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| + | ====== News ====== | ||
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| + | 24 May 2023 - Published online: Gap junctions desynchronize a neural circuit to stabilize insect flight - Hürkey S*, **Niemeyer N*, Schleimer J-H**, Ryglewski S, **Schreiber S#**, Duch C# (2023): Gap junctions desynchronize a neural circuit to stabilize insect flight. Nature https:// | ||
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| + | 2023-02-12 | ||
| + | X-Research Group - Digital twins for the treatment of hypertension | ||
| + | < | ||
| + | **M. König** receives funding of the Berlin University Alliance and the DFG | ||
| + | for the X-Student Research Group: **Digital twins for the treatment of | ||
| + | hypertension** | ||
| + | Understanding a drugs pharmacokinetics - how it is absorbed, | ||
| + | distributed, | ||
| + | pharmacodynamics - how it affects the body - is a key challenge in | ||
| + | treating people. Angiotensin converting enzyme (ACE) inhibitors and | ||
| + | diuretics are two classes of drugs used to treat high blood pressure. | ||
| + | Both are among the most commonly prescribed drugs due to the high | ||
| + | prevalence of hypertension in an ageing society. | ||
| + | In this X-student research group physiologically based pharmacokinetic | ||
| + | models of the diuretic hydrochlorothiazide and the ACE inhibitor | ||
| + | lisinopril will be developed. With the X-Student Research Groups | ||
| + | < | ||
| + | the Berlin University Alliance supports research teams consisting of | ||
| + | young researchers and students. The goal is to involve students in | ||
| + | current research projects of the alliance partners and to enable them to | ||
| + | participate in (cutting-edge) research already during their studies. | ||
| + | Funded under the Excellence Strategy of the Federal Government and the | ||
| + | Länder by the Berlin University Alliance | ||
| + | < | ||
| + | |||
| + | 2023-02-01 | ||
| + | ATLAS - AI and Simulation for Tumor Liver ASsessment | ||
| + | < | ||
| + | *The BMBF is funding the joint project ATLAS in the **" | ||
| + | Sciences - AI Methods for Systems Medicine" | ||
| + | Liver cancer is the second most common cause of cancer-related death. | ||
| + | Diagnosis and treatment are time-critical and require highly | ||
| + | patient-specific diagnostic and treatment pathways. Medical | ||
| + | decision-making is based on a variety of interdependent factors related | ||
| + | to different medical disciplines, | ||
| + | guidelines. Taking into account all decision factors in combination with | ||
| + | the possible therapeutic approaches is a major challenge for physicians | ||
| + | and often cannot be solved optimally even in an interdisciplinary tumor | ||
| + | board. In this project, we are developing ATLAS, a decision support tool | ||
| + | that will significantly assist clinicians in meeting this challenge. | ||
| + | Based on AI methods, ATLAS processes all relevant patient data from | ||
| + | databases, systems medicine and continuum biomechanical in silico | ||
| + | prognosis models as well as individual patient data. The tool is being | ||
| + | developed in a co-design approach by experts in surgical oncology, | ||
| + | mathematical modeling and machine learning. The selected technologies | ||
| + | will integrate automated understanding of a highly complex patient | ||
| + | situation through simulation of liver functions with expert knowledge | ||
| + | and ontology-driven learning with knowledge graphs from retrospective | ||
| + | liver tumor cases. ATLAS will be based on a detailed historical data | ||
| + | cohort of more than 6,000 patients with liver tumors and will be | ||
| + | evaluated on case studies at the University Hospital of Jena. The | ||
| + | integration of medical expert knowledge, mathematical modeling and | ||
| + | artificial intelligence represents a highly original and promising | ||
| + | approach for high-quality diagnosis and treatment of liver tumors, | ||
| + | resulting in patient-specific improvement of prognosis. The scientific | ||
| + | knowledge gained from these projects will provide opportunities for | ||
| + | transfer to malignancies in other organs, such as the lung, kidney or | ||
| + | brain. The development of tools and demonstrators will provide | ||
| + | sustainable exploitation pathways for future commercial applications. | ||
| + | https:// | ||
| + | |||
| + | The 3-year research project **" | ||
| + | model of parahippocampal phase precession" | ||
| + | Research Foundation (Deutsche Forschungsgemeinschaft, | ||
| + | Priority Program 1665 (Schwerpunktprogramm) " | ||
| + | manipulating neuronal networks in the mammalian brain – from | ||
| + | correlative to causal analysis" | ||
| + | 2016. Collaborators are **Prof. Michael Brecht** (Humboldt-Universität zu | ||
| + | Berlin), **Prof. Dietmar Schmitz** (Charité), and **Prof. Richard Kempter** | ||
| + | (Humboldt-Universität zu Berlin). | ||
| + | Further details can be found at | ||
| + | http:// | ||
| + | |||
| + | The 3-year research project **" | ||
| + | is funded by the German Federal Ministry of Education and Research | ||
| + | (BMBF) and the National Science Foundation (NSF). This " | ||
| + | German Collaboration in Computational Neuroscience" | ||
| + | 2016. Collaborators are **Prof. Catherine E. Carr** (University of | ||
| + | Maryland), **Prof. Christine Köppl** (Universität Oldenburg), and | ||
| + | **Prof. Richard Kempter** (Humboldt-Universität zu Berlin). | ||
| + | Further details can be found at | ||
| + | https:// | ||
| + | |||
| **Prof Werner Ebeling** Honorary Colloquium **" | **Prof Werner Ebeling** Honorary Colloquium **" | ||
| Selforganization" | Selforganization" | ||