Journal of breath research最新文献

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.

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.