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相关概念视频

Genetic Screens02:46

Genetic Screens

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
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Mutagenicity and Carcinogenicity01:25

Mutagenicity and Carcinogenicity

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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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In-vitro Mutagenesis01:16

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To learn more about the function of a gene, researchers can observe what happens when the gene is inactivated or “knocked out,” by creating genetically engineered knockout animals. Knockout mice have been particularly useful as models for human diseases such as cancer, Parkinson’s disease, and diabetes.
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相关实验视频

Updated: Jan 10, 2026

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
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在预测性遗传毒理学中使用In Silico方法.

Meetali Sinha1,2, Tanya Jamal1,2, Alok Dhawan3

  • 1REACT - Computational Toxicology Group, CSIR - Indian Institute of Toxicology Research, Vishvigyan Bhavan, Lucknow, Uttar Pradesh, India.

Methods in molecular biology (Clifton, N.J.)
|November 22, 2025
PubMed
概括

在毒理学中使用计算方法来更快,更经济,更无动物的遗传毒性预测. 这些方法,包括定量结构-活动关系 (QSAR) 模型,有助于监管决策,并加强化学品安全评估.

关键词:
基于专家的系统基于专家的系统.基因毒性 基因毒性在状的中.预测毒理学 预测毒理学在QSAR中使用QSAR.统计表现的统计表现.毒理学 毒理学 毒理学

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A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans
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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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相关实验视频

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科学领域:

  • 计算毒理学计算毒理学
  • 在形方法的方法.
  • 基因毒性预测基因毒性的预测.

背景情况:

  • 监管机构越来越多地强调计算方法,以减少化学毒性评估中的动物试验.
  • 将in silico预测与体外和体内数据相结合,可以提高对基因毒性评估的信心.
  • 虽然完全没有动物的毒理学未来是遥远的,但计算方法是重要的和不断发展的工具.

研究的目的:

  • 描述各种in silico毒理学方法来预测化学遗传毒性.
  • 概述进行这些预测的标准化协议.
  • 为了突出量化结构-活动关系 (QSAR) 模型结果的验证参数.

主要方法:

  • 使用基于专家的统计QSAR模型和跨读方法.
  • 遵守经合组织的QSAR验证原则和专家审查系统.
  • 以下是关键步骤:问题识别,数据收集,描述器生成,模型构建,验证和优化.

主要成果:

  • 在 silico 方法提供更快,更经济,更无动物替代品来解释遗传毒性.
  • 标准化的协议和验证参数对于可靠的in silico基因毒性预测至关重要.
  • 计算预测与实验数据的整合增强了预测的信心.

结论:

  • 无毒理学对于现代化学安全评估至关重要,它补充了传统方法.
  • 经过验证的计算方法是迈向无动物毒性测试的关键.
  • 这些方法的不断发展将利用科学和技术的进步,为环境和人类健康带来好处.