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Related Concept Videos

Passive Diffusion: Overview and Kinetics01:17

Passive Diffusion: Overview and Kinetics

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 their diffusion into...
Diffusion01:21

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Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
Diffusion01:12

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

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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...
Theories of Dissolution: Diffusion Layer Model01:15

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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.
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Updated: Jun 11, 2026

Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing (MTT)
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Published on: May 27, 2012

TOPODIFFUSIONNET: A TOPOLOGY-AWARE DIFFUSION MODEL.

Saumya Gupta1, Dimitris Samaras1, Chao Chen1

  • 1Department of Computer Science Stony Brook University Stony Brook, NY 11794, USA.

... International Conference on Learning Representations
|June 10, 2026
PubMed
Summary
This summary is machine-generated.

Diffusion models can now generate images with precise topology, thanks to TopoDiffusionNet (TDN). This novel approach ensures accurate Betti numbers, enhancing image generation for robotics and environmental modeling.

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A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates
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Published on: February 23, 2018

Related Experiment Videos

Last Updated: Jun 11, 2026

Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing (MTT)
12:19

Mapping Molecular Diffusion in the Plasma Membrane by Multiple-Target Tracing (MTT)

Published on: May 27, 2012

A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates
10:33

A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates

Published on: February 23, 2018

Area of Science:

  • Computer Vision
  • Topological Data Analysis
  • Machine Learning

Background:

  • Diffusion models generate high-quality images but lack topological control.
  • Betti numbers, a topological measure of structures, are not preserved by current diffusion models.
  • This limitation hinders applications requiring precise structural integrity.

Purpose of the Study:

  • To develop a novel method, TopoDiffusionNet (TDN), for enforcing desired topology in diffusion model image generation.
  • To integrate topological data analysis with diffusion models for enhanced control.

Main Methods:

  • Utilizing persistent homology from topological data analysis to extract image topology.
  • Designing a topology-based objective function to guide the diffusion model's denoising process.
  • Implementing TDN to preserve intended structures and reduce noise.

Main Results:

  • Demonstrated significant improvements in topological accuracy across four diverse datasets.
  • Successfully enforced desired Betti numbers in generated images.
  • Validated the effectiveness of the topology-based objective function.

Conclusions:

  • TopoDiffusionNet (TDN) is the first method to successfully integrate topology control into diffusion models.
  • TDN enhances diffusion model utility in fields like robotics and environmental modeling.
  • This work opens new research directions at the intersection of topology and generative AI.