Institution
Radboud University Nijmegen
Education•Nijmegen, Gelderland, Netherlands•
About: Radboud University Nijmegen is a education organization based out in Nijmegen, Gelderland, Netherlands. It is known for research contribution in the topics: Population & Randomized controlled trial. The organization has 35417 authors who have published 83035 publications receiving 3285064 citations. The organization is also known as: Catholic University of Nijmegen & Radboud University.
Papers published on a yearly basis
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TL;DR: The first example of naturally occurring mutations in a mammalian DNA methyltransferase gene is described, occurring in patients with a rare autosomal recessive disorder, termed the ICF syndrome, for immunodeficiency, centromeric instability, and facial anomalies.
Abstract: DNA methylation is an important regulator of genetic information in species ranging from bacteria to humans. DNA methylation appears to be critical for mammalian development because mice nullizygous for a targeted disruption of the DNMT1 DNA methyltransferase die at an early embryonic stage. No DNA methyltransferase mutations have been reported in humans until now. We describe here the first example of naturally occurring mutations in a mammalian DNA methyltransferase gene. These mutations occur in patients with a rare autosomal recessive disorder, which is termed the ICF syndrome, for immunodeficiency, centromeric instability, and facial anomalies. Centromeric instability of chromosomes 1, 9, and 16 is associated with abnormal hypomethylation of CpG sites in their pericentromeric satellite regions. We are able to complement this hypomethylation defect by somatic cell fusion to Chinese hamster ovary cells, suggesting that the ICF gene is conserved in the hamster and promotes de novo methylation. ICF has been localized to a 9-centimorgan region of chromosome 20 by homozygosity mapping. By searching for homologies to known DNA methyltransferases, we identified a genomic sequence in the ICF region that contains the homologue of the mouse Dnmt3b methyltransferase gene. Using the human sequence to screen ICF kindreds, we discovered mutations in four patients from three families. Mutations include two missense substitutions and a 3-aa insertion resulting from the creation of a novel 3′ splice acceptor. None of the mutations were found in over 200 normal chromosomes. We conclude that mutations in the DNMT3B are responsible for the ICF syndrome.
707 citations
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National Institutes of Health1, Society for Immunotherapy of Cancer2, French Institute of Health and Medical Research3, University of Paris4, University of Bern5, Providence Portland Medical Center6, University of Mainz7, Cornell University8, University of Erlangen-Nuremberg9, Medical University of Graz10, Karolinska Institutet11, Harvard University12, CEU San Pablo University13, Memorial Sloan Kettering Cancer Center14, Lund University15, University of Southampton16, Innsbruck Medical University17, University Health Network18, Princess Margaret Cancer Centre19, Keio University20, Yamaguchi University21, Kindai University22, Cancer Research Institute23, University of Tübingen24, Loyola University Chicago25, University of Manchester26, Université de Montréal27, Georgia Regents University28, Royal Melbourne Hospital29, University of Melbourne30, Sapporo Medical University31, Kurume University32, Umeå University33, Radboud University Nijmegen34, Xi'an Jiaotong University35, Dorset County Hospital NHS Foundation Trust36, University of Texas MD Anderson Cancer Center37, Mayo Clinic38, University of Chicago39, Oregon Health & Science University40
TL;DR: Evidence supports the notion to include immunological biomarkers, implemented as a tool for the prediction of prognosis and response to therapy, into traditional classification of cancer, designated TNM-I (TNM-Immune), and introduction of this parameter as a biomarker to classify cancers will facilitate clinical decision-making.
Abstract: Prediction of clinical outcome in cancer is usually achieved by histopathological evaluation of tissue samples obtained during surgical resection of the primary tumor. Traditional tumor staging (AJCC/UICC-TNM classification) summarizes data on tumor burden (T), presence of cancer cells in draining and regional lymph nodes (N) and evidence for metastases (M). However, it is now recognized that clinical outcome can significantly vary among patients within the same stage. The current classification provides limited prognostic information, and does not predict response to therapy. Recent literature has alluded to the importance of the host immune system in controlling tumor progression. Thus, evidence supports the notion to include immunological biomarkers, implemented as a tool for the prediction of prognosis and response to therapy. Accumulating data, collected from large cohorts of human cancers, has demonstrated the impact of immune-classification, which has a prognostic value that may add to the significance of the AJCC/UICC TNM-classification. It is therefore imperative to begin to incorporate the 'Immunoscore' into traditional classification, thus providing an essential prognostic and potentially predictive tool. Introduction of this parameter as a biomarker to classify cancers, as part of routine diagnostic and prognostic assessment of tumors, will facilitate clinical decision-making including rational stratification of patient treatment. Equally, the inherent complexity of quantitative immunohistochemistry, in conjunction with protocol variation across laboratories, analysis of different immune cell types, inconsistent region selection criteria, and variable ways to quantify immune infiltration, all underline the urgent requirement to reach assay harmonization. In an effort to promote the Immunoscore in routine clinical settings, an international task force was initiated. This review represents a follow-up of the announcement of this initiative, and of the J Transl Med. editorial from January 2012. Immunophenotyping of tumors may provide crucial novel prognostic information. The results of this international validation may result in the implementation of the Immunoscore as a new component for the classification of cancer, designated TNM-I (TNM-Immune).
705 citations
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TL;DR: Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains.
Abstract: Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI) in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR) effected by each decomposition, and decomposition 'dipolarity' defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA); best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison).
702 citations
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University of Utah1, University of Michigan2, The Royal Marsden NHS Foundation Trust3, Ohio State University4, Indiana University5, St. Jude Medical Center6, University of Queensland7, Beaumont Hospital8, BC Cancer Agency9, University of Rochester10, Harvard University11, American Society of Clinical Oncology12, National Institutes of Health13, Baylor College of Medicine14, Sungkyunkwan University15, University of the West of England16, Tohoku University17, Radboud University Nijmegen18, University of Toronto19, Saint Joseph Mercy Health System20, Mayo Clinic21, University of Washington22, Memorial Hospital of South Bend23, University of Texas at Austin24, Johns Hopkins University25
TL;DR: An international Expert Panel that conducted a systematic review and evaluation of the literature and developed recommendations for optimal IHC ER/PgR testing performance recommended that ER and PgR status be determined on all invasive breast cancers and breast cancer recurrences.
Abstract: Purpose To develop a guideline to improve the accuracy of immunohistochemical (IHC) estrogen receptor (ER) and progesterone receptor (PgR) testing in breast cancer and the utility of these receptors as predictive markers. Methods The American Society of Clinical Oncology and the College of American Pathologists convened an international Expert Panel that conducted a systematic review and evaluation of the literature in partnership with Cancer Care Ontario and developed recommendations for optimal IHC ER/PgR testing performance. Results Up to 20% of current IHC determinations of ER and PgR testing worldwide may be inaccurate (false negative or false positive). Most of the issues with testing have occurred because of variation in pre-analytic variables, thresholds for positivity, and interpretation criteria. Recommendations The Panel recommends that ER and PgR status be determined on all invasive breast cancers and breast cancer recurrences. A testing algorithm that relies on accurate, reproducible assay performance is proposed. Elements to reliably reduce assay variation are specified. It is recommended that ER and PgR assays be considered positive if there are at least 1% positive tumor nuclei in the sample on testing in the presence of expected reactivity of internal (normal epithelial elements) and external controls. The absence of benefit from endocrine therapy for women with ER-negative invasive breast cancers has been confirmed in large overviews of randomized clinical trials.
700 citations
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TL;DR: Evidence was obtained that at least some cell bodies in the medullary raphe nuclei and adjacent areas contained all three compounds, 5-hydroxytryptamine, thyrotropin releasing hormone (TRH) and substance P immunoreactive neurons in normal and colchicine-treated rats.
700 citations
Authors
Showing all 35749 results
Name | H-index | Papers | Citations |
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Charles A. Dinarello | 190 | 1058 | 139668 |
Richard H. Friend | 169 | 1182 | 140032 |
Yang Gao | 168 | 2047 | 146301 |
Ian J. Deary | 166 | 1795 | 114161 |
David T. Felson | 153 | 861 | 133514 |
Margaret A. Pericak-Vance | 149 | 826 | 118672 |
Fernando Rivadeneira | 146 | 628 | 86582 |
Shah Ebrahim | 146 | 733 | 96807 |
Mihai G. Netea | 142 | 1170 | 86908 |
Mingshui Chen | 141 | 1543 | 125369 |
George Alverson | 140 | 1653 | 105074 |
Barry Blumenfeld | 140 | 1909 | 105694 |
Harvey B Newman | 139 | 1594 | 88308 |
Tariq Aziz | 138 | 1646 | 96586 |
Stylianos E. Antonarakis | 138 | 746 | 93605 |