This study rationally engineered the esterase Aes72 based on its resolved crystal structure and quantum mechanical calculations, yielding a mutant with substantially enhanced degradation activity of polyurethane. The results provide an efficient enzymatic resource and mechanistic foundation for the biological recycling of polyurethane.

The National Institutes of Health has renewed support for Artificial Intelligence for Alzheimer’s Disease, or AI4AD. The new $12.6 million award to advance the project’s next phase, AI4AD2, brings its total investment in AI4AD to $30.7 million. Led by Paul M. Thompson, PhD, associate director of the USC Mark and Mary Stevens Neuroimaging and Informatics Institute (Stevens INI) at the Keck School of Medicine of USC, the multi-institutional initiative will develop artificial intelligence (AI) tools to uncover the biological causes of Alzheimer’s and related dementias, improve predictions of disease progression, and help develop more precise treatment options. AI4AD2 unites 10 investigators and 23 co-investigators from 10 institutions in pursuit of four interconnected research goals. The consortium will analyze large-scale datasets, including whole-genome sequencing, brain imaging, cognitive testing, and other biological data, to advance the diagnosis and treatment of dementia. This work builds on the original AI4AD initiative launched in 2020, which developed AI tools to detect Alzheimer’s-related patterns in brain scans and showed how machine learning can link imaging findings to underlying genetic risk. AI4AD2 will also develop new “genomic language models,” a type of AI inspired by the same broad family of technology used in language-based artificial intelligence systems. Instead of analyzing words, these models will analyze genomic sequences to identify combinations of DNA changes associated with Alzheimer’s disease, disease progression, and key biomarkers. The project will train and evaluate these methods using data from over 58,000 participants across 57 cohorts. In practical terms, that involves teaching AI to search vast genetic datasets for patterns that traditional methods could not identify.

Working with “digital twins” of patients’ hearts, doctors improved cardiac ablation outcomes for patients with life-threatening arrythmias.

In the first clinical trials for cardiac digital twins technology, researchers at Johns Hopkins University created digital replicas of patients’ hearts, then tested procedures on those twins before performing them on the real thing. Working with digital twins resulted in faster and significantly more accurate procedures that reduced recurrences of arrythmias for patients, compared to traditional methods.