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Recently, volatile organic compound (VOC) determination in exhaled breath has seen growing interest due to its promising potential in early diagnosis of several pathological conditions, including chronic kidney disease (CKD). Therefore, this study aimed to identify the breath VOC pattern providing an accurate, reproducible and fast CKD diagnosis at early stages of disease. A cross-sectional observational study was carried out, enrolling a total of 30 subjects matched for age and gender. More specifically, the breath samples were collected from (a) 10 patients with end-stage kidney disease (ESKD) before undergoing hemodialysis treatment (DIAL); (b) 10 patients with mild-moderate CKD (G) including 3 patients in stage G2 with mild albuminuria, and 7 patients in stage G3 and (c) 10 healthy controls (CTRL). For each volunteer, an end-tidal exhaled breath sample and an ambient air sample (AA) were collected at the same time on two sorbent tubes by an automated sampling system and analyzed by Thermal Desorption–Gas Chromatography–Mass Spectrometry. A total of 110 VOCs were detected in breath samples but only 42 showed significatively different levels with respect to AA. Nonparametric tests, such as Wilcoxon/Kruskal–Wallis tests, allowed us to identify the most weighting variables able to discriminate between AA, DIAL, G and CTRL breath samples. A promising multivariate data mining approach incorporating only selected variables (showing p-values lower than 0.05), such as nonanal, pentane, acetophenone, pentanone, undecane, butanedione, ethyl hexanol and benzene, was developed and cross-validated, providing a prediction accuracy equal to 87% and 100% in identifying patients with both mild–moderate CKD (G) and ESKD (DIAL), respectively. [ABSTRACT FROM AUTHOR]

Kidney diseases are a group of conditions related to the functioning of kidneys, which are in turn unable to properly filter waste and excessive fluids from the blood, resulting in the presence of dangerous levels of electrolytes, fluids, and waste substances in the human body, possibly leading to significant health effects. At the same time, the toxins amassing in the organism can lead to significant changes in breath composition, resulting in halitosis with peculiar features like the popular ammonia breath. Starting from this evidence, scientists have started to work on systems that can detect the presence of kidney diseases using a minimally invasive approach, minimizing the burden to the individuals, albeit providing clinicians with useful information about the disease's presence or its main related features. The electronic nose (e-nose) is one of such tools, and its applications in this specific domain represent the core of the present review, performed on articles published in the last 20 years on humans to stay updated with the latest technological advancements, and conducted under the PRISMA guidelines. This review focuses not only on the chemical and physical principles of detection of such compounds (mainly ammonia), but also on the most popular data processing approaches adopted by the research community (mainly those relying on Machine Learning), to draw exhaustive conclusions about the state of the art and to figure out possible cues for future developments in the field. [ABSTRACT FROM AUTHOR]

This paper presents a novel technique for extracting the alveolar part of human breath. Gas exchange occurs between blood and inhaled air in the alveoli, which is helpful in medical diagnostics based on breath analysis. Consequently, the alveolar portion of the exhaled air contains specific concentrations of endogenous EVOC (exogenous volatile organic compound), which, among other factors, depend on the person's health condition. As this part of the breath enables the screening for diseases, accurate sample collection for testing is crucial. Inaccurate sampling can significantly alter the composition of the specimen, alter the concentration of EVOC (biomarkers) and adversely affect the diagnosis. Furthermore, the volume of alveolar air is minimal (usually <350 mL), especially in the case of people affected by respiratory system problems. For these reasons, precise sampling is a key factor in the effectiveness of medical diagnostic systems. A new technique ensuring high accuracy and repeatability is presented in the article. It is based on analyzing the changes in carbon dioxide concentration in human breath using a fast and compensated non-dispersive infrared (NDIR) sensor and the simple moving adjacent average (SMAA) algorithm. Research has shown that this method accurately identifies exhalation phases with an uncertainty as low as 20 ms. This provides around 350 ms of breath duration for carrying out additional stages of the diagnostic process using various types of analyzers. [ABSTRACT FROM AUTHOR]

Introduction: Volatile organic compounds (VOCs) can arise from underlying metabolism and are detectable in exhaled breath, therefore offer a promising route to non-invasive diagnostics. Robust, precise, and repeatable breath measurement platforms able to identify VOCs in breath distinguishable from background contaminants are needed for the confident discovery of breath-based biomarkers. Objectives: To build a reliable breath collection and analysis method that can produce a comprehensive list of known VOCs in the breath of a heterogeneous human population. Methods: The analysis cohort consisted of 90 pairs of breath and background samples collected from a heterogenous population. Owlstone Medical's Breath Biopsy® OMNI® platform, consisting of sample collection, TD-GC-MS analysis and feature extraction was utilized. VOCs were determined to be "on-breath" if they met at least one of three pre-defined metrics compared to paired background samples. On-breath VOCs were identified via comparison against purified chemical standards, using retention indexing and high-resolution accurate mass spectral matching. Results: 1471 VOCs were present in > 80% of samples (breath and background), and 585 were on-breath by at least one metric. Of these, 148 have been identified covering a broad range of chemical classes. Conclusions: A robust breath collection and relative-quantitative analysis method has been developed, producing a list of 148 on-breath VOCs, identified using purified chemical standards in a heterogenous population. Providing confirmed VOC identities that are genuinely breath-borne will facilitate future biomarker discovery and subsequent biomarker validation in clinical studies. Additionally, this list of VOCs can be used to facilitate cross-study data comparisons for improved standardization. [ABSTRACT FROM AUTHOR]

Early diagnosis is crucial for effective treatment of socially significant diseases, such as type 1 diabetes mellitus (T1DM), pneumonia, and asthma. This study employs a diagnostic method based on infrared laser spectroscopy of human exhaled breath. The experimental setup comprises a quantum cascade laser, which emits in a pulsed mode with a peak power of up to 150 mW in the spectral range of 5.3–12.8 μm (780–1890 cm−1), and a Herriott multipass gas cell with a specific optical path length of 76 m. Using this setup, spectra of exhaled breath in the mid‐infrared range were obtained from 165 volunteers, including healthy individuals, patients with T1DM, asthma, and pneumonia. The study proposes a hybrid approach for classifying these spectra, utilizing a variational autoencoder for dimensionality reduction and a support vector machine method for classification. The results demonstrate that the proposed hybrid approach outperforms other machine learning method combinations. [ABSTRACT FROM AUTHOR]

Exhaled breath volatilomics is a powerful non-invasive tool for biomarker discovery in medical applications, but compound annotation is essential for pathophysiological insights and technology transfer. This study was aimed at investigating the interest of a hybrid approach combining real-time proton transfer reaction-time-of-flight mass spectrometry (PTR-TOF-MS) with comprehensive thermal desorption—two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (TD-GCxGC-TOF-MS) to enhance the analysis and characterization of VOCs in clinical research, using COVID-19 as a use case. VOC biomarker candidates were selected from clinical research using PTR-TOF-MS fingerprinting in patients with COVID-19 and matched to the Human Breathomic Database. Corresponding analytical standards were analysed using both a liquid calibration unit coupled to PTR-TOF-MS and TD-GCxGC-TOF-MS, together with confirmation on new clinical samples with TD-GCxGC-TOF-MS. From 26 potential VOC biomarkers, 23 were successfully detected with PTR-TOF-MS. All VOCs were successfully detected using TD-GCxGC-TOF-MS, providing effective separation of highly chemically related compounds, including isomers, and enabling high-confidence annotation based on two-dimensional chromatographic separation and mass spectra. Four VOCs were identified with a level 1 annotation in the clinical samples. For future applications, the combination of real-time PTR-TOF-MS and comprehensive TD-GCxGC-TOF-MS, at least on a subset of samples from a whole study, would enhance the performance of VOC annotation, offering potential advancements in biomarker discovery for clinical research. [ABSTRACT FROM AUTHOR]

Mi'an, freshly recovered from a serious illness, was preparing for a ceremony to honor Doctor X for his exceptional contributions in defending against disease attacks. Unexpectedly, remnants of the rebellion clandestinely laid traps for innocent civilians, initiating a diabetic onslaught on Mi'an. Thankfully, Doctor X promptly devised a solution. He established the "Medical Barking Team", using their keen sense of smell to detect diseases and temporarily alleviate the situation. However, the enemy proved to be cunning, and the crisis persisted. In a bold move, Doctor X decided to take a chance. He created a machine called the "E-nose", capable of detecting diseases day and night. Ultimately, Mi'an overcame the crisis with the assistance of the E-nose. From that point forward, the E-nose found applications in various fields for the benefit of all. [ABSTRACT FROM AUTHOR]

Background: The multitude of metabolites generated by physiological processes in the body can serve as valuable biomarkers for many clinical purposes. They can provide a window into relevant metabolic pathways for health and disease, as well as be candidate therapeutic targets. A subset of these metabolites generated in the human body are volatile, known as volatile organic compounds (VOCs), which can be detected in exhaled breath. These can diffuse from their point of origin throughout the body into the bloodstream and exchange into the air in the lungs. For this reason, breath VOC analysis has become a focus of biomedical research hoping to translate new useful biomarkers by taking advantage of the non-invasive nature of breath sampling, as well as the rapid rate of collection over short periods of time that can occur. Despite the promise of breath analysis as an additional platform for metabolomic analysis, no VOC breath biomarkers have successfully been implemented into a clinical setting as of the time of this review. Aim of review: This review aims to summarize the progress made to address the major methodological challenges, including standardization, that have historically limited the translation of breath VOC biomarkers into the clinic. We highlight what steps can be taken to improve these issues within new and ongoing breath research to promote the successful development of the VOCs in breath as a robust source of candidate biomarkers. We also highlight key recent papers across select fields, critically reviewing the progress made in the past few years to advance breath research. Key scientific concepts of review: VOCs are a set of metabolites that can be sampled in exhaled breath to act as advantageous biomarkers in a variety of clinical contexts. [ABSTRACT FROM AUTHOR]

Introduction: This study employs Proton-Transfer-Reaction Mass Spectrometry (PTR-MS) to analyze exhaled breath profiles of 504 healthy adults, focusing on nine common volatile organic compounds (VOCs): acetone, acetaldehyde, acetonitrile, ethanol, isoprene, methanol, propanol, phenol, and toluene. PTR-MS offers real-time VOC measurement, crucial for understanding breath biomarkers and their applications in health assessment. Objectives: The study aims to investigate how demographic factors-gender, age, and smoking history-affect VOC concentrations in exhaled breath. The objective is to enhance our understanding of breath biomarkers and their potential for health monitoring and clinical diagnosis. Methods: Exhaled breath samples were collected using PTR-MS, measuring concentrations of nine VOCs. The data were analyzed to discern distribution patterns across demographic groups. Results: Males showed higher average VOC levels for certain compounds. Propanol and methanol concentrations significantly increased with age. Smoking history influenced VOC levels, with differences among non-smokers, current smokers, and ex-smokers. Conclusion: This research provides valuable insights into demographic influences on exhaled VOC profiles, emphasizing the potential of breath analysis for health assessment. PTR-MS's real-time measurement capabilities are crucial for capturing dynamic VOC changes, offering advantages over conventional methods. These findings lay a foundation for advancements in non-invasive disease detection, highlighting the importance of considering demographics in breath biomarker research. [ABSTRACT FROM AUTHOR]

Abstract COVID-19 has caused a worldwide pandemic, creating an urgent need for early detection methods. Breath analysis has shown great potential as a non-invasive and rapid means for COVID-19 detection. The objective of this study is to detect patients infected with SARS-CoV-2 and even the possibility to screen between different SARS-CoV-2 variants by analysis of carbonyl compounds in breath. Carbonyl compounds in exhaled breath are metabolites related to inflammation and oxidative stress induced by diseases. This study included a cohort of COVID-19 positive and negative subjects confirmed by reverse transcription polymerase chain reaction between March and December 2021. Carbonyl compounds in exhaled breath were captured using a microfabricated silicon microreactor and analyzed by ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS). A total of 321 subjects were enrolled in this study. Of these, 141 (85 males, 60.3%) (mean ± SD age: 52 ± 15 years) were COVID-19 (55 during the alpha wave and 86 during the delta wave) positive and 180 (90 males, 50%) (mean ± SD age: 45 ± 15 years) were negative. Panels of a total of 34 ketones and aldehydes in all breath samples were identified for detection of COVID-19 positive patients. Logistic regression models indicated high accuracy/sensitivity/specificity for alpha wave (98.4%/96.4%/100%), for delta wave (88.3%/93.0%/84.6%) and for all COVID-19 positive patients (94.7%/90.1%/98.3%). The results indicate that COVID-19 positive patients can be detected by analysis of carbonyl compounds in exhaled breath. The technology for analysis of carbonyl compounds in exhaled breath has great potential for rapid screening and detection of COVID-19 and for other infectious respiratory diseases in future pandemics.

Background: Disorders of mucociliary clearance, such as cystic fibrosis (CF), primary ciliary dyskinesia (PCD) and bronchiectasis of unknown origin, are characterised by periods with increased respiratory symptoms, referred to as pulmonary exacerbations. These exacerbations are hard to predict and associated with lung function decline and the loss of quality of life. To optimise treatment and preserve lung function, there is a need for non-invasive and reliable methods of detection. Breath analysis might be such a method. Methods: We systematically reviewed the existing literature on breath analysis to detect pulmonary exacerbations in mucociliary clearance disorders. Extracted data included the study design, technique of measurement, definition of an exacerbation, identified compounds and diagnostic accuracy. Results: Out of 244 identified articles, 18 were included in the review. All studies included patients with CF and two also with PCD. Age and the definition of exacerbation differed between the studies. There were five that measured volatile organic compounds (VOCs) in exhaled breath using gas chromatography with mass spectrometry, two using an electronic nose and eleven measured organic compounds in exhaled breath condensate. Most studies showed a significant correlation between pulmonary exacerbations and one or multiple compounds, mainly hydrocarbons and cytokines, but the validation of these results in other studies was lacking. Conclusions: The detection of pulmonary exacerbations by the analysis of compounds in exhaled breath seems possible but is not near clinical application due to major differences in results, study design and the definition of an exacerbation. There is a need for larger studies, with a longitudinal design, international accepted definition of an exacerbation and validation of the results in independent cohorts. [ABSTRACT FROM AUTHOR]

Purpose: This feasibility study aimed to investigate the use of exhaled breath analysis to capture and quantify relative changes of metabolites during resolution of acute diabetic ketoacidosis under insulin and rehydration therapy. Methods: Breath analysis was conducted on 30 patients of which 5 with DKA. They inflated Nalophan bags, and their metabolic content was subsequently interrogated by secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS). Results: SESI-HRMS analysis showed that acetone, pyruvate, and acetoacetate, which are well known to be altered in DKA, were readily detectable in breath of participants with DKA. In addition, a total of 665 mass spectral features were found to significantly correlate with base excess and prompt metabolic trajectories toward an in-control state as they progress toward homeostasis. Conclusion: This study provides proof-of-principle for using exhaled breath analysis in a real ICU setting for DKA monitoring. This non-invasive new technology provides new insights and a more comprehensive overview of the effect of insulin and rehydration during DKA treatment. [ABSTRACT FROM AUTHOR]

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

Abstract Early diagnosis of lung cancer (LC) can significantly reduce its mortality rate. Considering the limitations of the high false positive rate and reliance on radiologists’ experience in computed tomography (CT)–based diagnosis, a multi-modal early LC screening model that combines radiology with other non-invasive, rapid detection methods is warranted. A high-resolution, multi-modal, and low-differentiation LC screening strategy named ensemble text and breath analysis (ETBA) is proposed that ensembles radiology report text analysis and breath analysis. In total, 231 samples (140 LC patients and 91 benign lesions [BL] patients) were screened using proton transfer reaction–time of flight–mass spectrometry and CT screening. Participants were randomly assigned to a training set and a validation set (4:1) with stratification. The report section of the radiology reports was used to train a text analysis (TA) model with a natural language processing algorithm. Twenty-two volatile organic compounds (VOCs) in the exhaled breath and the prediction results of the TA model were used as predictors to develop the ETBA model using an extreme gradient boosting algorithm. A breath analysis model was developed based on the 22 VOCs. The BA and TA models were compared with the ETBA model. The ETBA model achieved a sensitivity of 94.3%, a specificity of 77.3%, and an accuracy of 87.7% with the validation set. The radiologist diagnosis performance with the validation set had a sensitivity of 74.3%, a specificity of 59.1%, and an accuracy of 68.1%. High sensitivity and specificity were obtained by the ETBA model compared with radiologist diagnosis. The ETBA model has the potential to provide sensitivity and specificity in CT screening of LC. This approach is rapid, non-invasive, multi-dimensional, and accurate for LC and BL diagnosis.

Research has shown that the composition of breath can differ based on the human's behavioral patterns and mental and physical states immediately before being collected. These breath-collection techniques have also been extended to observe the general processes occurring in groups of humans and can link them to what those groups are collectively experiencing. In this research, we applied machine learning techniques to the breath data collected from cinema audiences. These techniques included XGBOOST Regression, Hierarchical Clustering, and Item Basket analyses created using the Apriori algorithm. They were conducted to find associations between the biomarkers in the crowd's breath and the movie's audio-visual stimuli and thematic events. This analysis enabled us to directly link what the group was experiencing and their biological response to that experience. We first extracted visual and auditory features from a movie to achieve this. We compared it to the biomarkers in the crowd's breath using regression and pattern mining techniques. Our results supported the theory that a crowd's collective experience directly correlates to the biomarkers in the crowd's breath. Consequently, these findings suggest that visual and auditory experiences have predictable effects on the human body that can be monitored without requiring expensive or invasive neuroimaging techniques. [ABSTRACT FROM AUTHOR]

Background: Acute respiratory distress syndrome (ARDS) poses challenges in early identification. Exhaled breath contains metabolites reflective of pulmonary inflammation. Aim: To evaluate the diagnostic accuracy of breath metabolites for ARDS in invasively ventilated intensive care unit (ICU) patients. Methods: This two-center observational study included critically ill patients receiving invasive ventilation. Gas chromatography and mass spectrometry (GC–MS) was used to quantify the exhaled metabolites. The Berlin definition of ARDS was assessed by three experts to categorize all patients into "certain ARDS", "certain no ARDS" and "uncertain ARDS" groups. The patients with "certain" labels from one hospital formed the derivation cohort used to train a classifier built based on the five most significant breath metabolites. The diagnostic accuracy of the classifier was assessed in all patients from the second hospital and combined with the lung injury prediction score (LIPS). Results: A total of 499 patients were included in this study. Three hundred fifty-seven patients were included in the derivation cohort (60 with certain ARDS; 17%), and 142 patients in the validation cohort (47 with certain ARDS; 33%). The metabolites 1-methylpyrrole, 1,3,5-trifluorobenzene, methoxyacetic acid, 2-methylfuran and 2-methyl-1-propanol were included in the classifier. The classifier had an area under the receiver operating characteristics curve (AUROCC) of 0.71 (CI 0.63–0.78) in the derivation cohort and 0.63 (CI 0.52–0.74) in the validation cohort. Combining the breath test with the LIPS does not significantly enhance the diagnostic performance. Conclusion: An exhaled breath metabolomics-based classifier has moderate diagnostic accuracy for ARDS but was not sufficiently accurate for clinical use, even after combination with a clinical prediction score. [ABSTRACT FROM AUTHOR]

PurposeThis feasibility study aimed to investigate the use of exhaled breath analysis to capture and quantify relative changes of metabolites during resolution of acute diabetic ketoacidosis under insulin and rehydration therapy.MethodsBreath analysis was conducted on 30 patients of which 5 with DKA. They inflated Nalophan bags, and their metabolic content was subsequently interrogated by secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS).ResultsSESI-HRMS analysis showed that acetone, pyruvate, and acetoacetate, which are well known to be altered in DKA, were readily detectable in breath of participants with DKA. In addition, a total of 665 mass spectral features were found to significantly correlate with base excess and prompt metabolic trajectories toward an in-control state as they progress toward homeostasis.ConclusionThis study provides proof-of-principle for using exhaled breath analysis in a real ICU setting for DKA monitoring. This non-invasive new technology provides new insights and a more comprehensive overview of the effect of insulin and rehydration during DKA treatment.

Recently, volatile organic compound (VOC) determination in exhaled breath has seen growing interest due to its promising potential in early diagnosis of several pathological conditions, including chronic kidney disease (CKD). Therefore, this study aimed to identify the breath VOC pattern providing an accurate, reproducible and fast CKD diagnosis at early stages of disease. A cross-sectional observational study was carried out, enrolling a total of 30 subjects matched for age and gender. More specifically, the breath samples were collected from (a) 10 patients with end-stage kidney disease (ESKD) before undergoing hemodialysis treatment (DIAL); (b) 10 patients with mild-moderate CKD (G) including 3 patients in stage G2 with mild albuminuria, and 7 patients in stage G3 and (c) 10 healthy controls (CTRL). For each volunteer, an end-tidal exhaled breath sample and an ambient air sample (AA) were collected at the same time on two sorbent tubes by an automated sampling system and analyzed by Thermal Desorption–Gas Chromatography–Mass Spectrometry. A total of 110 VOCs were detected in breath samples but only 42 showed significatively different levels with respect to AA. Nonparametric tests, such as Wilcoxon/Kruskal–Wallis tests, allowed us to identify the most weighting variables able to discriminate between AA, DIAL, G and CTRL breath samples. A promising multivariate data mining approach incorporating only selected variables (showing p-values lower than 0.05), such as nonanal, pentane, acetophenone, pentanone, undecane, butanedione, ethyl hexanol and benzene, was developed and cross-validated, providing a prediction accuracy equal to 87% and 100% in identifying patients with both mild–moderate CKD (G) and ESKD (DIAL), respectively.

Kidney diseases are a group of conditions related to the functioning of kidneys, which are in turn unable to properly filter waste and excessive fluids from the blood, resulting in the presence of dangerous levels of electrolytes, fluids, and waste substances in the human body, possibly leading to significant health effects. At the same time, the toxins amassing in the organism can lead to significant changes in breath composition, resulting in halitosis with peculiar features like the popular ammonia breath. Starting from this evidence, scientists have started to work on systems that can detect the presence of kidney diseases using a minimally invasive approach, minimizing the burden to the individuals, albeit providing clinicians with useful information about the disease’s presence or its main related features. The electronic nose (e-nose) is one of such tools, and its applications in this specific domain represent the core of the present review, performed on articles published in the last 20 years on humans to stay updated with the latest technological advancements, and conducted under the PRISMA guidelines. This review focuses not only on the chemical and physical principles of detection of such compounds (mainly ammonia), but also on the most popular data processing approaches adopted by the research community (mainly those relying on Machine Learning), to draw exhaustive conclusions about the state of the art and to figure out possible cues for future developments in the field.

This paper presents a novel technique for extracting the alveolar part of human breath. Gas exchange occurs between blood and inhaled air in the alveoli, which is helpful in medical diagnostics based on breath analysis. Consequently, the alveolar portion of the exhaled air contains specific concentrations of endogenous EVOC (exogenous volatile organic compound), which, among other factors, depend on the person’s health condition. As this part of the breath enables the screening for diseases, accurate sample collection for testing is crucial. Inaccurate sampling can significantly alter the composition of the specimen, alter the concentration of EVOC (biomarkers) and adversely affect the diagnosis. Furthermore, the volume of alveolar air is minimal (usually

Exhaled breath‐based disease diagnosis is an ancient technique, and the application of this technique is rapidly developing for disease quick testing, such as viral infection, asthma, chronic kidney disease, and so on. Among the diagnostic tools, an exhaled breath‐based test has demonstrated the merits of being non‐invasive, convenient, quick, and comfortable. In this review, the exhaled breath diagnosis via the gaseous part of the breath is the major focus. First, the summary of state‐of‐art studies based on exhaled gas detection is described. Second, typical disease‐related exhaled gas and their measurements are described. Finally, the various structure of field effect transistor (FET)‐type sensors for gas‐based disease detection is discussed in detail. This review may inspire new research ideas and directions for applying FET‐type sensors to quick disease detection via the gaseous route. [ABSTRACT FROM AUTHOR]

Increased acceptance of cannabis containing the psychoactive component, Δ9‐tetrahydrocannabinol (THC), raises concerns about the potential for impaired drivers and increased highway accidents. In contrast to the "breathalyzer" test, which is generally accepted for determining the alcohol level in a driver, there is no currently accepted roadside test for THC in a motorist. There is a need for an easily collectible biological sample from a potentially impaired driver coupled with an accurate on‐site test to measure the presence and quantity of THC in a driver. A novel breath collection device is described, which includes three separate sample collectors for collecting identical A, B, and C breath samples from a subject. A simple one‐step ethanol extraction of the "A" breath collector sample can be analyzed by UHPLC/selected ion monitoring (SIM) liquid chromatography/mass spectrometry (LC/MS) to provide qualitative and quantitative determination of THC in breath sample in less than 4 min for samples collected up to 6 h after smoking a cannabis cigarette. SIM LC/MS bioanalyses employed d3‐THC as the stable isotope internal standard fortified in negative control breath samples for quantitation including replicates of six calibrator standards and three quality control (QC) samples. Subsequent confirmation of the same breath sample in the B collectors was then confirmed by a reference lab by LC/MS/MS analysis. Fit‐for‐purpose bioanalytical validation consistent with pharmaceutical regulated bioanalyses produced pharmacokinetic (PK) curves for the two volunteer cannabis smokers. These results produced PK curves, which showed a rapid increase of THC in the breath of the subjects in the first hour followed by reduced THC levels in the later time points. A simpler single‐point calibration curve procedure with calibrators and QC prepared in ethanol provided similar results. Limitations to this approach include the higher cost and operator skill sets for the instrumentation employed and the inability to actually determine driver impairment. [ABSTRACT FROM AUTHOR]

Abstract Exhaled breath provides information of individual's holistic physiological condition, making non‐invasive exhaled breath testing a convenient and effective choice for personalized health management. This work proposes a mobile e‐nose prototype equipped with a miniaturized gas delivering unit, an advanced sensor array, a customized analog to digital conversion circuit board, an online data transmission, and machine learning algorithms that can effectively detect and analyze volatile compounds in exhaled breath, providing a systematic design strategy for non‐invasive disease diagnosis. The gas sensor array, as a core element, is composed of eight graphene‐based chemiresistive materials which are sensitive to trace gas analytes. In an online breath test study with 401 cases, patients with respiratory diseases can be distinguished from the healthy with an overall accuracy higher than 80%. In addition, classification of breath samples between patients with chronic obstructive pulmonary disease and the healthy higher than 85% is achieved. These results indicate the proposed portable e‐nose prototype holds great promise in personalized health state monitoring.

Lung cancer remains a formidable global health challenge, necessitating the exploration of innovative diagnostic approaches for early detection. This review paper delves into the burgeoning field of exhaled breath analysis using electronic nose (e-nose) technology for the identification of volatile organic compounds (VOCs) as potential biomarkers for lung cancer diagnosis. An electronic nose inspired by the human olfactory system comprises an array of sensors that can detect and differentiate complex odor profiles. This paper elucidates the principles behind e-nose technology and its application in capturing the unique VOC signatures present in exhaled breath, which serve as indicators of underlying physiological conditions. A significant portion of this review is dedicated to elucidating the methodology and advancements in e-nose-based VOC analysis, providing insights into the potential of this non-invasive approach for disease diagnosis. The exploration extends beyond general applications to specifically spotlight the utilization of e-nose technology in the realm of early lung cancer detection. E-nose-based disease diagnosis, and more specifically, lung cancer detection, is discussed in detail, with an emphasis on the recent studies and advancements. The potential of VOC profiling as a reliable and early diagnostic tool for lung cancer is explored, addressing both the promises and challenges associated with this cutting-edge approach. This comprehensive review amalgamates the current state of knowledge in the field, offering a roadmap for future research opportunities and the realization of e-nose technology’s promise in revolutionizing lung cancer diagnosis.

Detection of volatile organic compounds in exhaled air is a promising approach to non-invasive and scalable gastric cancer screening. This work proposes a new approach for the detection of volatile organic compounds by analyzing odor-evoked calcium responses in the rat olfactory bulb. We estimate the feasibility of gastric cancer biomarker detection added to the exhaled air of healthy participants. Our detector consists of a convolutional encoder and a similarity-based classifier over encoder outputs. To minimize overfitting on a small available training set, we involve a pre-training where the encoder is trained on synthetic data representing spatiotemporal patterns similar to real calcium responses in the olfactory bulb. We estimate the classification accuracy of exhaled air samples by matching their encodings with encodings of calibration samples of two classes: 1) exhaled air and 2) a mixture of exhaled air with the cancer biomarker. On our data, the accuracy increased from 0.68 on real data up to 0.74 if pre-training on synthetic data is involved. Our work is focused on proving the feasibility of proposed new approach rather than on comparing its efficiency with existing methods. Such detection is often performed with an electronic nose, but its output becomes unstable over time due to a sensor drift. In contrast to the electronic nose, rats can robustly detect low concentrations of biomarkers over lifetime. The feasibility of gastric cancer biomarker detection in exhaled air by bio-hybrid system is shown. Pre-training of neural models for images analysis increases the accuracy of detection.

Lung cancer remains a formidable global health challenge, necessitating the exploration of innovative diagnostic approaches for early detection. This review paper delves into the burgeoning field of exhaled breath analysis using electronic nose (e-nose) technology for the identification of volatile organic compounds (VOCs) as potential biomarkers for lung cancer diagnosis. An electronic nose inspired by the human olfactory system comprises an array of sensors that can detect and differentiate complex odor profiles. This paper elucidates the principles behind e-nose technology and its application in capturing the unique VOC signatures present in exhaled breath, which serve as indicators of underlying physiological conditions. A significant portion of this review is dedicated to elucidating the methodology and advancements in e-nose-based VOC analysis, providing insights into the potential of this non-invasive approach for disease diagnosis. The exploration extends beyond general applications to specifically spotlight the utilization of e-nose technology in the realm of early lung cancer detection. E-nose-based disease diagnosis, and more specifically, lung cancer detection, is discussed in detail, with an emphasis on the recent studies and advancements. The potential of VOC profiling as a reliable and early diagnostic tool for lung cancer is explored, addressing both the promises and challenges associated with this cutting-edge approach. This comprehensive review amalgamates the current state of knowledge in the field, offering a roadmap for future research opportunities and the realization of e-nose technology's promise in revolutionizing lung cancer diagnosis. [ABSTRACT FROM AUTHOR]

Infrared spectroscopy appears to be a promising analytical method for the metabolic analysis of breath. However, due to the presence of trace amounts in exhaled breath, the absorption strength of the metabolites remains extremely low. In such low detection limits, the nonlinear detection sensitivity of the infrared detector and electronic noise strongly modify the baseline of the acquired infrared spectra of breath. Fitting the reference molecular spectra with the baseline-modified spectral features of breath metabolites does not provide accurate identification. Therefore, baseline correction of the acquired infrared spectra of breath is the primary requirement for the success of breath-based infrared diagnosis. A selective spectral region-based, simple baseline correction method is proposed for the infrared spectroscopy of breath. [Display omitted] • VOCs in exhaled breath provide information about internal chemistry of the body. • IR spectroscopy is a promising technique for the identification of VOCs in breath. • Due to the presence of trace amount VOCs, in data processing, baseline looks distorted. • Proposed hierarchical correction method allows accurately identify and quantify VOCs. [ABSTRACT FROM AUTHOR]

[Display omitted] • Poly (MAA-co-EGDMA)-modified cotton fiber achieves 92% and 88% capture efficiencies. • Robust performance across batches and excellent stability over 16 days. • Temporal profiles revealed MDMB-4en-PINACA/ADB-4en-PINACA detectable for 210/150 min in exhaled breath. • Offers a low-cost, noninvasive method for enhancing drug abuse detection efforts. Breath analysis has emerged as a promising noninvasive method for detecting drug abuse, offering potential advantages over traditional blood or urine samples. However, the capture and analysis of trace analytes in exhaled breath (EB) remains a challenge for practical applications. In this study, we explore the utilization of poly (methacrylic anhydride-co-ethylene glycol dimethacrylate)-functionalized cotton fiber (poly (MAA-co-EGDMA)-functionalized cotton fiber) for the capture of MDMB-4en-PINACA and ADB-4en-PINACA, two popular synthetic cannabinoids, from exhaled breath. Capture efficiencies of 92 % and 88 % for MDMB-4en-PINACA and ADB-4en-PINACA were achieved demonstrating robust performance across batches and excellent stability. Animal experiments were conducted and exhaled breath samples were collected and analyzed. Shorter half-lives were shown for MDMB-4en-PINACA and ADB-4en-PINACA in EB than in plasma. The detection window for MDMB-4en-PINACA was 210 min, while 150 min for ADB-4en-PINACA. This pilot study presents a novel approach for the capture of MDMB-4en-PINACA and ADB-4en-PINACA in exhaled breath using functionalized cotton fiber. The demonstrated efficacy in capturing MDMB-4en-PINACA and ADB-4en-PINACA highlights the potential of this low-cost and stable platform for noninvasive drug abuse detection. By offering a viable alternative to invasive methods, our findings demonstrate a new noninvasive method of drug abuse detection using exhaled breath holds great promise for enhancing public health efforts to combat drug abuse. [ABSTRACT FROM AUTHOR]

Chronic obstructive pulmonary disease (COPD) is a widespread respiratory disorder with a high mortality rate. Current diagnostic methods have limitations, necessitating innovative diagnostic approaches. Breath analysis using electronic noses (e-noses) is promising but still faces challenges. The single temperature-modulated sensor (STMS) is one of the optimized models of e-noses. It uses a single sensor instead of an array of multiple sensors, which enhances commercial viability and ease of use. This pilot study explores the feasibility of using a STMS device for COPD detection. Breath samples were collected from 34 healthy individuals and 33 COPD patients. Features extracted from the sensor's response were analyzed, and a novel feature selection method was developed to discriminate between the two groups. Using this method and a linear support vector machine (SVM) classifier, a single commercial sensor, driven with half-sine-staircase and staircase heater waveforms, achieved 80.60 % accuracy, 78.79 % sensitivity, and 82.35 % specificity in differentiating between COPD patients and healthy controls. These results demonstrate the potential of an appropriately configured STMS to capture comprehensive breath information for COPD detection. Furthermore, our study highlights how advancements in machine learning can maximize the effectiveness of this approach. These promising pilot study results warrant further investigation with larger, more diverse cohorts to validate this approach for broader clinical implementation. [Display omitted] • Investigating temperature modulation of a single metal oxide gas sensor to detect COPD through exhaled breath analysis. • Evaluating machine learning algorithms for feature reduction and classification to achieve accurate discrimination, and proposing a novel feature selection method based on sensor signal information. • Demonstrating high accuracy, sensitivity, and specificity of the proposed system in detecting COPD patients using the feature selection method and the support vector machine (SVM) algorithm. [ABSTRACT FROM AUTHOR]

• Overcoming one-shot limitation of thermal desorption via sample re-collection. • Assessing recollection accuracy and precision. • Recollection and analyzing standards and breath samples using GC×GC-TOFMS. • Compounds' properties, sample moisture, and desorption parameters affect recollection. In this study, we aimed to assess the feasibility of re-collecting breath samples using the Centri® (Markes International, Bridgend, UK) followed by two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS) analysis. The work was conducted in two main phases. In the first phase, we evaluated the re-collection performance by analyzing two sets of standards, including a Grob mix primary solution and a standard mixture of 20 selected volatile compounds (VCs) covering different classes of organic species commonly found in breath samples. The intra-day and inter-day precision (reported as relative standard deviation (RSD),%) for the re-collection of the Grob mix primary solution were in the range of 1 % to14 % and 3 % to12 %, respectively. The re-collection accuracy ranged from 78 % to 97 %. The intra-day RSD for the re-collection of the standard mixture of selected VCs was within 20 % for all compounds, except for acetone and nonane. The precision was within 25 % for all compounds, except for nonane, n -hexane, 1,4-dichlorobenzene, and decane, which exhibited less than 36 % RSD. The re-collection accuracy was in the range of 67 % to 129 %. In the second phase of the study, the re-collection performance in breath analysis was evaluated via five repetitive splitting and re-collection of six breath samples obtained from healthy adults, realizing a total of 30 breath analyses. Initially, we evaluated the re-collection performance by considering all features obtained from breath analysis and then focused on the 20 VCs commonly found in breath samples. The re-collection accuracy for total breath features ranged from 86 to 103 %, and the RSDs were in the range of 1.0 % to 10.4 %. For the selected VCs, the re-collection accuracy of all compounds, except for undecane and benzene, was in the range of 71 % to 132 %. [ABSTRACT FROM AUTHOR]

The advancements of chemical sensors over the past 50 years have significantly expanded the diversity of available sensors making them more affordable, portable, sensitive, and partially chemically selective. Despite these advancements, the diversity and capability of sensors used in breath analysis has remained limited due to challenges that persist in this field. This encompasses fluctuations in humidity, temperature, and oxygen concentration within breath that all pose challenges that can significantly affect sensor output. This review article aims to introduce and compare the techniques used in current chemical sensor arrays for breath analysis. A brief overview of the discrete chemical sensors and arrays employed in breath research, at both commercial and research levels, is discussed. Furthermore, current trends in data analysis for chemical sensor arrays in breath analysis is described. Finally, a detailed outlook of recent diagnostic results from studies implementing sensor arrays from the last 5 years is outlined. [Display omitted] • Concise overview on the working principles of discrete chemical sensors utilised in breath analysis. • A discussion on the application of sensor arrays, both commercial and custom, in breath analysis. • Diagnostic outputs from studies implementing sensors in breath analysiswithin the last five years. • Elucidation of the major hurdles in sensor-based breath analysis. [ABSTRACT FROM AUTHOR]

Humanity endeavors to resume crewed missions to the Moon and prepares for the exploration of Mars. These missions will require sustained human presence in space for longer periods than ever before. Space exposes astronauts to demanding conditions, including microgravity, radiation, rapid light-dark cycles, and hazardous chemicals. Gas sensors will be pivotal in preserving astronaut health by providing critical health data (e.g., through breath analysis) and space-resolved environmental information. Here, we explore the recent progress of gas sensors to meet the key needs of space exploration. First, the fundamental sensing principles of electrochemical, chemoresistive, mass-sensitive, and optical sensors are briefly introduced. Then, we connect space-related health challenges with suitable breath markers and sensor solutions, encompassing areas like gut microbiome, muscle activity, cardiovascular health, hepatic and renal function, and circadian rhythm. Finally, environmental exposure guidelines and suitable sensor innovations for distributed air quality monitoring in space vehicles and habitats are presented. • Deep space exploration will require sustained human presence. • Breath analysis enables continuous health monitoring of astronauts. • Space-resolved air quality monitoring is needed for rapid pollutant detection. • Key breath markers and air pollutants with exposure thresholds are identified. • Electrochemical, chemoresistive, mass-sensitive, and optical sensors are reviewed. [ABSTRACT FROM AUTHOR]

Diabetes Mellitus (DM), a widespread metabolic disorder, poses lifelong health implications, demanding timely diagnosis and cautious monitoring for effective disease management. Traditional blood glucose tests are invasive and require medical expertise for intermittent checking, motivating the investigation of alternative, noninvasive methods. This study introduces an approach employing breath analysis through a set of 12 quartz tuning fork-based sensors enhanced using nanomaterials and dedicated artificial neural network (ANN) algorithms for data interpretation. The breath analysis methodology involves capturing unique breath signatures using the frequency-based sensor array. The accompanying neural network classification algorithm, customized for the sensor data, enables precise classification of data from 245 individuals as diabetic, prediabetic, or healthy. A neural network regression algorithm predicted blood glucose values and was compared with the actual values obtained from medical blood glucose measurement. The clinical relevance of the predicted blood glucose has been examined using error grids. The sensor array coupled with the ANN algorithm can identify diabetic, prediabetic, and control samples with 97% test accuracy. Blood glucose was predicted using neural network regression with a correlation coefficient of 0.89 and a mean square error of 0.13.

For decades, intake monitoring of drugs using urine as the matrix of choice is the gold standard in drug treatment centers. A properly conducted urine drug test can identify recent use of prescribed, non-prescribed and illicit drugs. However, issues like adulteration, substitution and privacy issues have driven the search for alternative matrices. This prospective pilot study evaluates the use of an impaction-based breath sampling device, Breath Explor ® , as an alternative to traditional urine-based drug monitoring. Breath samples were analyzed using a validated 32-component liquid chromatography-tandem mass spectrometry method. Recovery data represent the efficiency of extracting the analytes from the breath devices. Both automated and manual processing of the Breath Explor® devices showed mean recovery rates ranging from 39.5% to 55.4% for the 32 analytes. Despite the small number of subjects, breath analysis proved to be a convenient and easy-to-use methodology. An overall kappa-values of 0.5 indicated a moderate level of agreement with urine analysis, underscoring its potential as a complementary diagnostic tool. All participants tested positive in their breath sample for methadone (70% methadone and 100% EDDP), while a significant portion (90%) tested positive for 6-monoacetylmorphine. This innovative approach offers several advantages, including non-invasiveness, reduced risk of adulteration, and the ability to perfom repeated automated sampling and confirmation testing. These findings suggest that breath-based substance monitoring could complement or even replace traditional urine-based methods in clinical practice., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Untargeted analysis of volatile organic compounds (VOCs) from exhaled breath and culture headspace are influenced by several confounding factors not represented in reference standards. In this study, we propose a method of generating pooled quality control (QC) samples for untargeted VOC studies using a split-recollection workflow with thermal desorption tubes. Sample tubes were desorbed and split from each sample and recollected onto a single tube, generating a pooled QC sample. This QC sample was then repeatedly desorbed and recollected with a sequentially lower split ratio allowing injection of multiple QC samples. We found pooled QC samples to be representative of complex mixtures using principal component analysis and may be useful in future longitudinal, multi-centre, and validation studies to assess data quality and adjust for batch effects., (Creative Commons Attribution license.)

Surface-enhanced Raman scattering (SERS) offers a promising, cost-effective alternative for the rapid, sensitive, and quantitative analysis of potential biomarkers in exhaled gases, which is crucial for early disease diagnosis. However, a major challenge in SERS is the effective detection of gaseous analytes, primarily due to difficulties in enriching and capturing them within the substrate's "hotspot" regions. This study introduces an advanced gas sensor combining mesoporous gold (MesoAu) and metal-organic frameworks (MOFs), exhibiting high sensitivity and rapid detection capabilities. The MesoAu provides abundant active sites and interconnected mesopores, facilitating the diffusion of analytes for detection. A ZIF-8 shell enveloping MesoAu further enriches target molecules, significantly enhancing sensitivity. A proof-of-concept experiment demonstrated a detection limit of 0.32 ppb for gaseous benzaldehyde, indicating promising prospects for the rapid diagnosis of early stage lung cancer. This research also pioneers a novel approach for constructing hierarchical plasmonic nanostructures with immense potential in gas sensing.

Polymeric bags are a widely applied, simple, and cost-effective method for the storage and offline analysis of gaseous samples. Various materials have been used as sampling bags, all known to contain impurities and differing in their cost, durability, and storage capabilities. Herein, we present a comparative study of several well-known bag materials, Tedlar (PVF), Kynar (PVDF), Teflon (PTFE), and Nalophan (PET), as well as a new material, ethylene vinyl copolymer (EVOH), commonly used for storing food. We investigated the influences of storage conditions, humidity, bag cleaning, and light exposure on volatile organic compound concentration (acetone, acetic acid, isoprene, benzene, limonene, among others) in samples of exhaled human breath stored in bags for up to 48 h. Specifically, we show high losses of short-chain fatty acids (SCFAs) in bags of all materials (for most SCFAs, less than 50% after 8 h of storage). We found that samples in Tedlar, Nalophan, and EVOH bags undergo changes in composition when exposed to UV radiation over a period of 48 h. We report high initial impurity levels in all the bags and their doubling after a period of 48 h. We compare secondary electrospray ionization and proton transfer reaction mass spectrometry in the context of offline analysis after storage in sampling bags. We provide an analytical perspective on the temporal evolution of bag contents by presenting the intensity changes of all significant m / z features. We also present a simple, automated, and cost-effective offline sample introduction system, which enables controlled delivery of collected gaseous samples from polymeric bags into the mass spectrometer. Overall, our findings suggest that sampling bags exhibit high levels of impurities, are sensitive to several environmental factors (e.g. light exposure), and provide low recoveries for some classes of compounds, e.g. SCFAs., (Creative Commons Attribution license.)

The COVID-19 pandemic had profound global impacts on daily lives, economic stability, and healthcare systems. Diagnosis of COVID-19 infection via RT-PCR was crucial in reducing spread of disease and informing treatment management. While RT-PCR is a key diagnostic test, there is room for improvement in the development of diagnostic criteria. Identification of volatile organic compounds (VOCs) in exhaled breath provides a fast, reliable, and economically favorable alternative for disease detection. This meta-analysis analyzed the diagnostic performance of VOC-based breath analysis in detection of COVID-19 infection. A systematic review of twenty-nine papers using the grading criteria from Newcastle-Ottawa Scale (NOS) and PRISMA guidelines was conducted. The cumulative results showed a sensitivity of 0.92 (95 % CI, 90 %-95 %) and a specificity of 0.90 (95 % CI 87 %-93 %). Subgroup analysis by variant demonstrated strong sensitivity to the original strain compared to the Omicron and Delta variant in detection of SARS-CoV-2 infection. An additional subgroup analysis of detection methods showed eNose technology had the highest sensitivity when compared to GC-MS, GC-IMS, and high sensitivity-MS. Overall, these results support the use of breath analysis as a new detection method of COVID-19 infection. [ABSTRACT FROM AUTHOR]

• First study of breath profiles in cystic fibrosis patients using proton mass spectrometry. • Proton mass spectrometry is a promising method for determining breath profiles in cystic fibrosis. • Combined tidal and forced expiration analysis allows for more accurate comparison of breath profiles. • Specific exhaled biomarkers in adult patients with cystic fibrosis are indole, phenol, and dimethyl sulfide. Proton-transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS) is a promising tool for a rapid online determination of exhaled volatile organic compounds (eVOCs) profiles in patients with cystic fibrosis (CF). To detect VOC breath signatures specific to adult patients with CF compared with controls using PTR-TOF-MS. 102 CF patients (54 M/48, mean age 25.6 ± 7.8 yrs) and 97 healthy controls (56 M/41F, mean age 25.8 ± 6.0 yrs) were examined. Samples from normal quiet breathing and forced expiratory maneuvers were analyzed with PTR-TOF-MS (Ionicon, Austria) to obtain VOC profiles listed as ions at various mass-to-charge ratios (m / z). PTR-TOF-MS analysis was able to detect 167 features in exhaled breath from CF patients and healthy controls. According to cluster analysis and LASSO regression, patients with CF and controls were separated. The most significant VOCs for CF were indole, phenol, dimethyl sulfide, and not indicated: m / z = 297.0720 ([C12H13N2O7 and C17H13O5]H +), m / z = 281.0534 ([C19H7NO2, C12H11NO7 and C16H9O5]H +) during five-fold cross-validation both in forced expiratory maneuver and in normal quiet breathing. PTR-TOF-MS is a promising method for determining the molecular composition of exhaled air specific to CF. [ABSTRACT FROM AUTHOR]

Breath exhaled hydrogen cyanide (HCN) has been identified to be associated with several respiratory diseases. Accurately distinguishing the concentration and release rate of different HCN sources is of great value in clinical research. However, there are still significant challenges due to the high adsorption and low concentration characteristics of exhaled HCN. In this study, a two-compartment kinetic model method based on negative photoionization mass spectrometry was developed to simultaneously determine the kinetic parameters including concentrations and release rates in the airways and alveoli. The influences of the sampling line diameter, length, and temperature on the response time of the sampling system were studied and optimized, achieving a response time of 0.2 s. The negative influence of oral cavity-released HCN was reduced by employing a strategy based on anatomical lung volume calculation. The calibration for HCN in the dynamic range of 0.5–100 ppbv and limit of detection (LOD) at 0.3 ppbv were achieved. Subsequently, the experiments of smoking, short-term passive smoking, and intake of bitter almonds were performed to examine the influences of endogenous and exogenous factors on the dynamic parameters of the model method. The results indicate that compared with steady-state concentration measurements, the kinetic parameters obtained using this model method can accurately and significantly reflect the changes in different HCN sources, highlighting its potential for HCN-related disease research. [Display omitted] • Exhaled HCN profiling with a LOD of 300 pptv and a response time of 0.2 s. • Two-compartment model (2CM) parameters of airway and alveoli are determined. • Influence of smoking and food intake on 2CM parameters has been studied. [ABSTRACT FROM AUTHOR]

Invasive methods such as blood collection and biopsy are commonly used for testing liver and kidney function, which are painful, time-consuming, require trained personnel, and may not be easily accessible to people for their routine checkup. Early diagnosis of liver and kidney diseases can prevent severe symptoms and ensure better management of these patients. Emerging approaches such as breath and sweat analysis have shown potential as non-invasive methods for disease diagnosis. Among the many markers, ammonia is often used as a biomarker for the monitoring of liver and kidney functions. In this review we provide an insight into the production and expulsion of ammonia gas in the human body, the different diseases that could potentially use ammonia as biomarker and analytical devices such as chemiresistive gas sensors for non-invasive monitoring of this gas. The review also provides an understanding into the different materials, doping agents and substrates used to develop such multifunctional sensors. Finally, the current challenges and the possible future trends have been discussed. [ABSTRACT FROM AUTHOR]

The Peppermint Initiative, established within the International Association of Breath Research, introduced the peppermint protocol, a breath analysis benchmarking effort designed to address the lack of inter-comparability of outcomes across different breath sampling techniques and analytical platforms. Benchmarking with gas chromatography-ion mobility spectrometry (GC-IMS) using peppermint has been previously reported however, coupling micro-thermal desorption ( µ TD) to GC-IMS has not yet, been benchmarked for breath analysis. To benchmark µ TD-GC-IMS for breath analysis using the peppermint protocol. Ten healthy participants (4 males and 6 females, aged 20-73 years), were enrolled to give six breath samples into Nalophan bags via a modified peppermint protocol. Breath sampling after peppermint ingestion occurred over 6 h at t = 60, 120, 200, 280, and 360 min. The breath samples (120 cm 3 ) were pre-concentrated in the µ TD before being transferred into the GC-IMS for detection. Data was processed using VOCal, including background subtractions, peak volume measurements, and room air assessment. During peppermint washout, eucalyptol showed the highest change in concentration levels, followed by α -pinene and β -pinene. The reproducibility of the technique for breath analysis was demonstrated by constructing logarithmic washout curves, with the average linearity coefficient of R 2 TD-GC-IMS is reproducible and suitable technique for breath analysis, with benchmark values for eucalyptol comparable to the gold standard GC-MS.µ TD-GC-IMS is reproducible and suitable technique for breath analysis, with benchmark values for eucalyptol comparable to the gold standard GC-MS., (Creative Commons Attribution license.)

COVID-19 has caused a worldwide pandemic, creating an urgent need for early detection methods. Breath analysis has shown great potential as a non-invasive and rapid means for COVID-19 detection. The objective of this study is to detect patients infected with SARS-CoV-2 and even the possibility to screen between different SARS-CoV-2 variants by analysis of carbonyl compounds in breath. Carbonyl compounds in exhaled breath are metabolites related to inflammation and oxidative stress induced by diseases. This study included a cohort of COVID-19 positive and negative subjects confirmed by reverse transcription polymerase chain reaction between March and December 2021. Carbonyl compounds in exhaled breath were captured using a microfabricated silicon microreactor and analyzed by ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS). A total of 321 subjects were enrolled in this study. Of these, 141 (85 males, 60.3%) (mean ± SD age: 52 ± 15 years) were COVID-19 (55 during the alpha wave and 86 during the delta wave) positive and 180 (90 males, 50%) (mean ± SD age: 45 ± 15 years) were negative. Panels of a total of 34 ketones and aldehydes in all breath samples were identified for detection of COVID-19 positive patients. Logistic regression models indicated high accuracy/sensitivity/specificity for alpha wave (98.4%/96.4%/100%), for delta wave (88.3%/93.0%/84.6%) and for all COVID-19 positive patients (94.7%/90.1%/98.3%). The results indicate that COVID-19 positive patients can be detected by analysis of carbonyl compounds in exhaled breath. The technology for analysis of carbonyl compounds in exhaled breath has great potential for rapid screening and detection of COVID-19 and for other infectious respiratory diseases in future pandemics., (© 2024. The Author(s).)

There is an apparent need for novel non-invasive colorectal cancer (CRC) screening tests that are more acceptable to patients and can reliably detect CRC or reduce the number of unnecessary colonoscopies performed in cancer-free patients. An emerging number of studies demonstrate the potential value of exhaled volatile organic compounds (VOCs) as a diagnostic and triaging test for CRC. A systematic appraisal and meta-analysis of the published evidence was done to determine whether exhaled VOCs can be used in the detection and screening of CRC. Nine electronic databases were searched from inception of the databases until August 2020. Quantitative and descriptive data of CRC patients and healthy control (HC) participants who underwent VOCs breath analysis was extracted. In addition, where possible, sampling methods, analytical platforms, processors, and specific breath biomarkers found in each study were recorded. Fourteen articles were included in the systematic review with 491 colorectal patients and 754 HC participants (n=1245). Sub-group meta-analysis was conducted on nine of those articles and the pooled sensitivity was estimated to be 0.89 (95 % CI = 0.80-0.99) whereas specificity was 0.83 (95 % CI = 0.74-0.92). Heterogeneity of pooled sensitivity and specificity was estimated as I 2 =11.11 %. Although this study was limited by small sample size and different analytical platforms, the proposed future framework resolves such limitations and standardizes future research. It is reasonable to deduce that VOCs breath analysis is certainly a field of research that can progress to replace traditional methods within the framework of CRC screening and diagnosis., Competing Interests: The authors have no conflict of interest to disclose., (Copyright © 2024 Alsaadi et al.)

[Display omitted] • Abnormal concentrations of some components in exhaled gases may indicate the occurrence of disease. • The main respiratory markers include ammonia, nitric oxide, hydrogen sulfide, acetone, and formaldehyde. • This review details the development of exhaled gas analysis based on MOF electrical sensors. • Speculation and discussion on the development prospects and challenges of breath analysis based on MOF electrical sensor. The components of human-breathed gas have a direct impact on disease monitoring, and non-invasive breath analysis is a growing area of interest. More than ever, sensors with high sensitivity, excellent selectivity, affordability, and portability are in demand. Metal-organic frameworks (MOFs), which have high porosity, exceptional physical chemistry properties, and designability, as well as potent interactions with gas molecules, make them ideal for use in the sensing field. The various respiratory illness markers are outlined in this review along with the rationale behind MOFs-based electrical gas sensors and their advancements in various disease monitoring. Finally, the difficulties of employing MOFs-based electrical gas sensors for illness monitoring in clinical practice are discussed. [ABSTRACT FROM AUTHOR]

The measurement of volatile organic compounds (VOCs) originating from organisms allows continuous monitoring and a unique insight into the metabolism. One method offering the sensitivity to detect these VOCs is secondary electrospray ionization coupled to high-resolution mass spectrometry (SESI-HRMS). SESI was derived from electrospray ionization (ESI) and has found widespread application in clinical research and monitoring of animals. This review discusses the technical aspects behind SESI, the advancements, and the technical hurdles faced. Additionally, the recent advances in the applications of SESI in human and animal-centered research are presented. [Display omitted] • Breath Analysis: non-invasive insights into metabolism through VOCs. • Volatilomics: unveiling metabolic pathways through VOCs analysis. • Secondary Electrospray Ionization advances in clinical and animal research. • High-Resolution Mass Spectrometry: a sensitive tool to detect exhaled breath VOCs. [ABSTRACT FROM AUTHOR]

[Display omitted] • Early lung cancer detection is vital due to its high mortality in advanced stage. • We created a novel membrane-type surface stress sensor for breath analysis. • We conducted a pilot study on lung cancer diagnosability from perioperative breath. • We achieved the optimized prediction model with over 80% accuracy. • Breath analysis could be a promising non-invasive screening method for lung cancer. Breath analysis using a chemical sensor array combined with machine learning algorithms may be applicable for detecting and screening lung cancer. In this study, we examined whether perioperative breath analysis can predict the presence of lung cancer using a Membrane-type Surface stress Sensor (MSS) array and machine learning. Patients who underwent lung cancer surgery at an academic medical center, Japan, between November 2018 and November 2019 were included. Exhaled breaths were collected just before surgery and about one month after surgery, and analyzed using an MSS array. The array had 12 channels with various receptor materials and provided 12 waveforms from a single exhaled breath sample. Boxplots of the perioperative changes in the expiratory waveforms of each channel were generated and Mann-Whitney U test were performed. An optimal lung cancer prediction model was created and validated using machine learning. Sixty-six patients were enrolled of whom 57 were included in the analysis. Through the comprehensive analysis of the entire dataset, a prototype model for predicting lung cancer was created from the combination of array five channels. The optimal accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.809, 0.830, 0.807, 0.806, and 0.812, respectively. Breath analysis with MSS and machine learning with careful control of both samples and measurement conditions provided a lung cancer prediction model, demonstrating its capacity for non-invasive screening of lung cancer. [ABSTRACT FROM AUTHOR]

We present generalized information on the new type of universal quantum detectors operating on the basis of Yanson point contacts. The properties and capabilities of quantum point-contact detectors are totally different from those of conventional sensors based on the principle of changing electric conductance. To give an idea of the nature of these differences, we examine the spectral properties of Yanson point contacts, which are fundamental for understanding the operation mechanisms of quantum point-contact sensors. One of the most important features that determine the functioning of point-contact sensing elements is the quantum detection mechanisms discovered during the study of the point-contact gas-sensitive effect. The detection mechanisms based on the energy principles of analysis and conductance quantization are considered. The innovative methods developed using breath analysis with quantum point-contact sensors made it possible for the first time to detect in real time carcinogenic strains of the bacterium Helicobacter pylori and the hormonal background of the human body. The future work will be focused on universalizing the technology of quantum point-contact sensors, addressing various problems including evaluation of the general state of the human body and solutions to global ecological problems. [Display omitted] [ABSTRACT FROM AUTHOR]