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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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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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Related Experiment Video

Updated: Nov 19, 2025

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
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Manifold learning based data-driven modeling for soft biological tissues.

Qizhi He1, Devin W Laurence2, Chung-Hao Lee3

  • 1Department of Structural Engineering, University of California, San Diego, La Jolla, CA 92093, USA; Physical and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99354, USA.

Journal of Biomechanics
|January 30, 2021
PubMed
Summary
This summary is machine-generated.

Data-driven modeling, using the local convexity data-driven (LCDD) framework, can predict biological tissue mechanics. Sufficient data coverage is crucial for accuracy, outperforming earlier methods with noisy data.

Keywords:
Data-driven material modelingHyperelasticityLocal convexity data-driven methodManifold learningMitral heart valve

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Area of Science:

  • Computational mechanics
  • Biomaterials science
  • Machine learning applications

Background:

  • Data-driven modeling offers an alternative to traditional constitutive models for predicting material behavior.
  • Extending data-driven approaches to complex, large-deformation biological tissues remains a challenge.
  • Existing methods like distance-minimization data-driven (DMDD) have limitations with noisy data.

Purpose of the Study:

  • To extend the local convexity data-driven (LCDD) framework for modeling the mechanical response of biological tissues.
  • To investigate the predictability of the LCDD framework using different training data protocols (biaxial and pure shear).
  • To compare the LCDD framework's effectiveness against established phenomenological models.

Main Methods:

  • Application of the local convexity data-driven (LCDD) framework to a porcine heart mitral valve posterior leaflet.
  • Training the LCDD model with various combinations of biaxial and pure shear experimental data.
  • Comparative analysis against a modified full structural model and a Fung-type phenomenological model.

Main Results:

  • The LCDD framework's predictability showed less sensitivity to the type of loading protocols used in training.
  • Predictivity was more influenced by the coverage and richness of the experimental data than the loading protocol type.
  • The LCDD method demonstrated improved performance over DMDD, particularly in handling noisy data.

Conclusions:

  • The local convexity data-driven (LCDD) framework is a viable method for modeling the mechanics of large-deformation biological tissues.
  • Sufficiently rich and comprehensive experimental data coverage is paramount for the success of data-driven modeling approaches.
  • Data-driven computing, when supported by adequate data, presents a powerful alternative to traditional methods for complex biological materials.