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New podcast "Mikroben im Visier. Infektionen verstehen, Resistenzen besiegen!" of the Leibniz Research Alliance INFECTIONS has been launched

According to the WHO, antibiotic resistance is one of the greatest threats to global health – and not just in human medicine. The new podcast ‘Mikroben im Visier’ (Microbes in Focus) from the Leibniz Research Alliance INFECTIONS addresses this highly topical issue, explains complex interrelationships in an understandable way and highlights what we can all do to combat resistant pathogens.

Antimicrobial resistance (AMR) is a complex problem that affects our healthcare systems, the environment and agriculture in equal measure. In animal husbandry and agriculture, we are increasingly encountering pathogens that are no longer susceptible to medication. The consequences are serious: declining productivity, threatened food security – and a growing need for research. This is precisely where the interdisciplinary work of the Leibniz Research Alliance INFECTIONS comes in.

In the podcast ‘Mikroben im Visier. Infektionen verstehen, Resistenzen besiegen!’ (Microbes in the spotlight. Understanding infections, defeating resistance!), experts from various Leibniz institutes talk about the causes, consequences and countermeasures in the fight against resistant germs. Each episode highlights a key topic – from the spread of germs via water and air to the use of antibiotics in everyday life. We are delighted that our colleague Dr Tina Kabelitz will be contributing to the third part of this series on resistance in animal husbandry. It's well worth a listen!

Further information

Microbes in the spotlight. Understanding infections, defeating resistance! Podcast of the Leibniz research alliance ‘INFECTIONS’

Website

ATB contributes its expertise in agricultural and environmental research to the Leibniz Research Alliance INFECTIONS. Our focus is on how antimicrobial resistance (AMR) spreads via environmental pathways such as manure, water or bioaerosols – and how these sources of contamination can be identified and reduced.

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