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相关概念视频

Toxic Reactions: Overview01:26

Toxic Reactions: Overview

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When toxic substances penetrate the human body, they disseminate to various tissues, undergoing metabolic changes. This process yields reactive metabolites that may covalently bind with specific target molecules, resulting in toxicity.
Toxicity falls into two primary categories: local and systemic.
Local toxicity appears at the exposure site, such as protein denaturation caused by caustic substances.
In contrast, systemic toxicity requires the toxic agent's absorption and distribution,...
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Types of Toxins01:36

Types of Toxins

2.0K
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...
2.0K
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

1.2K
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
1.2K
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

167
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
167
Mutagenicity and Carcinogenicity01:25

Mutagenicity and Carcinogenicity

1.4K
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...
1.4K
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

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Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
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相关实验视频

Updated: Sep 16, 2025

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
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Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

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基于AI的毒性预测模型使用ToxCast数据:可解释模型的当前状态和未来方向

Donghyeon Kim1, Jinhee Choi1

  • 1School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea.

Toxicology
|July 11, 2025
PubMed
概括

使用ToxCast数据的人工智能 (AI) 模型正在推进化学毒性预测. 这些人工智能方法分析了大量的毒理学数据,以改善环境化学查和风险评估.

科学领域:

  • 环境毒理学和计算化学.
  • 人工智能在化学安全评估中的应用.

背景情况:

  • 美国环境保护局 (EPA) 的ToxCast计划提供了广泛的毒理数据,对于开发人工智能驱动的毒性预测模型至关重要.
  • ToxCast数据被广泛用于构建预测模型以选环境化学物质.

研究的目的:

  • 审查和分析自2015年以来使用ToxCast数据开发的人工智能 (AI) 模型.
  • 概述ToxCast基于数据的AI模型的现状,包括数据库结构,目标终点,分子表示和学习算法.

主要方法:

  • 自2015年以来发表的93篇同行评审论文的系统审查.
  • 对AI模型组件的分析:数据库结构,目标终点,分子表示 (例如指纹,描述符,图形,图像,文本) 和机器学习算法 (监督,半监督,无监督).

主要成果:

  • 大多数人工智能模型的重点是数据丰富的终点和器官特异性毒性,如内分泌干扰和肝毒性.
  • 最近的模型越来越多地使用先进的分子表示 (图形,图像,文本) 和深度学习,超越传统的指纹.
  • 有越来越多的趋势向半和无监督学习解决数据稀疏性,补充传统的监督方法.

结论:

  • 基于ToxCast数据的AI模型显示了加速化学毒性预测和环境风险评估的重大前景.
关键词:
人工智能的人工智能是人工智能.下一代风险评估新一代风险评估在 ToxCast 里面,你会看到 ToxCast.毒性预测 毒性预测

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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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A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans
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  • 未来的方向包括改进人工智能模型并将其整合到下一代风险评估 (NGRA) 框架中.
  • 持续开发人工智能算法和数据表示是克服当前局限性的关键.