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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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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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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...
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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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Realistic modelling of information spread using peer-to-peer diffusion patterns.

Bin Zhou1,2, Sen Pei3, Lev Muchnik4,5

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Information diffusion models in computational social science are improved by a new cascade model. This model incorporates power-law relationships and heterogeneous response times, accurately reproducing real-world diffusion tree structures.

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Area of Science:

  • Computational Social Science
  • Network Science
  • Information Diffusion Modeling

Background:

  • Epidemic-inspired models are common for simulating information spread.
  • Empirical data shows these models often fail to capture real-world diffusion tree structures.
  • A gap exists in understanding person-to-person information spread in social networks.

Purpose of the Study:

  • To develop a more realistic model for information diffusion.
  • To investigate the factors influencing information spread probability in social networks.
  • To reproduce key structural features of real-world diffusion trees.

Main Methods:

  • Analysis of comprehensive diffusion records and social network data from three online platforms.
  • Identification of a power-law relationship between diffusion probability and user network size (followers/followees).
  • Development of a cascade model incorporating the power-law finding and heterogeneous response times, with observational bias adjustment.

Main Results:

  • Diffusion probability along social ties follows a power-law distribution based on disseminator and receiver network sizes.
  • The proposed cascade model successfully reproduces structural characteristics of observed diffusion trees.
  • Model validation across three distinct online social platforms demonstrates robustness.

Conclusions:

  • The study presents a refined cascade model for information diffusion.
  • Findings highlight the importance of network structure and response times in information spread.
  • The developed model offers a practical method for creating more accurate generative models of information diffusion.