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Die Multisystematrophie (MSA) wird zu den Parkinsonsyndromen gezählt. Sie ist eine rasch fortschreitende Erkrankung des Gehirns, die mit einer Störung der Steuerung von Körperbewegungen, Blutdruckregulation, Harnblasenfunktion, Sprechen und Schlucken einhergeht. Für die Erkrankung steht bislang keine durchgreifende Behandlung zur Verfügung. Daher ist die Entwicklung ursächlicher Therapien zur Verlangsamung des Krankheitsverlaufs dringend notwendig. Dies setzt aber ein besseres Verständnis der Krankheitsmechanismen voraus.

Co-applicants:

Dr. rer. nat. Konrad Fischer (TU München)
Prof. Dr. vet. med. Rabea Hinkel (Deutsches Primatenzentrum, Göttingen)
Dr. med. Dionysios Koliogiannis (LMU München)
 

Project partner:

Prof. Stig Steen and Prof. Michael Olausson, Lund University, Sweden

Non-pharmacological treatment of comorbidities of heart failure such as sleep apnea may have an effect on ventricular arrhythmias, cardiac function and prognosis. Half of the patients with heart failure suffer from obstructive or central sleep apnea. Therefore, the aim of this project is, to test whether treatment of sleep apnea with adaptive servoventilation reduces the burden of nocturnal ventricular arrhythmias compared to the control group. Therefore, we perform a substudy of the large-scale ongoing investigator initated randomized controlled ADVENT-HF trial (850 participants).

Melanized fungi, e.g. cryptococci, are associated with a wide range of globally distributed infectious diseases. Reports of melanized fungi as important pathogens increased recently. Diseases caused by melanized fungi include among others allergies, wound infections and pneumonia. One major virulence factor of these fungi is the melanization. Melanin, stored in cell walls, stabilizes the cell and provides protection towards environmental stresses. Another factor contributing to virulence in fungi is the capability to form biofilms.

Co-applicants:

Prof. Dr. med. Torsten Tonn: DRK-Blutspendedienst
PD Dr. med. Michael Burger, Prof. Dr. med. Joachim Steinbach, Prof. Dr. med. Christian Senft: Zentrum der Neurologie und Neurochirurgie, Universitätsklinikum Frankfurt
Prof. Dr. med. Karl Plate: Edinger Institut, Universitätsklinikum Frankfurt
 

Project partner:

Universitätsklinikum Frankfurt
DRK-Blutspendedienst Frankfurt und Dresden

In clinical routine, the analysis of the leukocytes composition in peripheral blood represents a daily procedure. Cell counting is usually performed manually or automatically, however, all conventional methods require fresh blood samples (not older than 24 hours). Alternatively, it is possible to estimate the cellular composition based on epigenetic modifications, which are acquired during hematopoietic differentiation.

The future of radiation therapy lies in the dynamic adjustment of the target volume during irradiation in real time (image guided radiation therapy). Currently, organ movements are compensated by predicting respiratory curves via gating (interrupting radiation as soon as the target area moves out of the beam). In radiation oncology, adaptive irradiation methods that apply the dose spatially and temporally precisely are becoming increasingly popular.

Stroke is the leading cause for acquired disability in adults. Non-invasive brain stimulation of the primary motor cortex can enhance motor recovery. However, there is relevant intersubject variability in the response. Posteroparietal brain regions might serve as alternative targets. This project aims at investigating this approach in a group of chronic stroke patients. We will address stimulation-related changes in cortical excitability of the motor network and changes in motor functioning.

T cells are immune cells that protect against infections or cancer, but can also cause chronic inflammatory or autoimmune diseases. T cells recognize their target via the T cell receptor (TCR). The human body harbors billions of different TCRs. Therefore, assigning the correct TCR and its target is an enormous challenge. In the proposed project, we will combine innovative experimental methods with machine learning in a clinical cohort of yellow fever vaccinees to predict which target a TCR will recognize.