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相关概念视频

Toxicity Testing in Animals01:23

Toxicity Testing in Animals

63
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...
63

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以代谢学驱动的数据增强机器学习用于预测微塑料混合物的毒性.

Beilei Yuan1, Chengzhi Liu2, Shuang Chen1

  • 1College of Safety Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu 210009, China.

Ecotoxicology and environmental safety
|February 25, 2026
PubMed
概括

由于复杂的混合物,预测微塑料 (MP) 毒性具有挑战性. 一个由代谢学驱动的机器学习模型有效地预测了MP细胞毒性,为细胞能量代谢重编程提供了洞察力.

关键词:
细胞毒性 细胞毒性数据增强数据增强机器学习 机器学习微塑料是一种微塑料.在QSAR中使用QSAR.

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科学领域:

  • 环境科学 环境科学
  • 毒理学 毒理学 毒理学
  • 计算化学计算化学

背景情况:

  • 微塑料 (MP) 存在于环境中的复杂混合物中,阻碍了个体毒性评估.
  • 开发快速有效的方法来评估MP混合物的毒性对于风险评估至关重要.

研究的目的:

  • 开发用于评估微塑料混合物毒性的预测模型.
  • 为了比较定量结构-活性关系 (QSAR),定量生物活性关系 (QBAR) 和定量结构-生物活性关系 (QSBAR) 模型的性能.

主要方法:

  • 研究了三个模型框架:QSAR,QBAR和QSBAR.
  • 采用了六个机器学习算法与数据增强策略.
  • 利用代谢学数据来选QBAR模型中的生物描述剂.

主要成果:

  • 基于QBAR的极端梯度增强 (XGB-qbar) 模型表现出卓越的性能 (R2test = 0.8923).
  • 确定了影响毒性的关键生物描述因素.
  • 代谢学分析显示,MP混合物暴露可能会重编程细胞能量代谢.

结论:

  • 一种以代谢学为导向,数据增强的机器学习方法有效地预测复杂混合物中的微塑料毒性.
  • 这种方法提供了机制性见解和环境风险评估的可行途径.