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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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Mutagenicity and carcinogenicity refer to the ability of drugs to cause genetic defects and induce cancer, respectively. The International Agency for Research on Cancer (IARC) classifies agents into four groups based on their carcinogenic potential. Group 1 agents are known human carcinogens; group 2A agents are probably carcinogenic to humans; group 3 agents lack data to support their role in carcinogenesis; and group 4 includes agents for which data support that they are not likely to be...
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
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基于机器学习的毒理学建模用于选环境肥胖原体.

Siying Wu1, Linping Wang1, Daniel Schlenk2

  • 1MOE Key Laboratory of Environmental Remediation and Ecosystem Health, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China.

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概括
此摘要是机器生成的。

环境肥胖原体有助于肥胖. 这项研究开发了一种机器学习系统,使用分子启动事件 (MIE) 来预测肥胖化学物质,识别关键分子描述符并验证其对极度关注物质 (SVHC) 的有效性.

关键词:
脂质生成 (adipogenesis) 是一种环境中的肥胖原体机器学习是机器学习.分子启动事件的发生.毒理学建模 毒理学建模

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科学领域:

  • 环境毒理学环境毒理学
  • 计算化学是一种计算化学.
  • 内分泌学 在内分泌学.

背景情况:

  • 环境肥胖原体破坏能量平衡,导致肥胖,这是一个重大的公共卫生问题.
  • 分子启动事件 (MIE) 对于理解化学毒性和开发预测模型至关重要.
  • 集成MIE的机器学习 (ML) 模型提供了对毒性终点的增强预测和改进的模型解释性.

研究的目的:

  • 构建和验证基于ML的选系统,用于使用MIE预测化学肥胖潜在的可能性.
  • 为了确定与脂肪生成和肥胖相关的关键分子描述符.
  • 评估系统在预测极度关注物质 (SVHCs) 的肥胖效应方面的表现.

主要方法:

  • 将与脂肪生成和肥胖相关的六个MIE整合到一个预测系统中.
  • 使用分子描述符,如疏水性 (SlogP_VSA) 和静电相互作用 (PEOE_VSA).
  • 使用接收器运行特征 (ROC) 曲线进行外部验证,并使用3T3-L1脂肪生成试验进行实验验证.

主要成果:

  • 开发的系统在外部验证中实现了高精度,ROC曲线下的面积为0.78.8.
  • 分子水性和直接静电相互作用被确定为肥胖潜在的关键预测因素.
  • 该系统正确预测了12个测试的SVHC中10个SVHC的肥胖效应,并确定了四种新的潜在肥胖原体.

结论:

  • 基于MIE的综合查系统在预测脂肪生成潜力方面表现强.
  • 该系统有效地识别环境肥胖原体,包括新型化合物,有助于风险评估.
  • 这种方法提高了对化学物质诱导的肥胖症的理解,并支持对非常令人担忧的物质的监管工作.