Publications by authors named "Jan Rudolph"

11 Publications

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Evaluation of patients with respiratory infections during the first pandemic wave in Germany: characteristics of COVID-19 versus non-COVID-19 patients.

BMC Infect Dis 2021 Feb 10;21(1):167. Epub 2021 Feb 10.

Department of Radiology, University Hospital, LMU Munich, 81377, Munich, Germany.

Background: Characteristics of COVID-19 patients have mainly been reported within confirmed COVID-19 cohorts. By analyzing patients with respiratory infections in the emergency department during the first pandemic wave, we aim to assess differences in the characteristics of COVID-19 vs. Non-COVID-19 patients. This is particularly important regarding the second COVID-19 wave and the approaching influenza season.

Methods: We prospectively included 219 patients with suspected COVID-19 who received radiological imaging and RT-PCR for SARS-CoV-2. Demographic, clinical and laboratory parameters as well as RT-PCR results were used for subgroup analysis. Imaging data were reassessed using the following scoring system: 0 - not typical, 1 - possible, 2 - highly suspicious for COVID-19.

Results: COVID-19 was diagnosed in 72 (32,9%) patients. In three of them (4,2%) the initial RT-PCR was negative while initial CT scan revealed pneumonic findings. 111 (50,7%) patients, 61 of them (55,0%) COVID-19 positive, had evidence of pneumonia. Patients with COVID-19 pneumonia showed higher body temperature (37,7 ± 0,1 vs. 37,1 ± 0,1 °C; p = 0.0001) and LDH values (386,3 ± 27,1 vs. 310,4 ± 17,5 U/l; p = 0.012) as well as lower leukocytes (7,6 ± 0,5 vs. 10,1 ± 0,6G/l; p = 0.0003) than patients with other pneumonia. Among abnormal CT findings in COVID-19 patients, 57 (93,4%) were evaluated as highly suspicious or possible for COVID-19. In patients with negative RT-PCR and pneumonia, another third was evaluated as highly suspicious or possible for COVID-19 (14 out of 50; 28,0%). The sensitivity in the detection of patients requiring isolation was higher with initial chest CT than with initial RT-PCR (90,4% vs. 79,5%).

Conclusions: COVID-19 patients show typical clinical, laboratory and imaging parameters which enable a sensitive detection of patients who demand isolation measures due to COVID-19.
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http://dx.doi.org/10.1186/s12879-021-05829-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7875010PMC
February 2021

COVID-19 Pandemic and Upcoming Influenza Season-Does an Expert's Computed Tomography Assessment Differentially Identify COVID-19, Influenza and Pneumonias of Other Origin?

J Clin Med 2020 Dec 28;10(1). Epub 2020 Dec 28.

Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany.

(1) Background: Time-consuming SARS-CoV-2 RT-PCR suffers from limited sensitivity in early infection stages whereas fast available chest CT can already raise COVID-19 suspicion. Nevertheless, radiologists' performance to differentiate COVID-19, especially from influenza pneumonia, is not sufficiently characterized. (2) Methods: A total of 201 pneumonia CTs were identified and divided into subgroups based on RT-PCR: 78 COVID-19 CTs, 65 influenza CTs and 62 Non-COVID-19-Non-influenza (NCNI) CTs. Three radiology experts (blinded from RT-PCR results) raised pathogen-specific suspicion (separately for COVID-19, influenza, bacterial pneumonia and fungal pneumonia) according to the following reading scores: 0-not typical/1-possible/2-highly suspected. Diagnostic performances were calculated with RT-PCR as a reference standard. Dependencies of radiologists' pathogen suspicion scores were characterized by . (3) Results: Depending on whether the intermediate reading score 1 was considered as positive or negative, radiologists correctly classified 83-85% (vs. NCNI)/79-82% (vs. influenza) of COVID-19 cases (sensitivity up to 94%). Contrarily, radiologists correctly classified only 52-56% (vs. NCNI)/50-60% (vs. COVID-19) of influenza cases. The COVID-19 scoring was more specific than the influenza scoring compared with suspected bacterial or fungal infection. (4) Conclusions: High-accuracy COVID-19 detection by CT might expedite patient management even during the upcoming influenza season.
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http://dx.doi.org/10.3390/jcm10010084DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7795488PMC
December 2020

Expanding the Perseus Software for Omics Data Analysis With Custom Plugins.

Curr Protoc Bioinformatics 2020 09;71(1):e105

Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany.

The Perseus software provides a comprehensive framework for the statistical analysis of large-scale quantitative proteomics data, also in combination with other omics dimensions. Rapid developments in proteomics technology and the ever-growing diversity of biological studies increasingly require the flexibility to incorporate computational methods designed by the user. Here, we present the new functionality of Perseus to integrate self-made plugins written in C#, R, or Python. The user-written codes will be fully integrated into the Perseus data analysis workflow as custom activities. This also makes language-specific R and Python libraries from CRAN (cran.r-project.org), Bioconductor (bioconductor.org), PyPI (pypi.org), and Anaconda (anaconda.org) accessible in Perseus. The different available approaches are explained in detail in this article. To facilitate the distribution of user-developed plugins among users, we have created a plugin repository for community sharing and filled it with the examples provided in this article and a collection of already existing and more extensive plugins. © 2020 The Authors. Basic Protocol 1: Basic steps for R plugins Support Protocol 1: R plugins with additional arguments Basic Protocol 2: Basic steps for python plugins Support Protocol 2: Python plugins with additional arguments Basic Protocol 3: Basic steps and construction of C# plugins Basic Protocol 4: Basic steps of construction and connection for R plugins with C# interface Support Protocol 4: Advanced example of R Plugin with C# interface: UMAP Basic Protocol 5: Basic steps of construction and connection for python plugins with C# interface Support Protocol 5: Advanced example of python plugin with C# interface: UMAP Support Protocol 6: A basic workflow for the analysis of label-free quantification proteomics data using perseus.
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http://dx.doi.org/10.1002/cpbi.105DOI Listing
September 2020

Large Momentum Transfer Clock Atom Interferometry on the 689 nm Intercombination Line of Strontium.

Phys Rev Lett 2020 Feb;124(8):083604

Department of Physics, Stanford University, Stanford, California 94305, USA.

We report the first realization of large momentum transfer (LMT) clock atom interferometry. Using single-photon interactions on the strontium ^{1}S_{0}-^{3}P_{1} transition, we demonstrate Mach-Zehnder interferometers with state-of-the-art momentum separation of up to 141  ℏk and gradiometers of up to 81  ℏk. Moreover, we circumvent excited state decay limitations and extend the gradiometer duration to 50 times the excited state lifetime. Because of the broad velocity acceptance of the interferometry pulses, all experiments are performed with laser-cooled atoms at a temperature of 3  μK. This work has applications in high-precision inertial sensing and paves the way for LMT-enhanced clock atom interferometry on even narrower transitions, a key ingredient in proposals for gravitational wave detection and dark matter searches.
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http://dx.doi.org/10.1103/PhysRevLett.124.083604DOI Listing
February 2020

Sleep-wake cycles drive daily dynamics of synaptic phosphorylation.

Science 2019 10;366(6462)

Institute of Medical Psychology, Faculty of Medicine, LMU Munich, Germany.

The circadian clock drives daily changes of physiology, including sleep-wake cycles, through regulation of transcription, protein abundance, and function. Circadian phosphorylation controls cellular processes in peripheral organs, but little is known about its role in brain function and synaptic activity. We applied advanced quantitative phosphoproteomics to mouse forebrain synaptoneurosomes isolated across 24 hours, accurately quantifying almost 8000 phosphopeptides. Half of the synaptic phosphoproteins, including numerous kinases, had large-amplitude rhythms peaking at rest-activity and activity-rest transitions. Bioinformatic analyses revealed global temporal control of synaptic function through phosphorylation, including synaptic transmission, cytoskeleton reorganization, and excitatory/inhibitory balance. Sleep deprivation abolished 98% of all phosphorylation cycles in synaptoneurosomes, indicating that sleep-wake cycles rather than circadian signals are main drivers of synaptic phosphorylation, responding to both sleep and wake pressures.
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http://dx.doi.org/10.1126/science.aav3617DOI Listing
October 2019

A Network Module for the Perseus Software for Computational Proteomics Facilitates Proteome Interaction Graph Analysis.

J Proteome Res 2019 05 10;18(5):2052-2064. Epub 2019 Apr 10.

Computational Systems Biochemistry , Max-Planck Institute of Biochemistry , Am Klopferspitz 18 , 82152 Martinsried , Germany.

Proteomics data analysis strongly benefits from not studying single proteins in isolation but taking their multivariate interdependence into account. We introduce PerseusNet, the new Perseus network module for the biological analysis of proteomics data. Proteomics is commonly used to generate networks, e.g., with affinity purification experiments, but networks are also used to explore proteomics data. PerseusNet supports the biomedical researcher for both modes of data analysis with a multitude of activities. For affinity purification, a volcano-plot-based statistical analysis method for network generation is featured which is scalable to large numbers of baits. For posttranslational modifications of proteins, such as phosphorylation, a collection of dedicated network analysis tools helps in elucidating cellular signaling events. Co-expression network analysis of proteomics data adopts established tools from transcriptome co-expression analysis. PerseusNet is extensible through a plugin architecture in a multi-lingual way, integrating analyses in C#, Python, and R, and is freely available at http://www.perseus-framework.org .
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http://dx.doi.org/10.1021/acs.jproteome.8b00927DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6578358PMC
May 2019

4D-Flow MRI: Technique and Applications.

Rofo 2018 Nov 13;190(11):1025-1035. Epub 2018 Aug 13.

Department of Diagnostic and Interventional Radiology, University Hospital Technical University of Munich.

Background:  Blood flow through the cavities of the heart and great vessels is pulsatile and is subject to time and multidirectional variations. To date, the recording of blood flow in multiple directions and phases has been limited. 4D-flow MRI offers advantages for the recording, visualization and analysis of blood flow.

Method:  The status quo of the method was summarized through analysis with the PubMed database using the keywords "4D-flow MRI, phase-contrast magnetic resonance imaging, MR flow imaging/visualization, MR flow quantification, 3 D cine (time-resolved) phase-contrast CMR, three-directional velocity-encoding MRI".

Results/conclusion:  This review summarizes the current status of the technical development of 4D-flow MRI, discusses its advantages and disadvantages and describes clinical applications. Finally, the most important principles and parameters are explained to give the reader relevant information about clinical indications, postprocessing methods and limitations of the method.

Key Points:   · 4D-Fluss-MRT. · 3-dimensionale zeitaufgelöste Phasenkontrast-MRT. · Flussanalyse-MRT (Wall-Shear-Stress/Druckgradienten-Messung/Vortex-Fluss/turbulente kinetische Energie/Flussgeschwindigkeit/Flussrate).

Citation Format: · Sträter A, Huber A, Rudolph J et al. 4D-Flow MRI: Technique and Applications. Fortschr Röntgenstr 2018; 190: 1025 - 1035.
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http://dx.doi.org/10.1055/a-0647-2021DOI Listing
November 2018

MaxQuant goes Linux.

Nat Methods 2018 06;15(6):401

Computational Systems Biochemistry, Max Planck Institute for Biochemistry, Martinsried, Germany.

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http://dx.doi.org/10.1038/s41592-018-0018-yDOI Listing
June 2018

Elucidation of Signaling Pathways from Large-Scale Phosphoproteomic Data Using Protein Interaction Networks.

Cell Syst 2016 Dec;3(6):585-593.e3

Blavatnik School of Computer Sciences, Tel Aviv University, 69978 Tel Aviv, Israel. Electronic address:

Phosphoproteomic experiments typically identify sites within a protein that are differentially phosphorylated between two or more cell states. However, the interpretation of these data is hampered by the lack of methods that can translate site-specific information into global maps of active proteins and signaling networks, especially as the phosphoproteome is often undersampled. Here, we describe PHOTON, a method for interpreting phosphorylation data within their signaling context, as captured by protein-protein interaction networks, to identify active proteins and pathways and pinpoint functional phosphosites. We apply PHOTON to interpret existing and novel phosphoproteomic datasets related to epidermal growth factor and insulin responses. PHOTON substantially outperforms the widely used cutoff approach, providing highly reproducible predictions that are more in line with current biological knowledge. Altogether, PHOTON overcomes the fundamental challenge of delineating signaling pathways from large-scale phosphoproteomic data, thereby enabling translation of environmental cues to downstream cellular responses.
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http://dx.doi.org/10.1016/j.cels.2016.11.005DOI Listing
December 2016

System-wide Clinical Proteomics of Breast Cancer Reveals Global Remodeling of Tissue Homeostasis.

Cell Syst 2016 03 3;2(3):172-84. Epub 2016 Mar 3.

Department of Human Molecular Genetics and Biochemistry, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel. Electronic address:

The genomic and transcriptomic landscapes of breast cancer have been extensively studied, but the proteomes of breast tumors are far less characterized. Here, we use high-resolution, high-accuracy mass spectrometry to perform a deep analysis of luminal-type breast cancer progression using clinical breast samples from primary tumors, matched lymph node metastases, and healthy breast epithelia. We used a super-SILAC mix to quantify over 10,000 proteins with high accuracy, enabling us to identify key proteins and pathways associated with tumorigenesis and metastatic spread. We found high expression levels of proteins associated with protein synthesis and degradation in cancer tissues, accompanied by metabolic alterations that may facilitate energy production in cancer cells within their natural environment. In addition, we found proteomic differences between breast cancer stages and minor differences between primary tumors and their matched lymph node metastases. These results highlight the potential of proteomic technology in the elucidation of clinically relevant cancer signatures.
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http://dx.doi.org/10.1016/j.cels.2016.02.001DOI Listing
March 2016

JSBML 1.0: providing a smorgasbord of options to encode systems biology models.

Bioinformatics 2015 Oct 16;31(20):3383-6. Epub 2015 Jun 16.

University of California, San Diego, La Jolla, CA, USA, Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany.

Unlabelled: JSBML, the official pure Java programming library for the Systems Biology Markup Language (SBML) format, has evolved with the advent of different modeling formalisms in systems biology and their ability to be exchanged and represented via extensions of SBML. JSBML has matured into a major, active open-source project with contributions from a growing, international team of developers who not only maintain compatibility with SBML, but also drive steady improvements to the Java interface and promote ease-of-use with end users.

Availability And Implementation: Source code, binaries and documentation for JSBML can be freely obtained under the terms of the LGPL 2.1 from the website http://sbml.org/Software/JSBML. More information about JSBML can be found in the user guide at http://sbml.org/Software/JSBML/docs/.

Contact: jsbml-development@googlegroups.com or andraeger@eng.ucsd.edu

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btv341DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4595895PMC
October 2015