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Due to interference by the high moisture content and complicated compositions of human exhaled breath, the trace-level detection of ammonia (NH 3) with desirable selectivity and stability is a large challenge for exhaled breath analysis. Carboxyl-sensitized hydrogels can be activated by moisture to exhibit a significant response and excellent selectivity to NH 3. However, the high activity of carboxyl groups in hydrogels is a double-edged sword, resulting in poor chemical stability during NH 3 detection. Herein, organic acids were embedded into a cross-linked poly(ethylene glycol) diacrylate (PEGDA) hydrogel via thiol−ene photochemistry to form stable hydrogels for NH 3 detection in a humid atmosphere. As a result, under high humidity conditions (80% RH), the optimal sensors exhibited superior selectivity to NH 3 among various interfering gas species, a remarkably high NH 3 response (Z a /Z g =6.20) towards 20 ppm NH 3 , and an extremely low actual detection limit (50 ppb) at room temperature. Moreover, the sensors exhibited excellent chemical stability due to the moderate equilibrium water content of the hydrogel composites and acid dissociation constant of the acid groups. The moisture-activated NH 3 sensing mechanism was thoroughly investigated by complex impedance spectroscopy (CIS), quartz crystal microbalance (QCM) measurements, Fourier transform infrared (FT-IR) spectroscopy and X-ray photoelectron spectroscopy (XPS). To explore the application prospects of cross-linked hydrogel sensors for detecting NH 3 in exhaled breath, a simulated exhaled breath test was also performed. • The strategy for improving the repeatability of hydrogel NH 3 sensors was approached. • The CA/PEGDA sensor exhibited a remarkable NH 3 response (Za/Zg=6.20, 20 ppm) at 80% RH. • The extremely low detection limit (50 ppb) to ammonia at 80% RH was achieved. • The novel sensing mechanism gives a potential way to realize the exhaled breath ammonia detection. [ABSTRACT FROM AUTHOR]

• Air pollution is a significant environmental trigger of asthma. Accurate methods to detect asymptomatic children with asthma are lacking. • This study showed the E-nose had high accuracy to detect air pollution-induced small airway dysfunction among asymptomatic schoolchildren. • Inhalation is a crucial exposure route of air pollutants, but there is still a lack of methods to assess the internal exposure dose of air pollutants. • The breathomics analysis could become a new personal exposure monitoring method to assess the acute respiratory effects of air pollutants. Accurate methods that can be used to detect asymptomatic children with asthma in schools are lacking. Small airway dysfunction is a precursor of asthma. The objective of this study was to use the electronic nose (E-nose) to detect small airway dysfunction in elementary schoolchildren at risk of asthma and evaluate the acute effects of air pollution on respiration. We conducted this study in an elementary school. The study recruited 40 asymptomatic students with a history of asthma and 40 age- and sex-matched children without a history of asthma to take the breath test. We used an E-nose to analyze volatile metabolites and gas chromatography-mass spectrometry to analyze common air pollutants in exhaled breath. After excluding eight subjects in pilot tests, we included 72 subjects in the final analysis of the breath test. The sensitivity of detecting small airway dysfunction with the E-nose was 0.92, the specificity was 0.95, the positive predictive value was 0.79, the negative predictive value was 0.98, the overall accuracy was 0.94, and the leave-one-out cross-validation accuracy was 0.74. The area under the curve was 0.98 (95 % confidence interval: 0.96–1.00). Methyl tert-butyl ether was the only ambient air pollutant that had a significant negative correlation with the maximum mid-expiratory flow (r = -0.33, P < 0.05). The E-nose is highly accurate at detecting small airway dysfunction in children at high risk for asthma. An analysis of exhaled breath can also be used as a personal monitoring method to assess the acute effects of air pollution on respiration. [ABSTRACT FROM AUTHOR]

[Display omitted] • The zine ferrite sensing materials with different Zn/Fe ratios were synthesized by a facile solvothermal route. • Gas sensors based on Zn 0.4 Fe 2.6 O 4 showed the highest response to acetone and great selectivity. • The simulated breath analysis proved that the sensors possessed the advantage and potential in diabetes diagnosis. Seeking for advanced gas sensing materials with high responses to trace acetone gases is of great importance for the diagnosis of diabetes. However, it is still challenging to exhibit a reliable response for gas sensing devices in high humidity environment. Herein, zinc ferrite (Zn x Fe 3-x O 4) sensing materials with different zinc contents of x varying from 0 to 0.6 were synthesized by a facile solvothermal route. The acetone sensing properties of Zn x Fe 3-x O 4 -sensors showed a highly sensitive dependence with Zn/Fe ratio variations. As a result, the Zn 0.4 Fe 2.6 O 4 spheres based sensors displayed the highest response (R a /R g = 157) towards 100 ppm acetone and excellent selectivity compared to the other zinc ferrite samples. The sensor also showed obvious signal to trace acetone in the simulated breath of diabetic patients. Controlling zinc content in ferrites provides an alternative new strategy to realize effective acetone detection and facilitate human exhaled breath analysis. [ABSTRACT FROM AUTHOR]

Volatile organic compounds (VOCs) in exhaled breath have the potential to be used as biomarkers for screening and diagnosis of diseases. Clinical studies are often complicated by both modifiable and non-modifiable factors influencing the composition of VOCs in exhaled breath. Small laboratory animal studies contribute in obtaining fundamental insight in alterations in VOC composition in exhaled breath and thereby facilitate the design and analysis of clinical research. However, long term animal experiments are often limited by invasive breath collection methods and terminal experiments. To overcome this problem, a novel device was developed for non-invasive breath collection in mice using glass nose-only restrainers thereby omitting the need of anesthetics. C57Bl/6 J mice were used to test reproducibility and different air sampling settings for air-flow (ml min -1 ) and time (minutes). Exhaled air was collected on desorption tubes and analysed for VOCs by gas chromatography time-of-flight mass spectrometry (GC-tof-MS). In total 27 compounds were putatively identified and used to assess the variability of the VOC measurements in the breath collections. Best reproducibility is obtained when using an air flow of 185 ml min -1 and a collection time of 20 min. Due to the non-invasive nature of breath collections in murine models, this device has the potential to facilitate VOC research in relation to disturbed metabolism and or disease pathways., (Creative Commons Attribution license.)

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., (Creative Commons Attribution license.)

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]

[Display omitted] • The PAA/GA sensor showed excellent selectivity and high response to NH 3 under 80 % RH at room temperature. • The investigation of PAA/GA sensor reveals a novel humidity-activated NH 3 sensing mechanism. • The humidity-activated sensing mechanism gives a potential way to realize the exhaled breath NH 3 detection. High ammonia (NH 3) concentration in the exhaled breath can be used as a crucial biomarker for end-stage of renal disease. Detecting exhaled ammonia via gas sensors remains a huge challenge, because of the complex components and high humidity environment of exhaled gases. Herein, a creative design for NH 3 sensing is realized using a humidity-activated mechanism at room temperature. The investigation reveals that the carboxylate formation reaction specific recognition of NH 3 can be activated under high humid environment. An environmentally friendly and non-toxic biomass hydrogel NH 3 sensor was developed, with poly- l -aspartic acid (PAA) and l -glutamic acid (GA) as the sensing material. The response to 50 ppm NH 3 can reach 9.2 under 80 % RH at room temperature. The humidity-activated sensing mechanism is proposed as a complex process involving the acid-base adsorption, the formation of ionic conduction and the reaction between carboxylic acid groups and NH 3. It is believed that the PAA/GA sensor presents a new pathway for highly selective detection of NH 3 under high humidity condition, which is a promising method for analyzing the exhaled ammonia. [ABSTRACT FROM AUTHOR]

Acetone is known as the breath biomarker of diabetes. In this paper we have reported on a highly sensitive, stable cobalt chromite (CoCr 2 O 4) thick film-based trace acetone sensor with quick response and recovery times promising for the detection of diabetes from exhaled breath. CoCr 2 O 4 nanoparticles were prepared by a cost-effective and easy sol-gel method. The as prepared nanoparticles were well characterized by sophisticated characterization techniques, such as, XRD, FESEM. TEM, HRTEM, XPS, and BET. Further these nanoparticles were exploited to fabricate a Taguchi type chemoresistive sensor using a customized drop coater. The developed sensor was characterized by I–V measurement. The developed sensor exhibited high p-type response towards 1 ppm of acetone vapor (~3.81 folds), and appreciable resolution between 1 ppm, 2 ppm (response = ~4.82 folds), and 5 ppm (response = ~6.64 folds) acetone vapor at 300 °C. Further, the sensor exhibited fast response and recovery times of ~1.65 s and ~62 s, respectively. Also, the response of the sensor to 1 ppm acetone vapor is appreciably higher than that of 0.2 ppm ethanol, 0.25 ppm ammonia vapor, and saturated moisture. Finally, the sensor is stable for at least 6 months and exhibits repeatable measurements. Image 1 • Facile sol-gel synthesis of cobalt chromite (CoCr 2 O 4) nanoparticles. • High p-type response to 1 ppm acetone vapor (~3.81 folds). • Appreciable resolution between 1 ppm, 2 ppm, and 5 ppm acetone vapor. • Minimal cross-sensitivities to 0.25 ppm ammonia, 0.2 ppm ethanol, and saturated moisture. • Fast response (~1.65s) and recovery (~62s) times and prolonged stability (≥6 months). [ABSTRACT FROM AUTHOR]

Selected-Ion Flow-Tube Mass Spectrometry (SIFT-MS) has been applied in a clinical context as diagnostic tool for breath samples using target biomarkers. Exhaled breath sampling is non-invasive and therefore much more patient friendly compared to bronchoscopy, which is the golden standard for evaluating airway inflammation. In the actual pilot study, 55 exhaled breath samples of children with asthma, cystic-fibrosis and healthy individuals were included. Rather than focusing on the analysis of target biomarkers or on the identification of biomarkers, different data analysis strategies, including a variety of pretreatment, classification and discrimination techniques, are evaluated regarding their capacity to distinguish the three classes based on subtle differences in their full scan SIFT-MS spectra. Proper data-analysis strategies are required because these full scan spectra contain much external, i.e. unwanted, variation. Each SIFT-MS analysis generates three spectra resulting from ion-molecule reactions of analyte molecules with H 3 O+, NO+ and O 2 +. Models were built with Linear Discriminant Analysis, Quadratic Discriminant Analysis, Soft Independent Modelling by Class Analogy, Partial Least Squares - Discriminant Analysis, K-Nearest Neighbours, and Classification and Regression Trees. Perfect models, concerning overall sensitivity and specificity (100% for both) were found using Direct Orthogonal Signal Correction (DOSC) pretreatment. Given the uncertainty related to the classification models associated with DOSC pretreatments (i.e. good classification found also for random classes), other models are built applying other preprocessing approaches. A Partial Least Squares - Discriminant Analysis model with a combined pre-processing method considering single value imputation results in 100% sensitivity and specificity for calibration, but was less good predictive. Pareto scaling prior to Quadratic Discriminant Analysis resulted in 41/55 correctly classified samples for calibration and 34/55 for cross-validation. In future, the uncertainty with DOSC and the applicability of the promising preprocessing methods and models must be further studied applying a larger representative data set with a more extensive number of samples for each class. Nevertheless, this pilot study showed already some potential for the untargeted SIFT-MS application as a rapid pattern-recognition technique, useful in the diagnosis of clinical breath samples. Image 1 • Full scan SIFT-MS used as a non-invasive diagnostic tool for breath-sample analysis. • Spectra submitted to different pretreatment and classification approaches. • Promising PLS-DA models found. • Suitability of DOSC as data pretreatment technique needs further investigation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Sensitive, selective and rapid ammonia-sensing. • Suitable for monitoring end-stage chronic kidney disease. • Low limit of detection (212 ppb) and linear range (5–158 ppm) at 150 °C. • High intra-day repeatability and long-term stability over 56 days, once a week. • All by Au nanoparticle-sensitized V 2 O 5 /CuWO 4 designed for exhaled breath analysis. Marigold flower-like V 2 O 5 /CuWO 4 heterojunctions were synthesized and its volatile organic compound (VOC)-sensing properties were tested and significantly enhanced after sensitizing by Au nanoparticles. Detailed characterizations were carried out by SEM, XRD and XPS to determine the morphology, crystal structure, elemental and chemical composition of the sensing materials, respectively. The fabricated gold-sensitized sensor was found to be rapidly responsive (a few seconds), highly sensitive to ammonia with good selectivity as compared to various types of VOCs. The limit of detection and linear range of sensor at 150 °C were 212 ppb and 5–158 ppm, respectively, which is suitable for detection of exhaled breath ammonia of patients at their last stage of chronic kidney disease. Furthermore, it was found to be of high intra-day repeatability, which is properly explained by discussing the mechanism of NH 3 sensing. Very long-term stability of the sensor was investigated over 56 days, once a week. [ABSTRACT FROM AUTHOR]

In the present study we investigated whether multiple sclerosis (MS) can be detected via exhaled breath analysis using an electronic nose (eNose). The Aeonose TM (an eNose, 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 Aeonose TM can make a distinction between the breath patterns of patients with MS and healthy control subjects. In this mono-center, prospective, non-invasive study, 124 subjects with a confirmed diagnosis of MS and 129 control subjects each breathed into the Aeonose TM for 5 min. 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. 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). The study showed that the Aeonose TM 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 Aeonose TM is able to differentiate new MS patients from subjects who will not get the diagnosis., (© 2021 IOP Publishing Ltd.)

In this study, a secondary electrospray ionization-high resolution mass spectrometer (SESI-HRMS) system was employed to profile the real-time exhaled metabolome of ten subjects who had ingested a peppermint oil capsule. In total, six time points were sampled during the experiment. Using an untargeted way of profiling breath metabolome, 2333 m/z unique metabolite features were determined in positive mode, and 1322 in negative mode. To benchmark the performance of the SESI-HRMS setup, several additional checks were done, including determination of the technical variation, the biological variation of one subject within three days, the variation within a time point, and the variation across all samples, taking all m/z features into account. Reproducibility was good, with the median technical variation being ∼ 18% and the median variation within biological replicates being ∼ 34%. Both variations were lower than the variation across individuals. Washout profiles of compounds from the peppermint oil, including menthone, limonene, pulegone, menthol and menthofuran were determined in all subjects. Metabolites of the peppermint oil were also determined in breath, for example, cis/trans-carveol, perillic acid and menthol glucuronide. Butyric acid was found to be the major metabolite that reduce the uptake rate of limonene. Pathways related to limonene metabolism were examined, and meaningful pathways were identified from breath metabolomics data acquired by SESI using an untargeted analysis., (© 2021 IOP Publishing Ltd.)

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., (© 2020 The Author(s).)

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.

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., Competing Interests: The authors declare no conflict of interest.

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. [ABSTRACT FROM AUTHOR]

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.)

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).

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.

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.

Ammonia (NH 3 ) 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 CeO 2 overlayer by electron-beam evaporation. CuBr sensors coated with a 100 nm-thick CeO 2 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 NH 3 . The sensors thus show promising potential in detecting sub-parts per million-level NH 3 , regardless of humidity fluctuations, which can open up new applications in wearable devices for in situ medical diagnosis and indoor/outdoor environment monitoring.

PtO 2 nanocatalysts-loaded SnO 2 multichannel nanofibers (PtO 2 -SnO 2 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 PtO 2 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), PtO 2 -SnO 2 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.