•The MSDA is a global multi-stakeholder collaboration accelerating research insights in MS.•We envision a patient-centred data ecosystem in which all stakeholders contribute and use big data.•We aim ...to raise awareness about the importance of research using real world MS data.•We enable better discovery and access to real world MS data.•We promote trustworthy and transparent practices in the way real world MS data is used.
The Multiple Sclerosis Data Alliance (MSDA), a global multi-stakeholder collaboration, is working to accelerate research insights for innovative care and treatment for people with multiple sclerosis (MS) through better use of real-world data (RWD). Despite the increasing reliance on RWD, challenges and limitations complicate the generation, collection, and use of these data. MSDA aims to tackle sociological and technical challenges arising with scaling up RWD, specifically focused on MS data. MSDA envisions a patient-centred data ecosystem in which all stakeholders contribute and use big data to co-create the innovations needed to advance timely treatment and care of people with MS.
A key task for the pharmaceutical industry is to understand the compliance implications of engaging with a patient advocacy group (PAG). This presents challenges for the industry to negotiate the ...ethical and reputational issues that can arise when working with a PAG.
To gain the views of pharmaceutical industry executives on future compliance challenges when working with PAGs.
We conducted two surveys among two sets of industry executives: one group focussed on market access roles and the other focussed on non-market access roles.
Transparency was identified as the biggest challenge, followed by project rationale and then by project ownership.
We explore how this can be overcome and make recommendations on how best to work compliantly with PAGs.
Nurses play a critical role in caring for patients with multiple sclerosis (MS). The Multiple Sclerosis-Nurse Empowering Education (MS-NEED): European Survey was conducted to understand the role of ...nurses in MS and the provision of care across Europe. The survey focused on four key areas: clinical practice, advocacy, research and publication, and training and education. A total of 280 nurses were included from the UK, Germany, Italy, Poland, Finland and the Czech Republic. All participants were nurses actively working with MS patients. The role of the nurse in MS is diverse and varies substantially across Europe, leading to inequalities in patient care. A European consensus to define the roles and responsibilities of the MS nurse would facilitate consistency of care across all countries and help to achieve the best possible outcome for patients with MS in Europe.
Background: Multiple sclerosis (MS) is a complex disease with new drugs becoming available in the past years. There is a need for a reference tool compiling current data to aid professionals in ...treatment decisions. Objectives: To develop an evidence-based clinical practice guideline for the pharmacological treatment of people with MS. Methods: This guideline has been developed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology and following the updated EAN recommendations. Clinical questions were formulated in Patients-Intervention-Comparator-Outcome (PICO) format and outcomes were prioritized. The quality of evidence was rated into four categories according to the risk of bias. The recommendations with assigned strength (strong and weak) were formulated based on the quality of evidence and the risk-benefit balance. Consensus between the panelists was reached by use of the modified nominal group technique. Results: A total of 10 questions were agreed, encompassing treatment efficacy, response criteria, strategies to address suboptimal response and safety concerns and treatment strategies in MS and pregnancy. The guideline takes into account all disease-modifying drugs approved by the European Medicine Agency (EMA) at the time of publication. A total of 21 recommendations were agreed by the guideline working group after three rounds of consensus. Conclusion: The present guideline will enable homogeneity of treatment decisions across Europe.
The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer ...science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. Current decentralized systems still focus on data and knowledge as their main resource. Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and the support of agent systems with scaling and decentralized control. Synergy between grids, P2P systems, and agent technologies is the key to data- and knowledge-centered systems in large-scale environments. This, the sixth issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains eight extended and revised versions of papers selected from those presented at DEXA 2011. Topics covered include skyline queries, probabilistic logics and reasoning, theory of conceptual modeling, prediction in networks of moving objects, validation of XML integrity constraints, management of loosely structured multi-dimensional data, data discovery in the presence of annotations, and quality ranking for Web articles.
Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and ...throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences.
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