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相关概念视频

Passive Diffusion: Overview and Kinetics01:17

Passive Diffusion: Overview and Kinetics

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Passive diffusion is a critical process that allows small lipophilic drugs to cross the cell membrane along a concentration gradient. This mechanism's efficiency depends on four primary factors: the membrane's surface area, the drug's lipid-water partition coefficient, the concentration gradient, and the membrane's thickness.
When administered orally, drugs establish a substantial concentration gradient between the gastrointestinal (GI) lumen and the bloodstream, expediting...
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Protein Diffusion in the Membrane01:24

Protein Diffusion in the Membrane

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Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
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Drug Absorption Mechanism: Passive Membrane Transport01:23

Drug Absorption Mechanism: Passive Membrane Transport

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Passive transport is a method of drug absorption where small, lipid-soluble drugs can move across the cell membrane. This movement happens along the concentration gradient, which is a natural flow from higher to lower concentration areas. The speed at which the drug moves is directly related to its lipid–water partition coefficient. This means that the more a drug dissolves in lipids, the faster it diffuses or spreads throughout the body. It is important to note that most drugs are either...
6.4K
Diffusion01:12

Diffusion

215.8K
Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
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相关实验视频

Updated: Jan 11, 2026

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
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Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

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用主动学习和分子动力学预测PFAS扩散系数

Archana Jagadisan1, Hakim Boukhalfa1, Mohamed Mehana1

  • 1Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87454, United States.

Environmental science & technology
|November 11, 2025
PubMed
概括
此摘要是机器生成的。

科学家们开发了一种机器学习框架,以预测14000多种酸和多酸物质 (PFAS) 的环境扩散. 这种方法有效地模拟污染物运输,帮助风险评估和补救策略.

关键词:
在PFAS中,有很多方法.积极学习是积极学习.扩散系数 扩散系数环境命运建模环境命运建模机器学习是机器学习.分子动力学分子动力学

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Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy
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Controlled Synthesis and Fluorescence Tracking of Highly Uniform PolyN-isopropylacrylamide Microgels
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Controlled Synthesis and Fluorescence Tracking of Highly Uniform PolyN-isopropylacrylamide Microgels

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相关实验视频

Last Updated: Jan 11, 2026

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
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Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy
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Controlled Synthesis and Fluorescence Tracking of Highly Uniform PolyN-isopropylacrylamide Microgels
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科学领域:

  • 环境化学环境化学
  • 计算化学的计算化学
  • 毒理学 毒理学 毒理学

背景情况:

  • 和多醇基物质 (PFAS) 是持久性,有毒的合成化合物,具有广泛的环境污染.
  • 预测PFAS的环境命运对于风险评估至关重要,但由于缺乏扩散系数数据而受到阻碍.
  • 实验和传统的计算方法对于庞大的PFAS化学空间是不够的.

研究的目的:

  • 开发一个数据高效的计算框架来预测PFAS扩散系数.
  • 为了在广泛的PFAS中实现准确的环境运输建模.
  • 支持知情的环境风险评估和补救计划.

主要方法:

  • 集成机器学习 (ML) 和分子动力学 (MD) 与主动学习.
  • 用于ML模型的化学图形表示和物理化学描述符.
  • 采用以不确定性为基础的抽样,以指导有针对性的MD模拟和模型再培训.

主要成果:

  • 在扩散系数预测方面取得了显著的性能改善 (R2从0.095到0.907,平均相对误差减少88%).
  • 证明了基于不确定性的主动学习对随机抽样的有效性,以有效地进行化学空间探索.
  • 成功启用了数千个PFAS分子的属性预测.

结论:

  • 开发的框架提供了一个计算效率高,准确的方法来预测PFAS扩散系数.
  • 这种方法促进了大规模的环境命运评估和设计有效的补救策略.
  • 支持积极管理PFAS污染风险.