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Diabetes mellitus, a chronic condition affecting millions worldwide, necessitates continuous monitoring of blood glucose level (BGL). The increasing prevalence of diabetes has driven the development of non-invasive methods, such as electronic noses (e-noses), for analyzing exhaled breath and detecting biomarkers in volatile organic compounds (VOCs). Effective machine learning models require extensive patient data to ensure accurate BGL predictions, but previous studies have been limited by small sample sizes. This study addresses this limitation by employing conditional generative adversarial networks (CTGAN) to generate synthetic data from real-world tests involving 29 healthy and 29 diabetic participants, resulting in over 14,000 new synthetic samples. These data were used to validate machine learning models for diabetes detection and BGL prediction, integrated into a Tiny Machine Learning (TinyML) e-nose system for real-time analysis. The proposed models achieved an 86% accuracy in BGL identification using LightGBM (Light Gradient Boosting Machine) and a 94.14% accuracy in diabetes detection using Random Forest. These results demonstrate the efficacy of enhancing machine learning models with both real and synthetic data, particularly in non-invasive systems integrating e-noses with TinyML. This study signifies a major advancement in non-invasive diabetes monitoring, underscoring the transformative potential of TinyML-powered e-nose systems in healthcare applications. [ABSTRACT FROM AUTHOR]

Since certain types of diabetes are reversible and curable in the early stages, early detection is crucial for effective treatment. Exhaled breath analysis, a noninvasive technique, is increasingly recognized for its potential in health monitoring, providing insights into an organism's metabolic state more directly than blood glucose tests. This work developed an in situ method utilizing corona ion source mass spectrometry for real-time direct monitoring of volatile organic compounds (VOCs) in mouse exhaled breath. The method was used to track breath changes in two mouse models with streptozotocin-induced diabetes throughout disease progression. Notably, variations in certain VOCs in exhaled breath correlated with blood glucose levels (BGLs) during diabetes development. For instance, in the type 1 diabetes model, acetone levels remained stable within 8 days after hyperglycemia (not significant, n.s.) compared to those in controls but showed a significant increase after 10 days (p < 0.001). In the type 2 diabetes model, isopropanol levels decreased (p < 0.001) during the prediabetes phase, when BGL changes were not significant (n.s.) compared to those in the healthy state. These results indicate that specific substances in exhaled breath change prior to noticeable BGL alterations. This study thus established the viability of direct online mass spectrometry for breath analysis in early diabetes detection and showed that this technique has potential for identifying clear clinical markers for early diabetes screening. [ABSTRACT FROM AUTHOR]

Background: This proof-of-concept study aimed to assess the diagnostic potential of gas chromatography–mass spectrometry (GC-MS) in profiling volatile organic compounds (VOCs) from exhaled breath as a diagnostic tool for the chronic coronary syndrome (CCS). Methods: Exhaled air was collected from patients undergoing invasive coronary angiography (ICA), with all samples obtained prior to ICA. Post hoc, patients were divided into groups based on coronary lesion severity and indications for revascularization. VOCs in the breath samples were analyzed using GC-MS. Results: This study included 23 patients, of whom 11 did not require myocardial revascularization and 12 did. GC-MS analysis successfully classified 10 of the 11 patients without the need for revascularization (sensitivity of 91%), and 7 of the 12 patients required revascularization (specificity 58%). In subgroup analysis, GC-MS demonstrated 100% sensitivity in identifying patients with significant coronary lesions requiring intervention when the cohort was divided into three groups. A total of 36 VOCs, including acetone, ethanol, and phenol, were identified as distinguishing markers between patient groups. Conclusions: Patients with CCS exhibited a unique fingerprint of exhaled breath, which was detectable with GC-MS. These findings suggest that GC-MS analysis could be a reliable and non-invasive diagnostic tool for CCS. Further studies with larger cohorts are necessary to validate these results and explore the potential integration of VOC analysis into clinical practice. [ABSTRACT FROM AUTHOR]

Introduction: Thyroid cancer incidence rate has increased substantially worldwide in recent years. Fine needle aspiration biopsy (FNAB) is currently the golden standard of thyroid cancer diagnosis, which however, is invasive and costly. In contrast, breath analysis is a non-invasive, safe and simple sampling method combined with a promising metabolomics approach, which is suitable for early cancer diagnosis in high volume population. Objectives: This study aims to achieve a more comprehensive and definitive exhaled breath metabolism profile in papillary thyroid cancer patients (PTCs). Methods: We studied both end-tidal and mixed expiratory breath, solid-phase microextraction gas chromatography coupled with high resolution mass spectrometry (SPME-GC-HRMS) was used to analyze the breath samples. Multivariate combined univariate analysis was applied to identify potential breath biomarkers. Results: The biomarkers identified in end-tidal and mixed expiratory breath mainly included alkanes, olefins, enols, enones, esters, aromatic compounds, and fluorine and chlorine containing organic compounds. The area under the curve (AUC) values of combined biomarkers were 0.974 (sensitivity: 96.1%, specificity: 90.2%) and 0.909 (sensitivity: 98.0%, specificity: 74.5%), respectively, for the end-tidal and mixed expiratory breath, indicating of reliability of the sampling and analysis method Conclusion: This work not only successfully established a standard metabolomic approach for early diagnosis of PTC, but also revealed the necessity of using both the two breath types for comprehensive analysis of the biomarkers. [ABSTRACT FROM AUTHOR]

Analyzing exhaled breath for volatile organic compounds (VOCs) using thermal desorption–gas chromatography–mass spectrometry (TD-GC-MS) offers a non-invasive diagnostic approach for various diseases. Despite its promise, the method faces challenges like sampling heterogeneity and high costs. Following the European Respiratory Society's advocacy for methodological standardization, we developed the SPIRITAS (Standardized Product for Inexpensive Respiratory InvesTigation: A breath Sampler), a low-cost, disposable breath sampler. This study evaluates the SPIRITAS's effectiveness in detecting targeted VOCs. We tested the SPIRITAS using the Peppermint Experiment, a standardized protocol that allows for comparison between different breath sampling and analytical practices by assessing the ability to detect five peppermint-specific VOCs after ingestion of a 200-milligram peppermint oil capsule. We included ten subjects and performed six breath samples per participant, including a baseline measurement taken before ingestion. We used the Wilcoxon signed-rank test to evaluate whether baseline values were significantly lower than the peak values of the targeted VOCs. Additionally, we conducted an experiment utilizing humidified medical-grade air to identify any VOCs attributable to the SPIRITAS setup itself. Results showed successful detection of four out of five targeted "peppermint-associated" VOCs: alpha-pinene (p ≤ 0.01), beta-pinene (p ≤ 0.01), menthone (p = 0.01), and menthol (p = 0.02), indicating significant differences between the baseline and peak values in the volunteers' breath. However, detection of eucalyptol was inconsistent. In addition, we identified 16 VOCs that were released by the SPIRITAS, one of which remains unidentified. Our findings underscore the SPIRITAS's potential for clinical applications, paving the way for broader biomarker research. The combination of ease of use, low cost, reduced risk of contamination, and standardization makes SPIRITAS very suitable for large-scale international studies. Furthermore, we have demonstrated the SPIRITAS's effectiveness in detecting specific VOCs and identified 16 compounds originating from the SPIRITAS, ensuring that these compounds would not be mis-qualified as potential biomarkers in future clinical studies. [ABSTRACT FROM AUTHOR]

Breathomics is an emerging area focusing on monitoring and diagnosing pulmonary diseases, especially lung cancer. This research aims to employ metabolomic methods to create a breathprint in human-expelled air to rapidly identify lung cancer and its stages. An electronic nose (e-nose) system with five metal oxide semiconductor (MOS) gas sensors, a microcontroller, and machine learning algorithms was designed and developed for this application. The volunteers in this study include 114 patients with lung cancer and 147 healthy controls to understand the clinical potential of the e-nose system to detect lung cancer and its stages. In the training phase, in discriminating lung cancer from controls, the XGBoost classifier model with 10-fold cross-validation gave an accuracy of 91.67%. In the validation phase, the XGBoost classifier model correctly identified 35 out of 42 patients with lung cancer samples and 44 out of 51 healthy control samples providing an overall sensitivity of 83.33% and specificity of 86.27%. These results indicate that the exhaled breath VOC analysis method may be developed as a new diagnostic tool for lung cancer detection. The advantages of e-nose based diagnostics, such as an easy and painless method of sampling, and low-cost procedures, will make it an excellent diagnostic method in the future. [ABSTRACT FROM AUTHOR]

Diabetes is a metabolic disorder often diagnosed late and requires continuous monitoring of blood glucose. We introduce GlucoBreath, a user-centric, cost-effective, and portable pre-diagnostic solution to address this global challenge. GlucoBreath addresses the urgent need for an accessible and non-intrusive diabetes detection device, offering affordability, mobility, and comfortable non-invasive diabetes testing, especially among economically weaker sections of society. GlucoBreath comprises (i) a non-intrusive multi-sensor Internet of Things device comprising multiple sensors detecting volatile organic compounds in breath, (ii) BreathProfiles dataset encompasses information from 492 patients, which includes demographic details, physiological measurements, and sensor readings derived by analysing breath samples with our device, (iii) an innovative Machine Learning-based diabetes prediction system trained on the BreathProfiles dataset, and (iv) a user-friendly web interface for seamless device interaction and viewing diabetes reports. Given a person’s breath sample, demographic data, and body vitals as input, GlucoBreath predicts (a) whether the person has diabetes. (b) If the person has diabetes, then the blood glucose level (BGL) of the person is moderate or high. GlucoBreath’s groundbreaking approach supersedes current methods, achieving an impressive mean accuracy of 98.4% using a Logistic Regression-AdaBoost stack-metamodel, marking a substantial 43.3% improvement over an existing method. Due to its portability, non-intrusiveness, and rapid response, GlucoBreath is a valuable pre-diagnostic tool that can facilitate the early detection of diabetes in many individuals. Furthermore, GlucoBreath’s prediction of BGL can help alert people to control their sugar consumption in the case of moderate BGL or see a doctor for high BGL.

Diabetes mellitus, a chronic condition affecting millions worldwide, necessitates continuous monitoring of blood glucose level (BGL). The increasing prevalence of diabetes has driven the development of non-invasive methods, such as electronic noses (e-noses), for analyzing exhaled breath and detecting biomarkers in volatile organic compounds (VOCs). Effective machine learning models require extensive patient data to ensure accurate BGL predictions, but previous studies have been limited by small sample sizes. This study addresses this limitation by employing conditional generative adversarial networks (CTGAN) to generate synthetic data from real-world tests involving 29 healthy and 29 diabetic participants, resulting in over 14,000 new synthetic samples. These data were used to validate machine learning models for diabetes detection and BGL prediction, integrated into a Tiny Machine Learning (TinyML) e-nose system for real-time analysis. The proposed models achieved an 86% accuracy in BGL identification using LightGBM (Light Gradient Boosting Machine) and a 94.14% accuracy in diabetes detection using Random Forest. These results demonstrate the efficacy of enhancing machine learning models with both real and synthetic data, particularly in non-invasive systems integrating e-noses with TinyML. This study signifies a major advancement in non-invasive diabetes monitoring, underscoring the transformative potential of TinyML-powered e-nose systems in healthcare applications.

ZnO nanoparticles (NPs) were prepared by green synthesis using plant leaf extraction of Ochradenus baccatus and characterized by XRD, FESEM, HRTEM, and Raman spectroscopy techniques. Since elevated CO levels have been associated with inflammatory conditions, cardiovascular diseases, and respiratory disorders and the methane gas primarily produced by gut microbiota and linked to gastrointestinal disorders and other abnormal methane levels in breath samples, the nanoparticles were applied for gas sensor fabrication. Thus, the gas sensors fabricated using ZnO nanoparticles were investigated for CH4, H2, CO, and NO2 gases. The gas sensing was performed for the fabricated sensors at various operating temperatures and gas concentrations. Interestingly, leaf-extracted green synthesized ZnO NPs were more sensitive to CH4, CO, and NO2 gases than to H2. The results of sensing studies revealed that the nanoparticles exhibit a selectivity toward gas depending on the gas type. The sensor response was also studied against the humidity. These findings bridge between the laboratory and industry sectors for future gas sensors development, which can be used for exhaled breath analysis and serve as potential diagnostic tools for highly sensitive contagious diseases. [ABSTRACT FROM AUTHOR]

In recent times, the global landscape of disease detection and monitoring has been profoundly influenced by the convergence of nanotechnology and biosensing techniques. Biosensors have enormous potential to monitor human health, with flexible or wearable variants, through monitoring of biomarkers in clinical and biological behaviors and applications related to health and disease, with increasing biorecognition, sensitivity, selectivity, and accuracy. The emergence of nanomaterial-based biosensors has ushered in a new era of rapid and sensitive diagnostic tools, offering unparalleled capabilities in the realm of disease identification. Even after the declaration of the end of the COVID-19 pandemic, the demand for efficient and accessible diagnostic methodologies has grown exponentially. In response, the integration of nanomaterial biosensors into breathalyzer devices has gained considerable attention as a promising avenue for low-cost, non-invasive, and early detection of COVID-19. This review delves into the forefront of scientific advancements, exploring the potential of emerging nanomaterial biosensors within breathalyzers to revolutionize the landscape of COVID-19 detection, providing a comprehensive overview of their principles, applications, and implications. [ABSTRACT FROM AUTHOR]

Diabetes mellitus is a widespread chronic metabolic disorder that requires regular blood glucose level surveillance. Current invasive techniques, such as finger-prick tests, often result in discomfort, leading to infrequent monitoring and potential health complications. The primary objective of this study was to design a novel, portable, non-invasive system for diabetes detection using breath samples, named DiabeticSense, an affordable digital health device for early detection, to encourage immediate intervention. The device employed electrochemical sensors to assess volatile organic compounds in breath samples, whose concentrations differed between diabetic and non-diabetic individuals. The system merged vital signs with sensor voltages obtained by processing breath sample data to predict diabetic conditions. Our research used clinical breath samples from 100 patients at a nationally recognized hospital to form the dataset. Data were then processed using a gradient boosting classifier model, and the performance was cross-validated. The proposed system attained a promising accuracy of 86.6%, indicating an improvement of 20.72% over an existing regression technique. The developed device introduces a non-invasive, cost-effective, and user-friendly solution for preliminary diabetes detection. This has the potential to increase patient adherence to regular monitoring. [ABSTRACT FROM AUTHOR]

Chronic Obstructive Pulmonary Disease (COPD) is a chronic respiratory condition that often goes undiagnosed despite the availability of spirometry for diagnosis, and its exact prevalence remains uncertain. Exhaled breath has been proposed as a source of relevant health information, particularly Volatile Organic Compounds (VOCs), which can be easily obtained and applied in clinical practice. In this study, exhaled breath samples were collected from patients diagnosed with COPD of varying severity during their stable condition using specialized RTubeVOC tubes. Volatile compounds from the air samples were extracted using a 50/30 µm divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber and the analysis was performed using gas chromatography/mass spectrometry (GC/MS) technique. The patients were divided into two groups based on their history of exacerbations, and the aim was to identify VOCs associated with the risk of future COPD exacerbation, thus allowing for more personalized and objective COPD treatment. Blood eosinophil content was also taken into consideration. A panel of distinguishing mass-spectral features was identified between the two patient groups. The discriminating exhaled molecules were heptane 2,2,4,6,6-pentamethyl, gamma-terpinene, 2-ethylhexanol, and undecane demonstrating the potential of analyzing VOCs in exhaled breath for the detection and management of COPD, offering a promising avenue to improve COPD management and treatment approaches. [ABSTRACT FROM AUTHOR]

Analyzing exhaled breath for volatile organic compounds (VOCs) using thermal desorption–gas chromatography–mass spectrometry (TD-GC-MS) offers a non-invasive diagnostic approach for various diseases. Despite its promise, the method faces challenges like sampling heterogeneity and high costs. Following the European Respiratory Society’s advocacy for methodological standardization, we developed the SPIRITAS (Standardized Product for Inexpensive Respiratory InvesTigation: A breath Sampler), a low-cost, disposable breath sampler. This study evaluates the SPIRITAS’s effectiveness in detecting targeted VOCs. We tested the SPIRITAS using the Peppermint Experiment, a standardized protocol that allows for comparison between different breath sampling and analytical practices by assessing the ability to detect five peppermint-specific VOCs after ingestion of a 200-milligram peppermint oil capsule. We included ten subjects and performed six breath samples per participant, including a baseline measurement taken before ingestion. We used the Wilcoxon signed-rank test to evaluate whether baseline values were significantly lower than the peak values of the targeted VOCs. Additionally, we conducted an experiment utilizing humidified medical-grade air to identify any VOCs attributable to the SPIRITAS setup itself. Results showed successful detection of four out of five targeted “peppermint-associated” VOCs: alpha-pinene (p ≤ 0.01), beta-pinene (p ≤ 0.01), menthone (p = 0.01), and menthol (p = 0.02), indicating significant differences between the baseline and peak values in the volunteers’ breath. However, detection of eucalyptol was inconsistent. In addition, we identified 16 VOCs that were released by the SPIRITAS, one of which remains unidentified. Our findings underscore the SPIRITAS’s potential for clinical applications, paving the way for broader biomarker research. The combination of ease of use, low cost, reduced risk of contamination, and standardization makes SPIRITAS very suitable for large-scale international studies. Furthermore, we have demonstrated the SPIRITAS’s effectiveness in detecting specific VOCs and identified 16 compounds originating from the SPIRITAS, ensuring that these compounds would not be mis-qualified as potential biomarkers in future clinical studies.

Disease diagnosis through biological fluids, particularly exhaled breath analysis, has gained increasing importance. Volatile organic compounds (VOCs) present in exhaled breath offer diagnostic potential as they reflect altered and disease-specific metabolic pathways. While gas chromatography–mass spectrometry (GC–MS) has been traditionally used for VOCs detection, electronic noses have emerged as a promising alternative for disease screening. Metal oxide semiconductor (MOS) sensors play an essential role in these devices due to their simplicity and cost-effectiveness. However, their limited specificity and sensitivity pose challenges for accurate diagnosis at lower VOCs concentrations, typical of exhaled breath. To address specificity and sensitivity issues, temperature modulation (TM) has been proposed in this paper, introducing a custom-developed electronic nose based on multiple and heterogeneous gas sensors located within an analysis chamber. Four different TM patterns (i.e., square, sine, triangular, and a combination of square and triangular) were applied to the gas sensors to test their response to three different analytes at three distinct concentrations. Data were analyzed by extracting meaningful features from the sensor raw data, and dimensionality reduction using principal component analysis (PCA) was performed. The results demonstrated distinct clusters for each experimental condition, indicating successful discrimination of analytes and concentrations. In addition, an analysis of which set of sensors and modulation pattern yielded the best results was performed. In particular, the most promising TM pattern proved to be the square and triangular combination, with optimal discrimination accuracy between both concentrations and analytes. One specific sensor, namely, TGS2600 from Figaro USA, Inc., provided the best performance. While preliminary results highlighted the potential of TM to improve the sensitivity of gas sensors in electronic nose devices, paving the way for further advancements in the field of exhaled breath analysis. [ABSTRACT FROM AUTHOR]

Volatile Organic Compounds (VOCs) are not only present in the human living conditions, but also can be produced in the process of human metabolism. The types and kurtosis of VOCs produced by the human body are closely related to human health and disease status. In recent years, analyzing VOCs components and concentration in human exhaled breath through non-invasive detection can be used to indicate human health status, which has been widely concerned in the field of clinical diagnosis and health monitoring. This paper reviews the clinical application value and common detection methods of exhaled VOCs, and elucidates pros and cons of each method . In this paper, the revolutionary progress brought by the new electronic nose technology to the detection of exhaled VOCs is described, which brings more possibilities for the high sensitivity, high throughput and intelligent detection of exhaled VOCs.

The urgency of early lung cancer (LC) diagnosis and treatment has been more and more significant. Exhaled breath analysis using gas sensors is a promising way to find out if someone has LC due to its low-cost, non-invasive, and real-time monitoring compared with traditional invasive diagnostic techniques. Among sensor-based gas detection techniques, metal oxide semiconductor’s gas sensors are one of the most important types. This review presents the-state-of-art in metal oxide gas sensors for the diagnosis of early LC. First, the exhaled breath biomarkers are described with emphasis on the concentration of abnormal volatile organic compounds (VOCs) caused by the metabolic process of LC cells. Then, the research status of metal oxide gas sensors in LC diagnosis is summarized. The sensing performance and enhancement strategy of biomarkers provided by metal oxide semiconductor materials are reviewed. Another effective way to improve VOC detection performance is to build a gas sensor array. At the same time, various gas sensors combined with self-powered techniques are mentioned to display a broad development prospect in breath diagnosis. Finally, metal oxide gas sensor-based LC diagnosis is prospected.

ZnO nanoparticles (NPs) were prepared by green synthesis using plant leaf extraction of Ochradenus baccatus and characterized by XRD, FESEM, HRTEM, and Raman spectroscopy techniques. Since elevated CO levels have been associated with inflammatory conditions, cardiovascular diseases, and respiratory disorders and the methane gas primarily produced by gut microbiota and linked to gastrointestinal disorders and other abnormal methane levels in breath samples, the nanoparticles were applied for gas sensor fabrication. Thus, the gas sensors fabricated using ZnO nanoparticles were investigated for CH4, H2, CO, and NO2 gases. The gas sensing was performed for the fabricated sensors at various operating temperatures and gas concentrations. Interestingly, leaf-extracted green synthesized ZnO NPs were more sensitive to CH4, CO, and NO2 gases than to H2. The results of sensing studies revealed that the nanoparticles exhibit a selectivity toward gas depending on the gas type. The sensor response was also studied against the humidity. These findings bridge between the laboratory and industry sectors for future gas sensors development, which can be used for exhaled breath analysis and serve as potential diagnostic tools for highly sensitive contagious diseases.

There is a need for reliable biomarkers for the diagnosis of chronic lung allograft dysfunction (CLAD). In this light, we investigated the diagnostic value of exhaled breath analysis using an electronic nose (eNose) for CLAD, CLAD phenotype, and CLAD stage in lung transplant recipients (LTR). We performed eNose measurements in LTR with and without CLAD, visiting the outpatient clinic. Through supervised machine learning, the diagnostic value of eNose for CLAD was assessed in a random training and validation set. Next, we investigated the diagnostic value of the eNose measurements combined with known risk factors for CLAD. Model performance was evaluated using ROC-analysis. We included 152 LTR (median age 60 years, 49% females), of whom 38 with CLAD. eNose-based classification of patients with and without CLAD provided an AUC of 0.86 in the training set, and 0.82 in the validation set. After adding established risk factors for CLAD (age, gender, type of transplantation, time after transplantation and prior occurrence of acute cellular rejection) to a model with the eNose data, the discriminative ability of the model improved to an AUC of 0.94 (p = 0.02) in the training set and 0.94 (p = 0.04) in the validation set. Discrimination between BOS and RAS was good (AUC 0.95). Discriminative ability for other phenotypes (AUCs ranging 0.50-0.92) or CLAD stages (AUC 0.56) was limited. Exhaled breath analysis using eNose is a promising novel biomarker for enabling diagnosis and phenotyping CLAD. eNose technology could be a valuable addition to the diagnostic armamentarium for suspected graft failure in LTR. [ABSTRACT FROM AUTHOR]

In recent times, the global landscape of disease detection and monitoring has been profoundly influenced by the convergence of nanotechnology and biosensing techniques. Biosensors have enormous potential to monitor human health, with flexible or wearable variants, through monitoring of biomarkers in clinical and biological behaviors and applications related to health and disease, with increasing biorecognition, sensitivity, selectivity, and accuracy. The emergence of nanomaterial-based biosensors has ushered in a new era of rapid and sensitive diagnostic tools, offering unparalleled capabilities in the realm of disease identification. Even after the declaration of the end of the COVID-19 pandemic, the demand for efficient and accessible diagnostic methodologies has grown exponentially. In response, the integration of nanomaterial biosensors into breathalyzer devices has gained considerable attention as a promising avenue for low-cost, non-invasive, and early detection of COVID-19. This review delves into the forefront of scientific advancements, exploring the potential of emerging nanomaterial biosensors within breathalyzers to revolutionize the landscape of COVID-19 detection, providing a comprehensive overview of their principles, applications, and implications.

Chronic Obstructive Pulmonary Disease (COPD) is a chronic respiratory condition that often goes undiagnosed despite the availability of spirometry for diagnosis, and its exact prevalence remains uncertain. Exhaled breath has been proposed as a source of relevant health information, particularly Volatile Organic Compounds (VOCs), which can be easily obtained and applied in clinical practice. In this study, exhaled breath samples were collected from patients diagnosed with COPD of varying severity during their stable condition using specialized RTubeVOC tubes. Volatile compounds from the air samples were extracted using a 50/30 µm divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber and the analysis was performed using gas chromatography/mass spectrometry (GC/MS) technique. The patients were divided into two groups based on their history of exacerbations, and the aim was to identify VOCs associated with the risk of future COPD exacerbation, thus allowing for more personalized and objective COPD treatment. Blood eosinophil content was also taken into consideration. A panel of distinguishing mass-spectral features was identified between the two patient groups. The discriminating exhaled molecules were heptane 2,2,4,6,6-pentamethyl, gamma-terpinene, 2-ethylhexanol, and undecane demonstrating the potential of analyzing VOCs in exhaled breath for the detection and management of COPD, offering a promising avenue to improve COPD management and treatment approaches.

Nanomaterial-based room temperature gas sensors are used as a screening tool for diagnosing various diseases through breath analysis. The stable planar structure of boron carbide (B4C3) is utilized as a base material for adsorption of human breath exhaled VOCs, namely formaldehyde, methanol, acetone, toluene along, with interfering gases of carbon dioxide and water. The adsorption energy, charge density, density of states, energy band gap variation, recovery time, sensitivity, and work function of adsorbed molecules on pristine B4C3 are analyzed by density functional theory. The computed adsorption energies of VOC are in the range of − 0.176 to − 0.238 eV, and a larger interaction distance validate the physisorption behavior of these VOCs biomarkers on pristine boron carbide monolayer. Minute changes are determined from the electronic band structure of all adsorbed systems conserving the semiconducting nature of the B4C3 monolayer. The band gap variation upon adsorption of VOCs and interfering gases is examined between 0.05 and 0.52%. The 13.63 × 10–9 s recovery time of methanol is slower among VOCs, and 0.556 × 10–9 s of carbon dioxide (CO2) is faster for desorption. The results reveal that boron carbide can be utilized as a biosensor at room temperature for the analysis of exhaled VOCs from human breath. [ABSTRACT FROM AUTHOR]

Using the first-principles theory, the geometric and electronic properties of the Ru-doped PtTe2 (Ru-PtTe2) monolayer, and its sensing performance for three VOCs biomarkers, namely, 2-propenal (C3H4O), acetone (C3H6O) and isoprene (C5H8), were analyzed, to expound its potential for exhaled breath analysis and diagnosis of lung cancer. It was found that the Ru-substitution on the surface of the pristine PtTe2 surface with a Te atom is energy-favorable, with the formation energy of −1.22 eV. Upon adsorption of the three VOC gas species, chemisorption was identified with the adsorption energies of −1.72, −1.12 and −1.80 eV for C3H4O, C3H6O and C5H8, respectively. The Ru-doping results in a strong magnetic property for the PtTe2 monolayer, whereas the gas adsorption eliminates this magnetic behavior. The electronic properties reveal the sensing mechanism of the Ru-PtTe2 monolayer for gas detection, and the bandgap change indicates its admirable positive sensing response for the three gas species. Therefore, we conclude that the Ru-PtTe2 monolayer is a promising sensing material to realize the diagnosis of lung cancer through exhaled gas detection, with a remarkable decrease in its electrical conductivity. This work paves the way for further exploration of the PtTe2-based gas sensor for early diagnosis of lung cancer, and we hope that more sensing materials can be investigated using the PtTe2 monolayer. [ABSTRACT FROM AUTHOR]

In this paper, the first e-nose system coupled with machine learning algorithm for noninvasive measurement of total cholesterol level based on exhaled air sample was proposed. The study was conducted with the participation of 151 people, from whom a breath sample was collected, and the level of total cholesterol was measured. The breath sample was examined using e-nose and gas sensors, such as TGS1820, TGS2620, TGS2600, MQ3, Semeatech 7e4 NO2 and 7e4 H2S, SGX&#95;NO2, SGX&#95;H2S, K33, AL-03P, and AL-03S. The LGBMRegressor algorithm was used to predict cholesterol level based on the breath sample. Machine learning algorithms were developed for the entire measurement range and for the norm range ≤200 mg/dL achieving MAPE 13.7% and 8%, respectively. The results show that it is possible to develop a noninvasive device to measure total cholesterol level from breath.

Complications like acute cellular rejection (ACR) and infection are known risk factors for the development of chronic lung allograft dysfunction, impacting long-term patient and graft survival after lung transplantation (LTx). Differentiating between complications remains challenging and time-sensitive, highlighting the need for accurate and rapid diagnostic modalities. We assessed the ability of exhaled breath analysis using an electronic nose (eNose) to distinguish between ACR, infection, and mechanical complications in LTx recipients (LTR) presenting with suspected complications. LTR with suspected complications and subsequently proven diagnosis underwent exhaled breath analysis using an eNose. Supervised machine learning was used to assess the eNose's ability to discriminate between complications. Next, we determined the added value of the eNose measurement on top of standard clinical parameters. In 90 LTR, 161 measurements were performed during suspected complications, with 84 proven diagnoses. The eNose could distinguish between ACR, infection, and mechanical complications with 74% accuracy, and ACR and infection with 82% accuracy. Combining eNose measurements with standard clinical parameters improved diagnostic accuracy to 88% (P =.0139), with 94% sensitivity and 80% specificity. Exhaled breath analysis using eNose technology is a promising, noninvasive, diagnostic modality for distinguishing LTx complications, enabling timely diagnosis and interventions., Competing Interests: Declaration of competing interest The authors of this manuscript have conflicts of interest to disclose as described by the American Journal of Transplantation. Olivier C. Manintveld reports speaker engagement or advisory board fees from Astra Zeneca, Abbott, Boehringer Ingelheim, Daiichi Sankyo, Novartis, Novo Nordisk, Siemens, and Vifor. Nynke Wijbenga has no conflicts of interest to disclose. Bas J. Mathot has no conflicts of interest to disclose. Roel van Pel has no conflicts of interest to disclose. Leonard Seghers has no conflicts of interest to disclose. Catharina C. Moor has no conflicts of interest to disclose. Joachim G. J. V. Aerts has no conflicts of interest to disclose. Daniel Bos has no conflicts of interest to disclose. Merel E. Hellemons has no conflicts of interest to disclose., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Disease diagnosis through biological fluids, particularly exhaled breath analysis, has gained increasing importance. Volatile organic compounds (VOCs) present in exhaled breath offer diagnostic potential as they reflect altered and disease-specific metabolic pathways. While gas chromatography–mass spectrometry (GC–MS) has been traditionally used for VOCs detection, electronic noses have emerged as a promising alternative for disease screening. Metal oxide semiconductor (MOS) sensors play an essential role in these devices due to their simplicity and cost-effectiveness. However, their limited specificity and sensitivity pose challenges for accurate diagnosis at lower VOCs concentrations, typical of exhaled breath. To address specificity and sensitivity issues, temperature modulation (TM) has been proposed in this paper, introducing a custom-developed electronic nose based on multiple and heterogeneous gas sensors located within an analysis chamber. Four different TM patterns (i.e., square, sine, triangular, and a combination of square and triangular) were applied to the gas sensors to test their response to three different analytes at three distinct concentrations. Data were analyzed by extracting meaningful features from the sensor raw data, and dimensionality reduction using principal component analysis (PCA) was performed. The results demonstrated distinct clusters for each experimental condition, indicating successful discrimination of analytes and concentrations. In addition, an analysis of which set of sensors and modulation pattern yielded the best results was performed. In particular, the most promising TM pattern proved to be the square and triangular combination, with optimal discrimination accuracy between both concentrations and analytes. One specific sensor, namely, TGS2600 from Figaro USA, Inc., provided the best performance. While preliminary results highlighted the potential of TM to improve the sensitivity of gas sensors in electronic nose devices, paving the way for further advancements in the field of exhaled breath analysis.

Introduction: Coronary artery disease (CAD) is the leading cause of morbidity and mortality worldwide, and there is an unmet need for a simple, inexpensive, noninvasive tool aimed at CAD detection. The aim of this pilot study was to evaluate the possible use of breath analysis in detecting the presence of CAD. Materials and Methods: In a prospective study, breath from patients with no history of CAD who presented with acute chest pain to the emergency room was sampled using a designated portable electronic nose (eNose) system. First, breath samples from 60 patients were analyzed and categorized as obstructive, nonobstructive, and no-CAD according to the actual presence and extent of CAD as was demonstrated on cardiac imaging (either computerized tomography angiography or coronary angiography). Classification models were built according to the results, and their diagnostic performance was then examined in a blinded manner on a new set of 25 patients. The data were compared with the actual results of coronary arteries evaluation. Sensitivity, specificity, and accuracy were calculated for each model. Results: Obstructive CAD was correctly distinguished from nonobstructive and no-CAD with 89% sensitivity, 31% specificity, 83% negative predictive value (NPV), 42% positive predictive value (PPV), and 52% accuracy. In another model, any extent of CAD was successfully distinguished from no-CAD with 69% sensitivity, 67% specificity, 54% NPV, 79% PPV, and 68% accuracy. Conclusion: This proof-of-concept study shows that breath analysis has the potential to be used as a novel rapid, noninvasive diagnostic tool to help identify presence of CAD in patients with acute chest pain. [ABSTRACT FROM AUTHOR]

Introduction. Cardiopulmonary stress test provides significant diagnostic and prognostic information of the condition of patients with cardiovascular and pulmonary diseases. There is a serious problem, that final phase of stress testing is a physically difficult exercise for a person. There is a significant risk of occurrence and development of pathological conditions of the patient's cardiovascular system. One of the solutions is the development of methods for assessing the biological parameters of the patients at the end of a load protocol based on data from the initial stages of the test.Aim. Development of a method for finding an estimate of the maximum heart rate (HR) and of the peak oxygen consumption (OC) for the patients with chronic heart failure at the end of a cardiorespiratory exercise stress test, based on the results of the study obtained at the first initial stages of the test.Materials and methods. For the study, 149 anonymized records of rhythmograms and data of changes in the oxygen consumption of the patients with chronic heart failure were used. The patients underwent a cardiopulmonary stress test by a bicycle ergometer using step-by-step load protocol (the load power increase at each stage was 10 W, the duration of the load stage was 1 min)Results. Based on the analysis of the data obtained, a method for assessing the peak values of HR and of PC of the patients with chronic heart failure was developed.Conclusion. The relative error of the proposed estimate of the HR peak in most cases was no more than 10 %, which allows it to be used for practical purposes. It was established that when performing 70 % of the stress protocol, the error of the proposed estimate of the OC peak in most cases did not exceed 20 %. More research is needed to improve the accuracy of the assessment for using in medical applications aimed to the modernization of methods and equipment for stress testing of the patients.

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• Exhaled breath analysis can predict immunotherapy response in NSCLC patients. • Analysis should be performed early during treatment. • Application could spare ineffective therapy in 76% of non-responding patients. • Secondary resistance can be assessed during course of treatment. • Undesirable events can be prevented and unnecessary costs can be reduced. Exhaled breath analysis by electronic nose (eNose) has shown to be a potential predictive biomarker before start of anti-PD-1 therapy in patients with non-small cell lung carcinoma (NSCLC). We hypothesized that the eNose could also be used as an early monitoring tool to identify responders more accurately at early stage of treatment when compared to baseline. In this proof-of-concept study we aimed to definitely discriminate responders from non-responders after six weeks of treatment. This was a prospective observational study in patients with advanced NSCLC eligible for anti-PD-1 treatment. The efficacy of treatment was assessed by the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 at 3-month follow-up. We analyzed SpiroNose exhaled breath data of 94 patients (training cohort n = 62, validation cohort n = 32). Data analysis involved signal processing and statistics based on Independent Samples T-tests and Linear Discriminant Analysis (LDA) followed by Receiver Operating Characteristic (ROC) analysis. In the training cohort, a specificity of 73% was obtained at a 100% sensitivity level to identify objective responders. The Area Under the Curve (AUC) was 0.95 (CI: 0.89–1.00). In the validation cohort, these results were confirmed with an AUC of 0.97 (CI: 0.91–1.00). Exhaled breath analysis by eNose early during treatment allows for a highly accurate, non-invasive and low-cost identification of advanced NSCLC patients who benefit from anti-PD-1 therapy. [ABSTRACT FROM AUTHOR]

Using the first-principles theory, the geometric and electronic properties of the Ru-doped PtTe2 (Ru-PtTe2) monolayer, and its sensing performance for three VOCs biomarkers, namely, 2-propenal (C3H4O), acetone (C3H6O) and isoprene (C5H8), were analyzed, to expound its potential for exhaled breath analysis and diagnosis of lung cancer. It was found that the Ru-substitution on the surface of the pristine PtTe2 surface with a Te atom is energy-favorable, with the formation energy of −1.22 eV. Upon adsorption of the three VOC gas species, chemisorption was identified with the adsorption energies of −1.72, −1.12 and −1.80 eV for C3H4O, C3H6O and C5H8, respectively. The Ru-doping results in a strong magnetic property for the PtTe2 monolayer, whereas the gas adsorption eliminates this magnetic behavior. The electronic properties reveal the sensing mechanism of the Ru-PtTe2 monolayer for gas detection, and the bandgap change indicates its admirable positive sensing response for the three gas species. Therefore, we conclude that the Ru-PtTe2 monolayer is a promising sensing material to realize the diagnosis of lung cancer through exhaled gas detection, with a remarkable decrease in its electrical conductivity. This work paves the way for further exploration of the PtTe2-based gas sensor for early diagnosis of lung cancer, and we hope that more sensing materials can be investigated using the PtTe2 monolayer.

Lung cancer is generally diagnosed at advanced stages when surgical resection is not possible. Late diagnosis, along with development of chemoresistance, results in high mortality. Preventive approaches, including smoking cessation, chemoprevention and early detection are needed to improve survival. Smoking cessation combined with low-dose computed tomography screening has modestly improved survival. Chemoprevention has also shown some promise. Despite these successes, most lung cancer cases remain undetected until advanced stages. Additional early detection strategies may further improve survival and treatment outcome. Molecular alterations taking place during lung carcinogenesis have the potential to be used in early detection via noninvasive methods and may also serve as biomarkers for success of chemopreventive approaches. This review focuses on the utilization of molecular biomarkers to increase the efficacy of various preventive approaches. [ABSTRACT FROM AUTHOR]

• Breath test is suited to large-scale screening, which might contribute to early detection of lung cancer without specific symptom. • Gradient boost decision trees algorithm not only exhibits high classification performance but also provides an explainable model. • We borrowed the idea of bootstrap to estimate the uncertainty of diagnosis, which is important to decide the necessity of further detection. Lung cancer is continuously the leading cause of cancer related death, resulting from the lack of specific symptoms at early stage. A large-scale screening method may be the key point to find asymptomatic patients, leading to the reduction of mortality. An alternative method combining breath test and a machine learning algorithm is proposed. 236 breath samples were analyzed by TD-GCMS. Breath profile of each sample is composed of 308 features extracted from chromatogram. Gradient boost decision trees algorithm was employed to recognize lung cancer patients. Bootstrap is performed to simulate real diagnostic practice, with which we evaluated the confidence of our methods. An accuracy of 85 % is shown in 6-fold cross validations. In statistical bootstrap, 72 % samples are marked as "confident", and the accuracy of confident samples is 93 % throughout the cross validations. We have proposed such a non-invasive, accurate and confident method that might contribute to large-scale screening of lung cancer. As a consequence, more asymptomatic patients with early lung cancer may be detected. [ABSTRACT FROM AUTHOR]

Various compounds in human exhaled breath can provide important information on various diseases and health conditions. Major breakthroughs in new technologies such as infrared, electrochemistry, and chemiluminescence and the use of mass spectrometers have made it possible to accurately measure exhaled volatile organic compounds (VOCs) and aerosol particles at extremely low concentrations. Significant progress has been made in the field of detection. Respiratory testing has attracted widespread attention in scientific research and clinical applications because that can be used as a real-time, fast and non-invasive method to evaluate and monitor information on various diseases and health conditions. This review mainly summarizes the analysis methods of exhaled breath and their application in disease diagnosis, and aims to provide a new strategy for the real-time, rapid and non-invasive diagnosis of diseases in the future. [ABSTRACT FROM AUTHOR]

Exhaled breath analysis has emerged as a non-invasive and promising method for early detection of lung cancer, offering a novel approach for diagnosis through the identification of specific biomarkers present in a patient's breath. For this longitudinal study, 29 treatment-naive patients with lung cancer were evaluated before and after surgery. Secondary electrospray ionization high-resolution mass spectrometry was used for exhaled breath analysis. Volatile organic compounds with absolute log 2 fold change ⩾1 and q -values ⩾ 0.71 were selected as potentially relevant. Exhaled breath analysis resulted in a total of 3482 features. 515 features showed a substantial difference before and after surgery. The small sample size generated a false positive rate of 0.71, therefore, around 154 of these 515 features were expected to be true changes. Biological identification of the features with the highest consistency ( m / z -242.18428 and m / z -117.0539) revealed to potentially be 3-Oxotetradecanoic acid and Indole, respectively. Principal component analysis revealed a primary cluster of patients with a recurrent lung cancer, which remained undetected in the initial diagnostic and surgical procedures. The change of exhaled breath patterns after surgery in lung cancer emphasizes the potential for lung cancer screening and detection., (Creative Commons Attribution license.)

Development of early noninvasive methods for lung cancer diagnosis is among the most promising technologies, especially using exhaled breath as an object of analysis. Simple sample collection combined with easy and quick sample preparation, as well as the long-term stability of the samples, make it an ideal choice for routine analysis. The conditions of exhaled breath analysis by preconcentrating volatile organic compounds (VOCs) in sorbent tubes, two-stage thermal desorption and gas-chromatographic determination with flame-ionization detection have been optimized. These conditions were applied to estimate differences in exhaled breath VOC profiles of lung cancer patients and healthy volunteers. The combination of statistical methods was used to evaluate the ability of VOCs and their ratios to classify lung cancer patients and healthy volunteers. The performance of diagnostic models on the test data set was greater than 90 % for both VOC peak areas and their ratios. Some of the exhaled breath samples were analyzed using gas chromatography coupled with mass spectrometry (GC-MS) to identify VOCs present in exhaled breath at lower concentration levels. To confirm the endogenous origin of VOCs found in exhaled breath, GC-MS analysis of tumor tissues was conducted. Some of the VOCs identified in exhaled breath were found in tumor tissues, but their frequency of occurrence was significantly lower than in the case of exhaled breath.

Head and neck cancer (HNC) comprises a heterogeneous group of upper aerodigestive tract malignant neoplasms, the most frequent of which is squamous cell carcinoma. HNC forms the eighth most common cancer type and the incidence is increasing. However, survival has improved only moderately during the past decades. Currently, early diagnosis remains the mainstay for improving treatment outcomes in this patient population. Unfortunately, screening methods to allow early detection of HNC are not yet established. Therefore, many cases are still diagnosed at advanced stage, compromising outcomes. Exhaled breath analysis (EBA) is a diagnostic tool that has been recently introduced for many cancers. Breath analysis is non‐invasive, cost‐effective, time‐saving, and can potentially be applied for cancer screening. Here, we provide a summary of the accumulated evidence on the feasibility of EBA in the diagnosis of HNC. [ABSTRACT FROM AUTHOR]

Breath analysis has attracted human beings for centuries. It was one of the simplest methods to detect various diseases by using human smell sense only. Advances in technology enable to use more reliable and standardized methods, based on different gas sensing systems. Breath analysis requires the detection of volatile organic compounds (VOCs) of the concentrations below individual ppm (parts per million). Therefore, advanced detection methods have been proposed. Some of these methods use expensive and bulky equipment (e.g. optical sensors, mass spectrometry--MS), and require time-consuming analysis. Less accurate, but much cheaper, are resistive gas sensors. These sensors use porous materials and adsorptiondesorption processes, determining their physical parameters.We consider the problems of applying resistive gas sensors to breath analysis. Recent advances were underlined, showing that these economical gas sensors can be efficiently employed to analyse breath samples. General problems of applying resistive gas sensors are considered and illustrated with examples, predominantly related to commercial sensors and their long-term performance. A setup for collection of breath samples is considered and presented to point out the crucial parts and problematic issues. [ABSTRACT FROM AUTHOR]

The relationship of exhaled ethane and n-pentane to exhaled NO, carbonylated proteins, and indoor/outdoor atmospheric pollutants were examined in order to evaluate ethane and n-pentane as potential markers of airway inflammation and/or oxidative stress. Exhaled NO and carbonylated proteins were found to have no significant associations with either ethane (p = 0.96 and p = 0.81, respectively) or n-pentane (p = 0.44 and 0.28, respectively) when outliers were included. In the case where outliers were removed n-pentane was found to be inversely associated with carbonylated proteins. Exhaled hydrocarbons adjusted for indoor hydrocarbon concentrations were instead found to be positively associated with air pollutants (NO, NO(2) and CO), suggesting pollutant exposure is driving exhaled hydrocarbon concentrations. Given these findings, ethane and n-pentane do not appear to be markers of airway inflammation or oxidative stress.

With the global population prevalence of diabetes surpassing 463 million cases in 2019 and diabetes leading to millions of deaths each year, there is a critical need for feasible, rapid, and non-invasive methodologies for continuous blood glucose monitoring in contrast to the current procedures that are either invasive, complicated, or expensive. Breath analysis is a viable methodology for non-invasive diabetes management owing to its potential for multiple disease diagnoses, the nominal requirement of sample processing, and immense sample accessibility; however, the development of functional commercial sensors is challenging due to the low concentration of volatile organic compounds (VOCs) present in exhaled breath and the confounding factors influencing the exhaled breath profile. Given the complexity of the topic and the skyrocketing spread of diabetes, a multifarious review of exhaled breath analysis for diabetes monitoring is essential to track the technological progress in the field and comprehend the obstacles in developing a breath analysis-based diabetes management system. In this review, we consolidate the relevance of exhaled breath analysis through a critical assessment of current technologies and recent advancements in sensing methods to address the shortcomings associated with blood glucose monitoring. We provide a detailed assessment of the intricacies involved in the development of non-invasive diabetes monitoring devices. In addition, we spotlight the need to consider breath biomarker clusters as opposed to standalone biomarkers for the clinical applicability of exhaled breath monitoring. We present potential VOC clusters suitable for diabetes management and highlight the recent buildout of breath sensing methodologies, focusing on novel sensing materials and transduction mechanisms. Finally, we portray a multifaceted comparison of exhaled breath analysis for diabetes monitoring and highlight remaining challenges on the path to realizing breath analysis as a non-invasive healthcare approach.

IntroductionAlthough hyperbaric oxygen therapy (HBOT) has beneficial effects, some patients experience fatigue and pulmonary complaints after several sessions. The current limits of hyperbaric oxygen exposure to prevent pulmonary oxygen toxicity (POT) are based on pulmonary function tests (PFT), but the limitations of PFT are recognized worldwide. However, no newer modalities to detect POT have been established. Exhaled breath analysis in divers have shown volatile organic compounds (VOCs) of inflammation and methyl alkanes. This study hypothesized that similar VOCs might be detected after HBOT.MethodsTen healthy volunteers of the Royal Netherlands Navy underwent six HBOT sessions (95 min at 253 kPa, including three 5-min “air breaks”), i.e., on five consecutive days followed by another session after 2 days of rest. At 30 min before the dive, and at 30 min, 2 and 4 h post-dive, exhaled breath was collected and followed by PFT. Exhaled breath samples were analyzed using gas chromatography-mass spectrometry (GC-MS). After univariate tests and correlation of retention times, ion fragments could be identified using a reference database. Using these fragments VOCs could be reconstructed, which were clustered using principal component analysis. These clusters were tested longitudinally with ANOVA.ResultsAfter GC-MS analysis, eleven relevant VOCs were identified which could be clustered into two principal components (PC). PC1 consisted of VOCs associated with inflammation and showed no significant change over time. The intensities of PC2, consisting of methyl alkanes, showed a significant decrease (p = 0.001) after the first HBOT session to 50.8%, remained decreased during the subsequent days (mean 82%), and decreased even further after 2 days of rest to 58% (compared to baseline). PFT remained virtually unchanged.DiscussionAlthough similar VOCs were found when compared to diving, the decrease of methyl alkanes (PC2) is in contrast to the increase seen in divers. It is unknown why emission of methyl alkanes (which could originate from the phosphatidylcholine membrane in the alveoli) are reduced after HBOT. This suggests that HBOT might not be as damaging to the pulmonary tract as previously assumed. Future research on POT should focus on the identified VOCs (inflammation and methyl alkanes).

Introduction: Exposure to hyperbaric hyperoxic conditions can lead to pulmonary oxygen toxicity. Although a decrease in vital capacity has long been the gold standard, newer diagnostic modalities may be more accurate. In pulmonary medicine, much research has focussed on volatile organic compounds (VOCs) associated with inflammation in exhaled breath. In previous small studies after hyperbaric hyperoxic exposure several methyl alkanes were identified. This study aims to identify which VOCs mark the development of pulmonary oxygen toxicity.Methods: In this randomized crossover study, 12 divers of the Royal Netherlands Navy made two dives of one hour to 192.5 kPa (comparable to a depth of 9 msw) either with 100% oxygen or compressed air. At 30 min before the dive, and at 30 min and 1, 2, 3, and 4 h post-dive, exhaled breath was collected and followed by pulmonary function tests (PFT). Exhaled breath samples were analyzed using gas chromatography–mass spectrometry (GC–MS). After univariate tests and correlation of retention times, ion fragments could be identified using a standard reference database [National Institute of Standards and Technology (NIST)]. Using these fragments VOCs could be reconstructed, which were then tested longitudinally with analysis of variance.Results: After GC–MS analysis, seven relevant VOCs (generally methyl alkanes) were identified. Decane and decanal showed a significant increase after an oxygen dive (p = 0.020 and p = 0.013, respectively). The combined intensity of all VOCs showed a significant increase after oxygen diving (p = 0.040), which was at its peak (+35%) 3 h post-dive. Diffusion capacity of nitric oxide and alveolar membrane capacity showed a significant reduction after both dives, whereas no other differences in PFT were significant.Discussion: This study is the largest analysis of exhaled breath after in water oxygen dives to date and the first to longitudinally measure VOCs. The longitudinal setup showed an increase and subsequent decrease of exhaled components. The VOCs identified suggest that exposure to a one-hour dive with a partial pressure of oxygen of 192.5 kPa damages the phosphatidylcholine membrane in the alveoli, while the spirometry and diffusion capacity show little change. This suggests that exhaled breath analysis is a more accurate method to measure pulmonary oxygen toxicity.

With the rapid development of the concept of the Internet of Things (IoT), gas sensors with the function of simulating the human sense of smell became irreplaceable as a key element. Among them, ammonia (NH 3 ) sensors played an important role in respiration tests, environmental monitoring, safety, and other fields. However, the fabrication of the high-performance device with high stability and resistance to mechanical damages was still a challenge. In this work, polyurethane (PU) with excellent self-healing ability was applied as the substrate, and the sensor was designed from new sensitive material design and device structure optimization, through applying the organic molecule with groups which could absorb NH 3 and the laminated structure to shorten the electronic transmission path to achieve a low resistance state and favorable sensing properties. Accordingly, a room temperature flexible NH 3 sensor based on 6,6',6″-(nitrilotris(benzene-4,1-diyl))tris(5-phenylpyrazine-2,3-dicarbonitrile) (TPA-3DCNPZ) was successfully developed. The device could self-heal by means of a thermal evaporation assisted method. It exhibited a detection limit of 1 ppm at 98% relative humidity (RH), as well as great stability, selectivity, bending flexibility, and self-healing properties. The improved NH 3 sensing performance under high RH was further investigated by complex impedance plots (CIPs) and density functional theory (DFT), attributing to the enhanced adsorption of NH 3 . The TPA-3DCNPZ based NH 3 sensors proved to have great potential for application on simulated exhaled breath to determine the severity of kidney diseases and the progress of treatment. This work also provided new ideas for the construction of high-performance room temperature NH 3 sensors.

Volatile organic compounds (VOCs) can be subdivided into exogenous and endogenous categories based on their origin. Analyzing the endogenous VOCs can provide insights into maintaining the internal organs' homeostasis. Despite the ongoing development and the current understanding, studies have suggested a link between cardiovascular metabolic alterations in patients with ischemic heart disease and elevated levels of ethane and isoprene detectable through exhaled breath analysis. Conversely, patients with chronic heart failure exhibit elevated acetone and pentane in their exhaled air. These substances originate from disturbances in the heart tissue, including cellular and subcellular modulations. Hypothetically, ethane levels in the exhaled breath analysis can demonstrate the severity of ischemic heart disease and, consequently, the risk of death in the next 10 years due to cardiovascular disease (CVD). Real-time direct mass spectrometry is the preferred method for assessing VOCs in exhaled breath analysis. The accuracy of this analysis depends on several factors, including the selection of the relevant breath fraction, the type of breath collection container (if used), and the pre-concentration technique., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Background Immune checkpoint inhibitors have improved survival outcome of advanced non-small-cell lung cancer (NSCLC). However, most patients do not benefit. Therefore, biomarkers are needed that accurately predict response. We hypothesized that molecular profiling of exhaled air may capture the inflammatory milieu related to the individual responsiveness to anti-programmed death ligand 1 (PD-1) therapy. This study aimed to determine the accuracy of exhaled breath analysis at baseline for assessing nonresponders versus responders to anti-PD-1 therapy in NSCLC patients. Methods This was a prospective observational study in patients receiving checkpoint inhibitor therapy using both a training and validation set of NSCLC patients. At baseline, breath profiles were collected in duplicate by a metal oxide semiconductor electronic nose (eNose) positioned at the rear end of a pneumotachograph. Patients received nivolumab or pembrolizumab of which the efficacy was assessed by Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 at 3-month follow-up. Data analysis involved advanced signal-processing and statistics based on independent t -tests followed by linear discriminant and receiver operating characteristic (ROC) analysis. Results Exhaled breath data of 143 NSCLC patients (training: 92, validation: 51) were available at baseline. ENose sensors contributed significantly (P  < 0.05) at baseline in differentiating between patients with different responses at 3 months of anti-PD-1 treatment. The eNose sensors were combined into a single biomarker with an ROC-area under the curve (AUC) of 0.89 [confidence interval (CI) 0.82–0.96]. This AUC was confirmed in the validation set : 0.85 (CI 0.75–0.96). Conclusion ENose assessment was effective in the noninvasive prediction of individual patient responses to immunotherapy. The predictive accuracy and efficacy of the eNose for discrimination of immunotherapy responder types were replicated in an independent validation set op patients. This finding can potentially avoid application of ineffective treatment in identified probable nonresponders. [ABSTRACT FROM AUTHOR]

The interpretation of exhaled breath analysis needs to address to the influence of exogenous factors, especially to a transfer of confounding analytes by the test persons. A test person who was exposed to a disinfectant had exhaled breath analysis by MCC/IMS (Bioscout®) after different time intervals. Additionally, a new sampling method with inhalation of synthetic air before breath analysis was tested. After exposure to the disinfectant, 3-Pentanone monomer, 3-Pentanone dimer, Hexanal, 3-Pentanone trimer, 2-Propanamine, 1-Propanol, Benzene, Nonanal showed significantly higher intensities, in exhaled breath and air of the examination room, compared to the corresponding baseline measurements. Only one ingredient of the disinfectant (1-Propanol) was identical to the 8 analytes. Prolonging the time intervals between exposure and breath analysis showed a decrease of their intensities. However, the half-time of the decrease was different. The inhalation of synthetic air –more than consequently airing the examination room with fresh air –reduced the exogenous and also relevant endogenous analytes, leading to a reduction and even changing polarity of the alveolar gradient. The interpretation of exhaled breath needs further knowledge about the former residence of the proband and the likelihood and relevance of the inhalation of local, site-specific and confounding exogenous analytes by him. Their inhalation facilitates a transfer to the examination room and a detection of high concentrations in room air and exhaled breath, but also the exhalation of new analytes. This may lead to a misinterpretation of these analytes as endogenous resp. disease-specific ones. [ABSTRACT FROM AUTHOR]

Volatile organic compounds in breath can reflect host and pathogen metabolism and might be used to diagnose pneumonia. We hypothesized that rats with Streptococcus pneumoniae (SP) or Pseudomonas aeruginosa (PA) pneumonia can be discriminated from uninfected controls by thermal desorption-gas chromatography- mass-spectrometry (TD-GC-MS) and selected ion flow tubemass spectrometry (SIFT-MS) of exhaled breath. Male adult rats (n = 50) received an intratracheal inoculation of 1) 200 μl saline, or 2) 1 × 107 colony-forming units of SP or 3) 1 × 107 CFU of PA. Twenty-four hours later the rats were anaesthetized, tracheotomized, and mechanically ventilated. Exhaled breath was analyzed via TDGC- MS and SIFT-MS. Area under the receiver operating characteristic curves (AUROCCs) and correct classification rate (CCRs) were calculated after leave-one-out cross-validation of sparse partial least squares-discriminant analysis. Analysis of GC-MS data showed an AUROCC (95% confidence interval) of 0.85 (0.73-0.96) and CCR of 94.6% for infected versus noninfected animals, AUROCC of 0.98 (0.94-1) and CCR of 99.9% for SP versus PA, 0.92 (0.83-1.00), CCR of 98.1% for SP versus controls and 0.97 (0.92-1.00), and CCR of 99.9% for PA versus controls. For these comparisons the SIFT-MS data showed AUROCCs of 0.54, 0.89, 0.63, and 0.79, respectively. Exhaled breath analysis discriminated between respiratory infection and no infection but with even better accuracy between specific pathogens. Future clinical studies should not only focus on the presence of respiratory infection but also on the discrimination between specific pathogens. [ABSTRACT FROM AUTHOR]