Manchester Metropolitan University

About: Manchester Metropolitan University is a education organization based out in Manchester, Manchester, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 5435 authors who have published 16202 publications receiving 442561 citations. The organization is also known as: Manchester Polytechnic & MMU.

Changing practice: Implications of the World Report on Disability for responding to communication disability in under-served populations.

Abstract: The World Report on Disability provides a major challenge to the conceptualization and delivery of services for people with communication disabilities around the world. Many people, in both Majority and Minority World countries, receive limited or no support in relation to their communication disability. In this paper the prevalence of communication disability across the world (and the challenges to obtaining these data) are discussed, particularly in relation to disability more broadly. Populations that are under-served by speech-language pathology services in both Majority and Minority World countries are described. The paper describes organizational change theory and the potential it has, together with a biopsychosocial model of disability, to assist in understanding and influencing development of relevant services for people with communication disabilities (PWCD), particularly those who are under-served. Aspects of, and influences on, service delivery for PWCD are described. The need for novel ways of conceptualizing development of services, including population-based approaches, is highlighted. Finally, the challenges and opportunities for PWCD and for speech-language pathologists which arise from the nine recommendations of the World Report on Disability are considered and readers are encouraged to consider new and novel ways of developing equitable services for people with communication disabilities, in both majority and minority world settings.

2D Hexagonal Boron Nitride (2D-hBN) Explored for the Electrochemical Sensing of Dopamine.

Abstract: Crystalline 2D hexagonal boron nitride (2D-hBN) nanosheets are explored as a potential electrocatalyst toward the electroanalytical sensing of dopamine (DA). The 2D-hBN nanosheets are electrically wired via a drop-casting modification process onto a range of commercially available carbon supporting electrodes, including glassy carbon (GC), boron-doped diamond (BDD), and screen-printed graphitic electrodes (SPEs). 2D-hBN has not previously been explored toward the electrochemical detection/electrochemical sensing of DA. We critically evaluate the potential electrocatalytic performance of 2D-hBN modified electrodes, the effect of supporting carbon electrode platforms, and the effect of “mass coverage” (which is commonly neglected in the 2D material literature) toward the detection of DA. The response of 2D-hBN modified electrodes is found to be largely dependent upon the interaction between 2D-hBN and the underlying supporting electrode material. For example, in the case of SPEs, modification with 2D-hBN (3...

DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification

Abstract: Globally, in 2016, 1 out of 11 adults suffered from diabetes mellitus. Diabetic foot ulcers (DFU) are a major complication of this disease, which if not managed properly can lead to amputation. Current clinical approaches to DFU treatment rely on patient and clinician vigilance, which has significant limitations, such as the high cost involved in the diagnosis, treatment, and lengthy care of the DFU. We collected an extensive dataset of foot images, which contain DFU from different patients. In this DFU classification problem, we assessed the two classes as normal skin (healthy skin) and abnormal skin (DFU). In this paper, we have proposed the use of machine learning algorithms to extract the features for DFU and healthy skin patches to understand the differences in the computer vision perspective. This experiment is performed to evaluate the skin conditions of both classes that are at high risk of misclassification by computer vision algorithms. Furthermore, we used convolutional neural networks for the first time in this binary classification. We have proposed a novel convolutional neural network architecture, DFUNet, with better feature extraction to identify the feature differences between healthy skin and the DFU. Using 10-fold cross validation, DFUNet achieved an AUC score of 0.961. This outperformed both the traditional machine learning and deep learning classifiers we have tested. Here, we present the development of a novel and highly sensitive DFUNet for objectively detecting the presence of DFUs. This novel approach has the potential to deliver a paradigm shift in diabetic foot care among diabetic patients, which represent a cost-effective, remote, and convenient healthcare solution.