Breath analysis in disease diagnosis: methodological considerations and applications

Wearable sampling of proteins from human exhaled aerosols for nano-liquid chromatography-tandem mass spectrometry analysis: A pilot study

RATIONALE

Human exhaled breath usually contains unique proteins that may provide clues to characterize individual physiological activities and many diseases. However, the concentration of exhaled proteins in exhaled breath is extremely low and usually does not reach the detection limits of all online breath mass spectrometry instruments. Therefore, developing a new breath sampler for collecting and characterizing exhaled proteins is important.

METHODS

In this study, a new mask-based wearable sampler was developed by fixing metal materials into the inner surface of the KN95 mask. Human exhaled proteins could be directly adsorbed onto the metal material while wearing the mask. After sampling, the collected proteins were eluted, digested, and identified using nano-liquid chromatography-tandem mass spectrometry (nano-LC-MS/MS).

RESULTS

The adsorption of exhaled proteins was evaluated, showing that modified gold foil is an effective material for collecting exhaled proteins. Various endogenous proteins were successfully identified from exhaled breath, many of which can be potential biomarkers for disease diagnosis.

CONCLUSIONS

By coupling the newly developed mask sampler with nano-LC-MS/MS, human exhaled proteins were successfully collected and identified. Our results show that the mask sampler is wearable, simple, and convenient, and the method is noninvasive for investigating disease diagnosis and human health.

Porosity Engineering of Dried Smart Poly(N-isopropylacrylamide) Hydrogels for Gas Sensing

A recent study unveiled the potential of acrylamide-based stimulus-responsive hydrogels for volatile organic compound detection in gaseous environments. However, for gas sensing, a large surface area, that is, a highly porous material, offering many adsorption sites is crucial. The large humidity variation in the gaseous environment constitutes a significant challenge for preserving an initially porous structure, as the pores tend to be unstable and irreversibly collapse. Therefore, the present investigation focuses on enhancing the porosity of smart PNiPAAm hydrogels under the conditions of a gaseous environment and the preservation of the structural integrity for long-term use. We have studied the influence of polyethylene glycol (PEG) as a porogen and the application of different drying methods and posttreatment. The investigations lead to the conclusion that only the combination of PEG addition, freeze-drying, and subsequent conditioning in high relative humidity enables a long-term stable formation of a porous surface and inner structure of the material. The significantly enhanced swelling response in a gaseous environment and in the test gas acetone is confirmed by gravimetric experiments of bulk samples and continuous measurements of thin films on piezoresistive pressure sensor chips. These measurements are furthermore complemented by an in-depth analysis of the morphology and microstructure. While the study was conducted for PNiPAAm, the insights and developed processes are general in nature and can be applied for porosity engineering of other smart hydrogel materials for VOC detection in gaseous environments.

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 (H2 S) 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 H2 S (≈0.1 ppm) in high humidity conditions (exhaled breath) is challenging because of irreversible H2 S adsorption/desorption at the surface of chemiresistors. The study reports the synthesis of Fe-doped CuO hollow spheres as H2 S 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 H2 S 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 H2 S 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 H2 S using the Fe-doped CuO sensor suggested in this work opens a new possibility for halitosis self-monitoring.

Determination of breast cancer biomarkers with poly acrylic acid/ MIL-88(Fe)-NH2 hydrogel as a coating for stir bar sorptive extraction

The Poly acrylic acid/MIL-88(Fe)-NH2 composite material, carefully prepared, is employed as a sorbent for the stir bar. The best formula of the composite was selected by investigation of two parameters including the cross-linker of PAA and MIL-88(Fe)-NH2 content. The prepared stir bar was used for extraction of 2-pentanone, 2-heptanone, ethyl propionate, para-xylene, 1,2,4-trimethylbenzene, o-cresol, m-cresol in urine samples as breast cancer biomarkers with gas chromatography-flame ionization detector. The prepared Poly acrylic acid / MIL-88(Fe)-NH2 as sorbent for the stir bar demonstrate good repeatability of one bar (relative standard deviation (RSD%) < 4.61 %) and satisfactory reproducibility between two bars (RSD% < 6.85 %). The central composite design method was applied for the optimization of extraction parameters. Under the optimum conditions, linear dynamic ranges for compounds were in the acceptable range with correlation coefficients higher than 0.99. Detection limits of them were less than 1.71 µg L-1.

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.

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.

Standard operating procedure to reveal prostate cancer specific volatile organic molecules by infrared spectroscopy

The growing number of prostate cancer cases is a real concern in modern society. Over 1.4 million new cases and about 400 thousand (>26%) deaths were registered worldwide in 2020 due to prostate cancer. The high mortality rate of prostate cancer is due to the lack of reliable early detection of the disease. Till now the most reliable diagnosis of cancer is tissue biopsy, which is an invasive process. A non-invasive or minimally invasive technique could lead to a diagnostic tool that will allow for saving or prolonging the lifespan of millions of lives. Metabolite-based diagnostics may have a better chance of early cancer detection. However, reliable detection techniques need to be developed. Infrared spectroscopy based gaseous-biofluid holds great promise towards the development of non-invasive diagnostics. A pilot study based on breath analysis by infrared spectroscopy showed promising results in distinguishing prostate cancer patients from healthy volunteers. Details of the spectral metabolic analysis are presented.

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.

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.

Wireless, battery-free, multifunctional integrated bioelectronics for respiratory pathogens monitoring and severity evaluation

The rapid diagnosis of respiratory virus infection through breath and blow remains challenging. Here we develop a wireless, battery-free, multifunctional pathogenic infection diagnosis system (PIDS) for diagnosing SARS-CoV-2 infection and symptom severity by blow and breath within 110 s and 350 s, respectively. The accuracies reach to 100% and 92% for evaluating the infection and symptom severity of 42 participants, respectively. PIDS realizes simultaneous gaseous sample collection, biomarker identification, abnormal physical signs recording and machine learning analysis. We transform PIDS into other miniaturized wearable or portable electronic platforms that may widen the diagnostic modes at home, outdoors and public places. Collectively, we demonstrate a general-purpose technology for rapidly diagnosing respiratory pathogenic infection by breath and blow, alleviating the technical bottleneck of saliva and nasopharyngeal secretions. PIDS may serve as a complementary diagnostic tool for other point-of-care techniques and guide the symptomatic treatment of viral infections.

Biosensor development for low-level acetaldehyde gas detection using mesoporous carbon electrode printed on a porous polyimide film

Acetaldehyde, which is an intermediate product of alcohol metabolism, is known to induce symptoms, including alcohol flushing, vomiting, and headaches in humans. Therefore, real-time monitoring of acetaldehyde levels is crucial to mitigating these health issues. However, current methods for detecting low-concentration gases necessitate the use of complex measurement equipment. In this study, we developed a low-cost, low-detection-limit, enzyme-based electrochemical biosensor for acetaldehyde gas detection that does not require sophisticated equipment. The sensor was constructed by screen-printing electrodes onto a porous polyimide film, using grafted MgO-templated carbon (GMgOC) as working electrode material, carbon for the counter electrode, and silver/silver chloride for the reference electrode. Pyrroloquinoline-quinone-dependent aldehyde dehydrogenase was immobilized on the working electrode, and a chamber was attached to the electrode chip and filled with 1-methoxy-5-methylphenazinium methyl sulfate solution. The sensor can be used to measure acetaldehyde gas concentrations from 0.02 to 0.1 ppm, making it suitable for monitoring human skin gas. This low detection limit was achieved by delivering the analyte through the porous polyimide film on which the electrodes were printed and accumulating acetaldehyde in the mesoporous GMgOC of the working electrode. This mechanism suggests that this sensor design can be adapted to develop other low-detection limit gas sensors, such as those for screening skin gas biomarkers.

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.

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.

NIR and THz spectroscopy: An experimental investigation toward nicotine-related devices

This study examined the content of nicotine-delivery products using terahertz time-domain spectroscopy (THz-TDS) and breath ethylene investigated with CO2 laser photoacoustic spectroscopy (CO2 LPAS) system as a biomarker of oxidative stress after smoking. The THz-TDS method provided valuable information on the transmission spectra of tobacco and nicotine in smoking products. From the CO2 LPAS data it was observed that in cigarette (TC) smoking the mean breath ethylene was 687 parts per billion (ppb), while in electronic cigarettes and tobacco heating devices smoking the mean ethylene was 56 ppb and 48 ppb, respectively. The main finding was that TC showed higher transmission in the THz region producing a higher oxidative stress on the body.

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.

Breath analysis combined with cardiopulmonary exercise testing and echocardiography for monitoring heart failure patients: the AEOLUS protocol

This paper describes the AEOLUS pilot study which combines breath analysis with cardiopulmonary exercise testing (CPET) and an echocardiographic examination for monitoring heart failure (HF) patients. Ten consecutive patients with a prior clinical diagnosis of HF with reduced left ventricular ejection fraction were prospectively enrolled together with 15 control patients with cardiovascular risk factors, including hypertension, type II diabetes or chronic ischemic heart disease. Breath samples were collected at rest and during CPET coupled with exercise stress echocardiography (CPET-ESE) protocol by means of needle trap micro-extraction and were analyzed through gas-chromatography coupled with mass spectrometry. The protocol also involved using of a selected ion flow tube mass spectrometer for a breath-by-breath isoprene and acetone analysis during exercise. At rest, HF patients showed increased breath levels of acetone and pentane, which are related to altered oxidation of fatty acids and oxidative stress, respectively. A significant positive correlation was observed between acetone and the gold standard biomarker NT-proBNP in plasma (r= 0.646,p< 0.001), both measured at rest. During exercise, some exhaled volatiles (e.g., isoprene) mirrored ventilatory and/or hemodynamic adaptation, whereas others (e.g., sulfide compounds and 3-hydroxy-2-butanone) depended on their origin. At peak effort, acetone levels in HF patients differed significantly from those of the control group, suggesting an altered myocardial and systemic metabolic adaptation to exercise for HF patients. These preliminary data suggest that concomitant acquisition of CPET-ESE and breath analysis is feasible and might provide additional clinical information on the metabolic maladaptation of HF patients to exercise. Such information may refine the identification of patients at higher risk of disease worsening.

Suitability of Short- and Long-Term Storage of Volatile Organic Compounds Samples in Syringe-Based Containers: A Comparison Study

The employment of advanced analytical techniques and instrumentation enables the tracing of volatile organic compounds (VOCs) in vestigial concentrations (ppbv-pptv range) for several emerging applications, such as the research of disease biomarkers in exhaled air, the detection of metabolites in several biological processes, and the detection of pollutants for air quality control. In this scope, the storage of gaseous samples is crucial for preserving the integrity and stability of the collected set of analytes. This study aims to assess the suitability of three commercially available syringes as air containers (AC) that are commonly used for the collection, storage, isolation, and transportation of samples: glass syringes with glass plungers (AC1), and two plastic syringes, one with plastic plungers (AC2), and one with rubbered plungers (AC3). For this purpose, 99 air samples with different times of storage (from 10 min to 24 h) were analyzed using a Gas Chromatography-Ion Mobility Spectrometry device and the degradation of the samples was properly assessed by comparing the changes in the VOCs' emission profiles. The quality of the method was assured by via the measurement of the blank's spectra before each experimental run, as well as by the consecutive measurement of the three replicates for each sample. A statistical analysis of the changes in the VOCs' emission patterns was performed using principal component analysis (PCA). The results, with a total explained variance of 93.61%, indicate that AC3 is the most suitable option for the long-term storage of air samples. Thus, AC3 containers demonstrated a higher capacity to preserve the stability and integrity of the analytes compared to AC1 and AC2. The findings of the short-term effects analysis, up to 1 h, confirm the suitability of all analyzed syringe-based containers for sample-transferring purposes in onsite analysis.

Study of Electron Collisions with Isoprene, 1,2-Butadiene, and Their Isomers

Isoprene, 1,2-butadiene, and their isomers play an important role in aerosols in the atmosphere, interstellar media, and extraterrestrial life. Since electrons are everywhere, studying how electrons interact with these molecules is an important part of studying such environments. To date, however, little investigation has been conducted in this area. Bearing this in mind, we conducted a thorough investigation to report the various electron scattering cross sections of isoprene, 1,2-butadiene, and their isomers. The methods used for this purpose are reliable within the limits of adopted model potentials. The optical potential method was used to get the total elastic and inelastic cross sections, while the complex scattering potential ionization contribution method was used to get the total ionization cross section from the inelastic contribution. The results from these approximations are pretty close to the results from earlier experiments and theories. Furthermore, most of these isomers are being explored for the first time. Besides, their isomeric effect is also discussed. A correlation between the cross sections of molecules is demonstrated, which can be used to predict cross sections of those molecules where previous data are not available.

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.

Multiplexed DNA-functionalized graphene sensor with artificial intelligence-based discrimination performance for analyzing chemical vapor compositions

This study presents a new technology that can detect and discriminate individual chemical vapors to determine the chemical vapor composition of mixed chemical composition in situ based on a multiplexed DNA-functionalized graphene (MDFG) nanoelectrode without the need to condense the original vapor or target dilution. To the best of our knowledge, our artificial intelligence (AI)-operated arrayed electrodes were capable of identifying the compositions of mixed chemical gases with a mixed ratio in the early stage. This innovative technology comprised an optimized combination of nanodeposited arrayed electrodes and artificial intelligence techniques with advanced sensing capabilities that could operate within biological limits, resulting in the verification of mixed vapor chemical components. Highly selective sensors that are tolerant to high humidity levels provide a target for "breath chemovapor fingerprinting" for the early diagnosis of diseases. The feature selection analysis achieved recognition rates of 99% and above under low-humidity conditions and 98% and above under humid conditions for mixed chemical compositions. The 1D convolutional neural network analysis performed better, discriminating the compositional state of chemical vapor under low- and high-humidity conditions almost perfectly. This study provides a basis for the use of a multiplexed DNA-functionalized graphene gas sensor array and artificial intelligence-based discrimination of chemical vapor compositions in breath analysis applications.

Thin-film wafer-scale mid-IR Fabry Perot cavity gas sensor

We demonstrate a miniaturized wafer-scale optical gas sensor that combines the gas cell, an optical filter, and integrated flow channels. We present the design, fabrication and characterization of an integrated cavity-enhanced sensor. Using the module, we demonstrate absorption sensing of ethylene down to 100 ppm level.

Non-Invasive Disease Specific Biomarker Detection Using Infrared Spectroscopy: A Review

Many life-threatening diseases remain obscure in their early disease stages. Symptoms appear only at the advanced stage when the survival rate is poor. A non-invasive diagnostic tool may be able to identify disease even at the asymptotic stage and save lives. Volatile metabolites-based diagnostics hold a lot of promise to fulfil this demand. Many experimental techniques are being developed to establish a reliable non-invasive diagnostic tool; however, none of them are yet able to fulfil clinicians' demands. Infrared spectroscopy-based gaseous biofluid analysis demonstrated promising results to fulfil clinicians' expectations. The recent development of the standard operating procedure (SOP), sample measurement, and data analysis techniques for infrared spectroscopy are summarized in this review article. It has also outlined the applicability of infrared spectroscopy to identify the specific biomarkers for diseases such as diabetes, acute gastritis caused by bacterial infection, cerebral palsy, and prostate cancer.

Realization of Microfluidic Preconcentrator for N-Pentane Traces Impurities from the Gaseous Media

In this paper, we present the work of designing and fabricating a new generation of microelectromechanical systems (MEMS) based microfluidic preconcentrators (MFP) for volatile organic compounds (VOCs) quantification. The main objective of this work is to quantify the n-pentane impurities using MFP for sample preparation. The MFP was analyzed using Hewlett-Packard 5890 gas chromatography, having a flame ionization detector under isothermal conditions. The proposed MFP system includes two-microfluidic preconcentrators for continuous action and a system of four 3/2 solenoid valves with a control unit. Microfluidic preconcentrators were placed on metal plates and have circular channels filled with Al2O3 (50 μm), n-octane ResSil-C (80/100 mesh) sorbents of one nature and are hyphenated with the Peltier elements to regulate the temperature of sorption and desorption. The n-pentane quantitative determination was carried out using a calibration plot of gas mixtures on a successive dilution with the nitrogen. This study shows that the microfluidic preconcentrator system with Al2O3 and n-Octane ResSil-C sorbent concentrates the n-pentane traces up to 41 to 47 times from the gas mixture with the standard deviation of ≤5%. It has been observed that the n-octane ResSil-C based MFC shows very fast response (<5 min) and stability up to 300 cycles.

Accuracy of Volatile Organic Compound (VOC) Detection in Exhaled Breath Compared to Reverse-transcriptase Polymerase Chain Reaction (RT-PCR) for Diagnosis of COVID-19: An Evidence-based Case Report

Background: Coronavirus disease 2019 (COVID-19) is a contagious infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The World Health Organization (WHO) declared this infection a global pandemic in 2020. In addition, various methods have been developed to diagnose COVID-19 rapidly and accurately to reverse transcription-polymerase chain reaction (RT-PCR) as a gold standard method. One of these methods is the detection of volatile organic compounds (VOC) in exhaled breath. Objectives: The aim was to collect and investigate studies on the accuracy of VOC detection as a diagnostic method for COVID-19. Methods: A literature search was performed in five electronic databases, including PubMed, Cochrane Library, ProQuest, EBSCOhost, and Scopus, along with hand searching. The search was conducted in the titles and abstracts of articles using keywords and their equivalent terms, combined with the Boolean operators (OR and AND). The search results were then selected according to the inclusion and exclusion criteria and compatibility with the Population, Intervention, Control, and Outcomes (PICO) framework. Results: Based on the search results, two cross-sectional studies by Wintjens et al. and Ruszkiewicz et al. were selected, which were then critically appraised. Both studies showed good validity. Wintjens et al. reported 86% sensitivity and 54% specificity for their method, with a positive predictive value (PPV) and a negative predictive value (NPV) of 40% and 92%, respectively. Besides, Ruszkiewicz et al., who conducted a study in two different locations, reported 82.4% sensitivity and 75% specificity for their method in Edinburgh (UK), with PPV and NPV of 87.5% and 66.7%, respectively, while they reported 90% sensitivity and 80% specificity in Dortmund (Germany), with PPV and NPV of 45% and 97.8%, respectively. The accuracy of these three methods was 62%, 80%, and 82%, respectively. Conclusions: Detection of VOCs from exhaled breath can be a rapid, cost-effective, and simple method for diagnosing COVID-19. However, the accuracy of this method is still relatively low (62 - 82%) and inconsistent; therefore, it is only recommended for screening.

Variation of volatile organic compound levels within ambient room air and its impact upon the standardisation of breath sampling

The interest around analysis of volatile organic compounds (VOCs) within breath has increased in the last two decades. Uncertainty remains around standardisation of sampling and whether VOCs within room air can influence breath VOC profiles. To assess the abundance of VOCs within room air in common breath sampling locations within a hospital setting and whether this influences the composition of breath. A secondary objective is to investigate diurnal variation in room air VOCs. Room air was collected using a sampling pump and thermal desorption (TD) tubes in the morning and afternoon from five locations. Breath samples were collected in the morning only. TD tubes were analysed using gas chromatography coupled with time-of-flight mass spectrometry (GC-TOF-MS). A total of 113 VOCs were identified from the collected samples. Multivariate analysis demonstrated clear separation between breath and room air. Room air composition changed throughout the day and different locations were characterized by specific VOCs, which were not influencing breath profiles. Breath did not demonstrate separation based on location, suggesting that sampling can be performed across different locations without affecting results.

Analysis of volatile organic compounds from deep airway in the lung through intubation sampling

Exhaled volatile organic compounds (VOCs) have been widely applied for the study of disease biomarkers. Oral exhalation and nasal exhalation are two of the most common sampling methods. However, VOCs released from food residues and bacteria in the mouth or upper respiratory tract were also sampled and usually mistaken as that produced from body metabolism. In this study, exhalation from deep airway was first directly collected through intubation sampling and analyzed. The exhalation samples of 35 subjects were collected through a catheter, which was inserted into the trachea or bronchus through the mouth and upper respiratory tract. Then, the VOCs in these samples were detected by proton transfer reaction mass spectrometry (PTR-MS). In addition, fast gas chromatography proton transfer reaction mass spectrometry (FGC-PTR-MS) was used to further determine the VOCs with the same mass-to-charge ratios. The results showed that there was methanol, acetonitrile, ethanol, methyl mercaptan, acetone, isoprene, and phenol in the deep airway. Compared with that in oral exhalation, ethanol, methyl mercaptan, and phenol had lower concentrations. In detail, the median concentrations of ethanol, methyl mercaptan, and phenol were 7.3, 0.6, and 23.9 ppbv, while those in the oral exhalation were 80.0, 5.1, and 71.3 ppbv, respectively, which meant the three VOCs mainly originated from the food residues and bacteria in the mouth or upper respiratory tract, rather than body metabolism. The research results in our study can provide references for expiratory VOC research based on oral and nasal exhalation samplings, which are more feasible in clinical practice.

Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose

Breath pattern analysis based on an electronic nose (e-nose), which is a noninvasive, fast, and low-cost method, has been continuously used for detecting human diseases, including the coronavirus disease 2019 (COVID-19). Nevertheless, having big data with several available features is not always beneficial because only a few of them will be relevant and useful to distinguish different breath samples (i.e., positive and negative COVID-19 samples). In this study, we develop a hybrid machine learning-based algorithm combining hierarchical agglomerative clustering analysis and permutation feature importance method to improve the data analysis of a portable e-nose for COVID-19 detection (GeNose C19). Utilizing this learning approach, we can obtain an effective and optimum feature combination, enabling the reduction by half of the number of employed sensors without downgrading the classification model performance. Based on the cross-validation test results on the training data, the hybrid algorithm can result in accuracy, sensitivity, and specificity values of (86 ± 3)%, (88 ± 6)%, and (84 ± 6)%, respectively. Meanwhile, for the testing data, a value of 87% is obtained for all the three metrics. These results exhibit the feasibility of using this hybrid filter-wrapper feature-selection method to pave the way for optimizing the GeNose C19 performance.

Volatile Organic Compounds as Potential Biomarkers for Noninvasive Disease Detection by Nanosensors: A Comprehensive Review

Biomarkers are biological molecules associated with physiological changes of the body and aids in the detecting the onset of disease in patients. There is an urgent need for self-monitoring and early detection of cardiovascular and other health complications. Several blood-based biomarkers have been well established in diagnosis and monitoring the onset of diseases. However, the detection level of biomarkers in bed-side analysis is difficult and complications arise due to the endothelial dysfunction. Currently single volatile organic compounds (VOCs) based sensors are available for the detection of human diseases and no dedicated nanosensor is available for the elderly. Moreover, accuracy of the sensors based on a single analyte is limited. Hence, breath analysis has received enormous attention in healthcare due to its relatively inexpensive, rapid, and noninvasive methods for detecting diseases. This review gives a detailed analysis of how biomarker imprinted nanosensor can be used as a noninvasive method for detecting VOC to health issues early using exhaled breath analysis.

Smart Face Mask Based on an Ultrathin Pressure Sensor for Wireless Monitoring of Breath Conditions

A smart face mask that can conveniently monitor breath information is beneficial for maintaining personal health and preventing the spread of diseases. However, some challenges still need to be addressed before such devices can be of practical use. One key challenge is to develop a pressure sensor that is easily triggered by low pressure and has excellent stability as well as electrical and mechanical properties. In this study, a wireless smart face mask is designed by integrating an ultrathin self-powered pressure sensor and a compact readout circuit with a normal face mask. The pressure sensor is the thinnest (totally compressed thickness of ≈5.5 µm) and lightest (total weight of ≈4.5 mg) electrostatic pressure sensor capable of achieving a peak open-circuit voltage of up to ≈10 V when stimulated by airflow, which endows the sensor with the advantage of readout circuit miniaturization and makes the breath-monitoring system portable and wearable. To demonstrate the capabilities of the smart face mask, it is used to wirelessly measure and analyze the various breath conditions of multiple testers.

Effects of long-term vegan diet on breath composition

The composition of exhaled breath derives from an intricate combination of normal and abnormal physiological processes that are modified by the consumption of food and beverages, circadian rhythms, bacterial infections, and genetics as well as exposure to xenobiotics. This complexity, which results wide intra- and inter-individual variability and is further influenced by sampling conditions, hinders the identification of specific biomarkers and makes it difficult to differentiate between pathological and nominally healthy subjects. The identification of a "normal" breath composition and the relative influence of the aforementioned parameters would make breath analyses much faster for diagnostic applications. We thus compared, for the first time, the breath composition of age-matched volunteers following a vegan and a Mediterranean omnivorous diet in order to evaluate the impact of diet on breath composition. Mixed breath was collected from 38 nominally healthy volunteers who were asked to breathe into a two-liter handmade Nalophan bag. Exhalation flow rate and carbon dioxide values were monitored during breath sampling. An aliquot (100 mL) of breath was loaded into a sorbent tube (250 mg of Tenax GR, 60/80 mesh) before being analyzed by thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). Breath profiling using TD-GC-MS analysis identified five compounds (methanol, 1-propanol, pentane, hexane, and hexanal), thus enabling differentiation between samples collected from the different group members . Principal component analysis showed a clear separation between groups, suggesting that breath analysis could be used to study the influence of dietary habits in the fields of nutrition and metabolism. Surprisingly, one Italian woman and her brother showed extremely low breath isoprene levels (about 5 ppbv), despite their normal lipidic profile and respiratory data, such as flow rate and pCO2. Further investigations to reveal the reasons behind low isoprene levels in breath would help reveal the origin of isoprene in breath.

Breath analysis using electronic nose and gas chromatography-mass spectrometry: A pilot study on bronchial infections in bronchiectasis

BACKGROUND AND AIMS

In this work, breath samples from clinically stable bronchiectasis patients with and without bronchial infections by Pseudomonas Aeruginosa- PA) were collected and chemically analysed to determine if they have clinical value in the monitoring of these patients.

MATERIALS AND METHODS

A cohort was recruited inviting bronchiectasis patients (25) and controls (9). Among the former group, 12 members were suffering PA infection. Breath samples were collected in Tedlar bags and analyzed by e-nose and Gas Chromatography-Mass Spectrometry (GC-MS). The obtained data were analyzed by chemometric methods to determine their discriminant power in regards to their health condition. Results were evaluated with blind samples.

RESULTS

Breath analysis by electronic nose successfully separated the three groups with an overall classification rate of 84% for the three-class classification problem. The best discrimination was obtained between control and bronchiectasis with PA infection samples 100% (CI_95%: 84-100%) on external validation and the results were confirmed by permutation tests. The discrimination analysis by GC-MS provided good results but did not reach proper statistical significance after a permutation test.

CONCLUSIONS

Breath sample analysis by electronic nose followed by proper predictive models successfully differentiated between control, Bronchiectasis and Bronchiectasis PA samples.

Flexible Ni/NiOx-Based Sensor for Human Breath Detection

We developed a simple methodology to fabricate an Ni/NiOx-based flexible breath sensor by a single-step laser digital patterning process of solution-processed NiOx thin-film deposited using NiOx nanoparticle ink. Laser-induced reductive sintering phenomenon enables for the generation of three parts of Ni electrodes and two narrow NiOx-sensing channels in between, defined on a single layer on a thin flexible polymer substrate. The Ni/NiOx-based breath sensor efficiently detects human breath at a relatively low operating temperature (50 °C) with fast response/recovery times (1.4 s/1.7 s) and excellent repeatability. The mechanism of the gas-sensing ability enhancement of the sensor was investigated by X-ray photoelectron spectroscopy analysis. Furthermore, by decoupling of the temperature effect from the breathing gas, the response of the sensor due to the temperature alone and due to the chemical components in the breathing gas could be separately evaluated. Finally, bending and cyclic bending tests (10,000 cycles) demonstrated the superior mechanical stability of the flexible breath sensor.

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.

Ultrasensitive multispecies spectroscopic breath analysis for real-time health monitoring and diagnostics

Breath analysis enables rapid, noninvasive diagnostics, as well as long-term monitoring of human health, through the identification and quantification of exhaled biomarkers. Here, we demonstrate the remarkable capabilities of mid-infrared (mid-IR) cavity-enhanced direct-frequency comb spectroscopy (CE-DFCS) applied to breath analysis. We simultaneously detect and monitor as a function of time four breath biomarkers-[Formula: see text]OH, [Formula: see text], [Formula: see text]O, and HDO-as well as illustrate the feasibility of detecting at least six more ([Formula: see text]CO, [Formula: see text], OCS, [Formula: see text], [Formula: see text], and [Formula: see text]) without modifications to the experimental apparatus. We achieve ultrahigh detection sensitivity at the parts-per-trillion level. This is made possible by the combination of the broadband spectral coverage of a frequency comb, the high spectral resolution afforded by the individual comb teeth, and the sensitivity enhancement resulting from a high-finesse cavity. Exploiting recent advances in frequency comb, optical coating, and photodetector technologies, we can access a large variety of biomarkers with strong carbon-hydrogen-bond spectral signatures in the mid-IR.

Prospects and Challenges of MXenes as Emerging Sensing Materials for Flexible and Wearable Breath-Based Biomarker Diagnosis

A fully integrated, flexible, and functional sensing device for exhaled breath analysis drastically transforms conventional medical diagnosis to non-invasive, low-cost, real-time, and personalized health care. 2D materials based on MXenes offer multiple advantages for accurately detecting various breath biomarkers compared to conventional semiconducting oxides. High surface sensitivity, large surface-to-weight ratio, room temperature detection, and easy-to-assemble structures are vital parameters for such sensing devices in which MXenes have demonstrated all these properties both experimentally and theoretically. So far, MXenes-based flexible sensor is successfully fabricated at a lab-scale and is predicted to be translated into clinical practice within the next few years. This review presents a potential application of MXenes as emerging materials for flexible and wearable sensor devices. The biomarkers from exhaled breath are described first, with emphasis on metabolic processes and diseases indicated by abnormal biomarkers. Then, biomarkers sensing performances provided by MXenes families and the enhancement strategies are discussed. The method of fabrications toward MXenes integration into various flexible substrates is summarized. Finally, the fundamental challenges and prospects, including portable integration with Internet-of-Thing (IoT) and Artificial Intelligence (AI), are addressed to realize marketization.

The smell of lung disease: a review of the current status of electronic nose technology

There is a need for timely, accurate diagnosis, and personalised management in lung diseases. Exhaled breath reflects inflammatory and metabolic processes in the human body, especially in the lungs. The analysis of exhaled breath using electronic nose (eNose) technology has gained increasing attention in the past years. This technique has great potential to be used in clinical practice as a real-time non-invasive diagnostic tool, and for monitoring disease course and therapeutic effects. To date, multiple eNoses have been developed and evaluated in clinical studies across a wide spectrum of lung diseases, mainly for diagnostic purposes. Heterogeneity in study design, analysis techniques, and differences between eNose devices currently hamper generalization and comparison of study results. Moreover, many pilot studies have been performed, while validation and implementation studies are scarce. These studies are needed before implementation in clinical practice can be realised. This review summarises the technical aspects of available eNose devices and the available evidence for clinical application of eNose technology in different lung diseases. Furthermore, recommendations for future research to pave the way for clinical implementation of eNose technology are provided.

Towards reliable diagnostics of prostate cancer via breath

Early detection of cancer is a key ingredient for saving many lives. Unfortunately, cancers of the urogenital system are difficult to detect at early stage. The existing noninvasive diagnostics of prostate cancer (PCa) suffer from low accuracy (< 70%) even at advanced stages. In an attempt to improve the accuracy, a small breath study of 63 volunteers representing three groups: (1) of 19 healthy, (2) 28 with PCa, (3) with 8 kidney cancer (KC) and 8 bladder cancer (BC) was performed. Ultrabroadband mid-infrared Fourier absorption spectroscopy revealed eight spectral ranges (SRs) that differentiate the groups. The resulting accuracies of supervised analyses exceeded 95% for four SRs in distinguishing (1) vs (2), three for (1) vs (3) and four SRs for (1) vs (2) + (3). The SRs were then attributed to volatile metabolites. Their origin and involvement in urogenital carcinogenesis are discussed.

Breath Biomarkers in Diagnostic Applications

The detection of chemical compounds in exhaled human breath presents an opportunity to determine physiological state, diagnose disease or assess environmental exposure. Recent advancements in metabolomics research have led to improved capabilities to explore human metabolic profiles in breath. Despite some notable challenges in sampling and analysis, exhaled breath represents a desirable medium for metabolomics applications, foremost due to its non-invasive, convenient and practically limitless availability. Several breath-based tests that target either endogenous or exogenous gas-phase compounds are currently established and are in practical and/or clinical use. This review outlines the concept of breath analysis in the context of these unique tests and their applications. The respective breath biomarkers targeted in each test are discussed in relation to their physiological production in the human body and the development and implementation of the associated tests. The paper concludes with a brief insight into prospective tests and an outlook of the future direction of breath research.

Medical diagnosis at the point-of-care by portable high-field asymmetric waveform ion mobility spectrometry: a systematic review and meta-analysis

Non-invasive medical diagnosis by analysing volatile organic compounds (VOCs) at the point-of-care is becoming feasible owing to recent advances in portable instrumentation. A number of studies have assessed the performance of a state-of-the-art VOC analyser (micro-chip high-field asymmetric waveform ion mobility spectrometry, FAIMS) for medical diagnosis. However, a comprehensive meta-analysis is needed to investigate the overall diagnostic performance of these novel methods across different medical conditions. An electronic search was performed using the CAplus and MEDLINE database through the SciFinder platform. The review identified a total of 23 studies and 2312 individuals. Eighteen studies were used for meta-analysis. A pooled analysis found an overall sensitivity of 80% (95% CI, 74%-85%,I2= 62%), and specificity of 78% (95% CI, 70%-84%,I2= 80%), which corresponds to the overall diagnostic performance of micro-chip FAIMS across many different medical conditions. The diagnostic accuracy was particularly high for coeliac and inflammatory bowel disease (sensitivity and specificity from 74% to 97%). The overall diagnostic performance was similar across breath, urine, and faecal matrices with sparse logistic regression and random forests algorithms resulting in higher diagnostic accuracy. Sources of variability likely arise from differences in sample storage, sampling protocol, method of data analysis, type of disease, sample matrix, and potentially to clinical and disease factors. The results of this meta-analysis indicate that micro-chip FAIMS is a promising candidate for disease screening at the point-of-care, particularly for gastroenterology diseases. This review provides recommendations that should improve the techniques relevant to diagnostic accuracy of future VOC and point-of-care studies.

Photoacoustic detection of ammonia exhaled by individuals with chronic kidney disease

Ammonia (NH₃) has been reported as a breath biomarker for chronic kidney disease (CKD) usually detected at concentrations greater than 0.25 parts per million by volume (ppmV). NH₃ was detected in breath of individuals with CKD through gaseous photoacoustic spectroscopy (PAS). The efficiency of hemodialysis (HD) was demonstrated. Eight volunteers aged between 20 and 60 years and without previous respiratory disease were eligible, among which six were control volunteers (CV) and two volunteers with advanced CKD, named CKDV1 and CKDV2. The presence of CKD was confirmed by the calculation of creatinine clearance (CC) according to the Cockcroft-Gault equation. Before HD, the mean NH₃ concentration exhaled by CKDV1 was 0.9 ± 0.1 ppmV and after HD was 0.20 ± 0.03 ppmV, which demonstrated an efficiency of 76% NH₃ reduction in breath. The CKDV2 exhaled 1.27 ± 0.03 ppmV of NH₃ pre-HD and 0.42 ± 0.08 ppmV post-HD, which resulted in efficiency of about 67%. It was not possible to quantify NH₃ from CV, what led us to infer that all of them exhaled amounts below the detection limit, i.e., 0.20 ppmV. This assumption is underpinned by CC, whose values hovered at 90 ≤ CC ≤ 120 mL/ min, confirming normal renal function.

Isotopic gas analysis by means of mid-infrared photoacoustic spectroscopy targeting human exhaled air

There is a great need for cost-efficient non-invasive medical diagnostic tools for analyzing humanly exhaled air. Compared to present day methods, photoacoustic spectroscopy (PAS) can provide a compact and portable (bedside), sensitive and inexpensive solution. We demonstrate a novel portable photoacoustic spectroscopic platform for isotopic measurements of methane (CH₄). We identify and discriminate the ¹²CH₄- and ¹³CH₄ isotopologues and determine their mixing ratio. An Allan deviation analysis shows that the noise equivalent concentration for CH₄ is 200 ppt (pmol/mol) at 100 s of integration time, corresponding to a normalized noise equivalent absorption coefficient of 5.1×10^-9Wcm^-1Hz^-1/2, potentially making the PAS sensor a truly disruptive instrument for bedside monitoring using isotope tracers by providing real-time metabolism data to clinical personnel.

Breathomics: Review of Sample Collection and Analysis, Data Modeling and Clinical Applications

Metabolomics research is rapidly gaining momentum in disease diagnosis, on top of other Omics technologies. Breathomics, as a branch of metabolomics is developing in various frontiers, for early and noninvasive monitoring of disease. This review starts with a brief introduction to metabolomics and breathomics. A number of important technical issues in exhaled breath collection and factors affecting the sampling procedures are presented. We review the recent progress in metabolomics approaches and a summary of their applications on the respiratory and non-respiratory diseases investigated by breath analysis. Recent reports on breathomics studies retrieved from Scopus and Pubmed were reviewed in this work. We conclude that analyzing breath metabolites (both volatile and nonvolatile) is valuable in disease diagnoses, and therefore believe that breathomics will turn into a promising noninvasive discipline in biomarker discovery and early disease detection in personalized medicine. The problem of wide variations in the reported metabolite concentrations from breathomics studies should be tackled by developing more accurate analytical methods and sophisticated numerical analytical alogorithms.

Sport in Town: The Smart Healthy ENV Project, a Pilot Study of Physical Activity with Multiparametric Monitoring

Background: Increasing evidence links meteorological characteristics and air pollution to physiological responses during sports activities in urban areas with different traffic levels. Objective: The main objective of the Smart Healthy ENV (SHE, “Smart Monitoring Integrated System For A Healthy Urban Environment In Smart Cities”) project was to identify the specific responses of a group of volunteers during physical activity, by monitoring their heart rates and collecting breath samples, combined with data on meteorological determinants and pollution substances obtained through fixed sensor nodes placed along city routes and remotely connected to a dedicated data acquisition server. Methods: Monitoring stations were placed along two urban routes in Pisa, each two km long, with one located within the park beside the Arno river (green route) and the other in a crowded traffic zone (red route). Our sample participants were engaged in sports activities (N = 15, with different levels of ability) and were monitored through wearable sensors. They were first asked to walk back and forth (4 km) and then to run the same route. The experimental sessions were conducted over one day per route. A breath sample was also collected before each test. A questionnaire concerning temperature and fatigue perception was administered for all of the steps of the study over the two days. Results: The heart rates of the participants were monitored in the baseline condition, during walking, and while running, and were correlated with meteorological and pollutant data and with breath composition. Changes in the heart rates and breath composition were detected during the experimental sessions. These variations were related to the physical activity and to the meteorological conditions and air pollution levels. Conclusions: The SHE project can be considered a proof-of-concept study aimed at monitoring physiological and environmental variables during physical activity in urban areas, and can be used in future studies to provide useful information to those involved in sports and the broader community.

A Short Review of Cavity-Enhanced Raman Spectroscopy for Gas Analysis

The market of gas sensors is mainly governed by electrochemical, semiconductor, and non-dispersive infrared absorption (NDIR)-based optical sensors. Despite offering a wide range of detectable gases, unknown gas mixtures can be challenging to these sensor types, as appropriate combinations of sensors need to be chosen beforehand, also reducing cross-talk between them. As an optical alternative, Raman spectroscopy can be used, as, in principle, no prior knowledge is needed, covering nearly all gas compounds. Yet, it has the disadvantage of a low quantum yield through a low scattering cross section for gases. There have been various efforts to circumvent this issue by enhancing the Raman yield through different methods. For gases, in particular, cavity-enhanced Raman spectroscopy shows promising results. Here, cavities can be used to enhance the laser beam power, allowing higher laser beam-analyte interaction lengths, while also providing the opportunity to utilize lower cost equipment. In this work, we review cavity-enhanced Raman spectroscopy, particularly the general research interest into this topic, common setups, and already achieved resolutions.

Real-time versus thermal desorption selected ion flow tube mass spectrometry for quantification of breath volatiles

RATIONALE

Selected ion flow tube mass spectrometry (SIFT-MS) is versatile, rapidly provides result output and determines a wide range of volatiles, making it suitable for biomedical applications. When direct sampling into the SIFT-MS instrument is impractical, combining thermal desorption (TD) and SIFT-MS might offer a solution as it allows sample storage on sorbent tubes for later analysis. This work compares off-line TD SIFT-MS and real-time SIFT-MS for the quantification of selected breath volatiles.

METHODS

Ten healthy non-smoking individuals provided 60 breath samples per method. For off-line analysis, breath was collected onto sorbent tubes via a breath sampler provided with filtered inspiratory air. After TD, samples were re-collected in Tedlar bags which were then connected to the SIFT-MS instrument. For real-time analysis, breath was sampled directly into the instrument. In both cases the analytical method included a total of 155 product ions, and 14 selected volatiles were quantified. The agreement between the methods was assessed using Pearson correlation coefficients and Bland-Altman plots.

RESULTS

Overall, correlations between real-time and off-line analysis were moderate to very strong (r = 0.43-0.92) depending on the volatile of interest, except for 2,3-butanedione and styrene. The difference between real-time and off-line measured breath concentrations (average bias) ranged between -14.57 and 20.48 ppbv. For acetone and isoprene, it was 251.53 and 31.9 ppbv, respectively.

CONCLUSIONS

Real-time SIFT-MS and off-line TD SIFT-MS for quantification of selected breath volatiles did not show optimal agreement. Analyzing a multitude of analytes in breath via direct exhalation into a SIFT-MS instrument for real-time analysis is challenging. On the other hand, off-line analysis using a breath collection device also has its issues such as possible sample losses due to selective absorption depending on the sorbent used or during desorption and transfer to the instrument. Despite these drawbacks, both methods were moderately well correlated.

A highly electropositive ReS2 based ultra-sensitive flexible humidity sensor for multifunctional applications

Flexible 2D ReS₂ based humidity sensor for multifunctional applications.