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Introduction: Breath Volatile organic compounds (VOCs) are promising biomarkers for clinical purposes due to their unique properties. Translation of VOC biomarkers into the clinic depends on identification and validation: a challenge requiring collaboration, well-established protocols, and cross-comparison of data. Previously, we developed a breath collection and analysis method, resulting in 148 breath-borne VOCs identified. Objectives: To develop a complementary analytical method for the detection and identification of additional VOCs from breath. To develop and implement upgrades to the methodology for identifying features determined to be “on-breath” by comparing breath samples against paired background samples applying three metrics: standard deviation, paired t-test, and receiver-operating-characteristic (ROC) curve. Methods: A thermal desorption (TD)-gas chromatography (GC)-mass spectrometry (MS)-based analytical method utilizing a PEG phase GC column was developed for the detection of biologically relevant VOCs. The multi-step VOC identification methodology was upgraded through several developments: candidate VOC grouping schema, ion abundance correlation based spectral library creation approach, hybrid alkane-FAMES retention indexing, relative retention time matching, along with additional quality checks. In combination, these updates enable highly accurate identification of breath-borne VOCs, both on spectral and retention axes. Results: A total of 621 features were statistically determined as on-breath by at least one metric (standard deviation, paired t-test, or ROC). A total of 38 on-breath VOCs were able to be confidently identified from comparison to chemical standards. Conclusion: The total confirmed on-breath VOCs is now 186. We present an updated methodology for high-confidence VOC identification, and a new set of VOCs commonly found on-breath. [ABSTRACT FROM AUTHOR]

Bronchopulmonary dysplasia (BPD) is the most common respiratory disease in preterm and is still associated with increased mortality and morbidity. The great interest lies in identifying early biomarkers that can predict the development of BPD. This pilot study explores the potential of e-nose for the early identification of BPD risk in premature infants by analyzing volatile organic compounds (VOCs) in the exhaled breath condensate (EBC). Fourteen mechanically ventilated very preterm infants were included in this study. The clinical parameters and EBC were collected within the first 24 h of life. The discriminative ability of breath prints between preterms who did and did not develop BPD was investigated using pattern recognition, a machine learning algorithm, and standard statistical methods. We found that e-nose probes can significantly predict the outcome of "no-BPD" vs. "BPD". Specifically, a subset of probes (S18, S24, S14, and S6) were found to be significantly predictive, with an AUC of 0.87, 0.89, 0.82, 0.8, and p = 0.019, 0.009, 0.043, 0.047, respectively. The e-nose is an easy-to-use, handheld, non-invasive electronic device that quickly samples breath. Our preliminary study has shown that it has the potential for early prediction of BPD in preterms. [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]

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]

Multiphoton electron extraction spectroscopy (MEES) is an advanced analytical technique that has demonstrated exceptional sensitivity and specificity for detecting molecular traces on solid and liquid surfaces. Building upon the solid-state MEES foundations, this study introduces the first application of MEES in the gas phase (gas-phase MEES), specifically designed for quantitative detection of gas traces at sub-part per billion (sub-PPB) concentrations under ambient atmospheric conditions. Our experimental setup utilizes resonant multiphoton ionization processes using ns laser pulses under a high electrical field. The generated photoelectron charges are recorded as a function of the laser's wavelength. This research showcases the high sensitivity of gas-phase MEES, achieving high spectral resolution with resonant peak widths less than 0.02 nm FWHM. We present results from quantitative analysis of benzene and aniline, two industrially and environmentally significant compounds, demonstrating linear responses in the sub-PPM and sub-PPB ranges. The enhanced sensitivity and resolution of gas-phase MEES offer a powerful approach to trace gas analysis, with potential applications in environmental monitoring, industrial safety, security screening, and medical diagnostics. This study confirms the advantages of gas-phase MEES over many traditional optical spectroscopic methods and demonstrates its potential in direct gas-trace sensing in ambient atmosphere. [ABSTRACT FROM AUTHOR]

Background: Breath testing using an electronic nose has been recognized as a promising new technique for the early detection of lung cancer. Imbalanced data are commonly observed in electronic nose studies, but methods to address them are rarely reported. Objective: The objectives of this study were to assess the accuracy of electronic nose screening for lung cancer with imbalanced learning and to select the best mechanical learning algorithm. Methods: We conducted a case‒control study that included patients with lung cancer and healthy controls and analyzed metabolites in exhaled breath using a carbon nanotube sensor array. The study used five machine learning algorithms to build predictive models and a synthetic minority oversampling technique to address imbalanced data. The diagnostic accuracy of lung cancer was assessed using pathology reports as the gold standard. Results: We enrolled 190 subjects between 2020 and 2023. A total of 155 subjects were used in the final analysis, which included 111 lung cancer patients and 44 healthy controls. We randomly divided samples into one training set, one internal validation set, and one external validation set. In the external validation set, the summary sensitivity was 0.88 (95% CI 0.84–0.91), the summary specificity was 1.00 (95% CI 0.85–1.00), the AUC was 0.96 (95% CI 0.94–0.98), the pAUC was 0.92 (95% CI 0.89–0.96), and the DOR was 207.62 (95% CI 24.62–924.64). Conclusion: Electronic nose screening for lung cancer is highly accurate. The support vector machine algorithm is more suitable for analyzing chemical sensor data from electronic noses. [ABSTRACT FROM AUTHOR]

Early detection of colorectal cancer is crucial for improving outcomes and reducing mortality. While there is strong evidence of effectiveness, currently adopted screening methods present several shortcomings which negatively impact the detection of early stage carcinogenesis, including low uptake due to patient discomfort. As a result, developing novel, non-invasive alternatives is an important research priority. Recent advancements in the field of breathomics, the study of breath composition and analysis, have paved the way for new avenues for non-invasive cancer detection and effective monitoring. Harnessing the utility of Volatile Organic Compounds in exhaled breath, breathomics has the potential to disrupt colorectal cancer screening practices. Our goal is to outline key research efforts in this area focusing on machine learning methods used for the analysis of breathomics data, highlight challenges involved in artificial intelligence application in this context, and suggest possible future directions which are currently considered within the framework of the European project ONCOSCREEN. [ABSTRACT FROM AUTHOR]

Patients with COPD are at high risk of lung cancer developing, but no validated predictive biomarkers have been reported to identify these patients. Molecular profiling of exhaled breath by electronic nose (eNose) technology may qualify for early detection of lung cancer in patients with COPD. Can eNose technology be used for prospective detection of early lung cancer in patients with COPD? BreathCloud is a real-world multicenter prospective follow-up study using diagnostic and monitoring visits in day-to-day clinical care of patients with a standardized diagnosis of asthma, COPD, or lung cancer. Breath profiles were collected at inclusion in duplicate by a metal-oxide semiconductor eNose positioned at the rear end of a pneumotachograph (SpiroNose; Breathomix). All patients with COPD were managed according to standard clinical care, and the incidence of clinically diagnosed lung cancer was prospectively monitored for 2 years. Data analysis involved advanced signal processing, ambient air correction, and statistics based on principal component (PC) analysis, linear discriminant analysis, and receiver operating characteristic analysis. Exhaled breath data from 682 patients with COPD and 211 patients with lung cancer were available. Thirty-seven patients with COPD (5.4%) demonstrated clinically manifest lung cancer within 2 years after inclusion. Principal components 1, 2, and 3 were significantly different between patients with COPD and those with lung cancer in both training and validation sets with areas under the receiver operating characteristic curve of 0.89 (95% CI, 0.83-0.95) and 0.86 (95% CI, 0.81-0.89). The same three PCs showed significant differences (P <.01) at baseline between patients with COPD who did and did not subsequently demonstrate lung cancer within 2 years, with a cross-validation value of 87% and an area under the receiver operating characteristic curve of 0.90 (95% CI, 0.84-0.95). Exhaled breath analysis by eNose identified patients with COPD in whom lung cancer became clinically manifest within 2 years after inclusion. These results show that eNose assessment may detect early stages of lung cancer in patients with COPD. [ABSTRACT FROM AUTHOR]

Exhaled breath analysis using an e-nose is a groundbreaking tool for exhaled volatile organic compound (VOC) analysis, which has already shown its applicability in several respiratory and systemic diseases. It is still unclear whether food intake can be considered a confounder when analyzing the VOC-profile. We aimed to assess whether an e-nose can discriminate exhaled breath before and after predefined food intake at different time periods. We enrolled 28 healthy non-smoking adults and collected their exhaled breath as follows: (a) before food intake, (b) within 5 min after food consumption, (c) within 1 h after eating, and (d) within 2 h after eating. Exhaled breath was collected by a formerly validated method and analyzed by an e-nose (Cyranose 320). By principal component analysis, significant variations in the exhaled VOC-profile were shown for principal component 1 (capturing 63.4% of total variance) when comparing baseline vs. 5 min and vs. 1 h after food intake (both p < 0.05). No significance was shown in the comparison between baseline and 2 h after food intake. Therefore, the exhaled VOC-profile seems to be influenced by very recent food intake. Interestingly, two hours might be sufficient to avoid food induced alterations of exhaled VOC-spectrum when sampling for research protocols. [ABSTRACT FROM AUTHOR]

Background: Diagnostics for pulmonary tuberculosis (PTB) are usually inaccurate, expensive, or complicated. The breathomics-based method may be an attractive option for fast and noninvasive PTB detection. Method: Exhaled breath samples were collected from 518 PTB patients and 887 controls and tested on the real-time high-pressure photon ionization time-of-flight mass spectrometer. Machine learning algorithms were employed for breathomics analysis and PTB detection mode, whose performance was evaluated in 430 blinded clinical patients. Results: The breathomics-based PTB detection model achieved an accuracy of 92.6%, a sensitivity of 91.7%, a specificity of 93.0%, and an AUC of 0.975 in the blinded test set (n = 430). Age, sex, and anti-tuberculosis treatment does not significantly impact PTB detection performance. In distinguishing PTB from other pulmonary diseases (n = 182), the VOC modes also achieve good performance with an accuracy of 91.2%, a sensitivity of 91.7%, a specificity of 88.0%, and an AUC of 0.961. Conclusions: The simple and noninvasive breathomics-based PTB detection method was demonstrated with high sensitivity and specificity, potentially valuable for clinical PTB screening and diagnosis. Key messages: What is already known on this topic—Breath VOC analysis is a potential technology for PTB detection. However, it is still desirable for a real-time, robust, accurate, and simple breath analysis platform for clinical application. What this study adds—An online breath detection for PTB was proposed and demonstrated with high sensitivity and specificity in a large clinical cohort. How this study might affect research, practice, or policy—This study may promote the application of breath detection in clinical TB detection and related biomarker studies. [ABSTRACT FROM AUTHOR]

The measurement of exhaled volatile organic compounds (VOCs) in exhaled breath (breathomics) represents an exciting biomarker matrix for airways disease, with early research indicating a sensitivity to airway inflammation. One of the key aspects to analytical validity for any clinical biomarker is an understanding of the short-term repeatability of measures. We collected exhaled breath samples on 5 consecutive days in 14 subjects with severe asthma who had undergone extensive clinical characterisation. Principal component analysis on VOC abundance across all breath samples revealed no variance due to the day of sampling. Samples from the same patients clustered together and there was some separation according to T2 inflammatory markers. The intra-subject and between-subject variability of each VOC was calculated across the 70 samples and identified 30.35% of VOCs to be erratic: variable between subjects but also variable in the same subject. Exclusion of these erratic VOCs from machine learning approaches revealed no apparent loss of structure to the underlying data or loss of relationship with salient clinical characteristics. Moreover, cluster evaluation by the silhouette coefficient indicates more distinct clustering. We are able to describe the short-term repeatability of breath samples in a severe asthma population and corroborate its sensitivity to airway inflammation. We also describe a novel variance-based feature selection tool that, when applied to larger clinical studies, could improve machine learning model predictions. [ABSTRACT FROM AUTHOR]

The remarkable potential of breath analysis in medical care and diagnosis, and the consequent development of electronic noses, is currently attracting the interest of the research community. This is mainly due to the possibility of applying the technique for early diagnosis, screening campaigns, or tracking the effectiveness of treatment. Carbon nanotubes (CNTs) are known to be good candidates for gas sensing, and they have been recently considered for the development of electronic noses. The present work has the aim of reviewing the available literature on the development of CNTs-based electronic noses for breath analysis applications, detailing the functionalization procedure used to prepare the sensors, the breath sampling techniques, the statistical analysis methods, the diseases under investigation, and the population studied. The review is divided in two main sections: one focusing on the e-noses completely based on CNTs and one reporting on the e-noses that feature sensors based on CNTs, along with sensors based on other materials. Finally, a classification is presented among studies that report on the e-nose capability to discriminate biomarkers, simulated breath, and animal or human breath. [ABSTRACT FROM AUTHOR]

Microbial odors, produced by bacteria and fungi, play a significant role in our environment and can be useful in diagnosing infectious diseases. These odors are mediated by volatile organic compounds (VOCs) and can be detected through various methods such as gas chromatography/mass spectrometry and electronic nose technology. Detecting infections through scent has already been successful with the help of animals like rats and dogs, as well as through breath tests. Pathogenic microbes release VOCs to influence their hosts and enhance their own transmission. Understanding and utilizing these microbial odors could lead to new opportunities for improving human health. [Extracted from the article]

In this study, exhaled breath testing has been considered a promising method for the detection and monitoring of breast cancer (BC). A high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS) platform was used to detect volatile organic compounds (VOCs) in breath samples. Then, machine learning (ML) models were constructed on VOCs for the diagnosis of BC and its progression monitoring. Ultimately, 1981 women with useable breath samples were included in the study, of whom 937 (47.3 %) had been diagnosed with BC. VOC panels were used for ML model construction for BC detection and progression monitoring. On the blinded testing cohort, this VOC-based model successfully differentiated patients with and without BC with sensitivity, specificity, and area under receiver operator characteristic curve (AUC) values of 85.9 %, 90.4 %, and 0.946. The corresponding AUC values when differentiating between patients with and without lymph node metastasis (LNM) or between patients with tumor-node-metastasis (TNM) stage 0/I/II or III/IV disease were 0.840 and 0.708, respectively. While developed VOC-based models exhibited poor performance when attempting to differentiate between patients based on pathological patterns (Ductal carcinoma in situ (DCIS) vs Invasive BC (IBC)) or molecular subtypes (Luminal vs Human epidermal growth factor receptor 2 (HER2+) vs Triple-negative BC (TNBC)) of BC. Collectively, the HPPI-TOFMS-based breathomics approaches may offer value for the detection and progression monitoring of BC. Additional research is necessary to explore the fundamental mechanisms of the identified VOCs. [Display omitted] • Our study findings highlight a novel breast cancer detection and monitoring strategy. • HPPI-TOFMS-based breathomics approaches may offer value for the detection and progression monitoring of breast cancer. • Significant differences in the VOC profiles of breast cancer patients were detected relative to those in non-cancer patients. [ABSTRACT FROM AUTHOR]

Derzeit basieren die Diagnose von Exazerbationen bei Mukoviszidose-Patienten und die Überwachung der Krankheitsaktivität hauptsächlich auf klinischen Merkmalen und Lungenfunktionstests. Eine Atemgasanalyse könnte die Diagnostik unterstützen, da bestimmte Muster von flüchtigen organischen Verbindungen auf Mukoviszidose-assoziierte bakterielle Infektionen hinweisen und auch eine Früherkennung einer Mukoviszidose-Exazerbation ermöglichen. [ABSTRACT FROM AUTHOR]

Chlorine‐based disinfectants protect pool water from pathogen contamination but produce potentially harmful halogenated disinfection by‐products (DBPs). This study characterized the bioaccumulation and elimination of exhaled DBPs post‐swimming and investigated changes in exhaled breath profiles associated with chlorinated pool exposure. Nineteen participants provided alveolar‐enriched breath samples prior to and 5, 90, 300, 510, and 600 minutes post‐swimming. Known DBPs associated with chlorinated water were quantitated by thermal desorption‐gas chromatography‐mass spectrometry. Two distinct exhaled DBP elimination profiles were observed. Most participants (84%) reported peak concentrations immediately post‐swimming that reduced exponentially. A sub‐group exhibited a previously unobserved and delayed washout profile with peak levels at 90 minutes post‐exposure. Metabolomic investigations tentatively identified two candidate biomarkers associated with swimming pool exposure, demonstrating an upregulation in the hours after exposure. These data demonstrated a hitherto undescribed exhaled DBP elimination profile in a small number of participants which contrasts previous findings of uniform accumulation and exponential elimination. This sub‐group which exhibited delayed peak‐exhaled concentrations suggests the uptake, processing, and immediate elimination of DBPs are not ubiquitous across individuals as previously understood. Additionally, non‐targeted metabolomics highlighted extended buildup of compounds tentatively associated with swimming in a chlorinated pool environment that may indicate airway responses to DBP exposure. [ABSTRACT FROM AUTHOR]

Breathomics is an emerging field that aims to non-invasively diagnose and monitor respiratory diseases using the analysis of exhaled breath. In this review, we present a summary of the various analytical methods such as gas chromatography, and mass spectrometry that are used for the characterization of these VOCs and inorganic gases. We further elucidate on electrochemical transduction mechanism for ultrasensitive characterization of respiratory diseases in breathomics. Electrochemical sensors have several advantages over other detection methods, including high sensitivity, selectivity, and low cost. Electrochemical sensors can be miniaturized and integrated into portable devices, allowing for point-of-care diagnosis. Further advancements in sampling techniques, statistical analysis, and technology integration could pave the way for the widespread use of breath analysis in clinical practice. As research in breathomics continues to advance, electrochemical sensors are likely to play an increasingly significant part in the diagnosis and treatment of respiratory diseases. [Display omitted] • Breathomics is a powerful tool, widely being investigated for logitudal health monitoring • Breathomics provides mechanistic insights on the levels of endogeneously produced volatile organic compounds (VOCs). • Standard analytical methods , that are used for characterisation of VOCs and gases. • Overview of electrochemical transduction mechanism for breathomics screening. • Challenges and prospects for understanding and critically evaluating the scope of this field are also presented. [ABSTRACT FROM AUTHOR]

Background: Lipid peroxidation plays an important role in the pathogenesis of pneumoconiosis. Volatile organic compounds (VOCs) generated from lipid peroxidation might be used to detect pneumoconiosis. The objective of this study was to develop a breath test for pneumoconiosis.Methods: A case-control study was designed. Breath and ambient air were analysed by gas chromatography/mass spectrometry. After blank correction to prevent contamination from ambient air, we used canonical discriminant analysis (CDA) to assess the discrimination accuracy and principal component analysis (PCA) to generate a prediction score. The prediction accuracy was calculated and validated using the International Classification of Radiographs of the Pneumoconiosis criteria combined with an abnormal pulmonary function test as a reference standard. We generated a receiver operator characteristic (ROC) curve and calculated the area under the ROC curve (AUC) to estimate the screening accuracy of the breath test.Results: We enrolled 200 stone workers. After excluding 5 subjects with asthma and 16 subjects who took steroids or nonsteroidal anti-inflammatory drugs, a total of 179 subjects were used in the final analyses, which included 25 cases and 154 controls. By CDA, 88.8% of subjects were correctly discriminated by their exposure status and the presence of pneumoconiosis. After excluding the VOCs of automobile exhaust and cigarette smoking, pentane and C5-C7 methylated alkanes constituted the major VOCs in the breath of persons with pneumoconiosis. Using the prediction score generated from PCA, the ROC-AUC was 0.88 (95% CI = 0.80-0.95), and the mean ROC-AUC of 5-fold cross-validation was 0.90. The breath test had good accuracy for pneumoconiosis diagnosis.Conclusion: The analysis of breath VOCs has potential in the screening of pneumoconiosis for its non-invasiveness and high accuracy. We suggest that a multi-centre study is warranted and that all procedures must be standardized before clinical application. [ABSTRACT FROM AUTHOR]

We hypothesize that oxidative stress induced by trichloramine exposure during swimming could be related to etiopathogenesis of asthma among elite swimmers. Aim To investigate the effect of a swimming training session on oxidative stress markers of asthmatic compared to non-asthmatic elite swimmers using exhaled breath ( EB) metabolomics. Methods Elite swimmers annually screened in our department (n=27) were invited and those who agreed to participate (n=20, of which 9 with asthma) had EB collected (Tedlar® bags) before and after a swimming training session. SPME fiber ( DVB/ CAR/ PDMS) was used to extract EB metabolites followed by a multidimensional gas chromatography analysis ( GC× GC-To FMS). Dataset comprises eight metabolites end products of lipid peroxidation: five aliphatic alkanes (nonane, 2,2,4,6,6-pentamethylheptane, decane, dodecane, and tetradecane) and three aldehydes (nonanal, decanal, and dodecanal). To assess exercise impact on lipid peroxidation markers, data were analyzed using principle component analysis ( PCA), which was run on the original data set and on the data set constructed using differences in the metabolite total areas before and after exercise session. Results Heatmap representation revealed that metabolites content decreased after exercise, both for control and asthma groups; however, the greater decrease was observed for controls. Asthmatics and controls did not form separated clusters; however, control swimmers demonstrated a more varied response to the exercise being dispersed along all score plot. Conclusion In well-trained athletes, swimming is associated with a decrease in oxidative stress markers independently of the presence of asthma, although a more pronounced decrease was seen in controls. [ABSTRACT FROM AUTHOR]

The diagnosis of hospital-acquired pneumonia remains challenging. We hypothesized that analysis of volatile organic compounds (VOCs) in exhaled breath could be used to diagnose pneumonia or the presence of pathogens in the respiratory tract in intubated and mechanically-ventilated intensive care unit patients. In this prospective, single-centre, cross-sectional cohort study breath frommechanically ventilated patients was analysed using gas chromatography-mass spectrometry. Potentially relevant VOCs were selected with a p-value < 0.05 and an area under the receiver operating characteristics curve (AUROC) above 0.7. These VOCs were used for principal component analysis and partial least square discriminant analysis (PLS-DA). AUROC was used as a measure of accuracy. Ninety-three patients were included in the study. Twelve of 145 identified VOCs were significantly altered in patients with pneumonia compared to controls. In colonized patients, 52 VOCs were significantly different. Partial least square discriminant analysis classified patients with modest accuracy (AUROC: 0.73 (95% confidence interval (CI): 0.57-0.88) after leave-one-out cross-validation). For determining the colonization status of patients, the model had an AUROC of 0.69 (95% CI: 0.57-0.82) after leave-one-out cross-validation. To conclude, exhaled breath analysis can be used to discriminate pneumonia from controls with a modest to good accuracy. Furthermore breath profiling could be used to predict the presence and absence of pathogens in the respiratory tract. These findings need to be validated externally. [ABSTRACT FROM AUTHOR]

Current challenges of clinical breath analysis include large data size and non-clinically relevant variations observed in exhaled breath measurements, which should be urgently addressed with competent scientific data tools. In this study, three different baseline correction methods are evaluated within a previously developed data size reduction strategy for multi capillary column – ion mobility spectrometry (MCC-IMS) datasets. Introduced for the first time in breath data analysis, the Top-hat method is presented as the optimum baseline correction method. A refined data size reduction strategy is employed in the analysis of a large breathomic dataset on a healthy and respiratory disease population. New insights into MCC-IMS spectra differences associated with respiratory diseases are provided, demonstrating the additional value of the refined data analysis strategy in clinical breath analysis. [ABSTRACT FROM AUTHOR]

Breath-based metabolomics (breathomics) is an exciting developing area of biotechnology that centers on the capture, identification, and quantification of volatile organic compound (VOC) patterns in human breath and their utilization as tools in the diagnosis of a broad spectrum of medical problems. With the age of personalized medicines demanding rapid bespoke diagnosis and treatment, this area of molecular diagnostics is beginning to see an upsurge in biotechnological advancement. Here, we discuss recent improvements and directions in the development of breath VOC analysis and diagnosis platforms that offer the potential for disease biomarker discovery and disease prognosis. [ABSTRACT FROM AUTHOR]

Several volatile organic compounds have been identified in exhaled breath in healthy subjects and patients with respiratory diseases by gas chromatography/mass spectrometry. Identification of selective patterns of volatile organic compounds in exhaled breath could be used as a biomarker of inflammatory lung diseases. An electronic nose (e-nose) is an artificial sensor system that generally consists of an array of chemical sensors for detection of volatile organic compound profiles (breathprints) and an algorithm for pattern recognition. E-noses are handheld, portable devices that provide immediate results. E-noses discriminate between patients with respiratory disease, including asthma, COPD and lung cancer, and healthy control subjects, and also among patients with different respiratory diseases. E-nose breathprints are associated with airway inflammation activity. In combination with other 'omics' platforms, e-nose technology might contribute to the identification of new surrogate markers of pulmonary inflammation and subphenotypes of patients with respiratory diseases, provide a molecular basis to a personalized pharmacological treatment, and facilitate the development of new drugs. Copyright © 2012 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

[Display omitted] • HS-SPME/GC × GC–ToFMS development for EBC metabolome profiling. • Extraction of EBC, using DVB/CAR/PDMS coating (40.0 ± 0.1 °C and physiological pH) • Most detailed study on the endogenous and exogenous EBC volatile metabolites. • EBC metabolites were determined at pg level. • The use of surgical facemasks not significantly affect the levels of lipid peroxidation markers, neither SpO2. Metabolomics encompasses the acquisition of large-scale datasets from a single sample to identify and/or elucidate physiological or pathological mechanisms, or even to assess environmental exposure, being sample preparation and instrumental analysis two fundamental steps in the construction of a metabolomics workflow. The study of the human exhaled breath condensate (EBC) has gained tremendous interest recently owing to the advances in analytical techniques. Thus, this study aims to develop a methodology for EBC metabolites profiling based on headspace solid phase microextraction (SPME) combined with comprehensive two-dimensional gas chromatography (GC × GC). Metabolite's extraction efficiency was improved by the optimization of four SPME parameters using a real EBC matrix: fibre coating, sample pH and volume, and extraction temperature. EBC analysis was performed according to the optimized parameters, using aliquots of 500 μL of EBC, at its physiological pH, with the DVB/CAR/PDMS fibre coating, at 40.0 ± 0.1 °C, being able to detect analytes at pg/mL, with acceptable reproducibility. EBC volatile composition was provided, with around 400 chemical features detected, in which 130 were putatively identified as belonging to hydrocarbons, aldehydes, alcohols, ketones, esters, monoterpenic and sesquiterpenic compounds, ethers, norisoprenoids and furans, revealing therefore its high complexity. This research represents the most detailed study on the volatile composition of EBC and allows the assessment of a broad range of endogenous and exogenous metabolites, with further relevance on clinical or environmental exposure studies. To test the applicability of this methodology, an exploratory study was carried out to assess the impact of the use of surgical facemasks on young researcher's lipid peroxidation volatile markers during a normal working day in a laboratory. In young healthy adults (age ≤ 32), the use of surgical facemasks seems not significantly affect the levels of the volatile markers of lipid peroxidation (aliphatic alkanes and aldehydes), neither the O 2 saturation level - SpO 2. This methodology shows promise as a convenient way to follow specific physiological or pathological conditions based on EBC metabolome and may be paired as part of an aggregate analytical structure. [ABSTRACT FROM AUTHOR]

Background: Data handling in clinical bioinformatics is often inadequate. No freely available tools provide straightforward approaches for consistent, flexible metadata collection and linkage of related experimental data generated locally by vendor software. Results: To address this problem, we created LabPipe, a flexible toolkit which is driven through a local client that runs alongside vendor software and connects to a light-weight server. The toolkit allows re-usable configurations to be defined for experiment metadata and local data collection, and handles metadata entry and linkage of data. LabPipe was piloted in a multi-site clinical breathomics study. Conclusions: LabPipe provided a consistent, controlled approach for handling metadata and experimental data collection, collation and linkage in the exemplar study and was flexible enough to deal effectively with different data handling challenges. [ABSTRACT FROM AUTHOR]

Background: There is no definitive method of accurately diagnosing appendicitis before surgery. We evaluated the feasibility of collecting breath samples in children with abdominal pain and gathered preliminary data on the accuracy of breath tests. Methods: We conducted a prospective pilot study at a large tertiary referral paediatric hospital in the UK. We recruited 50 participants with suspected appendicitis, aged between 5 and 15 years. Five had primary diagnosis of appendicitis. The primary outcome was the number of breath samples collected. We also measured the number of samples processed within 2 h and had CO2 ≥ 3.5%. Usability was assessed by patient-reported pain pre- and post-sampling and user-reported sampling difficulty. Logistic regression analysis was used to predict appendicitis and evaluated using the area under the receiver operator characteristic curve (AUROC). Results: Samples were collected from all participants. Of the 45 samples, 36 were processed within 2 h. Of the 49 samples, 19 had %CO2 ≥ 3.5%. No difference in patient-reported pain was observed (p = 0.24). Sampling difficulty was associated with patient age (p = 0.004). The logistic regression model had AUROC = 0.86. Conclusions: Breath tests are feasible and acceptable to patients presenting with abdominal pain in clinical settings. We demonstrated adequate data collection with no evidence of harm to patients. The AUROC was better than a random classifier; more specific sensors are likely to improve diagnostic performance. Trial registration: ClinicalTrials.gov, NCT03248102. Registered 14 Aug 2017. [ABSTRACT FROM AUTHOR]