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Related Concept Videos

Clearance Models: Compartment Models01:25

Clearance Models: Compartment Models

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Clearance measures drug elimination from the central compartment, including plasma and highly perfused organs like kidneys and liver. Its calculation varies depending on pharmacokinetic models and administration routes. The one-compartment model, for instance, portrays the pharmacokinetics of polar drugs such as aminoglycoside antibiotics administered intravenously and readily excreted in urine. In this case, clearance is influenced by the terminal rate constant (λz) and the total volume...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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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.
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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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
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Molecular Models02:00

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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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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...
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Models, Theories, and Laws01:16

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Scientists frequently use models to help them comprehend a specific collection of phenomena. In physics, a model is a condensed version of a physical system that is too complex to study thoroughly. One such example is the light wave model; unlike water waves, light waves are typically invisible to us. Nonetheless, it is helpful to think of light as being composed of waves, since investigations show that light behaves like water waves. Since it is impossible to visually see what is genuinely...
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Related Experiment Video

Updated: Aug 20, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

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Can we share models if sharing data is not an option?

Zexi Li1, Feng Mao2,3, Chao Wu4,5

  • 1College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China.

Patterns (New York, N.Y.)
|November 24, 2022
PubMed
Summary
This summary is machine-generated.

Data sharing faces challenges due to privacy concerns. A new model-sharing strategy, powered by artificial intelligence, enables using closed data without direct sharing, advancing big data governance.

Keywords:
artificial intelligencebig datadata sharingfederated learningmodel-sharing strategyopen science

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Area of Science:

  • Data Science
  • Artificial Intelligence
  • Big Data Governance

Background:

  • The big data era generates vast datasets crucial for scientific discovery and socio-technical advancement.
  • The open data movement facilitates public data access, but privacy, ownership, and trust issues create challenges for "closed data."
  • Existing data sharing practices are insufficient for certain data types and holders, hindering the full potential of closed data.

Purpose of the Study:

  • To conceptualize data collaboration strategies that do not require direct data sharing.
  • To propose a novel "model-sharing" strategy for leveraging closed data.
  • To explore how artificial intelligence can support data-sharing-free collaboration.

Main Methods:

  • Conceptualization of data collaboration practices and technologies.
  • Development of the model-sharing strategy framework.
  • Analysis of the role of artificial intelligence in enabling model sharing.

Main Results:

  • The model-sharing strategy allows the utilization of closed data without compromising privacy or ownership.
  • Emerging artificial intelligence advances are key enablers for the proposed strategy.
  • The strategy offers a viable alternative to traditional data sharing for sensitive datasets.

Conclusions:

  • The model-sharing strategy represents a significant advancement in big data governance and collaboration.
  • This approach can unlock the potential of previously inaccessible closed data.
  • It paves the way for a new paradigm in secure and effective data utilization.