Human exhaled air analytics: biomarkers of diseases

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.

Graphene-enabled wearable sensors for healthcare monitoring

Wearable sensors in healthcare monitoring have recently found widespread applications in biomedical fields for their non- or minimal-invasive, user-friendly and easy-accessible features. Sensing materials is one of the major challenges to achieve these superiorities of wearable sensors for healthcare monitoring, while graphene-based materials with many favorable properties have shown great efficiency in sensing various biochemical and biophysical signals. In this paper, we review state-of-the-art advances in the development and modification of graphene-based materials (i.e., graphene, graphene oxide and reduced graphene oxide) for fabricating advanced wearable sensors with 1D (fibers), 2D (films) and 3D (foams/aerogels/hydrogels) macroscopic structures. We summarize the structural design guidelines, sensing mechanisms, applications and evolution of the graphene-based materials as wearable sensors for healthcare monitoring of biophysical signals (e.g., mechanical, thermal and electrophysiological signals) and biochemical signals from various body fluids and exhaled gases. Finally, existing challenges and future prospects are presented in this area.

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.

Review of the algorithms used in exhaled breath analysis for the detection of diabetes

Currently, intensive work is underway on the development of truly noninvasive medical diagnostic systems, including respiratory analysers based on the detection of biomarkers of several diseases including diabetes. In terms of diabetes, acetone is considered as a one of the potential biomarker, although is not the single one. Therefore, the selective detection is crucial. Most often, the analysers of exhaled breath are based on the utilization of several commercially available gas sensors or on specially designed and manufactured gas sensors to obtain the highest selectivity and sensitivity to diabetes biomarkers present in the exhaled air. An important part of each system are the algorithms that are trained to detect diabetes based on data obtained from sensor matrices. The prepared review of the literature showed that there are many limitations in the development of the versatile breath analyser, such as high metabolic variability between patients, but the results obtained by researchers using the algorithms described in this paper are very promising and most of them achieve over 90% accuracy in the detection of diabetes in exhaled air. This paper summarizes the results using various measurement systems, feature extraction and feature selection methods as well as algorithms such as Support Vector Machines, k-Nearest Neighbours and various variations of Neural Networks for the detection of diabetes in patient samples and simulated artificial breath samples.

Volatolomics in healthcare and its advanced detection technology

Various diseases increasingly challenge the health status and life quality of human beings. Volatolome emitted from patients has been considered as a potential family of markers, volatolomics, for diagnosis/screening. There are two fundamental issues of volatolomics in healthcare. On one hand, the solid relationship between the volatolome and specific diseases needs to be clarified and verified. On the other hand, effective methods should be explored for the precise detection of volatolome. Several comprehensive review articles had been published in this field. However, a timely and systematical summary and elaboration is still desired. In this review article, the research methodology of volatolomics in healthcare is critically considered and given out, at first. Then, the sets of volatolome according to specific diseases through different body sources and the analytical instruments for their identifications are systematically summarized. Thirdly, the advanced electronic nose and photonic nose technologies for volatile organic compounds (VOCs) detection are well introduced. The existed obstacles and future perspectives are deeply thought and discussed. This article could give a good guidance to researchers in this interdisciplinary field, not only understanding the cutting-edge detection technologies for doctors (medicinal background), but also making reference to clarify the choice of aimed VOCs during the sensor research for chemists, materials scientists, electronics engineers, etc.

Moisture-resistant, stretchable NOx gas sensors based on laser-induced graphene for environmental monitoring and breath analysis

The accurate, continuous analysis of healthcare-relevant gases such as nitrogen oxides (NO_x) in a humid environment remains elusive for low-cost, stretchable gas sensing devices. This study presents the design and demonstration of a moisture-resistant, stretchable NO_x gas sensor based on laser-induced graphene (LIG). Sandwiched between a soft elastomeric substrate and a moisture-resistant semipermeable encapsulant, the LIG sensing and electrode layer is first optimized by tuning laser processing parameters such as power, image density, and defocus distance. The gas sensor, using a needlelike LIG prepared with optimal laser processing parameters, exhibits a large response of 4.18‰ ppm^-1 to NO and 6.66‰ ppm^-1 to NO₂, an ultralow detection limit of 8.3 ppb to NO and 4.0 ppb to NO₂, fast response/recovery, and excellent selectivity. The design of a stretchable serpentine structure in the LIG electrode and strain isolation from the stiff island allows the gas sensor to be stretched by 30%. Combined with a moisture-resistant property against a relative humidity of 90%, the reported gas sensor has further been demonstrated to monitor the personal local environment during different times of the day and analyze human breath samples to classify patients with respiratory diseases from healthy volunteers. Moisture-resistant, stretchable NO_x gas sensors can expand the capability of wearable devices to detect biomarkers from humans and exposed environments for early disease diagnostics.

Depolarization Ratio of the ν1 Raman Band of Pure CH4 and Perturbed by N2 and CO2

In this work, the effect of nitrogen and carbon dioxide on the depolarization ratio of the ν₁ band of methane in the pressure range of 0.1–5 MPa is studied. A high-sensitivity single-pass Raman spectrometer was used to obtain accurate results. Moreover, we took into account the overlap of the ν₁ band by the ν₃ and ν₂ + ν₄ bands using the simulation of their spectra. The depolarization ratio of the ν₁ band in pure methane is within 0–0.001, and the effect of nitrogen and carbon dioxide on this parameter is negligible in the indicated pressure range. The obtained results are useful for correct simulation of the Raman spectrum of methane at different pressures, which is necessary to improve the accuracy of gas analysis methods using Raman spectroscopy.

Recent progress of self-powered respiration monitoring systems

Wearable and implantable medical devices are playing more and more key roles in disease diagnosis and health management. Various biosensors and systems have been used for respiration monitoring. Among them, self-powered sensors have some special characteristics such as low-cost, easy preparation, highly designable, and diversified. The respiratory airflow can drive the self-powered sensors directly to convert mechanical energy of the airflow into electricity. One of the major goals of the self-powered sensors and systems is realizing health monitoring and diagnosis. The relationship between the output signals and the models of respiratory diseases has not been studied deeply and clearly. Therefore, how to find an accurate relationship between them is a challenging and significant research topic. This review summarized the recent progress of the self-powered respiratory sensors and systems from aspects of device principle, output property, detecting index and so on. The challenges and perspectives have also been discussed for reference to the researchers who are interested in the field of self-powered sensors.

Analysis of 2D nanomaterial BC3 for COVID-19 biomarker ethyl butyrate sensor

Ethyl butyrate (EB) was identified in recent research as a prominent biomarker of COVID-19, as concentrations of EB were higher in exhaled breath of COVID-19 patients. Electronic sensitivities of pristine, Al- and Si-doped BC₃ nanosheets to the EB molecule were investigated in this study using density functional theory. It is found that the pure BC₃ was ineffective in sensing EB due to low adsorption energy and sensitivity. Aluminum- and silicon-doped BC₃ nanosheets were effective in forming a strong interaction with EB and were also sensitive. Our calculations show that the band gaps of the Al-doped and Si-doped BC₃ sheets were significantly decreased upon EB adsorption, which increased the electrical conductance of the sheets and the sensitivity. However, Si-doped BC₃ had a recovery time of almost 22 hours, making it less potent than Al-doped BC₃, which had a recovery time of just 7.7 minutes. The shorter recovery time of the Al-doped BC₃ sheet is due to its moderate adsorption energy of 25.8 kcal mol^-1. These results can help facilitate the development of an EB biosensor for COVID-19 testing and other similar applications.

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.

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.

Experimental Investigation on Water Adsorption Using Laser Photoacoustic Spectroscopy and Numerical Simulations

In the present research we propose a model to assess the water vapors adsorption capacity of a SiO₂ trap in the breathing circuit, aiming to reduce the loading of interfering compounds in human breath samples. In this study we used photoacoustic spectroscopy to analyze the SiO₂ adsorption of interfering compounds from human breath and numerical simulations to study the flow of expired breath gas through porous media. As a result, the highest adsorption rate was achieved with a flow rate of 300 sccm, while the lowest rate was achieved with a flow rate of 600 sccm. In the procedure of H₂O removal from the human breath air samples, we determined a quantity of 213 cm³ SiO₂ pearls to be used for a 750 mL sampling bag, in order to keep the detection of ethylene free of H₂O interference. The data from this study encourages the premise that the SiO₂ trap is efficient in the reduction of interfering compounds (like water vapors) from the human breath.

A multiple-method comparative study using GC-MS, AMDIS and in-house-built software for the detection and identification of "unknown" volatile organic compounds in breath

The human respiratory system is a highly complex matrix that exhales many volatile organic compounds (VOCs). Breath-exhaled VOCs are often "unknowns" and possess low concentrations, which make their analysis, peak digging and data processing challenging. We report a new methodology, applied in a proof-of-concept experiment, for the detection of VOCs in breath. For this purpose, we developed and compared four complementary analysis methods based on solid-phase microextraction and thermal desorption (TD) tubes with two GC-mass spectrometer (MS) methods. Using eight model compounds, we obtained an LOD range of 0.02-20 ng/ml. We found that in breath analysis, sampling the exhausted air from Tedlar bags is better when TD tubes are used, not only because of the preconcentration but also due to the stability of analytes in the TD tubes. Data processing (peak picking) was based on two data retrieval approaches with an in-house script written for comparison and differentiation between two populations: sick and healthy. We found it best to use "raw" AMDIS deconvolution data (.ELU) rather than its NIST (.FIN) identification data for comparison between samples. A successful demonstration of this method was conducted in a pilot study (n = 21) that took place in a closed hospital ward (Covid-19 ward) with the discovery of four potential markers. These preliminary findings, at the molecular level, demonstrate the capabilities of our method and can be applied in larger and more comprehensive experiments in the omics world.

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.

Recent Trends in Exhaled Breath Diagnosis Using an Artificial Olfactory System

Artificial olfactory systems are needed in various fields that require real-time monitoring, such as healthcare. This review introduces cases of detection of specific volatile organic compounds (VOCs) in a patient's exhaled breath and discusses trends in disease diagnosis technology development using artificial olfactory technology that analyzes exhaled human breath. We briefly introduce algorithms that classify patterns of odors (VOC profiles) and describe artificial olfactory systems based on nanosensors. On the basis of recently published research results, we describe the development trend of artificial olfactory systems based on the pattern-recognition gas sensor array technology and the prospects of application of this technology to disease diagnostic devices. Medical technologies that enable early monitoring of health conditions and early diagnosis of diseases are crucial in modern healthcare. By regularly monitoring health status, diseases can be prevented or treated at an early stage, thus increasing the human survival rate and reducing the overall treatment costs. This review introduces several promising technical fields with the aim of developing technologies that can monitor health conditions and diagnose diseases early by analyzing exhaled human breath in real time.

The peppermint breath test benchmark for PTR-MS and SIFT-MS

A major challenge for breath research is the lack of standardization in sampling and analysis. To address this, a test that utilizes a standardized intervention and a defined study protocol has been proposed to explore disparities in breath research across different analytical platforms and to provide benchmark values for comparison. Specifically, thePeppermint Experimentinvolves the targeted analysis in exhaled breath of volatile constituents of peppermint oil after ingestion of the encapsulated oil. Data from thePeppermint Experimentperformed by proton transfer reaction mass spectrometry (PTR-MS) and selected ion flow tube mass spectrometry (SIFT-MS) are presented and discussed herein, including the product ions associated with the key peppermint volatiles, namely limonene,α- andβ-pinene, 1,8-cineole, menthol, menthone and menthofuran. The breath washout profiles of these compounds from 65 individuals were collected, comprising datasets from five PTR-MS and two SIFT-MS instruments. The washout profiles of these volatiles were evaluated by comparing the log-fold change over time of the product ion intensities associated with each volatile. Benchmark values were calculated from the lower 95% confidence interval of the linear time-to-washout regression analysis for all datasets combined. Benchmark washout values from PTR-MS analysis were 353 min for the sum of monoterpenes and 1,8-cineole (identical product ions), 173 min for menthol, 330 min for menthofuran, and 218 min for menthone; from SIFT-MS analysis values were 228 min for the sum of monoterpenes, 281 min for the sum of monoterpenes and 1,8-cineole, and 370 min for menthone plus 1,8-cineole. Large inter- and intra-dataset variations were observed, whereby the latter suggests that biological variability plays a key role in how the compounds are absorbed, metabolized and excreted from the body via breath. This variability seems large compared to the influence of sampling and analytical procedures, but further investigations are recommended to clarify the effects of these factors.

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.

The investigation of the volatile metabolites of lung cancer from the microenvironment of malignant pleural effusion

For malignant pleural effusions, pleural fluid cytology is a diagnostic method, but sensitivity is low. The pleural fluid contains metabolites directly released from cancer cells. The objective of this study was to diagnose lung cancer with malignant pleural effusion using the volatilomic profiling method. We recruited lung cancer patients with malignant pleural effusion and patients with nonmalignant diseases with pleural effusion as controls. We analyzed the headspace air of the pleural effusion by gas chromatography-mass spectrometry. We used partial least squares discriminant analysis (PLS-DA) to identify metabolites and the support vector machine (SVM) to establish the prediction model. We split data into a training set (80%) and a testing set (20%) to validate the accuracy. A total of 68 subjects were included in the final analysis. The PLS-DA showed high discrimination with an R² of 0.95 and Q² of 0.58. The accuracy of the SVM in the test set was 0.93 (95% CI 0.66, 0.998), the sensitivity was 83%, the specificity was 100%, and kappa was 0.85, and the area under the receiver operating characteristic curve was 0.96 (95% CI 0.86, 1.00). Volatile metabolites of pleural effusion might be used in patients with cytology-negative pleural effusion to rule out malignancy.

Deciphering Exhaled Aerosol Fingerprints for Early Diagnosis and Personalized Therapeutics of Obstructive Respiratory Diseases in Small Airways

Respiratory diseases often show no apparent symptoms at their early stages and are usually diagnosed when permanent damages have been made to the lungs. A major site of lung pathogenesis is the small airways, which make it highly challenging to detect using current techniques due to the diseases’ location (inaccessibility to biopsy) and size (below normal CT/MRI resolution). In this review, we present a new method for lung disease detection and treatment in small airways based on exhaled aerosols, whose patterns are uniquely related to the health of the lungs. Proof-of-concept studies are first presented in idealized lung geometries. We subsequently describe the recent developments in feature extraction and classification of the exhaled aerosol images to establish the relationship between the images and the underlying airway remodeling. Different feature extraction algorithms (aerosol density, fractal dimension, principal mode analysis, and dynamic mode decomposition) and machine learning approaches (support vector machine, random forest, and convolutional neural network) are elaborated upon. Finally, future studies and frequent questions related to clinical applications of the proposed aerosol breath testing are discussed from the authors’ perspective. The proposed breath testing has clinical advantages over conventional approaches, such as easy-to-perform, non-invasive, providing real-time feedback, and is promising in detecting symptomless lung diseases at early stages.

Modular Breath Analyzer (MBA): Introduction of a Breath Analyzer Platform Based on an Innovative and Unique, Modular eNose Concept for Breath Diagnostics and Utilization of Calibration Transfer Methods in Breath Analysis Studies

Exhaled breath analysis for early disease detection may provide a convenient method for painless and non-invasive diagnosis. In this work, a novel, compact and easy-to-use breath analyzer platform with a modular sensing chamber and direct breath sampling unit is presented. The developed analyzer system comprises a compact, low volume, temperature-controlled sensing chamber in three modules that can host any type of resistive gas sensor arrays. Furthermore, in this study three modular breath analyzers are explicitly tested for reproducibility in a real-life breath analysis experiment with several calibration transfer (CT) techniques using transfer samples from the experiment. The experiment consists of classifying breath samples from 15 subjects before and after eating a specific meal using three instruments. We investigate the possibility to transfer calibration models across instruments using transfer samples from the experiment under study, since representative samples of human breath at some conditions are difficult to simulate in a laboratory. For example, exhaled breath from subjects suffering from a disease for which the biomarkers are mostly unknown. Results show that many transfer samples of all the classes under study (in our case meal/no meal) are needed, although some CT methods present reasonably good results with only one class.

Artificial Breath Classification Using XGBoost Algorithm for Diabetes Detection

Exhaled breath analysis has become more and more popular as a supplementary tool for medical diagnosis. However, the number of variables that have to be taken into account forces researchers to develop novel algorithms for proper data interpretation. This paper presents a system for analyzing exhaled air with the use of various sensors. Breath simulations with acetone as a diabetes biomarker were performed using the proposed e-nose system. The XGBoost algorithm for diabetes detection based on artificial breath analysis is presented. The results have shown that the designed system based on the XGBoost algorithm is highly selective for acetone, even at low concentrations. Moreover, in comparison with other commonly used algorithms, it was shown that XGBoost exhibits the highest performance and recall.

ZEUS (ZIF-based electrochemical ultrasensitive screening) device for isopentane analytics with focus on lung cancer diagnosis

Breath analytics is currently being explored for the development of point-of-care devices in non-invasive disease detection. It is based on the measurement of volatile organic compounds (VOCs) and gases that are produced by the body because of the metabolic pathways. The levels of these metabolites vary due to alteration in the endogenous oxidative stress-related metabolic pathways and can be correlated to understand the underlying disease condition. The levels of exhaled hydrocarbons in human breath can be used to design a rapid, easy to use method for lung cancer detection. This work outlines the development of an electrochemical sensing platform that can be used for the non-invasive diagnosis of lung cancer by monitoring isopentane levels in breath. This electrochemical sensor platform involves the use of [BMIM]BF4@ZIF-8 for sensing the target analyte. This synthesized nanocomposite offers advantages for gas sensing applications as it possesses unique properties such as an electrochemically active Room Temperature Ionic Liquid (RTIL) and a crosslinking Metal Organic Framework (MOF) that provides increased surface area for gas absorption. This is the first report of a hydrocarbon-based sensor platform developed for lung cancer diagnosis. The developed sensor platform displays sensitivity and specificity for the detection of isopentane up to 600 parts-per-billion. We performed structural and morphological characterization of the synthesized nanocomposite using various analytical techniques such as PXRD, FESEM, FTIR, and DLS. We further analyzed the electrochemical activity of the synthesized nanocomposite using a standard glassy carbon electrode. The application of the nanocomposite for isopentane sensing was done using a commercially available carbon screen printed electrode. The results so obtained helped in strengthening our hypothesis and serve as a proof-of-concept for the development of a breathomics-enabled electrochemical strategy. We illustrated the specificity of the developed nanocomposite by cross-reactivity studies. We envision that the detection platform will allow sensitive and specific sensing of isopentane levels such that it can used for point of care applications in noninvasive and early diagnosis of lung cancer, thereby leading to its early treatment and decrease in mortality rate.

Noninvasive Detection of Ammonia in the Breath of Hemodialysis Patients Using a Highly Sensitive Ammonia Sensor Based on a Polypyrrole/Sulfonated Graphene Nanocomposite

In this work, we fabricated fast-responsive and highly sensitive chemiresistive sensors based on nanocomposites of polypyrrole and graphitic materials such as graphene oxide (GO), reduced graphene oxide (RGO), and sulfonated graphene (SRGO) by an in situ chemical oxidative polymerization method. The synthesized nanocomposites were characterized using field emission scanning electron microscopy (FESEM), energy-dispersive spectroscopy (EDS), Fourier transform infrared (FT-IR) spectroscopy, and X-ray diffraction (XRD). The effects of the operating temperature of different nanocomposites were investigated at four temperatures (28, 40, 50, and 60 °C), and the results were compared with that of the polypyrrole-based sensor. The experimental results for sensors indicate that the proposed PPy/SRGO sensor could be an appropriate choice for NH₃ detection at 28 °C in the range of 0.50 parts per billion (ppb) to 12 parts per million (ppm). The PPy/SRGO nanocomposite gas sensor exhibited fast responsivity, good repeatability, and high chemical selectivity to low-concentration ammonia against humidity, methanol, ethanol, acetone, formaldehyde, dibutylamine, dimethylamine, methylamine, carbon monoxide, and nitrogen oxide at 28 °C. We utilized the PPy/SRGO sensor for studying the variation of the ammonia concentration in hemodialysis (HD) patients' breath before and after dialysis and correlated it with the blood urea nitrogen (BUN) levels. The results of the PPy/SRGO sensor indicated that the breath ammonia concentration significantly decreased after dialysis in agreement with BUN. The results demonstrate the potential application of the PPy/SRGO sensor for noninvasive detection of ammonia in breath and make this type of sensor a promising tool for the diagnosis of renal and liver diseases.

Enhanced ethanol sensing properties of WO3 modified TiO2 nanorods

Pristine and WO3 decorated TiO2 nanorods (NRs) were synthesised to investigate n-n-type heterojunction gas sensing properties. TiO2 NRs were fabricated via hydrothermal method on fluorine-doped tin oxide coated glass (FTO) substrates. Then, tungsten was sputtered on the TiO2 NRs and thermally oxidised to obtain WO3 nanoparticles. The heterostructure was characterised by X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive X-ray (EDX) spectroscopy. Fabricated sensor devices were exposed to VOCs such as toluene, xylene, acetone and ethanol, and humidity at different operation temperatures. Experimental results demonstrated that the heterostructure has better sensor response toward ethanol at 200 °C. Enhanced sensing properties are attributed to the heterojunction formation by decorating TiO2 NRs with WO3.

Exhaled Breath Reflects Prolonged Exercise and Statin Use during a Field Campaign

Volatile organic compounds (VOCs) in exhaled breath provide insights into various metabolic processes and can be used to monitor physiological response to exercise and medication. We integrated and validated in situ a sampling and analysis protocol using proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS) for exhaled breath research. The approach was demonstrated on a participant cohort comprising users of the cholesterol-lowering drug statins and non-statin users during a field campaign of three days of prolonged and repeated exercise, with no restrictions on food or drink consumption. The effect of prolonged exercise was reflected in the exhaled breath of participants, and relevant VOCs were identified. Most of the VOCs, such as acetone, showed an increase in concentration after the first day of walking and subsequent decrease towards baseline levels prior to walking on the second day. A cluster of short-chain fatty acids including acetic acid, butanoic acid, and propionic acid were identified in exhaled breath as potential indicators of gut microbiota activity relating to exercise and drug use. We have provided novel information regarding the use of breathomics for non-invasive monitoring of changes in human metabolism and especially for the gut microbiome activity in relation to exercise and the use of medication, such as statins.

Needle Trap Device-GC-MS for Characterization of Lung Diseases Based on Breath VOC Profiles

Volatile organic compounds (VOCs) have been assessed in breath samples as possible indicators of diseases. The present study aimed to quantify 29 VOCs (previously reported as potential biomarkers of lung diseases) in breath samples collected from controls and individuals with lung cancer, chronic obstructive pulmonary disease and asthma. Besides that, global VOC profiles were investigated. A needle trap device (NTD) was used as pre-concentration technique, associated to gas chromatography-mass spectrometry (GC-MS) analysis. Univariate and multivariate approaches were applied to assess VOC distributions according to the studied diseases. Limits of quantitation ranged from 0.003 to 6.21 ppbv and calculated relative standard deviations did not exceed 10%. At least 15 of the quantified targets presented themselves as discriminating features. A random forest (RF) method was performed in order to classify enrolled conditions according to VOCs' latent patterns, considering VOCs responses in global profiles. The developed model was based on 12 discriminating features and provided overall balanced accuracy of 85.7%. Ultimately, multinomial logistic regression (MLR) analysis was conducted using the concentration of the nine most discriminative targets (2-propanol, 3-methylpentane, (E)-ocimene, limonene, m-cymene, benzonitrile, undecane, terpineol, phenol) as input and provided an average overall accuracy of 95.5% for multiclass prediction.

The Influence of Smoking Status on Exhaled Breath Profiles in Asthma and COPD Patients

Breath analysis using eNose technology can be used to discriminate between asthma and COPD patients, but it remains unclear whether results are influenced by smoking status. We aim to study whether eNose can discriminate between ever- vs. never-smokers and smoking <24 vs. >24 h before the exhaled breath, and if smoking can be considered a confounder that influences eNose results. We performed a cross-sectional analysis in adults with asthma or chronic obstructive pulmonary disease (COPD), and healthy controls. Ever-smokers were defined as patients with current or past smoking habits. eNose measurements were performed by using the SpiroNose. The principal component (PC) described the eNose signals, and linear discriminant analysis determined if PCs classified ever-smokers vs. never-smokers and smoking <24 vs. >24 h. The area under the receiver–operator characteristic curve (AUC) assessed the accuracy of the models. We selected 593 ever-smokers (167 smoked <24 h before measurement) and 303 never-smokers and measured the exhaled breath profiles of discriminated ever- and never-smokers (AUC: 0.74; 95% CI: 0.66–0.81), and no cigarette consumption <24h (AUC 0.54, 95% CI: 0.43–0.65). In healthy controls, the eNose did not discriminate between ever or never-smokers (AUC 0.54; 95% CI: 0.49–0.60) and recent cigarette consumption (AUC 0.60; 95% CI: 0.50–0.69). The eNose could distinguish between ever and never-smokers in asthma and COPD patients, but not recent smokers. Recent smoking is not a confounding factor of eNose breath profiles.

Deficiency and absence of endogenous isoprene in adults, disqualified its putative origin

Background

Isoprene (C₅H₈) is a clinically important breath metabolite. Although, hundreds of studies have reported differential expressions in isoprene exhalation as breath biomarker for diverse diseases, the substance couldn't enter to clinical practice as diagnostic marker. Moreover, many experimental/basic observations upon breath isoprene remained unrelated to the corresponding pathophysiological effects on its putative metabolic origin (i.e. mevalonate pathway). Here, we investigated the fundamental reason that hindered the rational interpretation and translation of this marker from basic to clinical science.

Methods

Via high-resolution mass-spectrometry based breathomics in 1026 human subjects, we discovered adults with significant deficiency (order of magnitude lower than the normal) and complete absence of breath isoprene. We prospectively applied real-time breathomics, quantitative gene expression analysis of the mevalonate pathway enzymes, lipid-profiling and hemodynamic monitoring on those isoprene deficient subjects and controls. Additionally, the subject with absence of isoprene was followed up throughout different phases of her womanhood.

Results

In contrast to convention, we witnessed that adults can live healthy without exhaling isoprene or with significant deficiency. This rare phenotype represents a recessive inheritance. Despite physio-metabolic changes during menstrual cycle (that is known to profoundly affect isoprene exhalation) and profoundly increased plasma cholesterol during pregnancy and after childbirth, isoprene remained absent. All genes of mevalonate pathway enzymes were normally expressed in all participants, without any down-regulation or compensatory up-regulation.

Conclusions

Absence/deficiency of isoprene despite normal lipid profiles and no mevalonate pathway malfunction disqualifies the long-believed metabolic origin of isoprene from cholesterol biosynthesis. Thus, clinical translation of breath isoprene expressions should not be generally attributed to corresponding pathophysiological effects onto mevalonate/cholesterol pathway. Our finding has refined and optimized the clinical interpretation of isoprene as biomarker in volatile metabolomics and breathomics. Future studies will address the correct metabolic origin of isoprene to imply this important marker to routine practice.

Breath biopsy of breast cancer using sensor array signals and machine learning analysis

Breast cancer causes metabolic alteration, and volatile metabolites in the breath of patients may be used to diagnose breast cancer. The objective of this study was to develop a new breath test for breast cancer by analyzing volatile metabolites in the exhaled breath. We collected alveolar air from breast cancer patients and non-cancer controls and analyzed the volatile metabolites with an electronic nose composed of 32 carbon nanotubes sensors. We used machine learning techniques to build prediction models for breast cancer and its molecular phenotyping. Between July 2016 and June 2018, we enrolled a total of 899 subjects. Using the random forest model, the prediction accuracy of breast cancer in the test set was 91% (95% CI: 0.85–0.95), sensitivity was 86%, specificity was 97%, positive predictive value was 97%, negative predictive value was 97%, the area under the receiver operating curve was 0.99 (95% CI: 0.99–1.00), and the kappa value was 0.83. The leave-one-out cross-validated discrimination accuracy and reliability of molecular phenotyping of breast cancer were 88.5 ± 12.1% and 0.77 ± 0.23, respectively. Breath tests with electronic noses can be applied intraoperatively to discriminate breast cancer and molecular subtype and support the medical staff to choose the best therapeutic decision.

Exhaled breath analysis in disease detection

Investigating the use of exhaled breath analysis to diagnose and monitor different diseases has attracted much interest in recent years. This review introduces conventionally used methods and some emerging technologies aimed at breath analysis and their relevance to lung disease, airway inflammation, gastrointestinal disorders, metabolic disorders and kidney diseases. One section correlates breath components and specific diseases, whereas the other discusses some unique ideas, strategies, and devices to analyze exhaled breath for the diagnosis of some common diseases. This review aims to briefly introduce the potential application of exhaled breath analysis for the diagnosis and screening of various diseases, thereby providing a new avenue for the detection of non-invasive diseases.

Evaluation of salivary VOC profile composition directed towards oral cancer and oral lesion assessment

OBJECTIVES

Endogenous substances have been analyzed in biological samples in order to be related with metabolic dysfunctions and diseases. The study aimed to investigate profiles of volatile organic compounds (VOCs) from fresh and incubated saliva donated by healthy controls, individuals with oral tissue lesions and with oral cancer, in order to assess case-specific biomarkers of oxidative stress.

MATERIALS AND METHODS

VOCs were pre-concentrated using headspace-solid phase microextraction and analyzed using gas chromatography-mass spectrometry. Then, VOCs positively modulated by incubation process were subtracted, yielding profiles with selected features. Principal component analysis and hierarchical cluster analysis were used to inspect data distribution, while univariate statistics was applied to indicate potential markers of oral cancer. Machine learning algorithm was implemented, aiming multiclass prediction.

RESULTS

The removal of bacterial contribution to VOC profiles allowed the obtaining of more specific case-related patterns. Artificial neural network model included 9 most relevant compounds (1-octen-3-ol, hexanoic acid, E-2-octenal, heptanoic acid, octanoic acid, E-2-nonenal, nonanoic acid, 2,4-decadienal and 9-undecenoic acid). Model performance was assessed using 10-fold cross validation and receiver operating characteristic curves. Obtained overall accuracy was 90%. Oral cancer cases were predicted with 100% of sensitivity and specificity.

CONCLUSIONS

The selected VOCs were ascribed to lipid oxidation mechanism and presented potential to differentiate oral cancer from other inflammatory conditions.

CLINICAL RELEVANCE

These results highlight the importance of interpretation of saliva composition and the clinical value of salivary VOCs. Elucidated metabolic alterations have the potential to aid the early detection of oral cancer and the monitoring of oral lesions.

Breath Analysis: Comparison among Methodological Approaches for Breath Sampling

Despite promising results obtained in the early diagnosis of several pathologies, breath analysis still remains an unused technique in clinical practice due to the lack of breath sampling standardized procedures able to guarantee a good repeatability and comparability of results. The most diffuse on an international scale breath sampling method uses polymeric bags, but, recently, devices named Mistral and ReCIVA, able to directly concentrate volatile organic compounds (VOCs) onto sorbent tubes, have been developed and launched on the market. In order to explore performances of these new automatic devices with respect to sampling in the polymeric bag and to study the differences in VOCs profile when whole or alveolar breath is collected and when pulmonary wash out with clean air is done, a tailored experimental design was developed. Three different breath sampling approaches were compared: (a) whole breath sampling by means of Tedlar bags, (b) the end-tidal breath collection using the Mistral sampler, and (c) the simultaneous collection of the whole and alveolar breath by using the ReCIVA. The obtained results showed that alveolar fraction of breath was relatively less affected by ambient air (AA) contaminants (p-values equal to 0.04 for Mistral and 0.002 for ReCIVA Low) with respect to whole breath (p-values equal to 0.97 for ReCIVA Whole). Compared to Tedlar bags, coherent results were obtained by using Mistral while lower VOCs levels were detected for samples (both breath and AA) collected by ReCIVA, likely due to uncorrected and fluctuating flow rates applied by this device. Finally, the analysis of all data also including data obtained by explorative analysis of the unique lung cancer (LC) breath sample showed that a clean air supply might determine a further confounding factor in breath analysis considering that lung wash-out is species-dependent.

Applications of advanced materials in bio-sensing in live cells: Methods and applications

A wide variety of species, such as different ions, reactive oxygen species, and biomolecules play critical roles in many cell functions. These species are responsible for a range of cellular functions such as signaling, and disturbed levels could be involved in many diseases, such as diabetes, cancer, neurodegeneration etc. Thus, sensitive and specific detection methods for these biomarkers could be helpful for early disease detection and mechanistic investigations. New ultrasensitive sensors for detection of markers within living cells are a growing field of research. The present review provides updates in live cell-based biosensing, which have been published within the last decade. These sensors are mainly based on carbon, gold and other metals, and their physicochemical advantages and limitations are discussed. Advanced materials can be incorporated into probes for the detection of various analytes in living cells. The sensitivity is strongly influenced by the intrinsic properties of the nanomaterials as well their shape and size. The mechanisms of action and future challenges in the developments of new methods for live cell based biosensing are discussed.

Breath markers for therapeutic radiation

Radiation dose is important in radiotherapy. Too little, and the treatment is not effective, too much causes radiation toxicity. A biochemical measurement of the effect of radiotherapy would be useful in personalisation of this treatment. This study evaluated changes in exhaled breath volatile organic compounds (VOC) associated with radiotherapy with thermal desorption gas chromatography mass-spectrometry followed by data processing and multivariate statistical analysis. Further the feasibility of adopting gas chromatography ion mobility spectrometry for radiotherapy point-of-care breath was assessed. A total of 62 participants provided 240 end-tidal 1 dm³ breath samples before radiotherapy and at 1, 3, and 6 h post-exposure, that were analysed by thermal-desorption/gas-chromatography/quadrupole mass-spectrometry. Data were registered by retention-index and mass-spectra before multivariate statistical analyses identified candidate markers. A panel of sulfur containing compounds (thio-VOC) were observed to increase in concentration over the 6 h following irradiation. 3-methylthiophene (80 ng.m^-3 to 790 ng.m^-3) had the lowest abundance while 2-thiophenecarbaldehyde(380 ng.m^-3 to 3.85 μg.m^-3) the highest; note, exhaled 2-thiophenecarbaldehyde has not been observed previously. The putative tumour metabolite 2,4-dimethyl-1-heptene concentration reduced by an average of 73% over the same time. Statistical scoring based on the signal intensities thio-VOC and 3-methylthiophene appears to reflect individuals' responses to radiation exposure from radiotherapy. The thio-VOC are hypothesised to derive from glutathione and Maillard-based reactions and these are of interest as they are associated with radio-sensitivity. Further studies with continuous monitoring are needed to define the development of the breath biochemistry response to irradiation and to determine the optimum time to monitor breath for radiotherapy markers. Consequently, a single 0.5 cm³ breath-sample gas chromatography-ion mobility approach was evaluated. The calibrated limit of detection for 3-methylthiophene was 10 μg.m^-3 with a lower limit of the detector's response estimated to be 210 fg.s^-1; the potential for a point-of-care radiation exposure study exists.

An eNose-based method performing drift correction for online VOC detection under dry and humid conditions

Our recently demonstrated innovative concept of electronic nose (eNose) based on a combination of gas sensors is further tested and benchmarked in the present study. The system is a test bed for gas sensors of different principal technologies distributed within three compartments, which share a compact, very low volume, temperature-controlled sensing chamber. Here, the eNose-based analyser contains three sensing arrays of commercially available semiconducting metal oxide (MOX) gas sensors: one compartment contains 8 analog MOX sensors, while the other two compartments comprise 10 digital MOX sensors. The presented instrument is explicitly tested for the discrimination between mid-range (3-18 ppm) concentrations of different volatile organic compounds (VOCs) including acetaldehyde, acetone, ethanol, ethyl acetate, isoprene and n-pentane under dry and humid conditions, which are all considered relevant gases in future breath diagnostic applications. Since the experiments were performed in periods of time separated by around 20 days, they are affected by drift. For this reason, we explore the opportunity of drift mitigation using methods based on component removal computed by linear discriminant analysis, partial least squares discriminant analysis and direct orthogonalization, which lend themselves to future in-field applications of the developed device and sensing methodology.

'Warming up' a wind instrument: The time-dependent effects of exhaled air on the resonances of a trombone

To study the effect of 'warming up' a wind instrument, the acoustic impedance spectrum at the mouthpiece of a trombone was measured after different durations of playing. When an instrument filled with ambient air is played in a room at 26-27 °C, the resonance frequencies initially fall. This is attributed to CO₂ in the breath initially increasing the density of air in the bore and more than compensating for increased temperature and humidity. Soon after, the resonance frequencies rise to near or slightly above the ambient value as the effects of temperature and humidity compensate for that of increased CO₂. The magnitudes and quality factors of impedance maxima decrease with increased playing time whereas the minima increase. Using the measured change in resonance frequency, it proved possible to separate the changes in impedance due to changes in density and changes in acoustic losses due to water condensing in the bore. When the room and instrument temperature exceed 37 °C, condensation is not expected and, experimentally, smaller decreases in magnitudes and quality factors of impedance maxima are observed. The substantial compensation of the pitch fall due to CO₂ by the rise due to temperature and humidity is advantageous to wind players.

A Review of GC-Based Analysis of Non-Invasive Biomarkers of Colorectal Cancer and Related Pathways

Colorectal cancer (CRC) is the third most commonly diagnosed cancer in the world. In Europe, it is the second most common cause of cancer-related deaths. With the advent of metabolomics approaches, studies regarding the investigation of metabolite profiles related to CRC have been conducted, aiming to serve as a tool for early diagnosis. In order to provide further information about the current status of this field of research, 21 studies were systematically reviewed, regarding their main findings and analytical aspects. A special focus was given to the employment of matrices obtained non-invasively and the use of gas chromatography as the analytical platform. The relationship between the reported volatile and non-volatile biomarkers and CRC-related metabolic alterations was also explored, demonstrating that many of these metabolites are connected with biochemical pathways proven to be involved in carcinogenesis. The most commonly reported CRC indicators were hydrocarbons, aldehydes, amino acids and short-chain fatty acids. These potential biomarkers can be associated with both human and bacterial pathways and the analysis based on such species has the potential to be applied in the clinical practice as a low-cost screening method.

Exhaled breath analysis using cavity-enhanced optical techniques: a review

Cavity-enhanced absorption spectroscopies (CEAS) have gained importance in a wide range of applications in molecular spectroscopy. The development of optical sensors based on the CEAS techniques coupled with the continuous wave or pulsed laser sources operating in the mid-infrared or near-infrared spectral regime uniquely offers molecularly selective and ultra-sensitive detection of trace species in complex matrices including exhaled human breath. In this review, we discussed recent applications of CEAS for analyzing trace constituents within the exhaled breath matrix facilitating the non-invasive assessment of human health status. Next to a brief discussion on the mechanisms of formation of trace components found in the exhaled breath matrix related to particular disease states, existing challenges in CEAS and future development towards non-invasive clinical diagnostics will be discussed.

A novel lung alveolar cell model for exploring volatile biomarkers of particle-induced lung injury

Quartz can increase oxidative stress, lipid peroxidation, and inflammation. The objective of this study was to explore the volatile biomarkers of quartz-induced lung injury using a lung alveolar cell model. We exposed the human alveolar A549 cell line to 0, 200, and 500 μg/mL quartz particles for 24 h and used gas chromatography-mass spectrometry to measure the volatile metabolites in the headspace air of cells. We identified ten volatile metabolites that had concentration-response relationships with particles exposure, including 1,2,4-oxadiazole, 5-(4-nitrophenyl)-3-phenyl- (CAS: 28825-12-9), 2,6-dimethyl-6-trifluoroacetoxyoctane (CAS: 61986-67-2), 3-buten-1-amine, N,N-dimethyl- (CAS: 55831-89-5), 2-propanol, 2-methyl- (CAS: 75-65-0), glycolaldehyde dimethyl acetal (CAS: 30934-97-5), propanoic acid, 2-oxo-, ethyl ester (CAS: 617-35-6), octane (CAS: 111-65-9), octane, 3,3-dimethyl- (CAS: 4110-44-5), heptane, 2,3-dimethyl- (CAS: 3074-71-3) and ethanedioic acid, bis(trimethylsilyl) ester (CAS: 18294-04-7). The volatile biomarkers are generated through the pathways of propanoate and nitrogen metabolism. The volatile biomarkers of the alkanes and methylated alkanes are related to oxidative and lipid peroxidation of the cell membrane. The lung alveolar cell model has the potential to explore the volatile biomarkers of particulate-induced lung injury.

An exploratory study on online quantification of isoprene in human breath using cavity ringdown spectroscopy in the ultraviolet

Breath analysis offers a promising method of noninvasive analyses of volatile metabolites and xenobiotics present in human body. Isoprene is one of the highest abundant volatile organic compounds (VOCs) present in human exhaled breath. Breath isoprene (50-200 part per billion by volume (ppbv) or higher) can be analyzed by using mass spectroscopy-based methods, yet laser absorption spectral detection of breath isoprene has not been much reported, partially due to its ultraviolet (UV) absorption wavelength and the spectral overlap with other breath VOCs such as acetone in the same wavelength region. These facts make it challenging to develop a spectroscopy-based breath isoprene analyzer for a potential portable instrument. Here we report on the development of a cavity ringdown spectroscopy (CRDS) system for detection of breath isoprene in the UV region near 226 nm. First, we investigated spectral absorption interferences near 226 nm and selected an optimal detection wavelength at 226.56 nm with minimum to no spectral interference. We then measured absorption cross-sections of isoprene at 225.5-227.4 nm under controlled cavity pressures, and the measured absorption cross-section 1.93 × 10^-17 cm²/molecule at 226.56 nm was used to quantify isoprene in different cases including human breath gas samples. Finally, we validated the CRDS system by measuring breath gas samples from 19 human subjects using proton transfer reaction mass spectrometry (PTR-MS). The CRDS system shows good linear response (R² = 0.999), high stability (0.2%), and high accuracy (R² = 0.906 with PTR-MS). The limit of detection of the system was 0.47 ppbv, with average over 100 ringdown events (equivalent to 5 s). This work represents the first exploratory study of the detection of breath isoprene using CRDS. The results demonstrate the potential of developing a CRDS-based breath analyzer for online, near-real time, sensitive analysis of breath isoprene for further research that would help to elucidate its physiological and clinical significance.

Engineering of SnO2-Graphene Oxide Nanoheterojunctions for Selective Room-Temperature Chemical Sensing and Optoelectronic Devices

The development of high-performing sensing materials, able to detect ppb-trace concentrations of volatile organic compounds (VOCs) at low temperatures, is required for the development of next-generation miniaturized wireless sensors. Here, we present the engineering of selective room-temperature (RT) chemical sensors, comprising highly porous tin dioxide (SnO₂)-graphene oxide (GO) nanoheterojunction layouts. The optoelectronic and chemical properties of these highly porous (>90%) p-n heterojunctions were systematically investigated in terms of composition and morphologies. Optimized SnO₂-GO layouts demonstrate significant potential as both visible-blind photodetectors and selective RT chemical sensors. Notably, a low GO content results in an excellent UV light responsivity (400 A W^-1), with short rise and decay times, and RT high chemical sensitivity with selective detection of VOCs such as ethanol down to 100 ppb. In contrast, a high concentration of GO drastically decreases the RT response to ethanol and results in good selectivity to ethylbenzene. The feasibility of tuning the chemical selectivity of sensor response by engineering the relative amount of GO and SnO₂ is a promising feature that may guide the future development of miniaturized solid-state gas sensors. Furthermore, the excellent optoelectronic properties of these SnO₂-GO nanoheterojunctions may find applications in various other areas such as optoelectronic devices and (photo)electrocatalysis.

Breath analysis of smokers, non-smokers, and e-cigarette users

Solid phase micro extraction-Gas Chromatography/Mass Spectrometry (SPME-GC/MS) analysis was performed in exhaled breath samples of 48 healthy volunteers: 20 non-smokers, 10 smokers and 18 e-cigarette (EC, vape) users. Each volunteer provided 1 L of exhaled breath in a pre-cleaned Tedlar bag, in which an SPME fiber was exposed to absorb the emitted breath volatile organic compounds (VOCs). The acquired data were processed using multivariate data analysis (MDA) methods in order to identify the characteristic chemicals of the three groups. The results revealed that the breath of non-smokers demonstrated inverse correlation with a variety of molecules related to the breath from smokers including furan, toluene, 2-butanone and other organic substances. Vapers were distinguished from smokers by the chemical speciation of the e-liquids, such as that of esters (e.g. ethyl acetate), terpenes (e.g. α-pinene, β-pinene, d-limonene, p-cymene, etc.) and oxygenated compounds (e.g. 3-hexen-1-ol, benzaldehyde, hexanal, decanal, etc). Two classification models were developed (a) using principal component analysis (PCA) with hierarchical cluster analysis (HCA) and (b) using partial least squares-discriminant analysis (PLS-DA). Both models were validated using 8 new samples (4 vapers and 4 smokers), collected in addition to the 48 samples of the calibration set. The combination of GC/MS breath analysis and MDA contributed successfully in classifying the volunteers into their respective groups and highlighted the relevant characteristic VOCs. The respective dynamic combination (SPME-GC/MS and MDA) provides a means for long term non-invasive monitoring of the population's health status for early detection purposes.

Impact of the adsorbent material on volatile metabolites during in vitro and in vivo bio-sampling

The increased attraction of biological volatile compounds has opened the route to a wide variety of sampling techniques, amongst which trap tubes packed with adsorbent materials are commonly used. Many types of adsorbent materials are available and the choice of the adsorbent can impact the obtained results in untargeted analysis. Therefore, a proper combination of the adsorbent material and the sample is necessary to increase the robustness and reproducibility of biological studies. In this study, the sampling performance of thermal desorption tubes with six common adsorbent material combinations, i.e., Tenax® TA, Tenax® TA/Carbopack™ B, Tenax® TA/Sulficarb, Tenax® TA/Carbograph™ 5TD, Tenax® TA/Carbograph™ 1TD/Carboxen® 1003, and Carboxen® 1016/Carbograph™ 5TD, was evaluated in two different setups: in vitro and in vivo sampling. The in vitro setup consisted of the headspace dynamic extraction of spiked serum, and a mixture of 19 standards was evaluated in terms of response and reproducibility. The in vivo setup consisted into two parts: the first one was based the evaluation of the standard mixture, which was flash-vaporised into Tedlar® bags containing exhaled breath; the second part was based on the longitudinal monitoring of breath metabolites originating from a beverage intake (i.e., brewed coffee), over a 90 min time period. The tubes were all desorbed and analysed in a comprehensive two-dimensional gas chromatography system coupled to a high-resolution time-of-flight mass spectrometer (GC × GC-HR ToF MS). In both sampling setups, the widest analytes coverage and the overall best extraction yield on the selected compounds were obtained using Tenax® TA, followed by Tenax® TA/Carbopack™ B. Tenax® TA provided the highest sampling reproducibility with 12 %RSD, 10 %RSD and <5 %RSD of the response during the experiments using the in vitro setup, the in vivo setup, and during the longitudinal tracking, respectively.

Breakthroughs in the Design of Novel Carbon-Based Metal Oxides Nanocomposites for VOCs Gas Sensing

Nowadays, the detection of volatile organic compounds (VOCs) at trace levels (down to ppb) is feasible by exploiting ultra-sensitive and highly selective chemoresistors, especially in the field of medical diagnosis. By coupling metal oxide semiconductors (MOS e.g., SnO₂, ZnO, WO₃, CuO, TiO₂ and Fe₂O₃) with innovative carbon-based materials (graphene, graphene oxide, reduced graphene oxide, single-wall and multi-wall carbon nanotubes), outstanding performances in terms of sensitivity, selectivity, limits of detection, response and recovery times towards specific gaseous targets (such as ethanol, acetone, formaldehyde and aromatic compounds) can be easily achieved. Notably, carbonaceous species, highly interconnected to MOS nanoparticles, enhance the sensor responses by (i) increasing the surface area and the pore content, (ii) favoring the electron migration, the transfer efficiency (spillover effect) and gas diffusion rate, (iii) promoting the active sites concomitantly limiting the nanopowders agglomeration; and (iv) forming nano-heterojunctions. Herein, the aim of the present review is to highlight the above-mentioned hybrid features in order to engineer novel flexible, miniaturized and low working temperature sensors, able to detect specific VOC biomarkers of a human's disease.

Detection of the effects of triclosan (TCS) on the metabolism of VOCs in HepG2 cells by SPI-TOFMS

Volatile organic compounds (VOCs) emitted by organisms and cell metabolism have demonstrated great physiological and pathological values. At present, there is a great interest in the study of volatile metabolome to determine whether VOCs can serve as potential diagnostic biomarkers. In view of the sensitivity of VOCs to physiological changes, the aim of this study was to investigate alterations in VOC profiles in the in vitro headspace of HepG2 cells after exposure to triclosan (TCS). Since the in vivo biological effects of TCS are clearly defined, several TCS-related VOCs may potentially be traced back to common cellular processes. In this study, HepG2 cells were cultured in TCS-containing medium for 2 h, and the emitted VOCs in the headspace of the culture flask were detected using a single photon ionization time-of-flight mass spectrometry instrument. The control group and the TCS-treated group could be well separated by differential VOC profiles, which were related to the physiological states of the HepG2 cells. Compared to the control group, eleven and ten specific VOCs were identified in the 20 μm and 50 μm TCS-treated groups, respectively. Among them, five specific VOCs (m/z 62, 64, 70, 121 and 146) were commonly observed in these two TCS-treated groups. These results indicate that TCS can cause changes in cellular metabolic VOCs, and different concentrations of TCS lead to different VOCs profiles. Based on the findings of the study, the detection of VOCs in cell metabolism can be used as an auxiliary tool to explore the mechanism of drug action, and also as an exploratory method to determine whether drugs play a role in disease treatment.

A volatolomic approach using cerumen as biofluid to diagnose bovine intoxication by Stryphnodendron rotundifolium

An innovative volatolomic approach employs the detection of biomarkers present in cerumen (earwax) to identify cattle intoxication by Stryphnodendron rotundifolium Mart., Fabaceae (popularly known as barbatimão). S. rotundifolium is a poisonous plant with the toxic compound undefined and widely distributed throughout the Brazilian territory. Cerumen samples from cattle of two local Brazilian breeds ('Curraleiro Pé-Duro' and 'Pantaneiro') were collected during an experimental intoxication protocol and analyzed using headspace (HS)/GC-MS followed by multivariate analysis (genetic algorithm for a partial least squares, cluster analysis, and classification and regression trees). A total of 106 volatile organic metabolites were identified in the cerumen samples of bovines. The intoxication by S. rotundifolium influenced the cerumen volatolomic profile of the bovines throughout the intoxication protocol. In this way, it was possible to detect biomarkers for cattle intoxication. Among the biomarkers, 2-octyldecanol and 9-tetradecen-1-ol were able to discriminate all samples between intoxicated and nonintoxicated bovines. The cattle intoxication diagnosis by S. rotundifolium was accomplished by applying the cerumen analysis using HS/GC-MS, in an easy, accurate, and noninvasive way. Thus, the proposed bioanalytical chromatography protocol is a useful tool in veterinary applications to determine this kind of intoxication.

Urine headspace analysis with field asymmetric ion mobility spectrometry for detection of chronic kidney disease

Electronic noses (eNoses) are an emerging class of experimental diagnostic tools. They are based on the detection of volatile organic compounds. Urine is used as sample medium in several publications but neither the effect of chronic kidney disease (CKD) on the analysis nor the potential to detect CKD has been explored. Materials & methods: We utilized an eNose based on field asymmetric ion mobility spectrometry (FAIMS) technology to classify urine samples from CKD patients and controls. Results: We were able to differentiate extremes of kidney function with an accuracy of 81.4%. Conclusion: In this preliminary study, applying eNose technology we were able to distinguish the patients with impaired kidney function from those with normal kidney function.