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机器学习允许解释Fe-Doped CuO NM图书馆大数据集的毒性――环境实体案例研究

Janeck J Scott-Fordsmand1, Susana I L Gomes2, Suman Pokhrel3,4

  • 1Department of Ecoscience, Aarhus University, C.F. Mo̷llers Alle 4, DK-8000 Aarhus, Denmark.

ACS applied materials & interfaces
|August 1, 2024
PubMed
概括

评估纳米材料的安全性是具有挑战性的,因为它们的可变性. 机器学习发现,在长期生态毒理学研究中,对于预测毒性至关重要的是粒子特定的描述因素,而不仅仅是环境因素.

关键词:
先进的材料先进的材料.生态毒理学 生态毒理学机器学习是机器学习.通过更安全和可持续的设计 (SSbD)土壤土壤土壤土壤土壤

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Toxicity tests in animals are grounded on two main assumptions: first, the effects observed in laboratory animals can be extrapolated to humans, especially when adjusted for body surface area; second, high-dose exposure in animals is essential to identify potential human hazards from lower doses. This is based on the quantal dose-response concept, which faces the challenge of extrapolating results from relatively few test animals to much larger human populations. For example, a 0.01% incidence...