Institution
University of Erlangen-Nuremberg
Education•Erlangen, Bayern, Germany•
About: University of Erlangen-Nuremberg is a education organization based out in Erlangen, Bayern, Germany. It is known for research contribution in the topics: Population & Immune system. The organization has 42405 authors who have published 85600 publications receiving 2663922 citations.
Topics: Population, Immune system, Breast cancer, Catalysis, Transplantation
Papers published on a yearly basis
Papers
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TL;DR: Nucleic acid hybridization studies and immunological relationships of superoxide dismutase demonstrated that Streptococcus lactis, Lactobacillus xylosus, S. plantarum and S. raffinolactis are closely related to each other but not to other streptococci, and it is proposed that these taxa be transferred to a new genus Lactococcus.
475 citations
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TL;DR: Environmental factors add a substantial level of complexity to the understanding of IBD pathogenesis but also promote the fundamental notion that complex diseases such as IBD require complex therapies that go well beyond the current single-agent treatment approach.
Abstract: A number of environmental factors have been associated with the development of IBD. Alteration of the gut microbiota, or dysbiosis, is closely linked to initiation or progression of IBD, but whether dysbiosis is a primary or secondary event is unclear. Nevertheless, early-life events such as birth, breastfeeding and exposure to antibiotics, as well as later childhood events, are considered potential risk factors for IBD. Air pollution, a consequence of the progressive contamination of the environment by countless compounds, is another factor associated with IBD, as particulate matter or other components can alter the host's mucosal defences and trigger immune responses. Hypoxia associated with high altitude is also a factor under investigation as a potential new trigger of IBD flares. A key issue is how to translate environmental factors into mechanisms of IBD, and systems biology is increasingly recognized as a strategic tool to unravel the molecular alterations leading to IBD. Environmental factors add a substantial level of complexity to the understanding of IBD pathogenesis but also promote the fundamental notion that complex diseases such as IBD require complex therapies that go well beyond the current single-agent treatment approach. This Review describes the current conceptualization, evidence, progress and direction surrounding the association of environmental factors with IBD.
475 citations
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05 Dec 2003-Materials Science and Engineering A-structural Materials Properties Microstructure and Processing
TL;DR: Bio-inspired materials open new approaches for manufacturing implants for bone replacement using Functionally graded porous hydroxyapatite (HAP) ceramics, which are an appropriate material for in vitro growth of bone.
Abstract: Functional gradation is one characteristic feature of living tissue. Bio-inspired materials open new approaches for manufacturing implants for bone replacement. Different routes for new implant materials are presented using the principle of functional gradation. An artificial biomaterial for knee joint replacement has been developed by building a graded structure consisting of ultra-high molecular weight polyethylene (UHMWPE) fibre reinforced high-density polyethylene combined with a surface of UHMWPE. The ingrowth behaviour of titanium implants into hard tissue can be improved by depositing a graded biopolymer coating of fibronectin, collagen types I and III with a gradation, derived from the mechanisms occurring during healing in vivo. Functionally graded porous hydroxyapatite (HAP) ceramics can be produced using alternative routes, e.g. sintering of laminated structures of HAP tapes filled with polymer spheres or combining biodegradable polyesters such as polylactide, polylactide-co-glycolide and polyglycolide, with carbonated nanocrystalline hydroxyapatite. HAP–collagen I scaffolds are an appropriate material for in vitro growth of bone. The scaffold has to be functionally graded in order to create an optimised mechanical behaviour as well as the intended improvement of the cell ingrowth.
474 citations
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TL;DR: This paper is concerned with the inexact matching of attributed, relational graphs for structural pattern recognition and the matching procedure is based on a state space search utilizing heuristic information.
474 citations
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Cedars-Sinai Medical Center1, University of Erlangen-Nuremberg2, King Saud bin Abdulaziz University for Health Sciences3, University of Milan4, UCLA Medical Center5, Erasmus University Rotterdam6, Montreal Heart Institute7, Beaumont Hospital8, University of Ottawa9, NewYork–Presbyterian Hospital10, Ludwig Maximilian University of Munich11, Innsbruck Medical University12, University of Zurich13, Seoul National University Hospital14, University of British Columbia15, Unica Corporation16, Technion – Israel Institute of Technology17, Emory University18, Walter Reed Army Medical Center19, Durham University20
TL;DR: Machine learning combining clinical and CCTA data was found to predict 5-year all-cause mortality significantly better than existing clinical or C CTA metrics alone.
Abstract: Aims Traditional prognostic risk assessment in patients undergoing non-invasive imaging is based upon a limited selection of clinical and imaging findings Machine learning (ML) can consider a greater number and complexity of variables Therefore, we investigated the feasibility and accuracy of ML to predict 5-year all-cause mortality (ACM) in patients undergoing coronary computed tomographic angiography (CCTA), and compared the performance to existing clinical or CCTA metrics
Methods and results The analysis included 10 030 patients with suspected coronary artery disease and 5-year follow-up from the COronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter registry All patients underwent CCTA as their standard of care Twenty-five clinical and 44 CCTA parameters were evaluated, including segment stenosis score (SSS), segment involvement score (SIS), modified Duke index (DI), number of segments with non-calcified, mixed or calcified plaques, age, sex, gender, standard cardiovascular risk factors, and Framingham risk score (FRS) Machine learning involved automated feature selection by information gain ranking, model building with a boosted ensemble algorithm, and 10-fold stratified cross-validation Seven hundred and forty-five patients died during 5-year follow-up Machine learning exhibited a higher area-under-curve compared with the FRS or CCTA severity scores alone (SSS, SIS, DI) for predicting all-cause mortality (ML: 079 vs FRS: 061, SSS: 064, SIS: 064, DI: 062; P < 0001)
Conclusions Machine learning combining clinical and CCTA data was found to predict 5-year ACM significantly better than existing clinical or CCTA metrics alone
474 citations
Authors
Showing all 42831 results
Name | H-index | Papers | Citations |
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Hermann Brenner | 151 | 1765 | 145655 |
Richard B. Devereux | 144 | 962 | 116403 |
Manfred Paulini | 141 | 1791 | 110930 |
Daniel S. Berman | 141 | 1363 | 86136 |
Peter Lang | 140 | 1136 | 98592 |
Joseph Sodroski | 138 | 542 | 77070 |
Richard J. Johnson | 137 | 880 | 72201 |
Jun Lu | 135 | 1526 | 99767 |
Michael Schmitt | 134 | 2007 | 114667 |
Jost B. Jonas | 132 | 1158 | 166510 |
Andreas Mussgiller | 127 | 1059 | 73778 |
Matthew J. Budoff | 125 | 1449 | 68115 |
Stefan Funk | 125 | 506 | 56955 |
Markus F. Neurath | 124 | 934 | 62376 |
Jean-Marie Lehn | 123 | 1054 | 84616 |