Browsing by Author "OsloMet – storbyuniversitetet"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • No Thumbnail Available
    PublicationMetadata only
    PopAffiliator: Online calculator for individual affiliation to a major population group based on 17 autosomal short tandem repeat genotype profile
    (2011-09-01) Luísa Pereira; Farida Alshamali; Rune Andreassen; Ruth Ballard; Wasun Chantratita; Nam Soo Cho; Clotilde Coudray; Jean Michel Dugoujon; Marta Espinoza; Fabricio González-Andrade; Sibte Hadi; Uta Dorothee Immel; Catalin Marian; Antonio Gonzalez-Martin; Gerhard Mertens; Walther Parson; Carlos Perone; Lourdes Prieto; Haruo Takeshita; Héctor Rangel Villalobos; Zhaoshu Zeng; Lev Zhivotovsky; Rui Camacho; Nuno A. Fonseca; Universidade do Porto; General Department of Forensic Sciences and Criminology; OsloMet – storbyuniversitetet; California State University Sacramento; Mahidol University; National Institute of Scientific Investigation; Universite Paul Sabatier Toulouse III; Poder Judicial; Hospital Metropolitano; University of Central Lancashire; Martin-Universitat Halle-Wittenberg; Lombardi Comprehensive Cancer Center; Universidad Complutense de Madrid; Universitair Ziekenhuis Antwerpen; Medizinische Universitat Innsbruck; Universidade Federal de Minas Gerais; University Institute of Research Police Sciences (IUICP); Shimane University; Universidad de Guadalajara; Zhengzhou University; Russian Academy of Sciences; Instituto de Engenharia de Sistemas e Computadores Porto
    Because of their sensitivity and high level of discrimination, short tandem repeat (STR) maker systems are currently the method of choice in routine forensic casework and data banking, usually in multiplexes up to 15-17 loci. Constraints related to sample amount and quality, frequently encountered in forensic casework, will not allow to change this picture in the near future, notwithstanding the technological developments. In this study, we present a free online calculator named PopAffiliator ( http://cracs.fc.up.pt/popaffiliator ) for individual population affiliation in the three main population groups, Eurasian, East Asian and sub-Saharan African, based on genotype profiles for the common set of STRs used in forensics. This calculator performs affiliation based on a model constructed using machine learning techniques. The model was constructed using a data set of approximately fifteen thousand individuals collected for this work. The accuracy of individual population affiliation is approximately 86%, showing that the common set of STRs routinely used in forensics provide a considerable amount of information for population assignment, in addition to being excellent for individual identification. © 2010 Springer-Verlag.
  • No Thumbnail Available
    PublicationMetadata only
    Urinary extracellular vesicles: A position paper by the Urine Task Force of the International Society for Extracellular Vesicles
    (2021-05-01) Uta Erdbrügger; Charles J. Blijdorp; Irene V. Bijnsdorp; Francesc E. Borràs; Dylan Burger; Benedetta Bussolati; James Brian Byrd; Aled Clayton; James W. Dear; Juan M. Falcón-Pérez; Cristina Grange; Andrew F. Hill; Harry Holthöfer; Ewout J. Hoorn; Guido Jenster; Connie R. Jimenez; Kerstin Junker; John Klein; Mark A. Knepper; Erik H. Koritzinsky; James M. Luther; Metka Lenassi; Janne Leivo; Inge Mertens; Luca Musante; Eline Oeyen; Maija Puhka; Martin E. van Royen; Catherine Sánchez; Carolina Soekmadji; Visith Thongboonkerd; Volkert van Steijn; Gerald Verhaegh; Jason P. Webber; Kenneth Witwer; Peter S.T. Yuen; Lei Zheng; Alicia Llorente; Elena S. Martens-Uzunova; Siriraj Hospital; Swansea University Medical School; Department of Chemical Engineering, TU Delft; Edinburgh Medical School; Erasmus MC Cancer Institute; CIC BioGUNE; Oslo Universitetssykehus; Cardiff University School of Medicine; OsloMet – storbyuniversitetet; University of Michigan Medical School; Erasmus MC; Vanderbilt University Medical Center; Università degli Studi di Torino, Scuola di Medicina; University of Virginia Health System; Hospital Universitari Germans Trias i Pujol; QIMR Berghofer Medical Research Institute; Universitätsklinikum des Saarlandes Medizinische Fakultät der Universität des Saarlandes; University of Louisville Health Sciences Center; Univerza v Ljubljani Medicinska Fakulteta; Clinica Las Condes; Universiteit Antwerpen; Università degli Studi di Torino; National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK); La Trobe University; Turun yliopisto; National Heart, Lung, and Blood Institute (NHLBI); Universitätsklinikum Hamburg-Eppendorf; Helsingin Yliopisto; Southern Medical University; Radboud University Medical Center; Ottawa Hospital Research Institute; Amsterdam UMC - Free University Amsterdam; Johns Hopkins School of Medicine
    Urine is commonly used for clinical diagnosis and biomedical research. The discovery of extracellular vesicles (EV) in urine opened a new fast-growing scientific field. In the last decade urinary extracellular vesicles (uEVs) were shown to mirror molecular processes as well as physiological and pathological conditions in kidney, urothelial and prostate tissue. Therefore, several methods to isolate and characterize uEVs have been developed. However, methodological aspects of EV separation and analysis, including normalization of results, need further optimization and standardization to foster scientific advances in uEV research and a subsequent successful translation into clinical practice. This position paper is written by the Urine Task Force of the Rigor and Standardization Subcommittee of ISEV consisting of nephrologists, urologists, cardiologists and biologists with active experience in uEV research. Our aim is to present the state of the art and identify challenges and gaps in current uEV-based analyses for clinical applications. Finally, recommendations for improved rigor, reproducibility and interoperability in uEV research are provided in order to facilitate advances in the field.