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                                                            1. CCRC-HaunerEN
                                                            2. Clinical research
                                                            3. Scivias Study

                                                            Scivias Study

                                                            • Study Information
                                                            • Research
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                                                            Aims of the Study:


                                                            • Identification of new biomarkers for the overall assessment of the systemic health status of childrenDevelopment of a novel diagnostic tool in children 
                                                            • Establishment of a normal range for fundus photography, OCT and OCT angiography with regard to retinal changes in various age groups

                                                            • Evaluation of the value of optical fundus evaluation in (early) diagnosis of a rare disease
                                                            • Establishing a reference range for changes in the transcriptome, metabolome and proteome in various age groups and in various acute and chronic diseases
                                                            • Correlation of systems biology data with disease activities of rare diseases
                                                            • Specification of phenotyping of patients within different disease groups

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                                                            Scivias Study

                                                            Project Summary

                                                            Early detection of diseases is a central challenge for pediatric medicine. The earlier a disease is discovered, the easier it is to avoid complications and sequelae and to reduce long-term morbidity. This is particularly relevant for children with rare diseases in whom the diagnostic process is often delayed. Children with rare and chronic diseases are usually only diagnosed when their disease manifests or complications arise. Thus, there is an urgent need to develop and use new sensitive and specific diagnostic methods, preferably as non-invasive as possible.

                                                            Next generation sequencing technologies have revolutionized human genetics. A growing number of hereditary monogenic rare diseases has been identified, leading to a better understanding of molecular processes even in multifactorial diseases. In addition to genomics, other omics-technologies (e.g. transcriptomics, metabolomics, proteomics, immunomics) complement our scientific armamentarium to comprehensively assess states of diseases. A challenge of these technologies is to integrate and interpret these large datasets. Emerging data suggest that combining multi-layer omics data with digital clinical data will allow us to improve diagnostics, to optimize prevention, and to design definitive cures. Advances in machine learning enabling pattern recognition and statistical associations, offer new perspectives for developing innovative and non-invasive diagnostic methods.

                                                            In the context of this non-randomized, monocentric observation study, the benefit of using a combination of pattern recognition of image data of the retina by fundus photography and optical coherence tomography (OCT) in combination with the analysis of various OMICS data (genome, transcriptome, proteome and metabolome) will be explored in search of markers for rare and chronic childhood diseases. Retinal images and OMICS data are pseudonymized and subjected to machine learning algorithms. Starting from classical nosological entities, we will compare the data not only within defined groups but also across phenotypes, aiming to shed light on pleiotropic factors. Once associations between genomic and phenotypic data sets become apparent, new hypotheses will be developed and tested in suitable model systems. 

                                                            Multi-OMICS methods

                                                            Content will follow shortly.

                                                            A.I. analysis

                                                            Content will follow shortly.

                                                            Inquieries only via the official e-mail adress: Scivias.Hauner@med.uni-muenchen.de

                                                            Prof. Dr. med. Dr. sci. nat.  Christoph Klein
                                                            Studienleitung/Chefarzt
                                                            Mehr zur Person
                                                            Dr. med. Katharina Danhauser
                                                            Stellvertretende Studienleitung
                                                            Katharina.Danhauser@med.uni-muenchen.de
                                                            Mehr zur Person
                                                            PD Dr. Claudia Priglinger 
                                                            Stellvertretend Studienleitung Augenklinik
                                                            Claudia.Priglinger@med.uni-muenchen.de
                                                            Dr. med. Lena Birzele
                                                            Wissenschaftliche Mitarbeiterin, Koordination gesunde Kontrollen
                                                            Lena_Teresa.Birzele@med.uni-muenchen.de
                                                            Dr. med. Anna-Lisa Lanz
                                                            OMICs-Labor, Laborleitung
                                                            AnnaLisa.Lanz@med.uni-muenchen.de
                                                            Sophie Strobel
                                                            Studienärztin
                                                            Sophie.strobel@med.uni-muenchen.de
                                                            Dr. Benedikt Schworm 
                                                            Wissenschaftlicher Mitarbeiter Augenklinik
                                                            Benedikt.Schworm@med.uni-muenchen.de
                                                            Ulrike Hoffendahl
                                                            Kinderkrankenschwester/Child-Life-Specialist
                                                            Ulrike.Hoffendahl@med.uni-muenchen.de
                                                            Sachiko Kwaschnowitz
                                                            Study Nurse
                                                            Sachiko.Kwaschnowitz@med.uni-muenchen.de
                                                            Monika Prothmann
                                                            OMICs-Labor, leitende TA
                                                            monika.prothmann@med.uni-muenchen.de
                                                            Daniel Weiß
                                                            Informatiker
                                                            Daniel.Weiss@med.uni-muenchen.de
                                                            Dr. Susanne Pangratz-Fuehrer
                                                            Wissenschaftliche Mitarbeiterin, Projekt Management
                                                            Susanne.PangratzFuehrer@med.uni-muenchen.de

                                                            Scivias Study

                                                            Dr. von Hauner Children's Hospital, University Hospital LMU Munich

                                                            Lindwurmstrasse 4

                                                            80337

                                                            Munich

                                                            E-Mail

                                                            Thank you to all donors and supporters of the Scivias study

                                                            The Scivias study is kindly supported by Carl Zeiss AG and Munich Re, among others.

                                                            Kontakt

                                                            CCRC Hauner

                                                            Kinderklinik und Kinderpoliklinik

                                                            im Dr. von Haunerschen Kinderspital

                                                            Ludwig Maximilians Universität München

                                                            Lindwurmstr. 4

                                                            80337 Munich, Germany

                                                            ☎ +49-89-4400-57705


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                                                            Comprehensiv...

                                                            • Mission
                                                            • Research Labs
                                                              • Binder Lab
                                                              • Braun Lab
                                                              • Feuchtinger Lab
                                                              • Griese Lab
                                                              • Hauck Lab
                                                              • Hübner Lab
                                                              • Jeremias lab
                                                              • Kim-Hellmuth Lab
                                                              • Klein Lab
                                                              • Kotlarz Lab
                                                              • Lange-Sperandio Lab
                                                              • von Mutius Lab
                                                              • Schaub Lab
                                                              • Schwerd Lab
                                                              • Schmid Lab
                                                            • Clinical research
                                                              • Interdisciplinary pediatric study center (Hauner iPSC)
                                                              • Ped-COVID-19 Study
                                                              • Michael Albert
                                                              • Scivias Study
                                                              • TRACE Study
                                                            • PhD Program
                                                            • Technology platforms
                                                              • Bioinformatics
                                                              • Flow Cytometry
                                                              • High throughput sequencing
                                                              • Microscopy
                                                              • Pre-GMP Facility
                                                              • Mass spectrometry
                                                              • Hauner Biobank
                                                            • Events
                                                              • Meinhard von Pfaundler-Lectures
                                                              • Klaus Betke Symposium
                                                            • Join us!
                                                            • Contact