Blood Pressure Estimation using Photoplethysmography only: Comparison between Different Machine Learning Approaches

Introduction: Blood pressure (BP) has been a potential risk factor for cardiovascular diseases. BP measurement is one of the useful parameters for early diagnosis, prevention, and treatment of cardiovascular diseases. At present, BP measurement mainly relies on cuff-based techniques that cause inconvenience and discomfort to users. Although some of the present prototype cuffless BP measurement techniques are able to reach overall acceptable accuracies, they require an electrocardiogram (ECG) and photoplethysmograph (PPG) that makes them unsuitable for true wearable applications. Therefore, developing a single PPG based cuffless BP estimation algorithm with enough accuracy would be clinically and practically useful. Methods: The University of Queensland vital sign dataset (Online database) was accessed to extract raw PPG signals and its corresponding reference BPs (Systolic BP & Diastolic BP). The online database consisted of PPG waveforms of 32 cases from whom 8133 (good quality) signal segments (5s for each) were extracted, pre-processed and normalised in both width and amplitude. Three most significant features (Pulse area, Pulse Rising Time and Width 25%) with their corresponding reference BPs were used to train and test three machine learning algorithms (Regression Tree, Multiple Linear Regression (MLR) and Support Vector Machine (SVM)). A 10-fold cross-validation was applied to obtain over-all BP estimation accuracy, separately for the three machine learning algorithms. Their estimation accuracies were further analysed separately for three clinical BP categories (Normotensive, Hypertensive and Hypotensive). Finally, they were compared with the ISO standard for non-invasive BP device validation (average difference no greater than 5mmHg and SD no greater than 8mmHg). Results: In terms of overall measurement accuracy, the Regression Tree achieved the best overall accuracy for SBP (mean and SD of difference: -0.1±6.5mmHg) & DBP (mean and SD of difference: -0.6±5.2mmHg). MLR and SVM achieved the overall mean difference less than 5mmHg for both SBP and DBP but their SD of difference was >8mmHg. Regarding the measurement accuracy in each BP categories, only the Regression Tree achieved acceptable ISO standard for SBP (-1.1±5.7mmHg) & DBP (-0.03±5.6 mmHg) in the Normotensive category. MLR and SVM did not achieve acceptable accuracies in any BP categories. Conclusion: This study developed and compared three machine learning algorithms to estimate BPs using PPG only, and revealed that the Regression Tree algorithm was the best approach with overall acceptable accuracy to ISO standard for BP device validation. Furthermore, this study demonstrated that the Regression Tree algorithm achieved acceptable measurement accuracy only in the Normotensive category, suggesting that future algorithm development for BP estimation should be more specific for different BP categories

Exploring and Expanding the Fatty-Acid-Binding Protein Superfamily in Fasciola Species

The liver flukes Fasciola hepatica and F. gigantica infect livestock worldwide and threaten food security with climate change and problematic control measures spreading disease. Fascioliasis is also a food borne disease with up to 17 million humans infected. In the absence of vaccines, treatment depends on Triclabendazole (TCBZ) and over-use has led to widespread resistance, compromising future TCBZ control. Reductionist biology from many laboratories has predicted new therapeutic targets. To this end, the fatty acid binding protein (FABP) superfamily have proposed multi-functional roles, including functions intersecting vaccine and drug therapy, such as immune modulation and anthelmintic sequestration. Research is hindered by a lack of understanding of the full FABP superfamily complement. Although discovery studies predicted FABPs as promising vaccine candidates, it is unclear if uncharacterised FABPs are more relevant for vaccine formulations. We have coupled genome, transcriptome and EST data mining with proteomics and phylogenetics, to reveal a liver fluke FABP superfamily of 7 clades: previously identified clades I-III and newly identified clades IV-VII. All new clade FABPs were analysed using bioinformatics and cloned from both liver flukes. The extended FABP dataset will provide new study tools to research the role of FABPs in parasite biology and as therapy targets

Quantification of radial arterial pulse characteristics change during exercise and recovery

It is physiologically important to understand the arterial pulse waveform characteristics change during exercise and recovery. However, there is a lack of a comprehensive investigation. This study aimed to provide scientific evidence on the arterial pulse characteristics change during exercise and recovery. Sixty-five healthy subjects were studied. The exercise loads were gradually increased from 0 to 125 W for female subjects and to 150 W for male subjects. Radial pulses were digitally recorded during exercise and 4-min recovery. Four parameters were extracted from the raw arterial pulse waveform, including the pulse amplitude, width, pulse peak and dicrotic notch time. Five parameters were extracted from the normalized radial pulse waveform, including the pulse peak and dicrotic notch position, pulse Area, Area1 and Area2 separated by notch point. With increasing loads during exercise, the raw pulse amplitude increased significantly with decreased pulse period, reduced peak and notch time. From the normalized pulses, the pulse Area, pulse Area1 and Area2 decreased, respectively, from 38 ± 4, 61 ± 5 and 23 ± 5 at rest to 34 ± 4, 52 ± 6 and 13 ± 5 at 150-W exercise load. During recovery, an opposite trend was observed. This study quantitatively demonstrated significant changes of radial pulse characteristics during different exercise loads and recovery phases

Engagement with consumer smartwatches for tracking symptoms of individuals living with multiple long-term conditions (multimorbidity): A longitudinal observational study

From SAGE Publishing via Jisc Publications RouterHistory: received 2021-07-16, accepted 2021-11-08, epub 2021-11-30Publication status: PublishedFunder: University of Manchester/Medical Research Council; Grant(s): Confidence in Concepts funding scheme 6Introduction: People living with multiple long-term conditions (multimorbidity) (MLTC-M) experience an accumulating combination of different symptoms. It has been suggested that these symptoms can be tracked longitudinally using consumer technology, such as smartphones and wearable devices. Aim: The aim of this study was to investigate longitudinal user engagement with a smartwatch application, collecting survey questions and active tasks over 90 days, in people living with MLTC-M. Methods: ‘Watch Your Steps’ was a prospective observational study, administering multiple questions and active tasks over 90 days. Adults with more than one clinician-diagnosed long-term conditions were loaned Fossil® Sport smartwatches, pre-loaded with the study app. Around 20 questions were prompted per day. Daily completion rates were calculated to describe engagement patterns over time, and to explore how these varied by patient characteristics and question type. Results: Fifty three people with MLTC-M took part in the study. Around half were male ( = 26; 49%) and the majority had a white ethnic background (n = 45; 85%). About a third of participants engaged with the smartwatch app nearly every day. The overall completion rate of symptom questions was 45% inter-quartile range (IQR 23–67%) across all study participants. Older patients and those with greater MLTC-M were more engaged, although engagement was not significantly different between genders. Conclusion: It was feasible for people living with MLTC-M to report multiple symptoms per day over 3 months. User engagement appeared as good as other mobile health studies that recruited people with single health conditions, despite the higher daily data entry burden

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Identifying colorectal cancer caused by biallelic MUTYH pathogenic variants using tumor mutational signatures

Carriers of germline biallelic pathogenic variants in the MUTYH gene have a high risk of colorectal cancer. We test 5649 colorectal cancers to evaluate the discriminatory potential of a tumor mutational signature specific to MUTYH for identifying biallelic carriers and classifying variants of uncertain clinical significance (VUS). Using a tumor and matched germline targeted multi-gene panel approach, our classifier identifies all biallelic MUTYH carriers and all known non-carriers in an independent test set of 3019 colorectal cancers (accuracy = 100% (95% confidence interval 99.87-100%)). All monoallelic MUTYH carriers are classified with the non-MUTYH carriers. The classifier provides evidence for a pathogenic classification for two VUS and a benign classification for five VUS. Somatic hotspot mutations KRAS p.G12C and PIK3CA p.Q546K are associated with colorectal cancers from biallelic MUTYH carriers compared with non-carriers (p = 2 x 10(-23) and p = 6 x 10(-11), respectively). Here, we demonstrate the potential application of mutational signatures to tumor sequencing workflows to improve the identification of biallelic MUTYH carriers. Germline biallelic pathogenic MUTYH variants predispose patients to colorectal cancer (CRC); however, approaches to identify MUTYH variant carriers are lacking. Here, the authors evaluated mutational signatures that could distinguish MUTYH carriers in large CRC cohorts, and found MUTYH-associated somatic mutations