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Theories of Dissolution: Diffusion Layer Model01:15

Theories of Dissolution: Diffusion Layer Model

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Dissolution, the process by which drug particles dissolve in a solvent, is explained by the diffusion layer model, a theoretical framework that simulates the absorption of oral drugs and allows us to analyze experimental data.
This process starts with a thin layer, saturated with the drug, forming at the interface between the solid and liquid. The solute then diffuses from this layer into the main solution. The Noyes-Whitney equation suggests that the rate of dissolution relies on the diffusion...
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Protein Diffusion in the Membrane01:24

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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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Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

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Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
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Multicompartment Models: Overview01:14

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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
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Diffusion01:12

Diffusion

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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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Passive Diffusion: Overview and Kinetics01:17

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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.
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Related Experiment Video

Updated: Aug 23, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Finding influential edges in multilayer networks: Perspective from multilayer diffusion model.

Wei Lin1, Li Xu1, He Fang2

  • 1College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350117, Fujian, China.

Chaos (Woodbury, N.Y.)
|November 1, 2022
PubMed
Summary
This summary is machine-generated.

Identifying influential edges in multiplex social networks is challenging due to heterogeneous connections. This study proposes a new method to pinpoint these critical edges, significantly reducing information diffusion when removed.

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Area of Science:

  • Network Science
  • Information Diffusion Models
  • Social Network Analysis

Background:

  • Multiplex social networks present challenges in identifying influential edges due to layer-specific heterogeneity.
  • Existing edge centrality measures struggle with the complex nature of multiplex networks.

Purpose of the Study:

  • To develop a general information diffusion model for multiplex networks using adjacency tensors.
  • To propose an efficient edge eigenvector centrality measure for identifying influential heterogeneous edges.
  • To demonstrate the effectiveness of the proposed method in controlling information diffusion.

Main Methods:

  • Developed a general information diffusion model based on adjacency tensors for multiplex networks.
  • Introduced an efficient edge eigenvector centrality to quantify edge influence in heterogeneous networks.
  • Utilized n-mode singular value to control information diffusion levels.

Main Results:

  • The proposed diffusion model effectively uses n-mode singular value to control information spread.
  • Efficient edge eigenvector centrality successfully identifies influential heterogeneous edges.
  • Numerical results on synthetic and real-world networks show superior performance over existing measures.

Conclusions:

  • Influential heterogeneous edges in multiplex networks can be identified by considering network layer centrality.
  • Removing top influential edges significantly curtails information diffusion across network layers.
  • The proposed method offers a robust approach for managing information flow in complex social networks.