Diagnosis of pneumonia with an electronic nose: correlation of vapor signature with chest computed tomography scan findings

Exhaled breath profiles to detect lung infection with Staphylococcus aureus in children with cystic fibrosis

BACKGROUND

An electronic nose (eNose) can be used to detect volatile organic compounds (VOCs). Exhaled breath contains numerous VOCs and individuals' VOCs mixtures create distinct breath profiles. Previous reports have shown that eNose can detect lung infections. Whether eNose can detect Staphylococcus aureus airway infections in breath of children with cystic fibrosis (CF) is currently unclear.

METHODS

In this cross-sectional observational study, a cloud-connected eNose was used for breath profile analysis of clinically stable paediatric CF patients with airway microbiology cultures positive or negative for CF pathogens. Data-analysis involved advanced signal processing, ambient correction and statistics based on linear discriminant and receiver operating characteristics (ROC) analyses.

RESULTS

Breath profiles from 100 children with CF (median predicted FEV1 91%) were obtained and analysed. CF patients with positive airway cultures for any CF pathogen were distinguishable from no CF pathogens (no growth or usual respiratory flora) with accuracy of 79.0% (AUC-ROC 0.791; 95% CI: 0.669-0.913) and between patients positive for Staphylococcus aureus (SA) only and no CF pathogen with accuracy of 74.0% (AUC-ROC 0.797; 95% CI: 0.698-0.896). Similar differences were seen for Pseudomonas aeruginosa (PA) infection vs no CF pathogens (78.0% accuracy, AUC-ROC 0.876, 95% CI: 0.794-0.958). SA- and PA-specific signatures were driven by different sensors in the SpiroNose suggesting pathogen-specific breath signatures.

CONCLUSIONS

Breath profiles of CF patients with SA in airway cultures are distinct from those with no infection or PA infection, suggesting the utility of eNose technology in the detection of this early CF pathogen in children with CF.

Machine Learning-Based Rapid Detection of Volatile Organic Compounds in a Graphene Electronic Nose

Rapid detection of volatile organic compounds (VOCs) is growing in importance in many sectors. Noninvasive medical diagnoses may be based upon particular combinations of VOCs in human breath; detecting VOCs emitted from environmental hazards such as fungal growth could prevent illness; and waste could be reduced through monitoring of gases produced during food storage. Electronic noses have been applied to such problems, however, a common limitation is in improving selectivity. Graphene is an adaptable material that can be functionalized with many chemical receptors. Here, we use this versatility to demonstrate selective and rapid detection of multiple VOCs at varying concentrations with graphene-based variable capacitor (varactor) arrays. Each array contains 108 sensors functionalized with 36 chemical receptors for cross-selectivity. Multiplexer data acquisition from 108 sensors is accomplished in tens of seconds. While this rapid measurement reduces the signal magnitude, classification using supervised machine learning (Bootstrap Aggregated Random Forest) shows excellent results of 98% accuracy between 5 analytes (ethanol, hexanal, methyl ethyl ketone, toluene, and octane) at 4 concentrations each. With the addition of 1-octene, an analyte highly similar in structure to octane, an accuracy of 89% is achieved. These results demonstrate the important role of the choice of analysis method, particularly in the presence of noisy data. This is an important step toward fully utilizing graphene-based sensor arrays for rapid gas sensing applications from environmental monitoring to disease detection in human breath.

Diagnostic and prognostic prediction models in ventilator-associated pneumonia: Systematic review and meta-analysis of prediction modelling studies

PURPOSE

Existing expert systems have not improved the diagnostic accuracy of ventilator-associated pneumonia (VAP). The aim of this systematic literature review was to review and summarize state-of-the-art prediction models detecting or predicting VAP from exhaled breath, patient reports and demographic and clinical characteristics.

METHODS

Both diagnostic and prognostic prediction models were searched from a representative list of multidisciplinary databases. An extensive list of validated search terms was added to the search to cover papers failing to mention predictive research in their title or abstract. Two authors independently selected studies, while three authors extracted data using predefined criteria and data extraction forms. The Prediction Model Risk of Bias Assessment Tool was used to assess both the risk of bias and the applicability of the prediction modelling studies. Technology readiness was also assessed.

RESULTS

Out of 2052 identified studies, 20 were included. Fourteen (70%) studies reported the predictive performance of diagnostic models to detect VAP from exhaled human breath with a high degree of sensitivity and a moderate specificity. In addition, the majority of them were validated on a realistic dataset. The rest of the studies reported the predictive performance of diagnostic and prognostic prediction models to detect VAP from unstructured narratives [2 (10%)] as well as baseline demographics and clinical characteristics [4 (20%)]. All studies, however, had either a high or unclear risk of bias without significant improvements in applicability.

CONCLUSIONS

The development and deployment of prediction modelling studies are limited in VAP and related outcomes. More computational, translational, and clinical research is needed to bring these tools from the bench to the bedside.

REGISTRATION

PROSPERO CRD42020180218, registered on 05-07-2020.

Breath-Based Diagnosis of Infectious Diseases: A Review of the Current Landscape

Various analytical methods can be applied to concentrate, separate, and examine trace volatile organic metabolites in the breath, with the potential for noninvasive, rapid, real-time identification of various disease processes, including an array of microbial infections. Although biomarker discovery and validation in microbial infections can be technically challenging, it is an approach that has shown great promise, especially for infections that are particularly difficult to identify with standard culture and molecular amplification-based approaches. This article discusses the current state of breath analysis for the diagnosis of infectious diseases.

The Influence of Smoking Status on Exhaled Breath Profiles in Asthma and COPD Patients

Breath analysis using eNose technology can be used to discriminate between asthma and COPD patients, but it remains unclear whether results are influenced by smoking status. We aim to study whether eNose can discriminate between ever- vs. never-smokers and smoking <24 vs. >24 h before the exhaled breath, and if smoking can be considered a confounder that influences eNose results. We performed a cross-sectional analysis in adults with asthma or chronic obstructive pulmonary disease (COPD), and healthy controls. Ever-smokers were defined as patients with current or past smoking habits. eNose measurements were performed by using the SpiroNose. The principal component (PC) described the eNose signals, and linear discriminant analysis determined if PCs classified ever-smokers vs. never-smokers and smoking <24 vs. >24 h. The area under the receiver–operator characteristic curve (AUC) assessed the accuracy of the models. We selected 593 ever-smokers (167 smoked <24 h before measurement) and 303 never-smokers and measured the exhaled breath profiles of discriminated ever- and never-smokers (AUC: 0.74; 95% CI: 0.66–0.81), and no cigarette consumption <24h (AUC 0.54, 95% CI: 0.43–0.65). In healthy controls, the eNose did not discriminate between ever or never-smokers (AUC 0.54; 95% CI: 0.49–0.60) and recent cigarette consumption (AUC 0.60; 95% CI: 0.50–0.69). The eNose could distinguish between ever and never-smokers in asthma and COPD patients, but not recent smokers. Recent smoking is not a confounding factor of eNose breath profiles.

Development of a Compact, IoT-Enabled Electronic Nose for Breath Analysis

In this paper, we report on an in-house developed electronic nose (E-nose) for use with breath analysis. The unit consists of an array of 10 micro-electro-mechanical systems (MEMS) metal oxide (MOX) gas sensors produced by seven manufacturers. Breath sampling of end-tidal breath is achieved using a heated sample tube, capable of monitoring sampling-related parameters, such as carbon dioxide (CO2), humidity, and temperature. A simple mobile app was developed to receive real-time data from the device, using Wi-Fi communication. The system has been tested using chemical standards and exhaled breath samples from healthy volunteers, before and after taking a peppermint capsule. Results from chemical testing indicate that we can separate chemical standards (acetone, isopropanol and 1-propanol) and different concentrations of isobutylene. The analysis of exhaled breath samples demonstrate that we can distinguish between pre- and post-consumption of peppermint capsules; area under the curve (AUC): 0.81, sensitivity: 0.83 (0.59–0.96), specificity: 0.72 (0.47–0.90), p-value: <0.001. The functionality of the developed device has been demonstrated with the testing of chemical standards and a simplified breath study using peppermint capsules. It is our intention to deploy this system in a UK hospital in an upcoming breath research study.

Electronic Nose Analysis of Exhaled Breath Volatiles to Identify Lung Cancer Cases: A Systematic Review

The purpose of our review was to analyze evidence of the validity of electronic noses to discriminate persons with lung cancer from healthy control subjects and to advance implications for this technology in the care of people living with HIV. A computerized database search of the literature (published 1946-2018) was conducted to identify studies that used electronic nose-generated smellprints to discriminate persons with lung cancer from healthy control subjects. Fifteen articles met the sampling criteria. In 14 studies, mean sensitivity and specificity values from a single training sample were 84.1% and 80.9%, respectively. Five studies applied the prediction model obtained from the training sample to a separate validation sample; mean sensitivity was 88.2%, and mean specificity was 70.2%. Findings suggest that breath smellprints are valid markers of lung cancer and may be useful screening measures for cancer. No studies included people living with HIV; additional studies are needed to assess generalizability to this population.

ERS statement on chest imaging in acute respiratory failure

Chest imaging in patients with acute respiratory failure plays an important role in diagnosing, monitoring and assessing the underlying disease. The available modalities range from plain chest X-ray to computed tomography, lung ultrasound, electrical impedance tomography and positron emission tomography. Surprisingly, there are presently no clear-cut recommendations for critical care physicians regarding indications for and limitations of these different techniques.The purpose of the present European Respiratory Society (ERS) statement is to provide physicians with a comprehensive clinical review of chest imaging techniques for the assessment of patients with acute respiratory failure, based on the scientific evidence as identified by systematic searches. For each of these imaging techniques, the panel evaluated the following items: possible indications, technical aspects, qualitative and quantitative analysis of lung morphology and the potential interplay with mechanical ventilation. A systematic search of the literature was performed from inception to September 2018. A first search provided 1833 references. After evaluating the full text and discussion among the committee, 135 references were used to prepare the current statement.These chest imaging techniques allow a better assessment and understanding of the pathogenesis and pathophysiology of patients with acute respiratory failure, but have different indications and can provide additional information to each other.

The electronic nose technology in clinical diagnosis: A systematic review

Supplemental Digital Content is available in the text

Background:

Volatile organic compounds (VOC) are end products of human metabolism (normal and disease-associated) that can be mainly excreted in breath, urine, and feces. Therefore, VOC can be very useful as markers of diseases and helpful for clinicians since its sampling is noninvasive, inexpensive, and painless. Electronic noses, or eNoses, provide an easy and inexpensive way to analyze gas samples. Thus, this device may be used for diagnosis, monitoring or phenotyping diseases according to specific breathprints (breath profile).

Objective:

In this review, we summarize data showing the ability of eNose to be used as a noninvasive tool to improve diagnosis in clinical settings.

Methods:

A PRISMA-oriented search was performed in PubMed and Cochrane Library. Only studies performed in humans and published since 2000 were included.

Results:

A total of 48 original articles, 21 reviews, and 7 other documents were eligible and fully analyzed. The quality assessment of the selected studies was conducted according to the Standards for Reporting of Diagnostic Accuracy. Airway obstructive diseases were the most studied and Cyranose 320 was the most used eNose.

Conclusions:

Several case–control studies were performed to test this technology in diverse fields. More than a half of the selected studies showed good accuracy. However, there are some limitations regarding sampling methodology, analysis, reproducibility, and external validation that need to be standardized. Additionally, it is urgent to test this technology in intend-to-treat populations. Thus, it is possible to think in the contribution of VOC analysis by eNoses in a clinical setting.

Sensors for detecting pulmonary diseases from exhaled breath

This review presents and discusses a new frontier for fast, risk-free and potentially inexpensive diagnostics of respiratory diseases by detecting volatile organic compounds (VOCs) present in exhaled breath. One part of the review is a didactic presentation of the overlaying concept and the chemistry of exhaled breath. The other part discusses diverse sensors that have been developed and used for the detection of respiratory diseases (e.g. chronic obstructive pulmonary disease, asthma, lung cancer, pulmonary arterial hypertension, tuberculosis, cystic fibrosis, obstructive sleep apnoea syndrome and pneumoconiosis) by analysis of VOCs in exhaled breath. The strengths and pitfalls are discussed and criticised, particularly in the perspective in disseminating information regarding these advances. Ideas regarding the improvement of sensors, sensor arrays, sensing devices and the further planning of workflow are also discussed.

Detection of volatile organic compounds from exhaled breath by nanomaterial-based sensors is a new diagnostics frontier in the screening of pulmonary diseases.http://bit.ly/2JoBKXn

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.

Identification and Antibiotic-Susceptibility Profiling of Infectious Bacterial Agents: A Review of Current and Future Trends

Antimicrobial resistance is one of the most worrying threats to humankind with extremely high healthcare costs associated. The current technologies used in clinical microbiology to identify the bacterial agent and profile antimicrobial susceptibility are time-consuming and frequently expensive. As a result, physicians prescribe empirical antimicrobial therapies. This scenario is often the cause of therapeutic failures, causing higher mortality rates and healthcare costs, as well as the emergence and spread of antibiotic resistant bacteria. As such, new technologies for rapid identification of the pathogen and antimicrobial susceptibility testing are needed. This review summarizes the current technologies, and the promising emerging and future alternatives for the identification and profiling of antimicrobial resistance bacterial agents, which are expected to revolutionize the field of clinical diagnostics.

Influence of Water Molecules on the Detection of Volatile Organic Compounds (VOC) Cancer Biomarkers by Nanocomposite Quantum Resistive Vapor Sensors vQRS

The anticipated diagnosis of various fatal diseases from the analysis of volatile organic compounds (VOC) biomarkers of the volatolome is the object of very dynamic research. Nanocomposite-based quantum resistive vapor sensors (vQRS) exhibit strong advantages in the detection of biomarkers, as they can operate at room temperature with low consumption and sub ppm (part per million) sensitivity. However, to meet this application they need to detect some ppm or less amounts of biomarkers in patients' breath, skin, or urine in complex blends of numerous VOC, most of the time hindered by a huge amount of water molecules. Therefore, it is crucial to analyze the effects of moisture on the chemo-resistive sensing behavior of carbon nanotubes based vQRS. We show that in the presence of water molecules, the sensors cannot detect the right amount of VOC molecules present in their environment. These perturbations of the detection mechanism are found to depend on the chemical interactions between water and other VOC molecules, but also on their competitive absorption on sensors receptive sites, located at the nanojunctions of the conductive architecture. This complex phenomenon studied with down to 12.5 ppm of acetone, ethanol, butanone, toluene, and cyclohexane mixed with 100 ppm of water was worth to investigate in the prospect of future developments of devices analysing real breath samples in which water can reach a concentration of 6%.

In vitro detection of common rhinosinusitis bacteria by the eNose utilising differential mobility spectrometry

Acute rhinosinusitis (ARS) is a sudden, symptomatic inflammation of the nasal and paranasal mucosa. It is usually caused by respiratory virus infection, but bacteria complicate for a small number of ARS patients. The differential diagnostics between viral and bacterial pathogens is difficult and currently no rapid methodology exists, so antibiotics are overprescribed. The electronic nose (eNose) has shown the ability to detect diseases from gas mixtures. Differential mobility spectrometry (DMS) is a next-generation device that can separate ions based on their different mobility in high and low electric fields. Five common rhinosinusitis bacteria (Streptococcus pneumoniae, Haemophilus influenzae, Moraxella catarrhalis, Staphylococcus aureus, and Pseudomonas aeruginosa) were analysed in vitro with DMS. Classification was done using linear discriminant analysis (LDA) and k-nearest neighbour (KNN). The results were validated using leave-one-out cross-validation and separate train and test sets. With the latter, 77% of the bacteria were classified correctly with LDA. The comparative figure with KNN was 79%. In one train-test set, P. aeruginosa was excluded and the four most common ARS bacteria were analysed with LDA and KNN; the correct classification rate was 83 and 85%, respectively. DMS has shown its potential in detecting rhinosinusitis bacteria in vitro. The applicability of DMS needs to be studied with rhinosinusitis patients.

Towards precision medicine in sepsis: a position paper from the European Society of Clinical Microbiology and Infectious Diseases

BACKGROUND

Our current understanding of the pathophysiology and management of sepsis is associated with a lack of progress in clinical trials, which partly reflects insufficient appreciation of the heterogeneity of this syndrome. Consequently, more patient-specific approaches to treatment should be explored.

AIMS

To summarize the current evidence on precision medicine in sepsis, with an emphasis on translation from theory to clinical practice. A secondary objective is to develop a framework enclosing recommendations on management and priorities for further research.

SOURCES

A global search strategy was performed in the MEDLINE database through the PubMed search engine (last search December 2017). No restrictions of study design, time, or language were imposed.

CONTENT

The focus of this Position Paper is on the interplay between therapies, pathogens, and the host. Regarding the pathogen, microbiologic diagnostic approaches (such as blood cultures (BCs) and rapid diagnostic tests (RDTs)) are discussed, as well as targeted antibiotic treatment. Other topics include the disruption of host immune system and the use of biomarkers in sepsis management, patient stratification, and future clinical trial design. Lastly, personalized antibiotic treatment and stewardship are addressed (Fig. 1).

IMPLICATIONS

A road map provides recommendations and future perspectives. RDTs and identifying drug-response phenotypes are clear challenges. The next step will be the implementation of precision medicine to sepsis management, based on theranostic methodology. This highly individualized approach will be essential for the design of novel clinical trials and improvement of care pathways.

The potential role of exhaled breath analysis in the diagnostic process of pneumonia-a systematic review

Diagnostic strategies currently used for pneumonia are time-consuming, lack accuracy and suffer from large inter-observer variability. Exhaled breath contains thousands of volatile organic compounds (VOCs), which include products of host and pathogen metabolism. In this systematic review we investigated the use of so-called 'breathomics' for diagnosing pneumonia. A Medline search yielded 18 manuscripts reporting on animal and human studies using organic and inorganic molecules in exhaled breath, that all could be used to answer whether analysis of VOC profiles could potentially improve the diagnostic process of pneumonia. Papers were categorised based on their specific aims; the exclusion of pneumonia; the detection of specific respiratory pathogens; and whether targeted or untargeted VOC analysis was used. Ten studies reported on the association between VOCs and presence of pneumonia. Eight studies demonstrated a difference in exhaled VOCs between pneumonia and controls; in the individual studies this discrimination was based on unique sets of VOCs. Eight studies reported on the accuracy of a breath test for a specific respiratory pathogen: five of these concerned pre-clinical studies in animals. All studies were valued as having a high risk of bias, except for one study that used an external validation cohort. The findings in the identified studies are promising. However, as yet no breath test has been shown to have sufficient diagnostic accuracy for pneumonia. We are in need of studies that further translate the knowledge from discovery studies to clinical practice.

A Prediction Model with a Combination of Variables for Diagnosis of Lung Cancer

Background

Multivariate models with a combination of variables can predict disease more accurately than a single variable employed alone. We developed a logistic regression model with a combination of variables and evaluated its ability to predict lung cancer.

Material/Methods

The exhaled breath from 57 patients with lung cancer and 72 healthy controls without cancer was collected. The VOCs of exhaled breath were examined qualitatively and quantitatively by a novel electronic nose (Z-nose4200 equipment). The VOCs in the 2 groups were compared using the Mann-Whitney U test, and the baseline data were compared between the 2 groups using the chi-square test or ANOVA. Variables from VOCs and baseline data were selected by stepwise logistic regression and subjected to a prediction model for the diagnosis of lung cancer as combined factors. The receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of this prediction model.

Results

Nine VOCs in exhaled breath of lung cancer patients differed significantly from those of healthy controls. Four variables – age, hexane, 2,2,4,6,6-pentamethylheptane, and 1,2,6-trimethylnaphthalene – were entered into the prediction model, which could effectively separate the lung cancer samples from the control samples with an accuracy of 82.8%, a sensitivity of 76.0%, and a specificity of 94.0%.

Conclusions

The profile of VOCs in exhaled breath contained distinguishable biomarkers in the patients with lung cancers. The prediction model with 4 variables appears to provide a new technique for lung cancer detection.

The next generation of rapid point-of-care testing identification tools for ventilator-associated pneumonia

Ventilator-associated pneumonia (VAP) is a frequent issue in intensive care units (ICU), with a major impact on morbidity, mortality and cost of care. VAP diagnosis remains challenging: traditional culture-based microbiological techniques are still the gold-standard, but are too slow to enable clinicians to improve prognosis with timely antimicrobial therapy adjustment. Prolonged exposure to inappropriate antibiotics has also been shown to increase the incidence of multi-drug-resistant organisms (MDROs). Point-of-care testing (POCT) tools are diagnostic testing methods that can be used at or near the bedside, with delays ranging from a couple minutes to a few hours. The use of POCTs for VAP could allow for faster diagnosis and antimicrobial therapy adjustments. Despite uncertainty regarding their diagnostic value, C-reactive protein (CRP) and procalcitonin (PCT) can be detected using POCTs in few minutes. In VAP, CRP showed a sensitivity of 56% to 88% and specificity of 86% to 91%; PCT showed a sensitivity of 78% to 100% and a specificity between 75% and 97% using non-POCT methods. Automated microscopy could also be used in clinical ICU setting, with reported sensitivity of 100% and specificity of 97%, allowing for antibiotic susceptibility testing (AST) in less than 12 h. Multiplex polymerase chain reaction (MPCR) could allow for identification and AST approximation through the detection of drug-resistance genes in about 6 h, with reported sensitivity of 89.2% and specificity of 97.1%; although use as POCT was shown to result in test failure in about 40% of samples. Despite being at an early development stage, exhalome analysis, which allows for non-invasive fast identification, and chromogenic tests, more suited for the detection of drug-resistance enzymes, are also promising techniques for POCT diagnosis of VAP.

Exhaled Volatile Organic Compounds of Infection: A Systematic Review

With heightened global concern of microbial drug resistance, advanced methods for early and accurate diagnosis of infection are urgently needed. Analysis of exhaled breath volatile organic compounds (VOCs) toward detecting microbial infection potentially allows a highly informative and noninvasive alternative to current genomics and culture-based methods. We performed a systematic review of research literature reporting human and animal exhaled breath VOCs related to microbial infections. In this Review, we find that a wide range of breath sampling and analysis methods are used by researchers, which significantly affects interstudy method comparability. Studies either perform targeted analysis of known VOCs relating to an infection, or non-targeted analysis to obtain a global profile of volatile metabolites. In general, the field of breath analysis is still relatively immature, and there is much to be understood about the metabolic production of breath VOCs, particularly in a host where both commensal microflora as well as pathogenic microorganisms may be manifested in the airways. We anticipate that measures to standardize high throughput sampling and analysis, together with an increase in large scale collaborative international trials, will bring routine breath VOC analysis to improve diagnosis of infection closer to reality.

Antimicrobial resistance in the next 30 years, humankind, bugs and drugs: a visionary approach

PURPOSE

To describe the current standards of care and major recent advances with regard to antimicrobial resistance (AMR) and to give a prospective overview for the next 30 years in this field.

METHODS

Review of medical literature and expert opinion were used in the development of this review.

RESULTS

There is undoubtedly a large clinical and public health burden associated with AMR in ICU, but it is challenging to quantify the associated excess morbidity and mortality. In the last decade, antibiotic stewardship and infection prevention and control have been unable to prevent the rapid spread of resistant Gram-negative bacteria (GNB), in particular carbapenem-resistant Pseudomonas aeruginosa (and other non-fermenting GNB), extended-spectrum β-lactamase (ESBL)-producing and carbapenem-resistant Enterobacteriaceae (CRE). The situation appears more optimistic currently for Gram-positive, where Staphylococcus aureus, and particularly methicillin-resistant S. aureus (MRSA), remains a cardinal cause of healthcare-associated infections worldwide. Recent advancements in laboratory techniques allow for a rapid identification of the infecting pathogen and antibiotic susceptibility testing. Their impact can be particularly relevant in settings with prevalence of MDR, since they may guide fine-tuning of empirically selected regimen, facilitate de-escalation of unnecessary antimicrobials, and support infection control decisions. Currently, antibiotics are the primary anti-infective solution for patients with known or suspected MDR bacteria in intensive care. Numerous incentives have been provided to encourage researchers to work on alternative strategies to reverse this trend and to provide a means to treat these pathogens. Although some promising antibiotics currently in phase 2 and 3 of development will soon be licensed and utilized in ICU, the continuous development of an alternative generation of compounds is extremely important. There are currently several promising avenues available to fight antibiotic resistance, such as faecal microbiota, and phage therapy.

Analysis of heterogeneity in T2-weighted MR images can differentiate pseudoprogression from progression in glioblastoma

PURPOSE

To develop an image analysis technique that distinguishes pseudoprogression from true progression by analyzing tumour heterogeneity in T2-weighted images using topological descriptors of image heterogeneity called Minkowski functionals (MFs).

METHODS

Using a retrospective patient cohort (n = 50), and blinded to treatment response outcome, unsupervised feature estimation was performed to investigate MFs for the presence of outliers, potential confounders, and sensitivity to treatment response. The progression and pseudoprogression groups were then unblinded and supervised feature selection was performed using MFs, size and signal intensity features. A support vector machine model was obtained and evaluated using a prospective test cohort.

RESULTS

The model gave a classification accuracy, using a combination of MFs and size features, of more than 85% in both retrospective and prospective datasets. A different feature selection method (Random Forest) and classifier (Lasso) gave the same results. Although not apparent to the reporting radiologist, the T2-weighted hyperintensity phenotype of those patients with progression was heterogeneous, large and frond-like when compared to those with pseudoprogression.

CONCLUSION

Analysis of heterogeneity, in T2-weighted MR images, which are acquired routinely in the clinic, has the potential to detect an earlier treatment response allowing an early change in treatment strategy. Prospective validation of this technique in larger datasets is required.

An Adaptable Continuous Restricted Boltzmann Machine in VLSI for Fusing the Sensory Data of an Electronic Nose

An embedded system capable of fusing sensory data is demanded for many portable or implantable microsystems. The continuous restricted Boltzmann machine (CRBM) is a probabilistic neural network not only capable of classifying data reliably but also amenable to very-large-scale-integration (VLSI) implementation. Although the embedded system based on the CRBM has been demonstrated with analog VLSI, the precision required by the learning algorithm is hardly achievable with analog circuits. Therefore, this paper investigates the feasibility of realizing the CRBM as a digital embedded system for fusing the sensory data of an electronic nose (eNose). The fusion here refers to data clustering and dimensional reduction that facilitates reliable classification. The capability of the CRBM to model different types of eNose data is first examined by MATLAB simulation. Afterward, the CRBM algorithm is customdesigned as a digital embedded system within an eNose microsystem. The functionality of the embedded CRBM system is then tested and discussed. With on-chip learning ability, the CRBM-embedded eNose is able to adapt its parameters in response to new data inputs or environmental changes.

Electronic Nose Technology in Respiratory Diseases

Electronic noses (e-noses) are based on arrays of different sensor types that respond to specific features of an odorant molecule, mostly volatile organic compounds (VOCs). Differently from gas chromatography and mass spectrometry, e-noses can distinguish VOCs spectrum by pattern recognition. E-nose technology has successfully been used in commercial applications, including military, environmental, and food industry. Human-exhaled breath contains a mixture of over 3000 VOCs, which offers the postulate that e-nose technology can have medical applications. Based on the above hypothesis, an increasing number of studies have shown that breath profiling by e-nose could play a role in the diagnosis and/or screening of various respiratory and systemic diseases. The aim of the present study was to review the principal literature on the application of e-nose technology in respiratory diseases.

A Compendium of Volatile Organic Compounds (VOCs) Released By Human Cell Lines

Volatile organic compounds (VOCs) offer unique insights into ongoing biochemical processes in healthy and diseased humans. Yet, their diagnostic use is hampered by the limited understanding of their biochemical or cellular origin and their frequently unclear link to the underlying diseases. Major advancements are expected from the analyses of human primary cells, cell lines and cultures of microorganisms. In this review, a database of 125 reliably identified VOCs previously reported for human healthy and diseased cells was assembled and their potential origin is discussed. The majority of them have also been observed in studies with other human matrices (breath, urine, saliva, feces, blood, skin emanations). Moreover, continuing improvements of qualitative and quantitative analyses, based on the recommendations of the ISO-11843 guidelines, are suggested for the necessary standardization of analytical procedures and better comparability of results. The data provided contribute to arriving at a more complete human volatilome and suggest potential volatile biomarkers for future validation.

Dedication: This review is dedicated to the memory of Prof. Dr. Anton Amann, who sadly passed away on January 6, 2015. He was motivator and motor for the field of breath research.

Sulfonated poly(ether ether ketone) [SPEEK] nanocomposites based on hybrid nanocarbons for the detection and discrimination of some lung cancer VOC biomarkers

The analysis of a volatolome is a promising approach to allow the early diagnosis of diseases such as cancers. However, one important challenge is to take the chemical fingerprint of the complex blend of volatiles, for many of them only present at the sub-ppm level. We have investigated a facile route to differentiate the chemo-resistive behaviour of quantum resistive vapour sensors (vQRS) and provide them with a strong methanol selectivity by simply changing the sulfonation degree of poly(ether ether ketone) up to 85%. The hybridization of carbon nanotubes (CNTs) with fullerene (C₆₀) structured in a 3D architecture by spray layer-by-layer (sLbL) has allowed us to boost significantly the sensitivity of sensors to reach the sub-ppm level (340 ppb). After their integration into an e-nose, PEEK-nanocarbon sensors were found to effectively discriminate both single and binary mixtures of volatile organic compounds (VOCs) and among all biomarkers to detect preferentially methanol with a high signal to noise ratio (200).

Significance of Exhaled Breath Test in Clinical Diagnosis: A Special Focus on the Detection of Diabetes Mellitus

Analysis of volatile organic compounds (VOCs) emanating from human exhaled breath can provide deep insight into the status of various biochemical processes in the human body. VOCs can serve as potential biomarkers of physiological and pathophysiological conditions related to several diseases. Breath VOC analysis, a noninvasive and quick biomonitoring approach, also has potential for the early detection and progress monitoring of several diseases. This paper gives an overview of the major VOCs present in human exhaled breath, possible biochemical pathways of breath VOC generation, diagnostic importance of their analysis, and analytical techniques used in the breath test. Breath analysis relating to diabetes mellitus and its characteristic breath biomarkers is focused on. Finally, some challenges and limitations of the breath test are discussed.

Breath analysis for noninvasively differentiating Acinetobacter baumannii ventilator-associated pneumonia from its respiratory tract colonization of ventilated patients

A number of multiresistant pathogens including Acinetobacter baumannii (A. baumannii) place a heavy burden on ventilator-associated pneumonia (VAP) patients in intensive care units (ICU). It is critically important to differentiate between bacterial infection and colonization to avoid prescribing unnecessary antibiotics. Quantitative culture of lower respiratory tract (LRT) specimens, however, requires invasive procedures. Nowadays, volatile organic compounds (VOCs) have been studied in vitro and in vivo to identify pathogen-derived biomarkers. Therefore, an exploratory pilot study was conceived for a proof of concept that the appearance and level of A. baumannii-derived metabolites might be correlated with the presence of the pathogen and its ecological niche (i.e. the infection and colonization states) in ICU ventilated patients. Twenty patients with A. baumannii VAP (infection group), 20 ventilated patients with LRT A. baumannii colonization (colonization group) and 20 ventilated patients with neurological disorders, but without pneumonia or A. baumannii colonization (control group) were enrolled in the in vivo pilot study. A clinical isolate of A. baumannii strains was used for the in vitro culture experiment. The adsorptive preconcentration (solid-phase microextraction fiber and Tenax(®) TA) and analysis technique of gas chromatography-mass spectrometry were applied in the studies. Breath profiles could be visually differentiated between A. baumannii cultivation in vitro and culture medium, and among in vivo groups. In the in vitro experiment, nine compounds of interest (2,5-dimethyl-pyrazine, 1-undecene, isopentyl 3-methylbutanoate, decanal, 1,3-naphthalenediol, longifolene, tetradecane, iminodibenzyl and 3-methyl-indene) in the headspace were found to be possible A. baumannii derivations. While there were eight target VOCs (1-undecene, nonanal, decanal, 2,6,10-trimethyl-dodecane, 5-methyl-5-propyl-nonane, longifolene, tetradecane and 2-butyl-1-octanol) exhibiting characteristics of A. baumannii VAP derivations. The selected VOC profile in vivo could be adopted to efficiently differentiate the presence of LRT A. baumannii from its absence, and LRT A. baumannii infection from its colonization (AUC  =  0.89 and 0.88, respectively). It is not feasible to simply transfer the metabolic biomarkers from the in vitro condition to in vivo. The direct detection of exhaled A. baumannii-derived VOCs may be adopted for an early alert of the LRT bacterial presence in ventilated ICU patients, and even in different parasitic states of A. baumannii (i.e. infection and colonization). However, further refinement and validation are required before its clinical use.

Analysis of volatile organic compounds in exhaled breath to diagnose ventilator-associated pneumonia

Ventilator-associated pneumonia (VAP) is a nosocomial infection occurring in the intensive care unit (ICU). The diagnostic standard is based on clinical criteria and bronchoalveolar lavage (BAL). Exhaled breath analysis is a promising non-invasive method for rapid diagnosis of diseases and contains volatile organic compounds (VOCs) that can differentiate diseased from healthy individuals. The aim of this study was to determine whether analysis of VOCs in exhaled breath can be used as a non-invasive monitoring tool for VAP. One hundred critically ill patients with clinical suspicion of VAP underwent BAL. Before BAL, exhaled air samples were collected and analysed by gas chromatography time-of-flight mass spectrometry (GC-tof-MS). The clinical suspicion of VAP was confirmed by BAL diagnostic criteria in 32 patients [VAP(+)] and rejected in 68 patients [VAP(−)]. Multivariate statistical comparison of VOC profiles between VAP(+) and VAP(−) revealed a subset of 12 VOCs that correctly discriminated between those two patient groups with a sensitivity and specificity of 75.8% ± 13.5% and 73.0% ± 11.8%, respectively. These results suggest that detection of VAP in ICU patients is possible by examining exhaled breath, enabling a simple, safe and non-invasive approach that could diminish diagnostic burden of VAP.

Electronic nose analysis of exhaled breath to diagnose ventilator-associated pneumonia

BACKGROUND

Exhaled breath analysis is an emerging technology in respiratory disease and infection. Electronic nose devices (e-nose) are small and portable with a potential for point of care application. Ventilator-associated pneumonia (VAP) is a common nosocomial infection occurring in the intensive care unit (ICU). The current best diagnostic approach is based on clinical criteria combined with bronchoalveolar lavage (BAL) and subsequent bacterial culture analysis. BAL is invasive, laborious and time consuming. Exhaled breath analysis by e-nose is non-invasive, easy to perform and could reduce diagnostic time. Aim of this study was to explore whether an e-nose can be used as a non-invasive in vivo diagnostic tool for VAP.

METHODS

Seventy-two patients met the clinical diagnostic criteria of VAP and underwent BAL. In thirty-three patients BAL analysis confirmed the diagnosis of VAP [BAL+(VAP+)], in thirty-nine patients the diagnosis was rejected [BAL-]. Before BAL was performed, exhaled breath was sampled from the expiratory limb of the ventilator into sterile Tedlar bags and subsequently analysed by an e-nose with metal oxide sensors (DiagNose, C-it, Zutphen, The Netherlands). From further fifty-three patients without clinical suspicion of VAP or signs of respiratory disease exhaled breath was collected to serve as a control group [control(VAP-]). The e-nose data from exhaled breath were analysed using logistic regression.

RESULTS

The ROC curve comparing [BAL+(VAP+)] and [control(VAP-)] patients had an area under the curve (AUC) of 0.82 (95% CI 0.73-0.9). The sensitivity was 88% with a specificity of 66%. The comparison of [BAL+(VAP+)] and [BAL-] patients revealed an AUC of 0.69; 95% CI 0.57-0.81) with a sensitivity of 76% with a specificity of 56%.

CONCLUSION

E-nose lacked sensitivity and specificity in the diagnosis of VAP in the present study for current clinical application. Further investigation into this field is warranted to explore the diagnostic possibilities of this promising new technique.

Using the Electronic Nose to Identify Airway Infection during COPD Exacerbations

BACKGROUND

The electronic nose (e-nose) detects volatile organic compounds (VOCs) in exhaled air. We hypothesized that the exhaled VOCs print is different in stable vs. exacerbated patients with chronic obstructive pulmonary disease (COPD), particularly if the latter is associated with airway bacterial infection, and that the e-nose can distinguish them.

METHODS

Smell-prints of the bacteria most commonly involved in exacerbations of COPD (ECOPD) were identified in vitro. Subsequently, we tested our hypothesis in 93 patients with ECOPD, 19 of them with pneumonia, 50 with stable COPD and 30 healthy controls in a cross-sectional case-controlled study. Secondly, ECOPD patients were re-studied after 2 months if clinically stable. Exhaled air was collected within a Tedlar bag and processed by a Cynarose 320 e-nose. Breath-prints were analyzed by Linear Discriminant Analysis (LDA) with "One Out" technique and Sensor logic Relations (SLR). Sputum samples were collected for culture.

RESULTS

ECOPD with evidence of infection were significantly distinguishable from non-infected ECOPD (p = 0.018), with better accuracy when ECOPD was associated to pneumonia. The same patients with ECOPD were significantly distinguishable from stable COPD during follow-up (p = 0.018), unless the patient was colonized. Additionally, breath-prints from COPD patients were significantly distinguished from healthy controls. Various bacteria species were identified in culture but the e-nose was unable to identify accurately the bacteria smell-print in infected patients.

CONCLUSION

E-nose can identify ECOPD, especially if associated with airway bacterial infection or pneumonia.

In-vitro diagnosis of single and poly microbial species targeted for diabetic foot infection using e-nose technology

Background

Effective management of patients with diabetic foot infection is a crucial concern. A delay in prescribing appropriate antimicrobial agent can lead to amputation or life threatening complications. Thus, this electronic nose (e-nose) technique will provide a diagnostic tool that will allow for rapid and accurate identification of a pathogen.

Results

This study investigates the performance of e-nose technique performing direct measurement of static headspace with algorithm and data interpretations which was validated by Headspace SPME-GC-MS, to determine the causative bacteria responsible for diabetic foot infection. The study was proposed to complement the wound swabbing method for bacterial culture and to serve as a rapid screening tool for bacteria species identification. The investigation focused on both single and poly microbial subjected to different agar media cultures. A multi-class technique was applied including statistical approaches such as Support Vector Machine (SVM), K Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA) as well as neural networks called Probability Neural Network (PNN). Most of classifiers successfully identified poly and single microbial species with up to 90% accuracy.

Conclusions

The results obtained from this study showed that the e-nose was able to identify and differentiate between poly and single microbial species comparable to the conventional clinical technique. It also indicates that even though poly and single bacterial species in different agar solution emit different headspace volatiles, they can still be discriminated and identified using multivariate techniques.

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.

Advances in electronic-nose technologies for the detection of volatile biomarker metabolites in the human breath

Recent advancements in the use of electronic-nose (e-nose) devices to analyze human breath profiles for the presence of specific volatile metabolites, known as biomarkers or chemical bio-indicators of specific human diseases, metabolic disorders and the overall health status of individuals, are providing the potential for new noninvasive tools and techniques useful to point-of-care clinical disease diagnoses. This exciting new area of electronic disease detection and diagnosis promises to yield much faster and earlier detection of human diseases and disorders, allowing earlier, more effective treatments, resulting in more rapid patient recovery from various afflictions. E-nose devices are particularly suited for the field of disease diagnostics, because they are sensitive to a wide range of volatile organic compounds (VOCs) and can effectively distinguish between different complex gaseous mixtures via analysis of electronic aroma sensor-array output profiles of volatile metabolites present in the human breath. This review provides a summary of some recent developments of electronic-nose technologies, particularly involving breath analysis, with the potential for providing many new diagnostic applications for the detection of specific human diseases associated with different organs in the body, detectable from e-nose analyses of aberrant disease-associated VOCs present in air expired from the lungs.

On the Slow Diffusion of Point-of-Care Systems in Therapeutic Drug Monitoring

Recent advancements in point-of-care (PoC) technologies show great transformative promises for personalized preventative and predictive medicine. However, fields like therapeutic drug monitoring (TDM), that first allowed for personalized treatment of patients' disease, still lag behind in the widespread application of PoC devices for monitoring of patients. Surprisingly, very few applications in commonly monitored drugs, such as anti-epileptics, are paving the way for a PoC approach to patient therapy monitoring compared to other fields like intensive care cardiac markers monitoring, glycemic controls in diabetes, or bench-top hematological parameters analysis at the local drug store. Such delay in the development of portable fast clinically effective drug monitoring devices is in our opinion due more to an inertial drag on the pervasiveness of these new devices into the clinical field than a lack of technical capability. At the same time, some very promising technologies failed in the clinical practice for inadequate understanding of the outcome parameters necessary for a relevant technological breakthrough that has superior clinical performance. We hope, by over-viewing both TDM practice and its yet unmet needs and latest advancement in micro- and nanotechnology applications to PoC clinical devices, to help bridging the two communities, the one exploiting analytical technologies and the one mastering the most advanced techniques, into translating existing and forthcoming technologies in effective devices.

Exhaled breath analysis using electronic nose in cystic fibrosis and primary ciliary dyskinesia patients with chronic pulmonary infections

The current diagnostic work-up and monitoring of pulmonary infections may be perceived as invasive, is time consuming and expensive. In this explorative study, we investigated whether or not a non-invasive exhaled breath analysis using an electronic nose would discriminate between cystic fibrosis (CF) and primary ciliary dyskinesia (PCD) with or without various well characterized chronic pulmonary infections. We recruited 64 patients with CF and 21 with PCD based on known chronic infection status. 21 healthy volunteers served as controls. An electronic nose was employed to analyze exhaled breath samples. Principal component reduction and discriminant analysis were used to construct internally cross-validated receiver operator characteristic (ROC) curves. Breath profiles of CF and PCD patients differed significantly from healthy controls p = 0.001 and p = 0.005, respectively. Profiles of CF patients having a chronic P. aeruginosa infection differed significantly from to non-chronically infected CF patients p = 0.044. We confirmed the previously established discriminative power of exhaled breath analysis in separation between healthy subjects and patients with CF or PCD. Furthermore, this method significantly discriminates CF patients suffering from a chronic pulmonary P. aeruginosa (PA) infection from CF patients without a chronic pulmonary infection. Further studies are needed for verification and to investigate the role of electronic nose technology in the very early diagnostic workup of pulmonary infections before the establishment of a chronic infection.

Ultrasensitive QRS made by supramolecular assembly of functionalized cyclodextrins and graphene for the detection of lung cancer VOC biomarkers

A novel electronic nose system comprising functionalized β-cyclodextrin wrapped reduced graphene oxide (RGO) sensors with distinct ability of discrimination of a set of volatile organic compounds has been developed. Non-covalent modification of chemically functionalized cyclodextrin with RGO is carried out by using pyrene adamantane as a linker wherever necessary, in order to construct a supramolecular assembly. The chemical functionality on cyclodextrin is varied utilising the principle of selective chemical modification of cyclodextrin. In the present study, the combined benefits of the host-guest inclusion complex formation ability and tunable chemical functionality of cyclodextrin, as well as the high surface area and electrical conductivity of graphene, are utilized for the development of a set of highly selective quantum resistive chemical vapour sensors (QRS), which can be assembled in an electronic nose.

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.

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.

Analysis of Airborne Biomarkers for Point-of-Care Diagnostics

Treatable diseases continue to exact a heavy burden worldwide despite powerful advances in treatment. Diagnostics play crucial roles in the detection, management, and ultimate prevention of these diseases by guiding the allocation of precious medical resources. Motivated by globalization and evolving disease, and enabled by advances in molecular pathology, the scientific community has produced an explosion of research on miniaturized integrated biosensor platforms for disease detection. Low-cost, automated tests promise accessibility in low-resource settings by loosening constraints around infrastructure and usability. To address the challenges raised by invasive and intrusive sample collection, researchers are exploring alternative biomarkers in various specimens. Specifically, patient-generated airborne biomarkers suit minimally invasive collection and automated analysis. Disease biomarkers are known to exist in aerosols and volatile compounds in breath, odor, and headspace, media that can be exploited for field-ready diagnostics. This article reviews global disease priorities and the characteristics of low-resource settings. It surveys existing technologies for the analysis of bioaerosols and volatile organic compounds, and emerging technologies that could enable their translation to the point of care. Engineering advances promise to enable appropriate diagnostics by detecting chemical and microbial markers. Nonetheless, further innovation and cost reduction are needed for these technologies to broadly affect global health.

Exhaled breath analysis by electronic nose in airways disease. Established issues and key questions

Exhaled air contains many volatile organic compounds (VOCs) that are the result of normal and disease-associated metabolic processes anywhere in the body. Different omics techniques can assess the pattern of these VOCs. One such omics technique suitable for breath analysis is represented by electronic noses (eNoses), providing fingerprints of the exhaled VOCs, called breathprints. Breathprints have been shown to be altered in different disease states, including in asthma and COPD. This review describes the current status on clinical validation and application of breath analysis by electronic noses in the diagnosis and monitoring of chronic airways diseases. Furthermore, important methodological issues including breath sampling, modulating factors and incompatibility between eNoses are raised and discussed. Next steps towards clinical application of electronic noses are provided, including further validation in suspected disease, assessment of the influence of different comorbidities, the value in longitudinal monitoring of patients with asthma and COPD and the possibility to predict treatment responses. Eventually, a Breath Cloud may be constructed, a large database containing disease-specific breathprints. When collaborative efforts are put into optimization of this technique, it can provide a rapid and non-invasive first line diagnostic test.

Clinical application of volatile organic compound analysis for detecting infectious diseases

This review article introduces the significance of testing of volatile organic compounds (VOCs) in clinical samples and summarizes important features of some of the technologies. Compared to other human diseases such as cancer, studies on VOC analysis in cases of infectious diseases are limited. Here, we have described results of studies which have used some of the appropriate technologies to evaluate VOC biomarkers and biomarker profiles associated with infections. The publications reviewed include important infections of the respiratory tract, gastrointestinal tract, urinary tract, and nasal cavity. The results highlight the use of VOC biomarker profiles resulting from certain infectious diseases in discriminating between infected and healthy subjects. Infection-related VOC profiles measured in exhaled breath as well as from headspaces of feces or urine samples are a source of information with respect to disease detection. The volatiles emitted in clinical matrices may on the one hand represent metabolites of the infecting pathogen or on the other hand reflect pathogen-induced host responses or, indeed, a combination of both. Because exhaled-breath samples are easy to collect and online instruments are commercially available, VOC analysis in exhaled breath appears to be a promising tool for noninvasive detection and monitoring of infectious diseases.

Application of an electronic nose in the diagnosis of head and neck cancer

OBJECTIVES/HYPOTHESIS

Electronic nose (E-nose) technology has various applications such as the monitoring of air quality and the detection of explosive and chemical agents. We studied the diagnostic accuracy of volatile organic compounds (VOC) pattern analysis in exhaled breath by means of an E-nose in patients with head and neck squamous cell carcinoma (HNSCC).

STUDY DESIGN

Cohort study. Exhaled breath samples from patients with HNSCC were analyzed by using an E-Nose.

METHODS

Thirty-six patients diagnosed with HNSCC exhaled into a 5-litre Tedlar bag. The control group consisted of 23 patients visiting the outpatient clinic for other (benign) conditions. Air samples were analyzed using an E-nose.

RESULTS

Logistic regression showed a significant difference (P < 0.05) in VOC resistance patterns between patients diagnosed with HNSCC and the control group, with a sensitivity of 90% and a corresponding specificity of 80%.

CONCLUSIONS

E-nose application holds a promising potential for application in the diagnosis of HNSCC due to its rapid, simple, and noninvasive nature.

LEVEL OF EVIDENCE

3b.

A simple breath sampling method in intubated and mechanically ventilated critically ill patients

Volatile organic compounds (VOCs) in breath may serve as biomarkers of pulmonary infection or inflammation. We developed and validated a new breath sampling method for VOC analysis in ventilated patients. Breath was collected from the ventilatory circuit using cheap disposables. VOCs were identified by gas-chromatography and mass-spectrometry (GC-MS) at various minute volumes during ventilation of an artificial lung (in vitro) and ventilated patients (in vivo). Sixty-four VOCs emendated from the ventilator and tubing. Their concentrations had an inverse correlation with minute volume in in vitro experiments (median correlation coefficient: -0.61 [25-75th percentile: -0.66 to -0.43]). Forty-four of these "ventilator-associated VOCs" were also observed in vivo, without correlations with minute volume. In vivo experiments showed that only positive end-expiratory pressure influenced the concentration of breath VOCs. The sampling method was highly reproducible (median intra-class correlation 0.95 [25-75th percentile: 0.87-0.97]). In conclusion, a novel, simple and repeatable sampling method was developed and validated for capturing exhaled VOCs in ventilated patients, which could allow for large-scale breath analysis in clinical studies.

Application of the electronic nose technique to differentiation between model mixtures with COPD markers

The paper presents the potential of an electronic nose technique in the field of fast diagnostics of patients suspected of Chronic Obstructive Pulmonary Disease (COPD). The investigations were performed using a simple electronic nose prototype equipped with a set of six semiconductor sensors manufactured by FIGARO Co. They were aimed at verification of a possibility of differentiation between model reference mixtures with potential COPD markers (N,N-dimethylformamide and N,N-dimethylacetamide). These mixtures contained volatile organic compounds (VOCs) such as acetone, isoprene, carbon disulphide, propan-2-ol, formamide, benzene, toluene, acetonitrile, acetic acid, dimethyl ether, dimethyl sulphide, acrolein, furan, propanol and pyridine, recognized as the components of exhaled air. The model reference mixtures were prepared at three concentration levels-10 ppb, 25 ppb, 50 ppb v/v-of each component, except for the COPD markers. Concentration of the COPD markers in the mixtures was from 0 ppb to 100 ppb v/v. Interpretation of the obtained data employed principal component analysis (PCA). The investigations revealed the usefulness of the electronic device only in the case when the concentration of the COPD markers was twice as high as the concentration of the remaining components of the mixture and for a limited number of basic mixture components.

Secondary electrospray ionization-mass spectrometry (SESI-MS) breathprinting of multiple bacterial lung pathogens, a mouse model study

Bacterial pneumonia is one of the leading causes of disease-related morbidity and mortality in the world, in part because the diagnostic tools for pneumonia are slow and ineffective. To improve the diagnosis success rates and treatment outcomes for bacterial lung infections, we are exploring the use of secondary electrospray ionization-mass spectrometry (SESI-MS) breath analysis as a rapid, noninvasive method for determining the etiology of lung infections in situ. Using a murine lung infection model, we demonstrate that SESI-MS breathprints can be used to distinguish mice that are infected with one of seven lung pathogens: Haemophilus influenzae, Klebsiella pneumoniae, Legionella pneumophila, Moraxella catarrhalis, Pseudomonas aeruginosa, Staphylococcus aureus, and Streptococcus pneumoniae, representing the primary causes of bacterial pneumonia worldwide. After applying principal components analysis, we observed that with the first three principal components (primarily comprised of data from 14 peaks), all infections were separable via SESI-MS breathprinting (P < 0.0001). Therefore, we have shown the potential of this SESI-MS approach for rapidly detecting and identifying acute bacterial lung infections in situ via breath analysis.