Values for victims and vectors of disease.

John and Curran have convincingly shown that Scanlonian contractualism is a valuable resource for evaluating pandemic response policies, and that we should reject cost-benefit analysis in favour of a contractualist framework. However, they fail to consider the part of contractualism that Scanlon constructed precisely to deal with the question of when the state can restrict individuals from making choices that are harmful to themselves and others: the value of choice view (VoC). In doing so, they leave it open for opponents of lockdowns to misuse contractualism to justify mistaken policies. This is because the most powerful contractualist objections to locking down are likely to be based on the VoC.When we apply the value of choice view (VoC), we see that a lockdown policy's justifiability depends on the extent to which particular values of choice are found to be threatened by the policy in question, and what safeguards policy-makers have put in place to increase the value of choice and protect people from the harmful consequences of lockdown. Without the VoC, it is harder to explain why lockdowns, to be non-rejectable, must have certain features. With the VoC, the case for contractualism over cost benefit analysis (CBA) can be made even stronger.

[Impacts of COVID-19 Lockdown on Air Quality in Shenzhen in Spring 2022].

The short-term reduction of air pollutant emissions is an important emergency control measure for avoiding air pollution exceedances in Chinese cities. However, the impacts of short-term emission reductions on the air qualities in southern Chinese cities in spring has not been fully explored. We analyzed the changes in air quality in Shenzhen, Guangdong before, during, and after a city-wide lockdown associated with COVID-19 control during March 14 to 20, 2022. Stable weather conditions prevailed before and during the lockdown, such that local air pollution was strongly affected by local emissions. In-situ measurements and WRF-GC simulations over the Pearl River Delta (PRD) both showed that, due to reductions in traffic emissions during the lockdown, the concentrations of nitrogen dioxide (NO2), respirable particulate matter (PM10), and fine particulate matters (PM2.5) in Shenzhen decreased by (-26±9.5)%, (-28±6.4)%, and (-20±8.2)%, respectively. However, surface ozone (O3) concentration did not change significantly[(-1.0±6.5)%]. TROPOMI satellite observations of formaldehyde and nitrogen dioxide column concentrations indicated that the ozone photochemistry in the PRD in spring 2022 was mainly controlled by the volatile organic compound (VOCs) concentrations and was not sensitive to the reduction in nitrogen oxide (NOx) concentrations. Reduction in NOx may even have increased O3, because the titration of O3 by NOx was weakened. Due to the small spatial-temporal extent of emission reductions, the air quality effects caused by this short-term urban-scale lockdown were weaker than the air quality effects across China during the widespread COVID-19 lockdown in 2020. Future air quality management in South China cities should consider the impacts of NOx emission reduction on ozone and focus on the co-reduction scenarios of NOx and VOCs.

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.

Impacts of COVID-19's restriction measures on personal exposure to VOCs and aldehydes in Taipei City.

The Coronavirus Disease 2019 (COVID-19) pandemic provided an unprecedented natural experiment, that allowed us to investigate the impacts of different restrictive measures on personal exposure to specific volatile organic compounds (VOCs) and aldehydes and resulting health risks in the city. Ambient concentrations of the criteria air pollutants were also evaluated. Passive sampling for VOCs and aldehydes was conducted for graduate students and ambient air in Taipei, Taiwan, during the Level 3 warning (strict control measures) and Level 2 alert (loosened control measures) of the COVID-19 pandemic in 2021-2022. Information on the daily activities of participants and on-road vehicle counts nearby the stationary sampling site during the sampling campaigns were recorded. Generalized estimating equations (GEE) with adjusted meteorological and seasonal variables were used to estimate the effects of control measures on average personal exposures to the selected air pollutants. Our results showed that ambient CO and NO2 concentrations in relation to on-road transportation emissions were significantly reduced, which led to an increase in ambient O3 concentrations. Exposure to specific VOCs (benzene, methyl tert-butyl ether (MTBE), xylene, ethylbenzene, and 1,3-butadiene) associated with automobile emissions were remarkably decreased by ~40-80 % during the Level 3 warning, resulting in 42 % and 50 % reductions of total incremental lifetime cancer risk (ILCR) and hazard index (HI), respectively, compared with the Level 2 alert. In contrast, the exposure concentration and calculated health risks in the selected population for formaldehyde increased by ~25 % on average during the Level 3 warning. Our study improves knowledge of the influence of a series of anti-COVID-19 measures on personal exposure to specific VOCs and aldehydes and its mitigations.

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.

Measuring the quantity of harmful volatile organic compounds inhaled through masks.

An increase in the concentration of environmental particulate matter and the spread of the COVID-19 virus have dramatically increased our time spent wearing masks. If harmful chemicals are released from these masks, there may be harmful effects on human health. In this study, the concentration of volatile organic compounds (VOCs) emitted from some commonly used masks was assessed qualitatively and quantitatively under diverse conditions (including different mask material types, time between opening the product and wearing, and mask temperature). In KF94 masks, 1-methoxy-2-propanol (221 ± 356 µg m-3), N,N-dimethylacetamide (601 ± 450 µg m-3), n-hexane (268 ± 349 µg m-3), and 2-butanone (160 ± 244 µg m-3) were detected at concentrations 22.9-147 times higher than those found in masks made from other materials, such as cotton and other functional fabrics. In addition, in KF94 masks, the total VOC (TVOC) released amounted to 3730 ± 1331 µg m-3, about 14 times more than that released by the cotton masks (267.5 ± 51.6 µg m-3). In some KF94 masks, TVOC concentration reached over 4000 µg m-3, posing a risk to human health (based on indoor air quality guidelines established by the German Environment Agency). Notably, 30 min after KF94 masks were removed from their packaging, TVOC concentrations decreased by about 80% from their initial levels to 724 ± 5.86 µg m-3; furthermore, 6 h after removal, TVOC concentrations were found to be less than 200 µg m-3. When the temperature of the KF94 masks was raised to 40 oC, TVOC concentrations increased by 119-299%. Since the types and concentrations of VOCs that will be inhaled by mask wearers vary depending on the mask use conditions, it is necessary to comply with safe mask wearing conditions.

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.

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.

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.

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.

A review on characteristics and mitigation strategies of indoor air quality in underground subway stations.

Due to the rapidly increasing ridership and the relatively enclosed underground space, the indoor air quality (IAQ) in underground subway stations (USSs) has attracted more public attention. The air pollutants in USSs, such as particulate matter (PM), CO2 and volatile organic compounds (VOCs), are hazardous to the health of passengers and staves. Firstly, this paper presents a systematic review on the characteristics and sources of air pollutants in USSs. According to the review work, the concentrations of PM, CO2, VOCs, bacteria and fungi in USSs are 1.1-13.2 times higher than the permissible concentration limits specified by WHO, ASHRAE and US EPA. The PM and VOCs are mainly derived from the internal and outdoor sources. CO2 concentrations are highly correlated with the passenger density and the ventilation rate while the exposure levels of bacteria and fungi depend on the thermal conditions and the settled dust. Then, the online monitoring, fault detection and prediction methods of IAQ are summarized and the advantages and disadvantages of these methods are also discussed. In addition, the available control strategies for improving IAQ in USSs are reviewed, and these strategies are classified and compared from different viewpoints. Lastly, challenges of the IAQ management in the context of the COVID-19 epidemic and several suggestions for underground stations' IAQ management in the future are put forward. This paper is expected to provide a comprehensive guidance for further research and design of the effective prevention measures on air pollutants in USSs so as to achieve more sustainable and healthy underground environment.

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.

Chemical profile of anti-epidemic sachet based on multiple sample preparation coupled with gas chromatography-mass spectrometry analysis combined with an embedded peaks resolution method and their action mechanisms.

The anti-epidemic sachet (Fang Yi Xiang Nang, FYXN) in traditional Chinese medicine (TCM) can prevent COVID-19 through volatile compounds that can play the role of fragrant and dampness, heat-clearing and detoxifying, warding off filth and pathogenic factors. Nevertheless, the anti-(mutant) SARS-CoV-2 compounds and the compounds related to the mechanism in vivo, and the mechanism of FYXN are still vague. In this study, the volatile compound set of FYXN was constructed by gas chromatography-mass spectrometry (GC-MS) based on multiple sample preparation methods, which include headspace (HS), headspace solid phase microextraction (HS-SPME) and pressurized liquid extraction (PLE). In addition, selective ion analysis (SIA) was used to resolve embedded chromatographic peaks present in HS-SPME results. Preliminary analysis of active compounds and mechanism of FYXN by network pharmacology combined with disease pathway information based on GC-MS results. A total of 96 volatile compounds in FYXN were collected by GC-MS analysis. 39 potential anti-viral compounds were screened by molecular docking. 13 key pathways were obtained by KEGG pathway analysis (PI3K-Akt signaling pathway, HIF-1 signaling pathway, etc.) for FYXN to prevent COVID-19. 16 anti-viral compounds (C95, C91, etc.), 10 core targets (RELA, MAPK1, etc.), and 16 key compounds related to the mechanism in vivo (C56, C30, etc.) were obtained by network analysis. The relevant pharmacological effects of key pathways and key compounds were verified by the literature. Finally, molecular docking was used to verify the relationship between core targets and key compounds, which are related to the mechanism in vivo. A variety of sample preparation methods coupled with GC-MS analysis combined with an embedded peaks resolution method and integrated with network pharmacology can not only comprehensively characterize the volatile compounds in FYXN, but also expand the network pharmacology research ideas, and help to discover the active compounds and mechanisms in FYXN.

Characteristics, sources and health risk assessment of atmospheric carbonyls during multiple ozone pollution episodes in urban Beijing: Insights into control strategies.

Carbonyls have attracted continuous attention due to their critical roles in atmospheric chemistry and their potential hazards to the ecological environment and human health. In this study, atmospheric carbonyls were measured during several ground-level-ozone (O3) pollution episodes at three urban sites (CRAES, IEP and BJUT) in Beijing in 2019 and 2020. Comparative analysis revealed that the carbonyl concentrations were 20.25 ± 6.91 ppb and 13.43 ± 5.13 ppb in 2019 and 2020 in Beijing, respectively, with a significant spatial trend from north to south, and carbonyl levels in urban Beijing were in an upper-intermediate range in China, and higher than those in other countries reported in the literature. A particularly noteworthy phenomenon is the consistency of carbonyl concentrations with variations in O3 concentrations. On O3 polluted days, the carbonyl concentrations were 1.3-1.5 times higher than those on non-O3 polluted days. Secondary formation contributed more to formaldehyde (FA) and acetaldehyde (AA) on O3 polluted days, while the anthropogenic emissions were more significant for acetone (AC) on non-O3 polluted days. Vehicle exhaust and solvent utilization were the main primary contributors to carbonyls. Due to reduced anthropogenic emissions caused by the COVID-19 lockdown and the "Program for Controlling Volatile Organic Compounds in 2020" in China, the contributions of primary emissions to carbonyls decreased in 2020 in Beijing. Human cancer risks to exposed populations from FA and AA increased with elevated O3 levels, and the risks still remained on non-O3 polluted days. The residents around the BJUT site might experience relatively higher human cancer risks than those around the other two sites. The findings in this study confirmed that atmospheric carbonyl pollution and its potential human health hazards cannot be ignored in urban Beijing; therefore, more strict control strategies for atmospheric carbonyls are urgently needed to better protect human health in Beijing in the future.

Multigroup prediction in lung cancer patients and comparative controls using signature of volatile organic compounds in breath samples.

Early detection of lung cancer is a crucial factor for increasing its survival rates among the detected patients. The presence of carbonyl volatile organic compounds (VOCs) in exhaled breath can play a vital role in early detection of lung cancer. Identifying these VOC markers in breath samples through innovative statistical and machine learning techniques is an important task in lung cancer research. Therefore, we proposed an experimental approach for generation of VOC molecular concentration data using unique silicon microreactor technology and further identification and characterization of key relevant VOCs important for lung cancer detection through statistical and machine learning algorithms. We reported several informative VOCs and tested their effectiveness in multi-group classification of patients. Our analytical results indicated that seven key VOCs, including C4H8O2, C13H22O, C11H22O, C2H4O2, C7H14O, C6H12O, and C5H8O, are sufficient to detect the lung cancer patients with higher mean classification accuracy (92%) and lower standard error (0.03) compared to other combinations. In other words, the molecular concentrations of these VOCs in exhaled breath samples were able to discriminate the patients with lung cancer (n = 156) from the healthy smoker and nonsmoker controls (n = 193) and patients with benign pulmonary nodules (n = 65). The quantification of carbonyl VOC profiles from breath samples and identification of crucial VOCs through our experimental approach paves the way forward for non-invasive lung cancer detection. Further, our experimental and analytical approach of VOC quantitative analysis in breath samples may be extended to other diseases, including COVID-19 detection.

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.