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Growth Models with Integration: Problem Solving01:27

Growth Models with Integration: Problem Solving

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In population modeling, integration provides a systematic way to determine accumulated quantities from known rates of change. One such application arises in ecology, where the total weight of a fish population in a body of water is referred to as its biomass. When the rate of growth of this biomass is known as a function of time, calculus can be used to determine the total biomass at a future date.Growth Rate and Biomass FunctionLet the growth rate of the fish population be represented by a...
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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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Spin–Spin Coupling: Two-Bond Coupling (Geminal Coupling)01:20

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Two NMR-active nuclei bonded to a central atom can be involved in geminal or two-bond coupling. Geminal coupling is commonly seen between diastereotopic protons in chiral molecules and unsymmetrical alkenes, among others.
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Vicinal or three-bond coupling is commonly observed between protons attached to adjacent carbons. Here, nuclear spin information is primarily transferred via electron spin interactions between adjacent C‑H bond orbitals. This generally favors the antiparallel arrangement of spins, so 3J values are usually positive.
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The volume of distribution refers to the theoretical volume necessary to contain the entire amount of an administered drug at the same concentration observed in the blood plasma. The body's intracellular fluid compartment, which makes up two-thirds of the total body water, is contrasted with the extracellular fluid compartment—comprising plasma and interstitial fluid—that accounts for one-third. The volume of distribution can vary depending on the characteristics of the drug.
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G-protein coupled receptors are ligand binding receptors that indirectly affect changes in the cell. The actual receptor is a single polypeptide that transverses the cell membrane seven times creating intracellular and extracellular loops. The extracellular loops create a ligand specific pocket which binds to neurotransmitters or hormones. The intracellular loops holds onto the G-protein.
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A Mouse Model to Investigate the Role of Cancer-Associated Fibroblasts in Tumor Growth
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Coupling tumor growth and bio distribution models.

Raffaella Santagiuliana1, Miljan Milosevic2,3, Bogdan Milicevic2

  • 1Department of Civil, Environmental and Architectural Engineering, University of Padova, via Marzolo 9, 35131, Padova, Italy. santagiuliana.raffaella@gmail.com.

Biomedical Microdevices
|March 26, 2019
PubMed
Summary

This study integrates tumor growth and biodistribution models to simulate tumor development within an entire organ. This approach allows for realistic evaluation of therapeutic agent efficacy by modeling nutrient and drug distribution.

Keywords:
AngiogenesisBiodistributionCode couplingModelingMultiphaseSmearded finite element

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Area of Science:

  • Computational Biology
  • Mathematical Modeling
  • Oncology

Background:

  • Tumor growth is influenced by microenvironmental factors and nutrient availability.
  • Accurate simulation of drug distribution within an organ is crucial for predicting therapeutic efficacy.

Purpose of the Study:

  • To develop and couple a tumor growth model with a whole-organ biodistribution model.
  • To enable simulation of tumor development and therapeutic agent distribution in a complete organ.

Main Methods:

  • Coupling a microenvironment-embedded tumor growth model with a whole-organ biodistribution model.
  • The growth model simulates tumor cell population dynamics, pressure, ECM porosity, nutrient consumption, and angiogenesis.
  • The biodistribution model provides refined nutrient and molecular distribution on a static geometry.

Main Results:

  • The combined model allows for simulation of tumor evolution within an organ context.
  • Realistic distribution of therapeutic agents can be modeled.
  • The integrated approach facilitates the evaluation of treatment efficacy.

Conclusions:

  • The coupled modeling approach provides a comprehensive platform for studying tumor growth and therapeutic interventions.
  • This simulation framework can aid in optimizing drug delivery and treatment strategies for cancer.