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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.
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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Luminescence, the emission of light by a substance that has absorbed energy, is a process that involves the interaction of molecules with light. The energy-level diagram, or Jablonski diagram, is a graphical representation of these interactions, illustrating the various states and transitions a molecule can undergo. In a typical Jablonski diagram, the lowest horizontal line represents the ground-state energy of the molecule, which is usually a singlet state. This state represents the energies...
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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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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...
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Current Growth And Decay In RL Circuits01:30

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The current growth and decay in RL circuits can be understood by considering a series RL circuit consisting of a resistor, an inductor, a constant source of emf, and two switches. When the first switch is closed, the circuit is equivalent to a single-loop circuit consisting of a resistor and an inductor connected to a source of emf. In this case, the source of emf produces a current in the circuit. If there were no self-inductance in the circuit, the current would rise immediately to a steady...
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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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Related Experiment Video

Updated: Dec 27, 2025

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
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Decomposing Simon task BOLD activation using a drift-diffusion model framework.

James R McIntosh1, Paul Sajda2,3

  • 1Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA. j.mcintoshr@gmail.com.

Scientific Reports
|March 5, 2020
PubMed
Summary
This summary is machine-generated.

The Simon effect, a cognitive bias in spatial tasks, was modeled using sequential sampling models (SSM). Specific model parameters showed neural correlates in brain activity (BOLD response), advancing understanding of cognitive conflict.

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Area of Science:

  • Cognitive Psychology
  • Neuroscience
  • Computational Modeling

Background:

  • The Simon effect demonstrates slowed reaction times in spatial conflict tasks when stimulus-location mismatches response-mapping.
  • Previous fMRI studies on the Simon effect lacked direct integration with underlying cognitive models.
  • Existing Simon models often fail to capture individual subject behavioral variability.

Purpose of the Study:

  • To integrate sequential sampling models (SSM) with the Simon effect, creating a Simon effect SSM (SE-SSM).
  • To investigate neural correlates of SE-SSM parameters using fMRI BOLD signals.
  • To explore the relationship between task-specific model parameters and brain activity.

Main Methods:

  • Developed and applied a novel SE-SSM framework to individual subject behavioral data.
  • Incorporated parameters for automatic response bias and conflict counteraction within the SE-SSM.
  • Correlated SE-SSM parameters and expected decision-variables with fMRI BOLD responses.

Main Results:

  • The SE-SSM successfully captured behavioral data, including response distributions.
  • Two conflict-specific SE-SSM parameters demonstrated significant across-subject BOLD correlates.
  • Standard behavioral measures and other model parameters did not show significant BOLD correlates.
  • Analysis of the expected decision-variable revealed unique patterns of brain activation.

Conclusions:

  • The SE-SSM provides a robust framework for understanding the Simon effect at the individual level.
  • Specific cognitive parameters of the SE-SSM are directly reflected in neural activity (BOLD response).
  • Integrating computational models with neuroimaging offers novel insights into cognitive conflict processing.