Breath Concentration Research Articles

Gas-sensitive synergistic effect of Au-decorated ZnO nano-structured materials for rapid ethanol detection based on simulated sunlight activation

Local surface plasmon resonance (LSPR) and noble metal modification/doping are classical methods for improving the performance of metal-oxide-semiconductor (MOS)-based gas sensors. However, relatively less attention is paid to their synergies. In particular, research on synergistic gas-sensing technology that combines sunlight activation with other methods is significant for using friendly energy. In this study, Au-decorated ZnO (Au/ZnO) nano-structured materials (NMs) are successfully synthesised using a cost-effective nano-seed-assisted chemical bath and UV irradiation growth methods. The sensor based on this material exhibits superior performance in ethanol vapour under simulated sunlight, maintaining high sensitivity and repeatability at a low optimal working temperature. In particular, the sensitivity to 100 ppm of ethanol is 7.5 times better and the response time is 20 times shorter (tres < 1 s) in simulated sunlight than in the dark environment. The limit of detection (LOD) of ethanol is as low as 97 ppb, which is much lower than the concentration in exhaled breath of driving under the influence of alcohol according to Chinese law (20–80 ppm). This study provides a reliable and ultra-fast ethanol detection method, with potential applications in environmental monitoring and traffic safety. A possible gas-sensitive mechanism of the Au/ZnO ethanol vapour sensor is proposed based on the synergistic effect between simulated sunlight activation (LSPR, humidity resistance and thermal activation) and noble metal modification (electron sensitisation and chemical sensitisation). It provides a promising method for exploring the utilisation of sunlight for rapid gas detection.

Hollow CuO/Cu2O octahedrons for selective and stable detection of acetone gas

Acetone (C3H6O) is key member of the volatile organic compound family, which can cause health as well as environmental issues. The identification and sensing of acetone gas are important in many applications such as air quality control and biomarker-based diagnosis. Here, we synthesized hollow CuO/Cu2O octahedrons heterostructure composite through coprecipitation and subsequent heat treatment. The sensor manifested a response of 3.52 to 50 ppm acetone gas at 350 °C. Besides, it showed excellent stability in a 150-day test and excellent repeatability, with a response of 4.15. Also, exhaled gas sensing was carried out using standard method to estimate the acetone concentration in breath. The high sensing capability of the sensor was ascribed to the formation of p-p heterojunctions, the hollow morphology of octahedrons, and the presence of structural defects. The hollow CuO/Cu2O octahedron sensor with good performance may be employed for the detection and monitoring of acetone gas in real situations.

Photoionization ion mobility analyzer for on-site measurement of exhaled acetone by coupling miniature thermoelectric cooling dehydration

The breath concentration of acetone holds significant clinical importance, the development of on-site and accurate analyzer for exhaled acetone is highly demanded. A photoionization ion mobility analyzer based on a low-pressure krypton discharge lamp has been developed for direct and rapid determination of the acetone concentration. The difficulty for sensitive measurement of acetone by vacuum ultraviolet photoionization was the strong absorption of water molecules in the breath air. An online dehydration device with a miniature thermoelectric cooling quartz trap has been developed, which was capable for reducing the relative humidity from over 90 % to below 1 %. The dehydration could enhance the signal intensity of acetone about 85 times. The response time of current thermoelectric cooling dehydration was only 1/20 of the Nafion tube dehydration. A limit of detection 0.02 ppmV and linear response up to 2.0 ppmV were achieved. The interferences from ammonia in exhaled breath was eliminated using a calibration method based on ion number density, achieving recoveries ranging from 90 % to 110 %. In the final, the variation trends of exhaled acetone level in healthy individuals following the ingestion of glucose under fasting conditions were carried out to demonstrate the potential application of the analyzer.

Accelerating the Diagnosis of Pandemic Infection Based on Rapid Sampling Algorithm for Fast-Response Breath Gas Analyzers.

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.

A study of 9 common breath VOCs in 504 healthy subjects using PTR-TOF-MS.

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. 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. Exhaled breath samples were collected using PTR-MS, measuring concentrations of nine VOCs. The data were analyzed to discern distribution patterns across demographic groups. 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. 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.

YSZ-based mixed-potential acetone sensor with LaBaCo2O5+δ sensitive electrode for diabetic diagnosis

The acetone concentration in exhaled breath can be utilized for the noninvasive diagnosis of diabetes. In this study, to fulfil the requirements of breath analysis for diabetes diagnosis, high-performance mixed-potential acetone sensors based on yttria-stabilized zirconia were successfully developed by employing a novel sensitive electrode (SE) material, namely, LaBaCo2O5+δ. Electrochemical measurement results revealed that the LaBaCo2O5+δ SE, sintered at 1000 ℃, exhibited outstanding electrochemical catalytic activity toward acetone. The device incorporating this SE demonstrated a peak response of −46 mV to 100 ppm acetone at 655 ℃. Sensitive performance assessments indicated the capability of the sensor to detect acetone concentrations ranging from 200 ppb to 100 ppm. Based on the mixed-potential model, the sensitivities of the current device were quantified as −1.3 and −53.7 mV/decade within the acetone concentration ranges of 0.2−5 and 10−100 ppm, respectively. Furthermore, the fabricated sensor exhibited exceptional selectivity, repeatability, humidity resistance, and long-term stability. In addition, simulated expiratory experiments showed that the developed sensor has substantial potential for application in breath analysis for diabetes diagnosis and noninvasive monitoring of a patient’s physical status.

Image Sensing of Gaseous Acetone Using Secondary Alcohol Dehydrogenase-Immobilized Mesh for Exhaled Air.

Human-borne acetone is a potent marker of lipid metabolism. Here, an enzyme immobilization method for secondary alcohol dehydrogenase (S-ADH), which is suitable for highly sensitive and selective biosensing of acetone, was developed, and then its applicability was demonstrated for spatiotemporal imaging of concentration distribution. After various investigations, S-ADH-immobilized meshes could be prepared with less than 5% variation by cross-linking S-ADH with glutaraldehyde on a cotton mesh at 40 °C for 15 min. Furthermore, high activity was obtained by adjusting the concentration of the coenzyme nicotinamide adenine dinucleotide (NADH) solution added to the S-ADH-immobilized mesh to 500 μM and the solvent to a potassium phosphate buffer solution at pH 6.5. The gas imaging system using the S-ADH-immobilized mesh was able to image the decrease in NADH fluorescence (ex 340 nm, fl 490 nm) caused by the catalytic reaction of S-ADH and the acetone distribution in the concentration range of 0.1-10 ppm-v, including the breath concentration of healthy people at rest. The exhaled breath of two healthy subjects at 6 h of fasting was quantified as 377 and 673 ppb-v, which were consistent with the values quantified by gas chromatography-mass spectrometry.

Construction of mesoporous silica-implanted tungsten oxides for selective acetone gas sensing

As a key biomarker for noninvasive diagnosis of diabetes, the selective detection of trace acetone in exhaled gas using a portable and low-cost device remains a great challenge. Semiconductor metal oxide (SMO) based gas sensors have drawn signification attention due to their potential in miniaturization, user-friendliness, high cost-effectiveness and selective real-time detection for noninvasive clinical diagnosis. Herein, we propose a one-pot solvent evaporation induced tricomponent co-assembly strategy to design a novel ordered mesoporous SMO of silica-implanted WO3 (SiO2/WO3) as sensing materials for trace acetone detection. The controlled co-assembly of silicon and tungsten precursors and amphiphilic diblock copolymer poly(ethylene oxide)-block-polystyrene (PEO-b-PS), and the subsequent thermal treatment enable the local lattice disorder of WO3 induced by the amorphous silica and the formation of ordered mesoporous SiO2/WO3 hybrid walls with a unique metastable ε-phase WO3 framework. The obtained mesoporous SiO2/WO3 composites possess highly crystalline framework with large uniform pore size (12.0–13.3 nm), high surface area (99–113 m2/g) and pore volume (0.17–0.23 cm3/g). Typically, the as-fabricated gas sensor based on mesoporous 2.5 %SiO2/WO3 exhibits rapid response/recovery rate (5/17 s), superior sensitivity (Rair/Rgas = 105 for 50 ppm acetone), as well as high selectivity towards acetone. The limit of detection is as low as 0.25 ppm, which is considerably lower than the thresh value of acetone concentration (>1.1 ppm) in the exhaled breath of diabetic patients, demonstrating its great prospect in real-time monitoring in diabetes diagnosis. Moreover, the mesoporous 2.5 %SiO2/WO3 sensor is integrated into a wireless sensing module connected to a smart phone, providing a convenient real-time detection of acetone.

Periodic open and closed resonators as a biosensor using two computational methods

The volatile particles and molecules in our dry exhaled breath can reveal enormous information about the health of any person, such as the person’s respiratory and metabolic functioning. Beyond the carbon dioxide level is an indicator of life, it provides important health-related data like people’s metabolic rate. This study considers periodic open and closed resonators for measuring carbon dioxide concentration in dry exhaled breath. Transfer matrix and green methods are used to simulate the interaction between acoustic waves and the proposed sensor. The band gaps using the green method coincide with the transmittance spectra by the transfer matrix. The suggested sensor recorded a sensitivity of 5.3Hz.m-1.s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$5.3 Hz.{m}^{-1}.s$$\\end{document}, a figure of merit of 10,254 m-1.s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${m}^{-1}.s$$\\end{document}, a detection limit of 5×10-6m.s-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$5\ imes {10}^{-6} m.{s}^{-1}$$\\end{document}, and a quality factor of 3×106\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3\ imes {10}^{6}$$\\end{document}. Furthermore, the efficiency shows that the proposed design is appropriate as a diagnostic sensor for different diseases such as chronic obstructive pulmonary. Besides, cylindrical-adapted sensors are urgently needed in medicine, industry, and biology because they can simultaneously be used for fluid transport and detection.

An efficient convolutional neural network-extreme gradient boosting hybrid deep learning model for disease detection applications

In this paper, we present an efficient deep-learning hybrid model comprising an extreme gradient boosting (XGBoost) supervised learning algorithm and convolutional neural networks (CNN) for the automated detection of diseases. The proposed model is implemented and tested to detect type-2 diabetes by measuring the acetone concentration in the exhaled breath. Acetone will be present in much higher concentrations in type-2 diabetic patients compared to non-diabetic people. A novel sensing module is designed and implemented in our study to measure the acetone concentration in exhaled breath. The proposed approach delivered good results, with a classification accuracy of 97.14%. The findings of this study show how effectively the proposed detection module functions in disease diagnosis applications. As the detection process is simple and non-invasive, people can undergo routine checks for diabetes with the proposed detection module.

Measurement uncertainty of ethyl alcohol concentration in the exhaled breath of drivers and determination of sobriety at the time of incident.

The aim of the study was to determine the components of measurement uncertainty in the concentration of alcohol in exhaled breath and to determine the state of sobriety at the time of incident. Based on the literature review and the authors' experience in providing opinions for law enforcement and the judiciary, the influence of various factors on the final interpretation of sobriety state is described on the basis of measurement uncertainty of breath analyzers, uncertainty of retrospective and prospective calculations, and uncertainty related to the conversion of alcohol concentrations detected during breath and blood tests. The paper pays particular attention to interpreting the concentrations of ethanol in exhaled breath close to the legal limits of the state of sobriety and the state after alcohol use, or the state after alcohol use and the state of insobriety. Analyzing the results of an exhaled breath test concerning concentrations close to the values of 0.1 mg/dm3 and 0.25 mg/dm3, it is necessary to take into account the factors affecting the measurements obtained, including the measurement uncertainty of the determination of alcohol in exhaled breath, the processes of absorption, distribution and metabolism of ethyl alcohol, and the possibility of the presence of alcohol lingering in the oral cavity. The incorrect execution of measurements of the tested person's alcohol concentration is also a problematic issue. When determining sobriety state by means of retrospective and prospective calculations, it is important to remember that the uncertainty of the result is affected by a number of factors and depends, among other things, on the information provided by the suspect. Hence, the expert should draw conclusions particularly cautiously and any overestimation or underestimation of the components of uncertainty can lead to erroneous conclusions. Awareness of the uncertainties inherent in the results of a sobriety test or alcohol calculation allows for meaningful interpretation of test results and determination of the sobriety state of the person tested.

Boosting acetone response of p-type Co3O4 sensor via Sn and Ni co-doping for diabetes diagnosis

The hole accumulation layer (HAL) configuration of p-type oxides (including Co3O4) in ambient air causes intrinsically low gas response and hinders their promising applications in exhaled gas analysis. Herein, Sn and Ni co-doping has been proposed to trigger the response of chemiresistive Co3O4 sensor toward acetone (biomarker of diabetes). Via incorporating 1 at% Sn and 0.5 at% Ni doping (Co2.95Sn0.03Ni0.02O4), the response to 100 ppm acetone has been boosted ∼2 orders (from 1.24 to 125.6) at 70 °C, the limit of detection (LoD) has been reduced ∼4 times (from 47.9 to 12.4 ppb), the optimal operation temperature has been decreased from 130 °C to ∼70 °C. Various characterizations suggest that co-doping induced abundant surface asymmetric oxygen vacancy defects (Co-□-Ni, Sn-□-Ni), which facilitate the catalytic oxidation of acetone molecules at relatively low operation temperature. In addition to excellent reproducibility and long-term stability, Co2.95Sn0.03Ni0.02O4 sensor could also operate under highly humid air background and reliably detect acetone concentration in exhaled breath at 150 °C, opening the opportunity for the practical application of p-type oxide sensors for diabetes diagnosis.

The effect of high ethanol concentration on E-nose response for diabetes detection in exhaled breath: Laboratory studies

The growing number of people with diabetes has paved the way for the creation of non-invasive devices to measure blood glucose levels and enable the detection of diabetes non-invasively, for example, through exhaled air. This paper presents the development of a breath detection system using multiple sensors (e-nose system) for the non-invasive estimation of blood glucose levels. The system employs commercially available ethanol, CO2, and acetone sensors, with synthetic breath mixtures used for testing, including four scenarios with high ethanol concentrations (0–570 ppm) as an influence factor. Results showed a mean absolute error of 0.245 ppm when analysing commonly observed acetone concentrations in diabetic breath using four sensors and the XGBoost Regressor. In mixtures with high ethanol concentrations and varying acetone concentrations (0–8.62 ppm), the CatBoost Regressor outperformed other machine learning algorithms with a mean absolute error of 0.568 ppm. This study emphasises the significant impact of ethanol on acetone detection and the need to consider ethanol levels in the developing of non-invasive devices for blood glucose prediction based on the exhaled acetone measurements. The research shows that a set of three gas sensors is optimal for estimating acetone concentrations in gas mixtures. The presented results constitute a preliminary step towards developing a non-invasive device for estimating blood glucose levels based on breath analysis, with the novelty of considering alcohol intake as a potential influencing factor.

Complexity of Translating Analytics to Recent Cannabis Use and Impairment.

While current analytical methodologies can readily identify cannabis use, definitively establishing recent use within the impairment window has proven to be far more complex, requiring a new approach. Recent studies have shown no direct relationship between impairment and Δ9-tetra-hydrocannabinol (Δ9-THC) concentrations in blood or saliva, making legal "per se" Δ9-THC limits scientifically unjustified. Current methods that focus on Δ9-THC and/or metabolite concentrations in blood, saliva, urine, or exhaled breath can lead to false-positive results for recent use due to the persistence of Δ9-THC well outside of the typical 3-4 h window of potential impairment following cannabis inhalation. There is also the issue of impairment due to other intoxicating substances-just because a subject exhibits signs of impairment and cannabis use is detected does not rule out the involvement of other drugs. Compounding the matter is the increasing popularity of hemp-derived cannabidiol (CBD) products following passage of the 2018 Farm Bill, which legalized industrial hemp in the United States. Many of these products contain varying levels of Δ9-THC, which can lead to false-positive tests for cannabis use. Furthermore, hemp-derived CBD is used to synthesize Δ8-THC, which possesses psychoactive properties similar to Δ9-THC and is surrounded by legal controversy. For accuracy, analytical methods must be able to distinguish the various THC isomers, which have identical masses and exhibit immunological cross-reactivity. A new testing approach has been developed based on exhaled breath and blood sampling that incorporates kinetic changes and the presence of key cannabinoids to detect recent cannabis use within the impairment window without the false-positive results seen with other methods. The complexity of determining recent cannabis use that may lead to impairment demands such a comprehensive method so that irresponsible users can be accurately detected without falsely accusing responsible users who may unjustly suffer harsh, life-changing consequences.

The Association Between Personal Air Pollution Exposures and Fractional Exhaled Nitric Oxide (FeNO): A Systematic Review.

Airway inflammation is a common biological response to many types of environmental exposures and can lead to increased nitric oxide (NO) concentrations in exhaled breath. In recent years, several studies have evaluated airway inflammation using fractional exhaled nitric oxide (FeNO) as a biomarker of exposures to a range of air pollutants. This systematic review aims to summarize the studies that collected personal-level air pollution data to assess the air pollution-induced FeNO responses and to determine if utilizing personal-level data resulted in an improved characterization of the relationship between air pollution exposures and FeNO compared to using only ambient air pollution exposure data. Thirty-six eligible studies were identified. Overall, the studies included in this review establish that an increase in personal exposure to particulate and gaseous air pollutants can significantly increase FeNO. Nine out of the 12 studies reported statistically significant FeNO increases with increasing personal PM2.5 exposures, and up to 11.5% increase in FeNO per IQR increase in exposure has also been reported between FeNO and exposure to gas-phase pollutants, such as ozone, NO2, and benzene. Furthermore, factors such as chronic respiratory diseases, allergies, and medication use were found to be effect modifiers for air pollution-induced FeNO responses. About half of the studies that compared the effect estimates using both personal and ambient air pollution exposure methods reported that only personal exposure yielded significant associations with FeNO response. The evidence from the reviewed studies confirms that FeNO is a sensitive biomarker for air pollutant-induced airway inflammation. Personal air pollution exposure assessment is recommended to accurately assess the air pollution-induced FeNO responses. Furthermore, comprehensive adjustments for the potential confounding factors including the personal exposures of the co-pollutants, respiratory disease status, allergy status, and usage of medications for asthma and allergies are recommended while assessing the air pollution-induced FeNO responses.

AI‐Assisted Multimodal Breath Sensing System with Semiconductive Polymers for Accurate Monitoring of Ammonia Biomarkers

AbstractBreath ammonia is an essential biomarker for patients with many chronic illnesses, such as chronic kidney disease (CKD), chronic liver disease (CLD), urea cycle disorders (UCD), and hepatic encephalopathy. However, existing breath ammonia sensors fail to compensate for the impact of breath humidity and complex breathing motions associated with a human breath sample. Here, a multimodal breath sensing system is presented that integrates an ammonia sensor based on a thermally cleaved conjugated polymer, a humidity sensor based on reduced graphene oxide (rGO), and a breath dynamics sensor based on a 3D folded strain‐responsive mesostructure. The miniaturized construction and module‐based configuration offer flexible integration with a broad range of masks. Experimental results present the capabilities of the system in continuously detecting diagnostic ranges of breath ammonia under real, humid breath conditions with sufficient sensing accuracy and selectivity over 3 weeks. A machine‐learning algorithm based on K‐means clustering decodes multimodal signals collected from the breath sensor to differentiate between healthy and diseased breath concentrations of ammonia. The on‐body test highlights the operational simplicity and practicality of the system for noninvasively tracing ammonia biomarkers.

Detection of PPB-Level H2S Concentrations in Exhaled Breath Using Au Nanosheet Sensors with Small Variability, High Selectivity, and Long-Term Stability.

The continuous monitoring of hydrogen sulfide (H2S) in exhaled breath enables the detection of health issues such as halitosis and gastrointestinal problems. However, H2S sensors with high selectivity and parts per billion-level detection capability, which are essential for breath analysis, and facile fabrication processes for their integration with other devices are lacking. In this study, we demonstrated Au nanosheet H2S sensors with high selectivity, ppb-level detection capability, and high uniformity by optimizing their fabrication processes: (1) insertion of titanium nitride (TiN) as an adhesion layer to prevent Au agglomeration on the oxide substrate and (2) N2 annealing to improve nanosheet crystallinity. The fabricated Au nanosheets successfully detected H2S at concentrations as low as 5.6 ppb, and the estimated limit of detection was 0.5 ppb, which is superior to that of the human nose (8-13 ppb). In addition, the sensors detected H2S in the exhaled breath of simulated patients at concentrations as low as 175 ppb while showing high selectivity against interfering molecules, such as H2, alcohols, and humidity. Since Au nanosheets with uniform sensor characteristics enable easy device integration, the proposed sensor will be useful for facile health checkups based on breath analysis upon its integration into mobile devices.

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

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

Exhaled breath is found to be better than blood samples for determining propofol concentrations in the brain tissues of rats

The correlation between propofol concentration in exhaled breath (C E) and plasma (C P) has been well-established, but its applicability for estimating the concentration in brain tissues (C B) remains unknown. Given the impracticality of directly sampling human brain tissues, rats are commonly used as a pharmacokinetic model due to their similar drug-metabolizing processes to humans. In this study, we measured C E, C P, and C B in mechanically ventilated rats injected with propofol. Exhaled breath samples from the rats were collected every 20 s and analyzed using our team’s developed vacuum ultraviolet time-of-flight mass spectrometry. Additionally, femoral artery blood samples and brain tissue samples at different time points were collected and measured using high-performance liquid chromatography mass spectrometry. The results demonstrated that propofol concentration in exhaled breath exhibited stronger correlations with that in brain tissues compared to plasma levels, suggesting its potential suitability for reflecting anesthetic action sites’ concentrations and anesthesia titration. Our study provides valuable animal data supporting future clinical applications.

Exhaled breath of children swimmers conveniently collected in Teflon bags and used for trihalomethane determination by SPME-GC-MS.

Trihalomethanes (THMs) are the most common disinfection by-products in swimming pools; however, they exhibit strong cytotoxicity and genotoxicity, posing health risks. Children are more vulnerable to swimming-related health risks than adults; therefore, a rapid and accurate assessment of internal THM exposure in children swimmers is important for health risk assessment. For internal exposure measurement, collecting exhaled breath samples is more convenient, non-invasive, and easier to perform than collecting blood and urine. Therefore, this study aimed to develop a rapid, accurate, and reproducible method for determining THMs in children swimmers' exhaled breath using solid-phase microextraction (SPME)-gas chromatography-mass spectrometry (GC-MS). The factors influencing the pretreatment procedure, including selecting SPME fibers, extraction temperature, and time, were systematically evaluated. Under the optimized conditions, the instrumental linearity range was 1-200 ng L-1 with correlation coefficients >0.998. The limit of detection for this method was 0.3-0.5 ng L-1. The recovery values ranged between 76.87 and 111.49%. Detecting THMs at three different calibration levels using this method had an intra-day precision of 1.31-5.07%, while the inter-day precision was 1.59-11.10% (n = 6). Additionally, the SPME-GC-MS method was used to detect the concentration of THMs in children swimmers' exhaled breath before and after swimming. Trichloromethane was the most abundant THM in the air around the pool and children's exhaled breath, and THM concentrations in the children swimmers' exhaled breath increased significantly after swimming. This study found no significant differences in the concentrations of THMs in the children swimmers' exhaled breath with different swimming durations or frequencies, which may be a result of the unrepresentative sample population and small sample size, and more in-depth and comprehensive studies are needed to verify this conclusion.