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Transfer Learning Enables Robust Prediction of Cellular Toxicity from Environmental Micro- and Nanoplastics
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Transfer learning enables robust prediction of cellular toxicity from environmental micro- and nanoplastics
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Researchers developed a transfer learning approach to predict cellular toxicity from micro- and nanoplastics, overcoming the challenge of limited experimental data. By pre-training a model on a large nanoparticle dataset and fine-tuning it on plastic-specific data, they achieved strong predictive accuracy. The tool allows researchers to estimate the toxicity of various plastic particles based on their physical and chemical properties without extensive new experiments.
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Researchers reviewed evidence from cell and animal studies on the toxic effects of micro- and nanoplastics, finding that these particles can cause inflammation, oxidative stress, and organ damage in laboratory models, raising concern about what chronic low-level human exposure might mean for long-term health.
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Researchers examined how micro- and nanoplastics, along with their chemical additives such as bisphenols and phthalates, cause toxicity in terrestrial environments, highlighting how these synthetic polymer fragments disrupt soil ecosystems and expose organisms to compounded chemical hazards.
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