Fecal microbiota and volatile metabolome pattern alterations precede late-onset meningitis in preterm neonates

OBJECTIVE

The fecal microbiota and metabolome are hypothesized to be altered before late-onset neonatal meningitis (LOM), in analogy to late-onset sepsis (LOS). The present study aimed to identify fecal microbiota composition and volatile metabolomics preceding LOM.

METHODS

Cases and gestational age-matched controls were selected from a prospective, longitudinal preterm cohort study (born <30 weeks' gestation) at nine neonatal intensive care units. The microbial composition (16S rRNA sequencing) and volatile metabolome (gas chromatography-ion mobility spectrometry (GC-IMS) and GC-time-of-flight-mass spectrometry (GC-TOF-MS)), were analyzed in fecal samples 1-10 days pre-LOM.

RESULTS

Of 1397 included infants, 21 were diagnosed with LOM (1.5%), and 19 with concomitant LOS (90%). Random Forest classification and MaAsLin2 analysis found similar microbiota features contribute to the discrimination of fecal pre-LOM samples versus controls. A Random Forest model based on six microbiota features accurately predicts LOM 1-3 days before diagnosis with an area under the curve (AUC) of 0.88 (n=147). Pattern recognition analysis by GC-IMS revealed an AUC of 0.70-0.76 (P<0.05) in the three days pre-LOM (n=92). No single discriminative metabolites were identified by GC-TOF-MS (n=66).

CONCLUSION

Infants with LOM could be accurately discriminated from controls based on preclinical microbiota composition, while alterations in the volatile metabolome were moderately associated with preclinical LOM.

Faecal Volatile Organic Compound Analysis in De Novo Paediatric Inflammatory Bowel Disease by Gas Chromatography-Ion Mobility Spectrometry: A Case-Control Study

The gut microbiota and its related metabolites differ between inflammatory bowel disease (IBD) patients and healthy controls. In this study, we compared faecal volatile organic compound (VOC) patterns of paediatric IBD patients and controls with gastrointestinal symptoms (CGIs). Additionally, we aimed to assess if baseline VOC profiles could predict treatment response in paediatric IBD patients. We collected faecal samples from a cohort of de novo therapy-naïve paediatric IBD patients and CGIs. VOCs were analysed using gas chromatography-ion mobility spectrometry (GC-IMS). Response was defined as a combination of clinical response based on disease activity scores, without requiring treatment escalation. We included 109 paediatric IBD patients and 75 CGIs, aged 4 to 17 years. Faecal VOC profiles of paediatric IBD patients were distinguishable from those of CGIs (AUC ± 95% CI, p-values: 0.71 (0.64-0.79), <0.001). This discrimination was observed in both Crohn's disease (CD) (0.75 (0.67-0.84), <0.001) and ulcerative colitis (UC) (0.67 (0.56-0.78), 0.01) patients. VOC profiles between CD and UC patients were not distinguishable (0.57 (0.45-0.69), 0.87). Baseline VOC profiles of responders did not differ from non-responders (0.70 (0.58-0.83), 0.1). In conclusion, faecal VOC profiles of paediatric IBD patients differ significantly from those of CGIs.

Early Detection of Fusarium Basal Rot Infection in Onions and Shallots Based on VOC Profiles Analysis

Gas chromatography ion-mobility spectrometry (GC-IMS) technology is drawing increasing attention due to its high sensitivity, low drift, and capability for the identification of compounds. The noninvasive detection of plant pests and pathogens is an application area well suited to this technology. In this work, we employed GC-IMS technology for early detection of Fusarium basal rot in brown onion, red onion, and shallot bulbs and for tracking disease progression during storage. The volatile profiles of the infected and healthy control bulbs were characterized using GC-IMS and gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS). GC-IMS data combined with principal component analysis and supervised methods provided discrimination between infected and healthy control bulbs as early as 1 day after incubation with the pathogen, classification regarding the proportion of infected to healthy bulbs in a sample, and prediction of the infection's duration with an average R2 = 0.92. Furthermore, GC-TOF-MS revealed several compounds, mostly sulfides and disulfides, that could be uniquely related to Fusarium basal rot infection.

The Development of a Simple Projection-Based, Portable Olfactory Display Device

Olfactory displays are digital devices designed to provide the controlled release of odours to users. In this paper, we report on the design and development of a simple vortex-based olfactory display for a single user. By employing a vortex approach, we are able to minimize the amount of required odour, whilst still producing a good user experience. The olfactory display designed here is based on a steel tube with 3D-printed apertures and solenoid valve operation. A number of different design parameters (such as aperture size) were investigated, and the best combination was combined into a functional olfactory display. User testing was undertaken with four volunteers who were presented with four different odours, at two concentrations. It was found that the time to identify an odour was not strongly related to concentration. However, the intensity of the odour was correlated. We also found that there was a wide variance in human panel results when considering the length of time for a subject to identify an odour to its perceived intensity. This is likely linked to the subject group receiving no odour training before the experiments. However, we were able to produce a working olfactory display, based on a scent project method, which could be applicable to a range of application scenarios.

Finding a Husband: Using Explainable AI to Define Male Mosquito Flight Differences

Mosquito-borne diseases account for around one million deaths annually. There is a constant need for novel intervention mechanisms to mitigate transmission, especially as current insecticidal methods become less effective with the rise of insecticide resistance among mosquito populations. Previously, we used a near infra-red tracking system to describe the behaviour of mosquitoes at a human-occupied bed net, work that eventually led to an entirely novel bed net design. Advancing that approach, here we report on the use of trajectory analysis of a mosquito flight, using machine learning methods. This largely unexplored application has significant potential for providing useful insights into the behaviour of mosquitoes and other insects. In this work, a novel methodology applies anomaly detection to distinguish male mosquito tracks from females and couples. The proposed pipeline uses new feature engineering techniques and splits each track into segments such that detailed flight behaviour differences influence the classifier rather than the experimental constraints such as the field of view of the tracking system. Each segment is individually classified and the outcomes are combined to classify whole tracks. By interpreting the model using SHAP values, the features of flight that contribute to the differences between sexes are found and are explained by expert opinion. This methodology was tested using 3D tracks generated from mosquito mating swarms in the field and obtained a balanced accuracy of 64.5% and an ROC AUC score of 68.4%. Such a system can be used in a wide variety of trajectory domains to detect and analyse the behaviours of different classes, e.g., sex, strain, and species. The results of this study can support genetic mosquito control interventions for which mating represents a key event for their success.

Integrated Probe System for Measuring Soil Carbon Dioxide Concentrations

This article outlines the design and implementation of an internet-of-things (IoT) platform for the monitoring of soil carbon dioxide (CO₂) concentrations. As atmospheric CO₂ continues to rise, accurate accounting of major carbon sources, such as soil, is essential to inform land management and government policy. Thus, a batch of IoT-connected CO₂ sensor probes were developed for soil measurement. These sensors were designed to capture spatial distribution of CO₂ concentrations across a site and communicate to a central gateway using LoRa. CO₂ concentration and other environmental parameters, including temperature, humidity and volatile organic compound concentration, were logged locally and communicated to the user through a mobile (GSM) connection to a hosted website. Following three field deployments in summer and autumn, we observed clear depth and diurnal variation of soil CO₂ concentration within woodland systems. We determined that the unit had the capacity to log data continuously for a maximum of 14 days. These low-cost systems have great potential for better accounting of soil CO₂ sources over temporal and spatial gradients and possibly flux estimations. Future testing will focus on divergent landscapes and soil conditions.

Fecal Volatile Metabolomics Predict Gram-Negative Late-Onset Sepsis in Preterm Infants: A Nationwide Case-Control Study

Early detection of late-onset sepsis (LOS) in preterm infants is crucial since timely treatment initiation is a key prognostic factor. We hypothesized that fecal volatile organic compounds (VOCs), reflecting microbiota composition and function, could serve as a non-invasive biomarker for preclinical pathogen-specific LOS detection. Fecal samples and clinical data of all preterm infants (≤30 weeks’ gestation) admitted at nine neonatal intensive care units in the Netherlands and Belgium were collected daily. Samples from one to three days before LOS onset were analyzed by gas chromatography—ion mobility spectrometry (GC-IMS), a technique based on pattern recognition, and gas chromatography—time of flight—mass spectrometry (GC-TOF-MS), to identify unique metabolites. Fecal VOC profiles and metabolites from infants with LOS were compared with matched controls. Samples from 121 LOS infants and 121 matched controls were analyzed using GC-IMS, and from 34 LOS infants and 34 matched controls using GC-TOF-MS. Differences in fecal VOCs were most profound one and two days preceding Escherichia coli LOS (Area Under Curve; p-value: 0.73; p = 0.02, 0.83; p < 0.002, respectively) and two and three days before gram-negative LOS (0.81; p < 0.001, 0.85; p < 0.001, respectively). GC-TOF-MS identified pathogen-specific discriminative metabolites for LOS. This study underlines the potential for VOCs as a non-invasive preclinical diagnostic LOS biomarker.

Breath Analysis of COVID-19 Patients in a Tertiary UK Hospital by Optical Spectrometry: The E-Nose CoVal Study

Throughout the SARS-CoV-2 pandemic, diagnostic technology played a crucial role in managing outbreaks on a national and global level. One diagnostic modality that has shown promise is breath analysis, due to its non-invasive nature and ability to give a rapid result. In this study, a portable FTIR (Fourier Transform Infra-Red) spectrometer was used to detect chemical components in the breath from Covid positive symptomatic and asymptomatic patients versus a control cohort of Covid negative patients. Eighty-five patients who had a nasopharyngeal polymerase chain reaction (PCR) test for the detection of SARS-CoV-2 within the last 5 days were recruited to the study (36 symptomatic PCR positive, 23 asymptomatic PCR positive and 26 asymptomatic PCR negative). Data analysis indicated significant difference between the groups, with SARS-CoV-2 present on PCR versus the negative PCR control group producing an area under the curve (AUC) of 0.87. Similar results were obtained comparing symptomatic versus control and asymptomatic versus control. The asymptomatic results were higher than the symptomatic (0.88 vs. 0.80 AUC). When analysing individual chemicals, we found ethanol, methanol and acetaldehyde were the most important, with higher concentrations in the COVID-19 group, with symptomatic patients being higher than asymptomatic patients. This study has shown that breath analysis can provide significant results that distinguish patients with or without COVID-19 disease/carriage.

Discriminatory ability of gas chromatography-ion mobility spectrometry to identify patients hospitalised with COVID-19 and predict prognosis

Background

Rapid diagnostic and prognostic tests for coronavirus disease (COVID-19) are urgently required. We aimed to evaluate the diagnostic and prognostic ability of breath analysis using gas chromatography–ion mobility spectrometry (GC-IMS) in hospitalized patients with COVID-19.

Methods

Between February and May 2021, we took 1 breath sample for analysis using GC-IMS from participants who were admitted to the hospital for COVID-19, participants who were admitted to the hospital for other respiratory infections, and symptom-free controls, at the University Hospitals of Leicester NHS Trust, United Kingdom. Demographic, clinical, and radiological data, including requirement for continuous positive airway pressure (CPAP) ventilation as a marker for severe disease in the COVID-19 group, were collected.

Results

A total of 113 participants were recruited into the study. Seventy-two (64%) were diagnosed with COVID-19, 20 (18%) were diagnosed with another respiratory infection, and 21 (19%) were healthy controls. Differentiation between participants with COVID-19 and those with other respiratory tract infections with GC-IMS was highly accurate (sensitivity/specificity, 0.80/0.88; area under the receiver operating characteristics curve [AUROC], 0.85; 95% CI, 0.74–0.96). GC-IMS was also moderately accurate at identifying those who subsequently required CPAP (sensitivity/specificity, 0.62/0.80; AUROC, 0.70; 95% CI, 0.53–0.87).

Conclusions

GC-IMS shows promise as both a diagnostic tool and a predictor of prognosis in hospitalized patients with COVID-19 and should be assessed further in larger studies.

Preliminary Studies on Detection of Fusarium Basal Rot Infection in Onions and Shallots Using Electronic Nose

The evaluation of crop health status and early disease detection are critical for implementing a fast response to a pathogen attack, managing crop infection, and minimizing the risk of disease spreading. Fusarium oxysporum f. sp. cepae, which causes fusarium basal rot disease, is considered one of the most harmful pathogens of onion and accounts for considerable crop losses annually. In this work, the capability of the PEN 3 electronic nose system to detect onion and shallot bulbs infected with F. oxysporum f. sp. cepae, to track the progression of fungal infection, and to discriminate between the varying proportions of infected onion bulbs was evaluated. To the best of our knowledge, this is a first report on successful application of an electronic nose to detect fungal infections in post-harvest onion and shallot bulbs. Sensor array responses combined with PCA provided a clear discrimination between non-infected and infected onion and shallot bulbs as well as differentiation between samples with varying proportions of infected bulbs. Classification models based on LDA, SVM, and k-NN algorithms successfully differentiate among various rates of infected bulbs in the samples with accuracy up to 96.9%. Therefore, the electronic nose was proved to be a potentially useful tool for rapid, non-destructive monitoring of the post-harvest crops.

Application of MOS Gas Sensors Coupled with Chemometrics Methods to Predict the Amount of Sugar and Carbohydrates in Potatoes

Five potato varieties were studied using an electronic nose with nine MOS sensors. Parameters measured included carbohydrate content, sugar level, and the toughness of the potatoes. Routine tests were carried out while the signals for each potato were measured, simultaneously, using an electronic nose. The signals obtained indicated the concentration of various chemical components. In addition to support vector machines (SVMs that were used for the classification of the samples, chemometric methods, such as the partial least squares regression (PLSR) method, the principal component regression (PCR) method, and the multiple linear regression (MLR) method, were used to create separate regression models for sugar and carbohydrates. The predictive power of the regression models was characterized by a coefficient of determination (R²), a root-mean-square error of prediction (RMSEP), and offsets. PLSR was able to accurately model the relationship between the smells of different types of potatoes, sugar, and carbohydrates. The highest and lowest accuracy of models for predicting sugar and carbohydrates was related to Marfona potatoes and Sprite cultivar potatoes. In general, in all cultivars, the accuracy in predicting the amount of carbohydrates was somewhat better than the accuracy in predicting the amount of sugar. Moreover, the linear function had 100% accuracy for training and validation in the C-SVM method for classification of five potato groups. The electronic nose could be used as a fast and non-destructive method for detecting different potato varieties. Researchers in the food industry will find this method extremely useful in selecting the desired product and samples.

Prediction of Inflammatory Bowel Disease Course Based on Fecal Scent

The early prediction of changes in disease state allows timely treatment of patients with inflammatory bowel disease (IBD) to be performed, which improves disease outcome. The aim of this pilot study is to explore the potential of fecal volatile organic compound (VOC) profiles to predict disease course. In this prospective cohort, IBD patients were asked to collect two fecal samples and fill in a questionnaire at set intervals. Biochemically, active disease was defined by FCP ≥ 250 mg/g and remission was defined by FCP < 100 mg/g. Clinically, active disease was defined by a Harvey Bradshaw Index (HBI) ≥ 5 for Crohn’s disease or by a Simple Clinical Colitis Activity Index (SCCAI) ≥ 3 for ulcerative colitis. Clinical remission was defined by an HBI < 4 or SCCAI ≤ 2. Fecal VOC profiles were measured using gas chromatography-ion mobility spectrometry (GC-IMS). The fecal samples collected first were included for VOC analysis to predict disease state at the following collection. A total of 182 subsequently collected samples met the disease-state criteria. The fecal VOC profiles of samples displaying low FCP levels at the first measurements differed between patients preceding exacerbation versus those who remained in remission (AUC 0.75; p < 0.01). Samples with FCP levels at the first time point displayed different VOC profiles in patients preceding remission compared with those whose disease remained active (AUC 0.86; p < 0.01). Based on disease activity scores, there were no significant differences in any of the comparisons. Alterations in fecal VOC profiles preceding changes in FCP levels may be useful to detect disease-course alterations at an early stage. This could lead to earlier treatment, decreased numbers of complications, surgery and hospital admission.

Minimal Gluten Exposure Alters Urinary Volatile Organic Compounds in Stable Coeliac Disease

Coeliac disease (CD) patients are distinguishable from healthy individuals via urinary volatile organic compounds (VOCs) analysis. We exposed 20 stable CD patients on gluten-free diet (GFDs) to a 14-day, 3 g/day gluten challenge (GCh), and assessed urinary VOC changes. A control cohort of 20 patients continued on GFD. Urine samples from Days 0, 7, 14, 28 and 56 were analysed using Lonestar FAIMS and Markes Gas Chromatography-Time of Flight-Mass Spectrometer (GC-TOF-MS). VOC signatures on D (day) 7-56 were compared with D0. Statistical analysis was performed using R. In GCh patients, FAIMS revealed significant VOC differences for all time points compared to D0. GC-TOF-MS revealed significant changes at D7 and D14 only. In control samples, FAIMS revealed significant differences at D7 only. GC-TOF-MS detected no significant differences. Chemical analysis via GC-MS-TOF revealed 12 chemicals with significantly altered intensities at D7 vs. D0 for GCh patients. The alterations persisted for six chemicals at D14 and one (N-methyltaurine) remained altered after D14. This low-dose, short-duration challenge was well tolerated. FAIMS and GC-TOF-MS detected VOC signature changes in CD patients when undergoing a minimal GCh. These findings suggest urinary VOCs could have a role in monitoring dietary compliance in CD patients.

Detection of spontaneous preterm birth by maternal urinary volatile organic compound analysis: A prospective cohort study

Accurate prediction of preterm birth is currently challenging, resulting in unnecessary maternal hospital admittance and fetal overexposure to antenatal corticosteroids. Novel biomarkers like volatile organic compounds (VOCs) hold potential for predictive, bed-side clinical applicability. In a proof of principle study, we aimed to assess the predictive potential of urinary volatile organic compounds in the identification of pregnant women at risk for preterm birth. Urine samples of women with a high risk for preterm birth (≧24 + 0 until 36 + 6 weeks) were collected prospectively and analyzed for VOCs using gas chromatography coupled with an ion mobility spectrometer (GS-IMS). Urinary VOCs of women delivering preterm were compared with urine samples of women with suspicion of preterm birth collected at the same gestation period but delivering at term. Additionally, the results were also interpreted in combination with patient characteristics, such as physical examination at admission, microbial cultures, and placental pathology. In our cohort, we found that urinary VOCs of women admitted for imminent preterm birth were not significantly different in the overall group of women delivering preterm vs. term. However, urinary VOCs of women admitted for imminent preterm birth and delivering between 28 + 0 until 36 + 6 weeks compared to women with a high risk for preterm birth during the same gestation period and eventually delivering at term (>37 + 0 weeks) differed significantly (area under the curve: 0.70). In addition, based on the same urinary VOCs, we could identify women with a confirmed chorioamnionitis (area under the curve: 0.72) and urinary tract infection (area under the curve: 0.97). In conclusion, urinary VOCs hold potential for non-invasive, bedside prediction of preterm birth and on the spot identification of intra-uterine infection and urinary tract infections. We suggest these observations are further explored in larger populations.

Direct in situ spectroscopic evidence of the crucial role played by surface oxygen vacancies in the O2-sensing mechanism of SnO2

Conductometric gas sensors (CGS) provide a reproducible gas response at a low cost but their operation mechanisms are still not fully understood. In this paper, we elucidate the nature of interactions between SnO₂, a common gas-sensitive material, and O₂, a ubiquitous gas central to the detection mechanisms of CGS. Using synchrotron radiation, we investigated a working SnO₂ sensor under operando conditions via near-ambient pressure (NAP) XPS with simultaneous resistance measurements, and created a depth profile of the variable near-surface stoichiometry of SnO_2−x as a function of O₂ pressure. Our results reveal a correlation between the dynamically changing surface oxygen vacancies and the resistance response in SnO₂-based CGS. While oxygen adsorbates were observed in this study we conclude that these are an intermediary in oxygen transport between the gas phase and the lattice, and that surface oxygen vacancies, not the observed oxygen adsorbates, are central to response generation in SnO₂-based gas sensors.

NAP-XPS characterisation of SnO₂ under operando conditions shows that resistance change, band bending and surface O-vacancy concentration are correlated with ambient O₂ concentration, challenging current preconceptions of gas sensor function.

Urinary Volatiles and Chemical Characterisation for the Non-Invasive Detection of Prostate and Bladder Cancers

Bladder cancer (BCa) and prostate cancer (PCa) are some of the most common cancers in the world. In both BCa and PCa, the diagnosis is often confirmed with an invasive technique that carries a risk to the patient. Consequently, a non-invasive diagnostic approach would be medically desirable and beneficial to the patient. The use of volatile organic compounds (VOCs) for disease diagnosis, including cancer, is a promising research area that could support the diagnosis process. In this study, we investigated the urinary VOC profiles in BCa, PCa patients and non-cancerous controls by using gas chromatography-ion mobility spectrometry (GC-IMS) and gas chromatography time-of-flight mass spectrometry (GC-TOF-MS) to analyse patient samples. GC-IMS separated BCa from PCa (area under the curve: AUC: 0.97 (0.93-1.00)), BCa vs. non-cancerous (AUC: 0.95 (0.90-0.99)) and PCa vs. non-cancerous (AUC: 0.89 (0.83-0.94)) whereas GC-TOF-MS differentiated BCa from PCa (AUC: 0.84 (0.73-0.93)), BCa vs. non-cancerous (AUC: 0.81 (0.70-0.90)) and PCa vs. non-cancerous (AUC: 0.94 (0.90-0.97)). According to our study, a total of 34 biomarkers were found using GC-TOF-MS data, of which 13 VOCs were associated with BCa, seven were associated with PCa, and 14 VOCs were found in the comparison of BCa and PCa.

Non-Invasive Detection and Staging of Colorectal Cancer Using a Portable Electronic Nose

Electronic noses (e-nose) offer potential for the detection of cancer in its early stages. The ability to analyse samples in real time, at a low cost, applying easy-to-use and portable equipment, gives e-noses advantages over other technologies, such as Gas Chromatography-Mass Spectrometry (GC-MS). For diseases such as cancer with a high mortality, a technology that can provide fast results for use in routine clinical applications is important. Colorectal cancer (CRC) is among the highest occurring cancers and has high mortality rates, if diagnosed late. In our study, we investigated the use of portable electronic nose (PEN3), with further analysis using GC-TOF-MS, for the analysis of gases and volatile organic compounds (VOCs) to profile the urinary metabolome of colorectal cancer. We also compared the different cancer stages with non-cancers using the PEN3 and GC-TOF-MS. Results obtained from PEN3, and GC-TOF-MS demonstrated high accuracy for the separation of CRC and non-cancer. PEN3 separated CRC from non-cancerous group with 0.81 AUC (Area Under the Curve). We used data from GC-TOF-MS to obtain a VOC profile for CRC, which identified 23 potential biomarker VOCs for CRC. Thus, the PEN3 and GC-TOF-MS were found to successfully separate the cancer group from the non-cancer group.

Exploratory Study Using Urinary Volatile Organic Compounds for the Detection of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) biomarkers are lacking in clinical practice. We therefore explored the pattern and composition of urinary volatile organic compounds (VOCs) in HCC patients. This was done in order to assess the feasibility of a potential non-invasive test for HCC, and to enhance our understanding of the disease. This pilot study recruited 58 participants, of whom 20 were HCC cases and 38 were non-HCC cases. The non-HCC cases included healthy individuals and patients with various stages of non-alcoholic fatty liver disease (NAFLD), including those with and without fibrosis. Urine was analysed using gas chromatography-ion mobility spectrometry (GC-IMS) and gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS). GC-IMS was able to separate HCC from fibrotic cases with an area under the curve (AUC) of 0.97 (0.91-1.00), and from non-fibrotic cases with an AUC of 0.62 (0.48-0.76). For GC-TOF-MS, a subset of samples was analysed in which seven chemicals were identified and tentatively linked with HCC. These include 4-methyl-2,4-bis(p-hydroxyphenyl)pent-1-ene (2TMS derivative), 2-butanone, 2-hexanone, benzene, 1-ethyl-2-methyl-, 3-butene-1,2-diol, 1-(2-furanyl)-, bicyclo(4.1.0)heptane, 3,7,7-trimethyl-, [1S-(1a,3β,6a)]-, and sulpiride. Urinary VOC analysis using both GC-IMS and GC-TOF-MS proved to be a feasible method of identifying HCC cases, and was also able to enhance our understanding of HCC pathogenesis.

Development of a Tuneable NDIR Optical Electronic Nose

Electronic nose (E-nose) technology provides an easy and inexpensive way to analyse chemical samples. In recent years, there has been increasing demand for E-noses in applications such as food safety, environmental monitoring and medical diagnostics. Currently, the majority of E-noses utilise an array of metal oxide (MOX) or conducting polymer (CP) gas sensors. However, these sensing technologies can suffer from sensor drift, poor repeatability and temperature and humidity effects. Optical gas sensors have the potential to overcome these issues. This paper reports on the development of an optical non-dispersive infrared (NDIR) E-nose, which consists of an array of four tuneable detectors, able to scan a range of wavelengths (3.1–10.5 μm). The functionality of the device was demonstrated in a series of experiments, involving gas rig tests for individual chemicals (CO₂ and CH₄), at different concentrations, and discriminating between chemical standards and complex mixtures. The optical gas sensor responses were shown to be linear to polynomial for different concentrations of CO₂ and CH₄. Good discrimination was achieved between sample groups. Optical E-nose technology therefore demonstrates significant potential as a portable and low-cost solution for a number of E-nose applications.

Prediction of mortality in severe acute malnutrition in hospitalized children by faecal volatile organic compound analysis: proof of concept

Children with severe acute malnutrition (SAM) display immature, altered gut microbiota and have a high mortality risk. Faecal volatile organic compounds (VOCs) reflect the microbiota composition and may provide insight into metabolic dysfunction that occurs in SAM. Here we determine whether analysis of faecal VOCs could identify children with SAM with increased risk of mortality. VOC profiles from children who died within six days following admission were compared to those who were discharged alive using machine learning algorithms. VOC profiles of children who died could be separated from those who were discharged with fair accuracy (AUC) = 0.71; 95% CI 0.59-0.87; P = 0.004). We present the first study showing differences in faecal VOC profiles between children with SAM who survived and those who died. VOC analysis holds potential to help discover metabolic pathways within the intestinal microbiome with causal association with mortality and target treatments in children with SAM.Trial Registration: The F75 study is registered at clinicaltrials.gov/ct2/show/NCT02246296.

Volatile organic compounds (VOCs) for the non-invasive detection of pancreatic cancer from urine

Pancreatic ductal adenocarcinoma (PDAC) is a particularly challenging cancer, with very low 5-year survival rates. This low survival rate is linked to late stage diagnosis, associated with the lack of approved biomarkers. One approach that is receiving considerable attention is the use of volatile organic compounds (VOCs) that emanate from biological waste as biomarkers for disease. In this study, we used urine as our biological matrix and two VOC analysis platforms: gas chromatography - ion mobility spectrometry (GC-IMS) and GC time-of-flight mass spectrometry (GC-TOF-MS). We measured the urinary headspace of samples from patients with PDAC, chronic pancreatitis (CP) and healthy controls. In total, 123 samples were tested from these groups. Results indicate that both GC-IMS and GC-TOF-MS were able to discriminate PDAC from healthy controls with high confidence and an AUC (area under the curve) in excess of 0.85. However, both methods struggled to separate CP from PDAC, with the best result of AUC 0.58. This indicates that both conditions produce similar biomarkers in the urinary headspace. Chemical identification suggests that 2,6-dimethyl-octane, nonanal, 4-ethyl-1,2-dimethyl-benzene and 2-pentanone play an important role in separating these groups. Therefore, both techniques validate this approach in identifying subjects for further investigation in a clinical setting.

Early detection and follow-up of colorectal neoplasia based on faecal volatile organic compounds

AIM

Early detection and removal of colorectal cancer (CRC) and advanced adenomas (AAs) decreases the incidence of and mortality from the disease. We aimed to evaluate the potential of faecal volatile organic compounds (VOCs) for detection and follow-up of colorectal adenoma using advanced electronic nose technology.

METHOD

This was a prospective multi-centre case-control cohort including two district hospitals and one tertiary referral hospital. Patients undergoing colonoscopy were instructed to collect a faecal sample prior to bowel cleansing and were included in the study when CRC, AAs, large adenomas (LAs; 0.5-1.0 cm), small adenomas (SAs; 0.1-0.5 cm) or no endoscopic abnormalities (controls) were observed. Patients undergoing polypectomy and controls were asked for a second sample after 3 months. Faecal VOCs were measured with gas chromatography-ion mobility spectrometry. Random forest, support vector machine, Gaussian process and neural net classification were used to evaluate accuracy.

RESULTS

In total, 14 patients with CRC, 64 with AAs, 69 with LAs, 127 with SAs and 227 controls were included. A second sample was collected from 32 polypectomy patients and 32 controls. Faecal VOCs discriminated CRC and adenomas from control [AUC (95% CI): CRC vs control 0.96 (0.89-1); AA vs control 0.96 (0.93-1); LA vs control 0.96 (0.92-0.99); SA vs control 0.96 (0.94-0.99)]. There were no significant differences between CRC and adenoma groups. Patients with adenomas and controls were discriminated prior to polypectomy, whereas 3 months after polypectomy VOC profiles were similar [T0 adenoma vs control 0.98 (0.95-1); T1 adenoma vs control 0.55 (0.40-0.69)].

CONCLUSIONS

Faecal VOC profiles may be useful for early detection of CRC and adenomas and the timing of polyp surveillance as polypectomy led to a normalization of the VOC profile.

Sniffing Out Urinary Tract Infection-Diagnosis Based on Volatile Organic Compounds and Smell Profile

Current available methods for the clinical diagnosis of urinary tract infection (UTI) rely on a urine dipstick test or culturing of pathogens. The dipstick test is rapid (available in 1-2 min), but has a low positive predictive value, while culturing is time-consuming and delays diagnosis (24-72 h between sample collection and pathogen identification). Due to this delay, broad-spectrum antibiotics are often prescribed immediately. The over-prescription of antibiotics should be limited, in order to prevent the development of antimicrobial resistance. As a result, there is a growing need for alternative diagnostic tools. This paper reviews applications of chemical-analysis instruments, such as gas chromatography-mass spectrometry (GC-MS), selected ion flow tube mass spectrometry (SIFT-MS), ion mobility spectrometry (IMS), field asymmetric ion mobility spectrometry (FAIMS) and electronic noses (eNoses) used for the diagnosis of UTI. These methods analyse volatile organic compounds (VOCs) that emanate from the headspace of collected urine samples to identify the bacterial pathogen and even determine the causative agent's resistance to different antibiotics. There is great potential for these technologies to gain wide-spread and routine use in clinical settings, since the analysis can be automated, and test results can be available within minutes after sample collection. This could significantly reduce the necessity to prescribe broad-spectrum antibiotics and allow the faster and more effective use of narrow-spectrum antibiotics.

Volatile organic compound analysis, a new tool in the quest for preterm birth prediction-an observational cohort study

Preterm birth is the leading cause of death worldwide in children under five years. Due to its complex multifactorial nature, prediction is a challenge. Current research is aiming to develop accurate predictive models using patient history, ultrasound and biochemical markers. Volatile organic compound (VOC) analysis is an approach, which has good diagnostic potential to predict many disease states. Analysis of VOCs can reflect both the microbiome and host response to a condition. We aimed to ascertain if VOC analysis of vaginal swabs, taken throughout pregnancy, could predict which women go on to deliver preterm. Our prospective observational cohort study demonstrates that VOC analysis of vaginal swabs, taken in the midtrimester, is a fair test (AUC 0.79) for preterm prediction, with a sensitivity of 0.66 (95%CI 0.56–0.75) and specificity 0.89 (95%CI 0.82–0.94). Using vaginal swabs taken closest to delivery, VOC analysis is a good test (AUC 0.84) for the prediction of preterm birth with a sensitivity of 0.73 (95%CI 0.64–0.81) and specificity of 0.90 (95%CI 0.82–0.95). Consequently, VOC analysis of vaginal swabs has potential to be used as a predictive tool. With further work it could be considered as an additional component in models for predicting preterm birth.

The faecal scent of inflammatory bowel disease: Detection and monitoring based on volatile organic compound analysis

BACKGROUND

Inflammatory bowel disease (IBD) is diagnosed and monitored using endoscopic assessment, which is invasive and costly. In this study, potential of faecal volatile organic compounds (VOC) analysis for IBD detection and identification of disease activity was evaluated.

METHODS

IBD patients visiting outpatient clinics of participating tertiary hospitals were included. Active disease was defined as FCP ≥250 mg/g, remission as FCP <100 mg/g with Harvey Bradshaw Index <4 for Crohn's disease (CD) or Simple Clinical Colitis Activity Index <3 for ulcerative colitis (UC). Healthy controls (HC) were patients without mucosal abnormalities during colonoscopy. Faecal samples were measured using gas chromatography-ion mobility spectrometry.

RESULTS

A total of 280 IBD patients collected 107 CDa, 84 CDr, 80 UCa and 63 UCr samples. Additionally, 227 HC provided one faecal sample. UC and CD were discriminated from HC with high accuracy (AUC (95%CI): UCa vs HC 0.96(0.94-0.99); UCr vs HC 0.95(0.93-0.98); CDa vs HC 0.96(0.94-0.99); CDr vs HC 0.95(0.93-0.98)). There were small differences between UC and CD (0.55(0.50-0.6)) and no differences between active disease and remission (UCa vs UCr 0.63(0.44-0.82); CDa vs CDr 0.52(0.39-0.65)).

CONCLUSION

Our study outcomes imply that faecal VOC analysis holds potential for identifying biomarkers for IBD detection but not for monitoring disease activity.

Humidity-Tolerant Ultrathin NiO Gas-Sensing Films

When the gas sensor active layer film thickness is decreased, increased sensitivity to changes in the adsorbate concentration is expected when measuring the resistance of the layer, in particular when this thickness is on the order of the Debye length of the material (one-tens of nanometers); however, this is demonstrated only for a limited number of materials. Herein, ultrathin NiO films of different thicknesses (8-21 nm) have been deposited via chemical vapor deposition to fabricate gas sensor devices. Sensor performance for a range of NO₂ concentrations (800 part-per-billion to 7 part-per-million) was evaluated and an optimum operating temperature of 125 °C determined. The dependence of the potential relative changes with respect to the NO₂ concentration and of the sensor signal with respect to the geometrical parameters was qualitatively evaluated to derive a transduction model capable of fitting the experimental results. The selective sensitivity toward NO₂ was confirmed by the limited response for different reducing gases, CO, CH₄, NH₃, and SO₂, under optimum operating conditions, and the sensor signal toward NO₂ increased with decreasing thickness, demonstrating that the concept of a Debye length dependence of sensitivity is applicable for the p-type semiconductor NiO. In addition, these NiO sensors were exposed to different relative levels of humidity over a wide range of operating temperatures and were found to display humidity tolerance far superior to those in previous reports on SnO₂ materials.

The Detection of Wound Infection by Ion Mobility Chemical Analysis

Surgical site infection represents a large burden of care in the National Health Service. Current methods for diagnosis include a subjective clinical assessment and wound swab culture that may take several days to return a result. Both techniques are potentially unreliable and result in delays in using targeted antibiotics. Volatile organic compounds (VOCs) are produced by micro-organisms such as those present in an infected wound. This study describes the use of a device to differentiate VOCs produced by an infected wound vs. colonised wound. Malodourous wound dressings were collected from patients, these were a mix of post-operative wounds and vascular leg ulcers. Wound microbiology swabs were taken and antibiotics commenced as clinically appropriate. A control group of soiled, but not malodorous wound dressings were collected from patients who had a split skin graft (SSG) donor site. The analyser used was a G.A.S. GC-IMS. The results from the samples had a sensitivity of 100% and a specificity of 88%, with a positive predictive value of 90%. An area under the curve (AUC) of 91% demonstrates an excellent ability to discriminate those with an infected wound from those without. VOC detection using GC-IMS has the potential to serve as a diagnostic tool for the differentiation of infected and non-infected wounds and facilitate the treatment of wound infections that is cost effective, non-invasive, acceptable to patients, portable, and reliable.

Breath-based non-invasive diagnosis of Alzheimer's disease: a pilot study

Early detection of Alzheimer's disease (AD) will help researchers to better understand the disease and develop improved treatments. Recent developments have thus focused on identifying biomarkers for mild cognitive impairment due to AD (MCI) and AD during the preclinical phase. The aim of this pilot study is to determine whether exhaled volatile organic compounds (VOCs) can be used as a non-invasive method to distinguish controls from MCI, controls from AD and to determine whether there are differences between MCI and AD. The study used gas chromatography-ion mobility spectrometry (GC-IMS) techniques. Confounding factors, such as age, smoking habits, gender and alcohol consumption are investigated to demonstrate the efficacy of results. One hundred subjects were recruited including 50 controls, 25 AD and 25 MCI patients. The subject cohort was age- and gender-matched to minimise bias. Breath samples were analysed using a commercial GC-IMS instrument (G.A.S. BreathSpec, Dortmund, Germany). Data analysis indicates that the GC-IMS signal was consistently able to separate between diagnostic groups [AUC ± 95%, sensitivity, specificity], controls versus MCI: [0.77 (0.64-0.90), 0.68, 0.80], controls versus AD: [0.83 (0.72-0.94), 0.60, 0.96], and MCI versus AD: [0.70 (0.55-0.85), 0.60, 0.84]. VOC analysis indicates that six compounds play a crucial role in distinguishing between diagnostic groups. Analysis of possible confounding factors indicate that gender, age, smoking habits and alcohol consumption have insignificant influence on breath content. This pilot study confirms the utility of exhaled breath analysis to distinguish between AD, MCI and control subjects. Thus, GC-IMS offers great potential as a non-invasive, high-throughput, diagnostic technique for diagnosing and potentially monitoring AD in a clinical setting.

Detection of Group B Streptococcus in pregnancy by vaginal volatile organic compound analysis: a prospective exploratory study

Our objective was to assess whether volatile organic compound (VOC) analysis of vaginal swabs can detect maternal Group B Streptococcus (GBS) during pregnancy in a prospective exploratory study. Around 243 women attending a high-risk antenatal clinic at one university teaching hospital in the UK consented to take part and provide vaginal swabs throughout pregnancy. VOC analysis of vaginal swabs was undertaken and compared with the reference standard of GBS detected using enrichment culture method. The chemical components that emanated from the vaginal swabs were measured by gas chromatograph ion mobility spectrometry. This platform has both high sensitivity and good specificity to a range of chemical compounds. Our main outcome was to determine the sensitivity and specificity of VOC analysis for the detection of maternal GBS in vaginal swabs during pregnancy. Our study has demonstrated that the sensitivity and specificity of the VOC analysis by GC-IMS for the detection of GBS from vaginal swabs was 0.81 (95% confidence interval [CI], 0.71-0.89) and 0.97 (95% CI, 0.91-1) respectively. We conclude that the use of VOCs as biomarkers for the detection of maternal GBS in the vagina is a novel tool. As this test produces results within minutes and is of low unit test cost, it has the potential to be used in clinical settings, where fast diagnosis is important, for example, a patient in early labour.

Urinary volatile organic compound markers and colorectal anastomotic leakage

AIM

Inflammatory markers such as serum C-reactive protein (CRP) are used as routine markers to detect anastomotic leakage following colorectal surgery. However, CRP is characterized by a relatively low predictive value, emphasizing the need for the development of novel diagnostic approaches. Volatile organic compounds (VOCs) are gaseous metabolic products deriving from all conceivable bodily excrements and reflect (alterations in) the patient's physical status. Therefore, VOCs are increasingly considered as potential non-invasive diagnostic biomarkers. The aim of this study was to assess the diagnostic accuracy of urinary VOCs for colorectal anastomotic leakage.

METHODS

In this explorative multicentre study, urinary VOC profiles of 22 patients with confirmed anastomotic leakage and 27 uneventful control patients following colorectal surgery were analysed by field asymmetric ion mobility spectrometry (FAIMS).

RESULTS

Urinary VOCs of patients with anastomotic leakage could be distinguished from those of control patients with high accuracy: area under the receiver operating characteristics curve 0.91 (95% CI 0.81-1.00, P < 0.001), sensitivity 86% and specificity 93%. Serum CRP was significantly increased in patients with a confirmed anastomotic leak but with lower diagnostic accuracy compared to VOC analysis (area under the receiver operating characteristics curve 0.82, 95% CI 0.68-0.95, P < 0.001). Combining VOCs and CRP did not result in a significant improvement of the diagnostic performance compared to VOCs alone.

CONCLUSION

Analysis by FAIMS allowed for discrimination between urinary VOC profiles of patients with a confirmed anastomotic leak and control patients following colorectal surgery. A superior accuracy compared to CRP and apparently high specificity was observed, underlining the potential as a non-invasive biomarker for the detection of colorectal anastomotic leakage.

Investigation of paediatric PKU breath malodour, comparing glycomacropeptide with phenylalanine free L-amino acid supplements

In clinical practice, caregivers of children with phenylketonuria (PKU) report that their children have breath malodour. This might be linked to the regular consumption of low phenylalanine (Phe)/Phe-free protein substitutes (PS), which are an essential component of a low-Phe diet. Oral malodour can negatively affect interpersonal communication, lead to bullying, low self-esteem and social isolation. In this longitudinal cross-over study, exhaled volatile organic compounds (VOCs) were measured using gas chromatography-ion mobility spectrometry. 40 children (20 PKU, 20 controls) were recruited. Subjects with PKU took either L-Amino Acid (L-AA) or Casein Glycomacropeptide (CGMP-AA) exclusively for 1 week, in a randomised order. On the seventh day, seven exhaled breath samples were collected over a 10 h period. Subjects then transferred to the other PS for a week and on day seven, provided seven further breath samples. All subjects had a standardised menu using low-Phe food alternatives and all food intake was measured and recorded. In the PKU group, the aim was to collect samples 30 min after consuming PS. In 3 subjects, breath was collected 5 min post-PS consumption. Fasted L-AA and CGMP-AA breath samples contained a similar number of VOC peaks (10-12) as controls. Longitudinal breath testing results demonstrate that there was no significant difference in the number of exhaled VOCs, comparing L-AA or CGMP-AA with controls, or between PS (12-18 VOC peaks). Breath analysed immediately after consumption of PS (n = 3) showed an immediate increase in the number of VOC peaks (25-30), but these were no longer detectable at 30 min post-consumption. This suggests PS have a transient effect on exhaled breath. Measurements taken 30 min after consuming L-AA or CGMP-AA were not significantly different to controls. This indicates that timing food and drinks with PS consumption may be a potential solution for carers to reduce or eliminate unpleasant PS-related breath odours.

Colorectal cancer and adenoma screening using urinary volatile organic compound (VOC) detection: early results from a single-centre bowel screening population (UK BCSP)

Background

The United Kingdom (UK) bowel cancer screening programme has reduced mortality from colorectal cancer (CRC), but poor uptake with stool-based tests and lack of specificity of faecal occult blood testing (FOBT), has prompted investigation for a more suitable screening test. The aim of this study was to investigate the feasibility of a urinary volatile organic compounds (VOC)-based screening tool for CRC.

Methods

The urine from FOBT-positive patients was analysed using field asymmetric ion mobility spectrometry (FAIMS) and gas chromatography coupled with ion mobility spectrometry (GC–IMS). Data were analysed using a machine learning algorithm to calculate the test accuracy for correct classification of CRC against adenomas and other gastrointestinal pathology.

Results

One hundred and sixty-three patients were enrolled in the study. Test accuracy was high for differentiating CRC from control: area under the curve (AUC) 0.98 (95% CI 0.93–1) and 0.82 (95% CI 0.67–0.97) using FAIMS and GC–IMS respectively. Correct classification of CRC from adenoma was high with AUC range 0.83–0.92 (95% CI 0.43–1.0). Classification of adenoma from control was poor with AUC range 0.54–0.61 (95% CI 0.47–0.75) using both analytical modalities.

Conclusions

CRC was correctly distinguished from adenomas or no bowel pathology using urinary VOC markers, within the bowel screening population. This pilot study demonstrates the potential of this method for CRC detection, with higher test uptake and superior sensitivity than FOBT. In addition, this is the first application of GC–IMS in CRC detection which has shown high test accuracy and usability.

Electronic supplementary material

The online version of this article (10.1007/s10151-019-01963-6) contains supplementary material, which is available to authorized users.

Breath Analysis Using eNose and Ion Mobility Technology to Diagnose Inflammatory Bowel Disease-A Pilot Study

Early diagnosis of inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), remains a clinical challenge with current tests being invasive and costly. The analysis of volatile organic compounds (VOCs) in exhaled breath and biomarkers in stool (faecal calprotectin (FCP)) show increasing potential as non-invasive diagnostic tools. The aim of this pilot study is to evaluate the efficacy of breath analysis and determine if FCP can be used as an additional non-invasive parameter to supplement breath results, for the diagnosis of IBD. Thirty-nine subjects were recruited (14 CD, 16 UC, 9 controls). Breath samples were analysed using an in-house built electronic nose (Wolf eNose) and commercial gas chromatograph-ion mobility spectrometer (G.A.S. BreathSpec GC-IMS). Both technologies could consistently separate IBD and controls [AUC ± 95%, sensitivity, specificity], eNose: [0.81, 0.67, 0.89]; GC-IMS: [0.93, 0.87, 0.89]. Furthermore, we could separate CD from UC, eNose: [0.88, 0.71, 0.88]; GC-IMS: [0.71, 0.86, 0.62]. Including FCP did not improve distinction between CD vs UC; eNose: [0.74, 1.00, 0.56], but rather, improved separation of CD vs controls and UC vs controls; eNose: [0.77, 0.55, 1.00] and [0.72, 0.89, 0.67] without FCP, [0.81, 0.73, 0.78] and [0.90, 1.00, 0.78] with FCP, respectively. These results confirm the utility of breath analysis to distinguish between IBD-related diagnostic groups. FCP does not add significant diagnostic value to breath analysis within this study.

Non-Invasive Detection of Anastomotic Leakage Following Esophageal and Pancreatic Surgery by Urinary Analysis

BACKGROUND

Esophagectomy or pancreaticoduodenectomy is the standard surgical approach for patients with tumors of the esophagus or pancreatic head. Postoperative mortality is strongly correlated with the occurrence of anastomotic leakage (AL). Delay in diagnosis leads to delay in treatment, which ratifies the need for development of novel and accurate non-invasive diagnostic tests for detection of AL. Urinary volatile organic compounds (VOCs) reflect the metabolic status of an individual, which is associated with a systemic immunological response. The aim of this study was to determine the diagnostic accuracy of urinary VOCs to detect AL after esophagectomy or pancreaticoduodenectomy.

METHODS

In the present study, urinary VOCs of 63 patients after esophagectomy (n = 31) or pancreaticoduodenectomy (n = 32) were analyzed by means of field asymmetric ion mobility spectrometry. AL was defined according to international study groups.

RESULTS

AL was observed in 15 patients (24%). Urinary VOCs of patients with AL after pancreaticoduodenectomy could be distinguished from uncomplicated controls, area under the curve 0.85 (95% CI 0.76-0.93), sensitivity 76%, and specificity 77%. However, this was not observed following esophagectomy, area under the curve 0.51 (95% CI 0.37-0.65).

CONCLUSION

In our study population AL following pancreaticoduodenectomy could be discriminated from uncomplicated controls by means of urinary VOC analysis, NTC03203434.

Risk stratification of symptomatic patients suspected of colorectal cancer using faecal and urinary markers

AIM

Faecal markers, such as the faecal immunochemical test for haemoglobin (FIT) and faecal calprotectin (FCP), have been increasingly used to exclude colorectal cancer (CRC) and colonic inflammation. However, in those with lower gastrointestinal symptoms there are considerable numbers who have cancer but have a negative FIT test (i.e. false negative), which has impeded its use in clinical practice. We undertook a study of diagnostic accuracy CRC using FIT, FCP and urinary volatile organic compounds (VOCs) in patients with lower gastrointestinal symptoms.

METHOD

One thousand and sixteen symptomatic patients with suspected CRC referred by family physicians were recruited prospectively in accordance with national referring protocol. A total of 562 patients who completed colonic investigations, in addition to providing stool for FIT and FCP as well as urine samples for urinary VOC measurements, were included in the final outcome measures.

RESULTS

The sensitivity and specificity for CRC using FIT was 0.80 [95% confidence interval (CI) 0.66-0.93] and 0.93 (CI 0.91-0.95), respectively. For urinary VOCs, the sensitivity and specificity for CRC was 0.63 (CI 0.46-0.79) and 0.63 (CI 0.59-0.67), respectively. However, for those who were FIT-negative CRC (i.e. false negatives), the addition of urinary VOCs resulted in a sensitivity of 0.97 (CI 0.90-1.0) and specificity of 0.72 (CI 0.68-0.76).

CONCLUSIONS

When applied to the FIT-negative group, urinary VOCs improve CRC detection (sensitivity rises from 0.80 to 0.97), thus showing promise as a second-stage test to complement FIT in the detection of CRC.

Differentiation Between Pediatric Irritable Bowel Syndrome and Inflammatory Bowel Disease Based on Fecal Scent: Proof of Principle Study

BACKGROUND

The diagnostic work-up of pediatric irritable bowel syndrome (IBS) and functional abdominal pain-not otherwise specified (FAP-NOS) commonly includes invasive tests for discrimination from inflammatory bowel disease (IBD). As this carries a high burden on patients, an ongoing need exists for development of noninvasive diagnostic biomarkers for IBS and FAP-NOS. Several studies have shown microbiota alterations in IBS/FAP, which are considered to be reflected by fecal volatile organic compounds (VOCs). The object of the study was to evaluate whether pediatric IBS/FAP-NOS could be discriminated from IBD and healthy controls by fecal VOC analysis.

METHODS

IBS/FAP-NOS was diagnosed according to the ROME IV criteria, and de novo IBD patients and healthy controls (HCs) aged 4 to 17 years were matched on age and sex. Fecal VOCs were analyzed by means of field asymmetric ion mobility spectrometry.

RESULTS

Fecal VOCs of 15 IBS/FAP-NOS, 30 IBD (15 ulcerative colitis, 15 Crohn's disease) patients and 30 HCs were analyzed and compared. Differentiation between IBS/FAP-NOS and IBD was feasible with high accuracy (area under the curve [AUC], 0.94; 95% confidence interval [CI], 0.88-1; P < 0.00001). IBS/FAP-NOS profiles could not be differentiated from HCs (AUC, 0.59; 95% CI, 0.41-0.77; P = 0.167), whereas IBD profiles could with high accuracy (AUC, 0.96; 95% CI, 0.93-1; P < 0.00001).

CONCLUSION

Pediatric IBS/FAP-NOS could be differentiated from IBD by fecal VOC analysis with high accuracy, but not from healthy controls. The latter finding limits the potential of fecal VOCs to serve as a diagnostic biomarker for IBS/FAP-NOS. However, VOC could possibly serve as additional noninvasive biomarker to differentiate IBS/FAP-NOS from IBD. 10.1093/ibd/izy151_video1izy151.video15786446046001.

An improved machine learning pipeline for urinary volatiles disease detection: Diagnosing diabetes

Motivation

The measurement of disease biomarkers in easily–obtained bodily fluids has opened the door to a new type of non–invasive medical diagnostics. New technologies are being developed and fine–tuned in order to make this possibility a reality. One such technology is Field Asymmetric Ion Mobility Spectrometry (FAIMS), which allows the measurement of volatile organic compounds (VOCs) in biological samples such as urine. These VOCs are known to contain a range of information on the relevant person’s metabolism and can in principle be used for disease diagnostic purposes. Key to the effective use of such data are well–developed data processing pipelines, which are necessary to extract the most useful data from the complex underlying biological structure.

Results

In this study, we present a new data analysis pipeline for FAIMS data, and demonstrate a number of improvements over previously used methods. We evaluate the effect of a series of candidate operational steps during data processing, such as the use of wavelet transforms, principal component analysis (PCA), and classifier ensembles. We also demonstrate the use of FAIMS data in our pipeline to diagnose diabetes on the basis of a simple urine sample using machine learning classifiers. We present results for data generated from a case-control study of 115 urine samples, collected from 72 type II diabetic patients, with 43 healthy volunteers as negative controls. The resulting pipeline combines the steps that resulted in the best classification model performance. These include the use of a two–dimensional discrete wavelet transform, and the Wilcoxon rank–sum test for feature selection. We are able to achieve a best ROC curve AUC of 0.825 (0.747–0.9, 95% CI) for classification of diabetes vs control. We also note that this result is robust to changes in the data pipeline and different analysis runs, with AUC > 0.80 achieved in a range of cases. This is a substantial improvement in performance over previously used data processing methods in this area. Our ability to make strong statements about FAIMS ability to diagnose diabetes is sadly limited, as we found confounding effects from the demographics when including these data in the pipeline. The demographics alone produced a best AUC of 0.87 (0.795–0.94, 95% CI). While the combination of the demographics and FAIMS data resulted in an improvement on the AUC (0.907; 0.848–0.97, 95% CI), it did not prove to be a significant difference. Nevertheless, the pipeline itself shows a significant improvement in performance over more basic methods which have been used with FAIMS data in the past.

Optimized Sampling Conditions for Fecal Volatile Organic Compound Analysis by Means of Field Asymmetric Ion Mobility Spectrometry

Fecal volatile organic compounds (VOCs) are increasingly considered to be potential noninvasive, diagnostic biomarkers for various gastrointestinal diseases. Knowledge of the influence of sampling conditions on VOC outcomes is limited. We aimed to evaluate the effects of sampling conditions on fecal VOC profiles and to assess under which conditions an optimal diagnostic accuracy in the discrimination between pediatric inflammatory bowel disease (IBD) and controls could be obtained. Fecal samples from de novo treatment-naïve pediatric IBD patients and healthy controls (HC) were used to assess the effects of sampling conditions compared to the standard operating procedure (reference standard), defined as 500 mg of sample mass diluted with 10 mL tap water, using field asymmetric ion mobility spectrometry (FAIMS). A total of 17 IBD (15 CD (Crohn's disease) and 2 UC (ulcerative colitis)) and 25 HC were included. IBD and HC could be discriminated with high accuracy (accuracy = 0.93, AUC = 0.99, p < 0.0001). A smaller fecal sample mass resulted in a decreased diagnostic accuracy (300 mg accuracy = 0.77, AUC = 0.69, p = 0.02; 100 mg accuracy = 0.70, AUC = 0.74, p = 0.003). A loss of diagnostic accuracy was seen toward increased numbers of thaw–freeze cycles (one cycle, accuracy = 0.61, AUC = 0.80, p = 0.0004; two cycles, accuracy = 0.64, AUC = 0.56, p = 0.753; and three cycles, accuracy = 0.57, AUC = 0.50, p = 0.5101) and when samples were kept at room temperature for 180 min prior to analysis (accuracy = 0.60, AUC = 0.51, p = 0.46). Diagnostic accuracy of VOC profiles was not significantly influenced by storage duration differences of 20 months. The application of a 500 mg sample mass analyzed after one thaw–freeze cycle showed the best discriminative accuracy for the differentiation of IBD and HC. VOC profiles and diagnostic accuracy were significantly affected by sampling conditions, underlining the need for the implementation of standardized protocols in fecal VOC analysis.

Identifying volatile metabolite signatures for the diagnosis of bacterial respiratory tract infection using electronic nose technology: A pilot study

OBJECTIVES

New point of care diagnostics are urgently needed to reduce the over-prescription of antimicrobials for bacterial respiratory tract infection (RTI). We performed a pilot cross sectional study to assess the feasibility of gas-capillary column ion mobility spectrometer (GC-IMS), for the analysis of volatile organic compounds (VOC) in exhaled breath to diagnose bacterial RTI in hospital inpatients.

METHODS

71 patients were prospectively recruited from the Acute Medical Unit of the Royal Liverpool University Hospital between March and May 2016 and classified as confirmed or probable bacterial or viral RTI on the basis of microbiologic, biochemical and radiologic testing. Breath samples were collected at the patient's bedside directly into the electronic nose device, which recorded a VOC spectrum for each sample. Sparse principal component analysis and sparse logistic regression were used to develop a diagnostic model to classify VOC spectra as being caused by bacterial or non-bacterial RTI.

RESULTS

Summary area under the receiver operator characteristic curve was 0.73 (95% CI 0.61-0.86), summary sensitivity and specificity were 62% (95% CI 41-80%) and 80% (95% CI 64-91%) respectively (p = 0.00147).

CONCLUSIONS

GC-IMS analysis of exhaled VOC for the diagnosis of bacterial RTI shows promise in this pilot study and further trials are warranted to assess this technique.

A simple breath test for tuberculosis using ion mobility: A pilot study

Tuberculosis (TB) remains one of the world's major health burdens with 9.6 million new infections globally. Though considerable progress has been made in reduction of TB incidence and mortality, there is a continuous need for lower cost, simpler and more robust means of diagnosis. One method that may fulfil these requirements is in the area of breath analysis. In this study we analysed the breath of 21 patients with pulmonary or extra-pulmonary TB, recruited from a UK teaching hospital (University Hospital Coventry and Warwickshire) before or within 1 week of commencing treatment for TB. TB diagnosis was confirmed by reference tests (mycobacterial culture), histology or radiology. 19 controls were recruited to calculate specificity; these patients were all interferon-gamma release assay negative (T.SPOT(®).TB, Oxford Immunotec Ltd.). Whole breath samples were collected with subsequent chemical analysis undertaken by Ion Mobility Spectrometry. Our results produced a sensitivity of 81% and a specificity of 79% for all cases of TB (pulmonary and extra-pulmonary). Though lower than other studies analysing pulmonary TB alone, we believe that this technique shows promise, and a higher sensitivity could be achieved by further improving our sample capture methodology.

Breathomics--exhaled volatile organic compound analysis to detect hepatic encephalopathy: a pilot study

The current diagnostic challenge with diagnosing hepatic encephalopathy (HE) is identifying those with minimal HE as opposed to the more clinically apparent covert/overt HE. Rifaximin, is an effective therapy but earlier identification and treatment of HE could prevent liver disease progression and hospitalization. Our pilot study aimed to analyse breath samples of patients with different HE grades, and controls, using a portable electronic (e) nose. 42 patients were enrolled; 22 with HE and 20 controls. Bedside breath samples were captured and analysed using an uvFAIMS machine (portable e-nose). West Haven criteria applied and MELD scores calculated. We classify HE patients from controls with a sensitivity and specificity of 0.88 (0.73-0.95) and 0.68 (0.51-0.81) respectively, AUROC 0.84 (0.75-0.93). Minimal HE was distinguishable from covert/overt HE with sensitivity of 0.79 and specificity of 0.5, AUROC 0.71 (0.57-0.84). This pilot study has highlighted the potential of breathomics to identify VOCs signatures in HE patients for diagnostic purposes. Importantly this was performed utilizing a non-invasive, portable bedside device and holds potential for future early HE diagnosis.

Variation in Gas and Volatile Compound Emissions from Human Urine as It Ages, Measured by an Electronic Nose

The medical profession is becoming ever more interested in the use of gas-phase biomarkers for disease identification and monitoring. This is due in part to its rapid analysis time and low test cost, which makes it attractive for many different clinical arenas. One technology that is showing promise for analyzing these gas-phase biomarkers is the electronic nose—an instrument designed to replicate the biological olfactory system. Of the possible biological media available to “sniff”, urine is becoming ever more important as it is easy to collect and to store for batch testing. However, this raises the question of sample storage shelf-life, even at −80 °C. Here we investigated the effect of storage time (years) on stability and reproducibility of total gas/vapour emissions from urine samples. Urine samples from 87 patients with Type 2 Diabetes Mellitus were collected over a four-year period and stored at −80 °C. These samples were then analyzed using FAIMS (field-asymmetric ion mobility spectrometry—a type of electronic nose). It was discovered that gas emissions (concentration and diversity) reduced over time. However, there was less variation in the initial nine months of storage with greater uniformity and stability of concentrations together with tighter clustering of the total number of chemicals released. This suggests that nine months could be considered a general guide to a sample shelf-life.

Non-Invasive Distinction of Non-Alcoholic Fatty Liver Disease using Urinary Volatile Organic Compound Analysis: Early Results

Background & Aims: Non-Alcoholic Fatty Liver Disease (NAFLD) is the commonest cause of chronic liver disease in the western world. Current diagnostic methods including Fibroscan have limitations, thus there is a need for more robust non-invasive screening methods. The gut microbiome is altered in several gastrointestinal and hepatic disorders resulting in altered, unique gut fermentation patterns, detectable by analysis of volatile organic compounds (VOCs) in urine, breath and faeces. We performed a proof of principle pilot study to determine if progressive fatty liver disease produced an altered urinary VOC pattern; specifically NAFLD and Non-Alcoholic Steatohepatitis (NASH).Methods: 34 patients were recruited: 8 NASH cirrhotics (NASH-C); 7 non-cirrhotic NASH; 4 NAFLD and 15 controls. Urine was collected and stored frozen. For assay, the samples were defrosted and aliquoted into vials, which were heated to 40±0.1°C and the headspace analyzed by FAIMS (Field Asymmetric Ion Mobility Spectroscopy). A previously used data processing pipeline employing a Random Forrest classification algorithm and using a 10 fold cross validation method was applied.Results: Urinary VOC results demonstrated sensitivity of 0.58 (0.33 - 0.88), but specificity of 0.93 (0.68 - 1.00) and an Area Under Curve (AUC) 0.73 (0.55 -0.90) to distinguish between liver disease and controls. However, NASH/NASH-C was separated from the NAFLD/controls with a sensitivity of 0.73 (0.45 - 0.92), specificity of 0.79 (0.54 - 0.94) and AUC of 0.79 (0.64 - 0.95), respectively.Conclusions: This pilot study suggests that urinary VOCs detection may offer the potential for early non-invasive characterisation of liver disease using 'smell prints' to distinguish between NASH and NAFLD.

Differentiating coeliac disease from irritable bowel syndrome by urinary volatile organic compound analysis--a pilot study

Coeliac disease (CD), a T-cell-mediated gluten sensitive enteropathy, affects ∼ 1% of the UK population and can present with wide ranging clinical features, often being mistaken for Irritable Bowel Syndrome (IBS). Heightened clinical awareness and serological screening identifies those with potential coeliac disease; the diagnosis is confirmed with duodenal biopsies, and symptom improvement with a gluten-free diet. Limitations to diagnosis are false negative serology and reluctance to undergo biopsy. The gut microbiome is altered in several gastrointestinal disorders, causing altered gut fermentation patterns recognisable by volatile organic compounds (VOC) analysis in urine, breath and faeces. We aimed to determine if CD alters the urinary VOC pattern, distinguishing it from IBS. 47 patients were recruited, 27 with established CD, on gluten free diets, and 20 with diarrhoea-predominant IBS (D-IBS). Collected urine was stored frozen in 10 ml aliquots. For assay, the specimens were heated to 40 ± 0.1°C and the headspace analysed by Field Asymmetric Ion Mobility Spectrometry (FAIMS). Machine learning algorithms were used for statistical evaluation. Samples were also analysed using Gas chromatography and mass spectroscopy (GC-MS). Sparse logistic regression showed that FAIMS distinguishes VOCs in CD vs D-IBS with ROC curve AUC of 0.91 (0.83-0.99), sensitivity and specificity of 85% respectively. GCMS showed a unique peak at 4'67 found only in CD, not D-IBS, which correlated with the compound 1,3,5,7 cyclooctatetraene. This study suggests that FAIMS offers a novel, non-invasive approach to identify those with possible CD, and distinguishes from D-IBS. It offers the potential for monitoring compliance with a gluten-free diet at home. The presence of cyclooctatetraene in CD specimens will need further validation.

Detection of colorectal cancer (CRC) by urinary volatile organic compound analysis

Colorectal cancer (CRC) is a leading cause of cancer related death in Europe and the USA. There is no universally accepted effective non-invasive screening test for CRC. Guaiac based faecal occult blood (gFOB) testing has largely been superseded by Faecal Immunochemical testing (FIT), but sensitivity still remains poor. The uptake of population based FOBt testing in the UK is also low at around 50%. The detection of volatile organic compounds (VOCs) signature(s) for many cancer subtypes is receiving increasing interest using a variety of gas phase analytical instruments. One such example is FAIMS (Field Asymmetric Ion Mobility Spectrometer). FAIMS is able to identify Inflammatory Bowel disease (IBD) patients by analysing shifts in VOCs patterns in both urine and faeces. This study extends this concept to determine whether CRC patients can be identified through non-invasive analysis of urine, using FAIMS. 133 patients were recruited; 83 CRC patients and 50 healthy controls. Urine was collected at the time of CRC diagnosis and headspace analysis undertaken using a FAIMS instrument (Owlstone, Lonestar, UK). Data was processed using Fisher Discriminant Analysis (FDA) after feature extraction from the raw data. FAIMS analyses demonstrated that the VOC profiles of CRC patients were tightly clustered and could be distinguished from healthy controls. Sensitivity and specificity for CRC detection with FAIMS were 88% and 60% respectively. This study suggests that VOC signatures emanating from urine can be detected in patients with CRC using ion mobility spectroscopy technology (FAIMS) with potential as a novel screening tool.

Detection of potato storage disease via gas analysis: a pilot study using field asymmetric ion mobility spectrometry

Soft rot is a commonly occurring potato tuber disease that each year causes substantial losses to the food industry. Here, we explore the possibility of early detection of the disease via gas/vapor analysis, in a laboratory environment, using a recent technology known as FAIMS (Field Asymmetric Ion Mobility Spectrometry). In this work, tubers were inoculated with a bacterium causing the infection, Pectobacterium carotovorum, and stored within set environmental conditions in order to manage disease progression. They were compared with controls stored in the same conditions. Three different inoculation time courses were employed in order to obtain diseased potatoes showing clear signs of advanced infection (for standard detection) and diseased potatoes with no apparent evidence of infection (for early detection). A total of 156 samples were processed by PCA (Principal Component Analysis) and k-means clustering. Results show a clear discrimination between controls and diseased potatoes for all experiments with no difference among observations from standard and early detection. Further analysis was carried out by means of a statistical model based on LDA (Linear Discriminant Analysis) that showed a high classification accuracy of 92.1% on the test set, obtained via a LOOCV (leave-one out cross-validation).

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.

A microstereolithography resin based on thiol-ene chemistry: towards biocompatible 3D extracellular constructs for tissue engineering

A new class of degradable aliphatic poly(carbonate) resins for use in microstereolithographic process is described.