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Institution

Medical University of Vienna

EducationVienna, Austria
About: Medical University of Vienna is a education organization based out in Vienna, Austria. It is known for research contribution in the topics: Population & Transplantation. The organization has 16262 authors who have published 37468 publications receiving 1385244 citations. The organization is also known as: MedUni Wien & Medizinische Universität Wien.


Papers
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Journal ArticleDOI
TL;DR: The addition of temozolomide to radiotherapy for newly diagnosed glioblastoma resulted in a clinically meaningful and statistically significant survival benefit with minimal additional toxicity.
Abstract: methods Patients with newly diagnosed, histologically confirmed glioblastoma were randomly assigned to receive radiotherapy alone (fractionated focal irradiation in daily fractions of 2 Gy given 5 days per week for 6 weeks, for a total of 60 Gy) or radiotherapy plus continuous daily temozolomide (75 mg per square meter of body-surface area per day, 7 days per week from the first to the last day of radiotherapy), followed by six cycles of adjuvant temozolomide (150 to 200 mg per square meter for 5 days during each 28-day cycle). The primary end point was overall survival. results A total of 573 patients from 85 centers underwent randomization. The median age was 56 years, and 84 percent of patients had undergone debulking surgery. At a median follow-up of 28 months, the median survival was 14.6 months with radiotherapy plus temozolomide and 12.1 months with radiotherapy alone. The unadjusted hazard ratio for death in the radiotherapy-plus-temozolomide group was 0.63 (95 percent confidence interval, 0.52 to 0.75; P<0.001 by the log-rank test). The two-year survival rate was 26.5 percent with radiotherapy plus temozolomide and 10.4 percent with radiotherapy alone. Concomitant treatment with radiotherapy plus temozolomide resulted in grade 3 or 4 hematologic toxic effects in 7 percent of patients.

16,653 citations

Journal ArticleDOI
TL;DR: It is shown that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented and found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space.
Abstract: Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%. IQ-TREE is freely available at http://www.cibiv.at/software/iqtree.

13,668 citations

Journal ArticleDOI
TL;DR: ModelFinder is presented, a fast model-selection method that greatly improves the accuracy of phylogenetic estimates by incorporating a model of rate heterogeneity across sites not previously considered in this context and by allowing concurrent searches of model space and tree space.
Abstract: Model-based molecular phylogenetics plays an important role in comparisons of genomic data, and model selection is a key step in all such analyses. We present ModelFinder, a fast model-selection method that greatly improves the accuracy of phylogenetic estimates by incorporating a model of rate heterogeneity across sites not previously considered in this context and by allowing concurrent searches of model space and tree space.

7,425 citations

Journal ArticleDOI
TL;DR: This new classification system redefines the current paradigm of RA by focusing on features at earlier stages of disease that are associated with persistent and/or erosive disease, rather than defining the disease by its late-stage features.
Abstract: Objective The 1987 American College of Rheumatology (ACR; formerly the American Rheumatism Association) classifi cation criteria for rheumatoid arthritis (RA) have been criticised for their lack of sensitivity in early disease. This work was undertaken to develop new classifi cation criteria for RA. Methods A joint working group from the ACR and the European League Against Rheumatism developed, in three phases, a new approach to classifying RA. The work focused on identifying, among patients newly presenting with undifferentiated infl ammatory synovitis, factors that best discriminated between those who were and those who were not at high risk for persistent and/ or erosive disease—this being the appropriate current paradigm underlying the disease construct ‘RA’. Results In the new criteria set, classifi cation as ‘defi nite RA’ is based on the confi rmed presence of synovitis in at least one joint, absence of an alternative diagnosis better explaining the synovitis, and achievement of a total score of 6 or greater (of a possible 10) from the individual scores in four domains: number and site of involved joints (range 0–5), serological abnormality (range 0–3), elevated acute-phase response (range 0–1) and symptom duration (two levels; range 0–1). Conclusion This new classifi cation system redefi nes the current paradigm of RA by focusing on features at earlier stages of disease that are associated with persistent and/or erosive disease, rather than defi ning the disease by its late-stage features. This will refocus attention on the important need for earlier diagnosis and institution of effective disease-suppressing therapy to prevent or minimise the occurrence of the undesirable sequelae that currently comprise the paradigm underlying the disease construct ‘RA’.

7,120 citations

Journal ArticleDOI
Stephan Ripke1, Stephan Ripke2, Benjamin M. Neale2, Benjamin M. Neale1  +351 moreInstitutions (102)
24 Jul 2014-Nature
TL;DR: Associations at DRD2 and several genes involved in glutamatergic neurotransmission highlight molecules of known and potential therapeutic relevance to schizophrenia, and are consistent with leading pathophysiological hypotheses.
Abstract: Schizophrenia is a highly heritable disorder. Genetic risk is conferred by a large number of alleles, including common alleles of small effect that might be detected by genome-wide association studies. Here we report a multi-stage schizophrenia genome-wide association study of up to 36,989 cases and 113,075 controls. We identify 128 independent associations spanning 108 conservatively defined loci that meet genome-wide significance, 83 of which have not been previously reported. Associations were enriched among genes expressed in brain, providing biological plausibility for the findings. Many findings have the potential to provide entirely new insights into aetiology, but associations at DRD2 and several genes involved in glutamatergic neurotransmission highlight molecules of known and potential therapeutic relevance to schizophrenia, and are consistent with leading pathophysiological hypotheses. Independent of genes expressed in brain, associations were enriched among genes expressed in tissues that have important roles in immunity, providing support for the speculated link between the immune system and schizophrenia.

6,809 citations


Authors

Showing all 16488 results

NameH-indexPapersCitations
Jaakko Kaprio1631532126320
Hans Lassmann15572479933
Josef M. Penninger154700107295
Joseph L. Witztum13947274539
Elias Campo13576185160
Michael Weller134110591874
Peter M. Rothwell13477967382
Peter Tugwell129948125480
Eduardo Marbán12957949586
David N. Louis12842597370
Jack Cuzick12875479979
Erwin F. Wagner12537559688
Michel Goedert12533764671
Michael Wagner12435154251
Thomas Schwarz12370154560
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202376
2022362
20214,128
20203,642
20192,975
20182,574