In November 2025, a team led by Dr. Yuting Guo at the University of Chinese Academy of Sciences revealed how mitochondria-ER contact sites (MERCS) decode proteotoxic stress via calcium nanodomain regulation, activating adaptive mitochondrial responses. Published in Protein & Cell, the work proposes new therapeutic avenues for neurodegenerative diseases.

Artificial intelligence becomes a predictive tool that can provide assistance in defining a nutritional plan for preterm infants. This is the concept of an innovative study recently published in the Journal of Perinatology, part of the Nature portfolio. It is the joint work of researchers from the IRCCS San Gerardo dei Tintori Foundation (FSGT) and the Department of Electronics, Information and Bioengineering (DEIB) of the Politecnico di Milano.

Endoplasmic reticulum (ER) stress and the unfolded protein response are often implicated in the progression of metabolic dysfunction-associated steatotic liver disease (MASLD), from simple steatosis to inflammation-driven metabolic dysfunction-associated steatohepatitis and, ultimately, hepatocellular carcinoma. A new review paper from Hanyang University ERICA sheds light on the mechanisms underlying these processes, advocating for precise hepatic ER stress control to maintain normal hepatocyte function and develop therapeutic strategies against MASLD.

The strikingly orange yeast Rhodotorula toruloides is already known for its ability to produce valuable oils and pigments. Researchers from the Tallinn University of Techology uncovered a new talent for this yeast: manufacturing sugar-based polymers that could replace synthetic materials with more sustainable, bio-based alternatives.

For years, scientists have worked to uncover how the brain responds to mechanical forces and electromagnetic waves. Computer models offer useful simulations, but they don’t fully capture what goes on inside a living brain. Now, the team from Mizzou’s College of Engineering is working to close that gap by developing 3D-printed models of an artificial human brain.

As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective at capturing relationships between nodes and edges in data, but often overlook higher-order, complex connections. To address this challenge, a research team at The Hong Kong Polytechnic University (PolyU) has developed a new heterogeneous graph attention network, revolutionising the modelling of complex relationships in graph-structured data. This innovation is poised to break through AI application limitations in fields such as neuroscience, logistics, computer vision and biology.

Whatever challenges life throws, mothers often know best as they guide offspring through the risky stages of early development.