生体システムにおけるナノプラスチック：実験室でのメカニズムが現実世界での毒性について明らかにするもの

Nanoplastics (NPs) are increasingly detected across aquatic, atmospheric, and food systems. However, their biological relevance remains obscured by a persistent mismatch between high-dose laboratory studies and low, chronic environmental exposures. Here, we resolve this gap by introducing a mechanistic-scaling framework that explains how NPs toxicity emerges across doses. We show that the core pathways elicited in laboratory studies, such as cellular uptake, lysosomal rupture, mitochondrial dysfunction, oxidative stress, and inflammatory activation, remain qualitatively conserved at environmentally relevant concentrations but unfold with different frequencies and temporal dynamics. Environmental aging, eco-corona formation, and co-contaminant loading further amplify NPs reactivity, making even low particle abundances mechanistically potent under real-world conditions. Across cell, invertebrate, fish, and mammalian models, oxidative stress consistently serves as the integrative signature of NPs exposure, validating its central role in scalable toxicity. Further, this highlights how advances in artificial intelligence (AI) and machine learning (ML) are transforming NPs research, enabling sensitive detection, characterization of aged particles, prediction of NPs-pollutant interactions, and early identification of mechanistic responses. Together, these insights call for a shift from concentration-based assessments toward probabilistic, mechanism-informed models that capture the cumulative effects of chronic exposure. This framework provides a pathway for predictive, environmentally realistic evaluation of NPs risks and defines priorities for next-generation monitoring and regulatory strategies.