Toxicity of nanoplastics: machine learning combined with meta-analysis.

Abstract

Nanoplastics (NPs) are widespread in ecosystems, and their biohazards are of increasing concern. The hazards posed by NPs to aquatic and terrestrial plants as well as to aquatic animals have been extensively studied; however, their impact on mammals remains underexplored. Herein, we performed a meta-analysis to quantify the extent of the effects of NPs on mice and developed two machine learning methods to predict the correlations of the detrimental effects of NPs. We found that NPs have a wide range of toxic effects on various systems, and their adverse effects are mainly related to toxicity metrics, followed by the size, type, and mass concentration of NPs, as well as exposure routes, exposure time, and gender. These results suggest that the toxicity of NPs to mammals depends on diverse responses ranging from the molecular to the systemic scale and is influenced by the properties of NPs and environmental conditions, which complicate their toxicity and lead to a wide range of effects.