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 of NPs to aquatic and terrestrial plants and aquatic animals have been extensively studied, whereas the hazards to mammals are still being explored. Here, we performed a meta-analysis to quantify the general intensity of NPs effects on mice and developed two machine learning methods to predict the correlates of the detrimental effects of NPs. We found that NPs have a wide range of toxic effects on various systems, and their adverse effects were mainly related to toxicity metrics, followed by the size, type, and mass concentration of NPs, as well as exposure routes, exposure time, and mouse gender. These results suggest that the toxicity of NPs to mammals depends on diverse responses ranging from the molecular to the bioindividual scale, which are influenced by the properties of the NPs themselves and by environmental conditions that complicate their toxicity and have a wide range of effects.