Journal of breath research最新文献

Detection of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) relies on real-time-reverse-transcriptase polymerase chain reaction (RT-PCR) on nasopharyngeal swabs. The false-negative rate of RT-PCR can be high when viral burden and infection is localized distally in the lower airways and lung parenchyma. An alternate safe, simple and accessible method for sampling the lower airways is needed to aid in the early and rapid diagnosis of COVID-19 pneumonia. In a prospective unblinded observational study, patients admitted with a positive RT-PCR and symptoms of SARS-CoV-2 infection were enrolled from three hospitals in Ontario, Canada. Healthy individuals or hospitalized patients with negative RT-PCR and without respiratory symptoms were enrolled into the control group. Breath samples were collected and analyzed by laser absorption spectroscopy (LAS) for volatile organic compounds (VOCs) and classified by machine learning (ML) approaches to identify unique LAS-spectra patterns (breathprints) for SARS-CoV-2. Of the 135 patients enrolled, 115 patients provided analyzable breath samples. Using LAS-breathprints to train ML classifier models resulted in an accuracy of 72.2%-81.7% in differentiating between SARS-CoV2 positive and negative groups. The performance was consistent across subgroups of different age, sex, body mass index, SARS-CoV-2 variants, time of disease onset and oxygen requirement. The overall performance was higher than compared to VOC-trained classifier model, which had an accuracy of 63%-74.7%. This study demonstrates that a ML-based breathprint model using LAS analysis of exhaled breath may be a valuable non-invasive method for studying the lower airways and detecting SARS-CoV-2 and other respiratory pathogens. The technology and the ML approach can be easily deployed in any setting with minimal training. This will greatly improve access and scalability to meet surge capacity; allow early and rapid detection to inform therapy; and offers great versatility in developing new classifier models quickly for future outbreaks.

Exhaustive exercise can induce unique physiological responses in the lungs and other parts of the human body. The volatile organic compounds (VOCs) in exhaled breath are ideal for studying the effects of exhaustive exercise on the lungs due to the proximity of the breath matrix to the respiratory tract. As breath VOCs can originate from the bloodstream, changes in abundance should also indicate broader physiological effects of exhaustive exercise on the body. Currently, there is limited published data on the effects of exhaustive exercise on breath VOCs. Breath has great potential for biomarker analysis as it can be collected non-invasively, and capture real-time metabolic changes to better understand the effects of exhaustive exercise. In this study, we collected breath samples from a small group of elite runners participating in the 2019 Ultra-Trail du Mont Blanc ultra-marathon. The final analysis included matched paired samples collected before and after the race from 24 subjects. All 48 samples were analyzed using the Breath Biopsy Platform with GC-Orbitrap™ via thermal desorption gas chromatography-mass spectrometry. The Wilcoxon signed-rank test was used to determine whether VOC abundances differed between pre- and post-race breath samples (adjustedP-value < .05). We identified a total of 793 VOCs in the breath samples of elite runners. Of these, 63 showed significant differences between pre- and post-race samples after correction for multiple testing (12 decreased, 51 increased). The specific VOCs identified suggest the involvement of fatty acid oxidation, inflammation, and possible altered gut microbiome activity in response to exhaustive exercise. This study demonstrates significant changes in VOC abundance resulting from exhaustive exercise. Further investigation of VOC changes along with other physiological measurements can help improve our understanding of the effect of exhaustive exercise on the body and subsequent differences in VOCs in exhaled breath.

Disease detection and monitoring using volatile organic compounds (VOCs) is becoming increasingly popular. For a variety of (gastrointestinal) diseases the microbiome should be considered. As its output is to large extent volatile, faecal volatilomics carries great potential. One technical limitation is that current faecal headspace analysis requires specialized instrumentation which is costly and typically does not work in harmony with thermal desorption units often utilized in e.g. exhaled breath studies. This lack of harmonization hinders uptake of such analyses by the Volatilomics community. Therefore, this study optimized and compared two recently harmonized faecal headspace sampling platforms:High-capacity Sorptive extraction (HiSorb) probesand theMicrochamber thermal extractor (Microchamber). Statistical design of experiment was applied to find optimal sampling conditions by maximizing reproducibility, the number of VOCs detected, and between subject variation. To foster general applicability those factors were defined using semi-targeted as well as untargeted metabolic profiles. HiSorb probes were found to result in a faster sampling procedure, higher number of detected VOCs, and higher stability. The headspace collection using the Microchamber resulted in a lower number of detected VOCs, longer sampling times and decreased stability despite a smaller number of interfering VOCs and no background signals. Based on the observed profiles, recommendations are provided on pre-processing and study design when using either one of both platforms. Both can be used to perform faecal headspace collection, but altogether HiSorb is recommended.

Characteristics of extra-oral halitosis induced by functional constipation (FC) have never been revealed. To address this, this prospective cohort was conducted with 100 FC patients, who were divided into a halitosis group and a negative group. Organoleptic score (OLS) ⩾ 2 in nose breath was diagnosed as extra-oral halitosis. Concentration of overall volatile sulfur compounds (VSCs) measured by Halimeter, concentration of hydrogen sulfide (HS), methanethiol (MT), dimethyl sulfide (DMS) and their total amount measured by OralChroma in nose breath was recorded asC-VSC,C-HS,C-MT,C-DMS andC-sum respectively. We found that 82% (82/100) of the FC patients had extra-oral halitosis. However, only 12.5% (3/82) and 1.22% (1/82) of halitosis group were correctly diagnosed with the current diagnostic threshold ofC-VSC ⩾ 110 parts per billion (ppb) and ⩾150 ppb.C-VSC,C-DMS andC-sum were significantly higher in the halitosis group compared to the negative group (allP< 0.001), with ratios of about 2.2 times, 3.1 times and 2.1 times respectively.C-HS andC-MT were low and not significantly different between the groups. Positive correlations were observed among OLS,C-VSC,C-DMS andC-sum. The area under curve of receiver operating characteristics ofC-VSC, C-DMS andC-sum for predicting FC-induced halitosis was 0.909, 0.9073 and 0.962 respectively, with the threshold values of ⩾36 ppb, ⩾52 ppb and ⩾75 ppb respectively. Therefore, we conclude that: (1) DMS is the primary contributor to FC-induced extra-oral halitosis. (2) OLS, Halimeter and OralChroma are consistent in detecting FC-induced extra-oral halitosis. (3) The diagnostic threshold for Halimeter should be adjusted toC-VSC ⩾ 36 ppb and the diagnostic threshold for OralChroma should be set asC-DMS ⩾ 52 ppb for diagnosing FC-induced extra-oral halitosis.

Some studies have examined the impact of intra-oral halitosis on quality of life (QOL), but the impact of enterogenous extra-oral halitosis (EOH) on QOL has not been previously studied. We conducted a retrospective analysis of data from 88 patients with enterogenous EOH who visited our online halitosis clinic. A specialized halitosis associated life-quality test (HALT) questionnaire was used to assess QOL of these patients. Spearman correlation analysis was performed to investigate the relationship between HALT score and age. We found that 21 (23.86%) patients were male and 67 (76.14%) patients were female. HALT scores in females were significantly higher than in males (57.6 ± 13.6vs.45.5 ± 11.9,P< 0.001). Additionally, 13 of the 20 items of the HALT questionnaire showed significant differences between the sexes. No correlation was identified between HALT score and age. Therefore, we conclude that: (1) enterogenous EOH has a more severe impact on QOL in females compared to males. (2) More females with EOH visit the offline halitosis clinic compared to males. (3) The QOL of patients with enterogenous EOH does not decline with age.

The correlation between propofol concentration in exhaled breath (C_E) and plasma (C_P) has been well-established, but its applicability for estimating the concentration in brain tissues (C_B) remains unknown. Given the impracticality of directly sampling human brain tissues, rats are commonly used as a pharmacokinetic model due to their similar drug-metabolizing processes to humans. In this study, we measuredC_E,C_P, andC_Bin mechanically ventilated rats injected with propofol. Exhaled breath samples from the rats were collected every 20 s and analyzed using our team's developed vacuum ultraviolet time-of-flight mass spectrometry. Additionally, femoral artery blood samples and brain tissue samples at different time points were collected and measured using high-performance liquid chromatography mass spectrometry. The results demonstrated that propofol concentration in exhaled breath exhibited stronger correlations with that in brain tissues compared to plasma levels, suggesting its potential suitability for reflecting anesthetic action sites' concentrations and anesthesia titration. Our study provides valuable animal data supporting future clinical applications.

The direct analysis of molecules contained within human breath has had significant implications for clinical and diagnostic applications in recent decades. However, attempts to compare one study to another or to reproduce previous work are hampered by: variability between sampling methodologies, human phenotypic variability, complex interactions between compounds within breath, and confounding signals from comorbidities. Towards this end, we have endeavored to create an averaged healthy human 'profile' against which follow-on studies might be compared. Through the use of direct secondary electrospray ionization combined with a high-resolution mass spectrometry and in-house bioinformatics pipeline, we seek to curate an average healthy human profile for breath and use this model to distinguish differences inter- and intra-day for human volunteers. Breath samples were significantly different in PERMANOVA analysis and ANOSIM analysis based on Time of Day, Participant ID, Date of Sample, Sex of Participant, and Age of Participant (p< 0.001). Optimal binning analysis identify strong associations between specific features and variables. These include 227 breath features identified as unique identifiers for 28 of the 31 participants. Four signals were identified to be strongly associated with female participants and one with male participants. A total of 37 signals were identified to be strongly associated with the time-of-day samples were taken. Threshold indicator taxa analysis indicated a shift in significant breath features across the age gradient of participants with peak disruption of breath metabolites occurring at around age 32. Forty-eight features were identified after filtering from which a healthy human breath profile for all participants was created.

Comparing electronic nose (e-nose) performance is a challenging task because of a lack of standardised method. This paper proposes a method for defining and quantifying an indicator of the effectiveness of multi-sensor systems in detecting cancers by artificial breath analysis. To build this method, an evaluation of the performances of an array of metal oxide sensors built for use as a lung cancer screening tool was conducted. Breath from 20 healthy volunteers has been sampled in fluorinated ethylene propylene sampling bags. These healthy samples were analysed with and without the addition of nine volatile organic compound (VOC) cancer biomarkers, chosen from literature. The concentration of the VOC added was done in increasing amounts. The more VOC were added, the better the discrimination between 'healthy' samples (breath without additives) and 'cancer' samples (breath with additives) was. By determining at which level of concentration the e-nose fails to reliably discriminate between the two groups, we estimate its ability to well predict the presence of the disease or not in a realistic situation. In this work, a home-made e-nose is put to the test. The results underline that the biomarkers need to be about 5.3 times higher in concentration than in real breath for the home-made nose to tell the difference between groups with a sufficient confidence.

When attempts to lose body fat mass frequently fail, breath acetone (BA) monitoring may assist fat mass loss during a low-carbohydrate diet as it can provide real-time body fat oxidation levels. This randomized controlled study aimed to evaluate the effectiveness of monitoring BA levels and providing feedback on fat oxidation during a three-week low-carbohydrate diet intervention. Forty-seven participants (mean age = 27.8 ± 4.4 years, 53.3% females, body mass index = 24.1 ± 3.4 kg m^-2) were randomly assigned to three groups (1:1:1 ratio): daily BA assessment with a low-carbohydrate diet, body weight assessment (body scale (BS)) with a low-carbohydrate diet, and low-carbohydrate diet only. Primary outcome was the change in fat mass and secondary outcomes were the changes in body weight and body composition. Forty-five participants completed the study (compliance rate: 95.7%). Fat mass was significantly reduced in all three groups (allP< 0.05); however, the greatest reduction in fat mass was observed in the BA group compared to the BS (differences in changes in fat mass, -1.1 kg; 95% confidence interval: -2.3, -0.2;P= 0.040) and control (differences in changes in fat mass, -1.3 kg; 95% confidence interval: -2.1, -0.4;P= 0.013) groups. The BA group showed significantly greater reductions in body weight and visceral fat mass than the BS and control groups (allP< 0.05). In addition, the percent body fat and skeletal muscle mass were significantly reduced in both BA and BS groups (allP< 0.05). However, no significant differences were found in changes in body fat percentage and skeletal muscle mass between the study groups. Monitoring BA levels, which could have motivated participants to adhere more closely to the low-carbohydrate diet, to assess body fat oxidation rates may be an effective intervention for reducing body fat mass (compared to body weight assessment or control conditions). This approach could be beneficial for individuals seeking to manage body fat and prevent obesity.

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.