Face Mask Acceptability for Communal Religious Worship During the COVID-19 Pandemic in the United Kingdom: Results from the CONFESS Study.

The COVID-19 pandemic has led to restrictions such as social distancing and mandatory wearing of face masks. Singing and religious gatherings have been linked to infection clusters, and between 2020 and 2021 indoor congregational singing and chanting were prohibited in the United Kingdom. We evaluated attitudes to face mask use and their acceptability as well as changes within places of worship since their reopening in July up to autumn 2020. In this cross-sectional study, participants were recruited using convenience sampling through selective targeting of religious organisations and social media. Participants self-enrolled and completed an online questionnaire, which included open and closed questions. We used multivariable logistic regression to identify factors associated with face mask acceptability. We performed thematic analysis to evaluate responses to open questions. A total of 939 participants were included in the analysis. Median age was 52.7 years and 66.1% were female, while 80.7% identified as Christian. A majority (672/861; 78.0%) of participants would find it acceptable to wear a face mask and reduce their singing or chanting volume if required, even though 428/681 (49.1%) found face masks to be uncomfortable. Multivariable regression found that younger age was associated with a higher acceptability of face masks (adjusted OR (aOR): 0.98 (95% confidence interval (95% CI) 0.96-1.00), p = 0.0218). The majority of respondents stated that religious services had become shorter, attended by fewer people and with reduced singing or chanting. Most (869/893, 97.3%) stated their place of worship complied with government guidelines, with 803/887 (90.5%) reported that their place of worship enforced face mask wearing and 793/887 (89.4%) at least moderately happy with precaution measures. Our study demonstrates the significant impact of COVID-19 in places of worship but a high degree of compliance with guidelines. Face masks, despite practical difficulties, appeared to be more acceptable if there was an incentive of being able to sing and chant.

Flow rate accuracy of infusion devices within healthcare settings: a systematic review.

Background: One in five patients admitted to the hospital treated with intravenous (IV) fluid therapy suffer complications due to inappropriate administration. Errors have been reported in 13-84% of the preparation and administration of IV medications. The safe delivery of IV fluids requires precise rate administration. Objectives: This systematic review aims to determine the accuracy of infusion sets and devices and examine the factors that affect the flow rate accuracy of devices. Data Sources and Methods: Six databases (CINAHL, MEDLINE PubMed, EMBASE, Web of Science and Cochrane Database of systematic reviews) were systematically searched. Search terms included infusion pumps, infusion devices, flow rate accuracy, fluid administration rate, gravity-led infusion set and fluid balance. Studies were included if they examined infusion devices' flow rate accuracy and drop rates for fluids or non-oncological drugs. Findings were tabulated and synthesised qualitatively. The quality of the studies was examined based on the design of the studies due to their heterogeneity. Results: Eight studies were included: Four studies were conducted on human subjects in the hospital environment; studies recruited 182 participants between the ages of 18 and 94 years. Two studies examined flow rate accuracy in recruited patients across 509 observations and 2387 drip hours. No trials prospectively assessed the accuracy of infusion pumps in the clinical domain, and no studies were reported on patient safety outcomes. Four studies examined the impact of mechanical and physiological factors on the flow rate accuracies of infusion devices. Height and back pressure simulated vibrating conditions, the viscosity of IV fluid and the positions of patients were reported to have a significant impact on infusion volume and flow rates of infusion devices. Additionally, giving sets that vary from the manufacturer's specifications are reported to increase error percent by 10-20%. Conclusion: Infusion devices are an important source of error in administering IV fluids. Yet, there needs to be more prospective trial data to support their clinical accuracy and the impact on patient outcomes. Future flow variability and accuracy studies should capture their impact on patient safety and clinical outcomes.

Are the flow rate of infusion devices accurate in fluid administration? Background/Why was this study done? Nearly all patients in healthcare settings undergo treatment with fluid therapy that is administered through a vein. Inaccurate intravenous fluid administration causes patient harm. However, very little information in the literature explains how precisely intravenous fluid is administered. What did the researchers do? We reviewed the literature on flow rate accuracies of specific infusion devices and examined the factors that affect the flow rate accuracies of intravenous fluid administered to patients. What did the researchers find? We found that the flow rate accuracies of infusion devices vary greatly, and they are often affected by physiological and mechanical factors. However, the precise impact of this on patients' clinical outcomes is not often reported, representing a significant knowledge gap. What do the findings mean? We conclude that there is an urgent need to improve the reporting and precision of intravenous fluid rate administration and to understand how this impacts patient safety and clinical outcomes.

The grand challenges of Science Robotics.

One of the ambitions of Science Robotics is to deeply root robotics research in science while developing novel robotic platforms that will enable new scientific discoveries. Of our 10 grand challenges, the first 7 represent underpinning technologies that have a wider impact on all application areas of robotics. For the next two challenges, we have included social robotics and medical robotics as application-specific areas of development to highlight the substantial societal and health impacts that they will bring. Finally, the last challenge is related to responsible innovation and how ethics and security should be carefully considered as we develop the technology further.

Outlier detection and handling for robust 3-D active shape models search.

This paper presents a new outlier handling method for volumetric segmentation with three-dimensional (3-D) active shape models. The method is based on a shape metric that is invariant to scaling, rotation and translation by using the ratio of interlandmark distances as a local shape dissimilarity measure. Tolerance intervals for the descriptors are calculated from the training samples and used as a statistical tolerance model to infer the validity of the feature points. A replacement point is then suggested for each outlier based on the tolerance model and the position of the valid points. A geometrically weighted fitness measure is introduced for feature point detection, which limits the presence of outliers and improves the convergence of the proposed segmentation framework. The algorithm is immune to the extremity of the outliers and can handle a highly significant presence of erroneous feature points. The practical value of the technique is validated with 3-D magnetic resonance (MR) segmentation tasks of the carotid artery and myocardial borders of the left ventricle.

Powering the world's robots-10 years of ROS.

Robot Operating System (ROS) celebrates its 10th anniversary on 7 November 2017.

Big data for health.

This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging, clinical diagnosis, and long-term continuous physiological sensing of an individual. It is expected that recent advances in big data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to personalized treatment. The rise of big data, however, also raises challenges in terms of privacy, security, data ownership, data stewardship, and governance. This paper discusses some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled.

Multi-view stereo and advanced navigation for transanal endoscopic microsurgery.

Transanal endoscopic microsurgery (TEM), i.e., the local excision of rectal carcinomas by way of a bimanual operating system with magnified binocular vision, is gaining acceptance in lieu of more radical total interventions. A major issue with this approach is the lack of information on submucosal anatomical structures. This paper presents an advanced navigation system, wherein the intraoperative 3D structure is stably estimated from multiple stereoscopic views. It is registered to a preoperatively acquired anatomical volume based on subject-specific priors. The endoscope motion is tracked based on the 3D scene and its field-of-view is visualised jointly with the preoperative information. Based on in vivo data, this paper demonstrates how the proposed navigation system provides intraoperative navigation for TEM1.

A general framework for context-specific image segmentation using reinforcement learning.

This paper presents an online reinforcement learning framework for medical image segmentation. The concept of context-specific segmentation is introduced such that the model is adaptive not only to a defined objective function but also to the user's intention and prior knowledge. Based on this concept, a general segmentation framework using reinforcement learning is proposed, which can assimilate specific user intention and behavior seamlessly in the background. The method is able to establish an implicit model for a large state-action space and generalizable to different image contents or segmentation requirements based on learning in situ. In order to demonstrate the practical value of the method, example applications of the technique to four different segmentation problems are presented. Detailed validation results have shown that the proposed framework is able to significantly reduce user interaction, while maintaining both segmentation accuracy and consistency.

Pattern and degree of left ventricular remodeling following a tailored surgical approach for hypertrophic obstructive cardiomyopathy.

Background The role of a tailored surgical approach for hypertrophic cardiomyopathy (HCM) on regional ventricular remodelling remains unknown. The aims of this study were to evaluate the pattern, extent and functional impact of regional ventricular remodelling after a tailored surgical approach. Methods From 2005 to 2008, 44 patients with obstructive HCM underwent tailored surgical intervention. Of those, 14 were ineligible for cardiac magnetic resonance (CMR) studies. From the remainder, 14 unselected patients (42±12 years) underwent pre- and post-operative CMR studies at a median 12 months post-operatively (range 4-37 months). Regional changes in left ventricular (LV) thickness as well as global LV function following surgery were assessed using CMR Tools (London, UK). Results Pre-operative mean echocardiographic septal thickness was 21±4 mm and mean LV outflow gradient was 69±32 mmHg. Following surgery, there was a significant degree of regional regression of LV thickness in all segments of the LV, ranging from 16% in the antero-lateral midventricular segment to 41% in the anterior basal segment. Wall thickening was significantly increased in basal segments but showed no significant change in the midventricular or apical segments. Globally, mean indexed LV mass decreased significantly after surgery (120±29g/m2 versus 154±36g/m2; p<0.001). There was a trend for increased indexed LV end-diastolic volume (70±13 mL versus 65±11 mL; p=0.16) with a normalization of LV ejection fraction (68±7% versus 75±9%; p<0.01). Conclusion Following a tailored surgical relief of outflow obstruction for HCM, there is a marked regional reverse LV remodelling. These changes could have a significant impact on overall ventricular dynamics and function.

Reinforcement learning for context aware segmentation.

The ability to learn from user behavior during image segmentation to replicate the innate human ability to adapt shape delineation to contextually specific local information is an important area of study in image understanding. Current approaches to image segmentation usually incorporate specific designs, either relying on generic image features or specific prior knowledge, which usually prevent their application in different contextual settings. In this paper, a general segmentation framework based on reinforcement learning is proposed. It demonstrates how user-specific behavior can be assimilated in-situ for effective model adaptation and learning. It incorporates a two-layer reinforcement learning algorithm that constructs the model from accumulated experience during user interaction. As the algorithm learns 'pervasively' whilst the user performs manual segmentation, no additional steps are required for the training process, allowing the method to adapt and improve its accuracy as experience is acquired. Detailed validation of the method on in-vivo magnetic resonance (MR) data demonstrates the practical value of the technique in significantly reducing the level of user interaction required, whilst maintaining the overall segmentation accuracy.

Tracking of irregular graphical structures for tissue deformation recovery in minimally invasive surgery.

Tissue deformation tracking is an important topic of minimally invasive surgery with applications ranging from intra-operative guidance to augmented reality visualisation. In this paper, we present a technique for visual tracking of irregular structures with an arbitrary degree of connectivity in space. The variational formulation of the proposed method ensures that correlation is maximised between tracked points and their computed new positions while the overall structure shape variation is minimised, thus maintaining spatial coherence of the tracked structure. The proposed method is applied to surgical annotation and tracking in 3D for telementoring and path-planning. The results are validated both on a CT-scanned phantom model and in vivo, showing an average alignment error of 1.79 mm (+/- 0.72 mm).

Left-dominant arrhythmogenic cardiomyopathy: an under-recognized clinical entity.

OBJECTIVES: We sought to investigate the clinical-genetic profile of left-dominant arrhythmogenic cardiomyopathy (LDAC). BACKGROUND: In the absence of coronary disease and left ventricular (LV) systolic dysfunction, lateral T-wave inversion and arrhythmia of LV origin are often considered benign. Similarly, chest pain with enzyme release might be attributed to viral myocarditis. We hypothesized that these abnormalities might be manifestations of the "left-dominant" subtype of arrhythmogenic right ventricular cardiomyopathy. METHODS: The 42-patient cohort was established through clinical evaluation of individuals with unexplained (infero)lateral T-wave inversion, arrhythmia of LV origin, and/or proven LDAC/idiopathic myocardial fibrosis in the family. RESULTS: Patients presented from adolescence to age >80 years with arrhythmia or chest pain but not heart failure. Desmosomal mutations were identified in 8 of 24 families (15 of 33 patients). Magnetic resonance findings included LV late-enhancement in a subepicardial/midwall distribution, corresponding to fibrofatty replacement and fibrosis on histopathology. Fifty percent had previously been misdiagnosed with viral myocarditis, dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy, or idiopathic ventricular tachycardia. Arrhythmic events included presentation with ventricular fibrillatory arrest in 1 patient and 2 instances of sudden cardiac death during follow-up. CONCLUSIONS: Arrhythmogenic cardiomyopathy is distinguished from DCM by a propensity towards arrhythmia exceeding the degree of ventricular dysfunction. The left-dominant subtype is under-recognized owing to misattribution to other disorders and lack of specific diagnostic criteria. Clinicians are alerted to the possibility of LDAC in patients of any age with unexplained arrhythmia of LV origin, (infero)lateral T-wave inversion, apparent DCM (with arrhythmic presentation), or myocarditis (chest pain and enzyme rise with unobstructed coronary arteries).

Flow and myocardial interaction: an imaging perspective.

Heart failure due to coronary artery disease has considerable morbidity and poor prognosis. An understanding of the underlying mechanics governing myocardial contraction is a prerequisite for interpreting and predicting changes induced by heart disease. Gross changes in contractile behaviour of the myocardium are readily detected with existing techniques. For more subtle changes during early stages of cardiac dysfunction, however, a sensitive method for measuring, as well as a precise criterion for quantifying, normal and impaired myocardial function is required. The purpose of this paper is to outline the role of imaging, particularly cardiovascular magnetic resonance (CMR), for investigating the fundamental relationships between cardiac morphology, function and flow. CMR is emerging as an important clinical tool owing to its safety, versatility and the high-quality images it produces that allow accurate and reproducible quantification of cardiac structure and function. We demonstrate how morphological and functional assessment of the heart can be achieved by CMR and illustrate how blood flow imaging can be used to study flow and structure interaction, particularly for elucidating the underlying haemodynamic significance of directional changes and asymmetries of the cardiac looping. Future outlook on combining imaging with engineering approaches in subject-specific biomechanical simulation is also provided.

Evaluation of carotid artery wall volume measurement using novel semiautomated analysis software.

PURPOSE: To evaluate semiautomated analysis software for measuring the total carotid arterial wall volume (TWV) as a measure of atheroma burden. MATERIALS AND METHODS: Semiautomated-software and manual analyses of TWV measured by cardiovascular magnetic resonance (CMR) were compared in two phantom models, 10 subjects with no known carotid artery disease, and eight subjects with known carotid disease. The subjects were scanned twice for reproducibility. RESULTS: In subjects with no known carotid disease, semiautomated analysis of 98% of slices showed an improved interstudy coefficient of variation (COV) compared to manual analysis of 50% of slices (4.0% vs. 6.2%, P = 0.02). The proportion of matched cross-sectional slices usable for TWV measurement was superior (99% vs. 49%, P = 0.005) and the median analysis time was shorter (31 minutes vs. 90 minutes, P < 0.001) using the semiautomated software. In subjects with known carotid disease, semiautomated (99% of slices) and manual (56% of slices) analyses had comparable interstudy COVs (4.1% vs. 3.9%, P = 0.01). However, the proportion of matched cross-sectional slices usable for TWV measurement was greater using semiautomated contouring (96% vs. 56%, P = 0.01). CONCLUSION: Carotid CMR measurement of TWV using novel semiautomated analysis software shows good reproducibility, enables greater coverage of arterial vessel wall length, and is considerably faster compared to manual contouring.

Cardiovascular magnetic resonance in arrhythmogenic right ventricular cardiomyopathy revisited: comparison with task force criteria and genotype.

OBJECTIVES: We sought to assess the utility of cardiovascular magnetic resonance (CMR) in the evaluation of arrhythmogenic right ventricular cardiomyopathy (ARVC) in relation to diagnostic criteria and genotype. BACKGROUND: Timely diagnosis of ARVC is difficult as clinical findings may be subtle and nonspecific in early disease. The role of CMR is controversial owing to the absence of a standardized protocol, insufficient experience with the modality, and inherent difficulties in imaging the right ventricle. METHODS: Comprehensive CMR examination was performed in 232 patients undergoing evaluation for suspected ARVC. CMR outcomes were compared with: 1) prospective clinical diagnosis using Task Force guidelines, with and without the proposed modifications for familial ARVC; and 2) gene-carrier status in 35 individuals from genotyped families. RESULTS: CMR studies were positive in all 64 patients who prospectively fulfilled Task Force criteria, resulting in 100% sensitivity. Specificity in relation to Task Force criteria was low (29%). Of the 119 apparent false positives detected by CMR, however, 63 fulfilled modified diagnostic criteria for familial ARVC and 7 were obligate gene carriers, suggesting that CMR frequently identifies individuals with early disease, in whom Task Force criteria are relatively insensitive. This was borne out by evaluation of genotyped individuals (26 gene-positive and 9 gene-negative), in whom CMR had a sensitivity of 96% and a specificity of 78%. CONCLUSIONS: CMR is a valuable component of the diagnostic workup for ARVC when performed with a dedicated protocol by specialists with experience in analysis of volumes, right ventricular wall motion, and delayed-enhancement imaging.

Reduced k-space encoding for dual-contrast TrueFISP imaging.

Dual-contrast TrueFISP imaging relies on the use of two RF pulses with different RF flip-angles for enhancing image contrast and performing automatic tissue classification based on multispectrum clustering. The original technique, however, involves an extended imaging time, which limits its clinical application. The purpose of this study is to compare the applicability of two reduced k-space sampling techniques, reduced imaging using generalized series reconstruction (RIGR) and key-hole imaging, for minimizing the imaging time required. The performance of both approaches was evaluated theoretically and practically using 3D cine datasets acquired from eight asymptomatic subjects.

Progress towards patient-specific computational flow modeling of the left heart via combination of magnetic resonance imaging with computational fluid dynamics.

A combined computational fluid dynamics (CFD) and magnetic resonance imaging (MRI) methodology has been developed to simulate blood flow in a subject-specific left heart. The research continues from earlier experience in modeling the human left ventricle using time-varying anatomical MR scans. Breathing artifacts are reduced by means of a MR navigator echo sequence with feedback to the subject, allowing a near constant breath-hold diaphragm position. An improved interactive segmentation technique for the long- and short-axis anatomical slices is used. The computational domain is extended to include the proximal left atrium and ascending aorta as well as the left ventricle, and the mitral and aortic valve orifices are approximately represented. The CFD results show remarkable correspondence with the MR velocity data acquired for comparison purposes, as well as with previously published in vivo experiments (velocity and pressure). Coherent vortex formation is observed below the mitral valve, with a larger anterior vortex dominating the late-diastolic phases. Some quantitative discrepancies exist between the CFD and MRI flow velocities, owing to the limitations of the MR dataset in the valve region, heart rate differences in the anatomical and velocity acquisitions, and to certain phenomena that were not simulated. The CFD results compare well with measured ranges in literature.