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相关概念视频

Endocrine Signaling01:45

Endocrine Signaling

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Endocrine cells produce hormones to communicate with remote target cells found in other organs. The hormone reaches these distant areas using the circulatory system. This exposes the whole organism to the hormone but only those cells expressing hormone receptors or target cells are affected. Thus, endocrine signaling induces slow responses from its target cells but these effects also last longer.
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相关实验视频

Updated: Jul 18, 2025

Single-cell Transcriptomic Analyses of Mouse Pancreatic Endocrine Cells
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基于单细胞数据的内分泌干扰潜在识别的机器学习方法.

Zahir Aghayev1,2, Adam T Szafran3, Anh Tran4,5

  • 1Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT.

Chemical engineering science
|August 28, 2023
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概括
此摘要是机器生成的。

环境灾害增加了对有毒化学品的暴露. 一个新的计算框架准确地预测化学混合物的内分泌干扰,帮助快速评估风险和减轻风险的策略.

关键词:
分类分析对分类进行分析.内分泌干扰化学物质可以破坏内分泌.雌激素受体活性活动.高通量显微镜的显微镜.机器学习是机器学习.预测建模的预测建模.

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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
  • 计算生物学 计算生物学

背景情况:

  • 人类人口面临持续暴露于有毒物质的风险,环境灾难的风险加剧.
  • 灾害产生的危险化学混合物对人类和生态健康构成重大威胁.
  • 评估内分泌干扰,特别是通过雌激素受体α (ERα),对于减轻不良健康影响至关重要.

研究的目的:

  • 开发一个快速的,数据驱动的分类框架,以评估环境化合物的内分泌干扰潜力.
  • 创建工具,以便在环境灾害后减轻化学物质暴露风险的知情决策.

主要方法:

  • 利用基于高含量,高通量显微镜的生物传感器测试来测量单细胞水平的雌激素转录活性.
  • 结合计算建模与实验分析,使用主要组件分析 (PCA) 进行描述器投影.
  • 应用非线性机器学习算法,包括支持矢量机 (SVM) 和随机森林分类器,经过严格的数据预处理.

主要成果:

  • 开发了一个分类框架,在预测未见化学物质的ERα激动剂和对抗剂方面达到96%以上的准确性.
  • 证明了预处理和PCA在减少噪音和在复杂的生物数据集中识别模式方面的关键作用.
  • 通过图像分析描述器和机器学习,成功地区分了雌激素受体激活剂和对抗剂.

结论:

  • 开发的数据驱动框架提供了一个非常准确的方法来评估环境化学品的内分泌干扰潜力.
  • 计算机建模和机器学习为环境灾害后化学混合物的快速风险评估提供了强大的工具.
  • 这种方法有助于理解和减轻内分泌干扰化合物在各种环境场景中的健康影响.