Venous thromboembolism (VTE), including deep vein thrombosis and pulmonary embolism, is a life-threatening complication in patients with cervical cancer that can significantly worsen survival and treatment outcomes. Although several clinical risk assessment tools are available, they primarily rely on laboratory measurements obtained at a single time point, limiting their ability to capture the evolving biological processes that precede thrombosis. While deep learning models have demonstrated strong predictive performance, their limited interpretability and high computational demands remain barriers to routine clinical implementation.

Researchers at The University of Manchester have developed a physics-based artificial intelligence framework that, for the first time, enables global-scale predictions of dissolved organic carbon cycling between seawater and marine sediments. The approach reveals a previously unquantified component of Earth's carbon cycle and could improve climate models, support research into ocean-based climate mitigation strategies, and provide new insights into the long-term storage of carbon in marine environments.