Patient activation and psychological coping strategies to manage challenging circumstances during the COVID-19 pandemic in people with kidney disease.

BACKGROUND: Coping with health problems requires some degree of self-management; however, an individual's ability to self-manage can be threatened during challenging times, such as the COVID-19 pandemic. Exploring differences and changes in psychological well-being and coping strategies between those with low and high patient activation may inform appropriate interventions to support psychological coping. METHODS: People with chronic kidney disease (CKD) (non-dialysis and transplant) were recruited from 11 hospital sites across England between August and December 2020. Participants responded to an online survey study, including the Brief Coping Orientation to Problem Experienced (COPE) Inventory, Depression, Anxiety and Stress Scale (DASS-21), Short Health Anxiety Index (SHAI), and Patient Activation Measure (PAM-13). A follow-up survey was conducted 6-9 months later. Paired t tests assessed within-group changes, and chi-squared tests compared coping strategies utilised by low- and high-activated participants. General linear modelling was performed to determine the relationship between patient activation and coping strategies, and covariates. RESULTS: Two hundred and fourteen participants were recruited (mean age: 60.7, 51% male, mean eGFR: 38.9 ml/min/1.73 m2). Low-activated participants were significantly more anxious than high-activated participants (P = 0.045). Health anxiety significantly decreased (i.e., got better) for high-activated participants (P = 0.016). Higher patient activation scores were associated with greater use of problem-focused strategies (ß = 0.288, P < 0.001). Age (ß = - 0.174, P = 0.012), sex (ß = 0.188, P = 0.004), and education level (ß = 0.159, P = 0.019) significantly predicted use of problem-focused strategies. DISCUSSION: Those with higher activation had lower levels of anxiety, and more frequently used adaptive coping strategies during the pandemic. Targeted support and interventions may be required for people with CKD to enhance patient activation, encourage more positive adaptive coping strategies, and mitigate maladaptive coping strategies.

LightNet: a novel lightweight convolutional network for brain tumor segmentation in healthcare.

Diagnosis, treatment planning, surveillance, and the monitoring of clinical trials for brain diseases all benefit greatly from neuroimaging-based tumor segmentation. Recently, Convolutional Neural Networks (CNNs) have demonstrated promising results in enhancing the efficiency of image-based brain tumor segmentation. Most current work on CNNs, however, is devoted to creating increasingly complicated convolution modules to improve performance, which in turn raises the computing cost of the model. This work proposes a simple and effective feed-forward CNN, LightNet (Light Network). Based on multi-path and multi-level, it replaces traditional convolutional methods with light operations, which reduces network parameters and redundant feature maps. In the up-sampling stage, a light channel attention module is added to achieve richer multi-scale and spatial semantic feature information extraction of brain tumor. The performance of the network is evaluated in the Multimodal Brain Tumor Segmentation Challenge (BraTS 2015) dataset, and results are presented here alongside other high-performing CNNs. Results show comparable accuracy with other methods but with increased efficiency, segmentation performance, and reduced redundancy and computational complexity. The result is a high-performing network with a balance between efficiency and accuracy, allowing, for example, better energy performance on mobile devices.

SEDIQA: Sound Emitting Document Image Quality Assessment in a Reading Aid for the Visually Impaired.

For visually impaired people (VIPs), the ability to convert text to sound can mean a new level of independence or the simple joy of a good book. With significant advances in optical character recognition (OCR) in recent years, a number of reading aids are appearing on the market. These reading aids convert images captured by a camera to text which can then be read aloud. However, all of these reading aids suffer from a key issue-the user must be able to visually target the text and capture an image of sufficient quality for the OCR algorithm to function-no small task for VIPs. In this work, a sound-emitting document image quality assessment metric (SEDIQA) is proposed which allows the user to hear the quality of the text image and automatically captures the best image for OCR accuracy. This work also includes testing of OCR performance against image degradations, to identify the most significant contributors to accuracy reduction. The proposed no-reference image quality assessor (NR-IQA) is validated alongside established NR-IQAs and this work includes insights into the performance of these NR-IQAs on document images. SEDIQA is found to consistently select the best image for OCR accuracy. The full system includes a document image enhancement technique which introduces improvements in OCR accuracy with an average increase of 22% and a maximum increase of 68%.

A Systematic Review of Urban Navigation Systems for Visually Impaired People.

Blind and Visually impaired people (BVIP) face a range of practical difficulties when undertaking outdoor journeys as pedestrians. Over the past decade, a variety of assistive devices have been researched and developed to help BVIP navigate more safely and independently. In addition, research in overlapping domains are addressing the problem of automatic environment interpretation using computer vision and machine learning, particularly deep learning, approaches. Our aim in this article is to present a comprehensive review of research directly in, or relevant to, assistive outdoor navigation for BVIP. We breakdown the navigation area into a series of navigation phases and tasks. We then use this structure for our systematic review of research, analysing articles, methods, datasets and current limitations by task. We also provide an overview of commercial and non-commercial navigation applications targeted at BVIP. Our review contributes to the body of knowledge by providing a comprehensive, structured analysis of work in the domain, including the state of the art, and guidance on future directions. It will support both researchers and other stakeholders in the domain to establish an informed view of research progress.

Kidney Care during COVID-19 in the UK: Perspectives of Healthcare Professionals on Impacts on Care Quality and Staff Well-Being.

In light of the rapid changes in healthcare delivery due to COVID-19, this study explored kidney healthcare professionals' (HCPs) perspectives on the impact of these changes on care quality and staff well-being. Fifty-nine HCPs from eight NHS Trusts across England completed an online survey and eight took part in complementary semi-structured interviews between August 2020 and January 2021. Free-text survey responses and interviews were analysed using inductive thematic analysis. Themes described the rapid adaptations, concerns about care quality, benefits from innovations, high work pressure, anxiety and mental exhaustion in staff and the team as a well-being resource. Long-term retention and integration of changes and innovations can improve healthcare access and efficiency, but specification of conditions for its use is warranted. The impact of prolonged stress on renal HCPs also needs to be accounted for in quality planning. Results are further interpreted into a theoretical socio-technical framework.

CleanPage: Fast and Clean Document and Whiteboard Capture.

The move from paper to online is not only necessary for remote working, it is also significantly more sustainable. This trend has seen a rising need for the high-quality digitization of content from pages and whiteboards to sharable online material. However, capturing this information is not always easy nor are the results always satisfactory. Available scanning apps vary in their usability and do not always produce clean results, retaining surface imperfections from the page or whiteboard in their output images. CleanPage, a novel smartphone-based document and whiteboard scanning system, is presented. CleanPage requires one button-tap to capture, identify, crop, and clean an image of a page or whiteboard. Unlike equivalent systems, no user intervention is required during processing, and the result is a high-contrast, low-noise image with a clean homogenous background. Results are presented for a selection of scenarios showing the versatility of the design. CleanPage is compared with two market leader scanning apps using two testing approaches: real paper scans and ground-truth comparisons. These comparisons are achieved by a new testing methodology that allows scans to be compared to unscanned counterparts by using synthesized images. Real paper scans are tested using image quality measures. An evaluation of standard image quality assessments is included in this work, and a novel quality measure for scanned images is proposed and validated. The user experience for each scanning app is assessed, showing CleanPage to be fast and easier to use.

MATtrack: A MATLAB-Based Quantitative Image Analysis Platform for Investigating Real-Time Photo-Converted Fluorescent Signals in Live Cells.

We introduce here MATtrack, an open source MATLAB-based computational platform developed to process multi-Tiff files produced by a photo-conversion time lapse protocol for live cell fluorescent microscopy. MATtrack automatically performs a series of steps required for image processing, including extraction and import of numerical values from Multi-Tiff files, red/green image classification using gating parameters, noise filtering, background extraction, contrast stretching and temporal smoothing. MATtrack also integrates a series of algorithms for quantitative image analysis enabling the construction of mean and standard deviation images, clustering and classification of subcellular regions and injection point approximation. In addition, MATtrack features a simple user interface, which enables monitoring of Fluorescent Signal Intensity in multiple Regions of Interest, over time. The latter encapsulates a region growing method to automatically delineate the contours of Regions of Interest selected by the user, and performs background and regional Average Fluorescence Tracking, and automatic plotting. Finally, MATtrack computes convenient visualization and exploration tools including a migration map, which provides an overview of the protein intracellular trajectories and accumulation areas. In conclusion, MATtrack is an open source MATLAB-based software package tailored to facilitate the analysis and visualization of large data files derived from real-time live cell fluorescent microscopy using photoconvertible proteins. It is flexible, user friendly, compatible with Windows, Mac, and Linux, and a wide range of data acquisition software. MATtrack is freely available for download at eleceng.dit.ie/courtney/MATtrack.zip.

How to use: amplitude-integrated EEG (aEEG).

Amplitude-integrated electroencephalography (aEEG) is a method for continuous monitoring of brain activity that is increasingly used in the neonatal intensive care unit. In its simplest form, aEEG is a processed single-channel electroencephalogram that is filtered and time-compressed. Current evidence demonstrates that aEEG is useful to monitor cerebral background activity, diagnose and treat seizures and predict neurodevelopmental outcomes for preterm and term infants. This review aims to explain the fundamentals behind aEEG and its clinical applications.

Tracking protein dynamics with photoconvertible Dendra2 on spinning disk confocal systems.

Understanding the dynamic properties of cellular proteins in live cells and in real time is essential to delineate their function. In this context, we introduce the Fluorescence Recovery After Photobleaching-Photoactivation unit (Andor) combined with the Nikon Eclipse Ti E Spinning Disk (Andor) confocal microscope as an advantageous and robust platform to exploit the properties of the Dendra2 photoconvertible fluorescent protein (Evrogen) and analyse protein subcellular trafficking in living cells. A major advantage of the spinning disk confocal is the rapid acquisition speed, enabling high temporal resolution of cellular processes. Furthermore, photoconversion and imaging are less invasive on the spinning disk confocal as the cell exposition to illumination power is reduced, thereby minimizing photobleaching and increasing cell viability. We have tested this commercially available platform using experimental settings adapted to track the migration of fast trafficking proteins such as UBC9, Fibrillarin and have successfully characterized their differential motion between subnuclear structures. We describe here step-by-step procedures, with emphasis on cellular imaging parameters, to successfully perform the dynamic imaging and photoconversion of Dendra2-fused proteins at high spatial and temporal resolutions necessary to characterize the trafficking pathways of proteins.

A monocular marker-free gait measurement system.

This paper presents a new, user-friendly, portable motion capture and gait analysis system for capturing and analyzing human gait, designed as a telemedicine tool to monitor remotely the progress of patients through treatment. The system requires minimal user input and simple single-camera filming (which can be acquired from a basic webcam) making it very accessible to nontechnical, nonclinical personnel. This system can allow gait studies to acquire a much larger data set and allow trained gait analysts to focus their skills on the interpretation phase of gait analysis. The design uses a novel motion capture method derived from spatiotemporal segmentation and model-based tracking. Testing is performed on four monocular, sagittal-view, sample gait videos. Results of modeling, tracking, and analysis stages are presented with standard gait graphs and parameters compared to manually acquired data.