Should current indoor environment and air quality standards be doing more to protect young people in educational buildings?

Indoor environmental quality (IEQ) and indoor air quality (IAQ) were assessed in a recently refurbished educational building at Loughborough University, through a monitoring campaign in accordance with Building Bulletin (BB) 101. A particular focus of this work was on emissions from building materials. Volatile organic compounds (VOCs) were measured using diffusive (passive) methods involving Thermal Desorption (TD), Gas Chromatography (GC) and Mass Spectrometry (MS) techniques. The results show that although the building performs satisfactorily with respect to guidelines for overheating and ventilation performance according to BB101 (2018) the current guidelines only assess Total Volatile Organic Compound (TVOC) limits which fail to identify the source of IAQ problems. The presence of numerous VOCs indicates that quantification of individual compounds is necessary to assess long-term health risks

VOCCluster: Untargeted Metabolomics Feature Clustering Approach for Clinical Breath Gas Chromatography - Mass Spectrometry Data

<div>Our unsupervised clustering technique, VOCCluster, prototyped in Python, handles features of deconvolved GC-MS breath data. VOCCluster was created from a heuristic ontology based on the observation of experts undertaking data processing with a suite of software packages. VOCCluster identifies and clusters groups of volatile organic compounds (VOCs) from deconvolved GC-MS breath with similar mass spectra and retention index profiles.</div

Fast and automated biomarker detection in breath samples with machine learning

Volatile organic compounds (VOCs) in human breath can reveal a large spectrum of health conditions and can be used for fast, accurate and non-invasive diagnostics. Gas chromatography-mass spectrometry (GC-MS) is used to measure VOCs, but its application is limited by expert-driven data analysis that is time-consuming, subjective and may introduce errors. We propose a machine learning-based system to perform GC-MS data analysis that exploits deep learning pattern recognition ability to learn and automatically detect VOCs directly from raw data, thus bypassing expert-led processing. We evaluate this new approach on clinical samples and with four types of convolutional neural networks (CNNs): VGG16, VGG-like, densely connected and residual CNNs. The proposed machine learning methods showed to outperform the expert-led analysis by detecting a significantly higher number of VOCs in just a fraction of time while maintaining high specificity. These results suggest that the proposed novel approach can help the large-scale deployment of breath-based diagnosis by reducing time and cost, and increasing accuracy and consistency

New investigations into the stability of Mesna using LC-MS/MS and NMR

Both LC-MS/MS and NMR analyses confirmed the instability of Mesna and its conversion into Dimesna

Convolutional neural networks for automated targeted analysis of gas chromatography-mass spectrometry data

Through their breath, humans exhale hundreds of volatile organic compounds (VOCs) that can reveal pathologies, including many types of cancer at early stages. Gas chromatography–mass spectrometry (GC-MS) is an analytical method used to separate and detect compounds in the mixture contained in breath samples. The identification of VOCs is based on the recognition of their specific ion patterns in GC-MS data, which requires labour-intensive and time-consuming preprocessing and analysis by domain experts. This paper explores the original idea of applying supervised machine learning, and in particular convolutional neural networks (CNNs), to learn ion patterns directly from raw GC-MS data. The method adapts to machine specific characteristics, and once trained, can quickly analyse breath samples bypassing the time-consuming preprocessing phase. The CNN classification performance is compared to those of shallow neural networks and support vector machines. All considered machine learning tools achieved high accuracy in experiments with clinical data from participants. In particular, the CNN-based approach detected the lowest number of false positives. The results indicate that the proposed method is a promising tool to improve accuracy, specificity, and in particular speed in the detection of VOCs of interest in large-scale data analysis

Evaluation of the performance of elastomeric pumps in practice : are we under delivering on chemotherapy treatments?

Background and aims: Elastomeric pumps are widely used to facilitate ambulatory chemotherapy, and studies have shown that they are safe and well received by patients. Despite these advantages, their end of infusion time can fluctuate significantly. The aim of this research was to observe the performance of these pumps in real practice and to evaluate patients' satisfaction. Methods: This was a two-phase study conducted at three cancer units over 6 months. Phase-1 was an observational study recording the status of pumps at the scheduled disconnection time and noting remaining volume of infusion. Phase-2 was a survey of patients and their perception/satisfaction. Ethical approval was granted. Results: A total of 92 cases were observed covering 50 cases disconnected at hospital and 42 disconnected at home. The infusion in 40% of hospital disconnection cases was slow, with patients arriving at hospital with unfinished pumps; 58% of these had an estimated remaining volume which exceeded 10 mL with 35% exceeded 20 mL. In 73% of these cases, and regardless of the remaining volume, the patient was disconnected and the pump was discarded. Conclusions: The performance of pumps varied, which affected nurse workload and patients' waiting-times. A smart system is an option to monitor the performance of pumps and to predict their accuracy

Assessment of breath volatile organic compounds in acute cardiorespiratory breathlessness: a protocol describing a prospective real-world observational study

Introduction Patients presenting with acute undifferentiated breathlessness are commonly encountered in admissions units across the UK. Existing blood biomarkers have clinical utility in distinguishing patients with single organ pathologies but have poor discriminatory power in multifactorial presentations. Evaluation of volatile organic compounds (VOCs) in exhaled breath offers the potential to develop biomarkers of disease states that underpin acute cardiorespiratory breathlessness, owing to their proximity to the cardiorespiratory system. To date, there has been no systematic evaluation of VOC in acute cardiorespiratory breathlessness. The proposed study will seek to use both offline and online VOC technologies to evaluate the predictive value of VOC in identifying common conditions that present with acute cardiorespiratory breathlessness. Methods and analysis A prospective real-world observational study carried out across three acute admissions units within Leicestershire. Participants with self-reported acute breathlessness, with a confirmed primary diagnosis of either acute heart failure, community-acquired pneumonia and acute exacerbation of asthma or chronic obstructive pulmonary disease will be recruited within 24 hours of admission. Additionally, school-age children admitted with severe asthma will be evaluated. All participants will undergo breath sampling on admission and on recovery following discharge. A range of online technologies including: proton transfer reaction mass spectrometry, gas chromatography ion mobility spectrometry, atmospheric pressure chemical ionisation-mass spectrometry and offline technologies including gas chromatography mass spectroscopy and comprehensive two-dimensional gas chromatography-mass spectrometry will be used for VOC discovery and replication. For offline technologies, a standardised CE-marked breath sampling device (ReCIVA) will be used. All recruited participants will be characterised using existing blood biomarkers including C reactive protein, brain-derived natriuretic peptide, troponin-I and blood eosinophil levels and further evaluated using a range of standardised questionnaires, lung function testing, sputum cell counts and other diagnostic tests pertinent to acute disease. Ethics and dissemination The National Research Ethics Service Committee East Midlands has approved the study protocol (REC number: 16/LO/1747). Integrated Research Approval System (IRAS) 198921. Findings will be presented at academic conferences and published in peer-reviewed scientific journals. Dissemination will be facilitated via a partnership with the East Midlands Academic Health Sciences Network and via interaction with all UK-funded Medical Research Council and Engineering and Physical Sciences Research Council molecular pathology nodes. Trial registration number NCT0367299