Within-day and between-day repeatability of measurements with an electronic nose in patients with COPD

Advancing accuracy in breath testing for lung cancer: strategies for improving diagnostic precision in imbalanced data

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.

Engineering solutions to breath tests based on an e-nose system for silicosis screening and early detection in miners

OBJECTIVES

This study aims to develop an engineering solution to breath tests using an electronic nose (e-nose), and evaluate its diagnosis accuracy for silicosis. Influencing factors of this technique were explored.

METHODS

398 non-silicosis miners and 221 silicosis miners were enrolled in this cross-sectional study. Exhaled breath was analyzed by an array of 16 organic nanofiber sensors along with a customized sample processing system. Principal Component Analysis was used to visualize the breath data, and classifiers were trained by two improved cost-sensitive ensemble algorithms (RF and XGBoost) and two classical algorithms (KNN and SVM). All subjects were included to train the screening model, and an early detection model was run with silicosis cases in stage I. Both 5-fold cross-validation and external validation were adopted. Difference in classifiers caused by algorithms and subjects was quantified using a two-factor analysis of variance. The association between personal smoking habits and classification was investigated by the chi-square test.

RESULTS

Classifiers of ensemble learning performed well in both screening and early detection model, with an accuracy range of 0.817 to 0.987. Classical classifiers showed relatively worse performance. Besides, the ensemble algorithm type and silicosis cases inclusion had no significant effect on classification (p>0.05). There was no connection between personal smoking habits and classification accuracy.

CONCLUSION

Breath tests based on an e-nose consisted of 16x sensor array performed well in silicosis screening and early detection. Raw data input showed a more significant effect on classification compared with the algorithm. Personal smoking habits had little impact on models, supporting the applicability of models in large-scale silicosis screening. The e-nose technique and the breath analysis methods reported are expected to provide a quick and accurate screening for silicosis, and extensible for other diseases.

Noninvasive real-time breath test for controlling hormonal background of the human body: detection of serotonin and melatonin with quantum point-contact sensors

Significant progress in development of noninvasive diagnostic tools based on breath analysis can be expected if one employs a real-time detection method based on finding a spectral breath profile which would contain some energy characteristics of the analyzed gas mixture. Using the fundamental energy parameters of a quantum system, it is possible to determine with a high accuracy its quantitative and qualitative composition. Among the most efficient tools to measure energy characteristics of quantum systems are sensors based on Yanson point contacts. This paper reports the results of serotonin and melatonin detection as an example of testing the human hormonal background with point-contact sensors, which have already demonstrated their high efficiency in detecting carcinogenic strains ofHelicobacter pyloriand selective detection of complex gas mixtures. When comparing the values of serotonin and melatonin with the characteristic parameters of the spectral profile of the exhaled breath of each patient, high correlation dependences of the concentration of serotonin and melatonin with a number of characteristic parameters of the response curve of the point-contact sensor were found. The performed correlation analysis was complemented with the regression analysis. As a result, empiric regression relations were proposed to realize in practice the new non-invasive breath test for evaluation of the human hormonal background. Registration of the patient's breath profile using point-contact sensors makes it possible to easily monitor the dynamics of changes in the human hormonal background and perform a quantitative evaluation of serotonin and melatonin levels in the human body in real time without invasive interventions (blood collection) and expensive equipment or reagents.

The reliability and validity of the Turkish version of the European Health Interview Survey - Physical Activity Questionnaire (EHIS-PAQ)

Purpose: To translate and cross-culturally adapt the Turkish version of the EHIS-PAQ and to evaluate its reliability and validity in young adults. Methods: A total of 431 young adults were assessed with the Turkish version of the EHIS-PAQ and International Physical Activity Questionnaire-Short Form (IPAQ-SF). One week later, 117 participants refilled the Turkish EHIS-PAQ for the test-retest reliability. Reproducibility and construct validity was analyzed with intraclass correlation coefficient (ICC) and Pearson correlation coefficient, respectively. In addition, standard error of measurement (SEM) and minimal detectable change (MDC) were calculated for the Turkish EHIS-PAQ. Results: The mean age of the participants were 21.29.8 years. The ICC score for the total Physical Activity (PA) of Turkish EHIS-PAQ was 0.873 (CI:0.81-0.91). Test-retest reliability for the total score was excellent (ICC>0.80). The highest correlation was observed between the walking minute value of Turkish EHIS-PAQ and the walking PA sub-score of IPAQ-SF, as expected (r=0.625, p

Volatile organic compounds as disease predictors in newborn infants: a systematic review

Volatile organic compounds (VOC) detected in human breath, urine, stool, sweat, saliva, and blood result from metabolic processes in the body during health or disease. Using sophisticated measurement systems, small amounts of these compounds can be detected in the above bodily fluids. Multiple studies in adults and children have shown the potential of these compounds to differentiate between healthy individuals and patients by detecting profiles of compounds in non-invasively collected samples. However, the detection of biomarkers in VOCs from neonates is particularly attractive due to the non-invasive nature of its approach, and its ability to track disease progress by longitudinal sampling. In this work we have reviewed the literature on the use of VOCs in neonates and identified areas for future work.

Breath biopsy of breast cancer using sensor array signals and machine learning analysis

Breast cancer causes metabolic alteration, and volatile metabolites in the breath of patients may be used to diagnose breast cancer. The objective of this study was to develop a new breath test for breast cancer by analyzing volatile metabolites in the exhaled breath. We collected alveolar air from breast cancer patients and non-cancer controls and analyzed the volatile metabolites with an electronic nose composed of 32 carbon nanotubes sensors. We used machine learning techniques to build prediction models for breast cancer and its molecular phenotyping. Between July 2016 and June 2018, we enrolled a total of 899 subjects. Using the random forest model, the prediction accuracy of breast cancer in the test set was 91% (95% CI: 0.85–0.95), sensitivity was 86%, specificity was 97%, positive predictive value was 97%, negative predictive value was 97%, the area under the receiver operating curve was 0.99 (95% CI: 0.99–1.00), and the kappa value was 0.83. The leave-one-out cross-validated discrimination accuracy and reliability of molecular phenotyping of breast cancer were 88.5 ± 12.1% and 0.77 ± 0.23, respectively. Breath tests with electronic noses can be applied intraoperatively to discriminate breast cancer and molecular subtype and support the medical staff to choose the best therapeutic decision.

The Role of Electronic Noses in Phenotyping Patients with Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is a common progressive disorder of the respiratory system which is currently the third leading cause of death worldwide. Exhaled breath analysis is a non-invasive method to study lung diseases, and electronic noses have been extensively used in breath research. Studies with electronic noses have proved that the pattern of exhaled volatile organic compounds is different in COPD. More recent investigations have reported that electronic noses could potentially distinguish different endotypes (i.e., neutrophilic vs. eosinophilic) and are able to detect microorganisms in the airways responsible for exacerbations. This article will review the published literature on electronic noses and COPD and help in identifying methodological, physiological, and disease-related factors which could affect the results.

Diagnosis of ventilator-associated pneumonia using electronic nose sensor array signals: solutions to improve the application of machine learning in respiratory research

Background

Ventilator-associated pneumonia (VAP) is a significant cause of mortality in the intensive care unit. Early diagnosis of VAP is important to provide appropriate treatment and reduce mortality. Developing a noninvasive and highly accurate diagnostic method is important. The invention of electronic sensors has been applied to analyze the volatile organic compounds in breath to detect VAP using a machine learning technique. However, the process of building an algorithm is usually unclear and prevents physicians from applying the artificial intelligence technique in clinical practice. Clear processes of model building and assessing accuracy are warranted. The objective of this study was to develop a breath test for VAP with a standardized protocol for a machine learning technique.

Methods

We conducted a case-control study. This study enrolled subjects in an intensive care unit of a hospital in southern Taiwan from February 2017 to June 2019. We recruited patients with VAP as the case group and ventilated patients without pneumonia as the control group. We collected exhaled breath and analyzed the electric resistance changes of 32 sensor arrays of an electronic nose. We split the data into a set for training algorithms and a set for testing. We applied eight machine learning algorithms to build prediction models, improving model performance and providing an estimated diagnostic accuracy.

Results

A total of 33 cases and 26 controls were used in the final analysis. Using eight machine learning algorithms, the mean accuracy in the testing set was 0.81 ± 0.04, the sensitivity was 0.79 ± 0.08, the specificity was 0.83 ± 0.00, the positive predictive value was 0.85 ± 0.02, the negative predictive value was 0.77 ± 0.06, and the area under the receiver operator characteristic curves was 0.85 ± 0.04. The mean kappa value in the testing set was 0.62 ± 0.08, which suggested good agreement.

Conclusions

There was good accuracy in detecting VAP by sensor array and machine learning techniques. Artificial intelligence has the potential to assist the physician in making a clinical diagnosis. Clear protocols for data processing and the modeling procedure needed to increase generalizability.

Repeatability of exhaled breath fingerprint collected by a modern sampling system in asthmatic and healthy children

E-noses provide potential non-invasive metabolic biomarkers for diagnosing and monitoring pulmonary diseases. The primary aim of the present study was to assess the within-day and between-day repeatability of a modern breath sampling system (Pneumopipe^® plus an array of e-nose sensors) in asthmatic and healthy children. The secondary aim was to compare the repeatability of the breath sampling system, spirometry and exhaled nitric oxide (eNO). Fifteen children (age 6-11 years) with asthma and thirty healthy children matched by age and gender (1:2 allocation) were recruited; of them, three healthy children did not complete the study. All measurements were collected twice during the baseline visit, 30 min apart, and once during the final visit, after 7 d. Repeatability was assessed through the intra-cluster correlation coefficient (ICC), and a significance test was performed to detect an at least 'fair' repeatability (ICC > 0.2). In asthmatic children, the within-day (0-30 min) ICCs for e-nose sensors (8 sensors × 4 desorption temperatures) ranged from 0.24 to 0.84 (median 0.57, IQR 0.47-0.71), while the between-day (0-7 d) ICCs ranged from 0.25 to 0.83 (median 0.66, IQR 0.55-0.72). In healthy children, the within-day ICCs for e-nose sensors ranged from 0.29 to 0.85 (median 0.58, IQR 0.49-0.63), while the between-day ICCs ranged from 0.33 to 0.82 (median 0.55, IQR 0.49-0.63). In both groups, most of the within-day and between-day ICCs for e-nose sensors were statistically significant. Moreover, the within-day and between-day ICCs for all spirometry parameters and eNO were significant and similar to those of the most reliable sensors. The modern breath sampling system showed more than acceptable within-day and between-day repeatability, in both asthmatic and healthy children. The present study was registered on the central registration system ClinicalTrials.gov (ID: NCT03025061).

Electronic Noses in Medical Diagnostics

BACKGROUND

Electronic nose technology is being developed in order to analyse complex mixtures of volatiles in a way parallel to biologic olfaction. When applied in the field of medicine, the use of such devices should enable the identification and discrimination between different diseases. In this review, a comprehensive summary of research in medical diagnostics using electronic noses is presented. A special attention has been paid to the application of these devices and sensor technologies, in response to current trends in medicine.

METHODS

Peer-reviewed research literature pertaining to the subject matter was identified based on a search of bibliographic databases. The quality and relevance of retrieved papers was assessed using standard tools. Their content was critically reviewed and certain information contained therein was compiled in tabularized form.

RESULTS

The majority of reviewed studies show promising results, often surpassing the accuracy and sensitivity of established diagnostic methods. However, only a relatively small number of devices have been field tested. The methods used for sample collection and data processing in various studies were listed in a table, together with electronic nose models used in these investigations.

CONCLUSION

Despite the fact that devices equipped with arrays of chemical sensors are not routinely used in everyday medical practice, their prospective use would solve some established issues in medical diagnostics, as well as lead to developments in prophylactics by facilitating a widespread use of non-invasive screening tests.

Use of Electronic Noses for Diagnosis of Digestive and Respiratory Diseases through the Breath

The increased occurrence of chronic diseases related to lifestyle or environmental conditions may have a detrimental effect on long-term health if not diagnosed and controlled in time. For this reason, it is important to develop new noninvasive early diagnosis equipment that allows improvement of the current diagnostic methods. This, in turn, has led to an exponential development of technology applied to the medical sector, such as the electronic nose. In addition, the appearance of this type of technology has allowed the possibility of studying diseases from another point of view, such as through breath analysis. This paper presents a bibliographic review of past and recent studies, selecting those investigations in which a patient population was studied with electronic nose technology, in order to identify potential applications of this technology in the detection of respiratory and digestive diseases through the analysis of volatile organic compounds present in the breath.

Breath analysis in respiratory diseases: state-of-the-art and future perspectives

INTRODUCTION

The vast majority of respiratory diseases are associated with the production of volatile organic compounds (VOCs), the analysis of which might improve our knowledge about these disorders and their clinical management. The aim of this narrative review is to provide a comprehensive summary of current evidence supporting the application of breath analysis in the field of respiratory diseases, as well as suggesting potential applications available in the near future. Areas covered: A computerized literature search was performed to identify relevant articles reporting original data on the clinical use of breath analysis in respiratory diseases. Papers focusing on diseases other than respiratory, technical issues of VOC sampling and analysis, in vitro experiments or exogenous compounds were excluded. Expert commentary: Currently available evidence on the application of breath analysis in respiratory diseases is encouraging; however, it is mostly based on single-center studies without external validation. The standardization of the technique, together with multicenter clinical trials with external validation, will ensure it is ready for clinical use. Current and new applications in respiratory diseases may represent a major breakthrough in the field, so much so as to deserve further efforts in outlining the most effective way to apply VOC analysis for clinical purposes.

Evolution of clinical and environmental health applications of exhaled breath research: Review of methods and instrumentation for gas-phase, condensate, and aerosols

Human breath, along with urine and blood, has long been one of the three major biological media for assessing human health and environmental exposure. In fact, the detection of odor on human breath, as described by Hippocrates in 400 BC, is considered the first analytical health assessment tool. Although less common in comparison to contemporary bio-fluids analyses, breath has become an attractive diagnostic medium as sampling is non-invasive, unlimited in timing and volume, and does not require clinical personnel. Exhaled breath, exhaled breath condensate (EBC), and exhaled breath aerosol (EBA) are different types of breath matrices used to assess human health and disease state. Over the past 20 years, breath research has made many advances in assessing health state, overcoming many of its initial challenges related to sampling and analysis. The wide variety of sampling techniques and collection devices that have been developed for these media are discussed herein. The different types of sensors and mass spectrometry instruments currently available for breath analysis are evaluated as well as emerging breath research topics, such as cytokines, security and airport surveillance, cellular respiration, and canine olfaction.

A Study of Diagnostic Accuracy Using a Chemical Sensor Array and a Machine Learning Technique to Detect Lung Cancer

Lung cancer is the leading cause of cancer death around the world, and lung cancer screening remains challenging. This study aimed to develop a breath test for the detection of lung cancer using a chemical sensor array and a machine learning technique. We conducted a prospective study to enroll lung cancer cases and non-tumour controls between 2016 and 2018 and analysed alveolar air samples using carbon nanotube sensor arrays. A total of 117 cases and 199 controls were enrolled in the study of which 72 subjects were excluded due to having cancer at another site, benign lung tumours, metastatic lung cancer, carcinoma in situ, minimally invasive adenocarcinoma, received chemotherapy or other diseases. Subjects enrolled in 2016 and 2017 were used for the model derivation and internal validation. The model was externally validated in subjects recruited in 2018. The diagnostic accuracy was assessed using the pathological reports as the reference standard. In the external validation, the areas under the receiver operating characteristic curve (AUCs) were 0.91 (95% CI = 0.79–1.00) by linear discriminant analysis and 0.90 (95% CI = 0.80–0.99) by the supportive vector machine technique. The combination of the sensor array technique and machine learning can detect lung cancer with high accuracy.

Cluster analysis on breath print of newly diagnosed COPD patients: effects of therapy

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a highly heterogeneous disease and airflow limitation and symptoms only partially capture such heterogeneity. Since COPD is known to affect the production of volatile organic compounds (VOCs), we aimed to verify to which extent exhaled VOCs can characterize newly diagnosed COPD patients and changes in response to inhaled therapy.

MATERIALS AND METHODS

Fifty newly diagnosed COPD patients were consecutively recruited among those attending the pulmonary medicine outpatient clinic at 'Campus Bio-Medico' University Hospital. VOCs were collected using the Pneumopipe^® and analysed by the BIONOTE electronic nose both at baseline and after 12 weeks of inhaled therapy. Patients were grouped using K-mean cluster analysis on BIONOTE responses and the obtained clusters were compared via non-parametric tests.

RESULTS

We identified three clusters of subjects: (a) without remarkable comorbidities; (b) with air trapping and higher BODE index score (mean 1.2); (c) without air trapping and with a lower BODE index. Inhaled bronchodilators caused a quantitative reduction in the amound of VOCs, while inhaled steroids provided a qualitative modification of the breath profile.

CONCLUSION

VOCs patterns categorize newly diagnosed COPD subjects. VOCs production declines after bronchodilators administration and changes in quality after topic steroid treatment.

Breathomics for Assessing the Effects of Treatment and Withdrawal With Inhaled Beclomethasone/Formoterol in Patients With COPD

Background: Prospective pharmacological studies on breathomics profiles in COPD patients have not been previously reported. We assessed the effects of treatment and withdrawal of an extrafine inhaled corticosteroid (ICS)-long-acting β₂-agonist (LABA) fixed dose combination (FDC) using a multidimensional classification model including breathomics.

Methods: A pilot, proof-of-concept, pharmacological study was undertaken in 14 COPD patients on maintenance treatment with inhaled fluticasone propionate/salmeterol (500/50 μg b.i.d.) for at least 8 weeks (visit 1). Patients received 2-week treatment with inhaled beclomethasone dipropionate/formoterol (100/6 μg b.i.d.) (visit 2), 4-week treatment with formoterol alone (6 μg b.i.d.) (visit 3), and 4-week treatment with beclomethasone/formoterol (100/6 μg b.i.d.) (visit 4). Exhaled breath analysis with two e-noses, based on different technologies, and exhaled breath condensate (EBC) NMR-based metabolomics were performed. Sputum cell counts, sputum supernatant and EBC prostaglandin E₂ (PGE₂) and 15-F_2t-isoprostane, fraction of exhaled nitric oxide, and spirometry were measured.

Results: Compared with formoterol alone, EBC acetate and sputum PGE₂, reflecting airway inflammation, were reduced after 4-week beclomethasone/formoterol. Three independent breathomics techniques showed that extrafine beclomethasone/formoterol short-term treatment was associated with different breathprints compared with regular fluticasone propionate/salmeterol. Either ICS/LABA FDC vs. formoterol alone was associated with increased pre-bronchodilator FEF_25−75% and FEV₁/FVC (P = 0.008–0.029). The multidimensional model distinguished fluticasone propionate/salmeterol vs. beclomethasone/formoterol, fluticasone propionate/salmeterol vs. formoterol, and formoterol vs. beclomethasone/formoterol (accuracy > 70%, P < 0.01).

Conclusions: Breathomics could be used for assessing ICS treatment and withdrawal in COPD patients. Large, controlled, prospective pharmacological trials are required to clarify the biological implications of breathomics changes. EUDRACT number: 2012-001749-42.

Cutting Edge Methods for Non-Invasive Disease Diagnosis Using E-Tongue and E-Nose Devices

Biomimetic cross-reactive sensor arrays (B-CRSAs) have been used to detect and diagnose a wide variety of diseases including metabolic disorders, mental health diseases, and cancer by analyzing both vapor and liquid patient samples. Technological advancements over the past decade have made these systems selective, sensitive, and affordable. To date, devices for non-invasive and accurate disease diagnosis have seen rapid improvement, suggesting a feasible alternative to current standards for medical diagnostics. This review provides an overview of the most recent B-CRSAs for diagnostics (also referred to electronic noses and tongues in the literature) and an outlook for future technological development.

Diagnostic accuracy of breath tests for pneumoconiosis using an electronic nose

Breath analyses have attracted substantial attention as screens for occupational environmental lung disease. The objective of this study was to develop breath tests for pneumoconiosis by analysing volatile organic compounds using an electronic nose. A case-control study was designed. We screened 102 subjects from a cohort of stone workers. After excluding three subjects with poorly controlled diabetes mellitus and one subject with asthma, 98 subjects were enrolled, including 34 subjects with pneumoconiosis and 64 healthy controls. We analysed the subjects' breath using an electronic nose with 32 nanocomposite sensors. Data were randomly split into 80% for model building and 20% for validation. Using a linear discriminate analysis, the sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUROC) were 67.9%, 88.0%, 80.8%, and 0.91, respectively, in the training set and 66.7%, 71.4%, 70.0%, and 0.86, respectively, in the test set. In subgroup analysis divided by smoking status, the AUROCs for current smokers, former smokers, and subjects who never smoked were 0.94, 0.93, and 0.99, respectively. In subgroup analysis divided by gender, the AUROCs for males and females were 0.95 and 0.99, respectively. Breath tests may have potential as a screen for pneumoconiosis. A multi-centre study is warranted, and the procedures must be standardized before clinical application.

Applied upper-airway resistance instantly affects breath components: a unique insight into pulmonary medicine

Respiratory parameters such as flow or rate have complex effects on the exhalation of volatile substances and can hamper clinical interpretation of breath biomarkers. We have investigated the effects of progressively applied upper-airway resistances on the exhalation of volatile organic compounds (VOCs) in healthy humans. We performed real-time mass-spectrometric determination of breath volatiles in 50 subjects with parallel, non-invasive hemodynamic monitoring, breath-resolved spirometry and capnometry during controlled tidal breathing (12 breaths/min). Airway resistance was increased by changing the mouthpiece diameters from 2.5 cm to 1.0 cm and to 0.5 cm. At the smallest diameter, oxygen uptake increased (35%↑). Cardiac output decreased (6%↓) but end-tidal PCO₂ (8%↑) and exhalation of blood-borne isoprene (19%↑) increased. Carbon dioxide production remained constant. Furan, hydrogen sulphide mirrored isoprene. Despite lowered minute ventilation (4%↓) acetone concentrations decreased (3%↓). Exogenous acetonitrile, propionic acid, isopropanol, limonene mimicked acetone. VOC concentration changes could be modelled through substance volatility. Airway resistance-induced changes in hemodynamics, and ventilation can affect VOC exhalation and thereby interfere with breath biomarker interpretation. The effects of collateral ventilation, intra-alveolar pressure gradients and respiratory mechanics had to be considered to explain the exhalation kinetics of CO₂ and VOCs. Conventional breath sampling via smaller mouthpiece diameters (≤1.0 cm, e.g. via straw in Tedlar bags or canisters, etc) will immediately affect VOC exhalation and thereby mislead the analysis of the obtained results. Endogenous isoprene may probe respiratory muscle workload under obstructive conditions. Breath-gas analysis might enhance our understanding of diagnosis and management of obstructive lung diseases in the future.

Unhealthy smokers: scopes for prophylactic intervention and clinical treatment

BACKGROUND

Globally, tobacco use causes approximately 6 million deaths per year, and predictions report that with current trends; more than 8 million deaths are expected annually by 2030. Cigarette smokings is currently accountable for more than 480,000 deaths each year in United States (US) and is the leading cause of preventable death in the US. On average, smokers die 10 years earlier than nonsmokers and if smoking continues at its current proportion among adolescents, one in every 13 Americans aged 17 years or younger is expected to die prematurely from a smoking-related illness. Even though there has been a marginal smoking decline of around 5% in recent years (2005 vs 2015), smokers still account for 15% of the US adult population. What is also concerning is that 41,000 out of 480,000 deaths results from secondhand smoke (SHS) exposure. Herein, we provide a detailed review of health complications and major pathological mechanisms including mutation, inflammation, oxidative stress, and hemodynamic and plasma protein changes associated with chronic smoking. Further, we discuss prophylactic interventions and associated benefits and provide a rationale for the scope of clinical treatment.

CONCLUSIONS

Considering these premises, it is evident that much detailed translational and clinical studies are needed. Factors such as the length of smoking cessation for ex-smokers, the level of smoke exposure in case of SHS, pre-established health conditions, genetics (and epigenetics modification caused by chronic smoking) are few of the criteria that need to be evaluated to begin assessing the prophylactic and/or therapeutic impact of treatments aimed at chronic and former smokers (especially early stage ex-smokers) including those frequently subjected to second hand tobacco smoke exposure. Herein, we provide a detailed review of health complications and major pathological mechanisms including mutation, inflammation, oxidative stress, and hemodynamic and plasma protein changes associated with chronic smoking. Further, we discuss about prophylactic interventions and associated benefits and provide a rationale and scope for clinical treatment.

Lung Cancer Screening Based on Type-different Sensor Arrays

In recent years, electronic nose (e-nose) systems have become a focus method for diagnosing pulmonary diseases such as lung cancer. However, principles and patterns of sensor responses in traditional e-nose systems are relatively homogeneous. Less study has been focused on type-different sensor arrays. In this paper, we designed a miniature e-nose system using 14 gas sensors of four types and its subsequent analysis of 52 breath samples. To investigate the performance of this system in identifying and distinguishing lung cancer from other respiratory diseases and healthy controls, five feature extraction algorithms and two classifiers were adopted. Lastly, the influence of type-different sensors on the identification ability of e-nose systems was analyzed. Results indicate that when using the LDA fuzzy 5-NN classification method, the sensitivity, specificity and accuracy of discriminating lung cancer patients from healthy controls with e-nose systems are 91.58%, 91.72% and 91.59%, respectively. Our findings also suggest that type-different sensors could significantly increase the diagnostic accuracy of e-nose systems. These results showed e-nose system proposed in this study was potentially practicable in lung cancer screening with a favorable performance. In addition, it is important for type-different sensors to be considered when developing e-nose systems.

MMP-8, MMP-9 and Neutrophil Elastase in Peripheral Blood and Exhaled Breath Condensate in COPD

Chronic obstructive pulmonary disease (COPD) is characterised by progressive and irreversible airflow limitation associated with chronic inflammation involving cytokines and metalloproteinases (MMPs). MMP-8, MMP-9 and neutrophil elastase (NE) are known to be implicated in COPD but the factors influencing activation and suppression remain unclear. This study aimed to compare MMP-8, MMP-9 and NE in the peripheral blood of COPD patients and controls and to likewise assess exhaled breath condensate (EBC) for these MMPs. Peripheral blood micro(mi)RNA139-5p levels, which may regulate MMPs in COPD, were also measured. Blood and EBC were collected from COPD patients (stable and during exacerbations) and healthy controls. Expression of mRNA for MMP-8, MMP-9, NE and miRNA-139-5p expression in peripheral blood mononuclear cells (PBMCs) was measured using qRT-PCR. MMP-8, MMP-9 and NE protein in plasma as well as MMP-8 and MMP-9 protein in EBC were analysed by enzyme-linked immunoassays. PBMCs from COPD patients showed greater expression of mRNA for MMP-8 (p = 0.0004), MMP-9 (p = 0.0023) and NE (p = 0.0019). PBMC expression of mRNA for NE was significantly higher in COPD exacerbations compared to stable cases (p < 0.05). Expression of mRNA for MMP-9 and NE correlated negatively with spirometry in patients (p < 0.05). Plasma from COPD patients showed greater levels of protein for MMP-8 (p = 0.003), MMP-9 (p = 0.046) and NE (p = 0.018). MMP-8 protein levels were lower in the EBC of COPD patients (p < 0.0001). In PBMCs, enhanced expression of mRNA for MMP-9 and NE is associated with COPD and may correlate with disease severity and exacerbations.

Using a chemiresistor-based alkane sensor to distinguish exhaled breaths of lung cancer patients from subjects with no lung cancer

BACKGROUND

Breath alkanes are reported to be able to discriminate lung cancer patients from healthy people. A simple chemiresistor-based sensor was designed to respond to alkanes by a change in resistance measured by a digital multimeter connected to the sensor. In preclinical experiments, the sensor response was found to have a strong positive linear relationship with alkane compounds and not responsive to water. This study aimed to determine the ability of the alkane sensor to distinguish the exhaled breaths of lung cancer patients from that of chronic obstructive pulmonary disease (COPD) patients and control subjects without lung cancer.

METHODS

In this cross-sectional study, 12 treatment-naive patients with lung cancer, 12 ex- or current smokers with COPD and 13 never-smokers without lung disease were asked to exhale through a drinking straw into a prototype breath-in apparatus made from an empty 125 mL Vitagen^® bottle with the chemiresistor sensor attached at its inside bottom to measure the sensor peak output (percentage change of baseline resistance measured before exhalation to peak resistance) and the time taken for the baseline resistance to reach peak resistance.

RESULTS

Analysis of multivariate variance and post-hoc Tukey test revealed that the peak output and the time to peak values for the lung cancer patients were statistically different from that for both the COPD patients and the controls without lung disease, Pillai's Trace =0.393, F=3.909, df = (4, 64), P=0.007. A 2.20% sensor peak output and a 90-s time to peak gave 83.3% sensitivity and 88% specificity in diagnosing lung cancer. Tobacco smoking did not affect the diagnostic accuracy of the sensor.

CONCLUSIONS

The alkane sensor could discriminate patients with lung cancer from COPD patients and people without lung disease. Its potential utility as a simple, cheap and non-invasive test for early lung cancer detection needs further studies.

Artificial Nose Technology: Status and Prospects in Diagnostics

Biomimetic crossreactive sensor arrays have been used to detect and analyze a wide variety of vapor and liquid components in applications such as food science, public health and safety, and diagnostics. As technology has advanced over the past three decades, these systems have become selective, sensitive, and affordable. Currently, the need for noninvasive and accurate devices for early disease diagnosis remains a challenge. This Opinion article provides an overview of the various types of biomimetic crossreactive sensor arrays (also referred to as electronic noses or tongues in the literature), their current use and future directions, and an outlook for future technological development.

Breathomics in the setting of asthma and chronic obstructive pulmonary disease

Exhaled breath contains thousands of volatile organic compounds that reflect the metabolic process occurring in the host both locally in the airways and systemically. They also arise from the environment and airway microbiome. Comprehensive analysis of breath volatile organic compounds (breathomics) provides opportunities for noninvasive biomarker discovery and novel mechanistic insights. Applications in patients with obstructive lung diseases, such as asthma and chronic obstructive pulmonary disease, include not only diagnostics (especially in children and other challenging diagnostic areas) but also identification of clinical treatable traits, such as airway eosinophilia and risk of infection/exacerbation, that are not specific to diagnostic labels. Although many aspects of breath sampling and analysis are challenging, proof-of-concept studies with mass spectrometry and electronic nose technologies have provided independent studies with moderate-to-good diagnostic and phenotypic accuracies. The present review evaluates the data obtained by using breathomics in (1) predicting the inception of asthma or chronic obstructive pulmonary disease, (2) inflammatory phenotyping, (3) exacerbation prediction, and (4) treatment stratification. The current findings merit the current efforts of large multicenter studies using standardized sampling, shared analytic methods, and databases, including external validation cohorts. This will position this noninvasive technology in the clinical assessment and monitoring of chronic airways diseases.

eNose technology can detect and classify human pathogenic molds in vitro: a proof-of-concept study of Aspergillus fumigatus and Rhizopus oryzae

Invasive pulmonary mold disease (IPMD) is often fatal in neutropenic patients. This is because IPMD is difficult to diagnose timely, especially when non-Aspergillus molds are the causative agent, as they are usually not associated with a positive galactomannan assay. In 2013 we showed that exhaled breath analysis might be used to diagnose invasive aspergillosis through profiling of patterns in exhaled volatile organic compounds (VOCs) by electronic nose (eNose) technology. The current study aimed to determine (1) whether molds can be discriminated from other microorganisms (using two mold species: Aspergillus fumigatus and a pathogenic mold not associated with a positive galactomannan assay, i.c. Rhizopus oryzae) and (2) whether both molds can be discriminated from each other. First, we cultured strains of Streptococcus pneumoniae, Escherichia coli, Pseudomonas aeruginosa, Candida albicans, A. fumigatus and R. oryzae in separate airtight bottles. We examined whether an eNose (Cyranose 320) could discriminate the headspaces of bottles with molds from those with bacteria/yeasts. Second, we examined whether an eNose could discriminate A. fumigatus and R. oryzae. Diagnostic algorithms were created using canonical discriminant analysis after principle component analysis. Primary outcome parameter was the validated accuracy. The eNose discriminated A. fumigatus from bacteria/yeasts with a cross-validated accuracy of 92.9% (sensitivity 95.2%, specificity 91.9%). The eNose had an accuracy (validated using split-half analysis) of 100% in discriminating A. fumigatus from R. oryzae. Our study suggests that an eNose can identify and classify molds in vitro. This warrants prospective in vivo studies aimed at detecting and classifying IPMD using exhaled breath.

Volatile organic compounds in asthma diagnosis: a systematic review and meta-analysis

We aimed to assess the value and classification rate of exhaled volatile organic compounds (VOCs) in asthma diagnosis. A PRISMA-oriented systematic search for published studies regarding exhaled VOCs in asthma diagnosis was conducted based on predefined criteria. Studies presenting sensitivity and specificity values for the test were included in the meta-analysis. Pooled diagnosis odds ratios (DOR), area under the curve (AUC) and positive and negative likelihood ratios (LR) for exhaled VOC profiles were calculated; and publication bias, threshold effect and heterogeneity were estimated. Eighteen studies were selected for the qualitative analysis and six met the criteria for inclusion in the quantitative analysis. Mean (95% CI) pooled DOR, positive and negative LR were 49.3 (15.9-153.3), 5.86 (3.07-11.21) and 0.16 (0.10-0.26), respectively. The AUC value was 0.94. Only three of the 18 reviewed studies performed an external validation of the model using a different data set. The results from the revised studies suggest that exhaled VOCs are promising biomarkers for asthma diagnosis and that several compounds, mainly alkanes, may be significantly associated with asthma inflammation. However, there are still various constraints associated with standardization and externally validated studies are needed to introduce exhaled VOC profiling in a clinical scenario.

Metabolomics analysis identifies novel plasma biomarkers of cystic fibrosis pulmonary exacerbation

BACKGROUND

Cystic fibrosis (CF) lung disease is characterized by infection, inflammation, lung function decline, and intermittent pulmonary exacerbations. However, the link between pulmonary exacerbation and lung disease progression remains unclear. Global metabolomic profiling can provide novel mechanistic insight into a disease process in addition to putative biomarkers for future study. Our objective was to investigate how the plasma metabolomic profile changes between CF pulmonary exacerbation and a clinically well state.

METHODS

Plasma samples and lung function data were collected from 25 CF patients during hospitalization for a pulmonary exacerbation and during quarterly outpatient clinic visits. In collaboration with Metabolon, Inc., the metabolomic profiles of matched pair plasma samples, one during exacerbation and one at a clinic visit, were analyzed using gas and liquid chromatography coupled with mass spectrometry. Compounds were identified by comparison to a library of standards. Mixed effects models that controlled for nutritional status and lung function were used to test for differences and principal components analysis was performed.

RESULTS

Our population had a median age of 27 years (14-39) and had a median FEV1 % predicted of 65% (23-105%). 398 total metabolites were identified and after adjustment for confounders, five metabolites signifying perturbations in nucleotide (hypoxanthine), nucleoside (N4-acetylcytidine), amino acid (N-acetylmethionine), carbohydrate (mannose), and steroid (cortisol) metabolism were identified. Principal components analysis provided good separation between the two clinical phenotypes.

CONCLUSIONS

Our findings provide putative metabolite biomarkers for future study and allow for hypothesis generation about the pathophysiology of CF pulmonary exacerbation.

The risk and outcomes of pneumonia in patients on inhaled corticosteroids

Corticosteroids are frequently prescribed anti-inflammatory medications. Inhaled corticosteroids (ICS) are indicated for Chronic Obstructive Pulmonary Disease (COPD) and asthma. ICS are associated with a decrease in exacerbations and improved quality of life in COPD, however multiple studies have linked the chronic use of ICSs with an increased risk of developing pneumonia, though the effect on mortality is unclear. We review the association of ICS with the risk of pneumonia and the implications on clinical outcomes.

Exhaled breath analysis by electronic nose in respiratory diseases

Breath analysis via electronic nose is a technique oriented around volatile organic compound (VOC) profiling in exhaled breath for diagnostic and prognostic purposes. This approach, when supported by methodologies for VOC identification, has been often referred to as metabolomics or breathomics. Although breath analysis may have a substantial impact on clinical practice, as it may allow early diagnosis and large-scale screening strategies while being noninvasive and inexpensive, some technical and methodological limitations must be solved, together with crucial interpretative issues. By integrating a review of the currently available literature with more speculative arguments about the potential interpretation and application of VOC analysis, the authors aim to provide an overview of the main relevant aspects of this promising field of research.

Protein profile of exhaled breath condensate determined by high resolution mass spectrometry

A method based on liquid chromatography/high resolution tandem mass spectrometry coupled with electrophoretic separation, for determination and relative quantification of the protein composition of exhaled breath condensate (EBC), was developed. Application of the procedure to a sample of EBC, pooled from nine healthy subjects, resulted in the identification of 167 unique gene products, 113 of which not previously reported in EBC samples. The abundance of the protein identified was estimated by means of the exponentially modified protein abundance index protocol (emPAI). Cytokeratins were by far the most abundant proteins in EBC samples. Many of the identified proteins were associated with multiple cellular location with cytoplasm constituting the largest group. Cytosol, nucleus, membrane, cytoskeleton and extracellular were other abundantly represented locations. No amylase was detected, suggesting the absence of saliva protein contamination. The profile obtained represents the most comprehensive protein characterization of EBC so far reported and demonstrates that this approach provides a powerful tool for investigating the protein profile of EBC samples. Compared with analogous investigations, this study also shows that the protein profile of EBC is strongly affected by the sampling method adopted.

Developmental regulation of antioxidant enzymes and their impact on neonatal lung disease

SIGNIFICANCE

Deficient antioxidant defenses and compromised ability to respond to oxidative stress burden the immature lung. Routine neonatal therapies can cause increased oxidative stress with subsequent injury to the premature lung. Novel therapeutic approaches to protect the premature lung are greatly needed.

RECENT ADVANCES

Live cell imaging with targeted redox probes allows for the measurement of subcellular oxidative stress and for comparisons of oxidative stress across development. Comprehension of subcellular and cell-type-specific responses to oxidative stress may influence the targeting of future antioxidant therapies.

CRITICAL ISSUES

Challenges remain in identifying the optimal cellular targets, degree of enzyme activity, and appropriate antioxidant therapy. Further, the efficacy of delivering exogenous antioxidants to specific cell types or subcellular compartments remains under investigation. Treatment with a nonselective antioxidant could unintentionally compromise cellular function or impact cellular defense mechanisms and homeostasis.

FUTURE DIRECTIONS

Genetic and/or biomarker screening may identify infants at the greatest risk for oxidative lung injury and guide the use of more selective antioxidant therapies. Novel approaches to the delivery of antioxidant enzymes may allow cell type- or cellular organelle-specific therapy. Improved comprehension of the antioxidant enzyme regulation across cell type, cell compartment, gender, and developmental stage is critical to the design and optimization of therapy.

Markers of inflammation in the breath in paediatric inflammatory bowel disease

OBJECTIVES

Breath analysis and exhaled breath condensate (EBC) collection are simple and noninvasive processes whereby inflammatory mediators and other biomarkers can be assessed in diseases that affect the lung. It was hypothesised that markers of epithelial dysfunction and secretion, such as a low pH, 8-isoprostane, and release of epithelial factors such as trefoil factor 2 (TFF2) and mucin, would be elevated in the breath of those with inflammatory bowel disease (IBD). The aim was to compare the levels of these biomarkers in EBC and the fraction of expired nitric oxide (FENO) in children with Crohn disease (CD), in those with asthma, and in normal individuals in a pilot study.

METHODS

EBC was collected from patients in the 3 groups mentioned above in a cross-sectional design. pH, 8-isoprostane, TFF2, and mucin levels were measured in the EBC. Spirometry was performed in asthmatic patients and patients with IBD, whereas FENO and skin prick tests were performed in patients with IBD.

RESULTS

Breath samples including EBC were collected from 80 patients (30 CD, 30 asthma, 20 controls). Compared with controls, EBC pH was lower in children with IBD (P < 0.0001) or asthma (P = 0.0041). 8-Isoprostane levels differed between the 3 groups (P < 0.05). EBC TFF2 was mainly less than the limit of detection, whereas mucin levels did not differ significantly between the 3 groups. FENO was measurable in children with IBD, but did not correlate with disease activity or serum markers of inflammation.

CONCLUSIONS

A lower EBC pH may reflect inflammatory events either in the lung or systemically. 8-Isoprostane, FENO, and mucin were detected for the first time in the EBC of children with IBD. Further studies are required to assess the value of these assessments.

An efficient approach for preprocessing data from a large-scale chemical sensor array

In this paper, an artificial olfactory system (Electronic Nose) that mimics the biological olfactory system is introduced. The device consists of a Large-Scale Chemical Sensor Array (16, 384 sensors, made of 24 different kinds of conducting polymer materials) that supplies data to software modules, which perform advanced data processing. In particular, the paper concentrates on the software components consisting, at first, of a crucial step that normalizes the heterogeneous sensor data and reduces their inherent noise. Cleaned data are then supplied as input to a data reduction procedure that extracts the most informative and discriminant directions in order to get an efficient representation in a lower dimensional space where it is possible to more easily find a robust mapping between the observed outputs and the characteristics of the odors in input to the device. Experimental qualitative proofs of the validity of the procedure are given by analyzing data acquired for two different pure analytes and their binary mixtures. Moreover, a classification task is performed in order to explore the possibility of automatically recognizing pure compounds and to predict binary mixture concentrations.

Clinical and biological markers of asthma control

Asthma has substantial impact on the patient, their family and health systems, and its control has gained increasing attention. Perception of asthma control has varied widely among patients as well as healthcare providers. Several clinical markers have been developed to define and standardize the measurement of asthma control. They are based primarily on patients' symptoms and have been popular in clinical practice and in clinical studies. With the advances in basic research on the pathology of asthma, several biological markers have evolved that have the advantages of being objective, quantitative and more reflective of the underlying pathology, which makes them a better guide for selecting optimal therapy. In addition to the cost and expertise required, biological makers are influenced by multiple factors that limit their application in clinical practice. Ongoing research is expected to define the role of individual biological markers, the optimal method of their application, and their appropriate interpretation.

Detection of bloodstream infections and prediction of bronchopulmonary dysplasia in preterm neonates with an electronic nose

We show that smellprints of volatile organic components measured with an electronic nose (Cyranose 320; Smiths Detection Group Ltd, Watford, United Kingdom) differ between tracheal aspirates from preterm neonates with or without laboratory-confirmed bloodstream infections and with or without subsequent development of bronchopulmonary dysplasia. Tracheal aspirate smellprints could be useful noninvasive diagnostic markers for preterm neonates.

Multimarker screening of oxidative stress in aging

Aging is a complex process of organism decline in physiological functions. There is no clear theory explaining this phenomenon, but the most accepted one is the oxidative stress theory of aging. Biomarkers of oxidative stress, substances, which are formed during oxidative damage of phospholipids, proteins, and nucleic acids, are present in body fluids of diseased people as well as the healthy ones (in a physiological concentration). 8-iso prostaglandin F_2α is the most prominent biomarker of phospholipid oxidative damage, o-tyrosine, 3-chlorotyrosine, and 3-nitrotyrosine are biomarkers of protein oxidative damage, and 8-hydroxy-2^′-deoxyguanosine and 8-hydroxyguanosine are biomarkers of oxidative damage of nucleic acids. It is thought that the concentration of biomarkers increases as the age of people increases. However, the concentration of biomarkers in body fluids is very low and, therefore, it is necessary to use a sensitive analytical method. A combination of HPLC and MS was chosen to determine biomarker concentration in three groups of healthy people of a different age (twenty, forty, and sixty years) in order to find a difference among the groups.

Parameters of lung inflammation in asthmatic as compared to healthy children in a contaminated city

Background

The impact of air pollution on the respiratory system has been estimated on the basis of respiratory symptoms and lung function. However; few studies have compared lung inflammation in healthy and asthmatics children exposed to high levels of air pollution. The aim of the study was to elucidate the modulatory effect of air pollution on Cysteinyl-leukotrienes (Cys-LTs) levels in exhaled breath condensate (EBC) among healthy and asthmatic children.

Methods

We performed a cross-sectional comparative study. Children between 7–12 years of age, asthmatics and non-asthmatics, residents of a city with high levels of PM₁₀ were included. In all cases, forced spirometry, Cys-LTs levels in EBC, and the International Study of Asthma and Allergies in Childhood questionnaire were evaluated. We also obtained average of PM₁₀, CO, SO₂ and O₃ levels during the period of the study by the State Institute of Ecology.

Results

We studied 103 children (51 asthmatics and 52 non-asthmatics). Cys-LTs levels were higher in asthmatics than in non-asthmatics (77.3 ± 21.6 versus 60.3 ± 26.8 pg/ml; p = 0.0005). Also, Cys-LTs levels in children with intermittent asthma were lower than in children with persistent asthma (60.4 ± 20.4 versus 84.7 ± 19.2 pg/ml; p = 0.0001). In the multiple regression model, factors associated with levels of Cys-LTs were passive smoking (β = 13.1, p 0.04) and to be asthmatic (β = 11.5, p 0.03).

Conclusions

Cys-LTs levels are higher in asthmatic children than in healthy children in a contaminated city and its levels are also associated with passive smoking.

The thioredoxin reductase-1 inhibitor aurothioglucose attenuates lung injury and improves survival in a murine model of acute respiratory distress syndrome

AIMS

Inflammation and oxygen toxicity increase free radical production and contribute to the development of acute respiratory distress syndrome (ARDS), which is a significant cause of morbidity and mortality in intensive care patients. We have previously reported increased glutathione (GSH) levels in lung epithelial cells in vitro and attenuated adult murine hyperoxic lung injury in vivo after pharmacological thioredoxin reductase-1 (TrxR1) inhibition. Using a murine ARDS model, we tested the hypothesis that aurothioglucose (ATG) treatment increases pulmonary GSH levels, attenuates lung injury, and decreases mortality in a GSH-dependent manner.

RESULTS

Adult mice received a single intratracheal dose of 0.375 μg/g lipopolysaccharide (LPS) 12 h before a single intraperitoneal injection of 25 mg/kg ATG. Control mice received intratracheal and/or intraperitoneal saline. Mice were then exposed to room air or hyperoxia (>95% O2). Lung injury was assessed by bronchoalveolar lavage protein concentrations. Expression of glutamate-cysteine ligase modifier subunit (GCLM), GSH, cytokines, and chemokines was determined. Exposure to LPS/hyperoxia induced inflammation and lung injury. ATG treatment significantly attenuated lung injury, increased lung GCLM expression and GSH levels, and decreased mortality. GSH depletion completely prevented the protective effects of ATG in LPS/hyperoxia-exposed mice.

INNOVATION

ATG treatment significantly attenuates lung injury and enhances survival in a clinically relevant murine model of ARDS. The protective effects of ATG are GSH dependent.

CONCLUSION

Augmentation of GSH systems by TrxR1 inhibition could represent a promising therapeutic approach to attenuate oxidant-mediated lung injury and improve patient outcomes.

Predicting steroid responsiveness in patients with asthma using exhaled breath profiling

BACKGROUND

Exhaled breath contains disease-dependent volatile organic compounds (VOCs), which may serve as biomarkers distinguishing clinical phenotypes in asthma. Their measurement may be particularly beneficial in relation to treatment response.

OBJECTIVE

Our aim was to compare the performance of electronic nose (eNose) breath analysis with previously investigated techniques (sputum eosinophils, exhaled nitric oxide (FeNO) and airway hyperresponsiveness) to discriminate asthma from controls and identify steroid responsiveness in steroid-free patients. Trial registration ACTRN12613000038796.

METHODS

Twenty-five patients with mild/moderate asthma had their inhaled steroid treatment discontinued until loss of control or 28 days. They were subsequently treated with oral prednisone 30 mg/day for 14 days. Steroid responsiveness was defined as an increase of either > 12% FEV1 or > 2 doubling doses PC20 AMP. Steroid-free assessment of sputum eosinophils, FeNO and exhaled breath VOCs were used to construct algorithms predicting steroid responsiveness. Performance characteristics were compared by ROC analysis.

RESULTS

The eNose discriminated between asthma and controls (area under the curve = 0.766 ± 0.14; P = 0.002) with similar accuracy to FeNO (0.862 ± 0.12; P < 0.001) and sputum eosinophils (0.814 ± 0.15; P < 0.001). Steroid responsiveness was predicted with greater accuracy by VOC-analysis (AUC = 0.883 ± 0.16; P = 0.008) than FeNO (0.545 ± 0.28; P = 0.751) or sputum eosinophils (0.610 ± 0.29; P = 0.441).

CONCLUSIONS AND CLINICAL RELEVANCE

Breath analysis by eNose can identify asthmatic patients and may be used to predict their response to steroids with greater accuracy than sputum eosinophils or FeNO. This implies a potential role for breath analysis in the tailoring of treatment for asthma patients.

Review article: next generation diagnostic modalities in gastroenterology--gas phase volatile compound biomarker detection

BACKGROUND

The detection of airborne gas phase biomarkers that emanate from biological samples like urine, breath and faeces may herald a new age of non-invasive diagnostics. These biomarkers may reflect status in health and disease and can be detected by humans and other animals, to some extent, but far more consistently with instruments. The continued advancement in micro and nanotechnology has produced a range of compact and sophisticated gas analysis sensors and sensor systems, focussed primarily towards environmental and security applications. These instruments are now increasingly adapted for use in clinical testing and with the discovery of new gas volatile compound biomarkers, lead naturally to a new era of non-invasive diagnostics.

AIM

To review current sensor instruments like the electronic nose (e-nose) and ion mobility spectroscopy (IMS), existing technology like gas chromatography-mass spectroscopy (GC-MS) and their application in the detection of gas phase volatile compound biomarkers in medicine - focussing on gastroenterology.

METHODS

A systematic search on Medline and Pubmed databases was performed to identify articles relevant to gas and volatile organic compounds.

RESULTS

E-nose and IMS instruments achieve sensitivities and specificities ranging from 75 to 92% in differentiating between inflammatory bowel disease, bile acid diarrhoea and colon cancer from controls. For pulmonary disease, the sensitivities and specificities exceed 90% in differentiating between pulmonary malignancy, pneumonia and obstructive airways disease. These sensitivity levels also hold true for diabetes (92%) and bladder cancer (90%) when GC-MS is combined with an e-nose.

CONCLUSIONS

The accurate reproducible sensing of volatile organic compounds (VOCs) using portable near-patient devices is a goal within reach for today's clinicians.

The suitability of EBC-Pb as a new biomarker to assess occupational exposure to lead

Occupational exposure to lead (Pb) requires continuous surveillance to assure, as much as possible, safe and healthful working conditions. This study addresses the suitability of assessing Pb exposure in relevant workers using their exhaled breath condensate (EBC). This study enrolled workers of two different Pb processing industries characterized by moderate and high Pb exposure levels in the work environment, and a group of non-exposed individuals working in offices who served as baseline for Pb exposure. The EBC-Pb of workers reflected the Pb levels in the work environment of all three settings, although the relationship with B-Pb was not clear. The lack of correlation between EBC-Pb and B-Pb most probably indicates the time lag for Pb to enter in the two body pools. The EBC-Pb seems to reflect immediate exposure, providing a prompt signature of Pb in the environmental that may interact directly with the organ. By delivering short-term evaluation of exposure, EBC-Pb represents a clear advantage in biomonitoring and may become an interesting tool for estimating organ burden.

Solid-state gas sensors for breath analysis: a review

The analysis of volatile compounds is an efficient method to appraise information about the chemical composition of liquids and solids. This principle is applied to several practical applications, such as food analysis where many important features (e.g. freshness) can be directly inferred from the analysis of volatile compounds. The same approach can also be applied to a human body where the volatile compounds, collected from the skin, the breath or in the headspace of fluids, might contain information that could be used to diagnose several kinds of diseases. In particular, breath is widely studied and many diseases can be potentially detected from breath analysis. The most fascinating property of breath analysis is the non-invasiveness of the sample collection. Solid-state sensors are considered the natural complement to breath analysis, matching the non-invasiveness with typical sensor features such as low-cost, easiness of use, portability, and the integration with the information networks. Sensors based breath analysis is then expected to dramatically extend the diagnostic capabilities enabling the screening of large populations for the early diagnosis of pathologies. In the last years there has been an increased attention to the development of sensors specifically aimed to this purpose. These investigations involve both specific sensors designed to detect individual compounds and non-specific sensors, operated in array configurations, aimed at clustering subjects according to their health conditions. In this paper, the recent significant applications of these sensors to breath analysis are reviewed and discussed.