Journal of breath research最新文献

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. Them/z442 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. InPhase I, we comparedConBwithBrHin 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.ConBhad a lower eCO thanBrH,and eCO₂was frequently below the threshold of 4.6% compatible with inadequate alveolar sampling. The eCO measured by theConBandBrHmethods increased during the day and showed week-to-week variability. InPhase II, we compared theBrHandSrBtechniques by collecting prebreakfast samples weekly for four weeks in 30 healthy non-smokers (15 male,15 female). Comparing theSrBvs. theBrHmethod,SrBwas 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 IIstudy 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 registrationThe 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.

Small intestinal bacterial overgrowth (SIBO) and extraoral halitosis are often observed in functional dyspepsia (FD). We aimed to identify their associations for the first time. In this study, extraoral halitosis was diagnosed and assessed through the organoleptic score (OLS). Total symptom score (TSS) of FD, SIBO, gastricHelicobacter pylori(H. pylori) infection, and three exhaled volatile sulfur compounds (VSCs) (hydrogen sulfide, methyl mercaptan, and dimethyl sulfide (DMS)), were evaluated. Finally, 63 non-halitosis patients and 45 halitosis patients with extraoral halitosis were identified. Compared to non-halitosis patients, halitosis patients exhibited significantly higher TSS (86 [56, 123] vs 43 [34, 57],P< 0.001) and SIBO positivity rate (66.67% vs 11.11%,P< 0.001), but similarH. pyloripositivity rate. The adjusted odds ratios of TSS and SIBO were 1.06 and 5.02, respectively. The area under curve of the combination of TSS and SIBO for predicting extraoral halitosis was 0.89. Positive correlations were observed between TSS and OLS (r= 0.64), and between TSS and exhaled DMS level (r= 0.86), respectively. The other two VSCs were undetectable or of little value. We conclude that: (1) Extraoral halitosis is closely associated with FD and SIBO; (2) DMS is its primary contributing VSC; (3) FD patients with SIBO as opposed to gastricH. pyloriinfection are more prone to extraoral halitosis; (4) Clinicians should be aware of SIBO in the management of extraoral halitosis in FD.

Lung cancer is one of the most common malignancy in the world, and early detection of lung cancer remains a challenge. The exhaled breath condensate (EBC) from lung and trachea can be collected totally noninvasively. In this study, our aim is to identify differential metabolites between non-small cell lung cancer (NSCLC) and control EBC samples and discriminate NSCLC group from control group by orthogonal projections to latent structures-discriminant analysis (OPLS-DA) models. The EBC differential metabolites between NSCLC patients (n= 29) and controls (n= 24) (20 healthy and 4 benign individuals) were identified using ultra-performance liquid chromatography-high resolution mass spectrometry based untargeted metabolomics method. The upregulated metabolites in EBC of NSCLC included amino acids and derivatives (phenylalanine, tryptophan, 1-carboxyethylisoleucine/1-carboxyethylleucine, and 2-octenoylglycine), dipeptides (leucyl-phenylalanine, leucyl-leucine, leucyl-histidine/isoleucyl-histidine, and prolyl-valine), and fatty acids (tridecenoic acid, hexadecadienoic acid, tetradecadienoic acid, 9,12,13-trihydroxyoctadec-10-enoic acid/9,10,13-trihydroxyoctadec-11-enoic acid (9,12,13-TriHOME/9,10,13-TriHOME), 3-hydroxysebacic acid/2-hydroxydecanedioic acid, 9-oxooctadeca-10,12-dienoic acid/9,10-Epoxy-12,15-octadecadienoate (9-oxoODE/9(10)-EpODE), and suberic acid). The downregulated metabolites in EBC of NSCLC were 3,4-methylenesebacic acid, 2-isopropylmalic acid/3-isopropylmalic acid/2,3-dimethyl-3-hydroxyglutaric acid, and trimethylamine-N-oxide. The OPLS-DA model based on 5 EBC metabolites achieved 86.2% sensitivity, 83.3% specificity and 84.9% accuracy, showing a potential to distinguish NSCLC patients from controls.

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 CH₃SH, 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.