This review systematically examines the integration of machine learning (ML) and artificial intelligence (AI) in nanomedicine for cancer drug delivery. It demonstrates how ML algorithms—including support vector machines, neural networks, and deep learning models—are revolutionizing nanoparticle design, drug release prediction, and personalized therapy planning. The article outlines the complete ML workflow from data acquisition to model interpretation, compares key algorithms, and presents real-world case studies spanning multidrug carrier optimization and cancer diagnostics. While highlighting substantial preclinical advances, the authors identify critical barriers to clinical translation such as data heterogeneity, model opacity, and regulatory challenges. The review concludes with a forward-looking roadmap emphasizing data standardization, explainable AI, and clinical validation to bridge the gap between computational innovation and patient-ready nanomedicine.

A monitoring system devised by the University of Cordoba ascertains the flowering stages of each hive, with high precision, exploiting data on bees' behavior

A research team from China examined the entire process of the ecological use of reclaimed water from water recharge to ecological buffer zones to receiving water bodies and addresses the key challenges and future perspectives on the ecological use of reclaimed water. Strategies, such as establishing a comprehensive evaluation framework, developing ecological safety thresholds of key risk factors in reclaimed water, strengthening the functions of the ecological buffer zone, and optimizing ecosystem service value and the benefit of reclaimed water, have been put forward toward future safe and sustainable ecological use.