UNASSIGNED: Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease characterized by progressive inflammation and fibrosis of the bile ducts. PSC is a complex disease of largely unknown aetiology that is strongly associated with inflammatory bowel disease (IBD). Diagnosis, especially at an early stage, is difficult and to date there is no diagnostic biomarker. The present study aimed to assess the diagnostic potential of volatile organic compounds (VOCs) in exhaled breath to detect (early) PSC in an IBD population.
UNASSIGNED: Breath samples were obtained from 16 patients with PSC alone, 47 with PSC and IBD, and 53 with IBD alone during outpatient clinic visits. Breath sampling was performed using the ReCIVA breath sampler and subsequently analysed by gas chromatography mass spectrometry. Random forest modelling was performed to find discriminatory VOCs and create a predictive model that was tested using an independent test set.
UNASSIGNED: The final model to discriminate patients with PSC, with or without IBD, from patients with IBD alone included twenty VOCs and achieved a sensitivity, specificity, and area under the receiver-operating curve on the test set of 77%, 83%, and 0.84 respectively. Three VOCs (isoprene, 2-octanone and undecane) together correlated significantly with the Amsterdam-Oxford score for PSC disease prognosis. A sensitivity analysis showed stable results across early-stage PSC, including in those with normal alkaline phosphatase levels, as well as further progressed PSC.
UNASSIGNED: The present study demonstrates that exhaled breath can distinguish PSC cases from IBD and has potential as a non-invasive clinical breath test for (early) PSC.
UNASSIGNED: Primary sclerosing cholangitis is a complex chronic liver disease, which ultimately results in cirrhosis, liver failure, and death. Detection, especially in early disease stages, can be challenging, and therefore therapy typically starts when there is already some irreversible damage. The current study shows that metabolites in exhaled breath, so called volatile organic compounds, hold promise to non-invasively detect primary sclerosing cholangitis, including at early disease stages.

Ammonia (NH3) in exhaled breath (EB) has been a biomarker for kidney function, and accurate measurement of NH3 is essential for early screening of kidney disease. In this work, we report an optical sensor that combines ultraviolet differential optical absorption spectroscopy (UV-DOAS) and spectral reconstruction fitting neural network (SRFNN) for detecting NH3 in EB. UV-DOAS is introduced to eliminate interference from slow change absorption in the EB spectrum while spectral reconstruction fitting is proposed for the first time to map the original spectra onto the sine function spectra by the principle of least absolute deviations. The sine function spectra are then fitted by the least-squares method to eliminate noise signals and the interference of exhaled nitric oxide. Finally, the neural network is built to enable the detection of NH3 in EB at parts per billion (ppb) level. The laboratory results show that the detection range is 9.50-12425.82 ppb, the mean absolute percentage error (MAPE) is 0.83%, and the detection accuracy is 0.42%. Experimental results prove that the sensor can detect breath NH3 and identify EB in simulated patients and healthy people. Our sensor will serve as a new and effective system for detecting breath NH3 with high accuracy and stability in the medical field.

Tetrachloroethylene (PCE) is a widely utilized volatile chemical in industrial applications, including dry cleaning and metal degreasing. Exposure to PCE potentially presents a significant health risk to workers as well as communities near contamination sites. Adverse health effects arise not only from PCE, but also from PCE degradation products, such as trichloroethylene (TCE) and vinyl chloride (VC). PCE, TCE, and VC can contaminate water, soil, and air, leading to exposure through multiple pathways, including inhalation, ingestion, and dermal contact. This study focused on a community setting in Martinsville, Indiana, a working-class Midwestern community in the United States, where extensive PCE contamination has occurred due to multiple contamination sites (referring to \'plumes\'), including a Superfund site. Utilizing proton transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS), PCE, TCE, and VC concentrations were measured in the exhaled breath of 73 residents from both within and outside the plume areas. PCE was detected in 66 samples, TCE in 26 samples, and VC in 68 samples. Our results revealed a significant positive correlation between the concentrations of these compounds in exhaled breath and indoor air (Pearson correlation coefficients: PCE = 0.75, TCE = 0.71, and VC = 0.89). This study confirms the presence of PCE and its degradation products in exhaled breath in a community exposure investigation, demonstrating the potential of using exhaled breath analysis in monitoring exposure to environmental contaminants. This study showed the feasibility of utilizing PTR-TOF-MS in community investigations to assess exposure to PCE and its degradation products by measuring these compounds in exhaled breath and indoor air.

Highly sensitive and rapid detection of ethylene, the smallest alkene of great significance in human physiological metabolism remains a great challenge. In this study, we developed a new photoionization-induced substitution reaction chemical ionization time-of-flight mass spectrometry (PSCI-TOFMS) for trace exhaled ethylene detection. An intriguing ionization phenomenon involving a substitution reaction between the CH2Br2+ reactant ion and ethylene molecule was discovered and studied for the first time. The formation of readily identifiable [CH2Br·C2H4]+ product ion greatly enhanced the ionization efficiency of ethylene, which led to approximately 800-fold improvement of signal intensity over that in single photon ionization mode. The CH2Br2+ reactant ion intensity and ion-molecule reaction time were optimized, and a Nafion tube was employed to eliminate the influence of humidity on the ionization of ethylene. Consequently, a limit of detection (LOD) as low as 0.1 ppbv for ethylene was attained within 30 s at 100 % relative humidity. The application of PSCI-TOFMS on the rapid detection of trace amounts of exhaled ethylene from healthy smoker and non-smoker volunteers demonstrated the satisfactory performance and potential of this system for trace ethylene measurement in clinical diagnosis, atmospheric measurement, and process monitoring.

Lung cancer subtyping, particularly differentiating adenocarcinoma (ADC) from squamous cell carcinoma (SCC), is paramount for clinicians to develop effective treatment strategies. In this study, we aimed: (i) to discover volatile organic compound (VOC) biomarkers for precise diagnosis of ADC and SCC, (ii) to investigated the impact of risk factors on ADC and SCC prediction, and (iii) to explore the metabolic pathways of VOC biomarkers. Exhaled breath samples from patients with ADC (n= 149) and SCC (n= 94) were analyzed by gas chromatography-mass spectrometry. Both multivariate and univariate statistical analysis method were employed to identify VOC biomarkers. Support vector machine (SVM) prediction models were developed and validated based on these VOC biomarkers. The impact of risk factors on ADC and SCC prediction was investigated. A panel of 13 VOCs was found to differ significantly between ADC and SCC. Utilizing the SVM algorithm, the VOC biomarkers achieved a specificity of 90.48%, a sensitivity of 83.50%, and an area under the curve (AUC) value of 0.958 on the training set. On the validation set, these VOC biomarkers attained a predictive power of 85.71% for sensitivity and 73.08% for specificity, along with an AUC value of 0.875. Clinical risk factors exhibit certain predictive power on ADC and SCC prediction. Integrating these risk factors into the prediction model based on VOC biomarkers can enhance its predictive accuracy. This work indicates that exhaled breath holds the potential to precisely detect ADCs and SCCs. Considering clinical risk factors is essential when differentiating between these two subtypes.

A novel Fe2Mo3O8/MoO2@MoS2 nanocomposite is synthesized for extremely sensitive detection of NH3 in the breath of kidney disease patients at room temperature. Compared to MoS2, α-Fe2O3/MoS2, and MoO2@MoS2, it shows the optimal gas-sensing performance by optimizing the formation of Fe2Mo3O8 at 900 °C. The annealed Fe2Mo3O8/MoO2@MoS2 nanocomposite (Fe2Mo3O8/MoO2@MoS2-900 °C) sensor demonstrates a remarkably high selectivity of NH3 with a response of 875% to 30 ppm NH3 and an ultralow detection limit of 3.7 ppb. This sensor demonstrates excellent linearity, repeatability, and long-term stability. Furthermore, it effectively differentiates between patients at varying stages of kidney disease through quantitative NH3 measurements. The sensing mechanism is elucidated through the analysis of alterations in X-ray photoelectron spectroscopy (XPS) signals, which is supported by density functional theory (DFT) calculations illustrating the NH3 adsorption and oxidation pathways and their effects on charge transfer, resulting in the conductivity change as the sensing signal. The excellent performance is mainly attributed to the heterojunction among MoS2, MoO2, and Fe2Mo3O8 and the exceptional adsorption and catalytic activity of Fe2Mo3O8/MoO2@MoS2-900 °C for NH3. This research presents a promising new material optimized for detecting NH3 in exhaled breath and a new strategy for the early diagnosis and management of kidney disease.

Analyzing exhaled volatile organic compounds (VOCs) with an electronic nose (e-nose) is emerging in medical diagnostics as a non-invasive, quick, and sensitive method for disease detection and monitoring. This study investigates if activities like spirometry or physical exercise affect exhaled VOCs measurements in asthmatics and healthy individuals, a crucial step for e-nose technology\'s validation for clinical use. The study analyzed exhaled VOCs using an e-nose in 27 healthy individuals and 27 patients with stable asthma, before and after performing spirometry and climbing five flights of stairs. Breath samples were collected using a validated technique and analyzed with a Cyranose 320 e-nose. In healthy controls, the exhaled VOCs spectrum remained unchanged after both lung function test and exercise. In asthmatics, principal component analysis and subsequent discriminant analysis revealed significant differences post-spirometry (vs. baseline 66.7% cross validated accuracy [CVA],p< 0.05) and exercise (vs. baseline 70.4% CVA,p< 0.05). E-nose measurements in healthy individuals are consistent, unaffected by spirometry or physical exercise. However, in asthma patients, significant changes in exhaled VOCs were detected post-activities, indicating airway responses likely due to constriction or inflammation, underscoring the e-nose\'s potential for respiratory condition diagnosis and monitoring.

Aerosol microparticles in exhaled breath carry non-volatile compounds from the deeper parts of the lung. When captured and analyzed, these aerosol microparticles constitute a non-invasive and readily available specimen for drugs of abuse testing. The present study aimed to evaluate a simple breath collection device in a clinical setting. The device divides a breath sample into three parallel \"collectors\" that can be individually analyzed. Urine was used as the reference specimen, and parallel specimens were collected from 99 patients undergoing methadone maintenance treatment. Methadone was used as the primary validation parameter. A sensitive multi-analyte method using tandem liquid chromatography - mass spectrometry was developed and validated as part of the project. The method was successfully validated for 36 analytes with a limit of detection of 1 pg/collector for most compounds. Based on the validation results tetrahydrocannabinol THC), cannabidiol (CBD), and lysergic acid diethylamide (LSD) are suitable for qualitative analysis, but all other analytes can be quantitively assessed by the method. Methadone was positive in urine in 97 cases and detected in exhaled breath in 98 cases. Median methadone concentration was 64 pg/collector. The methadone metabolite 2-ethylidene-1,5-dimethyl-3,3-diphenylpyrrolidine (EDDP) was detected in 90 % of the cases but below 10 pg/collector in most. Amphetamine was also present in the urine in 17 cases and in exhaled breath in 16 cases. Several other substances were detected in the exhaled breath and urine samples, but at a lower frequency. This study concluded that the device provides a specimen from exhaled breath, that is useful for drugs of abuse testing. The results show that high analytical sensitivity is needed to achieve good detectability and detection time after intake.

BACKGROUND: Asthma is characterized by phenotypes of different clinical, demographic, and pathological characteristics. Identifying the profile of exhaled volatile organic compounds (VOCs) in asthma phenotypes may facilitate establishing biomarkers and understanding asthma background pathogenesis. This study aimed to identify exhaled VOCs that characterize severe asthma phenotypes among patients with asthma.
METHODS: This was a multicenter cross-sectional study of patients with severe asthma in Japan. Clinical data were obtained from medical records, and questionnaires were collected. Exhaled breath was sampled and subjected to thermal desorption gas chromatography-mass spectrometry (GC/MS).
RESULTS: Using the decision tree established in the previous nationwide asthma cohort study, 245 patients with asthma were divided into five phenotypes and subjected to exhaled VOC analysis with 50 healthy controls (HCs). GC/MS detected 243 VOCs in exhaled breath samples, and 142 frequently detected VOCs (50% of all samples) were used for statistical analyses. Cluster analysis assigning the groups with similar VOC profile patterns showed the highest similarities between phenotypes 3 and 4 (early-onset asthma phenotypes), followed by the similarities between phenotypes 1 and 2 (late-onset asthma phenotypes). Comparisons between phenotypes 1-5 and HC revealed 19 VOCs, in which only methanesulfonic anhydride showed p < 0.05 adjusted by false discovery rate (FDR). Comparison of these phenotypes yielded several VOCs showing different trends (p < 0.05); however, no VOCs showed p < 0.05 adjusted by FDR.
CONCLUSIONS: Exhaled VOC profiles may be useful for distinguishing asthma and asthma phenotypes; however, these findings need to be validated, and their pathological roles should be clarified.

Herein, SnO2QDs (<10 nm) with small size instead of conventional nanoparticles was employed to modify ZnFe2O4to synthesize porous and heterogeneous SnO2/ZnFe2O4(ZFSQ) composites for gas sensing. By an immersion process combined with calcination treatment, the resultant porous ZFSQ composites with different contents of SnO2QDs were obtained, and their sensing properties were investigated. Compared with bare ZnFe2O4and SnO2QDs, porous ZFSQ composites based-sensors showed much improved sensor response to acetone. For contrast, the sensor performance of ZFSQ composites was also compared with that of ZnFe2O4sphere modified by SnO2nanoparticles with different size. The porous ZFSQ composite with 5 wt% SnO2QDs (ZFSQ-5) showed a better acetone sensing response than that of other ZFSQ composites, and it exhibited a high response value of 110-100 ppm of acetone and a low detection limit of 0.3 ppm at 240 °C. In addition to the rich heterojunctions and porous structure, the size effect of SnO2QDs was other indispensable reasons for the improved sensor performance. Finally, the ZFSQ-5 composite sensor was attempted to be applied for acetone sensing in exhaled breath, suggesting its great potential in monitoring acetone.