We are excited to share our recent paper, Risk Prediction Tools for Pressure Injury Occurrence: An Umbrella Review of Systematic Reviews Reporting Model Development and Validation Methods. In this comprehensive umbrella review, we synthesized evidence from systematic reviews to evaluate the development, validation, and clinical utility of risk prediction tools for pressure injuries (PIs). PIs pose a significant burden on healthcare systems globally, making effective risk prediction essential for identifying at-risk patients and implementing preventative measures. Our findings highlight key gaps and opportunities for advancing the design and implementation of risk prediction models to improve patient care and outcomes. Pressure injuries (PIs) are a significant burden on healthcare systems worldwide, and risk prediction tools are essential to identify and prevent PIs in at-risk patients.

We are excited to introduce Evo, a genomic foundation model that enables prediction and generation tasks from the molecular to the genome scale using an AI architecture based on advances in deep signal processing, which was featured as the cover story in Science. Evo is scaled to 7 billion parameters with a context length of 131 kilobases at single-nucleotide resolution. Using information learned over whole genomes, Evo learns how small changes in nucleotide sequence affect whole-organism fitness and can generate DNA sequences with plausible genomic architecture more than 1 megabase in length. Beyond the technical innovations, we also incorporated essential elements of biosafety and ethics, ensuring that this work progresses responsibly and with societal impact in mind.