Breath Analysis Research Articles

Lung cancer detection in perioperative patients' exhaled breath with nanomechanical sensor array

IntroductionBreath analysis using a chemical sensor array combined with machine learning algorithms may be applicable for detecting and screening lung cancer. In this study, we examined whether perioperative breath analysis can predict the presence of lung cancer using a Membrane-type Surface stress Sensor (MSS) array and machine learning. MethodsPatients who underwent lung cancer surgery at an academic medical center, Japan, between November 2018 and November 2019 were included. Exhaled breaths were collected just before surgery and about one month after surgery, and analyzed using an MSS array. The array had 12 channels with various receptor materials and provided 12 waveforms from a single exhaled breath sample. Boxplots of the perioperative changes in the expiratory waveforms of each channel were generated and Mann-Whitney U test were performed. An optimal lung cancer prediction model was created and validated using machine learning. ResultsSixty-six patients were enrolled of whom 57 were included in the analysis. Through the comprehensive analysis of the entire dataset, a prototype model for predicting lung cancer was created from the combination of array five channels. The optimal accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.809, 0.830, 0.807, 0.806, and 0.812, respectively. ConclusionBreath analysis with MSS and machine learning with careful control of both samples and measurement conditions provided a lung cancer prediction model, demonstrating its capacity for non-invasive screening of lung cancer.

Apnea behavior in early- and late-stage mouse models of Parkinson's disease: Cineradiographic analysis of spontaneous breathing, acute stress, and swallowing

This study aimed to evaluate the timing and frequency of spontaneous apneas during breathing and swallowing by using cineradiography on mouse models of early/initial or late/advanced Parkinson’s disease (PD). C57BL/6 J mice received either 6-OHDA or vehicle injections into their right striatum, followed by respiratory movement recordings during spontaneous breathing and swallowing, and a stress challenge, two weeks later. Experimental group animals showed a significantly lower respiratory rate (158.66 ± 32.88 breaths/minute in late PD, 173.16 ± 25.19 in early PD versus 185.27 ± 25.36 in controls; p<0.001) and a significantly higher frequency of apneas (median 1 apnea/minute in both groups versus 0 in controls; p<0.001). Other changes included reduced food intake and the absence of swallow apneas in experimental mice. 6-OHDA-induced nigrostriatal degeneration in mice disrupted respiratory control, swallowing, stress responsiveness, and feeding behaviors, potentially hindering airway protection and elevating the risk of aspiration.

Alternative electrolyte solutions for untargeted breath metabolomics using secondary-electrospray ionization high-resolution mass spectrometry.

Secondary-electrospray ionization (SESI) coupled with high-resolution mass spectrometry is a powerful tool for the discovery of biomarkers in exhaled breath. A primary electrospray consisting of aqueous formic acid (FA) is currently used to charge the volatile organic compounds in breath. To investigate whether alternate electrospray compositions could enable different metabolite coverage and sensitivities, the electrospray dopants NaI and AgNO3 were tested. In a proof-of-principle manner, the exhaled breath of one subject was analyzed repeatedly with different electrospray solutions and with the help of a spectral stitching technique. Capillary diameter and position were optimized to achieve proper detection of exhaled breath. The detected features were then compared using formula annotation. Using an evaporation-based gas standard system, the signal response of the different solutions was probed. Principal component analysis revealed a substantial difference in features detected with AgNO3 . With silver, more sulfur-containing features and more unsaturated hydrocarbon compounds were detected. Furthermore, more primary amines were potentially ionized, as indicated by van Krewelen diagrams. In total, twice as many features were unique to AgNO3 than for other electrospray dopants. Using gas standards at known concentrations, the high sensitivity of FA as a dopant was demonstrated but also indicated alternate sensitivities of the other electrospray solutions. This work demonstrated the potential of AgNO3 as a complementary dopant for further biomarker discovery in SESI-based breath analysis.

Ultrasensitive Online NO Sensor Based on a Distributed Parallel Self-Regulating Neural Network and Ultraviolet Differential Optical Absorption Spectroscopy for Exhaled Breath Diagnosis.

The concentration of fractional exhaled nitric oxide (FeNO) is closely related to human respiratory inflammation, and the detection of its concentration plays a key role in aiding diagnosing inflammatory airway diseases. In this paper, we report a gas sensor system based on a distributed parallel self-regulating neural network (DPSRNN) model combined with ultraviolet differential optical absorption spectroscopy for detecting ppb-level FeNO concentrations. The noise signals in the spectrum are eliminated by discrete wavelet transform. The DPSRNN model is then built based on the separated multipeak characteristic absorption structure of the UV absorption spectrum of NO. Furthermore, a distributed parallel network structure is built based on each absorption feature region, which is given self-regulating weights and finally trained by a unified model structure. The final self-regulating weights obtained by the model indicate that each absorption feature region contributes a different weight to the concentration prediction. Compared with the regular convolutional neural network model structure, the proposed model has better performance by considering the effect of separated characteristic absorptions in the spectrum on the concentration and breaking the habit of bringing the spectrum as a whole into the model training in previous related studies. Lab-based results show that the sensor system can stably achieve high-precision detection of NO (2.59-750.66 ppb) with a mean absolute error of 0.17 ppb and a measurement accuracy of 0.84%, which is the best result to date. More interestingly, the proposed sensor system is capable of achieving high-precision online detection of FeNO, as confirmed by the exhaled breath analysis.

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

ABSTRACT 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.

Gas Sensing Performance of Zinc Oxide Nanoparticles Fabricated via Ochradenus baccatus Leaf

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

Exhaled Breath Analysis Detects the Clearance of Staphylococcus aureus from the Airways of Children with Cystic Fibrosis.

Electronic nose (eNose) technology can be used to characterize volatile organic compound (VOC) mixes in breath. While previous reports have shown that eNose can detect lung infections with pathogens such as Staphylococcus aureus (SA) in people with cystic fibrosis (CF), the clinical utility of eNose for longitudinally monitoring SA infection status is unknown. In this longitudinal study, a cloud-connected eNose, the SpiroNose, was used for the breath profile analysis of children with CF at two stable visits and compared based on changes in SA infection status between visits. Data analysis involved advanced sensor signal processing, ambient correction, and statistics based on the comparison of breath profiles between baseline and follow-up visits. Seventy-two children with CF, with a mean (IQR) age of 13.8 (9.8-16.4) years, were studied. In those with SA-positive airway cultures at baseline but SA-negative cultures at follow-up (n = 19), significant signal differences were detected between Baseline and Follow-up at three distinct eNose sensors, i.e., S4 (p = 0.047), S6 (p = 0.014), and S7 (p = 0.014). Sensor signal changes with the clearance of SA from airways were unrelated to antibiotic treatment. No changes in sensor signals were seen in patients with unchanged infection status between visits. Our results demonstrate the potential applicability of the eNose as a non-invasive clinical tool to longitudinally monitor pulmonary SA infection status in children with CF.

Breath analysis: Harnessing its potential for non-volatile drug caffeine monitoring in human body

Exhaled breath represents an appealing specimen for therapeutic drug monitoring (TDM). Despite the favorable correlation demonstrated between blood and breath concentrations for volatile drugs, the feasibility of breath analysis for TDM of non-volatile drugs (NVDs) remains uncertain. This uncertainty arises from the presence of NVDs as components of exhaled breath particles. This study aimed to definitively assess the potential of utilizing breath analysis in TDM for NVDs. Exhaled breath condensate (EBC) and blood samples were collected from seven volunteers through a specifically designed breathing maneuver (4-s inhalation and 11-s exhalation) within 0–12 h after administering caffeine as a model NVD. A reliable direct analysis method of caffeine in EBC samples was established using LC-MS/MS. Our findings indicated that the breath shared similar pharmacokinetic properties of caffeine with the blood, including elimination half-life (6.08 h vs 6.63 h), peak time (0.39 h vs 0.46 h), and mean residence time (4.68 h vs 4.90 h). Moreover, a significant linear correlation (r = 0.838) was observed between EBC and blood concentrations of caffeine. Notably, this marks the first instance of showcasing a robust correlation between breath and blood concentrations of NVDs in humans. By applying the correlation equation, blood caffeine concentration was calculated from breath concentration, yielding a predictive standard deviation of 0.689 μg/mL via leave-one-group-out statistics. Given caffeine's therapeutic range spanning from 5 to 25 μg/mL, this level of accuracy is deemed reasonably acceptable. In conclusion, exhaled breath analysis holds great promise as a non-invasive avenue for TDM of NVDs.

Ultratrace eNose Sensing of VOCs toward Breath Analysis Applications Utilizing an eNose-Based Analyzer.

This proof-of-principle study presents the ability of the recently developed iLovEnose to measure ultratrace levels of volatile organic compounds (VOCs) in simulated human breath based on the combination of multiple gas sensors. The iLovEnose was developed by our research team as a test bed for gas sensors that can be hosted in three serially connected compact low-volume and temperature-controlled compartments. Herein, the eNose system was equipped with conventional semiconducting metal oxide (MOX) gas sensors using a variety of base technologies providing 11 different sensor signals that were evaluated to determine six VOCs of interest at eight low to ultralow concentration levels (i.e., ranging from 3 to 0.075 ppm) at humid conditions (90% rh at 22 °C). The measurements were randomized and performed four times over a period of 2 weeks. Partial least-squares regression analysis was applied to estimate the concentration of these six analytes. It was shown that the iLovEnose system is able to discriminate between these VOCs and provide reliable quantitative information relevant for future applications in exhaled breath analysis as a diagnostic disease detection or monitoring device.

Exploring the Contribution of Intelligent Nanomaterials in Gas Sensing

AbstractGas sensors are crucial in various industries like chemicals, food processing, pharmaceuticals, health monitoring through breath analysis, as well as in household and environmental monitoring to ensure human safety. Chemo‐resistive gas sensors based on metal oxides convert gas information into electrical signals by interacting with adsorption and desorption processes. For metal oxide semiconductor gas sensors, sensitivity and selectivity enhancements are achieved through doping, functionalization, composite formations, heterojunction creation, and morphological adjustments. The formation of heterojunctions notably boosts the efficacy of the sensing material, leveraging synergistic effects to reinforce adsorption rates, catalytic activity acceleration, and the depletion interface for electrons and holes. Over the past few decades, nanomaterials have played a critical role in gas sensing applications. They have enabled the detection of a wide array of harmful and polluting gases, as well as volatile organic compounds serving as disease biomarkers. Additionally, incorporating heterostructure nanomaterials into heterojunctions significantly impacts sensing performance and detection speed. This review discussed various types of gas sensing devices, categorizing them based on the sensing elements employed, each possessing its own set of unique advantages and disadvantages. Nevertheless, the review underscored the importance of several key parameters and factors that influence the performance of gas sensors. Additionally, recent advancements in the utilization of nanomaterial composites for gas sensor applications, particularly in the detection of gases such as ammonia, hydrogen sulphide, and sulphur dioxide vapours, were discussed. Furthermore, the review highlights recent research endeavours and fundamental parameters, including sensitivity, detection limits, selectivity, response times, and recovery times.

Advances in secondary electrospray ionization for breath analysis and volatilomics

The measurement of volatile organic compounds (VOCs) originating from organisms allows continuous monitoring and a unique insight into the metabolism. One method offering the sensitivity to detect these VOCs is secondary electrospray ionization coupled to high-resolution mass spectrometry (SESI-HRMS). SESI was derived from electrospray ionization (ESI) and has found widespread application in clinical research and monitoring of animals. This review discusses the technical aspects behind SESI, the advancements, and the technical hurdles faced. Additionally, the recent advances in the applications of SESI in human and animal-centered research are presented.

Role of exhaled hydrogen sulfide in the diagnosis of colorectal cancer

BackgroundColorectal cancer (CRC) is often accompanied by increased excretion of hydrogen sulfide (H2S). This study aimed to explore the value of exhaled H2S in the diagnosis of CRC.MethodsA total of...

Volatile organic compounds in headspace characterize isolated bacterial strains independent of growth medium or antibiotic sensitivity.

Early and reliable determination of bacterial strain specificity and antibiotic resistance is critical to improve sepsis treatment. Previous research demonstrated the potential of headspace analysis of volatile organic compounds (VOCs) to differentiate between various microorganisms associated with pulmonary infections in vitro. This study evaluates whether VOC analysis can also discriminate antibiotic sensitive from resistant bacterial strains when cultured on varying growth media. Both antibiotic-sensitive and -resistant strains of Pseudomonas aeruginosa, Staphylococcus aureus and Klebsiella pneumonia were cultured on 4 different growth media, i.e. Brain Heart Infusion, Marine Broth, Müller-Hinton and Trypticase Soy Agar. After overnight incubation at 37°C, the headspace air of the cultures was collected on stainless steel desorption tubes and analyzed by gas chromatography time-of-flight mass spectrometry (GC-tof-MS). Statistical analysis was performed using regularized multivariate analysis of variance and cross validation. The three bacterial species could be correctly recognized based on the differential presence of 14 VOCs (p<0.001). This discrimination was not influenced by the different growth media. Interestingly, a clear discrimination could be made between the antibiotic-resistant and -sensitive variant of Pseudomonas aeruginosa (p<0.001) based on their species-specific VOC signature. This study demonstrates that isolated microorganisms, including antibiotic-sensitive and -resistant strains of Pseudomonas aeruginosa, could be identified based on their excreted VOCs independent of the applied growth media. These findings suggest that the discriminating volatiles are associated with the microorganisms themselves rather than with their growth medium. This study exemplifies the potential of VOC analysis as diagnostic tool in medical microbiology. However, validation of our results in appropriate in vivo models is critical to improve translation of breath analysis to clinical applications.

Real-time breath analysis towards a healthy human breath profile.

The direct analysis of molecules contained within human breath has had significant implications for clinical and diagnostic applications in recent decades. However, attempts to compare one study to another or to reproduce previous work are hampered by: variability between sampling methodologies, human phenotypic variability, complex interactions between compounds within breath, and confounding signals from comorbidities. Towards this end, we have endeavored to create an averaged healthy human 'profile' against which follow-on studies might be compared. Through the use of direct secondary electrospray ionization combined with a high-resolution mass spectrometry and in-house bioinformatics pipeline, we seek to curate an average healthy human profile for breath and use this model to distinguish differences inter- and intra-day for human volunteers. Breath samples were significantly different in PERMANOVA analysis and ANOSIM analysis based on Time of Day, Participant ID, Date of Sample, Sex of Participant, and Age of Participant (p< 0.001). Optimal binning analysis identify strong associations between specific features and variables. These include 227 breath features identified as unique identifiers for 28 of the 31 participants. Four signals were identified to be strongly associated with female participants and one with male participants. A total of 37 signals were identified to be strongly associated with the time-of-day samples were taken. Threshold indicator taxa analysis indicated a shift in significant breath features across the age gradient of participants with peak disruption of breath metabolites occurring at around age 32. Forty-eight features were identified after filtering from which a healthy human breath profile for all participants was created.

How well does your e-nose detect cancer? Application of artificial breath analysis for performance assessment

Comparing electronic nose (e-nose) performance is a challenging task because of a lack of standardised method. This paper proposes a method for defining and quantifying an indicator of the effectiveness of multi-sensor systems in detecting cancers by artificial breath analysis. To build this method, an evaluation of the performances of an array of metal oxide sensors built for use as a lung cancer screening tool was conducted. Breath from 20 healthy volunteers has been sampled in fluorinated ethylene propylene sampling bags. These healthy samples were analysed with and without the addition of nine volatile organic compound (VOC) cancer biomarkers, chosen from literature. The concentration of the VOC added was done in increasing amounts. The more VOC were added, the better the discrimination between ‘healthy’ samples (breath without additives) and ‘cancer’ samples (breath with additives) was. By determining at which level of concentration the e-nose fails to reliably discriminate between the two groups, we estimate its ability to well predict the presence of the disease or not in a realistic situation. In this work, a home-made e-nose is put to the test. The results underline that the biomarkers need to be about 5.3 times higher in concentration than in real breath for the home-made nose to tell the difference between groups with a sufficient confidence.

Self-powered triboelectric nanogenerator sensor for detecting humidity level and monitoring ethanol variation in a simulated exhalation environment

Respiration stands as a vital process reflecting physiological and pathological human health status. Exhaled breath analysis offers a facile, non-invasive, swift, and cost-effective approach for diagnosing and monitoring diseases by detecting concentration changes of specific biomarkers. In this study, we employed Polyethylene oxide/copper (I) oxide composite nanofibers (PCNFs), synthesized via the electrospinning method as the sensing material to measure ethanol levels (1–200 ppm) in an exhaled breath simulator environment. The integrated contact-separation triboelectric nanogenerator was utilized to power the self-powered PCNFs exhaled breath sensor. The PCNFs-based gas sensor demonstrates promising results with values of 0.9 and 3.2 for detecting 5 ppm and 200 ppm ethanol, respectively, in the presence of interfering gas at 90% relative humidity (RH). Notably, the sensor displayed remarkable ethanol selectivity, with ratios of 10:1 to methanol and 25:1 to acetone. Response and recovery times for 200 ppm ethanol at 90 RH% were rapid, at 2.7 s and 5.8 s, respectively. The PCNFs-based exhaled breath sensor demonstrated consistent and stable performance in practical conditions, showcasing its potential for integration into wearable devices. This self-powered breath sensor enabling continuous monitoring of lung cancer symptoms and facilitating compliance checks with legal alcohol consumption limits.

The assessment of halitosis with a new screening tool in medication-related osteonecrosis of the jaw.

This study aimed to identify the levels of halitosis in patients with Medication-related osteonecrosis of the jaw (MRONJ) and osteoporosis and to suggest a new MRONJ screening method using halitosis measurement. From October 2019 to April 2023, participants aged 19years or older without periodontal disease were selected. Seventy-five participants, 25 in each group, were divided into an MRONJ group, an osteoporosis group without MRONJ, and a control group without osteoporosis and not taking osteoporosis drugs or antibiotics. Each participant underwent halitosis assessment twice using an exhaled breath analyzer to measure halitosis twice by blowing a straw for 1min. Measured concentrations of hydrogen, hydrogen sulfide, and methyl mercaptan were compared between groups. Data from 22 patients in the MRONJ group, 25 in the osteoporosis group, and 25 in the control group were analyzed. The concentrations of hydrogen sulfide and methyl mercaptan were significantly higher in the MRONJ group than in the other groups, but the concentrations of hydrogen did not differ between the groups. When comparing the concentrations of hydrogen sulfide and methyl mercaptan in osteoporosis patients and solid cancer patients in the MRONJ group, there was a significant difference in hydrogen sulfide concentration, but there was no significant difference in methyl mercaptan. Quantifying the level of halitosis can be used to screen for MRONJ in patients taking bisphosphonates, such as patients with osteoporosis, prostate cancer, and breast cancer. MRONJ is accompanied by bad breath, and the concentrations of hydrogen sulfide and methyl mercaptan are associated with MRONJ.

GC-MS-based metabolomics of volatile organic compounds in exhaled breath: applications in health and disease. A review.

Exhaled breath analysis, with particular emphasis on volatile organic compounds, represents a growing area of clinical research due to its obvious advantages over other diagnostic tests. Numerous pathologies have been extensively investigated for the identification of specific biomarkers in exhalates through metabolomics. However, the transference of breath tests to clinics remains limited, mainly due to deficiency in methodological standardization. Critical steps include the selection of breath sample types, collection devices, and enrichment techniques. GC-MS is the reference analytical technique for the analysis of volatile organic compounds in exhalates, especially during the biomarker discovery phase in metabolomics. This review comprehensively examines and compares metabolomic studies focusing on cancer, lung diseases, and infectious diseases. In addition to delving into the experimental designs reported, it also provides a critical discussion of the methodological aspects, ranging from the experimental design and sample collection to the identification of potential pathology-specific biomarkers.

Exhaled Breath Analysis: from Laboratory Test to Wearable Sensing.

Breath analysis and monitoring have emerged as pivotal components in both clinical research and daily health management, particularly in addressing the global health challenges posed by respiratory and metabolic disorders. The advancement of breath analysis strategies necessitates a multidisciplinary approach, seamlessly integrating expertise from medicine, biology, engineering, and materials science. Recent innovations in laboratory methodologies and wearable sensing technologies have ushered in an era of precise, real-time, and in situ breath analysis and monitoring. This comprehensive review elucidates the physical and chemical aspects of breath analysis, encompassing respiratory parameters and both volatile and non-volatile constituents. It emphasizes their physiological and clinical significance, while also exploring cutting-edge laboratory testing techniques and state-of-the-art wearable devices. Furthermore, the review delves into the application of sophisticated data processing technologies in the burgeoning field of breathomics and examines the potential of breath control in human-machine interaction paradigms. Additionally, it provides insights into the challenges of translating innovative laboratory and wearable concepts into mainstream clinical and daily practice. Continued innovation and interdisciplinary collaboration will drive progress in breath analysis, potentially revolutionizing personalized medicine through entirely non-invasive breath methodology.

Emerging trends in metal oxide-based electronic noses for healthcare applications: a review.

An electronic nose (E-nose) is a technology fundamentally inspired by the human nose, designed to detect, recognize, and differentiate specific odors or volatile components in complex and chaotic environments. Comprising an array of sensors with meticulously designed nanostructured architectures, E-noses translate the chemical information captured by these sensors into useful metrics using complex pattern recognition algorithms. E-noses can significantly enhance the quality of life by offering preventive point-of-care devices for medical diagnostics through breath analysis, and by monitoring and tracking hazardous and toxic gases in the environment. They are increasingly being used in defense and surveillance, medical diagnostics, agriculture, environmental monitoring, and product validation and authentication. The major challenge in developing a reliable E-nose involves miniaturization and low power consumption. Various sensing materials are employed to address these issues. This review presents the key advancements over the last decade in E-nose technology, specifically focusing on chemiresistive metal oxide sensing materials. It discusses their sensing mechanisms, integration into portable E-noses, and various data analysis techniques. Additionally, we review the primary metal oxide-based E-noses for disease detection through breath analysis. Finally, we address the major challenges and issues in developing and implementing a portable metal oxide-based E-nose.