Breath analysis as a potential and non-invasive frontier in disease diagnosis: an overview

Precision detection of select human lung cancer biomarkers and cell lines using honeybee olfactory neural circuitry as a novel gas sensor

Human breath contains biomarkers (odorants) that can be targeted for early disease detection. It is well known that honeybees have a keen sense of smell and can detect a wide variety of odors at low concentrations. Here, we employ honeybee olfactory neuronal circuitry to classify human lung cancer volatile biomarkers at different concentrations and their mixtures at concentration ranges relevant to biomarkers in human breath from parts-per-billion to parts-per-trillion. We also validated this brain-based sensing technology by detecting human non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) cell lines using the 'smell' of the cell cultures. Different lung cancer biomarkers evoked distinct spiking response dynamics in the honeybee antennal lobe neurons indicating that those neurons encoded biomarker-specific information. By investigating lung cancer biomarker-evoked population neuronal responses from the honeybee antennal lobe, we classified individual human lung cancer biomarkers successfully (88% success rate). When we mixed six lung cancer biomarkers at different concentrations to create 'synthetic lung cancer' vs. 'synthetic healthy' human breath, honeybee population neuronal responses were able to classify those complex breath mixtures reliably with exceedingly high accuracy (93-100% success rate with a leave-one-trial-out classification method). Finally, we employed this sensor to detect human NSCLC and SCLC cell lines and we demonstrated that honeybee brain olfactory neurons could distinguish between lung cancer vs. healthy cell lines and could differentiate between different NSCLC and SCLC cell lines successfully (82% classification success rate). These results indicate that the honeybee olfactory system can be used as a sensitive biological gas sensor to detect human lung cancer.

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.

An inexpensive UV-LED photoacoustic based real-time sensor-system detecting exhaled trace-acetone

In this research we present a low-cost system for breath acetone analysis based on UV-LED photoacoustic spectroscopy. We considered the end-tidal phase of exhalation, which represents the systemic concentrations of volatile organic compounds (VOCs) - providing clinically relevant information about the human health. This is achieved via the development of a CO2-triggered breath sampling system, which collected alveolar breath over several minutes in sterile and inert containers. A real-time mass spectrometer is coupled to serve as a reference device for calibration measurements and subsequent breath analysis. The new sensor system provided a 3σ detection limit of 8.3 ppbV and an NNEA of 1.4E-9 Wcm-1Hz-0.5. In terms of the performed breath analysis measurements, 12 out of 13 fell within the error margin of the photoacoustic measurement system, demonstrating the reliability of the measurements in the field.

Highly Selective and Reversible Detection of Simulated Breath Hydrogen Sulfide Using Fe-Doped CuO Hollow Spheres: Enhanced Surface Redox Reaction by Multi-Valent Catalysts

The precise and reversible detection of hydrogen sulfide (H2S) at high humidity condition, a malodorous and harmful volatile sulfur compound, is essential for the self-assessment of oral diseases, halitosis, and asthma. However, the selective and reversible detection of trace concentrations of H2S (≈0.1 ppm) in high humidity conditions (exhaled breath) is challenging because of irreversible H2S adsorption/desorption at the surface of chemiresistors. The study reports the synthesis of Fe-doped CuO hollow spheres as H2S gas-sensing materials via spray pyrolysis. 4 at.% of Fe-doped CuO hollow spheres exhibit high selectivity (response ratio ≥ 34.4) over interference gas (ethanol, 1 ppm) and reversible sensing characteristics (100% recovery) to 0.1 ppm of H2S under high humidity (relative humidity 80%) at 175 °C. The effect of multi-valent transition metal ion doping into CuO on sensor reversibility is confirmed through the enhancement of recovery kinetics by doping 4 at.% of Ti- or Nb ions into CuO sensors. Mechanistic details of these excellent H2S sensing characteristics are also investigated by analyzing the redox reactions and the catalytic activity change of the Fe-doped CuO sensing materials. The selective and reversible detection of H2S using the Fe-doped CuO sensor suggested in this work opens a new possibility for halitosis self-monitoring.

Ultra-sensitive analysis of exhaled biomarkers in ozone-exposed mice via PAI-TOFMS assisted with machine learning algorithms

Ground-level ozone ranks sixth among common air pollutants. It worsens lung diseases like asthma, emphysema, and chronic bronchitis. Despite recent attention from researchers, the link between exhaled breath and ozone-induced injury remains poorly understood. This study aimed to identify novel exhaled biomarkers in ozone-exposed mice using ultra-sensitive photoinduced associative ionization time-of-flight mass spectrometry and machine learning. Distinct ion peaks for acetonitrile (m/z 42, 60, and 78), butyronitrile (m/z 70, 88, and 106), and hydrogen sulfide (m/z 35) were detected. Integration of tissue characteristics, oxidative stress-related mRNA expression, and exhaled breath condensate free-radical analysis enabled a comprehensive exploration of the relationship between ozone-induced biological responses and potential biomarkers. Under similar exposure levels, C57BL/6 mice exhibited pulmonary injury characterized by significant inflammation, oxidative stress, and cardiac damage. Notably, C57BL/6 mice showed free radical signals, indicating a distinct susceptibility profile. Immunodeficient non-obese diabetic Prkdc-/-/Il2rg-/- (NPI) mice exhibited minimal biological responses to pulmonary injury, with little impact on the heart. These findings suggest a divergence in ozone-induced damage pathways in the two mouse types, leading to alterations in exhaled biomarkers. Integrating biomarker discovery with comprehensive biopathological analysis forms a robust foundation for targeted interventions to manage health risks posed by ozone exposure.

User authentication system based on human exhaled breath physics

This work, in a pioneering approach, attempts to build a biometric system that works purely based on the fluid mechanics governing exhaled breath. We test the hypothesis that the structure of turbulence in exhaled human breath can be exploited to build biometric algorithms. This work relies on the idea that the extrathoracic airway is unique for every individual, making the exhaled breath a biomarker. Methods including classical multi-dimensional hypothesis testing approach and machine learning models are employed in building user authentication algorithms, namely user confirmation and user identification. A user confirmation algorithm tries to verify whether a user is the person they claim to be. A user identification algorithm tries to identify a user's identity with no prior information available. A dataset of exhaled breath time series samples from 94 human subjects was used to evaluate the performance of these algorithms. The user confirmation algorithms performed exceedingly well for the given dataset with over 97% true confirmation rate. The machine learning based algorithm achieved a good true confirmation rate, reiterating our understanding of why machine learning based algorithms typically outperform classical hypothesis test based algorithms. The user identification algorithm performs reasonably well with the provided dataset with over 50% of the users identified as being within two possible suspects. We show surprisingly unique turbulent signatures in the exhaled breath that have not been discovered before. In addition to discussions on a novel biometric system, we make arguments to utilise this idea as a tool to gain insights into the morphometric variation of extrathoracic airway across individuals. Such tools are expected to have future potential in the area of personalised medicines.

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.

Directly Self-Assembly of Aligned Ag NWs Films at the Air-Water Interface for the Detection of Pathogens in Artificial Breath Aerosols

Exhaled aerosols from humans, containing various pathogens, are crucial for early disease diagnosis. However, the traditional pathogen detection methods, such as polymerase chain reaction, are often slow and cumbersome due to complex sampling and procedures. This study introduces a novel, direct, and label-free detection method for pathogens in respiratory aerosols, utilizing a highly aligned silver nanowire (Ag NW) film combined with a filter membrane (Ag NWs@filter) as a surface-enhanced Raman spectroscopy-active substrate. A large-scale, ordered silver nanowire film was developed through a simplified self-assembly process. This process eliminates the need for an organic phase and complex surface modifications of Ag NWs, which are common in other preparation methods. Subsequently, the fabricated Ag NWs@filter demonstrated its capability to continuously capture and efficiently preconcentrate pathogens from aerosols, achieving a remarkable detection limit of 3 × 103 CFU/mL, demonstrated using Escherichia coli (E. coli) as a model pathogen. Moreover, the classification between E. coli and Pseudomonas aeruginosa achieved an overall accuracy of 96.5% by the principal component analysis with linear discriminant analysis models. The success of this sensing strategy illustrates its potential in detecting and identifying a variety of biomarkers present in respiratory aerosols, marking a significant step forward in the field of pathogen detection.

Non-invasive detection of renal disease biomarkers through breath analysis

Breath biomarkers are substances found in exhaled breath that can be used for non-invasive diagnosis and monitoring of medical conditions, including kidney disease. Detection techniques include mass spectrometry (MS), gas chromatography (GC), and electrochemical sensors. Biosensors, such as GC-MS or electronic nose (e-nose) devices, can be used to detect volatile organic compounds (VOCs) in exhaled breath associated with metabolic changes in the body, including the kidneys. E-nose devices could provide an early indication of potential kidney problems through the detection of VOCs associated with kidney dysfunction. This review discusses the sources of breath biomarkers for monitoring renal disease during dialysis and different biosensor approaches for detecting exhaled breath biomarkers. The future of using various types of biosensor-based real-time breathing diagnosis for renal failure is also discussed.

Breathomics Detect the Cardiovascular Disease: Delusion or Dilution of the Metabolomic Signature

Volatile organic compounds (VOCs) can be subdivided into exogenous and endogenous categories based on their origin. Analyzing the endogenous VOCs can provide insights into maintaining the internal organs' homeostasis. Despite the ongoing development and the current understanding, studies have suggested a link between cardiovascular metabolic alterations in patients with ischemic heart disease and elevated levels of ethane and isoprene detectable through exhaled breath analysis. Conversely, patients with chronic heart failure exhibit elevated acetone and pentane in their exhaled air. These substances originate from disturbances in the heart tissue, including cellular and subcellular modulations. Hypothetically, ethane levels in the exhaled breath analysis can demonstrate the severity of ischemic heart disease and, consequently, the risk of death in the next 10 years due to cardiovascular disease (CVD). Real-time direct mass spectrometry is the preferred method for assessing VOCs in exhaled breath analysis. The accuracy of this analysis depends on several factors, including the selection of the relevant breath fraction, the type of breath collection container (if used), and the pre-concentration technique.

Preservation of exhaled breath samples for analysis by off-line SESI-HRMS: proof-of-concept study

Secondary electrospray ionization-high resolution mass spectrometry (SESI-HRMS) is an established technique in the field of breath analysis characterized by its short analysis time, as well as high levels of sensitivity and selectivity. Traditionally, SESI-HRMS has been used for real-time breath analysis, which requires subjects to be at the location of the analytical platform. Therefore, it limits the possibilities for an introduction of this methodology in day-to-day clinical practice. However, recent methodological developments have shown feasibility on the remote sampling of exhaled breath in Nalophan® bags prior to measurement using SESI-HRMS. To further explore the range of applications of this method, we conducted a proof-of-concept study to assess the impact of the storage time of exhaled breath in Nalophan® bags at different temperatures (room temperature and dry ice) on the relative intensities of the compounds. In addition, we performed a detailed study of the storage effect of 27 aldehydes related to oxidative stress. After 2 h of storage, the mean of intensity of allm/zsignals relative to the samples analyzed without prior storage remained above 80% at both room temperature and dry ice. For the 27 aldehydes, the mean relative intensity losses were lower than 20% at 24 h of storage, remaining practically stable since the first hour of storage following sample collection. Furthermore, the mean relative intensity of most aldehydes in samples stored at room temperature was higher than those stored in dry ice, which could be related to water vapor condensation issues. These findings indicate that the exhaled breath samples could be preserved for hours with a low percentage of mean relative intensity loss, thereby allowing more flexibility in the logistics of off-line SESI-HRMS studies.

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.

Recent Advances in Gas Detection Methodologies with a Special Focus on Environmental Sensing and Health Monitoring Applications─A Critical Review

With the expansion of the Internet-of-Things (IoT), the use of gas sensors in the field of wearable technology, smart devices, and smart homes has increased manifold. These gas sensors have two key applications─one is the detection of gases present in the environment and the other is the detection of Volatile Organic Compounds (VOCs) that are found in the breath. In this review, we focus systematically on the advancements in the field of various spectroscopic methods such as mass spectrometry-based analysis and point-of-care approach to detect VOCs and gases for environmental monitoring and disease diagnosis. Additionally, we highlight the development of smart sensors that work on the principle of electrochemical detection and provide examples of the same through an extensive literature review. At the end of this review, we highlight various challenges and future perspectives.

Exhaled volatile organic compounds for diagnosis and monitoring of asthma

The asthmatic inflammatory process results in the generation of volatile organic compounds (VOCs), which are subsequently secreted by the airways. The study of these elements through gas chromatography-mass spectrometry (GC-MS), which can identify individual molecules with a discriminatory capacity of over 85%, and electronic-Nose (e-NOSE), which is able to perform a quick onboard pattern-recognition analysis of VOCs, has allowed new prospects for non-invasive analysis of the disease in an "omics" approach. In this review, we aim to collect and compare the progress made in VOCs analysis using the two methods and their instrumental characteristics. Studies have described the potential of GC-MS and e-NOSE in a multitude of relevant aspects of the disease in both children and adults, as well as differential diagnosis between asthma and other conditions such as wheezing, cystic fibrosis, COPD, allergic rhinitis and last but not least, the accuracy of these methods compared to other diagnostic tools such as lung function, FeNO and eosinophil count. Due to significant limitations of both methods, it is still necessary to improve and standardize techniques. Currently, e-NOSE appears to be the most promising aid in clinical practice, whereas GC-MS, as the gold standard for the structural analysis of molecules, remains an essential tool in terms of research for further studies on the pathophysiologic pathways of the asthmatic inflammatory process. In conclusion, the study of VOCs through GC-MS and e-NOSE appears to hold promise for the non-invasive diagnosis, assessment, and monitoring of asthma, as well as for further research studies on the disease.

Differences in the Volatilomic Urinary Biosignature of Prostate Cancer Patients as a Feasibility Study for the Detection of Potential Biomarkers

Prostate cancer (PCa) continues to be the second most common malignant tumour and the main cause of oncological death in men. Investigating endogenous volatile organic metabolites (VOMs) produced by various metabolic pathways is emerging as a novel, effective, and non-invasive source of information to establish the volatilomic biosignature of PCa. In this study, headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME/GC-MS) was used to establish the urine volatilomic profile of PCa and identify VOMs that can discriminate between the two investigated groups. This non-invasive approach was applied to oncological patients (PCa group, n = 26) and cancer-free individuals (control group, n = 30), retrieving a total of 147 VOMs from various chemical families. This included terpenes, norisoprenoid, sesquiterpenes, phenolic, sulphur and furanic compounds, ketones, alcohols, esters, aldehydes, carboxylic acid, benzene and naphthalene derivatives, hydrocarbons, and heterocyclic hydrocarbons. The data matrix was subjected to multivariate analysis, namely partial least-squares discriminant analysis (PLS-DA). Accordingly, this analysis showed that the group under study presented different volatomic profiles and suggested potential PCa biomarkers. Nevertheless, a larger cohort of samples is required to boost the predictability and accuracy of the statistical models developed.

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 sub-ppbv-level Acetone and Ethanol Quantum Cascade Laser Based Photoacoustic Sensor - Characterization and Multi-Component Spectra Recording in Synthetic Breath

Trace gas analysis in breath is challenging due to the vast number of different components. We present a highly sensitive quantum cascade laser based photoacoustic setup for breath analysis. Scanning the range between 8263 and 8270 nm with a spectral resolution of 48 pm, we are able to quantify acetone and ethanol within a typical breath matrix containing water and CO₂. We photoacoustically acquired spectra within this region of mid-infra-red light and prove that those spectra do not suffer from non-spectral interferences. The purely additive behavior of a breath sample spectrum was verified by comparing it with the independently acquired single component spectra using Pearson and Spearman correlation coefficients. A previously presented simulation approach is improved and an error attribution study is presented. With a 3σ detection limit of 6.5 ppbv in terms of ethanol and 250 pptv regarding acetone, our system is among the best performing presented so far.

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.

UHPLC-MS/MS-Based Identity Confirmation of Amino Acids Involved in Response to and Side Effects from Antiseizure Medications

Real-time breath analysis using secondary electrospray ionization coupled with high-resolution mass spectrometry is a fast and noninvasive method to access the metabolic state of a person. However, it lacks the ability to unequivocally assign mass spectral features to compounds due to the absence of chromatographic separation. This can be overcomed by using exhaled breath condensate and conventional liquid chromatography-mass spectrometry (LC-MS) systems. In this study, to the best of our knowledge, we confirm for the first time the presence of six amino acids (GABA, Oxo-Pro, Asp, Gln, Glu, and Tyr) previously reported to be involved in response to and side effects from antiseizure medications in exhaled breath condensate and by extension in exhaled human breath. Raw data are publicly available at MetaboLights with the accession number MTBLS6760.

Urinary volatile organic compounds for colorectal cancer screening, a systematic review and meta-analysis

BACKGROUND

The faecal immunochemical test (FIT) suffers from suboptimal performance and participation in colorectal cancer (CRC) screening. Urinary volatile organic compounds (VOCs) may be a useful alternative. We aimed to determine the diagnostic potential of urinary VOCs for CRC/adenomas. By relating VOCs to known pathways, we aimed to gain insight into the pathophysiology of colorectal neoplasia.

METHODS

A systematic search was performed in PubMed, EMBASE and Web of Science. Original studies on urinary VOCs for CRC/adenoma detection with a control group were included. QUADAS-2 tool was used for quality assessment. Meta-analysis was performed by adopting a bivariate model for sensitivity/specificity. Fagan's nomogram estimated the performance of combined FIT-VOC. Neoplasm-associated VOCs were linked to pathways using the KEGG database.

RESULTS

Sixteen studies-involving 837 CRC patients and 1618 controls-were included; 11 performed chemical identification and 7 chemical fingerprinting. In all studies, urinary VOCs discriminated CRC from controls. Pooled sensitivity and specificity for CRC based on chemical fingerprinting were 84% (95% CI 73-91%) and 70% (95% CI 63-77%), respectively. The most distinctive individual VOC was butanal (AUC 0.98). The estimated probability of having CRC following negative FIT was 0.38%, whereas 0.09% following negative FIT-VOC. Combined FIT-VOC would detect 33% more CRCs. In total 100 CRC-associated urinary VOCs were identified; particularly hydrocarbons, carboxylic acids, aldehydes/ketones and amino acids, and predominantly involved in TCA-cycle or alanine/aspartate/glutamine/glutamate/phenylalanine/tyrosine/tryptophan metabolism, which is supported by previous research on (colorectal)cancer biology. The potential of urinary VOCs to detect precancerous adenomas or gain insight into their pathophysiology appeared understudied.

CONCLUSION

Urinary VOCs hold potential for non-invasive CRC screening. Multicentre validation studies are needed, especially focusing on adenoma detection. Urinary VOCs elucidate underlying pathophysiologic processes.

Tetrakis(4-pyridylphenyl)ethylene-based Zinc Metal-Organic Framework with Aggregation-Induced Chemiluminescence Emission on a Paper Platform for Formaldehyde Detection in Breath

Volatile formaldehyde (FA) in exhaled breath (EB) is considered as a biomarker for lung cancer (LC). On-the-spot selective and sensitive detection of gaseous FA is rather important for LC screening and diagnosis. Herein, a tetrakis(4-pyridylphenyl)ethylene (Py-TPE)-based zinc metal-organic framework (MOF) with excellent aggregation-induced emission (AIE) property was utilized for absorption and selective detection of FA in EB. The porous Zn-Py-TPE served as a gaseous confinement cavity for the adsorption of FA in EB. Interestingly, Zn-Py-TPE was aggregated on paper, and then aggregation-induced chemiluminescence (CL) emission can be triggered by only adding bis(2,4,6-trichlorophenyl)oxalate (TCPO). Though without H₂O₂, the CL of Zn-Py-TPE-TCPO was enhanced greatly by FA. FA promoted the aggregation of Zn-Py-TPE on paper by forming halogen bonding between FA and Zn-Py-TPE, which contributed to the better selectivity. FA can also stimulate the production of more singlet oxygen (¹O₂) in the Zn-Py-TPE-TCPO CL system. Hence, FA could be detected via the proposed Zn-Py-TPE-TCPO system with a quantification linear range of 1.0-100.0 ppb and detection limit of 0.3 ppb. This portable, low-cost, and sensitive paper-based platform can achieve trace FA detection in EB and is expected to provide an on-the-spot screening platform for lung cancer.

VOCs from Exhaled Breath for the Diagnosis of Hepatocellular Carcinoma

BACKGROUND

Volatile organic compound (VOC) profiles as biomarkers for hepatocellular carcinoma (HCC) are understudied. We aimed to identify VOCs from the exhaled breath for HCC diagnosis and compare the performance of VOCs to alpha-fetoprotein (AFP). The performance of VOCs for predicting treatment response and the association between VOCs level and survival of HCC patients were also determined.

METHODS

VOCs from 124 HCC patients and 219 controls were identified using the XGBoost algorithm. ROC analysis was used to determine VOCs performance in differentiating HCC patients from controls and in discriminating treatment responders from non-responders. The association between VOCs and the survival of HCC patients was analyzed using Cox proportional hazard analysis.

RESULTS

The combination of 9 VOCs yielded 70.0% sensitivity, 88.6% specificity, and 75.0% accuracy for HCC diagnosis. When differentiating early HCC from cirrhotic patients, acetone dimer had a significantly higher AUC than AFP, i.e., 0.775 vs. 0.714, respectively, p = 0.001. Acetone dimer classified HCC patients into treatment responders and non-responders, with 95.7% sensitivity, 73.3% specificity, and 86.8% accuracy. Isopropyl alcohol was independently associated with the survival of HCC patients, with an adjusted hazard ratio of 7.23 (95%CI: 1.36-38.54), p = 0.020.

CONCLUSIONS

Analysis of VOCs is a feasible noninvasive test for diagnosing and monitoring HCC treatment response.

Harnessing insect olfactory neural circuits for detecting and discriminating human cancers

There is overwhelming evidence that presence of cancer alters cellular metabolic processes, and these changes are manifested in emitted volatile organic compound (VOC) compositions of cancer cells. Here, we take a novel forward engineering approach by developing an insect olfactory neural circuit-based VOC sensor for cancer detection. We obtained oral cancer cell culture VOC-evoked extracellular neural responses from in vivo insect (locust) antennal lobe neurons. We employed biological neural computations of the antennal lobe circuitry for generating spatiotemporal neuronal response templates corresponding to each cell culture VOC mixture, and employed these neuronal templates to distinguish oral cancer cell lines (SAS, Ca9-22, and HSC-3) vs. a non-cancer cell line (HaCaT). Our results demonstrate that three different human oral cancers can be robustly distinguished from each other and from a non-cancer oral cell line. By using high-dimensional population neuronal response analysis and leave-one-trial-out methodology, our approach yielded high classification success for each cell line tested. Our analyses achieved 76-100% success in identifying cell lines by using the population neural response (n = 194) collected for the entire duration of the cell culture study. We also demonstrate this cancer detection technique can distinguish between different types of oral cancers and non-cancer at different time-matched points of growth. This brain-based cancer detection approach is fast as it can differentiate between VOC mixtures within 250 ms of stimulus onset. Our brain-based cancer detection system comprises a novel VOC sensing methodology that incorporates entire biological chemosensory arrays, biological signal transduction, and neuronal computations in a form of a forward-engineered technology for cancer VOC detection.

Ability of animals to detect cancer odors

The olfactory capacity of animals has long been used by humans to help with various activities, e.g., hunting, detecting mines, locating people, and diagnosing diseases. Cancer is among the leading diseases causing death worldwide. Several recent studies have underscored the benefit of using scent to detect cancer, and this paper will review the studies using animals to detect tumor scents. A large variety of animals have been used for this purpose-dogs, rodents, insects, and nematodes-and have shown their capacity to detect cancer, with a success rate close to 90%. Here we discuss these studies, their methodologies, and the animal models used. Finally, we discuss the medical perspectives for cancer diagnosis using odors.

Highly Adsorptive Au-TiO2 Nanocomposites for the SERS Face Mask Allow the Machine-Learning-Based Quantitative Assay of SARS-CoV-2 in Artificial Breath Aerosols

Human respiratory aerosols contain diverse potential biomarkers for early disease diagnosis. Here, we report the direct and label-free detection of SARS-CoV-2 in respiratory aerosols using a highly adsorptive Au-TiO₂ nanocomposite SERS face mask and an ablation-assisted autoencoder. The Au-TiO₂ SERS face mask continuously preconcentrates and efficiently captures the oronasal aerosols, which substantially enhances the SERS signal intensities by 47% compared to simple Au nanoislands. The ultrasensitive Au-TiO₂ nanocomposites also demonstrate the successful detection of SARS-CoV-2 spike proteins in artificial respiratory aerosols at a 100 pM concentration level. The deep learning-based autoencoder, followed by the partial ablation of nondiscriminant SERS features of spike proteins, allows a quantitative assay of the 10¹-10⁴ pfu/mL SARS-CoV-2 lysates (comparable to 19-29 PCR cyclic threshold from COVID-19 patients) in aerosols with an accuracy of over 98%. The Au-TiO₂ SERS face mask provides a platform for breath biopsy for the detection of various biomarkers in respiratory aerosols.

Case Report: The effect of intravenous and oral antibiotics on the gut microbiome and breath volatile organic compounds over one year

BACKGROUND

Antimicrobial resistance (AMR) is a global concern and better understanding of the gut microbiome, a known 'amplifier' of AMR, may allow future clinicians to tailor therapy to minimise this risk and offer a personalised medicine approach. To examine the gut microbiome, patients are required to provide faecal samples; more convenient and cheaper solutions need to be found.

METHODS

As part of a pilot study looking at how routes of administration affect the gut microbiome in NHS patients undergoing routine clinical management for infections, we hypothesised that effects on the gut microbiome varied with the route and metabolism of antibiotic used, and these changes may be reflected in breath metabolites. We present a case report of a patient with an unusual clinical history, alongside breath metabolite and gut microbiome data taken before, during and after antibiotic therapy over a period of one year.

RESULTS

We noted a shift in the dominant Bacteroides strain in the patient's gut microbiome between pre- and post-therapy samples, along with an alteration in the composition of breath metabolites.

CONCLUSIONS

This study provides a framework for similar future work and highlights the need for further research on the relationships between changes in microbial gut communities and antimicrobial exposure, patient clinical status, and the metabolites of human breath.

Volatilomics as an Emerging Strategy to Determine Potential Biomarkers of Female Infertility: A Pilot Study

Due to its high prevalence, infertility has become a prominent public health issue, posing a significant challenge to modern reproductive medicine. Some clinical conditions that lead to female infertility include polycystic ovary syndrome (PCOS), endometriosis, and premature ovarian failure (POF). Follicular fluid (FF) is the biological matrix that has the most contact with the oocyte and can, therefore, be used as a predictor of its quality. Volatilomics has emerged as a non-invasive, straightforward, affordable, and simple method for characterizing various diseases and determining the effectiveness of their current therapies. In order to find potential biomarkers of infertility, this study set out to determine the volatomic pattern of the follicular fluid from patients with PCOS, endometriosis, and POF. The chromatographic data integration was performed through solid-phase microextraction (SPME), followed by gas chromatography–mass spectrometry (GC-MS). The findings pointed to specific metabolite patterns as potential biomarkers for the studied diseases. These open the door for further research into the relevant metabolomic pathways to enhance infertility knowledge and diagnostic tools. An extended investigation may, however, produce a new mechanistic understanding of the pathophysiology of the diseases.

Volatilomics: An Emerging and Promising Avenue for the Detection of Potential Prostate Cancer Biomarkers

Simple Summary

The lack of highly specific and sensitive biomarkers for the early detection of prostate cancer (PCa) is a major barrier to its management. Volatilomics emerged as a non-invasive, simple, inexpensive, and easy-to-use approach for cancer screening, characterization of disease progression, and follow-up of the treatment’s success. We provide a brief overview of the potential of volatile organic metabolites (VOMs) for the establishment of PCa biomarkers from non-invasive matrices. Endogenous VOMs have been investigated as potential biomarkers since changes in these VOMs can be characteristic of specific disease processes. Recent studies have shown that the conjugation of the prostate-specific antigen (PSA) screening with other methodologies, such as risk calculators, biomarkers, and imaging tests, can attenuate overdiagnosis and under-detection issues. This means that the combination of volatilomics with other methodologies could be extremely valuable for the differentiation of clinical phenotypes in a group of patients, providing more personalized treatments.

Abstract

Despite the spectacular advances in molecular medicine, including genomics, proteomics, transcriptomics, lipidomics, and personalized medicine, supported by the discovery of the human genome, prostate cancer (PCa) remains the most frequent malignant tumor and a leading cause of oncological death in men. New methods for prognostic, diagnostic, and therapy evaluation are mainly based on the combination of imaging techniques with other methodologies, such as gene or protein profiling, aimed at improving PCa management and surveillance. However, the lack of highly specific and sensitive biomarkers for its early detection is a major hurdle to this goal. Apart from classical biomarkers, the study of endogenous volatile organic metabolites (VOMs) biosynthesized by different metabolic pathways and found in several biofluids is emerging as an innovative, efficient, accessible, and non-invasive approach to establish the volatilomic biosignature of PCa patients, unravelling potential biomarkers. This review provides a brief overview of the challenges of PCa screening methods and emergent biomarkers. We also focus on the potential of volatilomics for the establishment of PCa biomarkers from non-invasive matrices.

3D-printed mouthpiece adapter for sampling exhaled breath in medical applications

The growing use of 3D printing in the biomedical sciences demonstrates its utility for a wide range of research and healthcare applications, including its potential implementation in the discipline of breath analysis to overcome current limitations and substantial costs of commercial breath sampling interfaces. This technical note reports on the design and construction of a 3D-printed mouthpiece adapter for sampling exhaled breath using the commercial respiration collector for in-vitro analysis (ReCIVA) device. The paper presents the design and digital workflow transition of the adapter and its fabrication from three commercial resins (Surgical Guide, Tough v5, and BioMed Clear) using a Formlabs Form 3B stereolithography (SLA) printer. The use of the mouthpiece adapter in conjunction with a pulmonary function filter is appraised in comparison to the conventional commercial silicon facemask sampling interface. Besides its lower cost - investment cost of the printing equipment notwithstanding - the 3D-printed adapter has several benefits, including ensuring breath sampling via the mouth, reducing the likelihood of direct contact of the patient with the breath sampling tubes, and being autoclaveable to enable the repeated use of a single adapter, thereby reducing waste and associated environmental burden compared to current one-way disposable facemasks. The novel adapter for breath sampling presented in this technical note represents an additional field of application for 3D printing that further demonstrates its widespread applicability in biomedicine.

Analyses of lung cancer-derived volatiles in exhaled breath and in vitro models

Lung cancer is one of the leading causes of cancer incidence and cancer-related deaths in the world. Early diagnosis of pulmonary tumors results in improved survival compared to diagnosis with more advanced disease, yet early disease is not reliably indicated by symptoms. Despite of the improved testing and monitoring techniques for lung cancer in the past decades, most diagnostic tests, such as sputum cytology or tissue biopsies, are invasive and risky, rendering them unfeasible for large population screening. The non-invasive analysis of exhaled breath has gained attentions as an innovative screening method to measure chemical alterations within the human volatilome profile as a result of oncogenesis. More importantly, volatile organic compounds (VOCs) have been correlated to the pathophysiology of disease since the source of volatile compounds relies mostly on endogenous metabolic processes that are altered as a result of disease onset. Therefore, studying VOCs emitted from human breath may assist lung cancer diagnosis, treatment monitoring, and other surveillance of this devastating disease. In this mini review, we evaluated recent human studies that have attempted to identify lung cancer-derived volatiles in exhaled breath of patients. We also examined reported volatiles in cell cultures of lung cancer to better understand the origins of cancer-associated VOCs. We highlight the metabolic processes of lung cancer that could be responsible for the endogenous synthesis of these VOCs and pinpoint the protein-encoding genes involved in these pathways. Finally, we highlight the potential value of a breath test in lung cancer and propose prominent areas for future research required for the incorporation of VOCs-based testing into clinical settings.

External Validation of a Breath-Based Prediction Model for Malignant Pleural Mesothelioma

Simple Summary

Malignant pleural mesothelioma (MPM) is an incurable asbestos-related thoracic cancer for which early-stage diagnosis remains a major challenge. Volatile organic compounds (VOCs), which are metabolites present in exhaled breath, have proven to be promising non-invasive biomarkers for MPM. However, without the necessary validation in an independent group of individuals, clinical implementation is hampered. Therefore, we performed external validation of a VOC-based prediction model for MPM, which initially revealed a poor performance and thus poor generalisability of the model. However, subsequent updating of the model improved its performance in the validation cohort, resulting in a more generalisable model with a screening potential, which could significantly impact MPM management.

Abstract

During the past decade, volatile organic compounds (VOCs) in exhaled breath have emerged as promising biomarkers for malignant pleural mesothelioma (MPM). However, as these biomarkers lack external validation, no breath test for MPM has been implemented in clinical practice. To address this issue, we performed the first external validation of a VOC-based prediction model for MPM. The external validation cohort was prospectively recruited, consisting of 47 MPM patients and 76 asbestos-exposed (AEx) controls. The predictive performance of the previously developed model was assessed by determining the degree of agreement between the predicted and actual outcome of the participants (patient/control). Additionally, to optimise the performance, the model was updated by refitting it to the validation cohort. External validation revealed a poor performance of the original model as the accuracy was estimated at only 41%, indicating poor generalisability. However, subsequent updating of the model improved the differentiation between MPM patients and AEx controls significantly (73% accuracy, 92% sensitivity, and 92% negative predictive value), substantiating the validity of the original predictors. This updated model will be more generalisable to the target population and exhibits key characteristics of a potential screening test for MPM, which could significantly impact MPM management.

Millimeter-wave gas spectroscopy for breath analysis of COPD patients in comparison to GC-MS

The analysis of human breath is a very active area of research, driven by the vision of a fast, easy, and non-invasive tool for medical diagnoses at the point of care. Millimeter-wave gas spectroscopy (MMWGS) is a novel, well-suited technique for this application as it provides high sensitivity, specificity and selectivity. Most of all, it offers the perspective of compact low-cost systems to be used in doctors' offices or hospitals. In this work, we demonstrate the analysis of breath samples acquired in a medical environment using MMWGS and evaluate validity, reliability, as well as limitations and perspectives of the method. To this end, we investigated 28 duplicate samples from chronic obstructive lung disease patients and compared the results to gas chromatography-mass spectrometry (GC-MS). The quantification of the data was conducted using a calibration-free fit model, which describes the data precisely and delivers absolute quantities. For ethanol, acetone, and acetonitrile, the results agree well with the GC-MS measurements and are as reliable as GC-MS. The duplicate samples deviate from the mean values by only 6% to 18%. Detection limits of MMWGS depend strongly on the molecular species. For example, acetonitrile can be traced down to 1.8 × 10^-12mol by the MMWGS system, which is comparable to the GC-MS system. We observed correlations of abundances between formaldehyde and acetaldehyde as well as between acetonitrile and acetaldehyde, which demonstrates the potential of MMWGS for breath research.

Haze Air Pollution Health Impacts of Breath-Borne VOCs

Here, we investigated the use of breath-borne volatile organic compounds (VOCs) for rapid monitoring of air pollution health effects on humans. Forty-seven healthy college students were recruited, and their exhaled breath samples (n = 235) were collected and analyzed for VOCs before, on, and after two separate haze pollution episodes using gas chromatography-ion mobility spectrometry (GC-IMS). Using a paired t-test and machine learning model (Gradient Boosting Machine, GBM), six exhaled VOC species including propanol and isoprene were revealed to differ significantly among pre-, on-, and post-exposure in both haze episodes, while none was found between clean control days. The GBM model was shown capable of differentiating between pre- and on-exposure to haze pollution with a precision of 90-100% for both haze episodes. However, poor performance was detected for the same model between two different clean days. In addition to gender and particular haze occurrence influences, correlation analysis revealed that NH₄⁺, NO₃^-, acetic acid, mesylate, CO, NO₂, PM_2.5, and O₃ played important roles in the changes in breath-borne VOC fingerprints following haze air pollution exposure. This work has demonstrated direct evidence of human health impacts of haze pollution while identifying potential breath-borne VOC biomarkers such as propanol and isoprene for haze air pollution exposure.

Case Report: The effect of intravenous and oral antibiotics on the gut microbiome and breath volatile organic compounds over one year

BACKGROUND

Antimicrobial resistance (AMR) is a global concern and better understanding of the gut microbiome, a known 'amplifier' of AMR, may allow future clinicians to tailor therapy to minimise this risk and offer a personalised medicine approach. To examine the gut microbiome, patients are required to provide faecal samples; more convenient and cheaper solutions need to be found.

METHODS

As part of a pilot study looking at how routes of administration affect the gut microbiome in NHS patients undergoing routine clinical management for infections, we hypothesised that effects on the gut microbiome varied with the route and metabolism of antibiotic used, and these changes may be reflected in breath metabolites. We present a case report of a patient with an unusual clinical history, alongside breath metabolite and gut microbiome data taken before, during and after antibiotic therapy over a period of one year.

RESULTS

We noted a shift in the dominant Bacteroides strain in the patient's gut microbiome between pre- and post-therapy samples, along with an alteration in the composition of breath metabolites.

CONCLUSIONS

This study provides a framework for similar future work and highlights the need for further research on the relationships between changes in microbial gut communities and antimicrobial exposure, patient clinical status, and the metabolites of human breath.

Sensors for Volatile Organic Compounds

This paper provides an overview of recent developments in the field of volatile organic compound (VOC) sensors, which are finding uses in healthcare, safety, environmental monitoring, food and agriculture, oil industry, and other fields. It starts by briefly explaining the basics of VOC sensing and reviewing the currently available and quickly progressing VOC sensing approaches. It then discusses the main trends in materials' design with special attention to nanostructuring and nanohybridization. Emerging sensing materials and strategies are highlighted and their involvement in the different types of sensing technologies is discussed, including optical, electrical, and gravimetric sensors. The review also provides detailed discussions about the main limitations of the field and offers potential solutions. The status of the field and suggestions of promising directions for future development are summarized.

Quartz-enhanced photoacoustic spectroscopy for multi-gas detection: A review

Multi-gas detection represents a suitable solution in many applications, such as environmental and atmospheric monitoring, chemical reaction and industrial process control, safety and security, oil&gas and biomedicine. Among optical techniques, Quartz-Enhanced Photoacoustic Spectroscopy (QEPAS) has been demonstrated to be a leading-edge technology for addressing multi-gas detection, thanks to the modularity, ruggedness, portability and real time operation of the QEPAS sensors. The detection module consists in a spectrophone, mounted in a vacuum-tight cell and detecting sound waves generated via photoacoustic excitation within the gas sample. As a result, the sound detection is wavelength-independent and the volume of the absorption cell is basically determined by the spectrophone dimensions, typically in the order of few cubic centimeters. In this review paper, the implementation of the QEPAS technique for multi-gas detection will be discussed for three main areas of applications: i) multi-gas trace sensing by exploiting non-interfering absorption features; ii) multi-gas detection dealing with overlapping absorption bands; iii) multi-gas detection in fluctuating backgrounds. The fundamental role of the analysis and statistical tools will be also discussed in detail in relation with the specific applications. This overview on QEPAS technique, highlighting merits and drawbacks, aims at providing ready-to-use guidelines for multi-gas detection in a wide range of applications and operating conditions.

Case Report: The effect of intravenous and oral antibiotics on the gut microbiome and breath volatile organic compounds over one year

BACKGROUND

Antimicrobial resistance (AMR) is a global concern and better understanding of the gut microbiome, a known 'amplifier' of AMR, may allow future clinicians to tailor therapy to minimise this risk and offer a personalised medicine approach. To examine the gut microbiome, patients are required to provide faecal samples; more convenient and cheaper solutions need to be found.

METHODS

As part of a pilot study looking at how routes of administration affect the gut microbiome in NHS patients undergoing routine clinical management for infections, we hypothesised that effects on the gut microbiome varied with the route and metabolism of antibiotic used, and these changes may be reflected in breath metabolites. We present a case report of a patient with an unusual clinical history, alongside breath metabolite and gut microbiome data taken before, during and after antibiotic therapy over a period of one year.

RESULTS

We noted a shift in the dominant Bacteroides strain in the patient's gut microbiome between pre- and post-therapy samples, along with an alteration in the composition of breath metabolites.

CONCLUSIONS

This study provides a framework for similar future work and highlights the need for further research on the relationships between changes in microbial gut communities and antimicrobial exposure, patient clinical status, and the metabolites of human breath.

A pilot study for the prediction of liver function related scores using breath biomarkers and machine learning

Volatile organic compounds (VOCs) present in exhaled breath can help in analysing biochemical processes in the human body. Liver diseases can be traced using VOCs as biomarkers for physiological and pathophysiological conditions. In this work, we propose non-invasive and quick breath monitoring approach for early detection and progress monitoring of liver diseases using Isoprene, Limonene, and Dimethyl sulphide (DMS) as potential biomarkers. A pilot study is performed to design a dataset that includes the biomarkers concentration analysed from the breath sample before and after study subjects performed an exercise. A machine learning approach is applied for the prediction of scores for liver function diagnosis. Four regression methods are performed to predict the clinical scores using breath biomarkers data as features set by the machine learning techniques. A significant difference was observed for isoprene concentration (p < 0.01) and for DMS concentration (p < 0.0001) between liver patients and healthy subject's breath sample. The R-square value between actual clinical score and predicted clinical score is found to be 0.78, 0.82, and 0.85 for CTP score, APRI score, and MELD score, respectively. Our results have shown a promising result with significant different breath profiles between liver patients and healthy volunteers. The use of machine learning for the prediction of scores is found very promising for use of breath biomarkers for liver function diagnosis.

Breath Analysis: A Promising Tool for Disease Diagnosis-The Role of Sensors

Early-stage disease diagnosis is of particular importance for effective patient identification as well as their treatment. Lack of patient compliance for the existing diagnostic methods, however, limits prompt diagnosis, rendering the development of non-invasive diagnostic tools mandatory. One of the most promising non-invasive diagnostic methods that has also attracted great research interest during the last years is breath analysis; the method detects gas-analytes such as exhaled volatile organic compounds (VOCs) and inorganic gases that are considered to be important biomarkers for various disease-types. The diagnostic ability of gas-pattern detection using analytical techniques and especially sensors has been widely discussed in the literature; however, the incorporation of novel nanomaterials in sensor-development has also proved to enhance sensor performance, for both selective and cross-reactive applications. The aim of the first part of this review is to provide an up-to-date overview of the main categories of sensors studied for disease diagnosis applications via the detection of exhaled gas-analytes and to highlight the role of nanomaterials. The second and most novel part of this review concentrates on the remarkable applicability of breath analysis in differential diagnosis, phenotyping, and the staging of several disease-types, which are currently amongst the most pressing challenges in the field.

ptairMS: real-time processing and analysis of PTR-TOF-MS data for biomarker discovery in exhaled breath

Motivation

Analysis of volatile organic compounds (VOCs) in exhaled breath by proton transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS) is of increasing interest for real-time, non-invasive diagnosis, phenotyping and therapeutic drug monitoring in the clinics. However, there is currently a lack of methods and software tools for the processing of PTR-TOF-MS data from cohorts and suited for biomarker discovery studies.

Results

We developed a comprehensive suite of algorithms that process raw data from patient acquisitions and generate the table of feature intensities. Notably, we included an innovative two-dimensional peak deconvolution model based on penalized splines signal regression for accurate estimation of the temporal profile and feature quantification, as well as a method to specifically select the VOCs from exhaled breath. The workflow was implemented as the ptairMS software, which contains a graphical interface to facilitate cohort management and data analysis. The approach was validated on both simulated and experimental datasets, and we showed that the sensitivity and specificity of the VOC detection reached 99% and 98.4%, respectively, and that the error of quantification was below 8.1% for concentrations down to 19 ppb.

Availability and implementation

The ptairMS software is publicly available as an R package on Bioconductor (doi: 10.18129/B9.bioc.ptairMS), as well as its companion experiment package ptairData (doi: 10.18129/B9.bioc.ptairData).

Supplementary information

Supplementary data are available at Bioinformatics online.

Colorimetric and Electrochemical Screening for Early Detection of Diabetes Mellitus and Diabetic Retinopathy-Application of Sensor Arrays and Machine Learning

In this review, a selection of works on the sensing of biomarkers related to diabetes mellitus (DM) and diabetic retinopathy (DR) are presented, with the scope of helping and encouraging researchers to design sensor-array machine-learning (ML)-supported devices for robust, fast, and cost-effective early detection of these devastating diseases. First, we highlight the social relevance of developing systematic screening programs for such diseases and how sensor-arrays and ML approaches could ease their early diagnosis. Then, we present diverse works related to the colorimetric and electrochemical sensing of biomarkers related to DM and DR with non-invasive sampling (e.g., urine, saliva, breath, tears, and sweat samples), with a special mention to some already-existing sensor arrays and ML approaches. We finally highlight the great potential of the latter approaches for the fast and reliable early diagnosis of DM and DR.

Breathomics profiling of metabolic pathways affected by major depression: Possibilities and limitations

BACKGROUND

Major depressive disorder (MDD) is one of the most common psychiatric disorders with multifactorial etiologies. Metabolomics has recently emerged as a particularly potential quantitative tool that provides a multi-parametric signature specific to several mechanisms underlying the heterogeneous pathophysiology of MDD. The main purpose of the present study was to investigate possibilities and limitations of breath-based metabolomics, breathomics patterns to discriminate MDD patients from healthy controls (HCs) and identify the altered metabolic pathways in MDD.

METHODS

Breath samples were collected in Tedlar bags at awakening, 30 and 60 min after awakening from 26 patients with MDD and 25 HCs. The non-targeted breathomics analysis was carried out by proton transfer reaction mass spectrometry. The univariate analysis was first performed by T-test to rank potential biomarkers. The metabolomic pathway analysis and hierarchical clustering analysis (HCA) were performed to group the significant metabolites involved in the same metabolic pathways or networks. Moreover, a support vector machine (SVM) predictive model was built to identify the potential metabolites in the altered pathways and clusters. The accuracy of the SVM model was evaluated by receiver operating characteristics (ROC) analysis.

RESULTS

A total of 23 differential exhaled breath metabolites were significantly altered in patients with MDD compared with HCs and mapped in five significant metabolic pathways including aminoacyl-tRNA biosynthesis (p = 0.0055), branched chain amino acids valine, leucine and isoleucine biosynthesis (p = 0.0060), glycolysis and gluconeogenesis (p = 0.0067), nicotinate and nicotinamide metabolism (p = 0.0213) and pyruvate metabolism (p = 0.0440). Moreover, the SVM predictive model showed that butylamine (p = 0.0005, p_FDR=0.0006), 3-methylpyridine (p = 0.0002, p_FDR = 0.0012), endogenous aliphatic ethanol isotope (p = 0.0073, p_FDR = 0.0174), valeric acid (p = 0.005, p_FDR = 0.0162) and isoprene (p = 0.038, p_FDR = 0.045) were potential metabolites within identified clusters with HCA and altered pathways, and discriminated between patients with MDD and non-depressed ones with high sensitivity (0.88), specificity (0.96) and area under curve of ROC (0.96).

CONCLUSION

According to the results of this study, the non-targeted breathomics analysis with high-throughput sensitive analytical technologies coupled to advanced computational tools approaches offer completely new insights into peripheral biochemical changes in MDD.

The variability of volatile organic compounds in the indoor air of clinical environments

The development of clinical breath-analysis is confounded by the variability of background volatile organic compounds (VOCs). Reliable interpretation of clinical breath-analysis at individual, and cohort levels requires characterisation of clinical-VOC levels and exposures. Active-sampling with thermal-desorption/gas chromatography-mass spectrometry recorded and evaluated VOC concentrations in 245 samples of indoor air from three sites in a large National Health Service (NHS) provider trust in the UK over 27 months. Data deconvolution, alignment and clustering isolated 7344 features attributable to VOC and described the variability (composition and concentration) of respirable clinical VOC. 328 VOC were observed in more than 5% of the samples and 68 VOC appeared in more than 30% of samples. Common VOC were associated with exogenous and endogenous sources and 17 VOC were identified as seasonal differentiators. The presence of metabolites from the anaesthetic sevoflurane, and putative-disease biomarkers in room air, indicated that exhaled VOC were a source of background-pollution in clinical breath-testing activity. With the exception of solvents, and waxes associated with personal protective equipment (PPE), exhaled VOC concentrations above 3µg m^-3are unlikely to arise from room air contamination, and in the absence of extensive survey-data, this level could be applied as a threshold for inclusion in studies, removing a potential environmental confounding-factor in developing breath-based diagnostics.

Diode Laser Spectrometer for Diagnostic Assessment of Exhaled Air Components

The main requirements for a screening test are simplicity, non-invasiveness, safety of testing procedures, high processing speed, and ability to detect diseases at an early stage. A multichannel gas analyzer for assessment of exhaled air composition (diode laser spectrometer), non-invasive screening, and biomedical testing was developed on the basis of near-infrared diode lasers with fiber output. The device measures the following exhaled air components: ¹²CO₂, ¹³CO₂, CH₄, NH₃, H₂O, and H₂S.

The concentration of molecules was measured in a multi-pass Herriot cell with a reference length of 40 cm, 1.8 L volume, and a total optical path length of 26 m. Three diode lasers manufactured by NTT Electronics (Japan) were used in the work. Detection of CH₄ was carried out in the 1.65 μm wavelength range, ¹²CO₂, ¹³CO₂, and H₂S levels were measured in the 1.60 μm range, NH₃ and H₂O in the 1.51 μm range. All measurements were taken in real time.

Clinical testing of the spectrometer was carried out at V.M. Buyanov City Clinical Hospital of Moscow Department of Health. More than 150 patients were examined. The tests included analysis and measurement of these molecular components in the exhaled air of patients with various diseases. The content of these components was studied in conditions of various changes in the human physiological state (dosed physical activity, relaxation, psychoemotional stress, etc.).

The studies have demonstrated efficacy of using the developed hardware system for assessment of exhaled air components in order to reveal functional disorders in various diseases of the digestive system, cardiorespiratory system, diseases caused by impaired nitrogen-excreting function of the kidneys, etc.

Selectivity of a ZnO@ZIF-71@PDMS Nanorod Array Gas Sensor Enhanced by Coating a Polymer Selective Separation Membrane

It is important for noninvasive diagnosis of diabetes to develop acetone gas sensors with high selectivity. ZnO@ZIF-71 has been reported as a highly sensitive and selective gas sensor on acetone detection. However, it is difficult to exclude the interference with similar molecular sizes gas in the gas-sensing process, like ethanol. To solve this problem, polydimethylsiloxane (PDMS) was synthesized on the surface of ZnO@ZIF-71 to form a ZnO@ZIF-71@PDMS sensor by vapor deposition. The new sensor shows inert response to ethanol and effective response to acetone simultaneously. The PDMS membrane acts as a molecular sieve, which shows the acetone selectivity performance and can totally eliminate the response to low concentration ethanol at low temperature. Theory calculations and solubility test are also employed to prove the role PDMS plays in this process. It demonstrated that the acetone selectivity performance comes from the hydrogen bond interaction between the ethanol gas molecules and PDMS, which increases difficulty for ethanol gas molecules to penetrate the PDMS membrane. Further, this work provides a new method for enhancing gas-sensing selectivity and promoting for miniaturization of gas sensors.

Highly sensitive and chemically stable NH3 sensors based on an organic acid-sensitized cross-linked hydrogel for exhaled breath analysis

Due to interference by the high moisture content and complicated compositions of human exhaled breath, the trace-level detection of ammonia (NH₃) with desirable selectivity and stability is a large challenge for exhaled breath analysis. Carboxyl-sensitized hydrogels can be activated by moisture to exhibit a significant response and excellent selectivity to NH₃. However, the high activity of carboxyl groups in hydrogels is a double-edged sword, resulting in poor chemical stability during NH₃ detection. Herein, organic acids were embedded into a cross-linked poly(ethylene glycol) diacrylate (PEGDA) hydrogel via thiol-ene photochemistry to form stable hydrogels for NH₃ detection in a humid atmosphere. As a result, under high humidity conditions (80% RH), the optimal sensors exhibited superior selectivity to NH₃ among various interfering gas species, a remarkably high NH₃ response (Z_a/Z_g=6.20) towards 20 ppm NH₃, and an extremely low actual detection limit (50 ppb) at room temperature. Moreover, the sensors exhibited excellent chemical stability due to the moderate equilibrium water content of the hydrogel composites and acid dissociation constant of the acid groups. The moisture-activated NH₃ sensing mechanism was thoroughly investigated by complex impedance spectroscopy (CIS), quartz crystal microbalance (QCM) measurements, Fourier transform infrared (FT-IR) spectroscopy and X-ray photoelectron spectroscopy (XPS). To explore the application prospects of cross-linked hydrogel sensors for detecting NH₃ in exhaled breath, a simulated exhaled breath test was also performed.

Breath volatile organic compound analysis: an emerging method for gastric cancer detection

Gastric cancer is a common malignancy, being the fifth most frequently diagnosed cancer and the fourth leading cause of cancer-related deaths worldwide. Diagnosis of gastric cancer at the early stage is critical to effectively improve the survival rate. However, a substantial proportion of patients with gastric cancer in the early stages lack specific symptoms or are asymptomatic. Moreover, the imaging techniques currently used for gastric cancer screening, such as computed tomography and barium examination, are usually radioactive and have low sensitivity and specificity. Even though endoscopy has high accuracy for gastric cancer screening, its application is limited by the invasiveness of the technique. Breath analysis is an economic, effective, easy to perform, non-invasive detection method, and has no undesirable side effects on subjects. Extensive worldwide research has been conducted on breath volatile organic compounds (VOCs), which reveals its prospect as a potential method for gastric cancer detection. Many interesting results have been obtained and innovative methods have been introduced in this subject; hence, an extensive review would be beneficial. By providing a comprehensive list of breath VOCs identified by gastric cancer would promote further research in this field. This review summarizes the commonly used technologies for exhaled breath analysis, focusing on the application of analytical instruments in the detection of breath VOCs in gastric cancers, and the alterations in the profile of breath biomarkers in gastric cancer patients are discussed as well.

Liver Impairment-The Potential Application of Volatile Organic Compounds in Hepatology

Liver diseases are currently diagnosed through liver biopsy. Its invasiveness, costs, and relatively low diagnostic accuracy require new techniques to be sought. Analysis of volatile organic compounds (VOCs) in human bio-matrices has received a lot of attention. It is known that a musty odour characterises liver impairment, resulting in the elucidation of volatile chemicals in the breath and other body fluids such as urine and stool, which may serve as biomarkers of a disease. Aims: This study aims to review all the studies found in the literature regarding VOCs in liver diseases, and to summarise all the identified compounds that could be used as diagnostic or prognostic biomarkers. The literature search was conducted on ScienceDirect and PubMed, and each eligible publication was qualitatively assessed by two independent evaluators using the SANRA critical appraisal tool. Results: In the search, 58 publications were found, and 28 were kept for inclusion: 23 were about VOCs in the breath, one in the bile, three in urine, and one in faeces. Each publication was graded from zero to ten. A graphical summary of the metabolic pathways showcasing the known liver disease-related VOCs and suggestions on how VOC analysis on liver impairment could be applied in clinical practice are given.