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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...
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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
Nonlinear Pharmacokinetics: Role of Transporters01:27

Nonlinear Pharmacokinetics: Role of Transporters

72
A drug's nonlinear kinetics can be influenced by a diverse range of transporter proteins that serve as crucial players in drug distribution. These transporters, found within cells, can enhance or reduce local drug concentrations by facilitating the influx or efflux of drugs. For instance, the expression of xenobiotic transporters can be influenced by factors such as age and gender, potentially impacting the linearity of drug response.
Polymorphisms occurring in drug transporters can alter...
72
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

81
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
81
Reynolds Transport Theorem01:24

Reynolds Transport Theorem

1.2K
The Reynolds transport theorem provides a framework to relate the time rate of change of an extensive property within a system to that in a control volume, which is crucial for analyzing fluid dynamics. Extensive properties, such as mass, velocity, acceleration, temperature, and momentum, can be expressed in terms of the mass of a fluid portion. These properties are called extensive because they depend on the system's size, while intensive properties are their corresponding values per unit...
1.2K
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...
116

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相关实验视频

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Author Spotlight: Advanced Techniques for Visualizing Endogenous Axonal Transport Dynamics
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Author Spotlight: Advanced Techniques for Visualizing Endogenous Axonal Transport Dynamics

Published on: February 16, 2024

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自动回归的最佳运输模型

Changbo Zhu1, Hans-Georg Müller2

  • 1Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN 46556, USA.

Journal of the Royal Statistical Society. Series B, Statistical methodology
|July 31, 2023
PubMed
概括
此摘要是机器生成的。

我们引入自回归运输模型来分析分布式时间序列. 这些模型通过在最佳的运输地图空间中运行来扩展经典的自回归方法,为复杂的数据模式提供新的见解.

关键词:
瓦斯斯坦空间的空间分布数据分析的数据分析.分布回归的分布回归.分布时间序列的分布时间序列.代的随机函数是一个代的随机函数.最佳的运输最佳的运输.

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科学领域:

  • 分布数据分析 分布数据分析
  • 最佳运输理论 最佳运输理论
  • 时间序列分析时间序列分析

背景情况:

  • 分布时间序列在各种应用中很常见.
  • 分析这些复杂的数据集带来了重大挑战.
  • 现有的方法可能无法完全捕捉分布数据的动态.

研究的目的:

  • 为分布式时间序列提出新的内在自回归模型.
  • 量化和分析分布序列中的时间依赖关系.
  • 将经典的自回归模型扩展到最佳运输地图的空间.

主要方法:

  • 开发基于回归最佳运输图的自行回归运输模型.
  • 在瓦瑟斯坦空间中利用地理测量技术来链接预测器和响应运输地图.
  • 建立了使用代随机函数的独特静止解决方案存在的条件.

主要成果:

  • 在特定的收缩条件下证明了独特的静止解决方案的存在.
  • 通过模拟展示了模型的适用性.
  • 用现实数据说明模型,包括房价分布和温度数据.

结论:

  • 自行回归式运输模型为分布式时间序列分析提供了一个强大的新框架.
  • 这些模型自然地将欧几里德的自回归方法扩展到最佳的运输空间.
  • 提出的方法为理解复杂的时间分布动态提供了强大的工具.