Association between PM2.5 from a coal mine fire and FeNO concentration 7.5 years later.

BACKGROUND AND AIM: There are few long-term studies of respiratory health effects of landscape fires, despite increasing frequency and intensity due to climate change. We investigated the association between exposure to coal mine fire PM2.5 and fractional exhaled nitric oxide (FeNO) concentration 7.5 years later. METHODS: Adult residents of Morwell, who were exposed to the 2014 Hazelwood mine fire over 6 weeks, and unexposed residents of Sale, participated in the Hazelwood Health Study Respiratory Stream in 2021, including measurements of FeNO concentration, a marker of eosinophilic airway inflammation. Individual exposure to coal mine fire PM2.5 was modelled and mapped to time-location diaries. The effect of exposure to PM2.5 on log-transformed FeNO in exhaled breath was investigated using multivariate linear regression models in the entire sample and stratified by potentially vulnerable subgroups. RESULTS: A total of 326 adults (mean age: 57 years) had FeNO measured. The median FeNO level (interquartile range [IQR]) was 17.5 [15.0] ppb, and individual daily exposure to coal mine fire PM2.5 was 7.2 [13.8] µg/m3. We did not identify evidence of association between coal mine fire PM2.5 exposure and FeNO in the general adult sample, nor in various potentially vulnerable subgroups. The point estimates were consistently close to zero in the total sample and subgroups. CONCLUSION: Despite previous short-term impacts on FeNO and respiratory health outcomes in the medium term, we found no evidence that PM2.5 from the Hazelwood coal mine fire was associated with any long-term impact on eosinophilic airway inflammation measured by FeNO levels.

Assessment of Maximum Oxygen Uptake in Elite Youth Soccer Players: A Comparative Analysis of Smartwatch Technology, Yoyo Intermittent Recovery Test 2, and Respiratory Gas Analysis.

The maximum oxygen uptake (VO2max) is a critical factor for endurance performance in soccer. Novel wearable technology may allow frequent assessment of V̇O2max during non-fatiguing warm-up runs of soccer players with minimal interference to soccer practice. The aim of this study was to assess the validity of VO2max provided by a consumer grade smartwatch (Garmin Forerunner 245, Garmin, Olathe, USA, Software:13.00) and the YoYo Intermittent Recovery Run 2 (YYIR2) by comparing it with respiratory gas analysis. 24 trained male youth soccer players performed different tests to assess VO2max: i) a treadmill test employing respiratory gas analysis, ii) YYIR2 and iii) during a non-fatiguing warm-up run of 10 min wearing a smartwatch as recommended by the device-manufacturer on 3 different days within 2 weeks. As the device-manufacturer indicates that validity of smartwatch-derived VO2max may differ with an increase in runs, 16 players performed a second run with the smartwatch to test this claim. The main evidence revealed that the smartwatch showed an ICC of 0.37 [95% CI: -0.25; 0.71] a mean absolute percentage error (MAPE) of 5.58% after one run, as well as an ICC of 0.54 [95% CI: -0.3; 8.4] and a MAPE of 1.06% after the second run with the smartwatch. The YYIR2 showed an ICC of 0.17 [95% CI: -5.7; 0.6]; and MAPE of 4.2%. When using the smartwatch for VO2max assessment in a non-fatiguing run as a warm-up, as suggested by the device manufacturer before soccer practice, the MAPE diminishes after two runs. Therefore, for more accurate VO2max assessment with the smartwatch, we recommend to perform at least two runs to reduce the MAPE and enhance the validity of the findings.

Predicting Helicobacter pylori infection from endoscopic features.

BACKGROUND: Helicobacter pylori infection, prevalent in more than half of the global population, is associated with various gastrointestinal diseases, including peptic ulcers and gastric cancer. The effectiveness of early diagnosis and treatment in preventing gastric cancer highlights the need for improved diagnostic methods. This study aimed to develop a simple scoring system based on endoscopic findings to predict H. pylori infection. METHODS: A retrospective analysis was conducted on 1,007 patients who underwent upper gastrointestinal endoscopy at Asan Medical Center from January 2019 to December 2021. Exclusion criteria included prior H. pylori treatment, gastric surgery, or gastric malignancies. Diagnostic techniques included rapid urease and 13C-urea breath tests, H. pylori culture, and assessment of endoscopic features following the Kyoto gastritis classification. A new scoring system based on endoscopic findings including regular arrangement of collecting venules (RAC), nodularity, and diffuse or spotty redness was developed for predicting H. pylori infection, utilizing logistic regression analysis in the development set. RESULTS: The scoring system demonstrated high predictive accuracy for H. pylori infection in the validation set. Scores of 2 and 3 were associated with 96% and 99% infection risk, respectively. Additionally, there was a higher prevalence of diffuse redness and sticky mucus in cases where the initial H. pylori eradication treatment failed. CONCLUSION: Our scoring system showed potential for improving diagnostic accuracy in H. pylori infection. H. pylori testing should be considered upon spotty redness, diffuse redness, nodularity, and RAC absence on endoscopic findings as determined by the predictive scoring system.

Features of exocrine pancreatic insufficiency in patients with non-alcoholic fatty liver disease in combination with type 2 diabetes and COVID-19.

OBJECTIVE: Aim: The aim of the research was to study the features of pancreatic exocrine insufficiency (EPI) in patients with nonalcoholic fatty liver disease (NAFLD) and type 2 diabetes mellitus (DM) at COVID-19. PATIENTS AND METHODS: Materials and Methods: 72 patients with NAFLD and COVID-19 were examined. The patients have been divided into two groups: group 1 included 42 patients with NAFLD and insulin resistance (IR); group 2 consisted of 30 patients with NAFLD in the combination with type 2 DM. EPI was detected by 13С-mixed triglyceride breath test (13С-MTBT) in all the patients. RESULTS: Results: The result of 13С-MTBT indicates EPI in the examined subjects of the 2 group. A significant decrease in the maximum concentration of 13СО2 between 150 and 210 min was also diagnosed in group 1 patients. research (up to 8.2 ± 0.9% - p < 0.05), however, the total concentration of 13СО2 at the end of 360 min. the study reached only 27.7 ± 1.1% (p < 0.05). CONCLUSION: Conclusions: Based on the results of laboratory-instrumental methods of research, patients with NAFLD and type 2 diabetes with COVID-19 were diagnosed with severe EPI. The results of 13С-MTBT in NAFLD and IR with COVID-19 indicate a decrease in the functional reserves of the pancreas and the formation of its EPI.

Evaluation of urea breath test as a diagnostic tool for Helicobacter pylori infection in adult dyspeptic patients.

In this editorial, we discuss the article in the World Journal of Gastroenterology. The article conducts a meta-analysis of the diagnostic accuracy of the urea breath test (UBT), a non-invasive method for detecting Helicobacter pylori (H. pylori) infection in humans. It is based on radionuclide-labeled urea. Various methods, both invasive and non-invasive, are available for diagnosing H. pylori infection, including endoscopy with biopsy, serology for immunoglobulin titers, stool antigen analysis, and UBT. Several guidelines recommend UBTs as the primary choice for diagnosing H. pylori infection and for reexamining after eradication therapy. It is used to be the first choice non-invasive test due to their high accuracy, specificity, rapid results, and simplicity. Moreover, its performance remains unaffected by the distribution of H. pylori in the stomach, allowing a high flow of patients to be tested. Despite its widespread use, the performance characteristics of UBT have been inconsistently described and remain incompletely defined. There are two UBTs available with Food and Drug Administration approval: The 13C and 14C tests. Both tests are affordable and can provide real-time results. Physicians may prefer the 13C test because it is non-radioactive, compared to 14C which uses a radioactive isotope, especially in young children and pregnant women. Although there was heterogeneity among the studies regarding the diagnostic accuracy of both UBTs, 13C-UBT consistently outperforms the 14C-UBT. This makes the 13C-UBT the preferred diagnostic approach. Furthermore, the provided findings of the meta-analysis emphasize the significance of precise considerations when choosing urea dosage, assessment timing, and measurement techniques for both the 13C-UBT and 14C-UBT, to enhance diagnostic precision.

Metabolic trajectories of diabetic ketoacidosis onset described by breath analysis.

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.

Identifying potential breath biomarkers for early diagnosis of papillary thyroid cancer based on solid-phase microextraction gas chromatography-high resolution mass spectrometry with metabolomics.

INTRODUCTION: Thyroid cancer incidence rate has increased substantially worldwide in recent years. Fine needle aspiration biopsy (FNAB) is currently the golden standard of thyroid cancer diagnosis, which however, is invasive and costly. In contrast, breath analysis is a non-invasive, safe and simple sampling method combined with a promising metabolomics approach, which is suitable for early cancer diagnosis in high volume population. OBJECTIVES: This study aims to achieve a more comprehensive and definitive exhaled breath metabolism profile in papillary thyroid cancer patients (PTCs). METHODS: We studied both end-tidal and mixed expiratory breath, solid-phase microextraction gas chromatography coupled with high resolution mass spectrometry (SPME-GC-HRMS) was used to analyze the breath samples. Multivariate combined univariate analysis was applied to identify potential breath biomarkers. RESULTS: The biomarkers identified in end-tidal and mixed expiratory breath mainly included alkanes, olefins, enols, enones, esters, aromatic compounds, and fluorine and chlorine containing organic compounds. The area under the curve (AUC) values of combined biomarkers were 0.974 (sensitivity: 96.1%, specificity: 90.2%) and 0.909 (sensitivity: 98.0%, specificity: 74.5%), respectively, for the end-tidal and mixed expiratory breath, indicating of reliability of the sampling and analysis method CONCLUSION: This work not only successfully established a standard metabolomic approach for early diagnosis of PTC, but also revealed the necessity of using both the two breath types for comprehensive analysis of the biomarkers.

Evaluation of the relationship between end-tidal carbon dioxide level and heart failure classification.

OBJECTIVE: Heart failure is a disease with cardiac dysfunction, and its morbidity and mortality are associated with the degree of dysfunction. The New York Heart Association classifies the heart failure stages based on the severity of symptoms and physical activity. End-tidal carbon dioxide refers to the level of carbon dioxide that a person exhales with each breath. End-tidal carbon dioxide levels can be used in many clinical conditions such as heart failure, asthma, and chronic obstructive pulmonary disease. The aim of the study was to reveal the relationship between end-tidal carbon dioxide levels and the New York Heart Association classification of heart failure stages. METHODS: This study was conducted at Kahramanmaras Sütçü Imam University Faculty of Medicine Adult Emergency Department between 01/03/2019 and 01/09/2019. A total of 80 patients who presented to the emergency department with a history of heart failure or were diagnosed with heart failure during admission were grouped according to the New York Heart Association classification of heart failure stages. The laboratory parameters, ejection fraction values, and end-tidal carbon dioxide levels of the patients were measured and recorded in the study forms. RESULTS: End-tidal carbon dioxide levels and ejection fraction values were found to be significantly lower in the stage 4 group compared to the other groups. Furthermore, pro-B-type natriuretic peptide (BNP) values were found to be significantly higher in stage 4 group compared to the other groups. CONCLUSION: It was concluded that end-tidal carbon dioxide levels could be used together with pro-BNP and ejection fraction values in determining the severity of heart failure.

Chemiresistors Based on Hybrid Nanostructures Obtained from Graphene and Conducting Polymers with Potential Use in Breath Methane Detection Associated with Irritable Bowel Syndrome.

This paper describes the process of producing chemiresistors based on hybrid nanostructures obtained from graphene and conducting polymers. The technology of graphene presumed the following: dispersion and support stabilization based on the chemical vapor deposition technique; transfer of the graphene to the substrate by spin-coating of polymethyl methacrylate; and thermal treatment and electrochemical delamination. For the process at T = 950 °C, a better settlement of the grains was noticed, with the formation of layers predominantly characterized by peaks and not by depressions. The technology for obtaining hybrid nanostructures from graphene and conducting polymers was drop-casting, with solutions of Poly(3-hexylthiophene (P3HT) and Poly[(9,9-dioctylfluorenyl-2,7-diyl)-co-bithiophene] (F8T2). In the case of F8T2, compared to P3HT, a 10 times larger dimension of grain size and about 7 times larger distances between the peak clusters were noticed. To generate chemiresistors from graphene-polymer structures, an ink-jet printer was used, and the metallization was made with commercial copper ink for printed electronics, leading to a structure of a resistor with an active surface of about 1 cm2. Experimental calibration curves were plotted for both sensing structures, for a domain of CH4 of up to 1000 ppm concentration in air. A linearity of the curve for the low concentration of CH4 was noticed for the graphene structure with F8T2, presenting a sensitivity of about 6 times higher compared with the graphene structure with P3HT, which makes the sensing structure of graphene with F8T2 more feasible and reliable for the medical application of irritable bowel syndrome evaluation.

Scheduling optimisation of alcohol test sites.

Drink driving is an infamous factor in road crashes and fatalities. Alcohol testing is a major countermeasure, and random breath tests (RBTs) deter tested drivers and passersby (observers who are not tested). We propose a genetic algorithm (GA)-based RBT scheduling optimisation method to achieve maximal deterrence of drink driving. The RBT schedule denotes the daily plan of where, when, and for how long tests should occur in the road network. The test results (positive and negative) and observing drivers are considered in the fitness function. The limited testing resource capacity is modeled by a number of constraints that consider the total duration of tests, the minimum and maximum duration of a single test site, and the total number of test sites during the day. Clustering of the alcohol-related crash data is used to estimate the matrix for drink driving on the scheduled day. The crash data and traffic flow data from Victoria, Australia are analysed and used to describe sober/drink driving. A detailed synthetic example is developed and a significant improvement with 150% more positive results and 59% more overall tests is observed using the proposed scheduling optimisation method.

The Classification of VOCs Based on Sensor Images Using a Lightweight Neural Network for Lung Cancer Diagnosis.

The application of artificial intelligence to point-of-care testing (POCT) disease detection has become a hot research field, in which breath detection, which detects the patient's exhaled VOCs, combined with sensor arrays of convolutional neural network (CNN) algorithms as a new lung cancer detection is attracting more researchers' attention. However, the low accuracy, high-complexity computation and large number of parameters make the CNN algorithms difficult to transplant to the embedded system of POCT devices. A lightweight neural network (LTNet) in this work is proposed to deal with this problem, and meanwhile, achieve high-precision classification of acetone and ethanol gases, which are respiratory markers for lung cancer patients. Compared to currently popular lightweight CNN models, such as EfficientNet, LTNet has fewer parameters (32 K) and its training weight size is only 0.155 MB. LTNet achieved an overall classification accuracy of 99.06% and 99.14% in the own mixed gas dataset and the University of California (UCI) dataset, which are both higher than the scores of the six existing models, and it also offers the shortest training (844.38 s and 584.67 s) and inference times (23 s and 14 s) in the same validation sets. Compared to the existing CNN models, LTNet is more suitable for resource-limited POCT devices.

Detection of Helicobacter pylori strain types and analysis of risk factors among subjects from Hainan Province, China.

OBJECTIVE: To explore the prevalence of type I and type II Helicobacter pylori infection and investigate risk factors in a population from Hainan Province in China. METHODS: Data came from a large, cross-sectional study conducted from August 2022 to April 2023 involving five cities of Hainan. Subjects with confirmed 14C-urea breath test (UBT) and positive serological assay were included. All subjects had a gastroscopy. According to presence or absence of CagA/VacA proteins, subjects were classified as either type I (present) or type II strains (absent). Gastroscopic findings and several socio-demographic factors were examined for correlation with antibody serotyping. RESULTS: In total, 410 subjects were investigated for H. pylori strain types. The overall prevalence of the highly virulent, type I H. pylori strain was 79% (324/410) and type II strain was 21% (86/410). There was a strong association between type I strain and peptic ulcer disease. Of several sociodemographic factors investigated, only smoking and data over baseline (DOB) values showed significant differences between type 1 and type II strains. Logistic regression analysis showed a lower risk of type I H. pylori infection in smokers compared with non-smokers, and a higher risk of H. pylori type I infection in subjects with medium and high data over baseline (DOB) values compared with subjects who had low DOB values. CONCLUSION: Highly virulent, type I H. pylori infections predominate in Hainan and the co-positivity of CagA and VacA antibodies are related to type I H. pylori infection. We found that Type I H. pylori was closely associated with peptic ulcer disease and the DOB values were generally high.

Critical appraisal of the SIBO hypothesis and breath testing: A clinical practice update endorsed by the European society of neurogastroenterology and motility (ESNM) and the American neurogastroenterology and motility society (ANMS).

BACKGROUND: There is compelling evidence that microbe-host interactions in the intestinal tract underlie many human disorders, including disorders of gut-brain interactions (previously termed functional bowel disorders), such as irritable bowel syndrome (IBS). Small intestinal bacterial overgrowth (SIBO) has been recognized for over a century in patients with predisposing conditions causing intestinal stasis, such as surgical alteration of the small bowel or chronic diseases, including scleroderma and is associated with diarrhea and signs of malabsorption. Over 20 years ago, it was hypothesized that increased numbers of small intestine bacteria might also account for symptoms in the absence of malabsorption in IBS and related disorders. This SIBO-IBS hypothesis stimulated significant research and helped focus the profession's attention on the importance of microbe-host interactions as a potential pathophysiological mechanism in IBS. PURPOSE: However, after two decades, this hypothesis remains unproven. Moreover, it has led to serious unintended consequences, namely the widespread use of unreliable and unvalidated breath tests as a diagnostic test for SIBO and a resultant injudicious use of antibiotics. In this review, we examine why the SIBO hypothesis remains unproven and, given the unintended consequences, discuss why it is time to reject this hypothesis and its reliance on breath testing. We also examine recent IBS studies of bacterial communities in the GI tract, their composition and functions, and their interactions with the host. While these studies provide important insights to guide future research, they highlight the need for further mechanistic studies of microbe-host interactions in IBS patients before we can understand their possible role in diagnosis and treatment of patient with IBS and related disorders.

Validation and application of an automated multitarget LC-MS/MS method for drugs of abuse testing using exhaled breath as specimen.

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.

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 5.3 H z . m - 1 . s , a figure of merit of 10,254 m - 1 . s , a detection limit of 5 × 10 - 6 m . s - 1 , and a quality factor of 3 × 10 6 . 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.

A comprehensive meta-analysis and systematic review of breath analysis in detection of COVID-19 through Volatile organic compounds.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: Overall, these results support the use of breath analysis as a new detection method of COVID-19 infection.

Efficient detection of nitric oxide a biomarker associated with COVID19 via N, P co-doped C60 fullerene: a computational study.

CONTEXT: Coronavirus (COVID-19) is a novel respiratory viral infection, causing a relatively large number of deaths especially in people who underly lung diseases such as chronic obstructive pulmonary and asthma, and humans are still suffering from the limited testing capacity. In this article, a solution is proposed for the detection of COVID-19 viral infections through the analysis of exhaled breath gasses, i.e., nitric oxide, a prominent biomarker released by respiratory epithelial, as a non-invasive and time-saving approach. Here, we designed a novel and low-cost N and P co-doped C60 fullerene-based breathalyzer for the detection of NO gas exhaled from the respiratory epithelial cells. This breathalyzer shows a quick response to the detection of NO gas by directly converting NO to NO2 without passing any energy barrier (0 kcal/mol activation energy). The recovery time of breathalyzer is very short (0.98 × 103 s), whereas it is highly selective for NO sensing in the mixture of CO2 and H2O gasses. The study provides an idea for the synthesis of low-cost (compared to previously reported Au atom decorated nanostructure and metal-based breathalyzer), efficient, and highly selective N and P co-doped C60 fullerene-based breathalyzer for COVID-19 detection. METHODS: The geometries of N and P-doped systems and gas molecules are simulated using spin-polarized density functional theory calculations.

Impact of breath sample collection method and length of storage of breath samples in Tedlar bags on the level of selected volatiles assessed using gas chromatography-ion mobility spectrometry (GC-IMS).

The analysis of volatile organic compounds (VOCs) in exhaled air has attracted the interest of the scientific community because it provides the possibility of monitoring physiological and metabolic processes and non-invasive diagnostics of various diseases. However, this method remains underused in clinical practice as well as in research because of the lack of standardized procedures for the collection, storage and transport of breath samples, which would guarantee good reproducibility and comparability of results. The method of sampling, as well as the storage time of the breath samples in the polymer bags used for sample storage and transport, affect the composition and concentration of VOCs present in the breath samples. The aim of our study was to compare breath samples obtained using two methods with fully disposable equipment: a Haldane sampling tube intended for direct breath collection and breath samples exhaled into a transparent Tedlar bag. The second task was to monitor the stability of selected compounds of real breath samples stored in a Tedlar bag for 6 h. Gas chromatography coupled with ion mobility spectrometry (GC-IMS) implemented in the BreathSpec®device was used to analyse exhaled breath. Our results showed a significant difference in the signal intensity of some volatiles when taking a breath sample with a Haldane tube and a Tedlar bag. Due to its endogenous origin, acetone levels were significantly higher when the Haldane tube sampler was used while elevated levels of 2-propanol and unidentified VOC (designated as VOC 3) in the Tedlar bag samples likely originated from contamination of the Tedlar bags. The VOC stability study revealed compound-specific signal intensity changes of the selected VOCs with storage time in the Tedlar bags, with some volatiles showing increasing signal intensity during storage in Tedlar bags. This limits the use of Tedlar bags only for very limited time and carefully selected purpose. Our results highlight the importance of careful design and implementation of experiments and clinical protocols to obtain relevant and reliable results.

Exhaled breath analysis in patients with potentially curative lung cancer undergoing surgery: a longitudinal study.

Exhaled breath analysis has emerged as a non-invasive and promising method for early detection of lung cancer, offering a novel approach for diagnosis through the identification of specific biomarkers present in a patient's breath. For this longitudinal study, 29 treatment-naive patients with lung cancer were evaluated before and after surgery. Secondary electrospray ionization high-resolution mass spectrometry was used for exhaled breath analysis. Volatile organic compounds with absolute log2fold change ⩾1 andq-values ⩾ 0.71 were selected as potentially relevant. Exhaled breath analysis resulted in a total of 3482 features. 515 features showed a substantial difference before and after surgery. The small sample size generated a false positive rate of 0.71, therefore, around 154 of these 515 features were expected to be true changes. Biological identification of the features with the highest consistency (m/z-242.18428 andm/z-117.0539) revealed to potentially be 3-Oxotetradecanoic acid and Indole, respectively. Principal component analysis revealed a primary cluster of patients with a recurrent lung cancer, which remained undetected in the initial diagnostic and surgical procedures. The change of exhaled breath patterns after surgery in lung cancer emphasizes the potential for lung cancer screening and detection.