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相关概念视频

Types of Toxins01:36

Types of Toxins

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Humans continually engage with an environment rich in potentially harmful chemicals. These are introduced to our bodies through inhalation, ingestion, or skin contact. These chemicals exist in various forms, such as air and environmental pollutants, agricultural chemicals, organic solvents, and heavy metals.
Air pollutants, primarily gases, pose significant threats to respiratory health, leading to conditions like hypoxia, lung cancer, and in extreme cases, death.
Environmental pollutants like...
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Assessment of the Acute Inhalation Toxicity of Airborne Particles by Exposing Cultivated Human Lung Cells at the Air-Liquid Interface
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消费品化学品的吸入毒性查使用经合组织测试指南基于数据的机器学习模型.

Donghyeon Kim1, Soyoung Cho2, Jong-June Jeon2

  • 1School of Environmental Engineering, University of Seoul, Seoul 02504, Republic of Korea.

Journal of hazardous materials
|August 18, 2024
PubMed
概括

机器学习模型选了消费品化学品的吸入毒性,确定了79种高度关注的物质. 这种人工智能方法揭示了日常产品中化学吸入风险的监管缺口.

关键词:
人工智能的人工智能消费品 消费品 化学品 化学品吸入中毒性 吸入中毒性定量结构 活动关系 活动关系毒性预测 毒性预测

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科学领域:

  • 环境化学环境化学
  • 毒理学 毒理学 毒理学
  • 计算化学计算化学

背景情况:

  • 消费品如空气清新剂和香水含有众多化学物质.
  • 评估这些化学品的吸入毒性对于公共卫生至关重要.
  • 现有的监管框架可能在解决消费品吸入风险方面存在差距.

研究的目的:

  • 使用机器学习来选消费品中化学品的吸入毒性.
  • 在消费产品的成分中识别引起高度关注的化学物质.
  • 评估AI在化学风险评估和优先考虑方面的潜力.

主要方法:

  • 来自经合组织数据库的1709种化学品的手动策划的吸入毒性数据.
  • 使用十个算法和四个分子指纹/描述器训练机器学习模型.
  • 对职业化学品的验证模型来评估预测不确定性.
  • 应用经过验证的模型来选消费品中使用的化学品.

主要成果:

  • 机器学习模型在测试数据集中实现了F1分数从51%到91%.
  • 虚拟选确定了79种消费品化学物质,这些化学物质对吸入毒性造成高度关注.
  • 优先考虑的化学品往往缺乏吸入毒性GHS分类.
  • 模型显示高灵敏度 (75%),但低特异性 (23%) 保守查.

结论:

  • 人工智能模型可以有效地选消费品化学品的吸入毒性.
  • 对于吸入消费品化学品的风险存在潜在的监管盲点.
  • 这项研究强调了人工智能在化学安全评估和监管优先级方面的实用性.