1
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Kiss AE, Venkatasubramani AV, Pathirana D, Krause S, Sparr AC, Hasenauer J, Imhof A, Müller M, Becker PB. Processivity and specificity of histone acetylation by the male-specific lethal complex. Nucleic Acids Res 2024:gkae123. [PMID: 38407474 DOI: 10.1093/nar/gkae123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/29/2024] [Accepted: 02/12/2024] [Indexed: 02/27/2024] Open
Abstract
Acetylation of lysine 16 of histone H4 (H4K16ac) stands out among the histone modifications, because it decompacts the chromatin fiber. The metazoan acetyltransferase MOF (KAT8) regulates transcription through H4K16 acetylation. Antibody-based studies had yielded inconclusive results about the selectivity of MOF to acetylate the H4 N-terminus. We used targeted mass spectrometry to examine the activity of MOF in the male-specific lethal core (4-MSL) complex on nucleosome array substrates. This complex is part of the Dosage Compensation Complex (DCC) that activates X-chromosomal genes in male Drosophila. During short reaction times, MOF acetylated H4K16 efficiently and with excellent selectivity. Upon longer incubation, the enzyme progressively acetylated lysines 12, 8 and 5, leading to a mixture of oligo-acetylated H4. Mathematical modeling suggests that MOF recognizes and acetylates H4K16 with high selectivity, but remains substrate-bound and continues to acetylate more N-terminal H4 lysines in a processive manner. The 4-MSL complex lacks non-coding roX RNA, a critical component of the DCC. Remarkably, addition of RNA to the reaction non-specifically suppressed H4 oligo-acetylation in favor of specific H4K16 acetylation. Because RNA destabilizes the MSL-nucleosome interaction in vitro we speculate that RNA accelerates enzyme-substrate turn-over in vivo, thus limiting the processivity of MOF, thereby increasing specific H4K16 acetylation.
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Affiliation(s)
- Anna E Kiss
- Biomedical Center, Molecular Biology Division, Ludwig-Maximilians-University of Munich, Planegg-Martinsried, Germany
| | - Anuroop V Venkatasubramani
- Biomedical Center, Molecular Biology Division, Ludwig-Maximilians-University of Munich, Planegg-Martinsried, Germany
| | - Dilan Pathirana
- Life and Medical Sciences (LIMES) Institute, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Silke Krause
- Biomedical Center, Molecular Biology Division, Ludwig-Maximilians-University of Munich, Planegg-Martinsried, Germany
| | - Aline Campos Sparr
- Biomedical Center, Molecular Biology Division, Ludwig-Maximilians-University of Munich, Planegg-Martinsried, Germany
| | - Jan Hasenauer
- Life and Medical Sciences (LIMES) Institute, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
- Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany
| | - Axel Imhof
- Biomedical Center, Molecular Biology Division, Ludwig-Maximilians-University of Munich, Planegg-Martinsried, Germany
| | - Marisa Müller
- Biomedical Center, Molecular Biology Division, Ludwig-Maximilians-University of Munich, Planegg-Martinsried, Germany
| | - Peter B Becker
- Biomedical Center, Molecular Biology Division, Ludwig-Maximilians-University of Munich, Planegg-Martinsried, Germany
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2
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Schälte Y, Fröhlich F, Jost PJ, Vanhoefer J, Pathirana D, Stapor P, Lakrisenko P, Wang D, Raimúndez E, Merkt S, Schmiester L, Städter P, Grein S, Dudkin E, Doresic D, Weindl D, Hasenauer J. pyPESTO: a modular and scalable tool for parameter estimation for dynamic models. Bioinformatics 2023; 39:btad711. [PMID: 37995297 PMCID: PMC10689677 DOI: 10.1093/bioinformatics/btad711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 10/02/2023] [Accepted: 11/22/2023] [Indexed: 11/25/2023] Open
Abstract
SUMMARY Mechanistic models are important tools to describe and understand biological processes. However, they typically rely on unknown parameters, the estimation of which can be challenging for large and complex systems. pyPESTO is a modular framework for systematic parameter estimation, with scalable algorithms for optimization and uncertainty quantification. While tailored to ordinary differential equation problems, pyPESTO is broadly applicable to black-box parameter estimation problems. Besides own implementations, it provides a unified interface to various popular simulation and inference methods. AVAILABILITY AND IMPLEMENTATION pyPESTO is implemented in Python, open-source under a 3-Clause BSD license. Code and documentation are available on GitHub (https://github.com/icb-dcm/pypesto).
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Affiliation(s)
- Yannik Schälte
- Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
- Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), 85764 Neuherberg, Germany
- Department of Mathematics, Technical University of Munich, 85748 Garching, Germany
| | - Fabian Fröhlich
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, United States
| | - Paul J Jost
- Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
| | - Jakob Vanhoefer
- Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
| | - Dilan Pathirana
- Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
| | - Paul Stapor
- Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), 85764 Neuherberg, Germany
- Department of Mathematics, Technical University of Munich, 85748 Garching, Germany
| | - Polina Lakrisenko
- Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), 85764 Neuherberg, Germany
- School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
| | - Dantong Wang
- Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), 85764 Neuherberg, Germany
- Department of Mathematics, Technical University of Munich, 85748 Garching, Germany
| | - Elba Raimúndez
- Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
- Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), 85764 Neuherberg, Germany
- Department of Mathematics, Technical University of Munich, 85748 Garching, Germany
| | - Simon Merkt
- Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
| | - Leonard Schmiester
- Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), 85764 Neuherberg, Germany
- Department of Mathematics, Technical University of Munich, 85748 Garching, Germany
| | - Philipp Städter
- Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), 85764 Neuherberg, Germany
- Department of Mathematics, Technical University of Munich, 85748 Garching, Germany
- Leibniz Institute for Natural Product Research and Infection Biology, 07745 Jena, Germany
| | - Stephan Grein
- Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
| | - Erika Dudkin
- Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
| | - Domagoj Doresic
- Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
| | - Daniel Weindl
- Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), 85764 Neuherberg, Germany
| | - Jan Hasenauer
- Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
- Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), 85764 Neuherberg, Germany
- Department of Mathematics, Technical University of Munich, 85748 Garching, Germany
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3
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Lakrisenko P, Stapor P, Grein S, Paszkowski Ł, Pathirana D, Fröhlich F, Lines GT, Weindl D, Hasenauer J. Efficient computation of adjoint sensitivities at steady-state in ODE models of biochemical reaction networks. PLoS Comput Biol 2023; 19:e1010783. [PMID: 36595539 PMCID: PMC9838866 DOI: 10.1371/journal.pcbi.1010783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 01/13/2023] [Accepted: 12/01/2022] [Indexed: 01/04/2023] Open
Abstract
Dynamical models in the form of systems of ordinary differential equations have become a standard tool in systems biology. Many parameters of such models are usually unknown and have to be inferred from experimental data. Gradient-based optimization has proven to be effective for parameter estimation. However, computing gradients becomes increasingly costly for larger models, which are required for capturing the complex interactions of multiple biochemical pathways. Adjoint sensitivity analysis has been pivotal for working with such large models, but methods tailored for steady-state data are currently not available. We propose a new adjoint method for computing gradients, which is applicable if the experimental data include steady-state measurements. The method is based on a reformulation of the backward integration problem to a system of linear algebraic equations. The evaluation of the proposed method using real-world problems shows a speedup of total simulation time by a factor of up to 4.4. Our results demonstrate that the proposed approach can achieve a substantial improvement in computation time, in particular for large-scale models, where computational efficiency is critical.
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Affiliation(s)
- Polina Lakrisenko
- Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Paul Stapor
- Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Stephan Grein
- University of Bonn, Life and Medical Sciences Institute, Bonn, Germany
| | | | - Dilan Pathirana
- University of Bonn, Life and Medical Sciences Institute, Bonn, Germany
| | - Fabian Fröhlich
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
| | | | - Daniel Weindl
- Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany
| | - Jan Hasenauer
- Computational Health Center, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany
- University of Bonn, Life and Medical Sciences Institute, Bonn, Germany
- * E-mail:
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4
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Villaverde AF, Pathirana D, Fröhlich F, Hasenauer J, Banga JR. A protocol for dynamic model calibration. Brief Bioinform 2022; 23:bbab387. [PMID: 34619769 PMCID: PMC8769694 DOI: 10.1093/bib/bbab387] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 08/06/2021] [Accepted: 08/29/2021] [Indexed: 12/23/2022] Open
Abstract
Ordinary differential equation models are nowadays widely used for the mechanistic description of biological processes and their temporal evolution. These models typically have many unknown and nonmeasurable parameters, which have to be determined by fitting the model to experimental data. In order to perform this task, known as parameter estimation or model calibration, the modeller faces challenges such as poor parameter identifiability, lack of sufficiently informative experimental data and the existence of local minima in the objective function landscape. These issues tend to worsen with larger model sizes, increasing the computational complexity and the number of unknown parameters. An incorrectly calibrated model is problematic because it may result in inaccurate predictions and misleading conclusions. For nonexpert users, there are a large number of potential pitfalls. Here, we provide a protocol that guides the user through all the steps involved in the calibration of dynamic models. We illustrate the methodology with two models and provide all the code required to reproduce the results and perform the same analysis on new models. Our protocol provides practitioners and researchers in biological modelling with a one-stop guide that is at the same time compact and sufficiently comprehensive to cover all aspects of the problem.
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Affiliation(s)
- Alejandro F Villaverde
- Universidade de Vigo, Department of Systems Engineering & Control, Vigo 36310, Galicia, Spain
| | - Dilan Pathirana
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn 53115, Germany
| | - Fabian Fröhlich
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany
| | - Jan Hasenauer
- Center for Mathematics, Technische Universität München, Garching 85748, Germany
- Harvard Medical School, Cambridge, MA 02115, USA
| | - Julio R Banga
- Bioprocess Engineering Group, IIM-CSIC, Vigo 36208, Galicia, Spain
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5
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Stapor P, Schmiester L, Wierling C, Merkt S, Pathirana D, Lange BMH, Weindl D, Hasenauer J. Mini-batch optimization enables training of ODE models on large-scale datasets. Nat Commun 2022; 13:34. [PMID: 35013141 PMCID: PMC8748893 DOI: 10.1038/s41467-021-27374-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Accepted: 11/11/2021] [Indexed: 11/09/2022] Open
Abstract
Quantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established parameter optimization approaches for mechanistic models become computationally extremely challenging. Mini-batch optimization methods, as employed in deep learning, have better scaling properties. In this work, we adapt, apply, and benchmark mini-batch optimization for ordinary differential equation (ODE) models, thereby establishing a direct link between dynamic modelling and machine learning. On our main application example, a large-scale model of cancer signaling, we benchmark mini-batch optimization against established methods, achieving better optimization results and reducing computation by more than an order of magnitude. We expect that our work will serve as a first step towards mini-batch optimization tailored to ODE models and enable modelling of even larger and more complex systems than what is currently possible.
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Affiliation(s)
- Paul Stapor
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, 85764, Neuherberg, Germany
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, 85748, Garching, Germany
| | - Leonard Schmiester
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, 85764, Neuherberg, Germany
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, 85748, Garching, Germany
| | | | - Simon Merkt
- Universität Bonn, Faculty of Mathematics and Natural Sciences, 53115, Bonn, Germany
| | - Dilan Pathirana
- Universität Bonn, Faculty of Mathematics and Natural Sciences, 53115, Bonn, Germany
| | | | - Daniel Weindl
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, 85764, Neuherberg, Germany
| | - Jan Hasenauer
- Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, 85764, Neuherberg, Germany.
- Technische Universität München, Center for Mathematics, Chair of Mathematical Modeling of Biological Systems, 85748, Garching, Germany.
- Universität Bonn, Faculty of Mathematics and Natural Sciences, 53115, Bonn, Germany.
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6
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Fröhlich F, Weindl D, Schälte Y, Pathirana D, Paszkowski Ł, Lines GT, Stapor P, Hasenauer J. AMICI: High-Performance Sensitivity Analysis for Large Ordinary Differential Equation Models. Bioinformatics 2021; 37:3676-3677. [PMID: 33821950 PMCID: PMC8545331 DOI: 10.1093/bioinformatics/btab227] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 03/18/2021] [Accepted: 04/01/2021] [Indexed: 11/14/2022] Open
Abstract
Summary Ordinary differential equation models facilitate the understanding of cellular signal transduction and other biological processes. However, for large and comprehensive models, the computational cost of simulating or calibrating can be limiting. AMICI is a modular toolbox implemented in C++/Python/MATLAB that provides efficient simulation and sensitivity analysis routines tailored for scalable, gradient-based parameter estimation and uncertainty quantification. Availabilityand implementation AMICI is published under the permissive BSD-3-Clause license with source code publicly available on https://github.com/AMICI-dev/AMICI. Citeable releases are archived on Zenodo. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Fabian Fröhlich
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Daniel Weindl
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Yannik Schälte
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany.,Center for Mathematics, Technische Universität München, 85748 Garching, Germany
| | - Dilan Pathirana
- Faculty of Mathematics and Natural Sciences, University of Bonn, 53113 Bonn, Germany
| | | | | | - Paul Stapor
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany.,Center for Mathematics, Technische Universität München, 85748 Garching, Germany
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany.,Center for Mathematics, Technische Universität München, 85748 Garching, Germany.,Faculty of Mathematics and Natural Sciences, University of Bonn, 53113 Bonn, Germany
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7
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Schmiester L, Schälte Y, Bergmann FT, Camba T, Dudkin E, Egert J, Fröhlich F, Fuhrmann L, Hauber AL, Kemmer S, Lakrisenko P, Loos C, Merkt S, Müller W, Pathirana D, Raimúndez E, Refisch L, Rosenblatt M, Stapor PL, Städter P, Wang D, Wieland FG, Banga JR, Timmer J, Villaverde AF, Sahle S, Kreutz C, Hasenauer J, Weindl D. PEtab-Interoperable specification of parameter estimation problems in systems biology. PLoS Comput Biol 2021; 17:e1008646. [PMID: 33497393 PMCID: PMC7864467 DOI: 10.1371/journal.pcbi.1008646] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 02/05/2021] [Accepted: 12/18/2020] [Indexed: 01/24/2023] Open
Abstract
Reproducibility and reusability of the results of data-based modeling studies are essential. Yet, there has been-so far-no broadly supported format for the specification of parameter estimation problems in systems biology. Here, we introduce PEtab, a format which facilitates the specification of parameter estimation problems using Systems Biology Markup Language (SBML) models and a set of tab-separated value files describing the observation model and experimental data as well as parameters to be estimated. We already implemented PEtab support into eight well-established model simulation and parameter estimation toolboxes with hundreds of users in total. We provide a Python library for validation and modification of a PEtab problem and currently 20 example parameter estimation problems based on recent studies.
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Affiliation(s)
- Leonard Schmiester
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Yannik Schälte
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | | | - Tacio Camba
- Department of Applied Mathematics II, University of Vigo, Vigo, Galicia, Spain
- BioProcess Engineering Group, IIM-CSIC, Vigo, Galicia, Spain
| | - Erika Dudkin
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
| | - Janine Egert
- Faculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Freiburg, Germany
- Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany
| | - Fabian Fröhlich
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA
| | - Lara Fuhrmann
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
| | - Adrian L. Hauber
- Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany
- Institute of Physics, University of Freiburg, Freiburg, Germany
| | - Svenja Kemmer
- Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany
- Institute of Physics, University of Freiburg, Freiburg, Germany
| | - Polina Lakrisenko
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Carolin Loos
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
- Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Simon Merkt
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
| | - Wolfgang Müller
- Heidelberg Institute for Theoretical Studies (HITS gGmbH), Heidelberg, Germany
| | - Dilan Pathirana
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
| | - Elba Raimúndez
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
| | - Lukas Refisch
- Faculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Freiburg, Germany
- Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany
| | - Marcus Rosenblatt
- Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany
- Institute of Physics, University of Freiburg, Freiburg, Germany
| | - Paul L. Stapor
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Philipp Städter
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Dantong Wang
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
| | - Franz-Georg Wieland
- Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany
- Institute of Physics, University of Freiburg, Freiburg, Germany
| | - Julio R. Banga
- BioProcess Engineering Group, IIM-CSIC, Vigo, Galicia, Spain
| | - Jens Timmer
- Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany
- Institute of Physics, University of Freiburg, Freiburg, Germany
- Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg, Germany
| | | | - Sven Sahle
- BioQUANT/COS, Heidelberg University, Heidelberg, Germany
| | - Clemens Kreutz
- Faculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Freiburg, Germany
- Freiburg Center for Data Analysis and Modeling (FDM), University of Freiburg, Freiburg, Germany
- Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg, Germany
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- Center for Mathematics, Technische Universität München, Garching, Germany
- Faculty of Mathematics and Natural Sciences, University of Bonn, Bonn, Germany
- * E-mail:
| | - Daniel Weindl
- Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
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8
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Pathirana D, Johnston B, Johnston P. The effect of including increased arterial stiffness in the upper body when modelling Coarctation of the Aorta. Comput Methods Biomech Biomed Engin 2019; 22:475-489. [PMID: 30714407 DOI: 10.1080/10255842.2018.1564821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
Coarctation of the Aorta is a congenital narrowing of the aorta and diagnosis can be difficult. Treatments result in idiopathic sequelae including hypertension. Untreated patients are known to develop increased arterial stiffness in the upper body, which worsens with time. We present results from simulations with a one-dimensional mathematical model, about the effect of stiffness, stenting, surgery and coarctation severity on blood pressure, Pulsatility and Resistivity Index. One conclusion is that increased stiffness may explain both hypertension in treated patients and why diagnosis can be difficult.
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Affiliation(s)
- Dilan Pathirana
- a School of Natural Sciences and Queensland Micro- and Nanotechnology Centre, Griffith University , Nathan 4111 , Australia
| | - Barbara Johnston
- a School of Natural Sciences and Queensland Micro- and Nanotechnology Centre, Griffith University , Nathan 4111 , Australia
| | - Peter Johnston
- a School of Natural Sciences and Queensland Micro- and Nanotechnology Centre, Griffith University , Nathan 4111 , Australia
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9
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Pathirana D, Johnston B, Johnston P. The effects of tapering and artery wall stiffness on treatments for Coarctation of the Aorta. Comput Methods Biomech Biomed Engin 2017; 20:1512-1524. [PMID: 29119836 DOI: 10.1080/10255842.2017.1382483] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Coarctation of the Aorta is a congenital narrowing of the aorta. Two commonly used treatments are resection and end-to-end anastomosis, and stent placements. We simulate blood flow through one-dimensional models of aortas. Different artery stiffnesses, due to treatments, are included in our model, and used to compare blood flow properties in the treated aortas. We expand our previously published model to include the natural tapering of aortas. We look at change in aorta wall radius, blood pressure and blood flow velocity, and find that, of the two treatments, the resection and end-to-end anastomosis treatment more closely matches healthy aortas.
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Affiliation(s)
- Dilan Pathirana
- a School of Natural Sciences and Queensland Micro- and Nanotechnology Centre , Griffith University , Nathan , Australia
| | - Barbara Johnston
- a School of Natural Sciences and Queensland Micro- and Nanotechnology Centre , Griffith University , Nathan , Australia
| | - Peter Johnston
- a School of Natural Sciences and Queensland Micro- and Nanotechnology Centre , Griffith University , Nathan , Australia
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Gross G, Becker N, Brockmeyer N, Esser S, Freitag U, Gebhardt M, Gissmann L, Hillemanns P, Grundhewer H, Ikenberg H, Jessen H, Kaufmann A, Klug S, Klussmann J, Nast A, Pathirana D, Petry K, Pfister H, Röllinghof U, Schneede P, Schneider A, Selka E, Singer S, Smola S, Sporbeck B, von Knebel Doeberitz M, Wutzler P. Impfprävention HPV-assoziierter Neoplasien. Laryngorhinootologie 2014; 93:848-56. [DOI: 10.1055/s-0034-1382013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- G. Gross
- Klinik und Poliklinik für Dermatologie und Venerologie, Universitätsmedizin, Universität Rostock, Rostock
| | - N. Becker
- Deutsches Krebsforschungszentrum (DKFZ), Epidemiologie von Krebserkrankungen (C020), Heidelberg
| | - N. Brockmeyer
- Klinik für Dermatologie und Allergologie der Ruhr-Universität, Bochum
| | - S. Esser
- Klinik für Dermatologie und Venerologie, Universitätsklinikum Essen, Essen
| | | | | | - L. Gissmann
- Deutsches Krebsforschungszentrum (DKFZ), FS Infektion und Krebs, Heidelberg
| | - P. Hillemanns
- Medizinische Hochschule Hannover (MHH), Frauenklinik, Abt. I für Frauenheilkunde und Geburtshilfe, Hannover
| | - H. Grundhewer
- Ausschuss Prävention des Berufsverbandes der Kinder- und Jugendärzte (BVKJ), Berlin
| | - H. Ikenberg
- MVZ für Zytologie und Molekularbiologie (CytoMol), Frankfurt/M
| | | | - A. Kaufmann
- Gynäkologische Tumorimmunologie, Gynäkologie mit Hochschulambulanz, Charité – Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin
| | - S. Klug
- Universitätsklinikum Carl Gustav Carus an der Technischen Universität Dresden, Dresden
| | - J. Klussmann
- Klinik und Poliklinik für Hals-Nasen-Ohrenheilkunde, Klinikum der Universität Gießen, Gießen
| | - A. Nast
- Division of Evidence Based Medicine (dEBM), Klinik für Dermatologie, Allergologie und Venerologie, Charité – Universitätsmedizin Berlin, Campus Mitte, Berlin
| | - D. Pathirana
- Division of Evidence Based Medicine (dEBM), Klinik für Dermatologie, Allergologie und Venerologie, Charité – Universitätsmedizin Berlin, Campus Mitte, Berlin
| | - K. Petry
- Klinikum Wolfsburg, Abteilung Gynäkologische Onkologie, Wolfsburg
| | - H. Pfister
- Institut für Virologie der Universität zu Köln
| | | | - P. Schneede
- Klinikum Memmingen, Klinik für Urologie, Memmingen
| | - A. Schneider
- Klinik und Poliklinik für Gynäkologie, Charité – Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin
| | - E. Selka
- VulvaKarzinom-SHG e. V., Wilhelmshaven
| | - S. Singer
- Universitätsmedizin der Johannes Gutenberg-Universität Mainz, Institut für Medizinische Biometrie, Epidemiologie und Informatik, Abt. Epidemiologie und Versorgungsforschung, Mainz
| | - S. Smola
- Institut für Virologie, Institut für Infektionsmedizin, Universität des Saarlandes, Homburg/Saar
| | - B. Sporbeck
- Division of Evidence Based Medicine (dEBM), Klinik für Dermatologie, Allergologie und Venerologie, Charité – Universitätsmedizin Berlin, Campus Mitte, Berlin
| | - M. von Knebel Doeberitz
- Abteilung für Molekulare Pathologie, Pathologisches Institut des Universitätsklinikum Heidelberg, Heidelberg
| | - P. Wutzler
- Universitätsklinikum Jena (Friedrich-Schiller-Universität), Institut für Virologie und Antivirale Therapie, Beutenberg Campus, Jena
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Gross G, Becker N, Brockmeyer NH, Esser S, Freitag U, Gebhardt M, Gissmann L, Hillemanns P, Grundhewer H, Ikenberg H, Jessen H, Kaufmann A, Klug S, Klußmann JP, Nast A, Pathirana D, Petry KU, Pfister H, Röllinghof U, Schneede P, Schneider A, Selka E, Singer S, Smola S, Sporbeck B, von Knebel Doeberitz M, Wutzler P. Vaccination against HPV-Associated Neoplasias: Recommendations from the Current S3 Guideline of the HPV Management Forum of the Paul-Ehrlich Society - AWMF Guidelines, Registry No. 082-002 (short version), valid until Dec. 31st, 2018. Geburtshilfe Frauenheilkd 2014; 74:233-241. [PMID: 27064858 DOI: 10.1055/s-0033-1360170] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022] Open
Affiliation(s)
- G Gross
- Klinik und Poliklinik für Dermatologie und Venerologie, Universitätsmedizin, Universität Rostock, Rostock
| | - N Becker
- Deutsches Krebsforschungszentrum (DKFZ), Epidemiologie von Krebserkrankungen (C020), Heidelberg
| | - N H Brockmeyer
- Klinik für Dermatologie und Allergologie der Ruhr-Universität, Bochum
| | - S Esser
- Klinik für Dermatologie und Venerologie, Universitätsklinikum Essen, Essen
| | | | | | - L Gissmann
- Deutsches Krebsforschungszentrum (DKFZ), FS Infektion und Krebs, Heidelberg
| | - P Hillemanns
- Medizinische Hochschule Hannover (MHH), Frauenklinik, Abt. I für Frauenheilkunde und Geburtshilfe, Hannover
| | - H Grundhewer
- Ausschuss Prävention des Berufsverbandes der Kinder- und Jugendärzte (BVKJ), Berlin
| | - H Ikenberg
- MVZ für Zytologie und Molekularbiologie (CytoMol), Frankfurt/M
| | - H Jessen
- Praxis Jessen + Kollegen, Berlin
| | - A Kaufmann
- Gynäkologische Tumorimmunologie, Gynäkologie mit Hochschulambulanz, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin
| | - S Klug
- Universitätsklinikum Carl Gustav Carus an der Technischen Universität Dresden, Dresden
| | - J P Klußmann
- Klinik und Poliklinik für Hals-Nasen-Ohrenheilkunde, Klinikum der Universität Gießen, Gießen
| | - A Nast
- Division of Evidence Based Medicine (dEBM), Klinik für Dermatologie, Allergologie und Venerologie, Charité - Universitätsmedizin Berlin, Campus Mitte, Berlin
| | - D Pathirana
- Division of Evidence Based Medicine (dEBM), Klinik für Dermatologie, Allergologie und Venerologie, Charité - Universitätsmedizin Berlin, Campus Mitte, Berlin
| | - K U Petry
- Klinikum Wolfsburg, Abteilung Gynäkologische Onkologie, Wolfsburg
| | - H Pfister
- Institut für Virologie der Universität zu Köln
| | | | - P Schneede
- Klinikum Memmingen, Klinik für Urologie, Memmingen
| | - A Schneider
- Klinik und Poliklinik für Gynäkologie, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin
| | - E Selka
- VulvaKarzinom-SHG e. V., Wilhelmshaven
| | - S Singer
- Universitätsmedizin der Johannes Gutenberg-Universität Mainz, Institut für Medizinische Biometrie, Epidemiologie und Informatik, Abt. Epidemiologie und Versorgungsforschung, Mainz
| | - S Smola
- Institut für Virologie, Institut für Infektionsmedizin, Universität des Saarlandes, Homburg/Saar
| | - B Sporbeck
- Division of Evidence Based Medicine (dEBM), Klinik für Dermatologie, Allergologie und Venerologie, Charité - Universitätsmedizin Berlin, Campus Mitte, Berlin
| | - M von Knebel Doeberitz
- Abteilung für Molekulare Pathologie, Pathologisches Institut des Universitätsklinikum Heidelberg, Heidelberg
| | - P Wutzler
- Universitätsklinikum Jena (Friedrich-Schiller-Universität), Institut für Virologie und Antivirale Therapie, Beutenberg Campus, Jena
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Lucka TC, Pathirana D, Sammain A, Bachmann F, Rosumeck S, Erdmann R, Schmitt J, Orawa H, Rzany B, Nast A. Efficacy of systemic therapies for moderate-to-severe psoriasis: a systematic review and meta-analysis of long-term treatment. J Eur Acad Dermatol Venereol 2012; 26:1331-44. [PMID: 22404617 DOI: 10.1111/j.1468-3083.2012.04492.x] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND Despite the chronicity of psoriasis, most systematic reviews focus on short-term treatment. METHODS The systematic search strategy and results from the German Psoriasis Guidelines were adapted. To update the data a literature search in Medline, Embase and the Cochrane Library was conducted. The proportion of participants achieving ≥75% decrease in Psoriasis Area and Severity Index (PASI) as well as Dermatology Life Quality Index (DLQI) reduction at different time points were assessed. Trials were summarized with respect to time periods and study designs. Suitable trials were included in a meta-analysis. Particular attention was paid to statistical approaches of handling dropouts. RESULTS A total of 33 articles including 27 trials totaling 6575 patients with active treatment were included in the systematic review. Seven randomized controlled trials were eligible for the meta-analysis. Over a 24 week treatment period infliximab [risk difference (RD) 78%, 95% confidence interval (CI) 72-83%] and ustekinumab 90 mg every 12 weeks (RD 77%, 95% CI 71-83%) were the most efficacious treatments. Adalimumab (RD: 60%, 95% CI 45-74%) showed results within the range of different etanercept dosages (etanercept 50 mg once weekly: RD 62%, 95% CI, 52-72%), (etanercept 25 mg twice weekly: RD 45%, 95% CI 34-56%), (etanercept 50 mg twice weekly: RD 56%, 95% CI 49-62%) and (etanercept 50 mg twice weekly until week 12, then 25 mg twice weekly: RD 50%, 95% CI 42-57%). After 24 weeks a decrease in efficacy for inflximab, adalimumab and etanercept was observed. CONCLUSIONS More sufficient data is required to draw reliable conclusions in extended long-term treatment and head-to-head comparisons are necessary.
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Affiliation(s)
- T C Lucka
- Division of Evidence Based Medicine, and Klinik für Dermatologie, Venerologie und Allergologie, Charité-Universitätsmedizin, Berlin
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Pathirana D, Nast A, Ormerod AD, Reytan N, Saiag P, Smith CH, Spuls P, Rzany B. On the development of the European S3 guidelines on the systemic treatment of psoriasis vulgaris: structure and challenges. J Eur Acad Dermatol Venereol 2010; 24:1458-67. [DOI: 10.1111/j.1468-3083.2010.03671.x] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Pathirana D, Sammain A, Nast A, Rzany B. Reply to Alberto Giannetti, MD, PhD commentary regarding the European S3-Guidelines on the systemic treatment of Psoriasis. J Eur Acad Dermatol Venereol 2010. [DOI: 10.1111/j.1468-3083.2009.03559.x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Pathirana D, Ormerod AD, Saiag P, Smith C, Spuls PI, Nast A, Barker J, Bos JD, Burmester GR, Chimenti S, Dubertret L, Eberlein B, Erdmann R, Ferguson J, Girolomoni G, Gisondi P, Giunta A, Griffiths C, Hönigsmann H, Hussain M, Jobling R, Karvonen SL, Kemeny L, Kopp I, Leonardi C, Maccarone M, Menter A, Mrowietz U, Naldi L, Nijsten T, Ortonne JP, Orzechowski HD, Rantanen T, Reich K, Reytan N, Richards H, Thio HB, van de Kerkhof P, Rzany B. European S3-Guidelines on the systemic treatment of psoriasis vulgaris. J Eur Acad Dermatol Venereol 2009; 23 Suppl 2:1-70. [DOI: 10.1111/j.1468-3083.2009.03389.x] [Citation(s) in RCA: 467] [Impact Index Per Article: 31.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Pathirana D, Ormerod AD, Saiag P, Smith C, Spuls PI, Nast A, Barker J, Bos JD, Burmester GR, Chimenti S, Dubertret L, Eberlein B, Erdmann R, Ferguson J, Girolomoni G, Gisondi P, Giunta A, Griffiths C, Hönigsmann H, Hussain M, Jobling R, Karvonen SL, Kemeny L, Kopp I, Leonardi C, Maccarone M, Menter A, Mrowietz U, Naldi L, Nijsten T, Ortonne JP, Orzechowski HD, Rantanen T, Reich K, Reytan N, Richards H, Thio HB, van de Kerkhof P, Rzany B. European S3-guidelines on the systemic treatment of psoriasis vulgaris. J Eur Acad Dermatol Venereol 2009. [PMID: 19712190 DOI: 10.1111/j.1468-3083.2009.03389] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Of the 131 studies on monotherapy or combination therapy assessed, 56 studies on the different forms of phototherapy fulfilled the criteria for inclusion in the guidelines. Approximately three-quarters of all patients treated with phototherapy attained at least a PASI 75 response after 4 to 6 weeks, and clearance was frequently achieved (levels of evidence 2 and 3). Phototherapy represents a safe and very effective treatment option for moderate to severe forms of psoriasis vulgaris. The onset of clinical effects occurs within 2 weeks. Of the unwanted side effects, UV erythema from overexposure is by far the most common and is observed frequently. With repeated or long-term use, the consequences of high, cumulative UV doses (such as premature aging of the skin) must be taken into consideration. In addition, carcinogenic risk is associated with oral PUVA and is probable for local PUVA and UVB. The practicability of the therapy is limited by spatial, financial, human, and time constraints on the part of the physician, as well as by the amount of time required by the patient. From the perspective of the cost-bearing institution, phototherapy has a good cost-benefit ratio. However, the potentially significant costs for, and time required of, the patient must be considered.
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Pathirana D, Hillemanns P, Petry KU, Becker N, Brockmeyer N, Erdmann R, Gissmann L, Grundhewer H, Ikenberg H, Kaufmann A, Klußmann J, Kopp I, Pfister H, Rzany B, Schneede P, Schneider A, Smola S, Winter-Koch N, Wutzler P, Gross G. Short version of the German evidence-based Guidelines for prophylactic vaccination against HPV-associated neoplasia. Vaccine 2009; 27:4551-9. [DOI: 10.1016/j.vaccine.2009.03.086] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2009] [Revised: 03/19/2009] [Accepted: 03/26/2009] [Indexed: 11/30/2022]
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Karunasekera KA, Pathirana D. A preliminary study on neonatal septicaemia in a tertiary referral hospital paediatric unit. Ceylon Med J 1999; 44:81-6. [PMID: 10565074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/14/2023]
Abstract
OBJECTIVE To estimate the incidence of neonatal septicaemia, and to identify risk factors, clinical presentations and causal organisms. DESIGN A cross-sectional study. SETTING Neonatal Care Unit, University Paediatric Unit, Colombo North Teaching Hospital. SUBJECTS Neonates admitted from January to December 1996 with clinical evidence of septicaemia. METHOD Gestational age, birth weight and mode of delivery were evaluated as risk factors for septicaemia. Although diagnosis of septicaemia was made on clinical grounds, blood cultures were performed in all babies. Data was analysed by using Epi Info version 6. RESULTS 98 babies had septicaemia. Incidence of septicaemia was 24.4 per 1000 live births and case fatality rate was 11.2%. Incidence was significantly higher in preterm babies, babies with low birth weight (LBW) and those born following instrumental delivery. 21.4% developed septicaemia on the first day of life, 74.5% between 2 and 7 days and 4.1% after the first week. Common presenting features were fever 61.2%, jaundice 52%, lethargy 37.8% refusal of feeds 25.5%, coffee grounds vomiting 22.4%, and fits 12.2%. Common bacteria identified were Klebsiella 26.5%, Staphylococcus aureus 15.3%, coliform bacilli 9.2% and spore forming bacilli 9.2%. Common sensitive antibiotics were amikacin 88.9%, amoxycillin + clavulanic acid 83%, ceftriaxone 78.1% and netilmicin 63.9%. CONCLUSIONS Septicaemia is an important cause of morbidity, particularly in preterm babies, in babies with LBW and those with instrumentation at birth. The high incidence of late onset septicaemia together with the findings of Klebsiella and Staphylococcus aureus as common and resistant pathogens for septicaemia indicate that the majority were nosocomial infections.
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