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相关概念视频

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

96
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.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
96
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...
115
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

569
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
569
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

64
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
64
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

177
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
177
Diffusion01:12

Diffusion

193.5K
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: Jul 17, 2025

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
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七参数扩散模型:在Stan中实现贝叶斯分析的实现

Franziska Henrich1, Raphael Hartmann2, Valentin Pratz3

  • 1Department of Psychology, University of Freiburg, Engelbergerstraße 41, D-79106, Freiburg, Germany. franziska.henrich@psychologie.uni-freiburg.de.

Behavior research methods
|August 28, 2023
PubMed
概括
此摘要是机器生成的。

我们在Stan中实施了一种灵活的七参数扩散模型,用于认知过程分析. 这种贝叶斯式方法准确地恢复参数,并验证响应时间数据的算法.

关键词:
贝叶斯的推理 贝叶斯的推理模型配件 模型配件拉特克利夫的扩散模型.函数 Stan 函数 Stan 是一个函数.

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科学领域:

  • 认知心理学 认知心理学
  • 计算神经科学是一种神经科学.
  • 心理测量 心理测量 心理测量

背景情况:

  • 扩散模型对于理解使用响应和响应时间数据的认知过程至关重要.
  • 现有的模型往往缺乏灵活性来捕捉试验间的变化.

研究的目的:

  • 在Stan概率编程语言中实现一个全面的七参数扩散模型.
  • 为了纳入漂移率,非决策时间和相对起点的试验间变化.
  • 为认知建模提供灵活的贝叶斯框架.

主要方法:

  • 在Stan环境中实施七参数扩散模型.
  • 使用贝叶斯框架,具有灵活的先前和模型结构定义.
  • 通过模拟研究进行性能评估,重点关注参数恢复和校准.

主要成果:

  • 模拟研究表明,扩散模型参数的恢复总体上很好.
  • 基于模拟的校准证实了在Stan.中实现的贝叶斯算法的有效性.
  • 这种实现为认知建模研究提供了更大的灵活性.

结论:

  • 斯坦实施的七参数扩散模型为认知科学研究提供了强大而灵活的工具.
  • 这种方法验证了在Stan中使用贝叶斯方法来分析复杂的认知数据.
  • 该模型处理试验间变化的能力提高了其用于详细认知过程分析的实用性.