Search Results (239)

Introduction: This study aimed to analyze exhaled breath condensate (EBC) for 8-oxoguanine (8-oxoGua), an oxidative stress biomarker among waterpipe (WP) smokers. Methods: In a within-subject pre-post exposure design, thirty waterpipe smokers completed two 45 min laboratory sessions. EBC was analyzed for 8-oxoGua before and after WP smoking. Median differences between time points (pre vs. post) were assessed using the Wilcoxon sign rank test, with significance defined as p < 0.05. Results: The analysis included 59 WP smoking sessions. Participants had a median age of 24 years (IQR: 21–25), with 62.1% being female. Most had a bachelor’s degree or less (62.1%), and over half were students (55.2%), while 34.5% were employed. The average age for first WP use was 18.6 years, with participants reporting a median of three WP smoking sessions per month. Results indicate a median increase in 8-oxoGua among participants from 5.4 ng/mL (IQR: 8.8) before the smoking session to 7.6 ng/mL after (IQR: 15.7; p < 0.001). Conclusions: This study is the first to examine 8-oxoGua in EBC. Findings provide strong evidence of WP smoking’s contribution to oxidative stress in the airways. It justifies the use of EBC to study the exposure to markers of oxidative stress with emerging tobacco use methods such as the waterpipe. Full article

Respiratory microbiota and lipids are closely associated with airway inflammation. This study aimed to analyze the correlations among the respiratory microbiome, the airway glycerophospholipid–sphingolipid profiles, and airway inflammation in patients with asthma. We conducted a cross-sectional study involving 61 patients with asthma and 17 healthy controls. Targeted phospholipidomics was performed on exhaled breath condensate (EBC) samples, and microbial composition was analyzed via the 16S rDNA sequencing of induced sputum. Asthma patients exhibited significant alterations in the EBC lipid profiles, with reduced levels of multiple ceramides (Cer) and glycerophospholipids, including phosphatidylethanolamine (PE) and phosphatidylcholine (PC), compared with healthy controls. These lipids were inversely correlated with the sputum interleukin-4 (IL-4) levels. Microbiome analysis revealed an increased abundance of Leptotrichia and Parasutterella in asthma patients, both positively associated with IL-4. Correlation analysis highlighted a potential interaction network involving PA, PE, ceramides, Streptococcus, Corynebacterium, Parasutterella, and Leptotrichia. Specific alterations in airway microbiota and phospholipid metabolism are associated with asthma-related inflammation, supporting the concept of a microbiota–phospholipid–immune axis and providing potential targets for future mechanistic and therapeutic studies. Full article

Measurement of nitric oxide (NO) concentration in exhaled breath (FeNO) is a quantitative, non-invasive, simple, and safe method for assessing airway inflammation. It serves as a complementary tool to other methods for evaluating airway diseases. However, little is known about the typical NO levels in healthy individuals, including individual differences and the influence of measurement timing. Therefore, this study classified measurement times into four periods and statistically analyzed NO levels in healthy individuals. The mean values among groups were compared using repeated measures ANOVA on six participants. The analysis showed large individual variations in NO levels, resulting in no significant difference (p = 0.29). Notably, greater fluctuations were observed in the morning. These findings align with previous studies suggesting the influence of circadian rhythms and the redundancy of repeated measurements. This study highlights the need to consider timing and individual variability when using FeNO as a physiological marker in healthy populations. Full article

The early detection of liver cirrhosis (LC) is crucial due to its high morbidity and mortality in advanced stages. Reliable, non-invasive diagnostic tools are essential for timely intervention. Exhaled human breath, reflecting metabolic changes, offers significant potential for disease diagnosis. This paper focuses on the emerging role of sensor array-based volatile organic compounds (VOCs) analysis of exhaled breath, particularly using electronic nose (e-nose) technology to differentiate LC patients from healthy controls (HCs). This study included 55 participants: 27 LC patients and 28 HCs. Sensor’s measurement data were analyzed using machine learning techniques, such as principal component analysis (PCA), discriminant function analysis (DFA), and support vector machines (SVMs) that were utilized to uncover meaningful patterns and facilitate accurate classification of sensor-derived information. The diagnostic accuracy was thoroughly assessed through receiver operating characteristic (ROC) curve analysis, with specific emphasis on assessing sensitivity and specificity metrics. The e-nose effectively distinguished LC from HC, with PCA explaining 92.50% variance and SVMs achieving 100% classification accuracy. This study demonstrates the significant potential of e-nose technology towards VOCs analysis in exhaled breath, as a valuable tool for LC diagnosis. It also explores feature extraction methods and suitable algorithms for effectively distinguishing between LC patients and controls. This research provides a foundation for advancing breath-based diagnostic technologies for early detection and monitoring of liver cirrhosis. Full article

The nicotine metabolite ratio (NMR) has been informative in selecting treatment choices for nicotine dependence and increasing treatment efficacy in Western settings; however, the clinical utility of the NMR among smokers in low-resource settings remains unclear. Prospective analysis was conducted using data from a randomized controlled trial of smoking cessation among adults living with HIV, to examine the association between the NMR and response to smoking cessation treatment. NMR was assessed using bio-banked urine samples collected at baseline. Self-reported smoking at 6 months was verified using a urine cotinine test and exhaled breath carbon monoxide (CO). We found no associations between the NMR and smoking abstinence (adjusted risk ratio (aRR) = 0.82; 95% CI: 0.45, 1.49; p = 0.53). No evidence of effect modification by treatment conditions was observed on the multiplicative scale (aRR = 1.17; 95% CI: 0.32, 4.30; p = 0.81) or additive scale (adjusted relative excess risk due to interaction (aRERI) = 0.10; 95% CI: −1.16, 1.36; p = 0.44). Our results suggest that the NMR may not be a viable approach for selecting smoking cessation treatment in this setting, given the minimal variability in our sample and racial/ethnic makeup of this population. Full article

Lymphangioleiomyomatosis (LAM) is a rare progressive disease that affects women of reproductive age and is characterized by cystic lung destruction, airflow obstruction, and lymphatic dysfunction. Current diagnostic methods are costly or lack sufficient specificity, highlighting the need for novel non-invasive approaches. Exhaled breath analysis using real-time proton mass spectrometry (PTR-MS) presents a promising strategy for identifying disease-specific volatile organic compounds (VOCs). This cross-sectional study analyzed exhaled breath samples from 51 LAM patients and 51 age- and sex-matched healthy controls. PTR-time-of-flight mass spectrometry (PTR-TOF-MS) was employed to identify VOC signatures associated with LAM. Data preprocessing, feature selection, and statistical analyses were performed using machine learning models, including gradient boosting classifiers (XGBoost), to identify predictive biomarkers of LAM and its complications. We identified several VOCs as potential biomarkers of LAM, including m/z = 90.06 (lactic acid) and m/z = 113.13. VOCs predictive of disease complications included m/z = 49.00 (methanethiol), m/z = 48.04 (O-methylhydroxylamine), and m/z = 129.07, which correlated with pneumothorax, obstructive ventilation disorders, and radiological findings of lung cysts and bronchial narrowing. The classifier incorporating these biomarkers demonstrated high diagnostic accuracy (AUC = 0.922). This study provides the first evidence that exhaled breath VOC profiling can serve as a non-invasive additional tool for diagnosing LAM and predicting its complications. These findings warrant further validation in larger cohorts to refine biomarker specificity and explore their clinical applications in disease monitoring and personalized treatment strategies. Full article

During the early stages of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, concerns arose regarding the susceptibility of asthmatic children, one of the most common chronic conditions in childhood and a major cause of hospitalization in pediatric settings. Unexpectedly, evidences showed milder clinical courses and fewer asthma exacerbations in these patients, even if cases of critical and fatal infection, often related to specific clinical features of the patient, are not negligible. In this regard, obesity is considered not only an important comorbidity in patients with difficult-to-treat asthma but also a risk factor for more severe forms of COVID-19. These observations are of even greater concern in the context of an increase in childhood obesity that began even before the SARS-CoV-2 pandemic and has continued also as a consequence of it. Given asthma’s heterogeneity, especially in children, an endotype-based approach is crucial. This is possible through a detailed analysis of the complex metabolic pathways that correlate asthma, COVID-19 infection and obesity thanks to new high-through-put technologies, especially metabolomics, which with minimally invasive sampling, including on exhaled breath condensate (EBC), can provide precise and unbiased evidence in support of existing endotypes, making it possible to identify not only the most vulnerable individuals and thus risk stratification through specific biomarkers, but also new molecular and therapeutic targets. This review explores asthma endotypes by highlighting their shared immunometabolic pathways with COVID-19. Findings suggest that metabolomics could enable more accurate risk stratification and guide personalized interventions during viral pandemics, especially in the presence of relevant comorbidities such as obesity. Full article

The respiration rate (RR) is an important vital sign for early detection of health deterioration in critically unwell patients. Its current measurement has limitations, relying on visual counting of chest movements. The design of a new RR measurement device utilizing a self-heating thermistor is described. The thermistor is integrated into a hand-held air chamber with a funnel attachment to sensitively detect respiratory airflow. The exhaled respiratory airflow reduces the temperature of the thermistor that is kept at a preset temperature, and its temperature recovers during inhalation. A microcontroller provides signal processing, while its display screen shows the respiratory signal and RR. The device was evaluated on 27 healthy adult volunteers, with a mean age of 32.8 years (standard deviation of 8.6 years). The RR measurements from the device were compared with the visual counting of chest movements, and the contact method of inductance plethysmography that was implemented using a commercial device (SOMNOtouch™ RESP). Statistical analysis, e.g., correlations were performed. The RR measurements from the new device and SOMNOtouch™ RESP, averaged across the 27 participants, were 14.6 breaths per minute (bpm) and 14.0 bpm, respectively. The device has a robust operation, is easy to use, and provides an objective measure of the RR in a noncontact manner. Full article

Background/Objectives: The increasing prevalence of diabetes underscores the urgent need for non-invasive, rapid, and cost-effective diagnostic alternatives. This study presents a breath-based multiclass diabetes classification system leveraging only three gas sensors (CO, alcohol, and acetone) to analyze exhaled breath composition. Methods: Breath samples were collected from 58 participants (22 healthy, 7 prediabetic, and 29 diabetic), with blood glucose levels serving as the reference metric. To enhance classification performance, we introduced a novel biomarker, the alcohol-to-acetone ratio, through a feature engineering approach. Class imbalance was addressed using the Synthetic Minority Over-Sampling Technique (SMOTE), ensuring a balanced dataset for model training. A nested cross-validation framework with 3 outer and 3 inner folds was implemented. Multiple machine learning classifiers were evaluated, with Random Forest and Gradient Boosting emerging as the top-performing models. Results: An ensemble combining both yielded the highest overall performance, achieving an average accuracy of 98.86%, precision of 99.07%, recall of 98.81% and F1 score of 98.87%. These findings highlight the potential of gas sensor-based breath analysis as a highly accurate, scalable, and non-invasive method for diabetes screening. Conclusions: The proposed system offers a promising alternative to blood-based diagnostic approaches, paving the way for real-world applications in point-of-care diagnostics and continuous health monitoring. Full article

Sepsis is a severe systemic condition due to an extreme response of the body to an infection. It is responsible for a significant number of deaths worldwide, and is still difficult to diagnose early. In this study, a system was developed for exhaled breath sampling in mechanically ventilated patients at the intensive care unit (ICU), together with a custom-made electronic nose (e-Nose) device for detecting sepsis in exhaled breath. The diagnostic performance of this system was evaluated in an animal sepsis model. Ten pigs (LPS group) were administered lipopolysaccharide (LPS) to induce a systemic inflammatory response. Nine other pigs received a placebo solution (control group). Exhaled breath samples were collected in Nalophan^TM bags and stored for temperature and humidity equilibration before e-Nose analysis. Measurements were corrected for the effects of different fractions of inspired oxygen (FiO₂) on e-Nose sensors. Two classification models using e-Nose and physiological measurements were developed and compared. One hour after LPS administration, the e-Nose data model with FiO₂ correction showed a higher accuracy (76.2% (95% confidence interval (CI) [58.0, 94.2])) than the physiological data model (59.0% (95% CI [39.5, 79.5])), indicating the potential of the early detection of sepsis with an e-Nose. Full article

Exhaled breath condensate (EBC) has gained attention as a diagnostic gateway for lung diseases, brain–gut microbiota dysbiosis, and biobanking. Due to its non-invasive and fast collection method, EBC collection is not under temporal or volume limitations. Nonetheless, conventional EBC screening methods are complex and require high operational costs and expertise. Thus, the advent of nanotechnology has introduced efforts for using nanosensors as EBC analyzers. Over the past decade, multiple EBC-based studies reported the successful use of functionalized nanosensors to trace oxidative stress, tissue damage, and respiratory diseases. The EBC signature includes biomarkers such as gases (H₂O₂ and VOCs), cations (polyamines), fatty acids, cytokines, and aldehydes, in addition to traces of drugs and antibiotics. A common feature of nanosensors is their ability to amplify signals and rapidly detect EBC analytes with high sensitivity and specificity. Based on the collected data, standardizing the collection protocol and read-out methods across laboratories is essential for optimal data comparability. Larger cohorts should be considered to ensure a reliable reproducibility of the reported outputs. Future research directions should employ EBC-based nanosensors to unravel the unexplored omics of lung diseases, especially those linked to the brain–gut microbiota that might influence airway immunity. Full article

Background: Lung cancer (LC) is the leading cause of cancer-related deaths worldwide. Effective screening strategies for early diagnosis that could improve disease prognosis are lacking. Non-invasive breath analysis of volatile organic compounds (VOC) is a potential method for earlier LC detection. This study explores the association of VOC profiles with artificial intelligence (AI) to achieve a sensitive, specific, and fast method for LC detection. Patients and methods: Exhaled breath air samples were collected from 123 healthy individuals and 73 LC patients at two clinical sites. The enrolled patients had LC diagnosed with different stages. Breath samples were collected before undergoing any treatment, including surgery, and analyzed using gas chromatography coupled to ion-mobility spectrometry (GC-IMS). AI methods classified the overall chromatographic profiles. Results: GC-IMS is highly sensitive, yielding detailed chromatographic profiles. AI methods ranked the sets of exhaled breath profiles across both groups through training and validation steps, while qualitative information was deliberately not taking part nor influencing the results. The K-nearest neighbor (KNN) algorithm classified the groups with an accuracy of 90% (sensitivity = 87%, specificity = 92%). Narrowing the LC group to those only in early-stage IA, the accuracy was 90% (sensitivity = 90%, specificity = 93%). Conclusions: Evaluation of the global exhaled breath profiles using AI algorithms enabled LC detection and demonstrated that qualitative information may not be required, thus easing the frustration that many studies have experienced so far. The results show that this approach coupled with screening protocols may improve earlier detection of LC and hence its prognosis. Full article

Exhaled breath analysis using electronic noses (e-noses) is a promising non-invasive diagnostic tool. However, a lack of standardized protocols limits clinical implementation. This study evaluates the consistency of breathprints in healthy subjects using the Cyranose 320 e-nose to support standardization efforts. Breath samples from 139 healthy non-smoking subjects (age range 18–65 years) were collected using a standardized protocol. Participants exhaled into a Tedlar bag for immediate analysis with the Cyranose 320. Principal Component Analysis (PCA) was used to reduce data dimensionality, and K-means clustering grouped subjects based on breathprints. PCA identified four principal components explaining 97.15% of variance. K-means clustering revealed two clusters: 1 outlier and 138 subjects with highly similar breathprints. The median distance from the cluster center was 0.21 (IQR: 0.18–0.24), indicating low variability. Box plots confirmed breathprint consistency across subjects. The high consistency of breathprints in healthy subjects supports the feasibility of standardizing e-nose protocols. These findings highlight the potential of e-noses for clinical diagnostics, warranting further research in diverse populations and disease cohorts. Full article

The detection of hydrogen gas is crucial for both industrial fields, as a green energy carrier, and biomedical applications, where it is a biomarker for diagnosis. TiO₂ nanomaterials are stable and sensitive to hydrogen gas, but their gas response can be negatively affected by external factors such as humidity. Therefore, a strategy is required to mitigate these influences. The utilization of organic–inorganic hybrid gas sensors, specifically metal oxide gas sensors coated with ultra-thin copolymer films, is a relatively novel approach in this field. In this study, we examined the performance and long-term stability of novel TiO₂-based sensors that were coated with poly(trivinyltrimethylcyclotrisiloxane-co-tetrafluoroethylene) (P(V3D3-co-TFE)) co-polymers. The P(V3D3-co-TFE)/TiO₂ hybrid sensors exhibit high reliability even for more than 427 days. They exhibit excellent hydrogen selectivity, particularly in environments with high humidity. An optimum operating temperature of 300 °C to 350 °C was determined. The highest recorded response to H₂ was approximately 153% during the initial set of measurements at a relative humidity of 10%. The developed organic–inorganic hybrid structures open wide opportunities for gas sensor tuning and customization, paving the way for innovative applications in industry and biomedical fields, such as exhaled breath analysis, etc. Full article

End-tidal breath gases originate deep within the lungs, and their composition is an especially accurate reflection of the body’s metabolism and health status. Therefore, accurate collection of end-tidal breath gases is crucial to enhance electronic noses’ performance in breath detection. Regarding this issue, this study proposes a novel electronic nose system and employs a threshold control method based on exhaled gas flow characteristics to design a gas collection module. The module monitors real-time gas flow with a flow meter and integrates solenoid valves to regulate the gas path, enabling automatic collection of end-tidal breath gas. In this way, the design reduces dead space gas contamination and the impact of individual breathing pattern differences. The sensor array is designed to detect the collected gas, and the response chamber is optimized to improve the detection stability. At the same time, the control module realizes automation of the experiment process, including control of the gas path state, signal transmission, and data storage. Finally, the system is used for breath detection. We employ classical machine learning algorithms to classify breath samples from different health conditions with a classification accuracy of more than 90%, which is better than the accuracy achieved in other studies of this type. This is due to the improved quality of the gas we extracted, demonstrating the superiority of our proposed electronic nose system. Full article