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
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Radboud University Nijmegen1, University of Southern California2, University Medical Center Groningen3, QIMR Berghofer Medical Research Institute4, Utrecht University5, National Institutes of Health6, Broad Institute7, Harvard University8, University of Bergen9, Region Zealand10, Cincinnati Children's Hospital Medical Center11, University of California, Irvine12, University of California, San Diego13, University of Würzburg14, University of Tübingen15, Trinity College, Dublin16, New York University17, King's College London18, Heidelberg University19, Federal University of Rio de Janeiro20, University of California, Los Angeles21, Nathan Kline Institute for Psychiatric Research22, MIND Institute23, Otto-von-Guericke University Magdeburg24, Maastricht University25, Goethe University Frankfurt26, Haukeland University Hospital27, Beth Israel Deaconess Medical Center28, VU University Amsterdam29, Autonomous University of Barcelona30, State University of New York Upstate Medical University31
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
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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
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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
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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
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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
Name | H-index | Papers | Citations |
---|---|---|---|
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 |