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Background: 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)., Methods: 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., Results: 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., Conclusion: 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., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Advanced stage detection of liver cirrhosis (LCi) would lead to high mortality rates in patients. Therefore, accurate and non-invasive tools for its early detection are highly needed using human emanations that may reflect this disease. Human breath, along with urine and blood, has long been one of the three main biological media for assessing human health and environmental exposure. The primary objective of this study was to explore the potential of using volatile organic compounds (VOCs) assay of exhaled breath and urine samples for the diagnosis of patients with LCi and healthy controls (HC). For this purpose, we used a hybrid electronic nose (E-nose) combining two sensor families, consisting of an array of five commercial chemical gas sensors and six interdigitated chemical gas sensors based on pristine or metal-doped WO 3 nanowires for sensing volatile gases in exhaled breath. A voltammetric electronic tongue (VE-tongue), composed of five working electrodes, was dedicated to the analysis of urinary VOCs using cyclic voltammetry as a measurement technique. 54 patients were recruited for this study, comprising 22 patients with LCi, and 32 HC. The two-sensing systems coupled with pattern recognition methods, namely Principal Component Analysis (PCA) and Discriminant Function Analysis (DFA), were trained to classify data clusters associated with the health status of the two groups. The diagnostic performances of the E-nose and VE-tongue systems were studied by using the receiver operating characteristic (ROC) method. The use of the E-nose or the VE-tongue separately, trained with these appropriate classifiers, showed a slight overlap indicating no clear discrimination between LCi patients and HC. To improve the performance of both electronic sensing devices, an emerging strategy, namely a multi-sensor data fusion technique, was proposed as a second aim to overcome this shortcoming. The data fusion approach of the two systems, at a medium level of abstraction, has demonstrated the ability to assess human health and disease status using non-invasive screening tools based on exhaled breath and urinary VOC analysis. This suggests that exhaled breath as well as urinary VOCs are specific to a disease state and could potentially be used as diagnostic methods., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

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., (© The Author(s) 2019. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background & Aims: The spectrum of volatile organic compounds in the exhaled breath (breath-print, BP) has been shown to characterize patients with cirrhosis and with worse hepatic function. However, the association of different BPs with clinically relevant outcomes has not been described yet. Hence, we aimed to evaluate the association between BPs, mortality and hospitalization in cirrhotic patients and to compare it with that of the "classical" prognostic indices (Child-Pugh Classification [CPC] and MELD)., Methods: Eighty-nine cirrhotic patients (M/F 59/30, mean age 64.8 ± 11.3, CPC A/B/C 37/33/19) were recruited and followed up for a median time of 23 months. Clinical and biochemical data were collected. Breath collection and analysis were obtained through Pneumopipe ® and BIONOTE e-nose respectively., Results: Four different BP clusters (A, B, C, D) were identified. BP clusters A and D were associated with a significantly increased risk of mortality (HR 2.9, 95% confidence intervals [CI] 1.5-5.6) and hospitalization (HR 2.6, 95% CI 1.4-4.6), even in multiple adjusted models including CPC and MELD score (adjusted [a]HR 2.8, 95% CI 1.1-7.0 for mortality and aHR 2.2, 95% CI 1.1-4.2 for hospitalization). CPC C maintained the strongest association with both mortality (aHR 17.6, 95% CI 1.8-174.0) and hospitalization (aHR 12.4, 95% CI 2.0-75.8)., Conclusions: This pilot study demonstrates that BP clusters are associated with significant clinical endpoints (mortality and hospitalization) even independently from "classical" prognostic indices. Even though further studies are warranted on this topic, our findings suggest that the e-nose may become an adjunctive aid to stratify the risk of adverse outcomes in cirrhotic patients., (© 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

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]

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 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.

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.

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.

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]

Highlights • The Aeonose is a non-invasive diagnostic method that is able to detect lung cancer and its specific subtypes. • E-nose technology can be complementary in the early detection of lung cancer. • The Aeonose is able to discriminate between different types of lung cancer. Abstract Objectives Lung cancer is a leading cause of mortality. Exhaled-breath analysis of volatile organic compounds (VOC's) might detect lung cancer early in the course of the disease, which may improve outcomes. Subtyping lung cancers could be helpful in further clinical decisions. Materials and methods In a prospective, multi-centre study, using 10 electronic nose devices, 144 subjects diagnosed with NSCLC and 146 healthy subjects, including subjects considered negative for NSCLC after investigation, breathed into the Aeonose™ (The eNose Company, Zutphen, Netherlands). Also, analyses into subtypes of NSCLC, such as adenocarcinoma (AC) and squamous cell carcinoma (SCC), and analyses of patients with small cell lung cancer (SCLC) were performed. Results Choosing a cut-off point to predominantly rule out cancer resulted for NSCLC in a sensitivity of 94.4%, a specificity of 32.9%, a positive predictive value of 58.1%, a negative predictive value (NPV) of 85.7%, and an area under the curve (AUC) of 0.76. For AC sensitivity, PPV, NPV, and AUC were 81.5%, 56.4%, 79.5%, and 0.74, respectively, while for SCC these numbers were 80.8%, 45.7%, 93.0%, and 0.77, respectively. SCLC could be ruled out with a sensitivity of 88.9% and an NPV of 96.8% with an AUC of 0.86. Conclusion Electronic nose technology with the Aeonose™ can play an important role in rapidly excluding lung cancer due to the high negative predictive value for various, but not all types of lung cancer. Patients showing positive breath tests should still be subjected to further diagnostic testing. [ABSTRACT FROM AUTHOR]

Background Asthma is a chronic inflammatory airway disease, associated with episodes of exacerbations. Therapy with inhaled corticosteroids ( ICS) targets airway inflammation, which aims to maintain and restore asthma control. Clinical features are only modestly associated with airways inflammation. Therefore, we hypothesized that exhaled volatile metabolites identify longitudinal changes between clinically stable episodes and loss of asthma control. Objectives To determine whether exhaled volatile organic compounds ( VOCs) as measured by gas-chromatography/mass-spectrometry ( GC/ MS) and electronic nose ( eNose) technology discriminate between clinically stable and unstable episodes of asthma. Methods Twenty-three patients with (partly) controlled mild to moderate persistent asthma using ICS were included in this prospective steroid withdrawal study. Exhaled metabolites were measured at baseline, during loss of control and after recovery. Standardized sampling of exhaled air was performed, after which samples were analysed by GC/ MS and eNose. Univariate analysis of covariance ( ANCOVA), followed by multivariate principal component analysis ( PCA) was used to reduce data dimensionality. Next paired t tests were utilized to analyse within-subject breath profile differences at the different time-points. Finally, associations between exhaled metabolites and sputum inflammation markers were examined. Results Breath profiles by eNose showed 95% (21/22) correct classification for baseline vs loss of control and 86% (19/22) for loss of control vs recovery. Breath profiles using GC/ MS showed accuracies of 68% (14/22) and 77% (17/22) for baseline vs loss of control and loss of control vs recovery, respectively. Significant associations between exhaled metabolites captured by GC/ MS and sputum eosinophils were found (Pearson r≥.46, P<.01). Conclusions & Clinical Relevance Loss of asthma control can be discriminated from clinically stable episodes by longitudinal monitoring of exhaled metabolites measured by GC/ MS and particularly eNose. Part of the uncovered biomarkers was associated with sputum eosinophils. These findings provide proof of principle for monitoring and identification of loss of asthma control by breathomics. [ABSTRACT FROM AUTHOR]

Breath analysis via electronic nose is a technique oriented around volatile organic compound (VOC) profiling in exhaled breath for diagnostic and prognostic purposes. This approach, when supported by methodologies for VOC identification, has been often referred to as metabolomics or breathomics. Although breath analysis may have a substantial impact on clinical practice, as it may allow early diagnosis and large-scale screening strategies while being noninvasive and inexpensive, some technical and methodological limitations must be solved, together with crucial interpretative issues. By integrating a review of the currently available literature with more speculative arguments about the potential interpretation and application of VOC analysis, the authors aim to provide an overview of the main relevant aspects of this promising field of research. [ABSTRACT FROM AUTHOR]

[Display omitted] • Commercial resistive gas sensors were applied to detect dengue disease. • We developed a portable set-up to analyse exhaled breath samples. • The data processing applied differential parameters to reduce drifts impact. • The random forest classification algorithm in Orange software was utilized. This paper presents a procedure and a set-up of an electronic nose system analyzing exhaled breath to detect the patients suffering from dengue – a mosquito-borne tropical disease. Low-power resistive gas sensors (MiCS-6814, TGS8100) were used to detect volatile organic compounds (VOCs) in the exhaled breath. The end-tidal phase of patients exhaled breath was collected with a BioVOCTM breath sampler. Two strategies were assessed for breath samples measurement: either direct transfer from the BioVOCTM into the sensors test chamber, or storage in Tenax TA sorbent tubes followed by VOCs release through thermal desorption and then transfer into sensors test chamber. DC sensor resistances were recorded and processed by multivariate classifier algorithms to detect infected patients. The experimental studies were run on a group of 26 individuals (16 dengue diagnosed patients and 10 control volunteers). The detection accuracy of dengue patients was over 90%. [ABSTRACT FROM AUTHOR]

Early diagnosis of lung cancer is important due to high mortality in late stages of the disease. An ideal approach for population screening could be the breath analysis, due to its non-invasiveness, simplicity and cheapness. Using sensitive methods of analysis like gas chromatography/mass spectrometry in exhaled air of cancer patients were discovered some volatile organic compounds - possible candidates for cancer markers. However, these compounds were not specific for cancer cells. At the same time, integrative approaches used to analyze the exhaled breath have demonstrated high sensitivity and specificity of this method for lung cancer diagnosis. Such integrative approaches include detection of smell prints by electronic nose or integrated analysis of wide range of volatile organic compounds detected by gas chromatography/mass spectrometry or related methods. Modern statistical pattern recognition systems like logistic regression analysis, support vector machine or analysis by artificial neuronal network may improve diagnostic accuracy. [ABSTRACT FROM AUTHOR]