Exhaled breath analysis in patients with potentially curative lung cancer undergoing surgery: a longitudinal study

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 log2fold change ⩾1 andq-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 andm/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.

Detection of lung cancer and stages via breath analysis using a self-made electronic nose device

BACKGROUND

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.

RESEARCH DESIGN AND METHODS

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.

RESULTS

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%.

CONCLUSIONS

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.

Self-Healing, Laminated, and Low Resistance NH3 Sensor Based on 6,6',6″-(Nitrilotris(benzene-4,1-diyl))tris(5-phenylpyrazine-2,3-dicarbonitrile) Sensing Material Operating at Room Temperature

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 (NH3) 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 NH3 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 NH3 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 NH3 sensing performance under high RH was further investigated by complex impedance plots (CIPs) and density functional theory (DFT), attributing to the enhanced adsorption of NH3. The TPA-3DCNPZ based NH3 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 NH3 sensors.

DiabeticSense: A Non-Invasive, Multi-Sensor, IoT-Based Pre-Diagnostic System for Diabetes Detection Using Breath

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.

Alveolar gradients in breath analysis. A pilot study with comparison of room air and inhaled air by simultaneous measurements using ion mobility spectrometry

Analyzing exhaled breath samples, especially using a highly sensitive method such as MCC/IMS (multi-capillary column/ion mobility spectrometry), may also detect analytes that are derived from exogenous production. In this regard, there is a discussion about the optimal interpretation of exhaled breath, either by considering volatile organic compounds (VOCs) only in exhaled breath or by additionally considering the composition of room air and calculating the alveolar gradients. However, there are no data on whether the composition and concentration of VOCs in room air are identical to those in truly inhaled air directly before analyzing the exhaled breath. The current study aimed to determine whether the VOCs in room air, which are usually used for the calculation of alveolar gradients, are identical to the VOCs in truly inhaled air. For the measurement of inhaled air and room air, two IMS, each coupled with an MCC that provided a pre-separation of the VOCs, were used in parallel. One device was used for sampling room air and the other for sampling inhaled air. Each device was coupled with a newly invented system that cleaned room air and provided a clean carrier gas, whereas formerly synthetic air had to be used as a carrier gas. In this pilot study, a healthy volunteer underwent three subsequent runs of sampling of inhaled air and simultaneous sampling and analysis of room air. Three of the selected 11 peaks (P4-unknown, P5-1-Butanol, and P9-Furan, 2-methyl-) had significantly higher intensities during inspiration than in room air, and four peaks (P1-1-Propanamine, N-(phenylmethylene), P2-2-Nonanone, P3-Benzene, 1,2,4-trimethyl-, and P11-Acetyl valeryl) had higher intensities in room air. Furthermore, four peaks (P6-Benzaldehyde, P7-Pentane, 2-methyl-, P8-Acetone, and P10-2-Propanamine) showed inconsistent differences in peak intensities between inhaled air and room air. To the best of our knowledge, this is the first study to compare simultaneous sampling of room air and inhaled air using MCC/IMS. The simultaneous measurement of inhaled air and room air showed that using room air for the calculation of alveolar gradients in breath analysis resulted in different alveolar gradient values than those obtained by measuring truly inhaled air.

Diagnostic performance of electronic nose technology in chronic lung allograft dysfunction

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.

A review on electronic nose for diagnosis and monitoring treatment response in lung cancer

Lung cancer is one of the common malignancies with high mortality rate and a poor prognosis. Most lung cancer cases are diagnosed at an advanced stage either due to limited resources of infrastructure, trained human resources, or delay in clinical suspicion. Low-dose computed tomography (LDCT) has emerged as a screening tool for early lung cancer detection but may not be a feasible option for most developing countries. Electronic nose (eNOSE) is a unique non-invasive device that has been developed for lung cancer diagnosis and monitoring response by exhaled breath analysis of volatile organic compounds (VOCs). The breath-print have been shown to differ not only among lung cancer and other respiratory diseases, but also between various types of lung cancer. Hence, we postulate that the breath-print analysis by electronic nose could be a potential biomarker for the early detection of lung cancer along with monitoring treatment response in a resource-limited setting. In this review, we have consolidated the current published literature suggesting the use of an electronic nose in the diagnosis and monitoring treatment response of lung cancer.

A Dual Sensing Platform for Human Exhaled Breath Enabled by Fe-MIL-101-NH2 Metal-Organic Frameworks and its Derived Co/Ni/Fe Trimetallic Oxides

Limited by the insufficient active sites and the interference from breath humidity, designing reliable gas sensing materials with high activity and moisture resistance remains a challenge to analyze human exhaled breath for the translational application of medical diagnostics. Herein, the dual sensing and cooperative diagnosis is achieved by utilizing metal-organic frameworks (MOFs) and its derivative. The Fe-MIL-101-NH₂ serves as the quartz crystal microbalance humidity sensing layer, which exhibits high selectivity and rapid response time (16 s/15 s) to water vapor. Then, the Co²⁺ and Ni²⁺ cations are further co-doped into Fe-MIL-101-NH₂ host to obtain the derived Co/Ni/Fe trimetallic  oxides (CoNiFe-MOS-n). The chemiresistive CoNiFe-MOS-n sensor displays the high sensitivity (560) and good selectivity to acetone, together with a lower original resistance compared with Fe₂ O₃ and NiFe₂ O₄ . Moreover, as a proof-of-concept application, synergistic integration of Fe-MIL-101-NH₂ and derived CoNiFe-MOS-n is carried out. The Fe-MIL-101-NH₂ is applied as moisture sorbent materials, which realize a sensitivity compensation of CoNiFe-MOS-n sensors for the detection of acetone (biomarker gas of diabetes). The findings provide an insight for effective utilization of MOFs and the derived materials to achieve a trace gas detection in exhaled breath analysis.

Relationship between lung function and exhaled volatile organic compounds in healthy infants

OBJECTIVE

The aim of this study is to assess, for the first time, the relationship between the volatilome and lung function in healthy infants, which may be of help for the early detection of certain respiratory diseases. Lung function tests are crucial in chronic respiratory diseases diagnosis. Moreover, volatile organic compounds (VOCs) analysis in exhaled breath is a noninvasive technique that enables the monitorization of oxidative stress, typical of some forms of airway inflammation.

METHODS

Lung function was studied in 50 healthy infants of 3-8 months of age and the following parameters were obtained: forced vital capacity (FVC), forced expiratory volume at 0.5 s (FEV_0.5 ), forced expiratory flow at 75% of FVC (FEF₇₅ ), forced expiratory flow at 25%-75% of FVC (FEF_25-75 ), and FEV_0.5 /FVC. Lung function was measured according to the raised volume rapid thoracoabdominal compression technique. In addition, a targeted analysis of six endogenous VOCs (acetone, isoprene, decane, undecane, tetradecane, and pentadecane) in the exhaled breath of the children was carried out by means of thermal desorption coupled gas chromatography-single quadrupole mass spectrometry system.

RESULTS

A negatively significant relationship has been observed between levels of acetone, tetradecane, and pentadecane in exhaled breath and several of the lung function parameters. Levels of acetone (feature m/z = 58) were significantly negatively associated with FVC and FVE_0.5 , levels of tetradecane (feature m/z = 71) with FEV_0.5, and levels of pentadecane (feature m/z = 71) with FEV_0.5 and FEF_25-75 .

CONCLUSION

The findings of this study highlight a significant association between VOCs related to oxidative stress and lung function in healthy infants.

Review of the algorithms used in exhaled breath analysis for the detection of diabetes

Currently, intensive work is underway on the development of truly noninvasive medical diagnostic systems, including respiratory analysers based on the detection of biomarkers of several diseases including diabetes. In terms of diabetes, acetone is considered as a one of the potential biomarker, although is not the single one. Therefore, the selective detection is crucial. Most often, the analysers of exhaled breath are based on the utilization of several commercially available gas sensors or on specially designed and manufactured gas sensors to obtain the highest selectivity and sensitivity to diabetes biomarkers present in the exhaled air. An important part of each system are the algorithms that are trained to detect diabetes based on data obtained from sensor matrices. The prepared review of the literature showed that there are many limitations in the development of the versatile breath analyser, such as high metabolic variability between patients, but the results obtained by researchers using the algorithms described in this paper are very promising and most of them achieve over 90% accuracy in the detection of diabetes in exhaled air. This paper summarizes the results using various measurement systems, feature extraction and feature selection methods as well as algorithms such as Support Vector Machines, k-Nearest Neighbours and various variations of Neural Networks for the detection of diabetes in patient samples and simulated artificial breath samples.

Exhaled Breath Analysis for Diabetes Diagnosis and Monitoring: Relevance, Challenges and Possibilities

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.

Molecular approaches to lung cancer prevention

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.

Detecting Multiple Sclerosis via breath analysis using an eNose, a pilot study

OBJECTIVE

In the present study we investigated whether Multiple Sclerosis (MS) can be detected via exhaled breath analysis using an electronic nose. The AeonoseTM (an electronic nose, The eNose Company, Zutphen, The Netherlands) is a diagnostic test device to detect patterns of volatile organic compounds in exhaled breath. We evaluated whether the AeonoseTM can make a distinction between the breath patterns of patients with MS and healthy control subjects.

METHODS

In this mono-center, prospective, non-invasive study, 124 subjects with a confirmed diagnosis of MS and 129 control subjects each breathed into the AeonoseTM for 5 minutes. Exhaled breath data was used to train an artificial neural network (ANN) predictive model. To investigate the influence of medication intake we created a second predictive model with a subgroup of MS patients without medication prescribed for MS.

RESULTS

The ANN model based on the entire dataset was able to distinguish MS patients from healthy controls with a sensitivity of 0.75 [95% CI: 0.66-0.82] and specificity of 0.60 [0.51-0.69]. The model created with the subgroup of MS patients not using medication and the healthy control subjects had a sensitivity of 0.93 [0.82-0.98] and a specificity of 0.74 [0.65-0.81].

CONCLUSION

The study showed that the AeonoseTM is able to make a distinction between MS patients and healthy control subjects, and could potentially provide a quick screening test to assist in diagnosing MS. Further research is needed to determine whether the AeonoseTM is able to differentiate new MS patients from subjects who will not get the diagnosis.

Advances in Mid-Infrared Spectroscopy-Based Sensing Techniques for Exhaled Breath Diagnostics

Human exhaled breath consists of more than 3000 volatile organic compounds, many of which are relevant biomarkers for various diseases. Although gas chromatography has been the gold standard for volatile organic compound (VOC) detection in exhaled breath, recent developments in mid-infrared (MIR) laser spectroscopy have led to the promise of compact point-of-care (POC) optical instruments enabling even single breath diagnostics. In this review, we discuss the evolution of MIR sensing technologies with a special focus on photoacoustic spectroscopy, and its application in exhaled breath biomarker detection. While mid-infrared point-of-care instrumentation promises high sensitivity and inherent molecular selectivity, the lack of standardization of the various techniques has to be overcome for translating these techniques into more widespread real-time clinical use.

Proton-Conductive Gas Sensor: a New Way to Realize Highly Selective Ammonia Detection for Analysis of Exhaled Human Breath

The analysis of exhaled human breath has great significance for early noninvasive diagnosis. Poor selectivity and strong humidity are two bottlenecks for the application of gas sensors to exhaled breath analysis. In this work, we utilized the adsorption, dissolution, ionization, and migration processes of ammonia in wet nonconjugated hydrophilic polymers to realize effective ammonia detection. The indispensable high-humidity atmosphere of exhaled breath was turned into a favorable condition for ammonia sensing. Nonconjugated polymer sensors can distinguish ammonia from most other gases because of its extremely high solubility and good ionization ability. A sensor based on poly(vinyl pyrrolidone) (PVP) could detect 0.5 ppm ammonia with an extremely high selectivity. The ammonia-sensing mechanism was thoroughly investigated by complex impedance plots (CIPs) and a quartz crystal microbalance (QCM) measurement. Finally, the potential of the PVP sensor for ammonia detection in exhaled breath was evaluated in simulated environments.

Exhaled Breath Analysis in Diagnosis of Malignant Pleural Mesothelioma: Systematic Review

Malignant pleural mesothelioma (MPM) is mainly related to previous asbestos exposure. There is still dearth of information on non-invasive biomarkers to detect MPM at early stages. Human studies on exhaled breath biomarkers of cancer and asbestos-related diseases show encouraging results. The aim of this systematic review was to provide an overview on the current knowledge about exhaled breath analysis in MPM diagnosis. A systematic review was conducted on MEDLINE (PubMed), EMBASE and Web of Science databases to identify relevant studies. Quality assessment was done by the Newcastle-Ottawa Scale. Six studies were identified, all of which showed fair quality and explored volatile organic compounds (VOC) based breath profile using Gas Chromatography Coupled to Mass Spectrometry (GC-MS), Ion Mobility Spectrometry Coupled to Multi-capillary Columns (IMS-MCC) or pattern-recognition technologies. Sample sizes varied between 39 and 330. Some compounds (i.e, cyclohexane, P3, P5, P50, P71, diethyl ether, limonene, nonanal, VOC IK 1287) that can be indicative of MPM development in asbestos exposed population were identified with high diagnostic accuracy rates. E-nose studies reported breathprints being able to distinguish MPM from asbestos exposed individuals with high sensitivity and a negative predictive value. Small sample sizes and methodological diversities among studies limit the translation of results into clinical practice. More prospective studies with standardized methodologies should be conducted on larger populations.

Exhaled breath analysis in the diagnosis of head and neck cancer

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.

Prediction of response to anti-PD-1 therapy in patients with non-small-cell lung cancer by electronic nose analysis of exhaled breath

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.

Hybrid Analytical Platform Based on Field-Asymmetric Ion Mobility Spectrometry, Infrared Sensing, and Luminescence-Based Oxygen Sensing for Exhaled Breath Analysis

The reliable online analysis of volatile compounds in exhaled breath remains a challenge, as a plethora of molecules occur in different concentration ranges (i.e., ppt to %) and need to be detected against an extremely complex background matrix. Although this complexity is commonly addressed by hyphenating a specific analytical technique with appropriate preconcentration and/or preseparation strategies prior to detection, we herein propose the combination of three different detector types based on truly orthogonal measurement principles as an alternative solution: Field-asymmetric ion mobility spectrometry (FAIMS), Fourier-transform infrared (FTIR) spectroscopy-based sensors utilizing substrate-integrated hollow waveguides (iHWG), and luminescence sensing (LS). By carefully aligning the experimental needs and measurement protocols of all three methods, they were successfully integrated into a single compact analytical platform suitable for online measurements. The analytical performance of this prototype system was tested via artificial breath samples containing nitrogen (N₂), oxygen (O₂), carbon dioxide (CO₂), and acetone as a model volatile organic compound (VOC) commonly present in breath. All three target analytes could be detected within their respectively breath-relevant concentration range, i.e., CO₂ and O₂ at 3-5 % and at ~19.6 %, respectively, while acetone could be detected with LOQs as low as 165-405 ppt. Orthogonality of the three methods operating in concert was clearly proven, which is essential to cover a possibly wide range of detectable analytes. Finally, the remaining challenges toward the implementation of the developed hybrid FAIMS-FTIR-LS system for exhaled breath analysis for metabolic studies in small animal intensive care units are discussed.

Markers of Pulmonary Oxygen Toxicity in Hyperbaric Oxygen Therapy Using Exhaled Breath Analysis

Introduction

Although 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.

Methods

Ten 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.

Results

After 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.

Discussion

Although 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).

Pulmonary Oxygen Toxicity in Navy Divers: A Crossover Study Using Exhaled Breath Analysis After a One-Hour Air or Oxygen Dive at Nine Meters of Sea Water

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.

Ligand-Capped Ultrapure Metal Nanoparticle Sensors for the Detection of Cutaneous Leishmaniasis Disease in Exhaled Breath

Human cutaneous leishmaniasis, although designated as one of the most neglected tropical diseases, remains underestimated due to its misdiagnosis. The diagnosis is mainly based on the microscopic detection of amastigote forms, isolation of the parasite, or the detection of Leishmania DNA, in addition to its differential clinical characterization; these tools are not always available in routine daily practice, and they are expensive and time-consuming. Here, we present a simple-to-use, noninvasive approach for human cutaneous leishmaniasis diagnosis, which is based on the analysis of volatile organic compounds in exhaled breath with an array of specifically designed chemical gas sensors. The study was realized on a group of n = 28 volunteers diagnosed with human cutaneous leishmaniasis and a group of n = 32 healthy controls, recruited in various sites from Tunisia, an endemic country of the disease. The classification success rate of human cutaneous leishmaniasis patients achieved by our sensors test was 98.2% accuracy, 96.4% sensitivity, and 100% specificity. Remarkably, one of the sensors, based on CuNPs functionalized with 2-mercaptobenzoxazole, yielded 100% accuracy, 100% sensitivity, and 100% specificity for human cutaneous leishmaniasis discrimination. While AuNPs have been the most extensively used in metal nanoparticle-ligand sensing films for breath sensing, our results demonstrate that chemical sensors based on ligand-capped CuNPs also hold great potential for breath volatile organic compounds detection. Additionally, the chemical analysis of the breath samples with gas chromatography coupled to mass spectrometry identified nine putative breath biomarkers for human cutaneous leishmaniasis.

Flexible Room-Temperature NH3 Sensor for Ultrasensitive, Selective, and Humidity-Independent Gas Detection

Ammonia (NH₃) is an irritant gas with a unique pungent odor; sub-parts per million-level breath ammonia is a medical biomarker for kidney disorders and Helicobacter pylori bacteria-induced stomach infections. The humidity varies in both ambient environment and exhaled breath, and thus humidity dependence of gas-sensing characteristics is a great obstacle for real-time applications. Herein, flexible, humidity-independent, and room-temperature ammonia sensors are fabricated by the thermal evaporation of CuBr on a polyimide substrate and subsequent coating of a nanoscale moisture-blocking CeO₂ overlayer by electron-beam evaporation. CuBr sensors coated with a 100 nm-thick CeO₂ overlayer exhibits an ultrahigh response (resistance ratio) of 68 toward 5 ppm ammonia with excellent gas selectivity, rapid response, reversibility, and humidity-independent sensing characteristics at room temperature. In addition, the sensing performance remains stable after repetitive bending and long-term operation. Moreover, the sensors exhibit significant response to the simulated exhaled breath of patients with H. pylori infection; the simulated breath contains 50 ppb NH₃. The sensors thus show promising potential in detecting sub-parts per million-level NH₃, regardless of humidity fluctuations, which can open up new applications in wearable devices for in situ medical diagnosis and indoor/outdoor environment monitoring.

Nanoscale PtO2 Catalysts-Loaded SnO2 Multichannel Nanofibers toward Highly Sensitive Acetone Sensor

PtO₂ nanocatalysts-loaded SnO₂ multichannel nanofibers (PtO₂-SnO₂ MCNFs) were synthesized by single-spinneret electrospinning combined with apoferritin and two immiscible polymers, i.e., poly(vinylpyrrolidone) and polyacrylonitrile. The apoferritin, which can encapsulate nanoparticles within a small inner cavity (8 nm), was used as a catalyst loading template for an effective functionalization of the PtO₂ catalysts. Taking advantage of the multichannel structure with a high porosity, effective activation of catalysts on both interior and exterior site of MCNFs was realized. As a result, under high humidity condition (95% RH), PtO₂-SnO₂ MCNFs exhibited a remarkably high acetone response (R_air/R_gas = 194.15) toward 5 ppm acetone gases, superior selectivity to acetone molecules among various interfering gas species, and excellent stability during 30 cycles of response and recovery toward 1 ppm acetone gases. In this work, we first demonstrate the high suitability of multichannel semiconducting metal oxides structure functionalized by apoferritin-encapsulated catalytic nanoparticles as highly sensitive and selective gas-sensing layer.

Exceptional High-Performance of Pt-Based Bimetallic Catalysts for Exclusive Detection of Exhaled Biomarkers

Achieving an improved understanding of catalyst properties, with ability to predict new catalytic materials, is key to overcoming the inherent limitations of metal oxide based gas sensors associated with rather low sensitivity and selectivity, particularly under highly humid conditions. This study introduces newly designed bimetallic nanoparticles (NPs) employing bimetallic Pt-based NPs (PtM, where M = Pd, Rh, and Ni) via a protein encapsulating route supported on mesoporous WO₃ nanofibers. These structures demonstrate unprecedented sensing performance for detecting target biomarkers (even at p.p.b. levels) in highly humid exhaled breath. Sensor arrays are further employed to enable pattern recognition capable of discriminating between simulated biomarkers and controlled breath. The results provide a new class of multicomponent catalytic materials, demonstrating potential for achieving reliable breath analysis sensing.

Exhaled breath profiles in the monitoring of loss of control and clinical recovery in asthma

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.

Breath-print analysis by e-nose may refine risk stratification for adverse outcomes in cirrhotic patients

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.

Development of an Exhaled Breath Monitoring System with Semiconductive Gas Sensors, a Gas Condenser Unit, and Gas Chromatograph Columns

Various volatile organic compounds (VOCs) in breath exhaled by patients with lung cancer, healthy controls, and patients with lung cancer who underwent surgery for resection of cancer were analyzed by gas condenser-equipped gas chromatography-mass spectrometry (GC/MS) for development of an exhaled breath monitoring prototype system involving metal oxide gas sensors, a gas condenser, and gas chromatography columns. The gas condenser-GC/MS analysis identified concentrations of 56 VOCs in the breath exhaled by the test population of 136 volunteers (107 patients with lung cancer and 29 controls), and selected four target VOCs, nonanal, acetoin, acetic acid, and propanoic acid, for use with the condenser, GC, and sensor-type prototype system. The prototype system analyzed exhaled breath samples from 101 volunteers (74 patients with lung cancer and 27 controls). The prototype system exhibited a level of performance similar to that of the gas condenser-GC/MS system for breath analysis.

Exhaled breath analysis by electronic nose in respiratory diseases

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.

Predicting co-morbidities in chemically sensitive individuals from exhaled breath analysis

The exhaled breath of more than four hundred patients who presented at the Environmental Health Center – Dallas with chemical sensitivity conditions were analyzed for the relative abundance of their breath chemical composition by gas chromatography and mass spectrometry for volatile and semi-volatile organic compounds. All presenting patients had no fewer than four and as many as eight co-morbid conditions. Surprisingly, almost all the exhaled breath analyses showed the presence of a preponderance of lipophilic aliphatic and aromatic hydrocarbons. The hydrophilic compounds present were almost entirely of natural origin, i.e. expected metabolites of foods. The lipophile, primarily C3 to C16 hydrocarbons and believed to have come from inhalation of polluted air, were, in all cases, present at concentrations far below those known to be toxic to humans, but caused sensitivity and signs of chemical overload. The co-morbid health effects observed are believed to be caused by the sequential absorption of lipophilic and hydrophilic chemicals; an initial absorption and retention of lipophile followed by a subsequent absorption of hydrophilic species facilitated by the retained lipophile to produce chemical mixtures that are toxic at very low levels. It is hypothesized that co-morbid conditions in chemically sensitive individuals can be predicted from analysis of their exhaled breath.