A research team led by Prof. Dr. Maria Brehm, biochemist at the University of Siegen, has developed a 3D disease model of the intestine on a microchip. It is being used to investigate the role of von Willebrand factor in the development of intestinal bleeding.
Researchers can apply for the Saarland Research Award for Alternatives to Animal Testing until January 9. The award is endowed with €10,000.
Using a 3D placental model, researchers at the Helmholtz Centre for Environmental Research (UFZ) in cooperation with Dessau Municipal Hospital have discovered that PFAS, also known as persistent chemicals, disrupt the functionality of the placenta. This can increase the risk of miscarriage.
Long awaited – now it's here: the BimmoH (BioMed Model Hub) database has been made available. It can be used to find information on models relevant to humans that are used in biomedical research.
The pharmaceutical company Merck KGaA is developing a new drug to treat Parkinson's disease. To this end, it has partnered with the US company Valo Health, focusing on artificial intelligence.
As announced in a recent press release, researchers at the University of Basel have succeeded in replicating the cellular complexity of bone marrow from human cells in the laboratory. As is said, this system could reduce animal testing for many applications.
As several British ministries announced in a joint press release, animal testing in the UK is to be replaced by the introduction of animal-free methods. To this end, the country is providing £75 million (just over €85 million).
The University of Utrecht is currently conducting a survey among young researchers who work with or are interested in animal-free methods (New Approach Methodologies (NAMs)).
State-of-the-art AI programs can support drug development by predicting the interaction of proteins with small molecules. However, researchers at the University of Basel show that these programs only memorize patterns instead of understanding physical relationships. They often fail when it comes to new proteins that would be particularly interesting for innovative drugs.
An American-Korean research team from Dallas, Seattle, Seoul, and Wonju has developed a deep learning model for predicting protein complex structures in order to predict what proteins interact with which other proteins. Around 190 million human protein pairs were analyzed. They succeeded in predicting around 5,500 previously unknown interactions between human proteins, including those involved in immunity, metabolism, and cell signaling.