Discovery of new marine sponges supports hypothesis on animal evolution
Peer-Reviewed Publication
Updates every hour. Last Updated: 13-Dec-2025 13:11 ET (13-Dec-2025 18:11 GMT/UTC)
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.
Dr Shiva Khoshtinat is a postdoctoral researcher at the Department of Chemistry, Materials and Chemical Engineering 'Giulio Natta' at Politecnico di Milano. With an interdisciplinary background spanning civil engineering, architecture, materials science, and biology, she explores how nature’s strategies can inspire sustainable construction on Earth and beyond. Her research focuses on biomineralization and microbial co-cultures as self-sustaining systems for construction. In a recent publication in Frontiers in Microbiology, Khoshtinat and co-authors present a bold approach for construction on Mars, harnessing microbial partnerships to transform Martian regolith into structural materials, laying the scientific foundations for building the first habitats on the Red Planet.