Breath Volatile Organic Compounds Research Articles

Detection of Clostridioides difficile infection by assessment of exhaled breath volatile organic compounds

Clostridioides difficile infection (CDI) is the leading cause of hospital-acquired infective diarrhea. Current methods for diagnosing CDI have limitations; enzyme immunoassays for toxin have low sensitivity and Clostridioides difficile polymerase chain reaction cannot differentiate infection from colonization. An ideal diagnostic test that incorporates microbial factors, host factors, and host-microbe interaction might characterize true infection. Assessing volatile organic compounds (VOCs) in exhaled breath may be a useful test for identifying CDI. To identify a wide selection of VOCs in exhaled breath, we used thermal desorption-gas chromatography-mass spectrometry to study breath samples from 17 patients with CDI. Age- and sex-matched patients with diarrhea and negative C.difficile testing (no CDI) were used as controls. Of the 65 VOCs tested, 9 were used to build a quadratic discriminant model that showed a final cross-validated accuracy of 74%, a sensitivity of 71%, a specificity of 76%, and a receiver operating characteristic area under the curve of 0.72. If these findings are proven by larger studies, breath VOC analysis may be a helpful adjunctive diagnostic test for CDI.

Abstract 4433: Secondary electrospray ionization mass spectrometry for volatile analysis of lung cancer

Abstract Lung cancer remains a leading cause of cancer-related mortality globally, necessitating innovative approaches for early detection and improved understanding of its metabolic landscape and consequences. Secondary Electrospray Ionization Mass Spectrometry (SESI-MS), an innovative analytical technique, has emerged as a powerful tool for the analysis of volatile organic compounds (VOCs) associated with various diseases, from metabolic disorders to bacterial infections. In this proof-of-concept study, we first utilized SESI-MS analyses of four representative lung cancer cells of both non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) to demonstrate its capability to profile VOCs released by lung cancer cells. The technique allows for real-time, non-invasive analysis of the headspace above cultured cells, enabling the detection of unique VOC signatures associated with different lung cancer subtypes. Meanwhile, SESI-MS also facilitated the discrimination specific cells based on distinct VOC patterns. Furthermore, we also performed cisplatin treatment experiments and observed the metabolic signature shift post drug treatment. These results collectively offered potential applications in early diagnosis and personalized treatment strategies. Next, we continued with in vivo investigations to further extend the utility of SESI-MS in lung cancer research. Animal models, mimicking human lung cancer pathophysiology, have been subjected to SESI-MS analysis to identify volatile biomarkers indicative of tumor development and progression. Several representative cancer drug treatments were performed to the lung cancer mice model, and the drug-induced responses were also monitored via SESI-MS breath analyses. The in vivo SESI-MS approach detected many interesting breath VOC features that could provide valuable insights into the dynamic changes in VOC profiles during various stages of lung cancer, and therefore has the potential to offer a deeper understanding of the disease's metabolic alterations and potential avenues for targeted therapeutic interventions. In conclusion, SESI-MS-based volatile analysis presents a robust and versatile approach for investigating lung cancer, and our ongoing work has the potential to bridge the gap between in vitro and in vivo models for better translational research outcomes. The detection of unique VOC signatures associated with lung cancer cells and tumors holds significant promise for early detection, subtype classification, and monitoring of treatment responses. As technology continues to advance, SESI-MS stands poised to play a pivotal role in transforming the landscape of lung cancer diagnostics and research. Citation Format: Jiangjiang (Chris) Zhu, Fouad Choueiry. Secondary electrospray ionization mass spectrometry for volatile analysis of lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4433.

Identification of breath volatile organic compounds to distinguish pancreatic adenocarcinoma, pancreatic cystic neoplasm, and patients without pancreatic lesions.

Volatile organic compounds (VOCs) are a promising potential biomarker that may be able to identify the presence of cancers. To identify exhaled breath VOCs that distinguish pancreatic ductal adenocarcinoma (PDAC) from intraductal papillary mucinous neoplasm (IPMN) and healthy volunteers. We collected exhaled breath from histologically proven PDAC patients, radiological diagnosis IPMN, and healthy volunteers using the ReCIVA® device between 10/2021-11/2022. VOCs were identified by thermal desorption-gas chromatography/field-asymmetric ion mobility spectrometry and compared between groups. A total of 156 participants (44% male, mean age 62.6 ± 10.6) were enrolled (54 PDAC, 42 IPMN, and 60 controls). Among the nine VOCs identified, two VOCs that showed differences between groups were dimethyl sulfide [0.73 vs 0.74 vs 0.94 arbitrary units (AU), respectively; P = 0.008] and acetone dimers (3.95 vs 4.49 vs 5.19 AU, respectively; P < 0.001). After adjusting for the imbalance parameters, PDAC showed higher dimethyl sulfide levels than the control and IPMN groups, with adjusted odds ratio (aOR) of 6.98 (95%CI: 1.15-42.17) and 4.56 (1.03-20.20), respectively (P < 0.05 both). Acetone dimer levels were also higher in PDAC compared to controls and IPMN (aOR: 5.12 (1.80-14.57) and aOR: 3.35 (1.47-7.63), respectively (P < 0.05 both). Acetone dimer, but not dimethyl sulfide, performed better than CA19-9 in PDAC diagnosis (AUROC 0.910 vs 0.796). The AUROC of acetone dimer increased to 0.936 when combined with CA19-9, which was better than CA19-9 alone (P < 0.05). Dimethyl sulfide and acetone dimer are VOCs that potentially distinguish PDAC from IPMN and healthy participants. Additional prospective studies are required to validate these findings.

Quantifying exhaled acetone and isoprene through solid phase microextraction and gas chromatography-mass spectrometry

BackgroundAcetone, isoprene, and other volatile organic compounds (VOCs) in exhaled breath have been shown to be biomarkers for many medical conditions. Researchers use different techniques for VOC detection, including solid phase microextraction (SPME), to preconcentrate volatile analytes prior to instrumental analysis by gas chromatography-mass spectrometry (GC-MS). These techniques include a previously developed method to detect VOCs in breath directly using SPME, but it is uncommon for studies to quantify exhaled volatiles because it can be time consuming due to the need of many external/internal standards, and there is no standardized or widely accepted method. The objective of this study was to develop an accessible method to quantify acetone and isoprene in breath by SPME GC-MS. ResultsA system was developed to mimic human exhalation and expose VOCs to a SPME fiber in the gas phase at known concentrations. VOCs were bubbled/diluted with dry air at a fixed flow rate, duration, and volume that was comparable to a previously developed breath sampling method. Identification of acetone and isoprene through GC-MS was verified using standards and observing overlaps in chromatographic retention/mass spectral fragmentation. Calibration curves were developed for these two analytes, which showed a high degree of linear correlation. Acetone and isoprene displayed limits of detection/quantification equal to 12 ppb/37 ppb and 73 ppb/222 ppb respectively. Quantification results in healthy breath samples (n = 15) showed acetone concentrations spanned between 71 ppb and 294 ppb, and isoprene varied between 170 ppb and 990 ppb. Both concentration ranges for acetone and isoprene in this study overlap with those reported in existing literature. SignificanceResults indicate the development of a system to quantify acetone and isoprene in breath that can be adapted to diverse sampling methods and instrumental analyses beyond SPME GC-MS.

Exhaled breath analyses for bronchial thermoplasty in severe asthma patients

BackgroundBronchial thermoplasty (BT) is a bronchoscopic treatment for severe asthma. Although multiple trials have demonstrated clinical improvement after BT, optimal patient selection remains a challenge and the mechanism of action is incompletely understood.The aim of this study was to examine whether exhaled breath analysis can contribute to discriminate between BT-responders and non-responders at baseline and to explore pathophysiological insights of BT. MethodsExhaled breath was collected from patients at baseline and six months post-BT. Patients were defined as responders or non-responders based on a half point increase in asthma quality of life questionnaire scores. Gas chromatography-mass spectrometry was used for volatile organic compounds (VOCs) detection and analyses. Analytical workflow consisted of: 1) detection of VOCs that differentiate between responders and non-responders and those that differ between baseline and six months post-BT, 2) identification of VOCs of interest and 3) explore correlations between clinical biomarkers and VOCs. ResultsData was available from 14 patients. Nonanal, 2-ethylhexanol and 3-thujol showed a significant difference in intensity between responders and non-responders at baseline (p = 0.04, p = 0.01 and p = 0.03, respectively). After BT, no difference was found in the compound intensity of these VOCs. A negative correlation was observed between nonanal and IgE and BALF eosinophils (r = −0.68, p < 0.01 and r = −0.61, p = 0.02 respectively) and 3-thujol with BALF neutrophils (r = −0.54, p = 0.04). ConclusionsThis explorative study identified discriminative VOCs in exhaled breath between BT responders and non-responders at baseline. Additionally, correlations were found between VOC's and inflammatory BALF cells. Once validated, these findings encourage research in breath analysis as a non-invasive easy to apply technique for identifying airway inflammatory profiles and eligibility for BT or immunotherapies in severe asthma.

Breath analysis system with convolutional neural network (CNN) for early detection of lung cancer

Early diagnosis of lung cancer, the leading cause of cancer-related death worldwide, is critical for reducing mortality rate. However, current diagnostic methods are invasive, time-consuming, costly, and may not always provide accurate diagnoses. For early diagnosis, recent research has focused on noninvasive approaches, including the detection of volatile organic compounds (VOCs) in human exhaled breath. Changes in the composition and concentration of VOCs in exhaled breath may indicate lung cancer, and this approach offers several advantages over traditional diagnostic methods. Moreover, the combination of a breath gas sensing system and machine learning algorithms provides a more accurate diagnosis. In this study, for the early diagnosis of lung cancer, a breath analysis system was developed using a gas sensor array and deep learning algorithm. The breath analysis system was designed to detect multiple VOCs in exhaled breath using ten semiconductor metal oxide (SMO), one photoionization detector (PID), nine electrochemical (EC) gas sensors. In total, 181 clinical breath samples (from 74 healthy controls and 107 lung cancer patients) were collected and analyzed using a 1D convolutional neural network (CNN) algorithm. The results showed an overall accuracy of 97.8% in classifying healthy controls and lung cancer patients using a complete clinical dataset. Through a comparison of the single-sensor type data and multimodal sensor data and performance analysis of three different deep learning models (multilayer perceptron, recurrent neural network, and CNN), we validated the potential of the breath analyzer with a multimodal sensor system and a 1D CNN as a lung cancer diagnostic device.

Exhaled breath and urinary volatile organic compounds (VOCs) for cancer diagnoses, and microbial-related VOC metabolic pathway analysis: a systematic review and meta-analysis.

The gradual evolution of the detection and quantification of volatile organic compounds (VOCs) has been instrumental in cancer diagnosis. The primary objective of this study was to assess the diagnostic potential of exhaled breath and urinary VOCs in cancer detection. As VOCs are indicative of tumor and human metabolism, our work also sought to investigate the metabolic pathways linked to the development of cancerous tumors. An electronic search was performed in the PubMed database. Original studies on VOCs within exhaled breath and urine for cancer detection with a control group were included. A meta-analysis was conducted using a bivariate model to assess the sensitivity and specificity of the VOCs for cancer detection. Fagan's nomogram was designed to leverage the findings from our diagnostic analysis for the purpose of estimating the likelihood of cancer in patients. Ultimately, MetOrigin was employed to conduct an analysis of the metabolic pathways associated with VOCs in relation to both human and/or microbiota. The pooled sensitivity, specificity and the area under the curve for cancer screening utilizing exhaled breath and urinary VOCs were determined to be 0.89, 0.88, and 0.95, respectively. A pretest probability of 51% can be considered as the threshold for diagnosing cancers with VOCs. As the estimated pretest probability of cancer exceeds 51%, it becomes more appropriate to emphasize the 'ruling in' approach. Conversely, when the estimated pretest probability of cancer falls below 51%, it is more suitable to emphasize the 'ruling out' approach. A total of 14, 14, 6, and 7 microbiota-related VOCs were identified in relation to lung, colorectal, breast, and liver cancers, respectively. The enrichment analysis of volatile metabolites revealed a significant enrichment of butanoate metabolism in the aforementioned tumor types. The analysis of exhaled breath and urinary VOCs showed promise for cancer screening. In addition, the enrichment analysis of volatile metabolites revealed a significant enrichment of butanoate metabolism in four tumor types, namely lung, colorectum, breast and liver. These findings hold significant implications for the prospective clinical application of multiomics correlation in disease management and the exploration of potential therapeutic targets.

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.

Metabolic insights at the finish line: deciphering physiological changes in ultramarathon runners through breath VOC analysis

Exhaustive exercise can induce unique physiological responses in the lungs and other parts of the human body. The volatile organic compounds (VOCs) in exhaled breath are ideal for studying the effects of exhaustive exercise on the lungs due to the proximity of the breath matrix to the respiratory tract. As breath VOCs can originate from the bloodstream, changes in abundance should also indicate broader physiological effects of exhaustive exercise on the body. Currently, there is limited published data on the effects of exhaustive exercise on breath VOCs. Breath has great potential for biomarker analysis as it can be collected non-invasively, and capture real-time metabolic changes to better understand the effects of exhaustive exercise. In this study, we collected breath samples from a small group of elite runners participating in the 2019 Ultra-Trail du Mont Blanc ultra-marathon. The final analysis included matched paired samples collected before and after the race from 24 subjects. All 48 samples were analyzed using the Breath Biopsy Platform with GC-Orbitrap™ via thermal desorption gas chromatography-mass spectrometry. The Wilcoxon signed-rank test was used to determine whether VOC abundances differed between pre- and post-race breath samples (adjusted P-value < .05). We identified a total of 793 VOCs in the breath samples of elite runners. Of these, 63 showed significant differences between pre- and post-race samples after correction for multiple testing (12 decreased, 51 increased). The specific VOCs identified suggest the involvement of fatty acid oxidation, inflammation, and possible altered gut microbiome activity in response to exhaustive exercise. This study demonstrates significant changes in VOC abundance resulting from exhaustive exercise. Further investigation of VOC changes along with other physiological measurements can help improve our understanding of the effect of exhaustive exercise on the body and subsequent differences in VOCs in exhaled breath.

Disease diagnosis and severity classification in pulmonary fibrosis using carbonyl volatile organic compounds in exhaled breath

BackgroundPathophysiological conditions underlying pulmonary fibrosis remain poorly understood. Exhaled breath volatile organic compounds (VOCs) have shown promise for lung disease diagnosis and classification. In particular, carbonyls are a byproduct of oxidative stress, associated with fibrosis in the lungs. To explore the potential of exhaled carbonyl VOCs to reflect underlying pathophysiological conditions in pulmonary fibrosis, this proof-of-concept study tested the hypothesis that volatile and low abundance carbonyl compounds could be linked to diagnosis and associated disease severity. MethodsExhaled breath samples were collected from outpatients with a diagnosis of Idiopathic Pulmonary Fibrosis (IPF) or Connective Tissue related Interstitial Lung Disease (CTD-ILD) with stable lung function for 3 months before enrollment, as measured by pulmonary function testing (PFT) DLCO (%), FVC (%) and FEV1 (%). A novel microreactor was used to capture carbonyl compounds in the breath as direct output products. A machine learning workflow was implemented with the captured carbonyl compounds as input features for classification of diagnosis and disease severity based on PFT (DLCO and FVC normal/mild vs. moderate/severe; FEV1 normal/mild/moderate vs. moderately severe/severe). ResultsThe proposed approach classified diagnosis with AUROC=0.877 ± 0.047 in the validation subsets. The AUROC was 0.820 ± 0.064, 0.898 ± 0.040, and 0.873 ± 0.051 for disease severity based on DLCO, FEV1, and FVC measurements, respectively. Eleven key carbonyl VOCs were identified with the potential to differentiate diagnosis and to classify severity. ConclusionsExhaled breath carbonyl compounds can be linked to pulmonary function and fibrotic ILD diagnosis, moving towards improved pathophysiological understanding of pulmonary fibrosis.

Knowledge of Non-Invasive Biomarkers of Breast Cancer, Risk Factors, and BSE Practices Among Nursing Undergraduates in Farasan Island, KSA.

Mammograms are sometimes met with issues of overdiagnosis and underdiagnosis; therefore, they are less reliable in identifying cancer in women with dense breasts. As a result, it is critical to be aware of other sensitive screening techniques for the early diagnosis of breast cancer. The ultimate objective of this study was to assess the knowledge of nursing undergraduates regarding non-invasive biomarkers, such as volatile organic compounds in breath, nipple aspirate fluid, sweat, urine, and tears, for the early detection of breast cancer to help improve patient care, determine the risk factors, and encourage practice of breast self-examination. Cross-sectional research was done in the Department of Nursing at Farasan campus using a self-structured questionnaire as the study tool. A total of 260 students willingly participated. The study tool had evaluation questions focused on the non-invasive biomarkers of breast cancer, risk factors, and breast self-examination practices to collect data. The data were subjected to descriptive and inferential statistics. The statistical significance was calculated at P < .05. Data analyses were done using Microsoft Excel (2013). A significant knowledge gap existed among the study participants about the non-invasive biomarkers of breast cancer. A lesser percentage of students (25%) stated that they do breast self-examination on a monthly basis. The most common reasons for not doing the breast self-examination were "not knowing how to do the breast self-examination" (77.3%), fear of a positive diagnosis (53.9%), thinking that they are not at risk as all were in their teens and hence not required (44.7%), and lack of time (48.7%). Age and frequency of breast self-examination were significantly associated (P < .05) as those few students (22.7%) who were doing breast self-examination practices every 2-4 months belonged to a higher study year. Furthermore, knowledge regarding incidence rates and health care expenditure by the government on breast cancer was also significantly low (P < .05). Outcomes would help prioritize actions to help future nurses better understand breast cancer, allowing them to extend patient care in the best way possible.

Breath volatile organic compounds for chronic kidney disease progression monitoring.

Breath analysis may provide a convenient and non-invasive method for clinical monitoring of chronic kidney disease (CKD) progression. However, few breath volatile organic compounds (VOCs) indicating progression of CKD have been reported. In this study, we used gas chromatography-mass spectrometry (GC-MS) for untargeted detection of breath VOCs in stage 1, 3, and 5 CKD patients. The results showed that, the levels of breath 4-heptanone, n-octane, and n-dodecane gradually increased from CKD stage 1 to stage 5, and their increasing rates from CKD stage 3 to stage 5 were higher than those from CKD stage 1 to stage 3. Gender, smoking habits, age, and body mass index (BMI) had insignificant impact on the levels of the three breath VOCs. The accuracies of the polynomial support vector machine (SVM) and K-nearest neighbour (KNN) models based on 4-heptanone + n-octane + n-dodecane combination in distinguishing CKD stages 1, 3, and 5 were 76.3% and 72.8%, respectively. The combination of 4-heptanone + n-octane + n-dodecane was superior to any single component for monitoring CKD progression. These discoveries have valuable implications for long-term clinical monitoring of CKD and improving our understanding of CKD.

Direct Online Monitoring of Mouse Exhaled Breath for Diabetes Assessment Using Corona Ionization Mass Spectrometry

Since certain types of diabetes are reversible and curable in the early stages, early detection is crucial for effective treatment. Exhaled breath analysis, a noninvasive technique, is increasingly recognized for its potential in health monitoring, providing insights into an organism’s metabolic state more directly than blood glucose tests. This work developed an in situ method utilizing corona ion source mass spectrometry for real-time direct monitoring of volatile organic compounds (VOCs) in mouse exhaled breath. The method was used to track breath changes in two mouse models with streptozotocin-induced diabetes throughout disease progression. Notably, variations in certain VOCs in exhaled breath correlated with blood glucose levels (BGLs) during diabetes development. For instance, in the type 1 diabetes model, acetone levels remained stable within 8 days after hyperglycemia (not significant, n.s.) compared to those in controls but showed a significant increase after 10 days (p < 0.001). In the type 2 diabetes model, isopropanol levels decreased (p < 0.001) during the prediabetes phase, when BGL changes were not significant (n.s.) compared to those in the healthy state. These results indicate that specific substances in exhaled breath change prior to noticeable BGL alterations. This study thus established the viability of direct online mass spectrometry for breath analysis in early diabetes detection and showed that this technique has potential for identifying clear clinical markers for early diabetes screening.

Machine Learning-Assisted Janus Colorimetric Face Mask for Breath Ammonia Analysis.

Artificial olfactory systems have been widely used in medical fields such as in the analysis of volatile organic compounds (VOCs) in human exhaled breath. However, there is still an urgent demand for a portable, accurate breath VOC analysis system for the healthcare industry. In this work, we proposed a Janus colorimetric face mask (JCFM) for the comfortable evaluation of breath ammonia levels by combining the machine learning K-nearest neighbor (K-NN) algorithm. Such a Janus fabric is designed for the unidirectional penetration of exhaled moisture, which can reduce stickiness and ensure facial dryness and comfort. Four different pH indicators on the colorimetric array serve as recognition elements that cross-react with ammonia, capturing the optical fingerprint information on breath ammonia by mimicking the sophisticated olfactory structure of mammals. The Euclidean distance (ED) is used to quantitatively describe the ammonia concentration between 1 ppm and 10 ppm, indicating that there is a linear relationship between the ammonia concentration and the ED response (R2 = 0.988). The K-NN algorithm based on RGB response features aids in the analysis of the target ammonia level and achieves a prediction accuracy of 96%. This study integrates colorimetry, Janus design, and machine learning to present a wearable and portable sensing system for breath ammonia analysis.

A novel coupling technique based on thermal desorption gas chromatography with mass spectrometry and ion mobility spectrometry for breath analysis

Exhaled breath analysis is evolving into an increasingly important non-invasive diagnostic tool. Volatile organic compounds (VOCs) in breath contain information about health status and are promising biomarkers for several diseases, including respiratory infections caused by bacteria. To monitor the composition of VOCs in breath or the emission of VOCs from bacteria, sensitive analytical techniques are required. Next to mass spectrometry, ion mobility spectrometry (IMS) is considered a promising analytical tool for detecting gaseous analytes in the parts per billion by volume to parts per trillion by volume range. This work presents a new, dual coupling of thermal desorption gas chromatography to a quadrupole mass spectrometer (MS) and an IMS by operating a simple splitter. Nearly identical retention times can be reached in the range of up to 30 min with slight deviations of 0.06 min–0.24 min. This enables the identification of unknown compounds in the IMS chromatogram using unambiguous mass spectral identification, as there are still no commercially available databases for IMS. It is also possible to discriminate one of the detectors using the splitter to improve detection limits. Using a test liquid mixture of seven ketones, namely 2-butanone, 2-pentanone, 2-hexanone, 2-heptanone, 2-octanone, 2-nonanone, and 2-decanone with a concentration of 0.01 g l−1 reproducibilities ranging from 3.0% to 7.6% for MS and 2.2%–5.3%, for IMS were obtained, respectively. In order to test the system optimized here for the field of breath analysis, characteristic VOCs such as ethanol, isoprene, acetone, 2-propanol, and 1-propanol were successfully identified in exhaled air using the dual detector system due to the match of the corresponding IMS, and MS spectra. The presented results may be considered to be a starting point for the greater use of IMS in combination with MS within the medical field.

(Invited) Breath and Skin Gas Sensing for Rapid POC Medical Diagnostics

Antiquity’s first known physician, Hippocrates of Cos (460–370 BC), smelled his patients’ breath to diagnose disease and recommend the appropriate remedy. Some medical terms coined then survive today, such as “fetor hepaticus,” which describes the sweet, ketone- and ammonia- rich scent that indicates the late stages of liver failure. Yet, neither breath nor skin analysis, as a noninvasive means of disease detection, are common practice among physicians to-date nor are they available to the general population. Ceramic sensor nanotechnology and nanomedicine are, however, capable of making breath-based diagnostics. VOCs in breath and skin are products of core metabolic processes, while inorganic molecules are related to other health conditions and can be indicators of a potential disease, recent exposure to a drug or an environmental pollutant. Therefore, an abnormally high or low measured concentration of certain breath or skin gases, so-called biomarkers, could potentially provide clues for diagnosing corresponding diseases. This work focuses on electronic olfaction systems which we have developed and used in breath-based testing of Volatile Organic Compound (VOC) biomarkers with great success. It also addresses the concept of skin-gas “smelling” of disease. These gas sensing nanotechnologies allow for the rapid and reliable output signals which indicate the host’s response to infection or metabolic changes. Using the library of specific prints/unique patterns for each of the targeted diseases, the following types of diseases could be potentially diagnosed: Metabolic diseases: diabetes, cholesterol-induced heart disease; Neurological diseases: Alzheimers, Parkinson’s; Chronic diseases: obesity, sleep apnea; Pulmonary diseases: CF, asthma, COPD; GI tract diseases: IBS, colitis; Cancers: breast, lung, pancreatic, colon, and Infectious diseases: flu, COVID-19; as well as diseases commonly found in ICU patients: Urinary tract infections, pneumonia, infections of the blood stream. Focusing on the most common gaseous biomarkers in breath and skin, that is Nitric Oxide and Carbon Monoxide and VOCs (acetone, isoprene, ammonia, alcohols, sulfides) effective discrimination between the diseases mentioned above is possible, by capturing the relative sensor output signals from the detection of each of these biomarkers and analyzing them to reveal the distinct breath print for each disease. We focus here on an electronic olfaction device based on resistive gas sensing utilizing a small number, just four at a time, of metal-oxide sensors that are specifically sensitive to the biomarkers of interest.A single-scan skin testing device comprises an array of gas sensors, each of which satisfies the following stringent conditions: high sensitivity to the target gas, high selectivity, stable response over extended periods of time and rapid response. The concentration of a target gas over a selective sensor is quantified by measurement of the sensor resistance. We also present a novel wearable device interfacing with a smart phone for disease detection and monitoring. This device utilizes conducting polymer actuators, since conductive polymers exhibit a low operating potential (typically <2V), and a high mechanical strength. Quantitative medical diagnostics require simultaneous monitoring of multiple gases because markers are affected differently in different diseases and this device allows for continuous measurement of acetone for diabetes monitoring.

Identification of Sars-Cov-2 related volatile organic compounds from face masks

This study aims to identify SARS-CoV-2-related volatile organic compounds (VOCs) of the exhaled breath from face masks. Face masks collected from SARS-CoV-2 patients (n = 30) and the control group (n = 11) were analyzed to detect VOCs in the exhaled breath using the Headspace-Gas Chromatography Mass Spectrometry. Significant increases in concentrations of toluene, hexadecane, and phenol were detected in the face masks belonging to patient groups (p < 0.001). Clinical significance was further found in isobutyric acid levels considering Cohen's d value (p = 0.070). The findings of this study suggest that monitoring toluene, hexadecane, phenol, and isobutyric acid could provide considerable support in rapid screening of SARS-CoV-2.

Effect of chemotherapy on urinary volatile biomarkers for lung cancer by HS-SPME-GC-MS and chemometrics.

Volatile organic compounds (VOCs) have been studied as possible biomarkers in several diseases, including lung cancer. Early detection of cancer can improve long-term survival rates and the quality of life, so the study of VOCs in exhaled breath and urine has been increasing in recent years. This study aimed to assess the urinary VOCs that are modified after chemotherapy to identify those with the potential to be lung cancer biomarkers that can be monitored during treatment. Three urine samples from 10 men with stage IV lung adenocarcinoma were collected, as well as urine samples from 14 men with other types of cancer (control group). All samples were analyzed by headspace-solid phase microextraction gas chromatography coupled with mass spectrometry. A total of 21 urinary VOCs were found with different levels after the administration of chemotherapy, with 2-pentanone being one of those that significantly decreased. Furthermore, 2-pentanone and 3-hydroxy-2,4,4-trimethylpentyl-2-methylpropanoate showed statistically significant differences with the control group. Chemotherapy administered to patients with advanced lung adenocarcinoma modified the volatile profile of urine. 2-Pentanone, a final product of the increased rate of fatty acid oxidation and protein hypermetabolism, significantly decreased after chemotherapy. Therefore, monitoring its urinary excretion could be very useful since its decrease over time could indicate an adequate response to chemotherapy and arrest of cancer development. Another VOC that could be a potential lung cancer biomarker is 3 hydroxy-2,4,4-trimethylpentyl-2-methylpropanoate, whose origin may be due to inhibition of the propanoic acid metabolic pathway or increased aldehyde dehydrogenase activity.

Unraveling the potential of breath and sweat VOC capture devices for human disease detection: a systematic-like review of canine olfaction and GC-MS analysis.

The development of disease screening methods using biomedical detection dogs relies on the collection and analysis of body odors, particularly volatile organic compounds (VOCs) present in body fluids. To capture and analyze odors produced by the human body, numerous protocols and materials are used in forensics or medical studies. This paper provides an overview of sampling devices used to collect VOCs from sweat and exhaled air, for medical diagnostic purposes using canine olfaction and/or Gas Chromatography-Mass spectrometry (GC-MS). Canine olfaction and GC-MS are regarded as complementary tools, holding immense promise for detecting cancers and infectious diseases. However, existing literature lacks guidelines for selecting materials suitable for both canine olfaction and GC-MS. Hence, this review aims to address this gap and pave the way for efficient body odor sampling materials. The first section of the paper describes the materials utilized in training sniffing dogs, while the second section delves into the details of sampling devices and extraction techniques employed for exhaled air and sweat analysis using GC-MS. Finally, the paper proposes the development of an ideal sampling device tailored for detection purposes in the field of odorology. By bridging the knowledge gap, this study seeks to advance disease detection methodologies, harnessing the unique abilities of both dogs and GC-MS analysis in biomedical research.

Solid-Phase Microextraction (SPME) and Gas Chromatographic/Mass Spectrometry in Chronic Obstructive Pulmonary Disease (COPD): A Chemometric Approach

Chronic Obstructive Pulmonary Disease (COPD) is a chronic respiratory condition that often goes undiagnosed despite the availability of spirometry for diagnosis, and its exact prevalence remains uncertain. Exhaled breath has been proposed as a source of relevant health information, particularly Volatile Organic Compounds (VOCs), which can be easily obtained and applied in clinical practice. In this study, exhaled breath samples were collected from patients diagnosed with COPD of varying severity during their stable condition using specialized RTubeVOC tubes. Volatile compounds from the air samples were extracted using a 50/30 µm divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber and the analysis was performed using gas chromatography/mass spectrometry (GC/MS) technique. The patients were divided into two groups based on their history of exacerbations, and the aim was to identify VOCs associated with the risk of future COPD exacerbation, thus allowing for more personalized and objective COPD treatment. Blood eosinophil content was also taken into consideration. A panel of distinguishing mass-spectral features was identified between the two patient groups. The discriminating exhaled molecules were heptane 2,2,4,6,6-pentamethyl, gamma-terpinene, 2-ethylhexanol, and undecane demonstrating the potential of analyzing VOCs in exhaled breath for the detection and management of COPD, offering a promising avenue to improve COPD management and treatment approaches.