26 research outputs found

    Interaction of Electrode Materials with Neuronal and Glial Cells

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    Steigende Zahlen von Patienten mit neurodegenerativen Erkrankungen sind ein überzeugender Grund, das menschliche Gehirn und seinen fortschreitenden Verfall zu untersuchen, wobei aber viele essenzielle biochemische Funktionen bisher noch nicht vollends geklärt sind. In vitro Forschung zur Hirnfunktion auf geeigneten Plattformen ist ein vielversprechender Weg, diese Lücke zu schließen. Eigenschaften der brain-machine Grenzfläche müssen erforscht werden, um neue Biomaterialien effektiv für lab-on-a-chip Anwendungen wie bspw. Multielektrodenarrays (MEAs) einzusetzen. Diese brain-on-a-chip Anwendungen können dazu dienen, die Zahl der Tierexperimente zu reduzieren, damit Forschung zu beschleunigen und Kosten zu senken. In dieser Hinsicht erfordert die Miniaturisierung von MEAs für eine detailliertere Messung von neuronalen Funktionen die Entwicklung von neuen Biomaterialien mit vorteilhaften elektrischen Eigenschaften. Die Wechselwirkung dieser Biomaterialien mit Zellen muss untersucht werden, um gute Zelladhäsion, Proliferation und elektrische Kopplung zu gewährleisten. Die vorliegende Arbeit dient der Charakterisierung der Wechselwirkung von humanen neuronalen Zellen und Gliazellen (neuronenartige SH-SY5Y und gliaartige U-87 MG Zellen) mit dem Elektrodenmaterial Titannitrid mit nanokolumnarer Oberfläche (TiN nano) und dessen Vorteile bezüglich elektrischer und bioaktiver Eigenschaften im Vergleich mit Gold (Au) und Indiumzinnoxid (ITO), welche derzeit für MEAs und Neuroelektroden verwendet werden. Das Ziel der Arbeit ist die Implementierung neuer aus der theoretischen Physik, Mathematik und Computerwissenschaft entlehnten Techniken, um eine bildbasierte Methode zu entwickeln, die auf minimalen Experimenten beruht und trotzdem wichtige Hinweise zur Biokompatibiliät eines Materials liefert. Das schließt die Analyse von Zellnetzwerken, Zellverteilung, Adhäsion und elektrochemischer Eigenschaften in mono- und co-Kultur ein. Dazu werden Autokorrelation, selbstlernende Algorithmen und die Analyse nächstgelegener Nachbarn eingesetzt, um einen Weg von klassischen biochemischen Assays weg zu einem rechnerischen Ansatz zu finden. Die Ergebnisse zeigen eine Überlegenheit von Tin nano als potenzielles Biomaterial für lab-on-a-chip Anwendungen und in vivo neuraler Stimulation. Die präsentierte bildbasierte Analysemethode für die Untersuchung von Zellverteilungen erweist sich als wertvolles Werkzeug für die Bewertung von Biokompatibilität. Sie ist universell einsetzbar für verschiedene Zelltypen und quantifiziert die Wechselwirkung von Zellen mit Biomaterialien.Rising numbers of patients with neurodegenerative diseases are a compelling reason to study the human brain and its progressive deterioration but many essential biochemical functions are still under investigation. Conducting research on brain function in vitro with suitable platforms is a promising solution to close these gaps. Characteristics of the brain-machine interface need to be investigated to effectively employ new biomaterials for lab-on-a-chip devices, such as multielectrode arrays (MEAs) for example. These brain-on-a-chip devices will potentially reduce the number of conducted animal experiments and therewith accelerate future research and reduce costs. In this context, miniaturization of MEAs for more detailed measurements of neuronal function calls for new biomaterials with advantageous electrical characteristics. The interaction of these biomaterials with cells needs to be investigated to ensure good cell adhesion, proliferation, and electrical coupling. This thesis aims to study and characterize the interaction of human neuronal and glial cells (neuron-like SH-SY5Y and glia-like U-87 MG cells) with the electrode material titanium nitride with nanocolumnar surface topography (TiN nano) and its advantages in terms of electric and bioactive properties compared to gold (Au) and indium tin oxide (ITO) which are currently employed for MEAs and neuroelectrodes. The overall goal of this study is the implementation of new techniques drawn from theoretical physics, mathematics, and computer science to establish an image-based method that relies on minimal experimental effort but nevertheless yields important evidence of biocompatibility of the material. Analysis includes the investigation of cellular networks, cell distribution, adhesion, and electrochemical properties in mono- and co-culture experiments. To this end, autocorrelation function, self-learning algorithms, and nearest neighbor analysis are deployed to move away from classical biochemical assays toward a more computational approach. Results show the superiority of TiN nano as a potential biomaterial employed for lab-on-a-chip designs as well as for in vivo neural stimulation. The proposed image-based analysis method for the investigation of cellular distribution turns out to be a valuable tool for the assessment of biocompatibility. It is universally applicable to cell types other than neuronal and quantifies the interaction of cells with biomaterials

    Robots in Nursing - False Rhetoric or Future Reality?: How might robots contribute to hospital nursing in the future? A qualitative study of the perspectives of roboticists and nurses

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    Introduction. The challenge of the global nursing shortage coupled with a rising healthcare demand prompts consideration of technology as a potential solution. Technology in the form of robots is being developed for healthcare applications but the potential role in nursing has not been researched in the UK. Methods A three-phased qualitative study was undertaken: interviews with 5 robotic developers (Phase 1); nine focus groups /interviews with 25 hospital Registered Nurses (RN) in Phase 2, and 12 nurse leaders in four focus groups (Phase 3). Data was analysed using framework analysis for Phase 1 and reflexive thematic analysis for Phase 2 and 3 data based on the Fundamentals of Care framework. Results Roboticist interviews confirmed that a taxonomy of potential robotic automation was a useful tool for discussing the role of robots. In Phase 2, RNs described activities that robots might undertake and commented on those which they should not. RNs more readily agreed that robots could assist with physical activities than relational activities. Six potential roles that robots might undertake in future nursing practice were identified from the data and which have been labelled as advanced machine, social companion, responsive runner, helpful co-worker, proxy nurse bot, and feared substitute. Three cross-cutting themes were identified: • a fear of the future; • a negotiated reality and • a positive opportunity. In phase 3, nurse leaders considered the RN results and four themes were identified from their discussions: • First impressions of robot in nursing; • The essence of nursing; • We must do something and • Reframing the future. Conclusions Robots will be a future reality in nursing, playing an assistive role. Nursing must become technically proficient and engage with the development and testing of robots. Nurse leaders must lead policy development and reframe the narrative from substitution to assistance. A number of navigational tools have been developed including a taxonomy of nursing automation and the six robotic roles which may be useful to inform future debate in nursing

    Development of a supervisory internet of things (IoT) system for factories of the future

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    Big data is of great importance to stakeholders, including manufacturers, business partners, consumers, government. It leads to many benefits, including improving productivity and reducing the cost of products by using digitalised automation equipment and manufacturing information systems. Some other benefits include using social media to build the agile cooperation between suppliers and retailers, product designers and production engineers, timely tracking customers’ feedbacks, reducing environmental impacts by using Internet of Things (IoT) sensors to monitor energy consumption and noise level. However, manufacturing big data integration has been neglected. Many open-source big data software provides complicated capabilities to manage big data software for various data-driven applications for manufacturing. In this research, a manufacturing big data integration system, named as Data Control Module (DCM) has been designed and developed. The system can securely integrate data silos from various manufacturing systems and control the data for different manufacturing applications. Firstly, the architecture of manufacturing big data system has been proposed, including three parts: manufacturing data source, manufacturing big data ecosystem and manufacturing applications. Secondly, nine essential components have been identified in the big data ecosystem to build various manufacturing big data solutions. Thirdly, a conceptual framework is proposed based on the big data ecosystem for the aim of DCM. Moreover, the DCM has been designed and developed with the selected big data software to integrate all the three varieties of manufacturing data, including non-structured, semi-structured and structured. The DCM has been validated on three general manufacturing domains, including product design and development, production and business. The DCM cannot only be used for the legacy manufacturing software but may also be used in emerging areas such as digital twin and digital thread. The limitations of DCM have been analysed, and further research directions have also been discussed

    Additive manufacturing of mitral annuloplasty devices

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    Mitral valve annuloplasty is a common surgical procedure performed on thousands of patients each year across the world. A less invasive and more successful method of resolving mitral valve regurgitation, repair surgeries now outnumber replacement of the mitral valve in its entirety. As a result, a range of supportive annuloplasty ring devices for maintaining the surgical repair are now available for lifelong implantation. However, these devices underserve some populations leading to replacement surgeries, and rely on assumptions made on the natural, healthy anatomy of the mitral valve. Additive manufacturing (AM) has, for the last few decades, become increasingly adopted into the medical industry. With applications ranging from educational aids to surgical instruments and long-term implantable devices, this field is rapidly expanding and encompassing a greater breadth of medical specialities. In particular, the manufacturing of patient-specific products with reasonable cost and high fidelity is a key area of development for medical applications of additive manufacturing methods. Significant research has already been undertaken in the fields of orthopaedics, regenerative medicine, and pharmaceuticals, producing long-term implantable metal devices, complex polymer scaffolds, and novel drug delivery methods. Personalized annuloplasty rings could lead to greater surgery success rates enabling greater repair longevity, reduced reoperation rates, and reduced risk of future valve replacement. This project aimed to investigate the suitability of the AM technique, selective laser melting (SLM), to create annuloplasty rings tailored to each patient. To achieve this goal, this research focussed first on comparing the existing design assumptions applied to commercial annuloplasty devices against human anatomy using cadaveric dissection and measurement. These studies concluded that whilst the assumed 3:4 ratio applied in annuloplasty design was a good average across a population, the ratio was inconsistent between subjects and could lead to difficulties in sizing devices appropriately for an individual patient. Following this, methods of design and manufacturing were investigated, comparing various tools available in commercial medical-CAD software, Materialise Mimics®. The commonly applied “thresholding” method of isolating structures from patient scan data was found to be insufficient for isolation of soft tissue structures such as the mitral valve annulus from the surrounding cardiac tissue due to the similarity in densities reducing contrast on the scan. A method of single-point design using insertion points of the valve leaflets throughout the scan was shown to be sufficient to reproduce a mitral annular structure, which was then manufactured in the Ti6Al4V alloy, which has been shown to be biocompatible in some orthopaedic applications, using SLM. Post-processing techniques appropriate for the specific application of this device into the cardiovascular system were also investigated. The novel electrolyte jet machining process was employed to moderate surface unevenness caused by inherent properties of the powder bed SLM process, such as stepping or loose powder particles. This process was tested with a range of parameter sets producing varying topographies and therefore applied to different needs of the annuloplasty device. Firstly, the process was applied for reduction of coagulation on the surface of Ti6Al4V alloy samples, and then for amplification of fibroblastic cell growth. The primary parameter sets were found to produce a small reduction in platelet adhesion when compared against as-built SLM surfaces, however failed to reduce the platelet activity to that found on conventionally manufactured Ti6Al4V samples. The secondary parameter sets did not produce any improvement in fibroblastic proliferation in short term studies, however SLM samples were found to be significantly more favourable to fibroblast growth than conventionally manufactured surfaces of the same material grade. Finally, future avenues for work are discussed, including next steps for each of the three areas investigated in this thesis and a view to the future of novel annuloplasty devices as a whole. Recommendations for other applications of electrolyte jet machining are provided, including the potential for anti-biofouling surface processing given the lack of cell survivability found in these studies. Further design recommendations are considered, from computational modelling of the valve through to structured surgical prediction integrated with design of the annuloplasty device

    ‘Technology as a trusted companion for accountants and business professionals in the business unusual environment’

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    Abstract: A conceptual model of corporate social responsibility (CSR) and service quality for Islamic bank service users is developed in this paper. The model is expected to bring about a better understanding of customer perceptions relating to issues of social responsibility and its impact on service quality as it pertains to Islamic banking in South Africa. The conceptual model is developed by synthesising relevant literature and theories pertaining to this study. This conceptual model is proposed to be tested in due course of time by using customer perceptions derived from a survey instrument to assess the relationship between aspects of social responsibility and service quality at the Islamic bank. The proposed model has implications towards enhancing the CSR and service quality offered by the Islamic bank according to their customer expectations

    SHELDON Smart habitat for the elderly.

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    An insightful document concerning active and assisted living under different perspectives: Furniture and habitat, ICT solutions and Healthcare

    QUANTITATIVE NUCLEAR MEDICINE IMAGING USING ADVANCED IMAGE RECONSTRUCTION AND RADIOMICS

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    Our aim is to help put nuclear medicine at the forefront of quantitation on the path to the realization of personalized medicine. We propose and evaluate (Part I) advanced image reconstruction and (Part II) robust radiomics (large-scale data-oriented study of radiological images). The goal is to attain significantly improved diagnostic, prognostic and treatment-response assessment capabilities. Part I presents a new paradigm in point-spread function (PSF)-modeling, a partial volume correction method in PET imaging where resolution-degrading phenomena are modeled within the reconstruction framework. PSF-modeling improves resolution and enhances contrast, but significantly alters noise properties and induces edge-overshoots. Past efforts involve a dichotomy of PSF vs. no-PSF modeling; by contrast, we focus on a wide-spectrum of PSF models, including under- and over-estimation of the true PSF, for the potential of enhanced quantitation in standardized uptake values (SUVs). We show for the standard range of iterations employed in clinic (not excessive), edge enhancement due to overestimation actually lower SUV bias in small regions, while inter-voxel correlations suppress image roughness and enhance uniformity. An overestimated PSF yields improved contrast and limited edge-overshoot effects at lower iterations, enabling enhanced SUV quantitation. Overall, our framework provides an effective venue for quantitative task-based optimization. Part II proposes robust and reproducible radiomics methods. Radiomics workflows are complex, generating hundreds of features, which can lead to high variability and overfitting, and ultimately hampering performance. We developed and released a Standardized Environment for Radiomics Analysis (SERA) solution to enable robust radiomics analyses. We conduct studies on two unique imaging datasets – renal cell carcinoma SPECT and prostate cancer PET – identifying robust and reproducible radiomic features. In addition, we evaluate a novel hypothesis that radiomic features extracted from clinically normal (non-ischemic) myocardial perfusion SPECT (MPS) can predict coronary artery calcification (CAC; as extracted from CT). This has important implications, since CAC assessment is not commonly-performed nor reimbursed in wide community settings. SERA-derived radiomic features were utilized in a multi-step feature selection framework, followed by the application of machine learning to radiomic features. Our results show the potential to predict CAC from normal MPS, suggesting added usage and value for routine standard MPS

    Supporting Collaborative Business Processes: a BPaaS Approach.

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    Collaborative business processes are increasingly driven by business flexibility and agility. Cloud-based business process management services have provided small medium enterprises (SMEs) with a pay-per-use manner for their daily business needs, i.e. some simple business process applications, e.g. salesforce provides cloud-based CRM to boost SMEs' sales. This raises the question how cloud-based business process management solutions can support the fast pace of change of business collaborations among business partners? For example, collaborative processes for managing industrial incidents are short term, low frequency processes. This paper proposes an architecture meta-model, which is used to design the concrete architecture and to further analyse the performance of the proposed solution. A real world case of collaborative processes for incident and maintenance notifi cations is used to explain the design and implementation of the cloud-based solution for supporting collaborative business processes. Service improvement of the new solution and computing power costs are analysed accordingly

    Artificial intelligence: a light approach

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