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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: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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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.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

246
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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One-Compartment Open Model: Urinary Excretion Data and Determination of k01:11

One-Compartment Open Model: Urinary Excretion Data and Determination of k

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The one-compartment open model leverages urinary excretion data to estimate renal clearance, which gauges the kidney's capacity to expel a drug. This method offers several benefits, including directly measuring drug elimination and assessing the kidney's contribution to overall drug clearance. However, this approach has limitations. It assumes sole renal excretion of the drug, which is not true for all drugs. Accurate urinary excretion and plasma drug concentration measurement can also...
621
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

529
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...
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How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
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Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
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High-resolution data-driven model of the mouse connectome.

Joseph E Knox1, Kameron Decker Harris2, Nile Graddis1

  • 1Allen Institute for Brain Science, Seattle, Washington, USA.

Network Neuroscience (Cambridge, Mass.)
|February 23, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel voxel-scale mouse brain connectivity model, improving structural connectivity analysis. The new model offers higher resolution for understanding brain information processing.

Keywords:
ConnectomeMouseWhole-brain

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Systems Neuroscience

Background:

  • Understanding brain connectivity is crucial for deciphering information processing within and between brain regions.
  • Existing structural connectivity models often assume regional homogeneity, limiting their resolution.
  • High-resolution mapping of neuronal projections is essential for detailed connectome analysis.

Purpose of the Study:

  • To develop a high-resolution, voxel-scale model of whole-brain structural connectivity in mice.
  • To overcome the underdetermined nature of inferring connectivity from large-scale tracing data.
  • To improve the accuracy of connectivity predictions compared to existing regional models.

Main Methods:

  • Utilized the Allen Mouse Brain Connectivity Atlas, comprising 428 anterograde tracing experiments.
  • Developed a voxel-scale model assuming smooth variations in connection patterns across brain divisions.
  • Employed a radial basis kernel-weighted average to model connectivity at each voxel.

Main Results:

  • The voxel-scale model demonstrated superior performance in predicting held-out experimental data.
  • The model's predictions were more accurate than those of a previous regional model.
  • Validation against a human-curated dataset confirmed the model's efficacy.

Conclusions:

  • The developed voxel-scale mouse connectome model provides unprecedented resolution for connectivity studies.
  • Enables researchers to conduct higher-resolution analyses of structural connectivity.
  • Facilitates integration and comparison with functional imaging and other neurobiological datasets.