesthesia. Eight beagles were randomly divided into the ciprofol group (Group Cipro, n = 4) or the ciprofol + dobutamine group (Group Cipro+Dobu, n = 4). Ciprofol was intravenously infused at a rate of 0.125 mg/kg/h for 1 hour. In the Cipro+Dobu group, dobutamine was administered at 35 minutes to increase CO. Ce-cipro was continuously monitored using the vacuum ultraviolet and time-of-flight mass spectrometry (VUV-TOF MS). CO was monitored at 0, 30, and 50 minutes using Doppler ultrasound. Mean arterial pressure was maintained within ± 20% of baseline between 40 and 50 minutes by adjusting the dobutamine infusion rate. The results indicated that in both groups, Ce-cipro levels gradually increased and reached a pseudo-steady state at around 30 minutes. However, no significant difference in Ce-cipro was observed in the Cipro+Dobu group between the 35-40 minute (178.13  71.67 pptv) and 50-55 minute (181.89  77.07 pptv) intervals (P = 0.05). This study suggests that when mean arterial pressure is maintained within ± 20% of preoperative levels, changes in CO do not significantly affect Ce-cipro monitoring. This finding provides valuable evidence supporting the application of online Ce-cipro monitoring in clinical anesthesia.

Human skin is an important source of volatile organic compounds (VOCs) offering noninvasive methods to gain clinical metabolite information. This work was focused on the development of a skin sampling device based on a dynamic headspace sampling (DHS) method with the addition of temperature to increase VOC metabolite recovery. The device preconcentrates skin VOC emissions onto a sorbent substrate, which can either be preserved for offline analysis or attached to a real time sensor downstream. In this work, skin VOC samples were analyzed offline using thermal desorption-gas chromatography-mass spectrometry. A list of 10 common skin VOCs was pre-selected to optimize parameters of sampling time, sampling temperature, and sorbent selection. Overall, this study highlights an effective skin VOC sampling technology with a heating dimension (40 °C, rather than 30 °C or no heating) with a sampling time of 15 min (rather than 5 or 30 mins) and onto Tenax TA sorbent (rather than PDMS), which collectively increases the recovery of compounds with lower vapor pressure and decreases the observed variability in skin VOC measurements. Finally, a list of 79 skin VOC compounds were detected and identified within a cohort of 20 young, healthy volunteers.

Introduction and Background:&#xD;Lung cancer, the third leading cause of death in England, is challenging to diagnose early. Traditional methods are costly, time-consuming and uncomfortable. Exhaled breath condensate (EBC) analysis with the Inflammacheck® device offers a non-invasive alternative, employing advanced analytics like t-distributed Stochastic Neighbor Embedding (t-SNE), Bhattacharyya distances and network maps to differentiate respiratory conditions.&#xD;&#xD;Methods:&#xD;The VICTORY study recruited participants (age ≥16) with physician-confirmed respiratory conditions (asthma, COPD, bronchiectasis, interstitial lung disease, lung cancer, pneumonia or a breathing pattern disorder) from inpatient and outpatient settings at a single NHS university hospital. EBC was collected using the Inflammacheck® device, to assess seven parameters: H2O2 levels, peak CO2 percentage, peak breath humidity, peak breath temperature, exhalation flow rate, exhalation duration and sample collection time. After standardisation of EBC data, t-SNE was employed, Bhattacharyya distances calculated on tSNE components, network maps generated, and hierarchical clustering performed to illustrate the distinct classifications of the respiratory conditions based on the EBC parameters.&#xD;&#xD;Results:&#xD;The study included 282 participants. Multinomial logistic regression revealed elevated exhaled H₂O₂ increased the odds of pneumonia (25.7-fold) and lung cancer (3.6-fold). t-SNE analysis showed distinct disease clusters, with Bhattacharyya distances for lung cancer and pneumonia demonstrating good separability from other conditions. Hierarchical clustering confirmed clear group distinctions, as visualised in heatmaps and dendrograms.&#xD;&#xD;Conclusions:&#xD;The integration of advanced dimensionality reduction techniques (t-SNE), combined with Bhattacharyya distance-based network mapping to interpret the EBC results facilitated discrimination between respiratory diseases. These methods were chosen over standard machine-learning classifiers due to their ability to provide intuitive, interpretable visualisations of complex data relationships, complementing their strong discriminatory power. Harnessing these analytical tools facilitated disease discrimination, particularly for lung cancer and pneumonia, suggesting promise as a diagnostic aid, paving the way for improved clinical decision-making and patient care.

The identification and quantitation of volatile organic compounds (VOCs) in exhaled human breath has attracted considerable interest due to its potential application in medical diagnostics, environmental exposure assessment, and forensic applications. Secondary electrospray ionization-mass spectrometry (SESI-MS) is a method capable of detecting thousands of VOCs. Nevertheless, most studies using SESI-MS for breath analysis have relied primarily on MS¹ measurements for identifications and quantification, which are susceptible to misassignments and errors. In this study, we targeted several endogenous compounds (C5 to C10 aldehydes, limonene and pyridine), known to occur in breath. These compounds were measured and quantified in exhaled breath from 12 volunteers over several days using three different acquisition methods: full scan, targeted selected ion monitoring and parallel reaction monitoring. These methods were used for identification and quantification by comparing with measurements of external standards. High-abundance features such as limonene and pyridine were successfully identified and quantified in exhaled human breath with all three methods, with MS² measurements supporting identification, albeit with limitations to separate between limonene and α-/β-pinene. For low-abundance features, the study highlights the challenges of false assignments in SESI-MS, even with MS² measurements. This was demonstrated in the case of aldehydes, which could not be reliably separated from isomeric ketones present in breath, leading to incorrect quantification.

Mycobacterium tuberculosis is a deadly infectious agent that infects over 10 million people every year. Early detection of Mycobacterium tuberculosis infection is essential for effective treatment and reduction of emerging drug resistance. However, current diagnostic methods are limited by lengthy procedures, invasive sampling or low sensitivity. Especially in the case of HIV co-infection, pediatric patients, extrapulmonary tuberculosis (TB) and drug-resistant TB, obtaining adequate samples and detecting and treating TB is challenging. Breath analysis is an alternative tool for TB diagnosis that can potentially overcome the limitations associated with conventional techniques. Nevertheless, TB breath tests are still in their infancy. This review provides an overview of recent advances in breath analysis for TB detection. We discuss the different biomarkers found for TB detection in exhaled breath and their strengths and limitations for the disease diagnostics. We conclude that breath analysis could be a promising TB diagnosis tool, calling for standardization of breath collection and validation of data obtained with various analysis techniques to ensure both sensitivity and specificity required in practice.

Online breath analysis provides a non-invasive method for monitoring drug concentrations. Ciprofol, a novel intravenous anesthetic, shows potential for real-time monitoring. However, the impact of changes in cardiac output (CO) on ciprofol concentration in exhaled breath (Ce-cipro) remains unclear. This study aims to evaluate the effect of CO changes on Ce-cipro monitoring during anesthesia. Eight beagles were randomly divided into the ciprofol group (Group Cipro, n = 4) or the ciprofol + dobutamine group (Group Cipro+Dobu, n = 4). Ciprofol was intravenously infused at a rate of 0.125 mg/kg/h for 1 hour. In the Cipro+Dobu group, dobutamine was administered at 35 minutes to increase CO. Ce-cipro was continuously monitored using the vacuum ultraviolet and time-of-flight mass spectrometry (VUV-TOF MS). CO was monitored at 0, 30, and 50 minutes using Doppler ultrasound. Mean arterial pressure was maintained within ± 20% of baseline between 40 and 50 minutes by adjusting the dobutamine infusion rate. The results indicated that in both groups, Ce-cipro levels gradually increased and reached a pseudo-steady state at around 30 minutes. However, no significant difference in Ce-cipro was observed in the Cipro+Dobu group between the 35-40 minute (178.13  71.67 pptv) and 50-55 minute (181.89  77.07 pptv) intervals (P = 0.05). This study suggests that when mean arterial pressure is maintained within ± 20% of preoperative levels, changes in CO do not significantly affect Ce-cipro monitoring. This finding provides valuable evidence supporting the application of online Ce-cipro monitoring in clinical anesthesia.

Respiratory failure (RF) has a high mortality rate and poor prognosis, making the development of novel non-invasive biomarkers crucial. Hypoxia promotes lipolysis, increasing free fatty acid (FFA) and ketones. Exhaled breath acetone (EBA), a volatile component of ketone bodies, may be linked to the presence and severity of RF. In this study, 156 patients were enrolled and categorized based on arterial blood gas analysis into RF group (N = 74) and control group (N = 82). The EBA was compared between the two groups. RF patients were classified by PaO₂/FiO₂ (P/F): high P/F (200 ⩽ P/F < 300 mmHg; N = 42) and low P/F (P/F < 200 mmHg; N = 32), and subsequently EBA was compared. Multivariate and multiple-model logistic regression analyses were employed to investigate the impacts of EBA on the RF. Additionally, receiver operator characteristic curve was utilized to evaluate the diagnostic efficacy of EBA. The RF group presented a significantly higher EBA [1.61 (0.98–2.57) vs 1.24 (0.86–1.69) ppm, P= 0.001], compared to the control group. The EBA within the low P/F group was higher than within the high P/F group [2.43 (1.57–3.23) vs 1.37 (0.91–1.83) ppm, P < 0.001]. EBA was conspicuously negatively correlated with PaO₂/FiO₂, and positively correlated with beta-hydroxybutyrate and FFA. Logistic regression analyses demonstrated that EBA was correlated with the presence and severity of RF. The area under curve of EBA in the diagnosis of RF and low P/F were 0.651 (95% CI: 0.564–0.738, P = 0.001) and 0.763 (95% CI: 0.652–0.875, P < 0.001). EBA can serve as a valuable predictor for the presence and severity of RF.

We introduce a novel method for efficient collection of analytes of low volatility from human breath, liquid secondary adsorption (LSA), and the application of this method to drug detection with mass spectrometry. Cannabis legalization has occurred in many jurisdictions, creating a need for a simple method for detection of recency of use. Most existing breath sampling methods rely on a time consuming and complex process of adsorption of the analyte of interest, and still often result in low collection efficiencies. The pilot study shows the capability of a breath capture technique and mass spectrometry add on analysis device (Cannabix Breath Analysis System) to easily collect breath samples in the field and rapidly analyze them without complex sample preparation. The study also shows correlation between the breath data collected with this method and blood Δ⁹-tetrahydrocannabinol (THC) levels.

Occupational lung diseases, such as silicosis, are a significant global health concern, especially with increasing exposure to engineered stone dust. Early detection of silicosis is helpful for preventing disease progression, but existing diagnostic methods, including x-rays, computed tomography scans, and spirometry, often detect the disease only at late stages. This study investigates a rapid, non-invasive diagnostic approach using atmospheric pressure chemical ionization-mass spectrometry (APCI-MS) to analyze volatile organic compounds (VOCs) in exhaled breath from 31 silicosis patients and 60 healthy controls. Six different interpretable machine learning (ML) models with Shapley additive explanations (SHAP) were applied to classify these samples and determine VOC features that contribute the most significantly to model accuracy. The extreme gradient boosting classifier demonstrated the highest performance, achieving an area under the receiver-operator characteristic curve of 0.933 with the top ten SHAP features. The m/z 442 feature, potentially corresponding to leukotriene-E3, emerged as a significant predictor for silicosis. The VOC sampling and measurement process takes less than five minutes per sample, highlighting its potential suitability for large-scale population screening. Moreover, the ML models are interpretable through SHAP, providing insights into the features contributing to the model's predictions. This study suggests that APCI-MS breath analysis could enable early and non-invasive diagnosis of silicosis, helping to improve disease outcomes.

The measurement of exhaled carbon monoxide (eCO) is relevant to understanding normal physiology and disease states but has been limited by deficiencies in valid sampling protocols, accurate and feasible measurement methods, and the understanding of normal physiological variation. The purposes of this study were (1) to compare the three collection methods for eCO and (2) to gain a better understanding of patterns of normal variation by obtaining repeated daily and weekly measurements. We compared three techniques to sample eCO: continuous breathing (ConB), breath-holding (BrH), and short rebreathing (SrB). We used a Carbolyzer mBA-2000 instrument that involves an electrochemical method to quantify CO, with the final value corrected for ambient CO. In Phase I, we compared ConB with BrH in 10 healthy non-smokers (5 male, five female). On day 1, the eCO was determined from 07:30 to 17:00 (11 samples), and the first four morning time points were repeated on days 7, 14, and 21. ConB had a lower eCO than BrH, and eCO₂ was frequently below the threshold of 4.6% compatible with inadequate alveolar sampling. The eCO measured by the ConB and BrH methods increased during the day and showed week-to-week variability. In Phase II, we compared the BrH and SrB techniques by collecting prebreakfast samples weekly for four weeks in 30 healthy non-smokers (15 male,15 female). Comparing the SrB vs. the BrH method, SrB was the easier for the participants to perform, generated higher eCO (∼ 0.5 ppm), and produced higher eCO2 levels (5.2% ± 0.3 vs. 5.0% ± 0.2); Importantly, Phase II study revealed that week-to-week changes in prebreakfast fasting eCO for individual participants were ⩾1.0 ppm in ∼ 37%. This variability complicates the interpretation of the relationship between small changes in eCO and the underlying physiological or disease states.

Pulmonary function tests (PFTs) are the gold standard for diagnosing of Chronic obstructive pulmonary disease (COPD). Given its limitation in some scenarios, it is imperative to develop new high-throughput screening methods for biomarkers in diagnosing COPD. This study aims to explore the feasibility of screening novel diagnostic biomarkers based on salivary metabolomics for the limited availability of PFTs and difficulties in implementation at primary care facilities. Participants were recruited from the outpatient department of West China Hospital. Saliva samples were collected to analyze the metabolites through the UPLC-Q-Exactive Orbitrap-MS platform. The raw data from the mass spectrometer was preprocessed with R software after peak extraction. The Wilcoxon rank sum test, Fold change analysis, PCA and orthogonal partial least squares - discriminant analysis were used to identify potential biomarkers. The receiver operating characteristic curve was used to assess the diagnostic efficacy of the predictive model generated by potential biomarkers. Saliva samples were collected from 66 patients with COPD and 55 healthy volunteers. Significant differences in the salivary metabolome between COPD patients and healthy controls were identified, with 261 differential metabolites recognized, 16 of which were considered as potential biomarker. The diagnostic model generated by these 16 biomarkers can successfully distinguish COPD patients from healthy people. Salivary metabolomic profiling is likely to emerge as a promising method for screening potential diagnostic biomarkers of COPD. Further prospective studies with large sample size are needed to verify the predictive value of these biomarkers in COPD diagnosis.

Trial registration

The study is registered with the China Clinical Trial Registry (www.chictr.org.cn/searchprojEN.html) on 26 September 2022, registration number: ChiCTR2200064091.

Breath volatile organic compounds (VOCs) are increasingly under consideration as biomarkers of respiratory disease. Although numerous studies have identified VOCs that distinguish patient groups, a lack of standardisation among published studies has impeded translation into clinical diagnostics. Standardised breath collection protocols have been proposed for adults and children aged >4 years, but optimal methods for collecting breath from younger children remain to be determined. The aim of this study was to assess the feasibility and acceptability of breath sampling among a young paediatric cohort. A total of 61 children (age 6 months–12 years) were recruited prospectively to observational studies of chronic cough at two study sites. Mixed expiratory breath was collected into 1 l Tedlar Bags using either a drinking straw, mouthpiece, or mask. After concentrating onto thermal desorption tubes, the breath was analysed using two-dimensional gas chromatography coupled with time-of-flight mass spectrometry. Breath collection via a mouthpiece was highly feasible for children aged >2 years. Mask-based collection was required for younger children but was poorly tolerated. Drinking straw-based collections were unsuitable for some children aged <4 years due to challenges maintaining a sufficient seal. At least 700 ml of breath was sampled from 72.6% of children. The number of peaks per sample, total peak area per sample, and composition of breath VOCs were all consistent with successful breath sampling. The high feasibility of breath collection via a mouthpiece in our study suggests established protocols designed for children aged over 4 years can be used with confidence for children from as young as 2 years of age.