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Institution

Maastricht University

EducationMaastricht, 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
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Journal ArticleDOI
Martine Hoogman1, Janita Bralten1, Derrek P. Hibar2, Maarten Mennes, Marcel P. Zwiers, Lizanne S.J. Schweren3, Kimm J. E. van Hulzen1, Sarah E. Medland4, Elena Shumskaya1, Neda Jahanshad2, Patrick de Zeeuw5, Eszter Szekely6, Gustavo Sudre6, Thomas Wolfers1, Alberdingk M.H. Onnink1, Janneke Dammers1, Jeanette C. Mostert1, Yolanda Vives-Gilabert, Gregor Kohls, Eileen Oberwelland, Jochen Seitz, Martin Schulte-Rüther, Sara Ambrosino5, Alysa E. Doyle7, Alysa E. Doyle8, Marie F. Høvik9, Margaretha Dramsdahl10, Leanne Tamm11, Theo G.M. van Erp12, Anders M. Dale13, Andrew J. Schork13, Annette Conzelmann14, Annette Conzelmann15, Kathrin C. Zierhut14, Ramona Baur14, Hazel McCarthy16, Yuliya N. Yoncheva17, Ana Cubillo18, Kaylita Chantiluke18, Mitul A. Mehta18, Yannis Paloyelis18, Sarah Hohmann19, Sarah Baumeister19, Ivanei E. Bramati, Paulo Mattos20, Fernanda Tovar-Moll20, Pamela K. Douglas21, Tobias Banaschewski19, Daniel Brandeis, Jonna Kuntsi18, Philip Asherson18, Katya Rubia18, Clare Kelly17, Clare Kelly16, Adriana Di Martino17, Michael P. Milham22, Michael P. Milham23, Francisco X. Castellanos22, Francisco X. Castellanos17, Thomas Frodl24, Thomas Frodl16, Mariam Zentis24, Klaus-Peter Lesch25, Klaus-Peter Lesch14, Andreas Reif26, Paul Pauli14, Terry L. Jernigan13, Jan Haavik9, Jan Haavik27, Kerstin J. Plessen, Astri J. Lundervold9, Kenneth Hugdahl9, Kenneth Hugdahl27, Larry J. Seidman8, Larry J. Seidman28, Joseph Biederman8, Nanda Rommelse1, Dirk J. Heslenfeld29, Catharina A. Hartman3, Pieter J. Hoekstra3, Jaap Oosterlaan29, Georg von Polier, Kerstin Konrad, Oscar Vilarroya30, Josep Antoni Ramos-Quiroga30, Joan Carles Soliva30, Sarah Durston5, Jan K. Buitelaar1, Stephen V. Faraone9, Stephen V. Faraone31, Philip Shaw6, Paul M. Thompson2, Barbara Franke1 
TL;DR: Lifespan analyses suggest that, in the absence of well powered longitudinal studies, the ENIGMA cross-sectional sample across six decades of ages provides a means to generate hypotheses about lifespan trajectories in brain phenotypes.

749 citations

Journal ArticleDOI
TL;DR: Identification of optimal machine-learning methods for radiomic applications is a crucial step towards stable and clinically relevant radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor-phenotypic characteristics in clinical practice.
Abstract: Radiomics extracts and mines large number of medical imaging features quantifying tumor phenotypic characteristics Highly accurate and reliable machine-learning approaches can drive the success of radiomic applications in clinical care In this radiomic study, fourteen feature selection methods and twelve classification methods were examined in terms of their performance and stability for predicting overall survival A total of 440 radiomic features were extracted from pre-treatment computed tomography (CT) images of 464 lung cancer patients To ensure the unbiased evaluation of different machine-learning methods, publicly available implementations along with reported parameter configurations were used Furthermore, we used two independent radiomic cohorts for training (n = 310 patients) and validation (n = 154 patients) We identified that Wilcoxon test based feature selection method WLCX (stability = 084 ± 005, AUC = 065 ± 002) and a classification method random forest RF (RSD = 352%, AUC = 066 ± 003) had highest prognostic performance with high stability against data perturbation Our variability analysis indicated that the choice of classification method is the most dominant source of performance variation (3421% of total variance) Identification of optimal machine-learning methods for radiomic applications is a crucial step towards stable and clinically relevant radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor-phenotypic characteristics in clinical practice

749 citations

Journal ArticleDOI
TL;DR: Although AAS administration may affect erythropoiesis and blood haemoglobin concentrations, no effect on endurance performance was observed and little data about the effects of AAS on metabolic responses during exercise training and recovery are available and, therefore, do not allow firm conclusions.
Abstract: Androgenic-anabolic steroids (AAS) are synthetic derivatives of the male hormone testosterone. They can exert strong effects on the human body that may be beneficial for athletic performance. A review of the literature revealed that most laboratory studies did not investigate the actual doses of AAS currently abused in the field. Therefore, those studies may not reflect the actual (adverse) effects of steroids. The available scientific literature describes that short-term administration of these drugs by athletes can increase strength and bodyweight. Strength gains of about 5-20% of the initial strength and increments of 2-5 kg bodyweight, that may be attributed to an increase of the lean body mass, have been observed. A reduction of fat mass does not seem to occur. Although AAS administration may affect erythropoiesis and blood haemoglobin concentrations, no effect on endurance performance was observed. Little data about the effects of AAS on metabolic responses during exercise training and recovery are available and, therefore, do not allow firm conclusions. The main untoward effects of short- and long-term AAS abuse that male athletes most often self-report are an increase in sexual drive, the occurrence of acne vulgaris, increased body hair and increment of aggressive behaviour. AAS administration will disturb the regular endogenous production of testosterone and gonadotrophins that may persist for months after drug withdrawal. Cardiovascular risk factors may undergo deleterious alterations, including elevation of blood pressure and depression of serum high-density lipoprotein (HDL)-, HDL2- and HDL3-cholesterol levels. In echocardiographic studies in male athletes, AAS did not seem to affect cardiac structure and function, although in animal studies these drugs have been observed to exert hazardous effects on heart structure and function. In studies of athletes, AAS were not found to damage the liver. Psyche and behaviour seem to be strongly affected by AAS. Generally, AAS seem to induce increments of aggression and hostility. Mood disturbances (e.g. depression, [hypo-]mania, psychotic features) are likely to be dose and drug dependent. AAS dependence or withdrawal effects (such as depression) seem to occur only in a small number of AAS users. Dissatisfaction with the body and low self-esteem may lead to the so-called 'reverse anorexia syndrome' that predisposes to the start of AAS use. Many other adverse effects have been associated with AAS misuse, including disturbance of endocrine and immune function, alterations of sebaceous system and skin, changes of haemostatic system and urogenital tract. One has to keep in mind that the scientific data may underestimate the actual untoward effects because of the relatively low doses administered in those studies, since they do not approximate doses used by illicit steroid users. The mechanism of action of AAS may differ between compounds because of variations in the steroid molecule and affinity to androgen receptors. Several pathways of action have been recognised. The enzyme 5-alpha-reductase seems to play an important role by converting AAS into dihydrotestosterone (androstanolone) that acts in the cell nucleus of target organs, such as male accessory glands, skin and prostate. Other mechanisms comprises mediation by the enzyme aromatase that converts AAS in female sex hormones (estradiol and estrone), antagonistic action to estrogens and a competitive antagonism to the glucocorticoid receptors. Furthermore, AAS stimulate erythropoietin synthesis and red cell production as well as bone formation but counteract bone breakdown. The effects on the cardiovascular system are proposed to be mediated by the occurrence of AAS-induced atherosclerosis (due to unfavourable influence on serum lipids and lipoproteins), thrombosis, vasospasm or direct injury to vessel walls, or may be ascribed to a combination of the different mechanisms. AAS-induced increment of muscle tissue can be attributed to hypertrophy and the formation of new muscle fibres, in which key roles are played by satellite cell number and ultrastructure, androgen receptors and myonuclei.

748 citations

Journal ArticleDOI
TL;DR: In this paper, the authors model knowledge diffusion as a barter process in which agents exchange different types of knowledge and examine the relationship between network architecture and diffusion performance, finding that the steady-state level of average knowledge is maximal when the structure is a small world (that is, when most connections are local, but roughly 10 percent of them are long distance).

744 citations

Journal ArticleDOI
11 Dec 2009-Cell
TL;DR: The data identify polyP as a new class of mediator having fundamental roles in platelet-driven proinflammatory and procoagulant disorders, including Hermansky-Pudlak Syndrome patients, who lack platelet polyP.

737 citations


Authors

Showing all 19492 results

NameH-indexPapersCitations
Edward Giovannucci2061671179875
Julie E. Buring186950132967
Aaron R. Folsom1811118134044
John J.V. McMurray1781389184502
Alvaro Pascual-Leone16596998251
Lex M. Bouter158767103034
David T. Felson153861133514
Walter Paulus14980986252
Michael Conlon O'Donovan142736118857
Randy L. Buckner141346110354
Philip Scheltens1401175107312
Anne Tjønneland139134591556
Ewout W. Steyerberg139122684896
James G. Herman138410120628
Andrew Steptoe137100373431
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023107
2022344
20214,522
20203,881
20193,367
20183,019