
A major step for HCV research
Research team from Hannover adapts hepatitis C virus to infect mouse liver cells
Read moreAt TWINCORE, we combine the expertise of medical professionals and scientists from a wide range of disciplines to find answers to the pressing questions in infection research. Our focus: translational research – the bridge between basic science and clinical application.
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Research team from Hannover adapts hepatitis C virus to infect mouse liver cells
Read moreResearchers in Hannover have developed a new method for studying neuroinfections. This reduces errors in analysis and delivers more accurate results.
Read moreA research team at TWINCORE was able to establish that TLR8 influences the formation of disease-relevant cytokines.
Read moreWe conduct translational infection research to improve the prevention, diagnosis and treatment of infectious diseases in humans. We focus on three areas that characterize our research work. Find out here how we proceed and what results we achieve.
Under the leadership of our best scientists, various labs are working on different projects within our research topics.
Tham H, Chong L, Krishnan M, Khan A, Choi S, Tamura T, Yusoff K, Tan G, Song A
Klee B, Diexer S, Langer S, Gottschick C, Hartmann C, Glaser N, Horn J, Dorendorf E, Raupach-Rosin H, Hassan L, Rübsamen N, Meyer-Schlinkmann K, Guzman C, Heselich V, Battin E, Pietschmann T, Pieper D, Pletz M, Riese P, Trittel S, Thies S, von Kaisenberg C, Dressler F, Guthmann F, Oberhoff C, Schild R, Karch A, Mikolajczyk R
Jumde R, Jézéquel G, Saramago M, Frank N, Adam S, Cunha M, Bader C, Gunesch A, Köhler N, Johannsen S, Bousis S, Pietschmann T, Matos R, Müller R, Arraiano C, Hirsch A
Immunomodulatory drugs can have systemic side effects. This project is testing nanocarriers that deliver drugs specifically into myeloid immune cells in order to reduce side effects and increase the local effect.
The CoViPa consortium uses computer-assisted high-throughput virus discovery and evolutionary analyses to identify RNA viruses with high spillover risk and potential animal host reservoirs and to investigate new pathogenicity factors.
By applying statistical genetics methods to pathogen genome sequences, we aim to identify and validate genetic determinants of phenotypes such as pathogenicity, virulence and antibiotic resistance, e.g. in E. coli and P. aeruginosa.
Thanks to high-throughput sequencing, genome sequences of hundreds of bacterial strains can be analyzed efficiently, revealing differences of up to 60 % in gene content, as in E. coli. With the help of machine learning, we want to better predict the functions of accessory genes and decipher their contribution to survival in specialized niches.
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