Stanford Medicine introduces Synthemol, an AI model crafting new drugs against superbugs. Amid a global crisis with nearly five million deaths yearly from antibiotic resistance, this tool offers hope by designing and synthesizing six promising compounds in record time.

A spinout company from the University of Bristol, Impulsonics is developing a unique biotech platform for cell culture automation by making use of acoustic manipulation to help automate a number of the processes for scientists which are currently time-consuming and expensive.

Researchers from the University of Leicester developed an AI tool that has shown an 80 % accuracy rate in predicting lethal heart rhythm conditions. This tool, VA-ResNet-50, analyzed Holter ECGs from 270 adults, identifying those at risk of ventricular arrhythmias — a rapid heart condition that can lead to sudden death.

Researchers at Cemm have developed a revolutionary method, vpCells, for observing proteins within living cells using multiple fluorescent colors. This technique allows for simultaneous labeling of numerous proteins, preserving their natural functions and enhancing visualization.

Contrary to previous assumptions, nerve cells in the human neocortex are wired differently than in mice. A study found that human neurons communicate in one direction, while in mice, signals tend to flow in loops. This increases the efficiency and capacity of the human brain to process information.

Researchers have introduced a project that aims to reduce CO2 emissions through the creation of an AI model that will improve the accuracy of short-range weather forecasting for photovoltaic production.

Researchers at The University of Texas at Austin are making use of artificial intelligence and biosensors to develop drugs faster. They are currently utilizing AI and genetically modified bacteria to ferment the Alzheimer’s drug Galantamine.

USC researchers are pioneering an accessible AI education with a low-cost, customizable “robot friend” kit, aimed at demystifying AI technology for students. This hands-on approach, supported by the National Science Foundation, not only makes learning about robotics, machine learning, and ethics more engaging but also addresses the affordability gap in educational robotics.

A Nims research team has developed an AI technique capable of expediting the identification of materials with desirable characteristics. Using this technique, the team was able to discover high-performance water electrolyzer electrode materials free of platinum-group elements.

Machine learning algorithms and cognitive science have been combined to create a new predictive model which ‘gets at how we make choices about medical decisions.’