Progress and challenges of developing volatile metabolites from exhaled breath as a biomarker platform.

BACKGROUND: The multitude of metabolites generated by physiological processes in the body can serve as valuable biomarkers for many clinical purposes. They can provide a window into relevant metabolic pathways for health and disease, as well as be candidate therapeutic targets. A subset of these metabolites generated in the human body are volatile, known as volatile organic compounds (VOCs), which can be detected in exhaled breath. These can diffuse from their point of origin throughout the body into the bloodstream and exchange into the air in the lungs. For this reason, breath VOC analysis has become a focus of biomedical research hoping to translate new useful biomarkers by taking advantage of the non-invasive nature of breath sampling, as well as the rapid rate of collection over short periods of time that can occur. Despite the promise of breath analysis as an additional platform for metabolomic analysis, no VOC breath biomarkers have successfully been implemented into a clinical setting as of the time of this review. AIM OF REVIEW: This review aims to summarize the progress made to address the major methodological challenges, including standardization, that have historically limited the translation of breath VOC biomarkers into the clinic. We highlight what steps can be taken to improve these issues within new and ongoing breath research to promote the successful development of the VOCs in breath as a robust source of candidate biomarkers. We also highlight key recent papers across select fields, critically reviewing the progress made in the past few years to advance breath research. KEY SCIENTIFIC CONCEPTS OF REVIEW: VOCs are a set of metabolites that can be sampled in exhaled breath to act as advantageous biomarkers in a variety of clinical contexts.

A study of 9 common breath VOCs in 504 healthy subjects using PTR-TOF-MS.

INTRODUCTION: This study employs Proton-Transfer-Reaction Mass Spectrometry (PTR-MS) to analyze exhaled breath profiles of 504 healthy adults, focusing on nine common volatile organic compounds (VOCs): acetone, acetaldehyde, acetonitrile, ethanol, isoprene, methanol, propanol, phenol, and toluene. PTR-MS offers real-time VOC measurement, crucial for understanding breath biomarkers and their applications in health assessment. OBJECTIVES: The study aims to investigate how demographic factors-gender, age, and smoking history-affect VOC concentrations in exhaled breath. The objective is to enhance our understanding of breath biomarkers and their potential for health monitoring and clinical diagnosis. METHODS: Exhaled breath samples were collected using PTR-MS, measuring concentrations of nine VOCs. The data were analyzed to discern distribution patterns across demographic groups. RESULTS: Males showed higher average VOC levels for certain compounds. Propanol and methanol concentrations significantly increased with age. Smoking history influenced VOC levels, with differences among non-smokers, current smokers, and ex-smokers. CONCLUSION: This research provides valuable insights into demographic influences on exhaled VOC profiles, emphasizing the potential of breath analysis for health assessment. PTR-MS's real-time measurement capabilities are crucial for capturing dynamic VOC changes, offering advantages over conventional methods. These findings lay a foundation for advancements in non-invasive disease detection, highlighting the importance of considering demographics in breath biomarker research.

Identifying potential breath biomarkers for early diagnosis of papillary thyroid cancer based on solid-phase microextraction gas chromatography-high resolution mass spectrometry with metabolomics.

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

Combination of real-time and hyphenated mass spectrometry for improved characterisation of exhaled breath biomarkers in clinical research.

Exhaled breath volatilomics is a powerful non-invasive tool for biomarker discovery in medical applications, but compound annotation is essential for pathophysiological insights and technology transfer. This study was aimed at investigating the interest of a hybrid approach combining real-time proton transfer reaction-time-of-flight mass spectrometry (PTR-TOF-MS) with comprehensive thermal desorption-two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (TD-GCxGC-TOF-MS) to enhance the analysis and characterization of VOCs in clinical research, using COVID-19 as a use case. VOC biomarker candidates were selected from clinical research using PTR-TOF-MS fingerprinting in patients with COVID-19 and matched to the Human Breathomic Database. Corresponding analytical standards were analysed using both a liquid calibration unit coupled to PTR-TOF-MS and TD-GCxGC-TOF-MS, together with confirmation on new clinical samples with TD-GCxGC-TOF-MS. From 26 potential VOC biomarkers, 23 were successfully detected with PTR-TOF-MS. All VOCs were successfully detected using TD-GCxGC-TOF-MS, providing effective separation of highly chemically related compounds, including isomers, and enabling high-confidence annotation based on two-dimensional chromatographic separation and mass spectra. Four VOCs were identified with a level 1 annotation in the clinical samples. For future applications, the combination of real-time PTR-TOF-MS and comprehensive TD-GCxGC-TOF-MS, at least on a subset of samples from a whole study, would enhance the performance of VOC annotation, offering potential advancements in biomarker discovery for clinical research.

Breath metabolomics for diagnosis of acute respiratory distress syndrome.

BACKGROUND: Acute respiratory distress syndrome (ARDS) poses challenges in early identification. Exhaled breath contains metabolites reflective of pulmonary inflammation. AIM: To evaluate the diagnostic accuracy of breath metabolites for ARDS in invasively ventilated intensive care unit (ICU) patients. METHODS: This two-center observational study included critically ill patients receiving invasive ventilation. Gas chromatography and mass spectrometry (GC-MS) was used to quantify the exhaled metabolites. The Berlin definition of ARDS was assessed by three experts to categorize all patients into "certain ARDS", "certain no ARDS" and "uncertain ARDS" groups. The patients with "certain" labels from one hospital formed the derivation cohort used to train a classifier built based on the five most significant breath metabolites. The diagnostic accuracy of the classifier was assessed in all patients from the second hospital and combined with the lung injury prediction score (LIPS). RESULTS: A total of 499 patients were included in this study. Three hundred fifty-seven patients were included in the derivation cohort (60 with certain ARDS; 17%), and 142 patients in the validation cohort (47 with certain ARDS; 33%). The metabolites 1-methylpyrrole, 1,3,5-trifluorobenzene, methoxyacetic acid, 2-methylfuran and 2-methyl-1-propanol were included in the classifier. The classifier had an area under the receiver operating characteristics curve (AUROCC) of 0.71 (CI 0.63-0.78) in the derivation cohort and 0.63 (CI 0.52-0.74) in the validation cohort. Combining the breath test with the LIPS does not significantly enhance the diagnostic performance. CONCLUSION: An exhaled breath metabolomics-based classifier has moderate diagnostic accuracy for ARDS but was not sufficiently accurate for clinical use, even after combination with a clinical prediction score.

Ultrasensitive multispecies spectroscopic breath analysis for real-time health monitoring and diagnostics.

Breath analysis enables rapid, noninvasive diagnostics, as well as long-term monitoring of human health, through the identification and quantification of exhaled biomarkers. Here, we demonstrate the remarkable capabilities of mid-infrared (mid-IR) cavity-enhanced direct-frequency comb spectroscopy (CE-DFCS) applied to breath analysis. We simultaneously detect and monitor as a function of time four breath biomarkers-[Formula: see text]OH, [Formula: see text], [Formula: see text]O, and HDO-as well as illustrate the feasibility of detecting at least six more ([Formula: see text]CO, [Formula: see text], OCS, [Formula: see text], [Formula: see text], and [Formula: see text]) without modifications to the experimental apparatus. We achieve ultrahigh detection sensitivity at the parts-per-trillion level. This is made possible by the combination of the broadband spectral coverage of a frequency comb, the high spectral resolution afforded by the individual comb teeth, and the sensitivity enhancement resulting from a high-finesse cavity. Exploiting recent advances in frequency comb, optical coating, and photodetector technologies, we can access a large variety of biomarkers with strong carbon-hydrogen-bond spectral signatures in the mid-IR.

Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose.

Volatile organic compounds (VOCs) in exhaled human breath serve as pivotal biomarkers for disease identification and medical diagnostics. In the context of diabetes mellitus, the noninvasive detection of acetone, a primary biomarker using electronic noses (e-noses), has gained significant attention. However, employing e-noses requires pre-trained algorithms for precise diabetes detection, often requiring a computer with a programming environment to classify newly acquired data. This study focuses on the development of an embedded system integrating Tiny Machine Learning (TinyML) and an e-nose equipped with Metal Oxide Semiconductor (MOS) sensors for real-time diabetes detection. The study encompassed 44 individuals, comprising 22 healthy individuals and 22 diagnosed with various types of diabetes mellitus. Test results highlight the XGBoost Machine Learning algorithm's achievement of 95% detection accuracy. Additionally, the integration of deep learning algorithms, particularly deep neural networks (DNNs) and one-dimensional convolutional neural network (1D-CNN), yielded a detection efficacy of 94.44%. These outcomes underscore the potency of combining e-noses with TinyML in embedded systems, offering a noninvasive approach for diabetes mellitus detection.

An automated respiratory data pipeline for waveform characteristic analysis.

Comprehensive and accurate analysis of respiratory and metabolic data is crucial to modelling congenital, pathogenic and degenerative diseases converging on autonomic control failure. A lack of tools for high-throughput analysis of respiratory datasets remains a major challenge. We present Breathe Easy, a novel open-source pipeline for processing raw recordings and associated metadata into operative outcomes, publication-worthy graphs and robust statistical analyses including QQ and residual plots for assumption queries and data transformations. This pipeline uses a facile graphical user interface for uploading data files, setting waveform feature thresholds and defining experimental variables. Breathe Easy was validated against manual selection by experts, which represents the current standard in the field. We demonstrate Breathe Easy's utility by examining a 2-year longitudinal study of an Alzheimer's disease mouse model to assess contributions of forebrain pathology in disordered breathing. Whole body plethysmography has become an important experimental outcome measure for a variety of diseases with primary and secondary respiratory indications. Respiratory dysfunction, while not an initial symptom in many of these disorders, often drives disability or death in patient outcomes. Breathe Easy provides an open-source respiratory analysis tool for all respiratory datasets and represents a necessary improvement upon current analytical methods in the field. KEY POINTS: Respiratory dysfunction is a common endpoint for disability and mortality in many disorders throughout life. Whole body plethysmography in rodents represents a high face-value method for measuring respiratory outcomes in rodent models of these diseases and disorders. Analysis of key respiratory variables remains hindered by manual annotation and analysis that leads to low throughput results that often exclude a majority of the recorded data. Here we present a software suite, Breathe Easy, that automates the process of data selection from raw recordings derived from plethysmography experiments and the analysis of these data into operative outcomes and publication-worthy graphs with statistics. We validate Breathe Easy with a terabyte-scale Alzheimer's dataset that examines the effects of forebrain pathology on respiratory function over 2 years of degeneration.

Proteomic characteristics and diagnostic potential of exhaled breath particles in patients with COVID-19.

BACKGROUND: SARS-CoV-2 has been shown to predominantly infect the airways and the respiratory tract and too often have an unpredictable and different pathologic pattern compared to other respiratory diseases. Current clinical diagnostical tools in pulmonary medicine expose patients to harmful radiation, are too unspecific or even invasive. Proteomic analysis of exhaled breath particles (EBPs) in contrast, are non-invasive, sample directly from the pathological source and presents as a novel explorative and diagnostical tool. METHODS: Patients with PCR-verified COVID-19 infection (COV-POS, n = 20), and patients with respiratory symptoms but with > 2 negative polymerase chain reaction (PCR) tests (COV-NEG, n = 16) and healthy controls (HCO, n = 12) were prospectively recruited. EBPs were collected using a "particles in exhaled air" (PExA 2.0) device. Particle per exhaled volume (PEV) and size distribution profiles were compared. Proteins were analyzed using liquid chromatography-mass spectrometry. A random forest machine learning classification model was then trained and validated on EBP data achieving an accuracy of 0.92. RESULTS: Significant increases in PEV and changes in size distribution profiles of EBPs was seen in COV-POS and COV-NEG compared to healthy controls. We achieved a deep proteome profiling of EBP across the three groups with proteins involved in immune activation, acute phase response, cell adhesion, blood coagulation, and known components of the respiratory tract lining fluid, among others. We demonstrated promising results for the use of an integrated EBP biomarker panel together with particle concentration for diagnosis of COVID-19 as well as a robust method for protein identification in EBPs. CONCLUSION: Our results demonstrate the promising potential for the use of EBP fingerprints in biomarker discovery and for diagnosing pulmonary diseases, rapidly and non-invasively with minimal patient discomfort.

Mask device as a new wearable sampler for breath analysis: what can we expect in the future?

Human exhaled breath is becoming an attractive clinical source as it is foreseen to enable noninvasive diagnosis of many diseases. Because mask devices can be used for efficiently filtering exhaled substances, mask-wearing has been required in the past few years in daily life since the unprecedented COVID-19 pandemic. In recent years, there is a new development of mask devices as new wearable breath samplers for collecting exhaled substances for disease diagnosis and biomarker discovery. This paper attempts to identify new trends in mask samplers for breath analysis. The couplings of mask samplers with different (bio)analytical approaches, including mass spectrometry (MS), polymerase chain reaction (PCR), sensor, and others for breath analysis, are summarized. The developments and applications of mask samplers in disease diagnosis and human health are reviewed. The limitations and future trends of mask samplers are also discussed.

Noninvasive detection of soft tissue sarcoma using volatile organic compounds in exhaled breath: a pilot study.

Aim: The aim of this pilot study was to assess whether an electronic nose can detect patients with soft tissue sarcoma (STS) based on volatile organic compound profiles in exhaled breath. Patients & methods: In this cross-sectional pilot study, patients with primary STS and healthy controls, matched on sex and age, were included for breath analysis. Machine learning techniques were used to develop the best-fitting model. Results: Fifty-nine breath samples were collected (29 STS and 30 control) from March 2018 to March 2022. The final model yielded a c-statistic of 0.85 with a sensitivity of 83% and specificity of 60%. Conclusion: This study suggests that exhaled volatile organic compound analysis could serve as a noninvasive diagnostic biomarker for the detection of STS with a good performance.

Diagnosing soft tissue sarcoma (STS) among the large number of benign soft tissue tumors is challenging. There is a serious need for a novel and easy tool that could accurately detect patients with STS. This study aimed to assess how well an easy-to-use electronic nose could differentiate between patients with STS and those without STS based on their exhaled breath. This is the first pilot study to reveal that an electronic nose could serve as a diagnostic tool for the detection of STS with a good performance. Future studies are needed to validate the findings in larger cohorts.

Very Rapid Improvement in Extended Nitric Oxide Parameters Is Associated With Clinical and Functional Improvement in Patients With Chronic Rhinosinusitis With Nasal Polyps Treated With Dupilumab.

BACKGROUND AND OBJECTIVE: Dupilumab, an anti-IL-4 receptor a monoclonal antibody, was recently approved for the treatment of chronic rhinosinusitis with nasal polyps (CRSwNP) and moderate-to-severe asthma. Onset of its clinical effects is rapid. CRSwNP is characterized by extended type 2 inflammatory involvement that can be assessed using extended nitric oxide analysis. We investigated whether dupilumab was associated with a rapid improvement in extended nitric oxide parameters, lung function, and clinical outcomes in patients with CRSwNP. METHODS: Consecutive patients with CRSwNP and an indication for dupilumab were evaluated for extended nitric oxide analysis (exhaled, FeNO; bronchial, JawNO; alveolar, CalvNO; nasal, nNO) and lung function 15 and 30 days after initiation of treatment and for clinical outcomes (nasal polyps score [NPS], quality of life questionnaires, visual analog scale [VAS] for the main symptoms, and the Asthma Control Test [ACT]) 30 days after initiation of treatment. RESULTS: We enrolled 33 patients. All extended nitric oxide and lung function parameters improved significantly after 15 days of treatment, remaining stable at 30 days. Scores on the NPS, VAS for the main RSwNP symptoms, quality of life questionnaires, and the ACT improved significantly 30 days after initiation of treatment. CONCLUSION: Dupilumab is associated with very rapid improvement in type 2 inflammation in all airway areas. This is associated with improved lung function and clinical parameters in patients with CRSwNP.

Diagnostic performance of eNose technology in COVID-19 patients after hospitalization.

BACKGROUND: Volatile organic compounds (VOCs) produced by human cells reflect metabolic and pathophysiological processes which can be detected with the use of electronic nose (eNose) technology. Analysis of exhaled breath may potentially play an important role in diagnosing COVID-19 and stratification of patients based on pulmonary function or chest CT. METHODS: Breath profiles of COVID-19 patients were collected with an eNose device (SpiroNose) 3 months after discharge from the Leiden University Medical Centre and matched with breath profiles from healthy individuals for analysis. Principal component analysis was performed with leave-one-out cross validation and visualised with receiver operating characteristics. COVID-19 patients were stratified in subgroups with a normal pulmonary diffusion capacity versus patients with an impaired pulmonary diffusion capacity (DLCOc < 80% of predicted) and in subgroups with a normal chest CT versus patients with COVID-19 related chest CT abnormalities. RESULTS: The breath profiles of 135 COVID-19 patients were analysed and matched with 174 healthy controls. The SpiroNose differentiated between COVID-19 after hospitalization and healthy controls with an AUC of 0.893 (95-CI, 0.851-0.934). There was no difference in VOCs patterns in subgroups of COVID-19 patients based on diffusion capacity or chest CT. CONCLUSIONS: COVID-19 patients have a breath profile distinguishable from healthy individuals shortly after hospitalization which can be detected using eNose technology. This may suggest ongoing inflammation or a common repair mechanism. The eNose could not differentiate between subgroups of COVID-19 patients based on pulmonary diffusion capacity or chest CT.

Dynamic Stability of Volatile Organic Compounds in Respiratory Air in Schizophrenic Patients and Its Potential Predicting Efficacy of TAAR Agonists.

OBJECTIVES: Volatile organic compounds (VOCs) in the breathing air were found to be altered in schizophrenia patients compared to healthy participants. The aim of this study was to confirm these findings and to examine for the first time whether these VOCs are stable or change in concentration during the early treatment course. Moreover, it was investigated whether there is a correlation of the VOCs with the existing psychopathology of schizophrenia patients, i.e., whether the concentration of masses detected in the breath gas changes when the psychopathology of the participants changes. METHODS: The breath of a total of 22 patients with schizophrenia disorder was examined regarding the concentration of VOCs using proton transfer reaction mass spectrometry. The measurements were carried out at baseline and after two weeks at three different time points, the first time immediately after waking up in the morning, after 30 min, and then after 60 min. Furthermore, 22 healthy participants were investigated once as a control group. RESULTS: Using bootstrap mixed model analyses, significant concentration differences were found between schizophrenia patients and healthy control participants (m/z 19, 33, 42, 59, 60, 69, 74, 89, and 93). Moreover, concentration differences were detected between the sexes for masses m/z 42, 45, 57, 69, and 91. Mass m/z 67 and 95 showed significant temporal changes with decreasing concentration during awakening. Significant temporal change over two weeks of treatment could not be detected for any mass. Masses m/z 61, 71, 73, and 79 showed a significant relationship to the respective olanzapine equivalents. The length of hospital stay showed no significant relationship to the masses studied. CONCLUSION: Breath gas analysis is an easy-to-use method to detect differences in VOCs in the breath of schizophrenia patients with high temporal stability. m/z 60 corresponding to trimethylamine might be of potential interest because of its natural affinity to TAAR receptors, currently a novel therapeutic target under investigation. Overall, breath signatures seemed to stable over time in patients with schizophrenia. In the future, the development of a biomarker could potentially have an impact on the early detection of the disease, treatment, and, thus, patient outcome.

Effect of Food Intake on Exhaled Volatile Organic Compounds Profile Analyzed by an Electronic Nose.

Exhaled breath analysis using an e-nose is a groundbreaking tool for exhaled volatile organic compound (VOC) analysis, which has already shown its applicability in several respiratory and systemic diseases. It is still unclear whether food intake can be considered a confounder when analyzing the VOC-profile. We aimed to assess whether an e-nose can discriminate exhaled breath before and after predefined food intake at different time periods. We enrolled 28 healthy non-smoking adults and collected their exhaled breath as follows: (a) before food intake, (b) within 5 min after food consumption, (c) within 1 h after eating, and (d) within 2 h after eating. Exhaled breath was collected by a formerly validated method and analyzed by an e-nose (Cyranose 320). By principal component analysis, significant variations in the exhaled VOC-profile were shown for principal component 1 (capturing 63.4% of total variance) when comparing baseline vs. 5 min and vs. 1 h after food intake (both p < 0.05). No significance was shown in the comparison between baseline and 2 h after food intake. Therefore, the exhaled VOC-profile seems to be influenced by very recent food intake. Interestingly, two hours might be sufficient to avoid food induced alterations of exhaled VOC-spectrum when sampling for research protocols.

Volatile Markers for Cancer in Exhaled Breath-Could They Be the Signature of the Gut Microbiota?

It has been shown that the gut microbiota plays a central role in human health and disease. A wide range of volatile metabolites present in exhaled breath have been linked with gut microbiota and proposed as a non-invasive marker for monitoring pathological conditions. The aim of this study was to examine the possible correlation between volatile organic compounds (VOCs) in exhaled breath and the fecal microbiome by multivariate statistical analysis in gastric cancer patients (n = 16) and healthy controls (n = 33). Shotgun metagenomic sequencing was used to characterize the fecal microbiota. Breath-VOC profiles in the same participants were identified by an untargeted gas chromatography-mass spectrometry (GC-MS) technique. A multivariate statistical approach involving a canonical correlation analysis (CCA) and sparse principal component analysis identified the significant relationship between the breath VOCs and fecal microbiota. This relation was found to differ between gastric cancer patients and healthy controls. In 16 cancer cases, 14 distinct metabolites identified from the breath belonging to hydrocarbons, alcohols, aromatics, ketones, ethers, and organosulfur compounds were highly correlated with 33 fecal bacterial taxa (correlation of 0.891, p-value 0.045), whereas in 33 healthy controls, 7 volatile metabolites belonging to alcohols, aldehydes, esters, phenols, and benzamide derivatives correlated with 17 bacterial taxa (correlation of 0.871, p-value 0.0007). This study suggested that the correlation between fecal microbiota and breath VOCs was effective in identifying exhaled volatile metabolites and the functional effects of microbiome, thus helping to understand cancer-related changes and improving the survival and life expectancy in gastric cancer patients.

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.

Recent advances in facemask devices for in vivo sampling of human exhaled breath aerosols and inhalable environmental exposures.

Since the COVID-19 pandemic, the unprecedented use of facemasks has been requiring for wearing in daily life. By wearing facemask, human exhaled breath aerosols and inhaled environmental exposures can be efficiently filtered and thus various filtration residues can be deposited in facemask. Therefore, facemask could be a simple, wearable, in vivo, onsite and noninvasive sampler for collecting exhaled and inhalable compositions, and gain new insights into human health and environmental exposure. In this review, the recent advances in developments and applications of in vivo facemask sampling of human exhaled bacteria, viruses, proteins, and metabolites, and inhalable facemask contaminants and air pollutants, are reviewed. New features of facemask sampling are highlighted. The perspectives and challenges on further development and potential applications of facemask devices are also discussed.

Detecting Coronary Artery Disease Using Exhaled Breath Analysis.

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

Effect of household air pollutants on the composition of exhaled breath characterized by solid-phase microextraction and needle-trap devices.

Exposure to household air pollutants is becoming a serious environmental health risk. Various methods can be applied to assess humans' exposure status to indoor pollutants, with breath monitoring being among the best options. Breath sampling is fast and non-invasive, and contains compounds that can be used as markers for evaluating exposure length and estimating internal concentrations of pollutants. However, the distribution of compounds between gas and droplets in breath samples represents one of the key challenges associated with this analytical method. In this work, a needle-trap device (NTD) was prepared by packing the needle with a porous filter, divinyl benzene, and Carboxen to enable the exhaustive capture of both droplet-bound and gaseous components. Furthermore, fiber-based solid-phase microextraction (SPME) was also applied to extract compounds from only the gas phase to distinguish this portion of analytes from the total concentration in the sample. Dynamic, real-time breath sampling was enabled via a new sampling tube equipped with 2 one-way valves, which was specially designed for this work. Both methods provided satisfactory reproducibility, repeatability, and sensitivity, with detection limits as low as 0.05 ng mL-1. To investigate the real-world applicability of the proposed devices, breath samples were obtained from volunteers who had been exposed to candle and incense smoke and aerosol sprays, or had smoked cannabis. The results revealed the high concentration of organic air pollutants in inhaled air (maximum of 215 ng mL-1) and exhaled breath (maximum of 14.4 ng mL-1) and a correlation between the components in inhaled air and exhaled breath. Significantly, the findings further revealed that the developed NTD has enhanced breath-sample determinations, especially for polar compounds, which tend to remain trapped in breath droplets.