Diagnostic potential of breath analysis--focus on volatile organic compounds

A new sol-gel fluorescent sensor to track carbonyl compounds

Carbonyl compounds are ubiquitous quality trackers that provide information about food product degradation as well as air and water pollution levels. In addition, they are used as biomarkers for medical diagnoses. With more user-friendly sensors, their fast detection and easy quantification are highly relevant. The synthesis, characterization, and performance assessment of a new sensor based on aniline fluorescence to monitor carbonyls in real time is reported. A cost-effective synthesis using a straightforward sol-gel process led to the construction of a nontoxic silica-based material with high porosity, which can be used with almost no sample preparation. The material exhibits a rapid (< 1 min) fluorescence decrease upon interaction with carbonyl groups. The limit of detection is as low as ca. 5 × 10-4 mol·L-1 for hexanal, while fluorescence extinction occurs at much higher concentrations (5 × 10-1·mol L-1), which enables the sensor to be used with a very broad range of detection. Real-time monitoring is possible since the fluorescence loss correlates with the concentration of carbonyl moieties. The performance was validated in simulating as well as in real media, making this sensor suitable for use in a wide range of applications.

Volatile organic compound analysis of malignant pleural mesothelioma chorioallantoic membrane xenografts

Malignant pleural mesothelioma (MPM) is an aggressive cancer associated with asbestos exposure. MPM is often diagnosed late, at a point where limited treatment options are available, but early intervention could improve the chances of successful treatment for MPM patients. Biomarkers to detect MPM in at-risk individuals are needed to implement early diagnosis technologies. Volatile organic compounds (VOCs) have previously shown diagnostic potential as biomarkers when analysed in MPM patient breath. In this study, chorioallantoic membrane (CAM) xenografts of MPM cell lines were used as models of MPM tumour development for VOC biomarker discovery with the aim of generating targets for investigation in breath, biopsies or other complex matrices. VOC headspace analysis of biphasic or epithelioid MPM CAM xenografts was performed using solid-phase microextraction and gas chromatography-mass spectrometry. We successfully demonstrated the capture, analysis and separation of VOC signatures from CAM xenografts and controls. A panel of VOCs was identified that showed discrimination between MPM xenografts generated from biphasic and epithelioid cells and CAM controls. This is the first application of the CAM xenograft model for the discovery of VOC biomarkers associated with MPM histological subtypes. These findings support the potential utility of non-invasive VOC profiling from breath or headspace analysis of tissues for detection and monitoring of MPM.

Application of electronic nose technology in the diagnosis of gastrointestinal diseases: a review

Electronic noses (eNoses) are electronic bionic olfactory systems that use sensor arrays to produce response patterns to different odors, thereby enabling the identification of various scents. Gastrointestinal diseases have a high incidence rate and occur in 9 out of 10 people in China. Gastrointestinal diseases are characterized by a long course of symptoms and are associated with treatment difficulties and recurrence. This review offers a comprehensive overview of volatile organic compounds, with a specific emphasis on those detected via the eNose system. Furthermore, this review describes the application of bionic eNose technology in the diagnosis and screening of gastrointestinal diseases based on recent local and international research progress and advancements. Moreover, the prospects of bionic eNose technology in the field of gastrointestinal disease diagnostics are discussed.

BreathXplorer: Processing Online Breathomics Data Generated from Direct Analysis Using High-Resolution Mass Spectrometry

Nontargeted breath analysis in real time using high-resolution mass spectrometry (HRMS) is a promising approach for high coverage profiling of metabolites in human exhaled breath. However, the information-rich and unique non-Gaussian metabolic signal shapes of real-time HRMS-based data pose a significant challenge for efficient data processing. This work takes a typical real-time HRMS technique as an example, i.e. secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS), and presents BreathXplorer, an open-source Python package designed for the processing of real-time exhaled breath data comprising multiple exhalations. BreathXplorer is composed of four main modules. The first module applies either a topological algorithm or a Gaussian mixture model (GMM) to determine the start and end points of each exhalation. Next, density-based spatial clustering of applications with noise (DBSCAN) is employed to cluster m/z values belonging to the same metabolic feature, followed by applying an intensity relative standard deviation (RSD) filter to extract real breath metabolic features. BreathXplorer also offers functions of (1) feature alignment across the samples and (2) associating MS/MS spectra with their corresponding metabolic features for downstream compound annotation. Manual inspection of the metabolic features extracted from SESI-HRMS breath data suggests that BreathXplorer can achieve 100% accuracy in identifying the start and end points of each exhalation and acquire accurate quantitative measurements of each breath feature. In a proof-of-concept study on exercise breathomics using SESI-HRMS, BreathXplorer successfully reveals the significantly changed metabolites that are pertinent to exercise. BreathXplorer is publicly available on GitHub (https://github.com/HuanLab/breathXplorer). It provides a powerful and convenient-to-use tool for the researchers to process breathomics data obtained by directly analysis using HRMS.

Defining a core breath profile for healthy, non-human primates

Non-human primates remain the most useful and reliable pre-clinical model for many human diseases. Primate breath profiles have previously distinguished healthy animals from diseased, including non-human primates. Breath collection is relatively non-invasive, so this motivated us to define a healthy baseline breath profile that could be used in studies evaluating disease, therapies, and vaccines in non-human primates. A pilot study, which enrolled 30 healthy macaques, was conducted. Macaque breath molecules were sampled into a Tedlar bag, concentrated onto a thermal desorption tube, then desorbed and analyzed by comprehensive two-dimensional gas chromatography-time of flight mass spectrometry. These breath samples contained 2,017 features, of which 113 molecules were present in all breath samples. The core breathprint was dominated by aliphatic hydrocarbons, aromatic compounds, and carbonyl compounds. The data were internally validated with additional breath samples from a subset of 19 of these non-human primates. A critical core consisting of 23 highly abundant and invariant molecules was identified as a pragmatic breathprint set, useful for future validation studies in healthy primates.

Benchmarking breath analysis using peppermint approach with gas chromatography ion mobility spectrometer coupled to micro thermal desorber

The Peppermint Initiative, established within the International Association of Breath Research, introduced the peppermint protocol, a breath analysis benchmarking effort designed to address the lack of inter-comparability of outcomes across different breath sampling techniques and analytical platforms. Benchmarking with gas chromatography-ion mobility spectrometry (GC-IMS) using peppermint has been previously reported however, coupling micro-thermal desorption (µTD) to GC-IMS has not yet, been benchmarked for breath analysis. To benchmarkµTD-GC-IMS for breath analysis using the peppermint protocol. Ten healthy participants (4 males and 6 females, aged 20-73 years), were enrolled to give six breath samples into Nalophan bags via a modified peppermint protocol. Breath sampling after peppermint ingestion occurred over 6 h att= 60, 120, 200, 280, and 360 min. The breath samples (120 cm3) were pre-concentrated in theµTD before being transferred into the GC-IMS for detection. Data was processed using VOCal, including background subtractions, peak volume measurements, and room air assessment. During peppermint washout, eucalyptol showed the highest change in concentration levels, followed byα-pinene andβ-pinene. The reproducibility of the technique for breath analysis was demonstrated by constructing logarithmic washout curves, with the average linearity coefficient ofR2= 0.99. The time to baseline (benchmark) value for the eucalyptol washout was 1111 min (95% CI: 529-1693 min), obtained by extrapolating the average logarithmic washout curve. The study demonstrated thatµTD-GC-IMS is reproducible and suitable technique for breath analysis, with benchmark values for eucalyptol comparable to the gold standard GC-MS.

Exhaled VOC detection in lung cancer screening: a comprehensive meta-analysis

BACKGROUND

Lung cancer (LC), characterized by high incidence and mortality rates, presents a significant challenge in oncology. Despite advancements in treatments, early detection remains crucial for improving patient outcomes. The accuracy of screening for LC by detecting volatile organic compounds (VOCs) in exhaled breath remains to be determined.

METHODS

Our systematic review, following PRISMA guidelines and analyzing data from 25 studies up to October 1, 2023, evaluates the effectiveness of different techniques in detecting VOCs. We registered the review protocol with PROSPERO and performed a systematic search in PubMed, EMBASE and Web of Science. Reviewers screened the studies' titles/abstracts and full texts, and used QUADAS-2 tool for quality assessment. Then performed meta-analysis by adopting a bivariate model for sensitivity and specificity.

RESULTS

This study explores the potential of VOCs in exhaled breath as biomarkers for LC screening, offering a non-invasive alternative to traditional methods. In all studies, exhaled VOCs discriminated LC from controls. The meta-analysis indicates an integrated sensitivity and specificity of 85% and 86%, respectively, with an AUC of 0.93 for VOC detection. We also conducted a systematic analysis of the source of the substance with the highest frequency of occurrence in the tested compounds. Despite the promising results, variability in study quality and methodological challenges highlight the need for further research.

CONCLUSION

This review emphasizes the potential of VOC analysis as a cost-effective, non-invasive screening tool for early LC detection, which could significantly improve patient management and survival rates.

Advancements in breath-based diagnostics for pancreatic cancer: Current insights and future perspectives

Recent decades have seen a concerning surge in carcinogenic diseases, with cancer cases and deaths on the rise globally. Conventional diagnostic methods are often invasive and time-consuming, highlighting the need for fast, accurate, and painless alternatives. Non-invasive exhaled breath analysis emerges as a promising solution, with over 200 volatile organic compounds (VOCs) detected in exhaled air, showing potential as biomarkers for various diseases, including cancer. Despite the lack of standardized methodologies, advancements in analytical instruments have expanded detection capabilities, reaching 3500 VOCs. Studies have identified specific VOC patterns associated with different cancers, offering hope for non-invasive diagnosis. Techniques such as gas chromatography-mass spectrometry and electronic noses show promise in distinguishing between healthy individuals and cancer patients. However, further research and standardization are needed to realize the full clinical potential of breath-based diagnostics, particularly in the early detection of challenging cancers like pancreatic cancer.

Quantitation of circulating short-chain fatty acids in small volume blood samples from animals and humans

BACKGROUND

The role of gut microbiota in human health has been intensively studied and more recently shifted from emphasis on composition towards function. Function is partly mediated through formed metabolites. Short-chain fatty acids (SCFAs) such as acetate, propionate, and butyrate as well as their branched analogues represent major products from gut fermentation of dietary fibre and proteins, respectively. Robust and high-throughput analysis of SCFAs in small volume blood samples have proven difficult. Major obstacles come from the ubiquitous presence of SCFAs that leads to contaminations and unstable analytical results because of the high volatility of these small molecules. Comprehensive and comparable data on the variation of SCFAs in blood samples from different blood matrices and mammal species including humans is lacking. Therefore, our aim was to develop and evaluate a stable and robust method for quantitation of 8 SCFAs and related fermentation products in small volume blood plasma samples and to investigate their variation in humans and different animal species.

RESULTS

Derivatization was a successful approach for measurement of SCFAs in biological samples but quenching of the derivatization reaction was crucial to obtain long-term stability of the derivatized analytes. In total 9 compounds (including succinic acid) were separated in 5 min. The method was linear over the range 0.6-3200 nM formic (FA), acetic (AA), 0.3-1600 nM propionic (PA), and 0.16-800 nM for butyric (BA)-, isobutyric (IBA)-, valeric (VA)-, isovaleric (IVA)-, succinic (SA) and caproic acid (CA). The precision ranged ≤12 % within days and ≤28 % between days (except for CA and VA) in three different plasma quality control (QC) samples (29 batches analyzed over 3 months). The extraction recovery was on average 94 % for the different SCFAs. Typical interquartile range (IQR) concentrations (μM) of SCFAs in human plasma samples were 168 μM (FA), 64 μM (AA), 2.2 μM (PA), 0.54 μM (BA), 0.66 μM (IBA), 0.18 μM (VA), 0.40 μM (IVA), and 0.34 μM (CA). In total, 55 samples per batch/day were successfully analyzed and in total 5380 human plasma samples measured over a 3-year timespan.

SIGNIFICANCE

The developed UHPLC-MS based method was suitable for measuring SCFAs in small blood volume samples and enabled robust quantitative data.

Association between individual sensor behavior of an electronic nose and airways inflammation in children with asthma: a pilot study at alpine altitude climate

BACKGROUND

Markers of airway inflammation can be helpful in the management of childhood asthma. Residential activities, such as intensive asthma camps at alpine altitude climate (AAC), can help reduce bronchial inflammation in patients who fail to achieve optimal control of the disease. Analysis of volatile organic compounds (VOCs) can be obtained using electronic devices such as e-Noses. We aimed to identify alterations in urinary e-Nose sensors among children with asthma participating in an intensive camp at AAC and to investigate associations between urinary e-Nose analysis and airway inflammation.

METHODS

We analyzed data collected in children with asthma recruited between July and September 2020. All children were born and resided at altitudes below 600 m asl. Urinary VOCs (measured using the Cyranose 320® VOC analyzer), Fractional exhaled Nitric Oxide (FeNO) and spirometry were evaluated upon children's arrival at the Istituto Pio XII, Misurina (BL), Italy, at 1756 m asl (T0), and after 7 (T1) and 15 days (T2) of stay.

RESULTS

Twenty-two patients (68.2% males; median age: 14.5 years) were enrolled. From T0 to T1 and T2, the negative trend for FeNO was significant (p < .001). Significant associations were observed between e-Nose sensors S7 (p = .002), S12 (p = .013), S16 (p = .027), S17 (p = .017), S22 (p = .029), S29 (p = .021), S31 (p = .009) and ΔFeNO at T0-T1. ΔFeNO at T0-T2 was significantly associated with S17 (p = .015), S19 (p = .004), S21 (p = .020), S24 (p = .012), S25 (p = .018), S26 (p = .008), S27 (p = .002), S29 (p = .007), S30 (p = .013).

CONCLUSIONS

We showed that a decrease in FeNO levels after a short sojourn at AAC is associated with behaviors of individual urinary e-Nose sensors in children with asthma.

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

INTRODUCTION

Breath analysis using a chemical sensor array combined with machine learning algorithms may be applicable for detecting and screening lung cancer. In this study, we examined whether perioperative breath analysis can predict the presence of lung cancer using a Membrane-type Surface stress Sensor (MSS) array and machine learning.

METHODS

Patients who underwent lung cancer surgery at an academic medical center, Japan, between November 2018 and November 2019 were included. Exhaled breaths were collected just before surgery and about one month after surgery, and analyzed using an MSS array. The array had 12 channels with various receptor materials and provided 12 waveforms from a single exhaled breath sample. Boxplots of the perioperative changes in the expiratory waveforms of each channel were generated and Mann-Whitney U test were performed. An optimal lung cancer prediction model was created and validated using machine learning.

RESULTS

Sixty-six patients were enrolled of whom 57 were included in the analysis. Through the comprehensive analysis of the entire dataset, a prototype model for predicting lung cancer was created from the combination of array five channels. The optimal accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.809, 0.830, 0.807, 0.806, and 0.812, respectively.

CONCLUSION

Breath analysis with MSS and machine learning with careful control of both samples and measurement conditions provided a lung cancer prediction model, demonstrating its capacity for non-invasive screening of lung cancer.

Sub-PPB Detection with Gas-Phase Multiphoton Electron Extraction Spectroscopy under Ambient Conditions

Multiphoton electron extraction spectroscopy (MEES) is an advanced analytical technique that has demonstrated exceptional sensitivity and specificity for detecting molecular traces on solid and liquid surfaces. Building upon the solid-state MEES foundations, this study introduces the first application of MEES in the gas phase (gas-phase MEES), specifically designed for quantitative detection of gas traces at sub-part per billion (sub-PPB) concentrations under ambient atmospheric conditions. Our experimental setup utilizes resonant multiphoton ionization processes using ns laser pulses under a high electrical field. The generated photoelectron charges are recorded as a function of the laser's wavelength. This research showcases the high sensitivity of gas-phase MEES, achieving high spectral resolution with resonant peak widths less than 0.02 nm FWHM. We present results from quantitative analysis of benzene and aniline, two industrially and environmentally significant compounds, demonstrating linear responses in the sub-PPM and sub-PPB ranges. The enhanced sensitivity and resolution of gas-phase MEES offer a powerful approach to trace gas analysis, with potential applications in environmental monitoring, industrial safety, security screening, and medical diagnostics. This study confirms the advantages of gas-phase MEES over many traditional optical spectroscopic methods and demonstrates its potential in direct gas-trace sensing in ambient atmosphere.

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

BACKGROUND

Volatile organic compounds (VOCs) are a promising potential biomarker that may be able to identify the presence of cancers.

AIM

To identify exhaled breath VOCs that distinguish pancreatic ductal adenocarcinoma (PDAC) from intraductal papillary mucinous neoplasm (IPMN) and healthy volunteers.

METHODS

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.

RESULTS

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).

CONCLUSION

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.

Volatile organic compounds for diagnosis of early hepatocellular carcinoma in at-risk patients

BACKGROUND

Volatile organic compounds (VOCs) have been shown as promising biomarkers for hepatocellular carcinoma (HCC) diagnosis. We aimed to investigate the performance of VOCs for diagnosing early-stage HCC in patients at-risk for HCC.

METHODS

VOCs were identified in exhaled breath samples collected from 87 early-stage HCC patients, 90 cirrhotic patients, and 72 HBV-infected patients using thermal desorption-gas chromatography/field-asymmetric ion mobility spectrometry. The VOC levels were compared between the three groups. An association between VOCs and HCC was determined using logistic regression analysis. Diagnostic performance of VOCs was estimated using the AUROC and compared to serum alpha-fetoprotein (AFP).

RESULTS

The levels of acetone monomer, dimethyl sulfide, 1,4-pentadiene, isopropyl alcohol, and acetone dimer were significantly different between the three groups. After adjusting for liver function test and AFP, acetone dimer was significantly associated with HCC. Acetone dimer significantly outperformed AFP with 86.2 % vs. 61.2 % sensitivity, 87.6 % vs. 66.2 % specificity, 86.9 % vs. 63.5 % for accuracy, and AUROC of 0.908 vs. 0.665, p = 0.007, <0.001, <0.001, and 0.001, respectively, for differentiating between HCC and cirrhosis.

CONCLUSION

Acetone showed a better performance than AFP for diagnosing early HCC in at-risk patients. Further studies to validate the utility of VOCs as an HCC surveillance tool are needed.

Comb spectroscopy of CO2 produced from microbial metabolism

We have developed a direct frequency comb spectroscopy instrument, which we have tested on Saccharomyces cerevisiae (baker's yeast) by measuring its CO2 output and production rate as we varied the environmental conditions, including the amount and type of feed sugar, the temperature, and the amount of yeast. By feeding isotopically-enhanced sugar to the yeast, we demonstrate the capability of our device to differentiate between two isotopologues of CO2, with a concentration measurement precision of 260 ppm for 12C16O2 and 175 ppm for 13C16O2. We also demonstrate the ability of our spectrometer to measure the proportion of carbon in the feed sugar converted to CO2, and estimate the amount incorporated into the yeast biomass.

Interaction of the III-As monolayer with SARS-CoV-2 biomarkers: implications for biosensor development

The emergence of SARS-CoV-2 in 2019 led to the global COVID-19 pandemic, highlighting the urgency for developing cost-effective and non-invasive methods to detect diseases at an early stage. Human breath, rich in volatile organic compounds (VOCs), is promising for cost-effective and rapid disease detection, with specific VOCs like methanol, ethanal, butanone, acetone, and ethyl butyrate linked to COVID-19. Recent advances in biomarker detection and gas sensing with 2D materials, particularly III-As monolayers like BAs, GaAs, and AlAs, offer high sensitivity at low concentrations, providing a novel avenue for exploring their potential in detecting COVID-19 biomarkers. This article aims to examine the effects of adsorption on different properties of III-Arsenide (BAs, GaAs and AlAs) monolayers, particularly in connection with SARS-CoV-2 biomarkers. In order to examine the interaction between the monolayers and biomarkers, first-principles computations within the framework of density functional theory (DFT) are utilized. The present study involves an investigation of the modifications in the band structure, density of states (DOS), work function, electron density difference, and optical properties (reflectance and absorbance) of III-As monolayers, with the aim of assessing their viability for the detection of SARS-CoV-2 biomarkers along with interfering gases such as CO2 and H2O. It is observed that VOCs induce a notable change in the work function of GaAs which serves as an indicator of the presence of these biomarkers. However, the changes in work function are not as substantial as those for AlAs and BAs. Additionally, the chemiresistive sensitivity, optical sensitivity and recovery time of III-As are investigated. The findings suggest that the pristine GaAs monolayer displays a significant level of sensitivity and selectivity towards the SARS-CoV-2 biomarkers, rendering it a material with potential for utilization in sensing applications. Furthermore, it has been observed that the recovery time of the GaAs monolayer subsequent to its exposure to the VOC biomarkers lies within an acceptable threshold. Upon exposure to UV light, the recovery time is further reduced. The outcomes of our study indicate that GaAs monolayers exhibit considerable potential as chemiresistive, work function-based and optical sensors for the precise and discerning identification of VOCs linked to the SARS-CoV-2 virus compared to the other two III-As monolayers.

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 (adjustedP-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.

Breath Fingerprint of Colorectal Cancer Patients Based on the Gas Chromatography-Mass Spectrometry Analysis

The human body emits a multitude of volatile organic compounds (VOCs) via tissues and various bodily fluids or exhaled breath. These compounds collectively create a distinctive chemical profile, which can potentially be employed to identify changes in human metabolism associated with colorectal cancer (CRC) and, consequently, facilitate the diagnosis of this disease. The main goal of this study was to investigate and characterize the VOCs' chemical patterns associated with the breath of CRC patients and controls and identify potential expiratory markers of this disease. For this purpose, gas chromatography-mass spectrometry was applied. Collectively, 1656 distinct compounds were identified in the breath samples provided by 152 subjects. Twenty-two statistically significant VOCs (p-xylene; hexanal; 2-methyl-1,3-dioxolane; 2,2,4-trimethyl-1,3-pentanediol diisobutyrate; hexadecane; nonane; ethylbenzene; cyclohexanone; diethyl phthalate; 6-methyl-5-hepten-2-one; tetrahydro-2H-pyran-2-one; 2-butanone; benzaldehyde; dodecanal; benzothiazole; tetradecane; 1-dodecanol; 1-benzene; 3-methylcyclopentyl acetate; 1-nonene; toluene) were observed at higher concentrations in the exhaled breath of the CRC group. The elevated levels of these VOCs in CRC patients' breath suggest the potential for these compounds to serve as biomarkers for CRC.

Real-time breath analysis towards a healthy human breath profile

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

Advancing accuracy in breath testing for lung cancer: strategies for improving diagnostic precision in imbalanced data

BACKGROUND

Breath testing using an electronic nose has been recognized as a promising new technique for the early detection of lung cancer. Imbalanced data are commonly observed in electronic nose studies, but methods to address them are rarely reported.

OBJECTIVE

The objectives of this study were to assess the accuracy of electronic nose screening for lung cancer with imbalanced learning and to select the best mechanical learning algorithm.

METHODS

We conducted a case‒control study that included patients with lung cancer and healthy controls and analyzed metabolites in exhaled breath using a carbon nanotube sensor array. The study used five machine learning algorithms to build predictive models and a synthetic minority oversampling technique to address imbalanced data. The diagnostic accuracy of lung cancer was assessed using pathology reports as the gold standard.

RESULTS

We enrolled 190 subjects between 2020 and 2023. A total of 155 subjects were used in the final analysis, which included 111 lung cancer patients and 44 healthy controls. We randomly divided samples into one training set, one internal validation set, and one external validation set. In the external validation set, the summary sensitivity was 0.88 (95% CI 0.84-0.91), the summary specificity was 1.00 (95% CI 0.85-1.00), the AUC was 0.96 (95% CI 0.94-0.98), the pAUC was 0.92 (95% CI 0.89-0.96), and the DOR was 207.62 (95% CI 24.62-924.64).

CONCLUSION

Electronic nose screening for lung cancer is highly accurate. The support vector machine algorithm is more suitable for analyzing chemical sensor data from electronic noses.

Graphene as a Sensor for Lung Cancer: Insights into Adsorption of VOCs Using vdW DFT

The adsorption mechanism of individual volatile organic compounds (VOCs) on the surface of graphene is investigated using nonempirical van der Waals (vdW) density functional theory. The VOCs chosen as adsorbates are ethanol, benzene, and toluene, which are found in the exhaled breath of lung cancer patients. The most energetically favorable configurations of the adsorbed systems, adsorption energy profiles, charge transfer, and work function are calculated. The fundamental insight into the interactions between the considered VOC molecules and graphene through molecular doping, i.e., charge transfer, is estimated. It is found that the adsorption energy is highly sensitive to the vdW functionals. Adsorption energies calculated by revPBE-vdW are in good agreement with the available experimental data, and the revPBE-vdW functional can cover well the physical phenomena behind the adsorption of these VOCs on graphene. Bader charge analysis shows that 0.064, 0.042, and 0.061e of charge were transferred from the graphene surface to ethanol, benzene, and toluene, respectively. All of the considered VOCs act as electron acceptors from graphene. By analyzing the electronic structure of the adsorption systems, we found that the energy level of the highest occupied molecular orbitals of these considered VOCs is shifted backward toward the Fermi level. The interaction of the VOCs with the π and π* states of the C atoms in graphene breaks the symmetry of graphene, leading to the opening of a band gap at the Fermi level. The adsorption of these considered VOCs onto the pristine graphene produces a band gap of 5-12 meV.

Conductive and Eco-friendly Biomaterials-based Hydrogels for Noninvasive Epidermal Sensors: A Review

As noninvasive wearable electronic devices, epidermal sensors enable continuous, real-time, and remote monitoring of various human physiological parameters. Conductive biomaterials-based hydrogels as sensor matrix materials have good biocompatibility, biodegradability, and efficient stimulus response capabilities and are widely applied in motion monitoring, healthcare, and human-machine interaction. However, biomass hydrogel-based epidermal sensing devices still need excellent mechanical properties, prolonged stability, multifunctionality, and extensive practicality. Therefore, this paper reviews the common biomass hydrogel materials for epidermal sensing (proteins, polysaccharides, polyphenols, etc.) and the various types of noninvasive sensing devices (strain/pressure sensors, temperature sensors, glucose sensors, electrocardiograms, etc.). Moreover, this review focuses on the strategies of scholars to enhance sensor properties, such as strength, conductivity, stability, adhesion, and self-healing ability. This work will guide the preparation and optimization of high-performance biomaterials-based hydrogel epidermal sensors.

Impact of thermal desorption tubes on the variability of exhaled breath data

Due to the overall low abundance of volatile compounds in exhaled breath, it is necessary to preconcentrate the sample prior to traditional thermal desorption (TD) gas chromatography mass spectrometry analysis. While certain aspects of TD tubes, such as volatile storage, have been evaluated, many aspects remain uncharacterized. Two common TD tubes, Tenax TA and Biomonitoring 5TD tubes, were evaluated for background content and flow rate variability. The data illustrate that the Biomonitoring 5TD tubes have the highest number (23) and abundance of background contamination greater than 3x the mean noise when compared to Tenax TA (13) and empty tubes (9). Tentative identifications of the compounds in the background contamination experiment show that greater than 59% (16/27) of the compounds identified have been reported in the breath literature. The data illustrate the TD tube background abundance could account for more than 70% of the chromatographic signal from exhaled breath for these select compounds. Flow rate measurements of 200 Tenax TA and 200 Biomonitoring 5TD tubes show a large range in measured flow rates among the TD tubes (Tenax: 252.9-284.0 ml min-1, 5TD: 220.6-255.1 ml min-1). Finally, TD tubes of each type, Tenax TA and Biomonitoring 5TD, previously established to have high, medium, and low flow rates, show insignificant differences (p> 0.05) among the tubes of different flow rates, using both gas standards and an exhaled breath from a peppermint experiment. Collectively, these results establish overall background compounds attributed to each TD tube type tested. Additionally, while measured flow rate variability is present and plausibly impacts exhaled breath results, the data demonstrate no statistically significant difference was observed between tubes showing high, medium, and low flow rates from two separate sample types.

Volatile organic compounds analysis as promising biomarkers for Parkinson's disease diagnosis: A systematic review and meta-analysis

OBJECTIVE

Researchers are investigating the potential of volatile organic compounds (VOCs) obtained from exhaled breath and sebum as non-invasive tools for early Parkinson's disease (PD) diagnosis. The present study aims to assess the feasibility of using VOC analysis for PD diagnosis and determine the overall diagnostic accuracy of the proposed tests.

METHODS

We performed systematic searches based on the PRISMA guidelines to identify relevant studies on VOCs in PD diagnosis using exhaled breath or sebum samples. The selected articles were described, and meta-analysis was conducted on those that provided the sensitivity and specificity data.

RESULTS

Out of 1268 articles initially identified, 8 met the inclusion criteria and provided specific sensitivity and specificity data for PD, which were included in the current meta-analysis. The pooled analysis of these findings showed a mean area under the receiver operating characteristic curve of 0.85, a sensitivity of 0.81 (95% confidence interval (CI): 0.72, 0.88), and a specificity of 0.76 (95% CI: 0.66, 0.84).

CONCLUSION

The analysis of VOCs in exhaled breath and sebum has shown promise as a new avenue for non-invasive diagnosis of PD. VOCs' ability to distinguish PD from healthy controls suggests their potential clinical application in screening for the disease. Consequently, VOCs hold significant potential as biomarkers for PD diagnosis and offer a promising novel approach to identifying and diagnosing the condition.

Diabetes noninvasive diagnostics and monitoring through volatile biomarkers analysis in the exhaled breath using optical absorption spectroscopy

The review is aimed on the analysis the abilities of noninvasive diagnostics and monitoring of diabetes mellitus (DM) and DM-associated complications through volatile molecular biomarkers detection in the exhaled breath. The specific biochemical reactions in the body of DM patients and their associations with volatile molecular biomarkers in the breath are considered. The applications of optical spectroscopy methods, including UV, IR, and terahertz spectroscopy for DM-associated volatile molecular biomarkers measurements, are described. The applications of similar technique combined with machine learning methods in DM diagnostics using the profile of DM-associated volatile molecular biomarkers in exhaled air or "pattern-recognition" approach are discussed.

Conducting polymer composite-based biosensing materials for the diagnosis of lung cancer: A review

The development of a simple and fast cancer detection method is crucial since early diagnosis is a key factor in increasing survival rates for lung cancer patients. Among several diagnosis methods, the electrochemical sensor is the most promising one due to its outstanding performance, portability, real-time analysis, robustness, amenability, and cost-effectiveness. Conducting polymer (CP) composites have been frequently used to fabricate a robust sensor device, owing to their excellent physical and electrochemical properties as well as biocompatibility with nontoxic effects on the biological system. This review brings up a brief overview of the importance of electrochemical biosensors for the early detection of lung cancer, with a detailed discussion on the design and development of CP composite materials for biosensor applications. The review covers the electrochemical sensing of numerous lung cancer markers employing composite electrodes based on the conducting polyterthiophene, poly(3,4-ethylenedioxythiophene), polyaniline, polypyrrole, molecularly imprinted polymers, and others. In addition, a hybrid of the electrochemical biosensors and other techniques was highlighted. The outlook was also briefly discussed for the development of CP composite-based electrochemical biosensors for POC diagnostic devices.

Novel breath biomarkers identification for early detection of hepatocellular carcinoma and cirrhosis using ML tools and GCMS

According to WHO 2019, Hepatocellular carcinoma (HCC) is the fourth highest cause of cancer death worldwide. More precise diagnostic models are needed to enhance early HCC and cirrhosis quick diagnosis, treatment, and survival. Breath biomarkers known as volatile organic compounds (VOCs) in exhaled air can be used to make rapid, precise, and painless diagnoses. Gas chromatography and mass spectrometry (GCMS) are utilized to diagnose HCC and cirrhosis VOCs. In this investigation, metabolically generated VOCs in breath samples (n = 35) of HCC, (n = 35) cirrhotic, and (n = 30) controls were detected via GCMS and SPME. Moreover, this study also aims to identify diagnostic VOCs for distinction among HCC and cirrhosis liver conditions, which are most closely related, and cause misleading during diagnosis. However, using gas chromatography-mass spectrometry (GC-MS) to quantify volatile organic compounds (VOCs) is time-consuming and error-prone since it requires an expert. To verify GC-MS data analysis, we present an in-house R-based array of machine learning models that applies deep learning pattern recognition to automatically discover VOCs from raw data, without human intervention. All-machine learning diagnostic model offers 80% sensitivity, 90% specificity, and 95% accuracy, with an AUC of 0.9586. Our results demonstrated the validity and utility of GCMS-SMPE in combination with innovative ML models for early detection of HCC and cirrhosis-specific VOCs considered as potential diagnostic breath biomarkers and showed differentiation among HCC and cirrhosis. With these useful insights, we can build handheld e-nose sensors to detect HCC and cirrhosis through breath analysis and this unique approach can help in diagnosis by reducing integration time and costs without compromising accuracy or consistency.

Diagnosis of Carcinogenic Pathologies through Breath Biomarkers: Present and Future Trends

The assessment of volatile breath biomarkers has been targeted with a lot of interest by the scientific and medical communities during the past decades due to their suitability for an accurate, painless, non-invasive, and rapid diagnosis of health states and pathological conditions. This paper reviews the most relevant bibliographic sources aiming to gather the most pertinent volatile organic compounds (VOCs) already identified as putative cancer biomarkers. Here, a total of 265 VOCs and the respective bibliographic sources are addressed regarding their scientifically proven suitability to diagnose a total of six carcinogenic diseases, namely lung, breast, gastric, colorectal, prostate, and squamous cell (oesophageal and laryngeal) cancers. In addition, future trends in the identification of five other forms of cancer, such as bladder, liver, ovarian, pancreatic, and thyroid cancer, through perspective volatile breath biomarkers are equally presented and discussed. All the results already achieved in the detection, identification, and quantification of endogenous metabolites produced by all kinds of normal and abnormal processes in the human body denote a promising and auspicious future for this alternative diagnostic tool, whose future passes by the development and employment of newer and more accurate collection and analysis techniques, and the certification for utilisation in real clinical scenarios.

Water interference in the chromatographic analysis of exhaled breath samples: Challenges and mitigation strategies

This study demonstrates the adverse effects of water in exhaled breath samples on the accuracy of breath biomarker analysis when using gas chromatography. The presence of water in exhaled breath significantly modifies the retention times and peak areas of compounds, particularly for low-boiling, early eluting compounds. To tackle this issue, a two-step approach is introduced. The process begins with thorough desorption of the sorbent tube using a high split ratio and a short analysis duration, followed by a secondary analysis of the same tube. The efficacy of the new, straightforward approach was illustrated using humid breath samples and 57 compound standard mixture. This study highlights the importance of proper sample pretreatment and analysis to ensure reliable and accurate results in clinical research.

A feasibility study on exhaled breath analysis using UV spectroscopy to detect COVID-19

A 23-subject feasibility study is reported to assess how UV absorbance measurements on exhaled breath samples collected from silicon microreactors can be used to detect COVID-19. The silicon microreactor technology chemoselectively preconcentrates exhaled carbonyl volatile organic compounds and subsequent methanol elution provides samples for analysis. The underlying scientific rationale that viral infection will induce an increase in exhaled carbonyls appears to be supported by the results of the feasibility study. The data indicate statistically significant differences in measured UV absorbance values between healthy and symptomatic COVID-19 positive subjects in the wavelength range from 235 nm to 305 nm. Factors such as subject age were noted as potential confounding variables.

Putative volatile biomarkers of bovine tuberculosis infection in breath, skin and feces of cattle

Bovine tuberculosis (bTB) is an infectious disease with significant impact on animal health, public health and international trade. Standard bTB screening in live cattle consists in injecting tuberculin and measuring the swelling at the place of injection few days later. This procedure is expensive, time-consuming, logistically challenging, and is not conclusive before performing confirmatory tests and additional analysis. The analysis of the volatile organic compounds (VOCs) emitted by non-invasive biological samples can provide an alternative diagnostic approach suitable for bTB screening. In the present study, we analyzed VOC samples emitted through the breath, feces and skin of 18 cows diagnosed with bTB from three farms from Romania, as well as of 27 negative cows for bTB from the same farms. Analytical studies employing gas chromatography coupled to mass spectrometry revealed 80 VOCs emitted through the breath, 200 VOCs released by feces, and 80 VOCs emitted through the skin. Statistical analysis of these compounds allowed the identification of 3 tentative breath VOC biomarkers (acetone; 4-methyldecane; D-limonene), 9 tentative feces VOC biomarkers (toluene; [(1,1-dimethylethyl)thio]acetic acid; alpha-thujene; camphene; phenol; o-cymene; 3-(1,1-dimethylethyl)-2,2,4,4-tetramethyl-3-pentanol; 2,5-dimethylhexane-2,5-dihydroperoxide; 2,4-di-tert-butylphenol), and 3 tentative skin VOC biomarkers (ammonia; 1-methoxy-2-propanol; toluene). The possible pathway of these volatile biomarkers is discussed.

Research progress of electronic nose technology in exhaled breath disease analysis

Exhaled breath analysis has attracted considerable attention as a noninvasive and portable health diagnosis method due to numerous advantages, such as convenience, safety, simplicity, and avoidance of discomfort. Based on many studies, exhaled breath analysis is a promising medical detection technology capable of diagnosing different diseases by analyzing the concentration, type and other characteristics of specific gases. In the existing gas analysis technology, the electronic nose (eNose) analysis method has great advantages of high sensitivity, rapid response, real-time monitoring, ease of use and portability. Herein, this review is intended to provide an overview of the application of human exhaled breath components in disease diagnosis, existing breath testing technologies and the development and research status of electronic nose technology. In the electronic nose technology section, the three aspects of sensors, algorithms and existing systems are summarized in detail. Moreover, the related challenges and limitations involved in the abovementioned technologies are also discussed. Finally, the conclusion and perspective of eNose technology are presented.

Determining the clinical utility of a breath test for screening an asbestos-exposed population for pleural mesothelioma: baseline results

Pleural mesothelioma (PM) is an aggressive cancer of the serosal lining of the thoracic cavity, predominantly caused by asbestos exposure. Due to nonspecific symptoms, PM is characterized by an advanced-stage diagnosis, resulting in a dismal prognosis. However, early diagnosis improves patient outcome. Currently, no diagnostic biomarkers or screening tools are available. Therefore, exhaled breath was explored as this can easily be obtained and contains volatile organic compounds, which are considered biomarkers for multiple (patho)physiological processes. A breath test, which differentiates asbestos-exposed (AEx) individuals from PM patients with 87% accuracy, was developed. However, before being implemented as a screening tool, the clinical utility of the test must be determined. Occupational AEx individuals underwent annual breath tests using multicapillary column/ion mobility spectrometry. A baseline breath test was taken and their individual risk of PM was estimated. PM patients were included as controls. In total, 112 AEx individuals and six PM patients were included in the first of four screening rounds. All six PM patients were correctly classified as having mesothelioma (100% sensitivity) and out of 112 AEx individuals 78 were classified by the breath-based model as PM patients (30% specificity). Given the large false positive outcome, the breath test will be repeated annually for three more consecutive years to adhere to the 'test, re-test' principle and improve the false positivity rate. A low-dose computed tomography scan in those with two consecutive positive tests will correlate test positives with radiological findings and the possible growth of a pleural tumor. Finally, the evaluation of the clinical value of a breath-based prediction model may lead to the initiation of a screening program for early detection of PM in Aex individuals, which is currently lacking. This clinical study received approval from the Antwerp University Hospital Ethics Committee (B300201837007).

Prediction of systemic free and total valproic acid by off-line analysis of exhaled breath in epileptic children and adolescents

Therapeutic drug monitoring (TDM) of medications with a narrow therapeutic window is a common clinical practice to minimize toxic effects and maximize clinical outcomes. Routine analyses rely on the quantification of systemic blood concentrations of drugs. Alternative matrices such as exhaled breath are appealing because of their inherent non-invasive nature. This is especially the case for pediatric patients. We have recently showcased the possibility of predicting systemic concentrations of valproic acid (VPA), an anti-seizure medication by real-time breath analysis in two real clinical settings. This approach, however, comes with the limitation of the patients having to physically exhale into the mass spectrometer. This restricts the possibility of sampling from patients not capable or available to exhale into the mass spectrometer located on the hospital premises. In this work, we developed an alternative method to overcome this limitation by collecting the breath samples in customized bags and subsequently analyzing them by secondary electrospray ionization coupled to high-resolution mass spectrometry (SESI-HRMS). A total ofn= 40 patients (mean ± SD, 11.5 ± 3.5 y.o.) diagnosed with epilepsy and taking VPA were included in this study. The patients underwent three measurements: (i) serum concentrations of total and free VPA, (ii) real-time breath analysis and (iii) off-line analysis of exhaled breath collected in bags. The agreement between the real-time and the off-line breath analysis methods was evaluated using Lin's concordance correlation coefficient (CCC). CCC was computed for ten mass spectral predictors of VPA concentrations. Lin's CCC was >0.6 for all VPA-associated features, except for two low-signal intensity isotopic peaks. Finally, free and total serum VPA concentrations were predicted by cross validating the off-line data set. Support vector machine algorithms provided the most accurate predictions with a root mean square error of cross validation of 29.0 ± 7.4 mg l-1and 3.9 ± 1.4 mg l-1for total and free VPA (mean ± SD), respectively. As a secondary analysis, we explored whether exhaled metabolites previously associated with side-effects and response to medication could be rendered by the off-line analysis method. We found that five features associated with side effects showed a CCC > 0.6, whereas none of the drug response-associated peaks reached this cut-off. We conclude that the clinically relevant free fraction of VPA can be predicted by this combination of off-line breath collection with rapid SESI-HRMS analysis. This opens new possibilities for breath based TDM in clinical settings.

Alveolar gradients in breath analysis. A pilot study with comparison of room air and inhaled air by simultaneous measurements using ion mobility spectrometry

Analyzing exhaled breath samples, especially using a highly sensitive method such as MCC/IMS (multi-capillary column/ion mobility spectrometry), may also detect analytes that are derived from exogenous production. In this regard, there is a discussion about the optimal interpretation of exhaled breath, either by considering volatile organic compounds (VOCs) only in exhaled breath or by additionally considering the composition of room air and calculating the alveolar gradients. However, there are no data on whether the composition and concentration of VOCs in room air are identical to those in truly inhaled air directly before analyzing the exhaled breath. The current study aimed to determine whether the VOCs in room air, which are usually used for the calculation of alveolar gradients, are identical to the VOCs in truly inhaled air. For the measurement of inhaled air and room air, two IMS, each coupled with an MCC that provided a pre-separation of the VOCs, were used in parallel. One device was used for sampling room air and the other for sampling inhaled air. Each device was coupled with a newly invented system that cleaned room air and provided a clean carrier gas, whereas formerly synthetic air had to be used as a carrier gas. In this pilot study, a healthy volunteer underwent three subsequent runs of sampling of inhaled air and simultaneous sampling and analysis of room air. Three of the selected 11 peaks (P4-unknown, P5-1-Butanol, and P9-Furan, 2-methyl-) had significantly higher intensities during inspiration than in room air, and four peaks (P1-1-Propanamine, N-(phenylmethylene), P2-2-Nonanone, P3-Benzene, 1,2,4-trimethyl-, and P11-Acetyl valeryl) had higher intensities in room air. Furthermore, four peaks (P6-Benzaldehyde, P7-Pentane, 2-methyl-, P8-Acetone, and P10-2-Propanamine) showed inconsistent differences in peak intensities between inhaled air and room air. To the best of our knowledge, this is the first study to compare simultaneous sampling of room air and inhaled air using MCC/IMS. The simultaneous measurement of inhaled air and room air showed that using room air for the calculation of alveolar gradients in breath analysis resulted in different alveolar gradient values than those obtained by measuring truly inhaled air.

Detection of abused drugs in human exhaled breath using mass spectrometry: A review

RATIONALE

Human breath analysis has been attracting increasing interest in the detection of abused drugs in forensic and clinical applications because of its noninvasive sampling and distinctive molecular information. Mass spectrometry (MS)-based approaches have been proven to be powerful tools for accurately analyzing exhaled abused drugs. The major advantages of MS-based approaches include high sensitivity, high specificity, and versatile couplings with various breath sampling methods.

METHODS

Recent advances in the methodological development of MS analysis of exhaled abused drugs are discussed. Breath collection and sample pretreatment methods for MS analysis are also introduced.

RESULTS

Recent advances in technical aspects of breath sampling methods are summarized, highlighting active and passive sampling. MS methods for detecting different exhaled abused drugs are reviewed, emphasizing their features, advantages, and limitations. The future trends and challenges in MS-based breath analysis of exhaled abused drugs are also discussed.

CONCLUSIONS

The coupling of breath sampling methods with MS approaches has been proven to be a powerful tool for the detection of exhaled abused drugs, offering highly attractive results in forensic investigations. MS-based detection of exhaled abused drugs in exhaled breath is a relatively new field and is still in the early stages of methodological development. New MS technologies promise a substantial benefit for future forensic analysis.

Early Prediction of Asthma

The clinical manifestations of asthma in children are highly variable, are associated with different molecular and cellular mechanisms, and are characterized by common symptoms that may diversify in frequency and intensity throughout life. It is a disease that generally begins in the first five years of life, and it is essential to promptly identify patients at high risk of developing asthma by using different prediction models. The aim of this review regarding the early prediction of asthma is to summarize predictive factors for the course of asthma, including lung function, allergic comorbidity, and relevant data from the patient's medical history, among other factors. This review also highlights the epigenetic factors that are involved, such as DNA methylation and asthma risk, microRNA expression, and histone modification. The different tools that have been developed in recent years for use in asthma prediction, including machine learning approaches, are presented and compared. In this review, emphasis is placed on molecular mechanisms and biomarkers that can be used as predictors of asthma in children.

Identification of Volatile Markers of Colorectal Cancer from Tumor Tissues Using Volatilomic Approach

The human body releases numerous volatile organic compounds (VOCs) through tissues and various body fluids, including breath. These compounds form a specific chemical profile that may be used to detect the colorectal cancer CRC-related changes in human metabolism and thereby diagnose this type of cancer. The main goal of this study was to investigate the volatile signatures formed by VOCs released from the CRC tissue. For this purpose, headspace solid-phase microextraction gas chromatography-mass spectrometry was applied. In total, 163 compounds were detected. Both cancerous and non-cancerous tissues emitted 138 common VOCs. Ten volatiles (2-butanone; dodecane; benzaldehyde; pyridine; octane; 2-pentanone; toluene; p-xylene; n-pentane; 2-methyl-2-propanol) occurred in at least 90% of both types of samples; 1-propanol in cancer tissue (86% in normal one), acetone in normal tissue (82% in cancer one). Four compounds (1-propanol, pyridine, isoprene, methyl thiolacetate) were found to have increased emissions from cancer tissue, whereas eleven showed reduced release from this type of tissue (2-butanone; 2-pentanone; 2-methyl-2-propanol; ethyl acetate; 3-methyl-1-butanol; d-limonene; tetradecane; dodecanal; tridecane; 2-ethyl-1-hexanol; cyclohexanone). The outcomes of this study provide evidence that the VOCs signature of the CRC tissue is altered by the CRC. The volatile constituents of this distinct signature can be emitted through exhalation and serve as potential biomarkers for identifying the presence of CRC. Reliable identification of the VOCs associated with CRC is essential to guide and tune the development of advanced sensor technologies that can effectively and sensitively detect and quantify these markers.

Molecular modeling and simulation of transition metal-doped molybdenum disulfide biomarkers in exhaled gases for early detection of lung cancer

BACKGROUND

The presence of volatile organic compounds (VOCs) in the exhaled breath of lung cancer patients is the only available source for detecting the disease at its initial stage. Exhaled breath analysis depends purely on the performance of the biosensors. The interaction between VOCs and pristine MoS₂ is repulsive in nature. Therefore, modifying MoS₂ via surficial adsorption of the transition metal nickel is of prime importance. The surficial interaction of six VOCs with Ni-doped MoS₂ led to substantial variations in the structural and optoelectronic properties compared to those of the pristine monolayer. The remarkable improvement in the conductivity, thermostability, good sensing response, and recovery time of the sensor exposed to six VOCs revealed that a Ni-doped MoS₂ exhibits impressive properties for the detection of exhaled gases. Different temperatures have a significant impact on the recovery time. Humidity has no effect on the detection of exhaled gases upon exposure to VOCs. The obtained results may encourage the use of exhaled breath sensors by experimentalists and oncologists to enable potential advancements in lung cancer detection.

METHODS

The surface adsorption of transition metal and its interaction with volatile organic compounds on a MoS₂ surface was studied by using Spanish Initiative for Electronic Simulations with Thousands of Atoms (SIESTA). The pseudopotentials used in the SIESTA calculations are norm-conserving in their fully nonlocal forms. The atomic orbitals with finite support were used as a basis set, allowing unlimited multiple-zeta and angular momenta, polarization, and off-site orbitals. These basis sets are the key for calculating the Hamiltonian and overlap matrices in O(N) operations. The present hybrid density functional theory (DFT) is a combination of PW92 and RPBE methods. Additionally, the DFT+U approach was employed to accurately ascertain the coulombic repulsion in the transition elements.

Breath Testing in the Surgical Setting: Applications, Challenges, and Future Perspectives

BACKGROUND

The potential for exhaled breath to be a valuable diagnostic tool is often overlooked as it can be difficult to imagine how a barely visible sample of breath could hold such a rich source of information about the state of our health. However, technological advances over the last 50 years have enabled us to detect volatile organic compounds (VOCs) present in exhaled breath, and this provides the key to understanding the wealth of information contained within these readily available samples.

SUMMARY

VOCs are produced as a by-product of metabolism; hence, changes in the underlying physiological processes will be reflected in the exact composition of VOCs in exhaled breath. It has been shown that characteristic changes occur in the breath VOC profile associated with certain diseases including cancer, which may enable the non-invasive detection of cancer at primary care level for patients with vague symptoms. The use of breath testing as a diagnostic tool has many advantages. It is non-invasive and quick, and the test is widely accepted by patients and clinicians. However, breath samples provide a snapshot of the VOCs present in a particular patient at a given point in time, so this can be heavily influenced by external factors such as diet, smoking, and the environment. These must all be accounted for when attempting to draw conclusions about disease status. This review focuses on the current applications for breath testing in the field of surgery, as well as discussing the challenges encountered with developing a breath test in a clinical environment. The future of breath testing in the surgical setting is also discussed, including the translation of breath research into clinical practice.

KEY MESSAGES

Analysis of VOCs in exhaled breath can identify the presence of underlying disease including cancer as well as other infectious or inflammatory conditions. Despite the patient factors, environmental factors, storage, and transport considerations that must be accounted for, breath testing demonstrates ideal characteristics for a triage test, being non-invasive, simple, and universally acceptable to patients and clinicians. Many novel biomarkers and diagnostic tests fail to translate into clinical practice because their potential clinical application does not align with the requirements and unmet needs of the healthcare sector. Non-invasive breath testing, however, has the great potential to revolutionise the early detection of diseases, such as cancer, in the surgical setting for patients with vague symptoms.

Hydrogen-Terminated Two-Dimensional Germanane/Silicane Alloys as Self-Powered Photodetectors and Sensors

2D monoelemental materials, particularly germanene and silicene (the single layer of germanium and silicon), which are the base materials for modern electronic devices demonstrated tremendous attraction for their 2D layer structure along with the tuneable electronics and optical band gap. The major shortcoming of synthesized thermodynamically very unstable layered germanene and silicene with their inclination toward oxidation was overcome by topochemical deintercalation of a Zintl phase (CaGe₂, CaGe_1.5Si_0.5, and CaGeSi) in a protic environment. The exfoliated Ge-H, Ge_0.75Si_0.25H, and Ge_0.5Si_0.5H were successfully synthesized and employed as the active layer for photoelectrochemical photodetectors, which showed broad response (420-940 nm), unprecedented responsivity, and detectivity on the order of 168 μA W^-1 and 3.45 × 10⁸ cm Hz^1/2 W^-1, respectively. The sensing capability of exfoliated germanane and silicane composites was explored using electrochemical impedance spectroscopy with ultrafast response and recovery time of less than 1 s. These positive findings serve as the application of exfoliated germanene and silicene composites and can pave a new path to practical applications in efficient future devices.

Smelling the Disease: Diagnostic Potential of Breath Analysis

Breath analysis is a relatively recent field of research with much promise in scientific and clinical studies. Breath contains endogenously produced volatile organic components (VOCs) resulting from metabolites of ingested precursors, gut and air-passage bacteria, environmental contacts, etc. Numerous recent studies have suggested changes in breath composition during the course of many diseases, and breath analysis may lead to the diagnosis of such diseases. Therefore, it is important to identify the disease-specific variations in the concentration of breath to diagnose the diseases. In this review, we explore methods that are used to detect VOCs in laboratory settings, VOC constituents in exhaled air and other body fluids (e.g., sweat, saliva, skin, urine, blood, fecal matter, vaginal secretions, etc.), VOC identification in various diseases, and recently developed electronic (E)-nose-based sensors to detect VOCs. Identifying such VOCs and applying them as disease-specific biomarkers to obtain accurate, reproducible, and fast disease diagnosis could serve as an alternative to traditional invasive diagnosis methods. However, the success of VOC-based identification of diseases is limited to laboratory settings. Large-scale clinical data are warranted for establishing the robustness of disease diagnosis. Also, to identify specific VOCs associated with illness states, extensive clinical trials must be performed using both analytical instruments and electronic noses equipped with stable and precise sensors.

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

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

A 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.

Analysis of the volatile organic compounds of epidural analgesia-ameliorated metabolic disorder in pregnant women with gestational diabetes mellitus based on untargeted metabolomics

Gestational diabetes mellitus (GDM) is a metabolic disease with an increasing annual incidence. Our previous observational study found that pregnant women with gestational diabetes had mild cognitive decline, which may be related to methylglyoxal (MGO). This study aimed to investigate whether labor pain aggravates the increase in MGO and explored the protective effect of epidural analgesia on metabolism in pregnant women with GDM based on solid-phase microextraction gas chromatography/mass spectrometry (SPME/GC-MS). Pregnant women with GDM were divided into a natural birth group (ND group, n = 30) and epidural analgesia group (PD group, n = 30). After fasting for ≥ 10 h overnight, venous blood samples were collected pre- and post-delivery to detect MGO, interleukin-6 (IL-6), and 8-epi-prostaglandin F2 alpha (8-iso-PGF2α) by ELISA. Serum samples were analyzed for volatile organic compounds (VOCs) using SPME-GC-MS. MGO, IL-6, and 8-iso-PGF2α levels in the ND group increased significantly post-delivery (P < 0.05) and were significantly higher in this group than the levels in the PD group (P < 0.05). Compared to the PD group, VOCs in the ND group increased significantly post-delivery. Further results indicated that propionic acid may be associated with metabolic disorders in pregnant women with GDM. Epidural analgesia can effectively improve the metabolism and immune function in pregnant women with GDM.

Volatile organic compounds as potential biomarkers of irritable bowel syndrome: A systematic review

BACKGROUND

Irritable bowel syndrome (IBS) is a prevalent gastrointestinal disease characterized by intermittent abdominal pain with altered bowel habits. Due to the condition's chronicity, patients suffer from poor quality of life, while the healthcare burden continues to grow. There is currently no reliable biomarker for the diagnosis of IBS, and the current approach depends on ruling-out organic diseases such as inflammatory bowel disease (IBD) and colorectal cancer by markers of inflammation like fecal calprotectin and C-reactive protein, or invasive procedures like a colonoscopy. Volatile organic compounds (VOCs) are growing in popularity as a biomarker due to its accuracy and ease of use.

PURPOSE

This systematic review of Medline and Cochrane's databases aimed to identify VOCs in the diagnosis of IBS. 57% of the studies proved that VOCs could identify IBS patients from healthy controls with AUC ranging from 0.83 to 0.99. Studies that distinguished IBS from IBD patients had slightly higher AUC of 0.87-0.98. Combining VOC into panels allowed the creation of discriminative algorithms. Though current research is limited by areas of heterogeneity in VOC sampling and small sample sizes, our review shows that VOC analysis has the potential to be a noninvasive point-of-care test that differentiates IBS from other organic gastrointestinal diseases.

Human exhaled air diagnostic markers for respiratory tract infections in subjects receiving mechanical ventilation

Diagnosing respiratory tract infections (RTIs) in critical care settings is essential for appropriate antibiotic treatment and lowering mortality. The current diagnostic method, which primarily relies on clinical symptoms, lacks sensitivity and specificity, resulting in incorrect or delayed diagnoses, putting patients at a heightened risk. In this study we developed a noninvasive diagnosis method based on collecting non-volatile compounds in human exhaled air. We hypothesized that non-volatile compound profiles could be effectively used for bacterial RTI diagnosis. Exhaled air samples were collected from subjects receiving mechanical ventilation diagnosed with or without bacterial RTI in intensive care units at the Johns Hopkins Hospital. Truncated proteoforms, a class of non-volatile compounds, were characterized by top-down proteomics, and significant features associated with RTI were identified using feature selection algorithms. The results showed that three truncated proteoforms, collagen type VI alpha three chain protein, matrix metalloproteinase-9, and putative homeodomain transcription factor II were independently associated with RTI with thep-values of 2.0 × 10^-5, 1.1 × 10^-4, and 1.7 × 10^-3, respectively, using multiple logistic regression. Furthermore, a score system named 'TrunScore' was constructed by combining the three truncated proteoforms, and the diagnostic accuracy was significantly improved compared to that of individual truncated proteoforms, with an area under the receiver operator characteristic curve of 96.9%. This study supports the ability of this noninvasive breath analysis method to provide an accurate diagnosis for RTIs in subjects receiving mechanical ventilation. The results of this study open the doors to be able to potentially diagnose a broad range of diseases using this non-volatile breath analysis technique.

Recent Advances in Sensing Materials Targeting Clinical Volatile Organic Compound (VOC) Biomarkers: A Review

In general, volatile organic compounds (VOCs) have a high vapor pressure at room temperature (RT). It has been reported that all humans generate unique VOC profiles in their exhaled breath which can be utilized as biomarkers to diagnose disease conditions. The VOCs available in exhaled human breath are the products of metabolic activity in the body and, therefore, any changes in its control level can be utilized to diagnose specific diseases. More than 1000 VOCs have been identified in exhaled human breath along with the respiratory droplets which provide rich information on overall health conditions. This provides great potential as a biomarker for a disease that can be sampled non-invasively from exhaled breath with breath biopsy. However, it is still a great challenge to develop a quick responsive, highly selective, and sensitive VOC-sensing system. The VOC sensors are usually coated with various sensing materials to achieve target-specific detection and real-time monitoring of the VOC molecules in the exhaled breath. These VOC-sensing materials have been the subject of huge interest and extensive research has been done in developing various sensing tools based on electrochemical, chemoresistive, and optical methods. The target-sensitive material with excellent sensing performance and capturing of the VOC molecules can be achieved by optimizing the materials, methods, and its thickness. This review paper extensively provides a detailed literature survey on various non-biological VOC-sensing materials including metal oxides, polymers, composites, and other novel materials. Furthermore, this review provides the associated limitations of each material and a summary table comparing the performance of various sensing materials to give a better insight to the readers.

From Piezoelectric Nanogenerator to Non-Invasive Medical Sensor: A Review

Piezoelectric nanogenerators (PENGs) not only are able to harvest mechanical energy from the ambient environment or body and convert mechanical signals into electricity but can also inform us about pathophysiological changes and communicate this information using electrical signals, thus acting as medical sensors to provide personalized medical solutions to patients. In this review, we aim to present the latest advances in PENG-based non-invasive sensors for clinical diagnosis and medical treatment. While we begin with the basic principles of PENGs and their applications in energy harvesting, this review focuses on the medical sensing applications of PENGs, including detection mechanisms, material selection, and adaptive design, which are oriented toward disease diagnosis. Considering the non-invasive in vitro application scenario, discussions about the individualized designs that are intended to balance a high performance, durability, comfortability, and skin-friendliness are mainly divided into two types: mechanical sensors and biosensors, according to the key role of piezoelectric effects in disease diagnosis. The shortcomings, challenges, and possible corresponding solutions of PENG-based medical sensing devices are also highlighted, promoting the development of robust, reliable, scalable, and cost-effective medical systems that are helpful for the public.