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Toxicity of nanoplastics: machine learning combined with meta-analysis
Summary
This meta-analysis combined data from multiple studies and used machine learning to assess nanoplastic toxicity in mice. The findings showed that nanoplastics cause a wide range of harmful effects across multiple body systems, with the severity depending on particle size, type, exposure route, and duration. These results suggest that the nanoplastics we encounter daily could have complex, varied effects on mammalian health.
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.
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