Institution
Maastricht University
Education•Maastricht, Limburg, Netherlands•
About: Maastricht University is a education organization based out in Maastricht, Limburg, Netherlands. It is known for research contribution in the topics: Population & Health care. The organization has 19263 authors who have published 53291 publications receiving 2266866 citations. The organization is also known as: Universiteit Maastricht & UM.
Papers published on a yearly basis
Papers
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University of Groningen1, University of Cambridge2, Queen Mary University of London3, University of Rochester4, University of Navarra5, King Saud University6, Medical University of Vienna7, University of Padua8, Maastricht University9, King Abdulaziz University10, Johns Hopkins University11, National and Kapodistrian University of Athens12, University of Geneva13, University of Amsterdam14
TL;DR: The use of LMWH for VTE prevention in cancer patients undergoing laparoscopic surgery may be recommended as for laparotomy and mechanical methods are not recommended as monotherapy except when pharmacological methods are contraindicated.
607 citations
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TL;DR: This review establishes detailed best practices, methods and techniques for characterizing CNM particle morphology, surface chemistry, surface charge, purity, crystallinity, rheological properties, mechanical properties, and toxicity for two distinct forms of CNMs: cellulose nanocrystals and cellulose Nanofibrils.
Abstract: A new family of materials comprised of cellulose, cellulose nanomaterials (CNMs), having properties and functionalities distinct from molecular cellulose and wood pulp, is being developed for applications that were once thought impossible for cellulosic materials. Commercialization, paralleled by research in this field, is fueled by the unique combination of characteristics, such as high on-axis stiffness, sustainability, scalability, and mechanical reinforcement of a wide variety of materials, leading to their utility across a broad spectrum of high-performance material applications. However, with this exponential growth in interest/activity, the development of measurement protocols necessary for consistent, reliable and accurate materials characterization has been outpaced. These protocols, developed in the broader research community, are critical for the advancement in understanding, process optimization, and utilization of CNMs in materials development. This review establishes detailed best practices, methods and techniques for characterizing CNM particle morphology, surface chemistry, surface charge, purity, crystallinity, rheological properties, mechanical properties, and toxicity for two distinct forms of CNMs: cellulose nanocrystals and cellulose nanofibrils.
606 citations
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TL;DR: Cognitive load theory was introduced in the 1980s as an instructional design theory based on several uncontroversial aspects of human cognitive architecture as discussed by the authors, which had a limited impact on the field of instructional design with most instructional design recommendations proceeding as though working memory and long-term memory did not exist.
Abstract: Cognitive load theory was introduced in the 1980s as an instructional design theory based on several uncontroversial aspects of human cognitive architecture. Our knowledge of many of the characteristics of working memory, long-term memory and the relations between them had been well-established for many decades prior to the introduction of the theory. Curiously, this knowledge had had a limited impact on the field of instructional design with most instructional design recommendations proceeding as though working memory and long-term memory did not exist. In contrast, cognitive load theory emphasised that all novel information first is processed by a capacity and duration limited working memory and then stored in an unlimited long-term memory for later use. Once information is stored in long-term memory, the capacity and duration limits of working memory disappear transforming our ability to function. By the late 1990s, sufficient data had been collected using the theory to warrant an extended analysis resulting in the publication of Sweller et al. (Educational Psychology Review, 10, 251–296, 1998). Extensive further theoretical and empirical work have been carried out since that time and this paper is an attempt to summarise the last 20 years of cognitive load theory and to sketch directions for future research.
605 citations
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TL;DR: How DBS has facilitated advances in the authors' understanding of how circuit malfunction can lead to brain disorders is considered and the key unmet challenges and future directions in the DBS field are outlined.
Abstract: The clinical use of deep brain stimulation (DBS) is among the most important advances in the clinical neurosciences in the past two decades. As a surgical tool, DBS can directly measure pathological brain activity and can deliver adjustable stimulation for therapeutic effect in neurological and psychiatric disorders correlated with dysfunctional circuitry. The development of DBS has opened new opportunities to access and interrogate malfunctioning brain circuits and to test the therapeutic potential of regulating the output of these circuits in a broad range of disorders. Despite the success and rapid adoption of DBS, crucial questions remain, including which brain areas should be targeted and in which patients. This Review considers how DBS has facilitated advances in our understanding of how circuit malfunction can lead to brain disorders and outlines the key unmet challenges and future directions in the DBS field. Determining the next steps in DBS science will help to define the future role of this technology in the development of novel therapeutics for the most challenging disorders affecting the human brain. Over the past 20 years, deep brain stimulation (DBS) has transformed the treatment of movement disorders. Now, new therapeutic possibilities for DBS are emerging for other neurological and psychiatric disorders. This Review considers the clinical and scientific advances facilitated by DBS and the crucial questions, challenges and opportunities that face this technology.
604 citations
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TL;DR: Overall, this review shows that variability over time and dynamic patterns of reactivity to the environment are essential features of psychopathological experiences that need to be captured for a better understanding of their phenomenology and underlying mechanisms.
Abstract: A growing body of research suggests that momentary assessment technologies that sample experiences in the context of daily life constitute a useful and productive approach in the study of behavioural phenotypes and a powerful addition to mainstream cross-sectional research paradigms. Momentary assessment strategies for psychopathology are described, together with a comprehensive review of research findings illustrating the added value of daily life research for the study of (1) phenomenology, (2) aetiology, (3) psychological models, (4) biological mechanisms, (5) treatment and (6) gene-environment interactions in psychopathology. Overall, this review shows that variability over time and dynamic patterns of reactivity to the environment are essential features of psychopathological experiences that need to be captured for a better understanding of their phenomenology and underlying mechanisms. The Experience Sampling Method (ESM) allows us to capture the film rather than a snapshot of daily life reality of patients, fuelling new research into the gene-environment-experience interplay underlying psychopathology and its treatment.
603 citations
Authors
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Name | H-index | Papers | Citations |
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Edward Giovannucci | 206 | 1671 | 179875 |
Julie E. Buring | 186 | 950 | 132967 |
Aaron R. Folsom | 181 | 1118 | 134044 |
John J.V. McMurray | 178 | 1389 | 184502 |
Alvaro Pascual-Leone | 165 | 969 | 98251 |
Lex M. Bouter | 158 | 767 | 103034 |
David T. Felson | 153 | 861 | 133514 |
Walter Paulus | 149 | 809 | 86252 |
Michael Conlon O'Donovan | 142 | 736 | 118857 |
Randy L. Buckner | 141 | 346 | 110354 |
Philip Scheltens | 140 | 1175 | 107312 |
Anne Tjønneland | 139 | 1345 | 91556 |
Ewout W. Steyerberg | 139 | 1226 | 84896 |
James G. Herman | 138 | 410 | 120628 |
Andrew Steptoe | 137 | 1003 | 73431 |