The role of measuring exhaled breath biomarkers in sarcoidosis: A systematic review

Introduction: Sarcoidosis is a chronic granulomatous disease of unknown aetiology with a variable clinical course and prognosis. There is a growing need to identify non-invasive biomarkers to differentiate between clinical phenotypes, identify those at risk of disease progression and monitor response to treatment. Objectives: We undertook a systematic review and meta-analysis, to evaluate the utility of breath-based biomarkers in discriminating sarcoidosis from healthy controls, alongside correlation with existing non-breath based biomarkers used in clinical practice, radiological stage, markers of disease activity and response to treatment. Methods: Electronic searches were undertaken during November 2017 using PubMed, Ebsco, Embase and Web of Science to capture relevant studies evaluating breath-based biomarkers in adult patients with sarcoidosis. Results: 353 papers were screened; 21 met the inclusion criteria and assessed 25 different biomarkers alongside VOCs in exhaled breath gas or condensate. Considerable heterogeneity existed amongst the studies in terms of participant characteristics, sampling and analytical methods. Elevated biomarkers in sarcoidosis included 8-isoprostane, carbon monoxide, neopterin, TGF-β1, TNFα, CysLT and several metallic elements including chromium, silicon and nickel. Three studies exploring VOCs were able to distinguish sarcoidosis from controls. Meta-analysis of four studies assessing alveolar nitric oxide showed no significant difference between sarcoidosis and healthy controls (2.22ppb; 95% CI -0.83, 5.27) however, a high degree of heterogeneity was observed with an I2 of 93.4% (p<0.001). Inconsistent or statistically insignificant results were observed for correlations between several biomarkers and radiological stage, markers of disease activity or treatment. Conclusions: The evidence for using breath biomarkers to diagnose and monitor sarcoidosis remains inconclusive with many studies limited by small sample sizes and lack of standardisation. VOCs have shown promising potential but further research is required to evaluate their prognostic role

Towards a Physarum learning chip

Networks of protoplasmic tubes of organism Physarum polycehpalum are macro-scale structures which optimally span multiple food sources to avoid repellents yet maximize coverage of attractants. When data are presented by configurations of attractants and behaviour of the slime mould is tuned by a range of repellents, the organism preforms computation. It maps given data configuration into a protoplasmic network. To discover physical means of programming the slime mould computers we explore conductivity of the protoplasmic tubes; proposing that the network connectivity of protoplasmic tubes shows pathway-dependent plasticity. To demonstrate this we encourage the slime mould to span a grid of electrodes and apply AC stimuli to the network. Learning and weighted connections within a grid of electrodes is produced using negative and positive voltage stimulation of the network at desired nodes; low frequency (10 Hz) sinusoidal (0.5 V peak-to-peak) voltage increases connectivity between stimulated electrodes while decreasing connectivity elsewhere, high frequency (1000 Hz) sinusoidal (2.5 V peak-to-peak) voltage stimulation decreases network connectivity between stimulated electrodes. We corroborate in a particle model. This phenomenon may be used for computation in the same way that neural networks process information and has the potential to shed light on the dynamics of learning and information processing in non-neural metazoan somatic cell networks

Urinary volatile organic compounds for the detection of prostate cancer

© 2015 Khalid et al.This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The aim of this work was to investigate volatile organic compounds (VOCs) emanating from urine samples to determine whether they can be used to classify samples into those from prostate cancer and non-cancer groups. Participants were men referred for a trans-rectal ultrasound-guided prostate biopsy because of an elevated prostate specific antigen (PSA) level or abnormal findings on digital rectal examination. Urine samples were collected from patients with prostate cancer (n = 59) and cancer-free controls (n = 43), on the day of their biopsy, prior to their procedure. VOCs from the headspace of basified urine samples were extracted using solid-phase micro-extraction and analysed by gas chromatography/mass spectrometry. Classifiers were developed using Random Forest (RF) and Linear Discriminant Analysis (LDA) classification techniques. PSA alone had an accuracy of 62-64% in these samples. A model based on 4 VOCs, 2,6-dimethyl-7-octen-2-ol, pentanal, 3-octanone, and 2-octanone, was marginally more accurate 63-65%. When combined, PSA level and these four VOCs had mean accuracies of 74% and 65%, using RF and LDA, respectively. With repeated double cross-validation, the mean accuracies fell to 71% and 65%, using RF and LDA, respectively. Results from VOC profiling of urine headspace are encouraging and suggest that there are other metabolomic avenues worth exploring which could help improve the stratification of men at risk of prostate cancer. This study also adds to our knowledge on the profile of compounds found in basified urine, from controls and cancer patients, which is useful information for future studies comparing the urine from patients with other disease states

The 42nd Symposium Chromatographic Methods of Investigating Organic Compounds : Book of abstracts

The 42nd Symposium Chromatographic Methods of Investigating Organic Compounds : Book of abstracts. June 4-7, 2019, Szczyrk, Polan

Gas chromatography-mass spectrometry analyses of volatile organic compounds from potato tubers inoculated with Phytophthora infestans or Fusarium coeruleum

Volatile organic compounds (VOCs) collected from potato tubers inoculated with Phytophthora infestans (late blight), Fusarium coeruleum (dry rot) or sterilized distilled water (as a control) were analysed using gas chromatography-mass spectrometry (GC-MS) and gas chromatography-flame ionization detection (GC-FID). A total of 52 volatiles were identified by GC-MS in the headspaces above P. infestans- and F. coeruleum-inoculated tubers after incubation for 42 days in the dark at 10°C. Of these VOCs, the six most abundant were common to both pathogens. These were benzothiazole (highest abundance), 2-ethyl-1-hexanol (second highest abundance), and at approximately equal third abundance, hexanal, 2-methylpropanoic acid-2,2-dimethyl-1-(2-hydroxy-1-methylethyl)-propyl ester, 2-methylpropanoic acid-3-hydroxy-2,4,4-trimethyl-pentyl ester and phenol. In addition, styrene also occurred at approximately equal third abundance in the headspace of F. coeruleum-inoculated tubers, but at lower abundance in the headspace of P. infestans-inoculated tubers. Some VOCs were specific to each pathogen. Butanal, 3-methylbutanal, undecane and verbenone were found at low levels only in the headspace of tubers inoculated with P. infestans, while 2-pentylfuran and copaene were found only in the headspace of tubers inoculated with F. coeruleum. Additionally GC-FID analysis identified ethanol and 2-propanol in the liquid exudate from both P. infestans- and F. coeruleum-inoculated tubers after incubation for 35 days, and in the headspace after incubation for 42 days. These data provide key information for developing a sensor-based early warning system for the detection of postharvest diseases in stored potato tubers

Development of a sensor system for the early detection of soft rot in stored potato tubers

A number of sensor types were fabricated and tested for their electrical resistance changes to compounds known to be evolved by potato tubers with soft rot caused by the bacterium Erwinia carotovora. On the basis of these tests, three sensors were selected for incorporation into a prototype device. The device was portable and could be used without computer control after threshold values and sensor settling criteria had been downloaded. The prototype was assessed for its discriminating power under simulated storage conditions. The device was capable of detecting one tuber with soft rot in 100 kg of sound tubers in a simulated storage crate. The device was also able to detect a tuber inoculated with E. carotovora, but without visible signs of soft rot, within 10 kg of sound tubers. The same system was able to follow the progression of the disease in a tuber stored amongst 10 kg of sound tubers when operated at 4 °C and 85% relative humidity (conditions typical of a refrigerated storage facility)

The use of a gas chromatograph coupled to a metal oxide sensor for rapid assessment of stool samples from irritable bowel syndrome and inflammatory bowel disease patients

There is much clinical interest in the development of a low-cost and reliable test for diagnosing inflammatory bowel disease (IBD) and irritable bowel syndrome (IBS), two very distinct diseases that can present with similar symptoms. The assessment of stool samples for the diagnosis of gastro-intestinal diseases is in principle an ideal non-invasive testing method. This paper presents an approach to stool analysis using headspace gas chromatography and a single metal oxide sensor coupled to artificial neural network software. Currently, the system is able to distinguish samples from patients with IBS from patients with IBD with a sensitivity and specificity of 76% and 88% respectively, with an overall mean predictive accuracy of 76%. © 2014 IOP Publishing Ltd