Journal of breath research最新文献

There is an unmet need for breath-based markers for pulmonary vascular disease (PVD). We developed a fully-automatic algorithm to analyze expiratory CO₂flow from resting ventilation and evaluated the clinical associations of our readouts. We enrolled patients with chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD), pulmonary arterial hypertension (PAH) and healthy controls and evaluated fractionated volumes for dead space, mixed space (MSV) and alveolar space, their respective CO₂volumes and ventilatory equivalents for CO₂(EqCO₂) and the maximum slope of the first derivative of the cumulative expiratory CO₂volume over expired volume (MSV-slope) as primary readouts. Differences between groups were analyzed using non-parametric tests. Associations were analyzed by Spearman correlation. The discriminatory power was determined with receiver operating characteristic analysis. Eleven COPD (median (IQR) age 64 (63-69) years), 10 ILD (61 (54-77) years), 10 PAH (64 (61-73) years) and 21 healthy controls (56 (52-61) years) were investigated. Patients vs healthy controls showed increased MSV and mixed space CO₂(221 (164-270) ml vs 144 (131-167) ml, and 3.9 (3.2-4.9) ml vs 3.0 (2.7-3.4) ml,p< 0.001 andp= 0.002) and EqCO₂(38 (34-42) vs 30 (29-35),p< 0.001), and decreased MSV-slopes (0.16 (0.12-0.21) vs 0.27 (0.23-0.32) l CO₂l^-2,p< 0.001). Area under the curve (AUC) for MSV and MSV-slope for disease prediction was 0.81 (95% CI 0.69-0.93) and 0.84 (0.73-0.95), respectively. MSV and mixed space CO₂were only strongly increased in COPD and ILD but not PAH, resulting in a significant difference between PAH and COPD&ILD (AUC 0.74 (95% CI: 0.56-0.92). MSV and MSV-slope were significantly correlated with DLCO (ρ=-0.69 andρ= 0.72, respectively; bothp< 0.001). Fully-automatic high-fidelity expiratory CO₂flow analysis is technically feasible, easy and safe to perform, and may represent a novel approach to detect PVD with or without structural changes of the airways and lung parenchyma. Prospective studies with larger sample size are needed to validate these findings.

Chios mastic gum, derived from Pistacia lentiscus variation chia, has emerged as a significant natural remedy to improve oral health and mitigate halitosis. This study aimed to examine the effect of mastic toothpaste on halitosis, plaque, and gingival indices in adolescents undergoing orthodontic treatment with fixed appliances. This study was a double-blind, placebo-controlled, parallel-group, randomized clinical trial. Thirty-two patients were randomly divided into two groups: A) mastic-toothpaste group and B) placebo-toothpaste group. Participants in both groups used the assigned toothpaste three times daily for 14 d. The primary outcome was objective hydrogen sulfide (H₂S) levels in breath, measured using a gas chromatograph. The secondary outcomes were dimethyl sulfide and methyl-mercaptan levels, as well as the Silness and Löe Gingival Index (GI) and the Modified Silness and Löe Plaque Index (PI-M). Assessments were conducted at baseline and after two weeks. Data were analyzed using the Mann-WhitneyUtest and Student'st-test. A statistically significant difference was found between interventions, in favor of the mastic group's H₂S (p= 0.001). The H₂S median levels decreased from 158 parts per billion (ppb) to 26 ppb. Neither treatment group experienced a different decline in the levels of the other two components. Statistically significant differences were observed in the periodontal parameters, favoring the mastic group. The GI index decreased from 1.8 to 1 [p< 0.001, 95% CI: -0.7, -0.4], whereas the PI-M index decreased from 1.2 to 0.8 [p< 0.001, (95% CI: -0.5, -0.2)]. Mastic toothpaste may be an alternative option to reduce halitosis in adolescents undergoing orthodontic treatment with fixed appliances. Regular use of this toothpaste may lead to a clinically meaningful reduction in plaque and gingival indices (ClinicalTrials.gov, NCT06766097).

Nonalcoholic fatty liver disease (NAFLD) is now the leading cause of global chronic liver disease, affecting approximately 32.4% of the population in various regions and imposing healthcare and economic burdens. The gold standard for the diagnosis of NAFLD, such as liver biopsy, has numerous limitations in large-scale screening. Recent studies have explored the use of machine learning to diagnose NAFLD. In this study, we investigated the effect of the lactulose breath test (LBT) on a machine-learning model for predicting NAFLD. The input variables for machine learning included three combination sets to assess the effect of the LBT results: anthropometric characteristics and blood test results; anthropometric characteristics and LBT results; and anthropometric characteristics, blood test results, and LBT results. The machine learning models developed in this study included linear regression, support vector machine, K-nearest neighbour, Random forest, and extreme gradient boosting (XGBoost) with 536 participants. The model performance was evaluated using six metrics: Accuracy, Area Under the Receiver Operating Characteristic curve (AUROC), specificity, sensitivity, precision, and F1 score. Among the six models, XGBoost had the highest AUROC at 0.88. The AUROC results from the three combination variable sets indicate that the LBT results significantly improve the model performance. LBT results improve the NAFLD prediction model and provide an opportunity for additional NAFLD screening in patients receiving LBT.

Volatile organic compounds (VOCs) offer potential for non-invasive diagnosis as biomarkers of disease and metabolism. In complex biological matrices, such as breath however, identifying useful biomarkers from hundreds, or even thousands of VOCs can be challenging. Models of disease, such as cellular or animal models, offer a means to elucidate VOC metabolisms, for accurate targeted studies in patient samples. Neurodegenerative conditions, such as Parkinson's have been associated with changed VOCs, offering a potential for early diagnostics and interventions improving treatments and outcomes for patients. Here, three separate models including; human HEK-293t cells, isolated primary rat glial cells, andDrosophilafruit flies (wild type and a mutant of the Parkinson's associated gene,DJ-1β) were grown for an extended period and levels of the VOC chloroform investigated using custom static headspace sampling chambers. Samples were analysed using targeted gas chromatography mass spectroscopy with selected ion monitoring mode, measuring chloroform at masses 83/85. Chloroform levels were shown to dramatically increase in all models over time. HEK-293t cells revealed a 60-fold increase after 10 weeks, glial cells revealed a 10-fold increase after 3-4 weeks andDJ-1βmutant flies revealed significant increases compared to control flies at 4 weeks. These results, taken together, suggest that chloroform release is related to ageing in these models and may provide a target for neurodegenerative studies. We present here the first evidence of chloroform being actively produced by human and rat cells and the first observation of volatile metabolisms inDrosophila. Recent clinical studies have also identified increased chloroform flux in the breath of patients, supporting the translational potential of our findings.

Chronic kidney disease (CKD) is a long-term progressive disease. The key to treatment lies in early diagnosis and timely intervention. How to achieve early diagnosis of CKD has always been an important challenge. Exhaled breath sample analysis, as an emerging method, has attracted much attention due to its non-invasiveness and the convenience of sample collection. Compared with the complex traditional detection methods, it is more suitable for large-scale screening. The main purpose of this review is to extensively collect relevant literature on the research of exhaled breath biomarkers for CKD, summarize the potential biomarkers discovered in these studies, and compare the similarities and differences. Through in-depth analysis of the causes of these differences and commonalities, this review aims to explore whether these potential exhaled breath biomarkers could serve as reliable indicators for the early diagnosis of CKD.

Halitosis is known to be associated with oral bacteria; however, its specific relationship with particular bacterial species within the oral microbiota remains uncertain. Our objective was to identify oral bacterial species associated with volatile sulfur compound (VSC) production that contribute to halitosis in a community-based Japanese population. This study included 1018 participants. Tongue plaque samples were collected and the oral microbiome was analyzed via 16S rDNA amplicon sequencing. Participants with VSC levels greater than 250 ppb were categorized as having oral malodor. Linear discriminant analysis effect size was used to compare bacterial compositions between participants with and without halitosis. In this study, we identified 37 bacterial genera in tongue plaque samples. Significant differences in bacterial composition were found between the malodor and control groups.Porphyromonas, Fusobacterium, andSolobacteriumwere more abundant in the malodor group, whereasStreptococcusandRothiawere more prevalent in the control group. Multiple regression analysis further revealed thatPorphyromonasandSolobacteriumabundances were positively correlated with oral malodor. We found that halitosis in this Japanese population is associated primarily with pathogenic periodontal bacteria (members of the red and orange complexes) andSolobacterium moorei. The bacterial community composition of individuals with halitosis differs significantly from that of healthy individuals, emphasizing the role of specific bacterial species in oral malodor development. These findings increase our understanding of the microbial basis of halitosis and suggest that targetingSolobacterium, along with treating periodontal disease, may be effective in combating halitosis.

We aimed to identify biomarkers in breath analysis with multicapillary column-ion mobility spectrometry (MCC-IMS) to monitor the haemodialysis for chronic kidney disease (CKD) patients fast and non-invasive. Six patients' breath was analyzed via MCC-IMS before and after dialysis and compared to blood plasma samples analyzed via ultra performance liquid chromatography-fluorescence detector for potential renal failure biomarkers. Additionally, breath from six healthy control persons was analyzed. Phenol was found as a breath marker for CKD. For three patients the phenol concentration in breath and plasma was elevated before and decreased during dialysis and reached values in the range of healthy control persons. The peak-intensity of phenol-monomer peaks ofP01-P04 was reduced from an average of 16.58 (5.42-27.28) a.U. to 7.03 (0.00-13.65) a.U., which is a reduction by 42.51 (-10.55-100.00) %. The control group has an average peak-intensity of 8.50 (5.00-12.00) a.U. This study shows that the measurement of phenol via breath analysis could be used to monitor the haemodialysis for CKD-patients and might also be usable for the calculation of haemodialysis dose in the future.The study is registered in the German Clinical Trials Register under number DRKS00029679.

Coronavirus 2019 (COVID-19) leads to substantial morbidity and excess mortality all over the world which may be aggravated by the propensity of Severe Acute Respiratory Syndrome Coronavirus 2 to mutate. Mechanisms for development of severe COVID-19 are poorly understood. The air we exhale contains endogenous proteins and represents a highly accessible yet unexploited biological sample that can be collected without use of invasive procedures. We collected exhaled breath condensate samples from 28 patients hospitalised due to COVID-19 at admission and discharge using RTubes™. Bottom-up proteomic analysis of tandem mass-tag-labelled single exhaled breath samples was performed in 25 exhaled breath samples collected at admission and 13 samples collected at discharge using discovery-based nano-liquid chromatography-tandem mass spectrometry. In total, 232 proteins were identified in the exhaled breath samples after stringent data filtering. Most of the exhaled proteins were related to the immune systems function and regulation. The levels of four proteins, KRT77, DCD, CASP14 and SERPINB12 decreased from admission to discharge as patients generally recovered from the infection. These proteins are expressed in lung epithelium or macrophages and are associated with the regulation of inflammation drivers in COVID-19. In particular, the alarmins S100A8 and S100A9 accounted for 8% of the exhaled breath proteins. In conclusion, our study demonstrates that analysis of the exhaled breath protein composition can give insights into mechanisms related to inflammation and response to infections, and hereby also of severe COVID-19.Clinical Trial No: NCT04598620.

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 (EBC) derived miRNAs to discriminate between asthma and COPD. This cross-sectional study included 65 subjects each with asthma (mean/SD age: 39/13 years, Malen/%: 27/42%), COPD (mean/SD age: 61/9 years, Malen/%: 53/81%) and healthy controls (mean/SD age: 34.4/12 years, Malen/%: 50/77%). 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.

Analysis of volatile organic compounds by electronic nose (e-nose) may address gaps in non-invasive screening for neoplasia. Machine learning impacts study design and sample size requirements, but guidance on clinical study design is limited. This study evaluates how neoplasia prevalence, augmented data, and the number of e-nose devices impact sample size requirements. Simulated e-nose breath test data were created using real-world study data. We examined the effect of varying neoplasia prevalence (50%-5%) and data augmentation on model performance, as well as the impact of using multiple devices. Prediction models were developed using single value decomposition and random forest, and convolutional neural networks. Model performance was displayed as area under the receiver operating characteristics curve and F1-score. Stable model performance was defined as the phase where additional data no longer increases model performance. We found that lower neoplasia prevalence significantly increased sample size requirements, with low-prevalence settings (5%) requiring up to five times more data than high-prevalence settings (50%) for stable model performance. Model performance varied between devices, and integrating data from multiple devices required larger sample sizes. Approximately 400 data points per device at 50% prevalence, and 2100 data points at 5% prevalence, were necessary to reach stable model performance. Concluding, sample size requirements for e-nose studies are heavily influenced by disease prevalence and the number of devices used. Limiting device variability and ensuring sufficient case and control samples per device are crucial for achieving reliable predictive performance. Specific requirements will vary based on sensor and disease characteristics.ClinicalTrials.gov Identifier:Clinicaltrials.gov Identifier NCT03346005 (model study) and NCT04357158 (validation study).