Institution
Heidelberg University
Education•Heidelberg, Germany•
About: Heidelberg University is a education organization based out in Heidelberg, Germany. It is known for research contribution in the topics: Population & Transplantation. The organization has 62066 authors who have published 119109 publications receiving 4678423 citations. The organization is also known as: Ruprecht-Karls-Universität Heidelberg & University of Heidelberg.
Topics: Population, Transplantation, Galaxy, Cancer, Stars
Papers published on a yearly basis
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TL;DR: A meta-analysis of 9 genome-wide association studies, including 10,052 breast cancer cases and 12,575 controls of European ancestry, and identified 29,807 SNPs for further genotyping suggests that more than 1,000 additional loci are involved in breast cancer susceptibility.
Abstract: Breast cancer is the most common cancer among women Common variants at 27 loci have been identified as associated with susceptibility to breast cancer, and these account for ∼9% of the familial risk of the disease We report here a meta-analysis of 9 genome-wide association studies, including 10,052 breast cancer cases and 12,575 controls of European ancestry, from which we selected 29,807 SNPs for further genotyping These SNPs were genotyped in 45,290 cases and 41,880 controls of European ancestry from 41 studies in the Breast Cancer Association Consortium (BCAC) The SNPs were genotyped as part of a collaborative genotyping experiment involving four consortia (Collaborative Oncological Gene-environment Study, COGS) and used a custom Illumina iSelect genotyping array, iCOGS, comprising more than 200,000 SNPs We identified SNPs at 41 new breast cancer susceptibility loci at genome-wide significance (P < 5 × 10(-8)) Further analyses suggest that more than 1,000 additional loci are involved in breast cancer susceptibility
1,048 citations
TL;DR: In this paper, the authors present a class of C*-algebras and point out its close relationship to topological Markov chains, whose theory is part of symbolic dynamics.
Abstract: In this paper we present a class of C*-algebras and point out its close relationship to topological Markov chains, whose theory is part of symbolic dynamics. The C*-algebra construction starts from a matrix A =(A (i,j))i,~ z, Z a finite set, A(i,j)c{0, l}, and where every row and every column of A is non-zero. (That A(i,j)e{O, 1} is assumed for convenience only. All constructions and results extend to matrices with entries in 2~+. We comment on this in Remark 2.18.) A C*-algebra 6~ A is then generated by partial isometries Si~O(i~X ) that act on a Hilbert space in such a way that their support projections Qi=S*S~ and their range projections P~ =SIS* satisfy the relations
1,042 citations
TL;DR: Intravenous desmoteplase administered 3 to 9 hours after acute ischemic stroke in patients selected with perfusion/diffusion mismatch is associated with a higher rate of reperfusion and better clinical outcome compared with placebo.
Abstract: Background and Purpose— Most acute ischemic stroke patients arrive after the 3-hour time window for recombinant tissue plasminogen activator (rtPA) administration. The Desmoteplase In Acute Ischemic Stroke trial (DIAS) was a dose-finding randomized trial designed to evaluate the safety and efficacy of intravenous desmoteplase, a highly fibrin-specific and nonneurotoxic thrombolytic agent, administered within 3 to 9 hours of ischemic stroke onset in patients with perfusion/diffusion mismatch on MRI. Methods— DIAS was a placebo-controlled, double-blind, randomized, dose-finding phase II trial. Patients with National Institute of Health Stroke Scale (NIHSS) scores of 4 to 20 and MRI evidence of perfusion/diffusion mismatch were eligible. Of 104 patients, the first 47 (referred to as Part 1) were randomized to fixed doses of desmoteplase (25 mg, 37.5 mg, or 50 mg) or placebo. Because of an excessive rate of symptomatic intracranial hemorrhage (sICH), lower weight-adjusted doses escalating through 62.5 μg/kg, ...
1,042 citations
TL;DR: The PHQ-9 has now proven to be a responsive and reliable measure of depression treatment outcomes and an attractive tool for gauging response to treatment in individual patient care as well as in clinical research.
Abstract: Background:Although effective treatment of depressed patients requires regular follow-up contacts and symptom monitoring, an efficient method for assessing treatment outcome is lacking. We investigated responsiveness to treatment, reproducibility, and minimal clinically important difference of the P
1,042 citations
Vanderbilt University1, University of Southern California2, University of Texas MD Anderson Cancer Center3, University of Wisconsin-Madison4, University of California, Los Angeles5, National Center for Genome Resources6, Portland VA Medical Center7, University of Colorado Boulder8, University of Pennsylvania9, Hannover Medical School10, Johns Hopkins University11, Oregon Health & Science University12, Cornell University13, University of Michigan14, University of Tennessee Health Science Center15, Washington University in St. Louis16, University of Toronto17, University of Memphis18, Medical Research Council19, University of Massachusetts Medical School20, Hebrew University of Jerusalem21, Université de Montréal22, Purdue University23, University of California, Davis24, Academy of Sciences of the Czech Republic25, University at Buffalo26, Emory University27, University of Cincinnati28, University of Texas Southwestern Medical Center29, New York University30, University of Groningen31, Rutgers University32, Stanford University33, Max Planck Society34, National Institutes of Health35, University of Alabama at Birmingham36, International Livestock Research Institute37, Heidelberg University38, Medical College of Wisconsin39, Icahn School of Medicine at Mount Sinai40, Oak Ridge National Laboratory41, Charité42, University of Antwerp43, RWTH Aachen University44, Paul Sabatier University45, University of California, San Francisco46, McGill University47, Pasteur Institute48, University of Western Australia49, Yale University50, University of Oxford51, Case Western Reserve University52, Roswell Park Cancer Institute53, University of Kentucky54, University of Helsinki55, University of Nebraska–Lincoln56, Harvard University57, Merck & Co.58, King's College London59, Northwestern University60, Shriners Hospitals for Children61, Thomas Jefferson University62, Novartis63, University of North Carolina at Chapel Hill64, Southern Illinois University Carbondale65, University of Rochester66
TL;DR: The Collaborative Cross will provide a common reference panel specifically designed for the integrative analysis of complex systems and will change the way the authors approach human health and disease.
Abstract: The goal of the Complex Trait Consortium is to promote the development of resources that can be used to understand, treat and ultimately prevent pervasive human diseases. Existing and proposed mouse resources that are optimized to study the actions of isolated genetic loci on a fixed background are less effective for studying intact polygenic networks and interactions among genes, environments, pathogens and other factors. The Collaborative Cross will provide a common reference panel specifically designed for the integrative analysis of complex systems and will change the way we approach human health and disease.
1,040 citations
Authors
Showing all 62427 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Nicholas G. Martin | 192 | 1770 | 161952 |
| Jing Wang | 184 | 4046 | 202769 |
| Chris Sander | 178 | 713 | 233287 |
| Kenneth C. Anderson | 178 | 1138 | 126072 |
| Zena Werb | 168 | 473 | 122629 |
| Marc Weber | 167 | 2716 | 153502 |
| Volker Springel | 165 | 746 | 123399 |
| Ira Pastan | 160 | 1286 | 110069 |
| Wolfgang Wagner | 156 | 2342 | 123391 |
| Jovan Milosevic | 152 | 1433 | 106802 |
| Hermann Brenner | 151 | 1765 | 145655 |
| Robert J. Sternberg | 149 | 1066 | 89193 |
| Margaret A. Pericak-Vance | 149 | 826 | 118672 |
| Andreas Pfeiffer | 149 | 1756 | 131080 |
| Rajesh Kumar | 149 | 4439 | 140830 |