Journal of breath research最新文献

Exhaled breath analysis represents a promising non-invasive approach for disease monitoring through volatile organic compounds (VOC) detection. However, the lack of standardized sam-pling methods do not enable direct clinical translation. This study compared three widely used offline breath sampling techniques (Tedlar® bags, BioVOC-2®, and ReCIVA®) using the estab-lished peppermint benchmarking protocol and comprehensive two-dimensional gas chroma-tography coupled to mass spectrometry (GC×GC-MS). Seven healthy participants completed the peppermint experiment, with breath samples col-lected at multiple time points following capsule ingestion. Washout curves for targeted terpe-noid compounds were analyzed to assess analytical performance, reproducibility, and back-ground contamination across devices. Clinical feasibility was evaluated through focus groups with clinicians, researchers, and study participants. Tedlar® bags demonstrated reliable performance with lowest overall pooled relative standard deviations, though sensitive to exogenous contamination. ReCIVA® showed higher overall vari-ability, superior selectivity and reduced background interference compared to Tedlar® bags (p<0.01). However, ReCIVA® showed higher complexity, cost, reduced comfort and potential for saliva contamination during extended sampling. BioVOC-2® offered operational simplicity but was limited by small sampling volume (129 mL) reducing its sensitivity and manual handling variability. No single device emerged as universally optimal. Tedlar® bags, when accompanied by rigorous standard operating procedures (SOPS), remain most suitable for large-scale studies, BioVOC-2® for rapid targeted screening, and ReCIVA® for controlled research requiring high selectivity. Successful clinical implementation will require balancing analytical performance with practical considerations including patient comfort, cost-effectiveness, and workflow integration. These findings support ongoing standardization efforts within breathomics community and extend peppermint database for exhaled breath sampling. .

This review addresses several important confounding factors that are often overlooked in the analysis of volatiles contained in exhaled breath, which, if ignored, will significantly limit the interpretation of volatile data from exhaled breath and thus prevent meaningful outcomes. Crucial confounding factors that tend to be neglected are those that influence the alveolar volatile concentrations according to the Farhi equation, namely cardiac output, alveolar ventilation, blood:air partition coefficients and mixed-venous blood volatile concentrations. Another potential confounding factor is associated with the contributions of volatiles produced in the oral cavity through microbial activity. In addition, the concentration of an exhaled breath volatile will be affected if that volatile is also present in the ambient inhaled air. The purpose of this review is to show how these confounding factors can be accounted for. We will demonstrate how mathematical modeling and an understanding of the Farhi equation aid in the interpretation of the exhaled breath volatile concentration measurements. We will discuss the limitations of the alveolar gradient method used to determine the effects of inhaled volatiles. An alternative method is presented that correctly allows for any inhaled volatile contribution to the exhaled concentration of that volatile. The review concludes with suggested recommendations that, if adopted, will improve the quality of breath data leading to an improved interpretation of exhaled volatiles.

Exhaled breath analysis presents a promising approach for drug monitoring. While the range of drugs known to undergo pulmonary exhalation remains limited, innovative experimental models are needed to explore this field. This study aimed to develop anex-vivoplatform as a general experimental setup for studying volatile and semi-volatile compounds in exhaled breath, using propofol as a pragmatic validation compound because it can be reliably detected both in exhaled air and in blood under our experimental conditions. A porcine lung model was created using lungs from a commercial slaughter. Each experiment was performed with a single isolated lung in an individual setup. The lungs were perfused and ventilated. Propofol exhalation was validated under various conditions (boluses, infusion rates, blood flow, and ventilation) using a propofol calibrated multi-capillary column-ion mobility spectrometer (MCC-IMS). Blood gas analysis and plasma concentration samples were collected every 20 min to continuously monitor propofol plasma levels, pulmonary respiration, and metabolism. We established a functionalex-vivoplatform using nine commercially slaughtered porcine lungs, enabling extended measurements for up to 13 h. Hematocrit was set to 35% and hemoglobin to approximately 12 g dl^-1, with glucose supplementation of 921 mg h^-1. Lactate increased by 308% over the perfusion period. The lungs were ventilated in volume-controlled mode (V_t700 ml, RR 14 min^-1, PEEP 8 mbar, I:E 1:1) and perfused at a standard blood flow of 1.0 l min^-1, and mean pH was 7.31 over the perfusion period. Exhaled Propofol was detected on average 19 min after the first administration using MCC-IMS. Changes in blood flow and minute volume were accompanied by corresponding changes in the time course and magnitude of exhaled propofol. Thisex-vivomodel of perfused and ventilated porcine lungs provides a controlled setting to study the appearance of intravenously administered drugs in exhaled air under defined ventilation and perfusion conditions. The platform enabled prolonged measurements and detection of exhaled propofol signals and may support future screening of candidate drugs for breath-based drug monitoring pending validation across additional compounds and conditions.

Halitosis, frequently associated with volatile sulfur compounds (VSCs) produced by oral microbiota, affects a large proportion of adults. Among VSCs, methyl mercaptan (CH₃SH) is a critical biomarker for periodontitis-related halitosis due to its strong correlation with periodontal pocket depth and attachment loss. This study investigated the utility of a methionine challenge protocol to selectively stimulate CH₃SH production and enhance the standardization of oral air-based screening for periodontal disease. Thirty adults were enrolled and divided equally into control and periodontitis groups. Mouth air samples were collected from oral cavity air using a straw-based sampling method connected to a portable gas-sensing device, which continuously monitored VSCs, including CH₃SH and hydrogen sulfide (H₂S), across eight time points. Participants underwent an 8 h fast prior to baseline oral air collection, followed by standardized toothbrushing. After a 60 min rest period, they swilled with a methionine solution, with oral air samples collected immediately after and at 10 min intervals for 40 min. Both groups showed increased CH₃SH levels following methionine stimulation, with the periodontitis group exhibiting a significantly greater increase from pre- to post-stimulation (p< 0.001) and higher cumulative exposure (p< 0.001). In contrast, H₂S levels remained consistently elevated in the periodontitis group but did not fluctuate significantly over time. Furthermore, correlations between CH₃SH and H₂S decreased immediately post-stimulation and gradually recovered in the periodontitis group. These findings indicate that the methionine challenge effectively induces CH₃SH production linked to periodontal dysbiosis, supporting its potential as a non-invasive screening and indicator tool for the presence of periodontitis, rather than for staging disease severity. The protocol offers a promising approach to improve diagnostic accuracy while minimizing variability related to oral hygiene. (The study is registered with the Clinical Research Information Service under number KCT0010328.).

Background: Melioidosis is a life-threatening infectious disease caused by Burkholderia pseudomallei (Bp). Rapid diagnosis and appropriate antimicrobial treatment are critical to reduce mortality, yet diagnosis is hindered by diverse clinical manifestations, mimicry with other diseases, and reliance on slow culture-based methods. Detecting volatile compounds offers a non-invasive approach for rapid infection detection. In this study, we aim to identify volatile compounds in patients' breath that can aid in diagnosing melioidosis and indicating response to treatment. Methods: Breath samples were collected from 17 patients with culture-confirmed melioidosis and eight patients with other febrile illnesses. Longitudinal samples were collected from five of the 17 melioidosis patients over approximately one month of antibiotic treatment. Breath samples were analyzed using comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry. Data analysis involved statistical comparison and machine learning-based feature selection. Results: We identified three breath markers -camphene, 1-butanol, and 3-methylheptyl acetate -that discriminated melioidosis (n=7) from febrile controls (n=6) with an area under the receiver operating characteristic curve of 1.00. These three markers correctly classified 11 additional samples from 11 melioidosis patients, with one febrile control misclassified. Separately, we selected four breath markers, three of which were hydrocarbons, that differentiated samples associated with a positive Bp culture from those with a negative Bp culture, with a random forest model developed upon these four markers showing a sensitivity of 98% and specificity of 95%. Moreover, we identified a set of 16 volatile compounds that significantly correlated (correlation coefficient > 0.6) with blood C-reactive protein levels. Lastly, a panel of 144 volatile compounds was identified that corresponded to treatment time, indicating that the breath profile may reflect treatment response or shifts in disease severity. Conclusion: This pilot study reports candidate breath-based markers for diagnosing melioidosis and assessing treatment outcome, supporting further validation in larger studies. .

Halitosis, or oral malodour, is primarily caused by volatile sulphur compounds (VSCs) such as methyl mercaptan, which are produced by anaerobic bacteria includingPorphyromonas gingivalis(P. gingivalis) andFusobacterium nucleatum (F. nucleatum). While conventional antibacterial mouthwashes are widely used, their adverse effects-such as microbial imbalance and dental staining-necessitate safer, natural alternatives. This study aimed to identify plant-derived agents with anti-halitosis, antibacterial, and anti-inflammatory activity, with low cytotoxicity. A total of 252 medicinal plant extracts were screened for biological activity. Antibacterial effects againstP. gingivalisandF. nucleatumwere evaluated using spectrophotometry and agar well diffusion assays. Anti-inflammatory activity was assessed by nitric oxide inhibition in LPS-stimulated macrophages. Cytotoxicity was measured in gingival epithelial cells and oral keratinocytes. The anti-halitosis effect was determined based on inhibition of methyl mercaptan production using Oral Chroma, a gas chromatography-based device. Of the 252 extracts, 32 demonstrated significant antibacterial activity, VSC suppression, and low cytotoxicity. Among these,Polygoni Cuspidati Radix, Rhei Rhizoma, Pini Ramulus, andPiperis Longi Fructusexhibited the most potent inhibition of methyl mercaptan production. Combination treatments using these extracts maintained efficacy at lower concentrations. The findings suggest that these herbal extracts may serve as effective, safe, and accessible anti-halitosis agents. Their dual antibacterial and anti-inflammatory effects support their potential use in oral hygiene formulations, offering a natural alternative for the prevention and management of halitosis.

Multi-cancer early detection (MCED) is critical for reducing cancer mortality, however current screening technologies have limitations in accessibility, cost, and early stage sensitivity. Breath-based detection using volatile organic compounds (VOCs) offers a non-invasive and scalable alternative, with trained detection dogs demonstrating exceptional olfactory sensitivity in clinical studies. However, the widespread deployment of canine scent detection has been hindered by subjectivity and lack of standardization. This perspective article proposes a novel canine olfaction based MCED platform that analyzes olfactory recognition using electroencephalography (EEG) signals from trained dogs, combined with behavioral and physiological biomarkers (respiration, heart rate, vision tracking), and integrates these modalities via machine learning to produce a cancer risk score. We review the biological and computational foundations of this approach, present preliminary validation strategies, and outline a phased experimental roadmap from EEG signal capture to clinical deployment. This bio-digital framework exemplified by the Dognosis platform offers a pathway toward reproducible, portable, scalable and low-cost cancer detection systems, particularly suited for deployment in low-resource settings. The proposed system may also extend to detection of infections, neurological disorders, and other VOC-associated diseases.

This research assessed the relationship between oral malodor, measured by halimetry and organoleptic grading, and gingivitis as measured by number of gingival bleeding sites (GBS). Two complementary clinical studies were examined. The first was a cross-sectional trial evaluating the association between volatile sulfur compounds measured by halimetry and organoleptic scores (0-5 scale). The second study was a randomized, 2-treatment, parallel group trial evaluating oral malodor using a 1-9 hedonic scale between a control group and an oral hygiene regimen over 8 d. In both trials, GBS were derived from Löe-Silness gingival index scores. Multiple types of regression analyses were used to assess the relationship in both studies. In study 1, 980 participants were included in the analysis with a mean age of 39.6 years (range 18-89 years). 75% were female. Mean (SD) baseline values were 14.18 (23.22) for number of GBS, 188 ppb (66.08) for halimeter score, and 3.6 (1.01) for organoleptic score. There was a significant (p= 0.003) association between halimeter score and number of GBS, driven by participants defined with localized/generalized gingivitis (≥10% GBS). Generalized gingivitis (>30% GBS), localized gingivitis (10%-30% GBS) and generally healthy participants (<10% GBS) had mean halimeter scores of 223.29 ppb, 191.19 ppb, and 183.52 ppb, respectively. Similar results were observed comparing organoleptic scores by gingivitis case type. In study 2, 60 participants were included in the analysis with mean age of 36.7 years (range 18-60); 68% were female. Mean (SD) baseline values were 80.92 (24.508) for number of GBS and 8.29 (0.709) for hedonic score. A statistically significant (p< 0.001) correlation between hedonic score reduction (change from baseline) and reduction in number of GBS (change from baseline) was observed,r= 0.73. This research shows a relationship between GBS and oral malodor, as assessed by either halimetry or organoleptic/hedonic scoring.

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

To evaluate the effectiveness and sustainability of oral probiotics (Streptococcus salivariusK12), tongue brushing, and their combination in reducing halitosis. The study was a prospective, randomized, placebo-controlled trial. A total of 80 participants were randomly allocated to four groups: control (n= 20), oral probiotics (n= 20), tongue brushing (n= 20), and a combined group of tongue brushing and oral probiotics (n= 20). Assessments were taken at three time points: at baseline; after a 4-week intervention; and at a 4-week intervention-free follow-up. The outcome measurements included volatile sulfur compound (VSC), self-reported halitosis, organoleptic score, and tongue coating index. After the 4-week intervention, VSC levels were significantly decreased in the oral probiotics, tongue brushing, and combined groups (allp< 0.05), with the greatest reduction observed in the combined group. At the 4-week intervention-free follow-up, the combined group VSC levels remained significantly lower than baseline, despite slight rebounds. Self-reported halitosis scores decreased after intervention and follow-up (p< 0.05). Organoleptic scores improved only in the combined group post-intervention (p< 0.01). Tongue coating scores decreased in both tongue brushing and combined groups, but not in the oral probiotics group. Both tongue brushing and oral probiotics were effective in reducing halitosis, with the combined intervention producing the most significant and sustained improvements.