Diego Di Bernardo of the Telethon Institute of Genetics and Medicine (TIGEM) has received a €2.5 million ERC Advanced Grant for DIMERCIRCUITS, a project that combines synthetic biology, artificial intelligence, and systems biology to create programmable genetic circuits. These tools aim to precisely and reversibly control gene expression, paving the way for safer, more personalized gene therapies. Using rare diseases like Friedreich’s ataxia as testbeds, the project could unlock new treatment strategies for a broad range of genetic conditions..

Leho Tedersoo, Professor of Mycorrhizal Studies at the University of Tartu, has received the Advanced Grant from the European Research Council to systematise and describe members of the 95% majority of previously unclassified microscopic fungi, and other eukaryotic organisms not yet included in the current tree of life system. Using the kingdom Fungi as an example, a new system for DNA-based classification of organisms can then be created.

Cellulose-based textile material can make the clothing sector more sustainable. Currently, cellulose-based textiles are mainly made from wood, but a study headed by researchers from Chalmers University of Technology points to the possibility of using agricultural waste from wheat and oat. The method is easier and requires fewer chemicals than manufacturing forest-based cellulose, and can enhance the value of waste products from agriculture.

A research team at The University of Tokyo has discovered that inhalational anesthetics activate a protein called type 1 ryanodine receptor (RyR1), which contributes to the induction of general anesthesia.

This finding shed light on a long-standing mystery: the mechanism of action of inhalational anesthetics, which has remained only partially understood for nearly 180 years. A precise understanding of how anesthetics work could pave the way for the development of more effective anesthetic agents and improved methods of administration.

What if people could detect cancer and other diseases with the same speed and ease of a pregnancy test or blood glucose meter? Researchers at the Carl R. Woese Institute for Genomic Biology are a step closer to realizing this goal by integrating machine learning-based analysis into point-of-care biosensing technologies. 

The new method, dubbed LOCA-PRAM, was reported in the journal Biosensors and Bioelectronics and improves the accessibility of biomarker detection by eliminating the need for technical experts to perform the image analysis.