Who waits longest for a kidney? : Inequalities in access to kidney transplantation among Black and Asian Minority Ethnic (BAME) groups in the UK

This version deposited with the permission of the publisher 6/15Black and Asian Minority Ethnic (BAME) groups are over-represented on active kidney transplantation waiting lists and have relatively long waiting times. This inequality arises from a particularly high need for kidney transplantation combined with a low rate of deceased donation among BAME groups which limits the availability of a well-matched graft. This paper outlines the major barriers to both registration as a potential donor and family consent to donation. It then describes initiatives to increase donation and transplantation in terms of system changes, organisational changes and community interventions, and considers requirements for effective strategies.Peer reviewedFinal Published versio

How do patients from South Asian backgrounds experience life on haemodialysis in the UK? : A multicentre qualitative study

© Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.OBJECTIVES: End-stage kidney disease disproportionately affects people of South Asian origin. This study aimed to uncover the lived experiences of this group of patients on centre-based haemodialysis (HD), the most prevalent dialysis modality. DESIGN: The study utilised a qualitative focus group methodology. Seven focus groups were conducted across four NHS Trusts in the UK including three in Gujarati and two each in Punjabi and Urdu. This provided an inclusive opportunity for South Asian patients to contribute in their language of origin. A total of 24 patients participated. Focus groups were facilitated by bilingual project workers and data were forward translated and analysed using thematic analysis. RESULTS: Four themes were identified. This included (1) 'treatment imposition', which comprised of the restrictive nature of HD, the effects of treatment and the feeling of being trapped in an endless process. (2) The 'patient-clinician relationship' centred around the impact of a perceived lack of staff time, and inadequacies in the quality of interactions. (3) 'Coping strategies' highlighted the role of cognitive reappraisal, living in the moment and family support networks in facilitating adjustment. (4) 'Pursuit of transplantation' included equating this form of treatment with restoring normality, alongside cultural factors limiting hopefulness for receiving an organ. CONCLUSIONS: In general, the experiences of South Asian patients receiving HD were not unique to this ethnic group. We did find distinct issues in relation to interactions with healthcare professionals, views on access to transplantation and the importance of family support networks. The study provides useful insights which may help enhance culturally tailored renal care.Peer reviewe

‘I feel weak, useless and dependent on others’ : South Asian patient experiences of haemodialysis

Shivani Sharma, Roisin Mooney, Andrew Davenport, Clara Day, Neil Duncan, David Wellsted, Maria Da Silva Gane, Kirit Modi, Ken Farrington, ‘‘I feel weak, useless and dependent on others’: South Asian patient experiences of haemodialysis’, poster presented at the Annual Conference British Renal Society, Leeds, UK, 30 June – 2 July, 2015.OBJECTIVE: Much of what is known about patient experiences of haemodialysis (HD) has been gleaned from research with White English speaking groups. People from South Asian backgrounds- originating from India, Pakistan and Bangladesh- have a three to five fold greater risk of needing treatment for renal failure. Owing to language and cultural barriers, less is known about how patients from specific ethnic minorities experience HD, although such knowledge would help shape efforts to provide suitable support. In this study, we invited those who communicate predominately or exclusively in Gujarati, Punjabi or Urdu to participate in focus groups and with the aim of exploring thoughts and feelings related to HD and its impact on day-to-day life. METHOD: Seven focus groups were held and across four NHS Trusts with high representation of patients from South Asian backgrounds. They were facilitated by a team of bilingual researchers with experience of working in healthcare contexts. Twenty-eight patients participated (15 males and 13 females; mean age 57.4 years). Focus groups were transcribed verbatim and translated into English, paying attention to retaining meaning as opposed to literal interpretation. Thematic Analysis was used to elucidate emerging themes, and using NVivo 10- a software programme designed to aid robust analysis of qualitative data. RESULTS: Patients reported numerous aspects of the ‘imposition of treatment’ that altered their sense of self and left them feeling as though they were ‘living in limbo’. Various ‘support mechanisms’ were seen as crucial in helping maintain some form of normality and these were both internally and externally derived. Hope for optimising outcomes was constrained by awareness of cultural barriers to ‘access to transplantation’ with patients’ conscious that their current situation added to ‘family stress’. Furthermore, perceptions of the ‘patient-clinician relationship’ often compounded the overall experience of HD- leaving the majority feeling trapped by their situation. CONCLUSION: Our findings highlight communalities in patient experiences of HD across different ethnic and cultural groups- delineating the aspects of treatment that patients struggle to contend with. Unique to our sample, we also expose concerns about access to kidney transplantation, with cultural factors limiting hope for improving quality of life. Targeted organ donation campaigns have a role to play here in furthering patient optimism for the future. It is apparent that patients require support in managing their altered sense of self and this can be facilitated in many ways including strengthening personal resources for coping alongside peer support.Peer reviewe

DBGC:Dimension-Based Generic Convolution Block for Object Recognition

The object recognition concept is being widely used a result of increasing CCTV surveillance and the need for automatic object or activity detection from images or video. Increases in the use of various sensor networks have also raised the need of lightweight process frameworks. Much research has been carried out in this area, but the research scope is colossal as it deals with open-ended problems such as being able to achieve high accuracy in little time using lightweight process frameworks. Convolution Neural Networks and their variants are widely used in various computer vision activities, but most of the architectures of CNN are application-specific. There is always a need for generic architectures with better performance. This paper introduces the Dimension-Based Generic Convolution Block (DBGC), which can be used with any CNN to make the architecture generic and provide a dimension-wise selection of various height, width, and depth kernels. This single unit which uses the separable convolution concept provides multiple combinations using various dimension-based kernels. This single unit can be used for height-based, width-based, or depth-based dimensions; the same unit can even be used for height and width, width and depth, and depth and height dimensions. It can also be used for combinations involving all three dimensions of height, width, and depth. The main novelty of DBGC lies in the dimension selector block included in the proposed architecture. Proposed unoptimized kernel dimensions reduce FLOPs by around one third and also reduce the accuracy by around one half; semi-optimized kernel dimensions yield almost the same or higher accuracy with half the FLOPs of the original architecture, while optimized kernel dimensions provide 5 to 6% higher accuracy with around a 10 M reduction in FLOPs

Development of Fog-based Dynamic Load Balancing Framework for Healthcare using Fog Computing

Fog computing has become one of the leading technologies by conquering the many significant challenges in IoT, Big Data, and Cloud. Computing models are inclining toward Fog than Cloud due to faster processing. The numerous idle devices near the users help overcome the issue of latency found in the Cloud. Resource management through load balancing plays an essential role in efficient data processing. Based on the current pandemic situation, Emergency patient’s vital sign monitoring system for COVID and other variants is implemented with support of dynamic resource load balancing environment. Apart from this, previously, we have faced many such diseases such as plague and flu which were pandemic and have become normal diseases now. Apart from them, there are many critical conditions and diseases such as hypertension, kidney failure, heart attack, cancer, lung, and liver disease that need continuous monitoring. It is not feasible to treat all patients at the hospital as the count is increasing very speedily. There is a need for infrastructure to handle resource issues without any delay in the treatment of patients using the fog computing. The proposed approach DynaReLoad would provide prompt health services and prevent early deaths due to critical conditions. An immediate alert to the doctors will be generated when detecting any abnormality. The effectiveness of DynaReLoad has been analyzed with other load balancing algorithms to achieve a low latency with minimum MakeSpan, better scheduling time, and response time, maximizing load balancing level and resource utilization using iFogSim

Histogram of Oriented Gradient-Based Fusion of Features for Human Action Recognition in Action Video Sequences

Human Action Recognition (HAR) is the classification of an action performed by a human. The goal of this study was to recognize human actions in action video sequences. We present a novel feature descriptor for HAR that involves multiple features and combining them using fusion technique. The major focus of the feature descriptor is to exploits the action dissimilarities. The key contribution of the proposed approach is to built robust features descriptor that can work for underlying video sequences and various classification models. To achieve the objective of the proposed work, HAR has been performed in the following manner. First, moving object detection and segmentation are performed from the background. The features are calculated using the histogram of oriented gradient (HOG) from a segmented moving object. To reduce the feature descriptor size, we take an averaging of the HOG features across non-overlapping video frames. For the frequency domain information we have calculated regional features from the Fourier hog. Moreover, we have also included the velocity and displacement of moving object. Finally, we use fusion technique to combine these features in the proposed work. After a feature descriptor is prepared, it is provided to the classifier. Here, we have used well-known classifiers such as artificial neural networks (ANNs), support vector machine (SVM), multiple kernel learning (MKL), Meta-cognitive Neural Network (McNN), and the late fusion methods. The main objective of the proposed approach is to prepare a robust feature descriptor and to show the diversity of our feature descriptor. Though we are using five different classifiers, our feature descriptor performs relatively well across the various classifiers. The proposed approach is performed and compared with the state-of-the-art methods for action recognition on two publicly available benchmark datasets (KTH and Weizmann) and for cross-validation on the UCF11 dataset, HMDB51 dataset, and UCF101 dataset. Results of the control experiments, such as a change in the SVM classifier and the effects of the second hidden layer in ANN, are also reported. The results demonstrate that the proposed method performs reasonably compared with the majority of existing state-of-the-art methods, including the convolutional neural network-based feature extractors

CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope

Computer vision is becoming an increasingly trendy word in the area of image processing. With the emergence of computer vision applications, there is a significant demand to recognize objects automatically. Deep CNN (convolution neural network) has benefited the computer vision community by producing excellent results in video processing, object recognition, picture classification and segmentation, natural language processing, speech recognition, and many other fields. Furthermore, the introduction of large amounts of data and readily available hardware has opened new avenues for CNN study. Several inspirational concepts for the progress of CNN have been investigated, including alternative activation functions, regularization, parameter optimization, and architectural advances. Furthermore, achieving innovations in architecture results in a tremendous enhancement in the capacity of the deep CNN. Significant emphasis has been given to leveraging channel and spatial information, with a depth of architecture and information processing via multi-path. This survey paper focuses mainly on the primary taxonomy and newly released deep CNN architectures, and it divides numerous recent developments in CNN architectures into eight groups. Spatial exploitation, multi-path, depth, breadth, dimension, channel boosting, feature-map exploitation, and attention-based CNN are the eight categories. The main contribution of this manuscript is in comparing various architectural evolutions in CNN by its architectural change, strengths, and weaknesses. Besides, it also includes an explanation of the CNN’s components, the strengths and weaknesses of various CNN variants, research gap or open challenges, CNN applications, and the future research direction