Journal of breath research最新文献

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).

Lung cancer, the third leading cause of death in England, is challenging to diagnose early. Traditional methods are costly, time-consuming and uncomfortable. Exhaled breath condensate (EBC) analysis with the Inflammacheck® device offers a non-invasive alternative, employing advanced analytics like t-distributed stochastic neighbour embedding (t-SNE), Bhattacharyya distances and network maps to differentiate respiratory conditions. The VICTORY study recruited participants (age ⩾ 16) with physician-confirmed respiratory conditions (asthma, chronic obstructive pulmonary disease, bronchiectasis, interstitial lung disease, lung cancer, pneumonia or a breathing pattern disorder) from inpatient and outpatient settings at a single NHS university hospital. EBC was collected using the Inflammacheck® device, to assess seven parameters: H₂O₂levels, peak CO₂percentage, peak breath humidity, peak breath temperature, exhalation flow rate, exhalation duration and sample collection time. After standardisation of EBC data, t-SNE was employed, Bhattacharyya distances calculated on tSNE components, network maps generated, and hierarchical clustering performed to illustrate the distinct classifications of the respiratory conditions based on the EBC parameters. The study included 282 participants. Multinomial logistic regression revealed elevated exhaled H₂O₂increased the odds of pneumonia (25.7-fold) and lung cancer (3.6-fold). t-SNE analysis showed distinct disease clusters, with Bhattacharyya distances for lung cancer and pneumonia demonstrating good separability from other conditions. Hierarchical clustering confirmed clear group distinctions, as visualised in heatmaps and dendrograms. The integration of advanced dimensionality reduction techniques t-SNE, combined with Bhattacharyya distance-based network mapping to interpret the EBC results facilitated discrimination between respiratory diseases. These methods were chosen over standard machine-learning classifiers due to their ability to provide intuitive, interpretable visualisations of complex data relationships, complementing their strong discriminatory power. Harnessing these analytical tools facilitated disease discrimination, particularly for lung cancer and pneumonia, suggesting promise as a diagnostic aid, paving the way for improved clinical decision-making and patient care.

Mycobacterium tuberculosis(TB) is a deadly infectious agent that infects over 10 million people every year. Early detection ofM. TBinfection is essential for effective treatment and reduction of emerging drug resistance. However, current diagnostic methods are limited by lengthy procedures, invasive sampling or low sensitivity. Especially in the case of HIV co-infection, pediatric patients, EPTB and drug-resistant TB, obtaining adequate samples and detecting and treating TB is challenging. Breath analysis is an alternative tool for TB diagnosis that can potentially overcome the limitations associated with conventional techniques. Nevertheless, TB breath tests are still in their infancy. This review provides an overview of recent advances in breath analysis for TB detection. We discuss the different biomarkers found for TB detection in exhaled breath and their strengths and limitations for the disease diagnostics. We conclude that breath analysis could be a promising TB diagnosis tool, calling for standardization of breath collection and validation of data obtained with various analysis techniques to ensure both sensitivity and specificity required in practice.

Human skin is an important source of volatile organic compounds (VOCs) offering noninvasive methods to gain clinical metabolite information. This work was focused on the development of a skin sampling device based on a dynamic headspace sampling method with the addition of temperature to increase VOC metabolite recovery. The device preconcentrates skin VOC emissions onto a sorbent substrate, which can either be preserved for offline analysis or attached to a real time sensor downstream. In this work, skin VOC samples were analyzed offline using thermal desorption-gas chromatography-mass spectrometry. A list of 10 common skin VOCs was pre-selected to optimize parameters of sampling time, sampling temperature, and sorbent selection. Overall, this study highlights an effective skin VOC sampling technology with a heating dimension (40 °C, rather than 30 °C or no heating) with a sampling time of 15 min (rather than 5 or 30 mins) and onto Tenax TA sorbent (rather than PDMS), which collectively increases the recovery of compounds with lower vapor pressure and decreases the observed variability in skin VOC measurements. Finally, a list of 79 skin VOC compounds were detected and identified within a cohort of 20 young, healthy volunteers.

Online breath analysis provides a non-invasive method for monitoring drug concentrations. Ciprofol, a novel intravenous anesthetic, shows potential for real-time monitoring. However, the impact of changes in cardiac output (CO) on ciprofol concentration in exhaled breath (Ce-cipro) remains unclear. This study aims to evaluate the effect of CO changes on Ce-cipro monitoring during anesthesia. Eight beagles were randomly divided into the ciprofol group (Group Cipro,n= 4) or the ciprofol + dobutamine group (Group Cipro + Dobu,n= 4). Ciprofol was intravenously infused at a rate of 0.125 mg kg^-1h^-1for 1 h. In the Cipro + Dobu group, dobutamine was administered at 35 min to increase CO. Ce-cipro was continuously monitored using the vacuum ultraviolet and time-of-flight mass spectrometry (VUV-TOF MS). CO was monitored at 0, 30, and 50 min using Doppler ultrasound. Mean arterial pressure (MAP) was maintained within ±20% of baseline between 40 and 50 min by adjusting the dobutamine infusion rate. The results indicated that in both groups, Ce-cipro levels gradually increased and reached a pseudo-steady state at around 30 min. However, no significant difference in Ce-cipro was observed in the Cipro + Dobu group between the 35-40 min (178.13 ± 71.67 pptv) and 50-55 min (181.89 ± 77.07 pptv) intervals (P= 0.05). This study suggests that when MAP is maintained within ±20% of preoperative levels, changes in CO do not significantly affect Ce-cipro monitoring. This finding provides valuable evidence supporting the application of online Ce-cipro monitoring in clinical anesthesia.

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 MS¹measurements 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, targeted selected ion monitoring and parallel reaction monitoring. 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 MS²measurements supporting identification, albeit with limitations to separate between limonene andα-/β-pinene. For low-abundance features, the study highlights the challenges of false assignments in SESI-MS, even with MS²measurements. This was demonstrated in the case of aldehydes, which could not be reliably separated from isomeric ketones present in breath, leading to incorrect quantification.

Respiratory failure (RF) has a high mortality rate and poor prognosis, making the development of novel non-invasive biomarkers crucial. Hypoxia promotes lipolysis, increasing free fatty acid (FFA) and ketones. Exhaled breath acetone (EBA), a volatile component of ketone bodies, may be linked to the presence and severity of RF. In this study, 156 patients were enrolled and categorized based on arterial blood gas analysis into RF group (N= 74) and control group (N= 82). The EBA was compared between the two groups. RF patients were classified by PaO₂/FiO₂(P/F): high P/F (200 ⩽ P/F < 300 mmHg;N= 42) and low P/F (P/F < 200 mmHg;N= 32), and subsequently EBA was compared. Multivariate and multiple-model logistic regression analyses were employed to investigate the impacts of EBA on the RF. Additionally, receiver operator characteristic curve was utilized to evaluate the diagnostic efficacy of EBA. The RF group presented a significantly higher EBA [1.61 (0.98-2.57) vs 1.24 (0.86-1.69) ppm,P= 0.001], compared to the control group. The EBA within the low P/F group was higher than within the high P/F group [2.43 (1.57-3.23) vs 1.37 (0.91-1.83) ppm,P< 0.001]. EBA was conspicuously negatively correlated with PaO₂/FiO₂, and positively correlated with beta-hydroxybutyrate and FFA. Logistic regression analyses demonstrated that EBA was correlated with the presence and severity of RF. The area under curve of EBA in the diagnosis of RF and low P/F were 0.651 (95% CI: 0.564-0.738,P= 0.001) and 0.763 (95% CI: 0.652-0.875,P< 0.001). EBA can serve as a valuable predictor for the presence and severity of RF.

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.