Filter and export

Publications

What is a Publication?
6 Publications visible to you, out of a total of 6
Mathematical modeling of Lynch syndrome carcinogenesis

Abstract (Expand)

Cancer is one of the leading causes of disease-related death worldwide. In recent years, large amounts of data on cancer genetics and molecular characteristics have become available and accumulated … with increasing speed. However, the current understanding of cancer as a disease is still limited by the lack of suitable models that allow interpreting these data in proper ways. Thus, the highly interdisciplinary research field of mathematical oncology has evolved to use mathematics, modeling, and simulations to study cancer with the overall goal to improve clinical patient care. This dissertation aims at developing mathematical models and tools for different spatial scales of cancer development at the example of colorectal cancer in Lynch syndrome, the most common inherited colorectal cancer predisposition syndrome. We derive model-driven approaches for carcinogenesis at the DNA, cell, and crypt level, as well as data-driven methods for cancer-immune interactions at the DNA level and for the evaluation of diagnostic procedures at the Lynch syndrome population level. The developed models present an important step toward an improved understanding of hereditary cancer as a disease aiming at rapid implementation into clinical management guidelines and into the development of novel, innovative approaches for prevention and treatment.

Author: Saskia Haupt

Date Published: 28th Apr 2023

Publication Type: Doctoral Thesis

A “two-in-one hit” model of shortcut carcinogenesis in MLH1 Lynch syndrome carriers

Abstract

Not specified

Authors: Aysel Ahadova, Albrecht Stenzinger, Toni Seppälä, Robert Hüneburg, Matthias Kloor, Hendrik Bläker, Jan-Niklas Wittemann, Volker Endris, Leonie Gerling, Veit Bertram, Marie Theres Neumuth, Johannes Witt, Sebastian Graf, Glen Kristiansen, Oliver Hommerding, Saskia Haupt, Alexander Zeilmann, Vincent Heuveline, Daniel Kazdal, Johannes Gebert, Magnus von Knebel Doeberitz, Jukka-Pekka Mecklin, Jacob Nattermann

Date Published: 11th Mar 2023

Publication Type: Journal

Mortality by age, gene and gender in carriers of pathogenic mismatch repair gene variants receiving surveillance for early cancer diagnosis and treatment: a report from the prospective Lynch syndrome database

Abstract

Not specified

Authors: Mev Dominguez-Valentin, Saskia Haupt, Toni T. Seppälä, Julian R. Sampson, Lone Sunde, Inge Bernstein, Mark A. Jenkins, Christoph Engel, Stefan Aretz, Maartje Nielsen, Gabriel Capella, Francesc Balaguer, Dafydd Gareth Evans, John Burn, Elke Holinski-Feder, Lucio Bertario, Bernardo Bonanni, Annika Lindblom, Zohar Levi, Finlay Macrae, Ingrid Winship, John-Paul Plazzer, Rolf Sijmons, Luigi Laghi, Adriana Della Valle, Karl Heinimann, Tadeusz Dębniak, Robert Fruscio, Francisco Lopez-Koestner, Karin Alvarez-Valenzuela, Lior H. Katz, Ido Laish, Elez Vainer, Carlos Vaccaro, Dirce Maria Carraro, Kevin Monahan, Elizabeth Half, Aine Stakelum, Des Winter, Rory Kennelly, Nathan Gluck, Harsh Sheth, Naim Abu-Freha, Marc Greenblatt, Benedito Mauro Rossi, Mabel Bohorquez, Giulia Martina Cavestro, Leonardo S. Lino-Silva, Karoline Horisberger, Maria Grazia Tibiletti, Ivana do Nascimento, Huw Thomas, Norma Teresa Rossi, Leandro Apolinário da Silva, Attila Zaránd, Juan Ruiz-Bañobre, Vincent Heuveline, Jukka-Pekka Mecklin, Kirsi Pylvänäinen, Laura Renkonen-Sinisalo, Anna Lepistö, Päivi Peltomäki, Christina Therkildsen, Mia Gebauer Madsen, Stefan Kobbelgaard Burgdorf, John L. Hopper, Aung Ko Win, Robert W. Haile, Noralane Lindor, Steven Gallinger, Loïc Le Marchand, Polly A. Newcomb, Jane Figueiredo, Daniel D. Buchanan, Stephen N. Thibodeau, Magnus von Knebel Doeberitz, Markus Loeffler, Nils Rahner, Evelin Schröck, Verena Steinke-Lange, Wolff Schmiegel, Deepak Vangala, Claudia Perne, Robert Hüneburg, Silke Redler, Reinhard Büttner, Jürgen Weitz, Marta Pineda, Nuria Duenas, Joan Brunet Vidal, Leticia Moreira, Ariadna Sánchez, Eivind Hovig, Sigve Nakken, Kate Green, Fiona Lalloo, James Hill, Emma Crosbie, Miriam Mints, Yael Goldberg, Douglas Tjandra, Sanne W. ten Broeke, Revital Kariv, Guy Rosner, Suresh H. Advani, Lidiya Thomas, Pankaj Shah, Mithun Shah, Florencia Neffa, Patricia Esperon, Walter Pavicic, Giovana Tardin Torrezan, Thiago Bassaneze, Claudia Alejandra Martin, Gabriela Moslein, Pål Moller

Date Published: 1st Mar 2023

Publication Type: Journal

Machine Learning Methods Improve Specificity in Newborn Screening for Isovaleric Aciduria

Abstract (Expand)

Isovaleric aciduria (IVA) is a rare disorder of leucine metabolism and part of newborn screening (NBS) programs worldwide. However, NBS for IVA is hampered by, first, the increased birth prevalence due …prevalence due to the identification of individuals with an attenuated disease variant (so-called “mild” IVA) and, second, an increasing number of false positive screening results due to the use of pivmecillinam contained in the medication. Recently, machine learning (ML) methods have been analyzed, analogous to new biomarkers or second-tier methods, in the context of NBS. In this study, we investigated the application of machine learning classification methods to improve IVA classification using an NBS data set containing 2,106,090 newborns screened in Heidelberg, Germany. Therefore, we propose to combine two methods, linear discriminant analysis, and ridge logistic regression as an additional step, a digital-tier, to traditional NBS. Our results show that this reduces the false positive rate by 69.9% from 103 to 31 while maintaining 100% sensitivity in cross-validation. The ML methods were able to classify mild and classic IVA from normal newborns solely based on the NBS data and revealed that besides isovalerylcarnitine (C5), the metabolite concentration of tryptophan (Trp) is important for improved classification. Overall, applying ML methods to improve the specificity of IVA could have a major impact on newborns, as it could reduce the newborns’ and families’ burden of false positives or over-treatment.

Authors: Elaine Zaunseder, Ulrike Mütze, Sven F. Garbade, Saskia Haupt, Patrik Feyh, Georg F. Hoffmann, Vincent Heuveline, Stefan Kölker

Date Published: 1st Feb 2023

Publication Type: Journal

A simple approach for detecting HLA‐A *02 alleles in archival formalin‐fixed paraffin‐embedded tissue samples and an application example for studying cancer immunoediting

Abstract

Not specified

Authors: Johannes Witt, Saskia Haupt, Aysel Ahadova, Lena Bohaumilitzky, Vera Fuchs, Alexej Ballhausen, Moritz Jakob Przybilla, Michael Jendrusch, Toni T. Seppälä, Daniel Fürst, Thomas Walle, Elena Busch, Georg Martin Haag, Robert Hüneburg, Jacob Nattermann, Magnus von Knebel Doeberitz, Vincent Heuveline, Matthias Kloor

Date Published: 25th Oct 2022

Publication Type: Journal

Towards Cognition-Guided Patient-Specific Numerical Simulation for Cardiac Surgery Assistance

Abstract (Expand)

Motivation. Patient-specific, knowledge-based, holistic surgical treatment planning is of utmost importance when dealing with complex surgery. Surgeons need to account for all available medical patient … data, keep track of technical developments, and stay on top of current surgical expert knowledge to define a suitable surgical treatment strategy. There is a large potential for computer assistance, also, and in particular, regarding surgery simulation which gives surgeons the opportunity not only to plan but to simulate, too, some steps of an intervention and to forecast relevant surgical situations. Purpose. In this work, we particularly look at mitral valve reconstruction (MVR) surgery, which is to re-establish the functionality of an incompetent mitral valve (MV) through implantation of an artificial ring that reshapes the valvular morphology. We aim at supporting MVR by providing surgeons with biomechanical FEM-based MVR surgery simulations that enable them to assess the simulated behavior of the MV after an MVR. However, according to the above requirements, such surgery simulation is really beneficial to surgeons only if it is patient-specific, surgical expert knowledge-based, comprehensive in terms of the underlying model and the patient’s data, and if its setup and execution is fully automated and integrated into the surgical treatment workflow. Methods. This PhD work conducts research on simulation-enhanced, cognition-guided, patient-specific cardiac surgery assistance. First, we derive a biomechanical MV/MVR model and develop an FEM-based MVR surgery simulation using the FEM software toolkit HiFlow3. Following, we outline the functionality and features of the Medical Simulation Markup Language (MSML) and how it simplifies the biomechanical modeling workflow. It is then detailed, how, by means of the MSML and a set of dedicated MVR simulation reprocessing operators, patient-individual medical data can comprehensively be analyzed and processed in order for the fully automated setup of MVR simulation scenarios. Finally, the presented work is integrated into the cognitive system architecture of the joint research project Cognition-Guided Surgery. We particularly look at its semantic knowledge and data infrastructure as well as at the setup of its cognitive software components, which eventually facilitate cognition-guidance and patient-specifity for the overall simulation-enhanced MVR assistance pipeline. Results and Discussion. We have proposed and implemented, for the first time, a prototypic system for simulation-enhanced, cognition-guided, patient-specific cardiac surgery assistance. The overall system was evaluated in terms of functionality and performance. Through its cognitive, data-driven pipeline setup, medical patient data and surgical information is analyzed and processed comprehensively, efficiently and fully automatically, and the hence set-up simulation scenarios yield reliable, patient-specific MVR surgery simulation results. This indicates the system’s usability and applicability. The proposed work thus presents an important step towards a simulation-enhanced, cognition-guided, patient-specific cardiac surgery assistance, and can – once operative – be expected to significantly enhance MVR surgery. Concluding, we discuss possible further research contents and promising applications to build upon the presented work.

Editor:

Date Published: 8th Feb 2017

Publication Type: Doctoral Thesis

Powered by
 (v.1.15.2)
About HITS Publication SEEK | About HITS-Publication-SEEK | Funding and Programmes | Credits
Copyright © 2008 - 2024 The University of Manchester and HITS gGmbH