Electronic Nose Development and Preliminary Human Breath Testing for Rapid, Non-Invasive COVID-19 Detection.

We adapted an existing, spaceflight-proven, robust "electronic nose" (E-Nose) that uses an array of electrical resistivity-based nanosensors mimicking aspects of mammalian olfaction to conduct on-site, rapid screening for COVID-19 infection by measuring the pattern of sensor responses to volatile organic compounds (VOCs) in exhaled human breath. We built and tested multiple copies of a hand-held prototype E-Nose sensor system, composed of 64 chemically sensitive nanomaterial sensing elements tailored to COVID-19 VOC detection; data acquisition electronics; a smart tablet with software (App) for sensor control, data acquisition and display; and a sampling fixture to capture exhaled breath samples and deliver them to the sensor array inside the E-Nose. The sensing elements detect the combination of VOCs typical in breath at parts-per-billion (ppb) levels, with repeatability of 0.02% and reproducibility of 1.2%; the measurement electronics in the E-Nose provide measurement accuracy and signal-to-noise ratios comparable to benchtop instrumentation. Preliminary clinical testing at Stanford Medicine with 63 participants, their COVID-19-positive or COVID-19-negative status determined by concomitant RT-PCR, discriminated between these two categories of human breath with a 79% correct identification rate using "leave-one-out" training-and-analysis methods. Analyzing the E-Nose response in conjunction with body temperature and other non-invasive symptom screening using advanced machine learning methods, with a much larger database of responses from a wider swath of the population, is expected to provide more accurate on-the-spot answers. Additional clinical testing, design refinement, and a mass manufacturing approach are the main steps toward deploying this technology to rapidly screen for active infection in clinics and hospitals, public and commercial venues, or at home.

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.

Virus-induced breath biomarkers: A new perspective to study the metabolic responses of COVID-19 vaccinees.

Coronavirus disease 2019 (COVID-19) vaccines can protect people from the infection; however, the action mechanism of vaccine-mediated metabolism remains unclear. Herein, we performed breath tests in COVID-19 vaccinees that revealed metabolic reprogramming induced by protective immune responses. In total, 204 breath samples were obtained from COVID-19 vaccinees and non-vaccinated controls, wherein numerous volatile organic compounds (VOCs) were detected by comprehensive two-dimensional gas chromatography and time-of-flight mass spectrometry system. Subsequently, 12 VOCs were selected as biomarkers to construct a signature panel using alveolar gradients and machine learning-based procedure. The signature panel could distinguish vaccinees from control group with a high prediction performance (AUC, 0.9953; accuracy, 94.42%). The metabolic pathways of these biomarkers indicated that the host-pathogen interactions enhanced enzymatic activity and microbial metabolism in the liver, lung, and gut, potentially constituting the dominant action mechanism of vaccine-driven metabolic regulation. Thus, our findings of this study highlight the potential of measuring exhaled VOCs as rapid, non-invasive biomarkers of viral infections. Furthermore, breathomics appears as an alternative for safety evaluation of biological agents and disease diagnosis.

Quantitatively Visualizing Airborne Disease Transmission Risks of Different Exhalation Activities through CO2 Imaging.

Aerosol transmission has played a leading role in COVID-19 pandemic. However, there is still a poor understanding about how it is transmitted. This work was designed to study the exhaled breath flow dynamics and transmission risks under different exhaling modes. Using an infrared photography device, exhaled flow characteristics of different breathing activities, such as deep breathing, dry coughing, and laughing, together with the roles of mouth and nose were characterized by imaging CO2 flow morphologies. Both mouth and nose played an important role in the disease transmission though in the downward direction for the nose. In contrast to the trajectory commonly modeled, the exhaled airflows appeared with turbulent entrainments and obvious irregular movements, particularly the exhalations involving mouth were directed horizontal and had a higher propagation capacity and transmission risk. While the cumulative risk was high for deep breathing, those transient ones from dry coughing, yawning, and laughing were also shown to be significant. Various protective measures including masks, canteen table shields, and wearable devices were visually demonstrated to be effective for altering the exhaled flow directions. This work is useful to understanding the risk of aerosol infection and guiding the formulation of its prevention and control strategies. Experimental data also provide important information for refining model boundary conditions.

Comparing associations between mood and breath alcohol concentration in the laboratory and natural environment.

OBJECTIVE: Associations between mood and drinking are part of many theoretical models of problematic alcohol use. Laboratory and ecological momentary assessment (EMA) research on associations between mood and drinking behavior has produced mixed findings, and these constructs are often measured using different methods depending on research context. The present study compares associations between mood and alcohol consumption across research contexts (laboratory vs. daily life) and measurement methods (breathalyzer vs. self-report). METHOD: Forty-five young adults (53% women, Mage = 24.5) who drank moderate-to-heavy amounts completed an alcohol administration session and then 6 weeks of EMA with ambulatory breathalyzer samples. Participants reported their current mood (happy, nervous, upset, and excited) in both the laboratory and during EMA. Momentary, day, and person-level mood variables were examined in multilevel models predicting objective alcohol consumption [breath alcohol concentration (BrAC); lab and EMA] and subjective consumption (self-reported drinking occurrence and number of drinks; EMA). RESULTS: We identified discrepant mood-BrAC associations across laboratory and EMA contexts. Momentary excitement was negatively associated with BrAC in the lab, but positively associated with BrAC during EMA (ps < .01). We also identified discrepancies within EMA depending on the alcohol consumption measure used (BrAC or self-reported number of drinks) and the level of analysis (momentary or day). CONCLUSIONS: Studies testing theoretical models involving directional mood-alcohol associations (e.g., affective reinforcement models) need to carefully consider how research context and methods may influence findings of associations between mood and drinking. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Potential for Early Noninvasive COVID-19 Detection Using Electronic-Nose Technologies and Disease-Specific VOC Metabolic Biomarkers.

The established efficacy of electronic volatile organic compound (VOC) detection technologies as diagnostic tools for noninvasive early detection of COVID-19 and related coronaviruses has been demonstrated from multiple studies using a variety of experimental and commercial electronic devices capable of detecting precise mixtures of VOC emissions in human breath. The activities of numerous global research teams, developing novel electronic-nose (e-nose) devices and diagnostic methods, have generated empirical laboratory and clinical trial test results based on the detection of different types of host VOC-biomarker metabolites from specific chemical classes. COVID-19-specific volatile biomarkers are derived from disease-induced changes in host metabolic pathways by SARS-CoV-2 viral pathogenesis. The unique mechanisms proposed from recent researchers to explain how COVID-19 causes damage to multiple organ systems throughout the body are associated with unique symptom combinations, cytokine storms and physiological cascades that disrupt normal biochemical processes through gene dysregulation to generate disease-specific VOC metabolites targeted for e-nose detection. This paper reviewed recent methods and applications of e-nose and related VOC-detection devices for early, noninvasive diagnosis of SARS-CoV-2 infections. In addition, metabolomic (quantitative) COVID-19 disease-specific chemical biomarkers, consisting of host-derived VOCs identified from exhaled breath of patients, were summarized as possible sources of volatile metabolic biomarkers useful for confirming and supporting e-nose diagnoses.

Breath analysis by ultra-sensitive broadband laser spectroscopy detects SARS-CoV-2 infection.

Rapid testing is essential to fighting pandemics such as coronavirus disease 2019 (COVID-19), the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Exhaled human breath contains multiple volatile molecules providing powerful potential for non-invasive diagnosis of diverse medical conditions. We investigated breath detection of SARS-CoV-2 infection using cavity-enhanced direct frequency comb spectroscopy (CE-DFCS), a state-of-the-art laser spectroscopic technique capable of a real-time massive collection of broadband molecular absorption features at ro-vibrational quantum state resolution and at parts-per-trillion volume detection sensitivity. Using a total of 170 individual breath samples (83 positive and 87 negative with SARS-CoV-2 based on reverse transcription polymerase chain reaction tests), we report excellent discrimination capability for SARS-CoV-2 infection with an area under the receiver-operating-characteristics curve of 0.849(4). Our results support the development of CE-DFCS as an alternative, rapid, non-invasive test for COVID-19 and highlight its remarkable potential for optical diagnoses of diverse biological conditions and disease states.

Portable Breath-Based Volatile Organic Compound Monitoring for the Detection of COVID-19 During the Circulation of the SARS-CoV-2 Delta Variant and the Transition to the SARS-CoV-2 Omicron Variant.

Importance: Breath analysis has been explored as a noninvasive means to detect COVID-19. However, the impact of emerging variants of SARS-CoV-2, such as Omicron, on the exhaled breath profile and diagnostic accuracy of breath analysis is unknown. Objective: To evaluate the diagnostic accuracies of breath analysis on detecting patients with COVID-19 when the SARS-CoV-2 Delta and Omicron variants were most prevalent. Design, Setting, and Participants: This diagnostic study included a cohort of patients who had positive and negative test results for COVID-19 using reverse transcriptase polymerase chain reaction between April 2021 and May 2022, which covers the period when the Delta variant was overtaken by Omicron as the major variant. Patients were enrolled through intensive care units and the emergency department at the University of Michigan Health System. Patient breath was analyzed with portable gas chromatography. Main Outcomes and Measures: Different sets of VOC biomarkers were identified that distinguished between COVID-19 (SARS-CoV-2 Delta and Omicron variants) and non-COVID-19 illness. Results: Overall, 205 breath samples from 167 adult patients were analyzed. A total of 77 patients (mean [SD] age, 58.5 [16.1] years; 41 [53.2%] male patients; 13 [16.9%] Black and 59 [76.6%] White patients) had COVID-19, and 91 patients (mean [SD] age, 54.3 [17.1] years; 43 [47.3%] male patients; 11 [12.1%] Black and 76 [83.5%] White patients) had non-COVID-19 illness. Several patients were analyzed over multiple days. Among 94 positive samples, 41 samples were from patients in 2021 infected with the Delta or other variants, and 53 samples were from patients in 2022 infected with the Omicron variant, based on the State of Michigan and US Centers for Disease Control and Prevention surveillance data. Four VOC biomarkers were found to distinguish between COVID-19 (Delta and other 2021 variants) and non-COVID-19 illness with an accuracy of 94.7%. However, accuracy dropped substantially to 82.1% when these biomarkers were applied to the Omicron variant. Four new VOC biomarkers were found to distinguish the Omicron variant and non-COVID-19 illness (accuracy, 90.9%). Breath analysis distinguished Omicron from the earlier variants with an accuracy of 91.5% and COVID-19 (all SARS-CoV-2 variants) vs non-COVID-19 illness with 90.2% accuracy. Conclusions and Relevance: The findings of this diagnostic study suggest that breath analysis has promise for COVID-19 detection. However, similar to rapid antigen testing, the emergence of new variants poses diagnostic challenges. The results of this study warrant additional evaluation on how to overcome these challenges to use breath analysis to improve the diagnosis and care of patients.

Data-driven design of a multiplexed, peptide-sensitized transistor to detect breath VOC markers of COVID-19.

Exhaled human breath contains a rich mixture of volatile organic compounds (VOCs) whose concentration can vary in response to disease or other stressors. Using simulated odorant-binding proteins (OBPs) and machine learning methods, we designed a multiplex of short VOC- and carbon-binding peptide probes that detect a characteristic "VOC fingerprint". Specifically, we target VOCs associated with COVID-19 in a compact, molecular sensor array that directly transduces vapor composition into multi-channel electrical signals. Rapidly synthesizable, chimeric VOC- and solid-binding peptides were derived from selected OBPs using multi-sequence alignment with protein database structures. Selective peptide binding to targeted VOCs and sensor surfaces was validated using surface plasmon resonance spectroscopy and quartz crystal microbalance. VOC sensing was demonstrated by peptide-sensitized, exposed-channel carbon nanotube transistors. The data-to-device pipeline enables the development of novel devices for non-invasive monitoring, diagnostics of diseases, and environmental exposure assessment.

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.

Intelligent COVID-19 screening platform based on breath analysis.

The spread of coronavirus disease 2019 (COVID-19) results in an increasing incidence and mortality. The typical diagnosis technique for severe acute respiratory syndrome coronavirus 2 infection is reverse transcription polymerase chain reaction, which is relatively expensive, time-consuming, professional, and suffered from false-negative results. A reliable, non-invasive diagnosis method is in urgent need for the rapid screening of COVID-19 patients and controlling the epidemic. Here we constructed an intelligent system based on the volatile organic compound (VOC) biomarkers in human breath combined with machine learning models. The VOC profiles of 122 breath samples (65 of COVID-19 infections and 57 of controls) were identified with a portable gas chromatograph-mass spectrometer. Among them, eight VOCs exhibited significant differences (p< 0.001) between the COVID-19 and the control groups. The cross-validation algorithm optimized support vector machine (SVM) model was employed for the prediction of COVID-19 infection. The proposed SVM model performed a powerful capability in discriminating COVID-19 patients from healthy controls, with an accuracy of 97.3%, a sensitivity of 100%, a specificity of 94.1%, and a precision of 95.2%, and anF1 score of 97.6%. The SVM model was also compared with other common machine models, including artificial neural network,k-nearest neighbor, and logistic regression, and demonstrated obvious superiority in the prediction of COVID-19 infection. Furthermore, user-friendly software was developed based on the optimized SVM model. The developed intelligent platform based on breath analysis provides a new strategy for the point-of-care screening of COVID and shows great potential in clinical application.

Assessment of an e-nose performance for the detection of COVID-19 specific biomarkers.

Early, rapid and non-invasive diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is needed for the prevention and control of coronavirus disease 2019 (COVID-19). COVID-19 mainly affects the respiratory tract and lungs. Therefore, analysis of exhaled breath could be an alternative scalable method for reliable SARS-CoV-2 screening. In the current study, an experimental protocol using an electronic-nose ('e-nose') for attempting to identify a specific respiratory imprint in COVID-19 patients was optimized. Thus the analytical performances of the Cyranose®, a commercial e-nose device, were characterized under various controlled conditions. In addition, the effect of various experimental conditions on its sensor array response was assessed, including relative humidity, sampling time and flow rate, aiming to select the optimal parameters. A statistical data analysis was applied to e-nose sensor response using common statistical analysis algorithms in an attempt to demonstrate the possibility to detect the presence of low concentrations of spiked acetone and nonanal in the breath samples of a healthy volunteer. Cyranose®reveals a possible detection of low concentrations of these two compounds, in particular of 25 ppm nonanal, a possible marker of SARS-CoV-2 in the breath.

Determination of global chemical patterns in exhaled breath for the discrimination of lung damage in postCOVID patients using olfactory technology.

The objective of this work was to evaluate the use of an electronic nose and chemometric analysis to discriminate global patterns of volatile organic compounds (VOCs) in breath of postCOVID syndrome patients with pulmonary sequelae. A cross-sectional study was performed in two groups, the group 1 were subjects recovered from COVID-19 without lung damage and the group 2 were subjects recovered from COVID-19 with impaired lung function. The VOCs analysis was executed using a Cyranose 320 electronic nose with 32 sensors, applying principal component analysis (PCA), Partial Least Square-Discriminant Analysis, random forest, canonical discriminant analysis (CAP) and the diagnostic power of the test was evaluated using the ROC (Receiver Operating Characteristic) curve. A total of 228 participants were obtained, for the postCOVID group there are 157 and 71 for the control group, the chemometric analysis results indicate in the PCA an 84% explanation of the variability between the groups, the PLS-DA indicates an observable separation between the groups and 10 sensors related to this separation, by random forest, a classification error was obtained for the control group of 0.090 and for the postCOVID group of 0.088 correct classification. The CAP model showed 83.8% of correct classification and the external validation of the model showed 80.1% of correct classification. Sensitivity and specificity reached 88.9% (73.9%-96.9%) and 96.9% (83.7%-99.9%) respectively. It is considered that this technology can be used to establish the starting point in the evaluation of lung damage in postCOVID patients with pulmonary sequelae.

Design and Fabrication of a Gas Sensor Based on a Polypyrrole/Silver Nanoparticle Film for the Detection of Ammonia in Exhaled Breath of COVID-19 Patients Suffering from Acute Kidney Injury.

One of the serious complications of COVID-19 is acute kidney injury (AKI), leading to a decrease in kidney function and even death. The concentration of ammonia (NH3) in the exhaled breath (EB) of COVID-19 patients suffering from AKI symptoms will be significantly increased. In this work, the detection of breath NH3 was performed at gold interdigital electrodes modified with a soluble polypyrrole microparticle and silver nanoparticle film (Au-IDEs/S-PPyMPs/AgNPs) as a noninvasive chemiresistor gas sensor. The response behavior of unmodified and modified gas sensors toward NH3 and other interfering compounds was studied. The Au-IDEs/S-PPyMPs/AgNPs exhibited NH3 detection in the linear dynamic range of 1.00-19.23 ppm, with a limit of detection of 0.12 ppm. Finally, the fabricated gas sensor was used to monitor the NH3 concentration in the EB of COVID-19 patients suffering from AKI symptoms. For this purpose, the gas sensor was validated in 19 EB samples (seven COVID-19-positive patients, four COVID-19-negative patients, and eight post-COVID-19 patients). The gas sensor was directly exposed to the EB samples, followed by recording the changes in electrical resistance via a low-cost digital multimeter. The sensing mechanism was explained as the interaction between breath NH3 and sensing materials. The breath NH3 concentrations have a desirable correlation (R2 = 0.8463) with the estimated glomerular filtration rate (eGFR) values in COVID-19-positive patients. The fabricated gas sensor can distinguish COVID-19-positive patients suffering from AKI symptoms from COVID-19-negative patients and post-COVID-19 patients. The present work can pave the way for the development of a simple and efficient analytical approach for COVID-19 patients with AKI without the need for sample pretreatment.

Ultrasensitive NO Sensor Based on a Nickel Single-Atom Electrocatalyst for Preliminary Screening of COVID-19.

A new coronavirus, SARS-CoV-2, has caused the coronavirus disease-2019 (COVID-19) epidemic. A rapid and economical method for preliminary screening of COVID-19 may help to control the COVID-19 pandemic. Here, we report a nickel single-atom electrocatalyst that can be printed on a paper-printing sensor for preliminary screening of COVID-19 suspects by efficient detection of fractional exhaled nitric oxide (FeNO). The FeNO value is confirmed to be related to COVID-19 in our exploratory clinical study, and a machine learning model that can accurately classify healthy subjects and COVID-19 patients is established based on FeNO and other features. The nickel single-atom electrocatalyst consists of a single nickel atom with N2O2 coordination embedded in porous acetylene black (named Ni-N2O2/AB). A paper-printed sensor was fabricated with the material and showed ultrasensitive response to NO in the range of 0.3-180 ppb. This ultrasensitive sensor could be applied to preliminary screening of COVID-19 in everyday life.

Advanced setup for safe breath sampling and patient monitoring under highly infectious conditions in the clinical environment.

Being the proximal matrix, breath offers immediate metabolic outlook of respiratory infections. However, high viral load in exhalations imposes higher transmission risk that needs improved methods for safe and repeatable analysis. Here, we have advanced the state-of-the-art methods for real-time and offline mass-spectrometry based analysis of exhaled volatile organic compounds (VOCs) under SARS-CoV-2 and/or similar respiratory conditions. To reduce infection risk, the general experimental setups for direct and offline breath sampling are modified. Certain mainstream and side-stream viral filters are examined for direct and lab-based applications. Confounders/contributions from filters and optimum operational conditions are assessed. We observed immediate effects of infection safety mandates on breath biomarker profiles. Main-stream filters induced physiological and analytical effects. Side-stream filters caused only systematic analytical effects. Observed substance specific effects partly depended on compound's origin and properties, sampling flow and respiratory rate. For offline samples, storage time, -conditions and -temperature were crucial. Our methods provided repeatable conditions for point-of-care and lab-based breath analysis with low risk of disease transmission. Besides breath VOCs profiling in spontaneously breathing subjects at the screening scenario of COVID-19/similar test centres, our methods and protocols are applicable for moderately/severely ill (even mechanically-ventilated) and highly contagious patients at the intensive care.

A glimpse in post-COVID pathophysiology: the role of exhaled breath condensate pH as an early marker of residual alveolar inflammation.

BACKGROUND: Residual alveolar inflammation seems to be paramount in post-COVID pathophysiology. Currently, we still lack a reliable marker to detect and track alveolar phlogosis in these patients. Exhaled Breath Condensate (EBC) pH has robust evidences highlighting its correlation with lung phlogosis in various diseases. We aim to define the reliability of alveolar and bronchial EBC pH in the assessment and in the follow up of post-COVID-related inflammation. RESEARCH DESIGN AND METHODS: We enrolled 10 patients previously hospitalized due to COVID-19 pneumonia. We performed a complete follow-up after 3 months and 6 months from discharge. Each visit included routine blood tests, arterial blood gas analysis, 6-minute walking test, spirometry, diffusing capacity and body plethysmography. Finally, bronchial and alveolar EBC were collected at the end of each visit. RESULTS: Alveolar EBC pH was significantly lower than bronchial EBC pH at T1, alveolar EBC pH tended to be more acid after 3 months from hospital discharge compared to the same sample 6 months later. Serum inflammatory biomarkers showed no significant differences from T1 to T2. Alveolar EBC pH was positively correlated with neutrophil-lymphocyte ratio. CONCLUSIONS: Collecting EBC pH could help to understand pathophysiologic mechanism as well as monitoring alveolar inflammation in the post-COVID syndrome.