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.

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.

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.

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.

Pulmonary function is usually assessed by measuring Vital Capacity (VC) using equipment such as a spirometer or ventilometer, but these are not always available to the population, as they are relatively expensive tests, difficult to transport and require trained professionals. However, the single breath counting technique (SBCT) appears as a possible alternative to respiratory function tests, to help in the pathophysiological understanding of lung diseases. The objective is to verify the applicability of the SBCT as a parameter for evaluating VC. This is a systematic review registered in the International Prospective Register of Systematic Reviews (CRD42023383706) and used for PubMed^®, Scientific Electronic Library Online, LILACS, EMBASE, and Web of Science databases of articles published until January 2023. Methodological quality regarding the risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 and National Institutes of Health tools. Eleven of a total of 574 studies were included, of these, nine showed a correlation between VC and SBCT (weak in healthy, moderate in neuromuscular and strong in hospitalized patients). One study of hospitalized patients accurately identified a count value of 21 for a VC of 20 ml kg⁻¹ (Sensitivity = 94% and Specificity = 77%), and another estimated a count lower than 41 for a VC below 80% of predicted in patients with neuromuscular dystrophy (Sensitivity = 89% and Specificity = 62%), and another showed good intra and inter-examiner reproducibility in young, adult, and elderly populations. A meta-analysis of three studies showed a moderate correlation in subjects with neuromuscular diseases (r = 0.62, 95% CI = 0.52–0.71, p < 0.01). A high risk of bias was identified regarding the justification of the sample size and blinding of the evaluators. SBCT has been presented as an alternative to assess VC in the absence of specific equipment. There is a clear relationship between SBCT and VC, especially in neuromuscular and hospitalized individuals. New validation studies conducted with greater control of potential bias risks are necessary.

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 for detecting stomach cancer. This review discusses both 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 engaged to analyze Volatile Organic Compounds (VOCs) associated with stomach cancer, with gas chromatography-mass spectrometry (GC-MS) being one of the most widely used techniques. This review discusses non-invasive breath methods, along with the integration of e-nose systems. These techniques enable the detection and analysis of VOCs, offering a promising route for early stomach cancer diagnosis. To overcome the challenges associated with conventional methods, the e-nose system has been introduced as a cost-effective and portable alternative for VOC detection. 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.

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 (FS), targeted Selected Ion Monitoring (t-SIM) and Parallel Reaction Monitoring (PRM). 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.

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.

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 (FS), targeted Selected Ion Monitoring (t-SIM) and Parallel Reaction Monitoring (PRM). 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.

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.

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.

Halitosis presents a significant global health concern, necessitating the development of precise and efficient testing methodologies owing to the high prevalence and the associated social and psychological effects. The measurement of volatile sulfur compounds (VSCs), recognized as primary contributors to halitosis, is particularly significant. While gas chromatography (GC-MS) offers accurate measurements, its bulky and expensive nature limits widespread accessibility. Hence, this study endeavors to devise a compact yet highly accurate AI-based halitosis measurement apparatus, termed 'Kunkun dental' and validate its efficacy. Specifically, we intend to compare the VSC concentrations obtained from halitosis patients' breath samples using Kunkun dental against those from conventional GC-MS to assess the criterion validity of the new testing method. The study cohort comprised 68 halitosis patients aged 20 years or older, attending the breath freshening outpatient clinic at Tokyo Medical and Dental University Hospital between October 2022 and March 2023, who consented to participate and underwent routine measurements. Participants completed an age and sex questionnaire, while VSC concentrations were determined using both GC-MS and Kunkun dental (H₂S, CH₃SH, (CH₃)₂S), enabling a comparative analysis of the results. Pearson product-moment correlation coefficients between GC-MS and Kunkun dental indicated significant correlations for all three gases: 0.719 for H₂S, 0.821 for CH₃ SH, and 0.637 for (CH₃)₂S. Moreover, sensitivity and specificity in accordance with the predefined thresholds were confirmed, and their values ranged from 0.59 to 0.86 and 0.53–0.77, respectively. Furthermore, grouping Kunkun dental measurements into low-, medium-, and high-concentration groups revealed significantly higher GC-MS VSC concentrations in samples with elevated Kunkun dental readings. The amalgamation of AI technology and a semiconductor gas sensor holds great promise in creating a compact and precise halitosis analyzer. This study underscores the feasibility and effectiveness of Kunkun dental as a reliable tool for halitosis assessment, affirming its utility in clinical practice.

The concentrations of nasal nitric oxide (nNO) vary in patients with chronic rhinosinusitis (CRS) supposedly depending upon whether the paranasal ostia are open or obstructed. Our aim was to assess whether nNO levels and their response to topical xylometazoline (a local vasoconstrictor used to alleviate nasal congestion) in patients with CRS differ between those with open or obstructed ostia and if the results were altered by the use of nasal corticosteroids. Sixty-six patients with CRS (43% with nasal polyps) or recurrent acute rhinosinusitis and 23 healthy controls were included. Nasal NO was measured (EcoMedics CLD 88p analyser) before and after two xylometazoline sprays during three consecutive visits: with the medication they were using when they were referred, after 4 weeks of medication pause, and after 4 weeks of using intranasal fluticasone propionate. The relative difference between the nNO before and after dosing of xylometazoline was calculated, and ostial obstruction was evaluated with cone-beam computed tomography at every visit. The nNO measurements were lowest in the patients with CRS and obstructed paranasal ostia. The presence or absence of nasal polyps did not affect the results. Xylometazoline did not significantly affect nNO in the subjects with obstructed ostia, but there was a significant reduction of nNO in those with open ostia. The Xylometazoline-induced change in nNO between the groups with open or obstructed ostia was significantly different at each visit: 'on previous medication' 10% (−5–25) versus −14% (−19 to −9), p = 0.004, 'after medication pause' 6% (−5–17) versus −16% (−23 to −9), p = 0.001 and 'after regular fluticasone spray' 6% (−3–15) versus −9% (−16 to −3), p = 0.04. The native nNO and xylometazoline-induced change in nNO can be used to detect the status of ostial obstruction in patients with CRS irrespective of their topical corticosteroid usage.

Exhaled breath volatile organic compounds (VOCs) are often collected and stored in sorbent tubes before thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS) analysis. Information about the stability of VOCs during storage is needed to account for potential artifacts and monitor for losses. Additionally, information about the stability of VOC standards in solution is required to assess their performance as quality control and internal standards. We evaluated the stability of a standard mixture of 42 VOCs in dual-sorbent tubes containing Tenax® TA and Carbotrap 1TD over 60 d at commonly used storage conditions: room temperature (∼21 °C), 4 °C, and −80 °C. The same 42 VOCs were also evaluated for their stability in methanol over 60 d while stored at −20 °C. All samples were analyzed using TD-GC-MS. During storage, most VOCs were stable on sorbent after 60 d: 36/42 (86%), 39/42 (93%), and 41/42 (98%) had not statistically changed for room temperature, 4 °C and −80 °C, respectively, based on Spearman rank correlation coefficients and linear regression analysis. The isotopically labeled VOCs tested here are well-suited to serve as internal standards for pre-analysis or storage. Degradation of VOCs in solution was apparent after 60 d: 27/42 (64%) of VOCs had statistically decreased. The total VOC mixture had dropped to 90% of its original intensity after ∼22 d and a subset of VOCs typically used as internal standards dropped to 90% in ∼16 d. Analysts using similar mixtures should make a fresh solution at least every two weeks to ensure analytical accuracy. This study provides important insights into storage practices for both sorbent tubes and standard solutions, guiding analysts toward improved reliability and accuracy in exhaled breath analysis.