Clinical Trials

In collaboration with the DZL

DZL Deutsches Zentrum für Lungenforschung

GI-COVID

Granulocyte Macrophage Colony Stimulating Factor (GM-CSF) Inhalation to prevent ARDS in COVID-19 pneumonia

The scope of this trial is to improve host defense of the lung and at the same time drive repair of the injured organ in hospitalized patients with COVID-19 pneumonia by inhalation of granulocyte-macrophage colony stimulating factor (GM-CSF; Molgramostim), a cytokine that has been shown to exert both of these effects in the lung. This study is funded by the BMBF as part of the „Nationales Forschungsnetz zoonotische Infektionskrankheiten“

GI-Hope

Granulocyte Macrophage-Colony Stimulating Factor (GM-CSF) Inhalation to Improve Host Defense and Pulmonary Barrier Restoration

In collaboration with the German Center for Infection Research DZIF

German Center for Infection Research Logo

R-Net

R-Net investigates the effect of various interventions aimed at limiting the spread of infections due to multi-drug resistant organisms (MDRO) – including infection prevention and control, decolonization, antimicrobial stewardship (AMS) and the development of new antimicrobials active against MDRO. Key epidemiological, microbiological, and clinical background data on multi-drug resistant pathogens will be collected over a period of four years at six DZIF partner sites.

In collaboration with the Network University Medicine NUM

Logo Netzwerk Universitäts Medizin

COVIM

COVIM (COllaboratiVe IMmunity Platform of the NUM) is a nationwide network of leading scientists and clinicians from the fields of immunology, virology, clinical infectious diseases, epidemiology, and data science.

In collaboration with the Volkswagen Foundation

Swarm Learning for precision medicine in infectious diseases and pandemic preparedness

The growing availability of extensive medical data, particularly high-resolution (single-cell) multi-omics data, underscores the importance of integrating machine learning and artificial intelligence (ML/AI) in advancing data-driven precision medicine. Significant challenges, however, persist for instance in utilizing single-cell patient data across diverse clinical settings, addressing data protection and privacy concerns, ensuring generalizability and reliability of ML/AI applications, and navigating ethical implications. A collaborative interdisciplinary team of experts aims to tackle these issues within the context of infectious diseases and pandemic preparedness by utilizing the Swarm Learning principle.

As participating site

Infectious disease area: HIV

Infectious disease area: Hepatitis D

Infectious disease area: Clostridium difficile

Infectious Disease Area: Pneumonia