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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

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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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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...
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Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
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ATP Driven Pumps I: An Overview01:27

ATP Driven Pumps I: An Overview

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ATP-driven pumps, also known as transport ATPases, are integral membrane proteins. They have binding sites for ATP located on the membrane's cytosolic side and the ion-conducting domain in the transmembrane region. These pumps use the free energy released from ATP hydrolysis to move the solutes across cell membranes against an electrochemical gradient.
There are four main types of ATP-driven pumps - P-type, V-type, F-type, and ABC transporter. All these pumps are of varying complexities and...
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Perspectives on Neuroscience
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Data-driven models in human neuroscience and neuroengineering.

Bingni W Brunton1, Michael Beyeler2

  • 1Department of Biology, University of Washington, Seattle, WA 98195, USA; Institute for Neuroengineering, University of Washington, Seattle, WA 98195, USA; eScience Institute, University of Washington, Seattle, WA 98195, USA.

Current Opinion in Neurobiology
|July 21, 2019
PubMed
Summary
This summary is machine-generated.

Modern neuroscience relies on data-driven modeling to understand the brain, advancing neuroimaging, neural responses, and neuroengineering. Progress hinges on addressing challenges in model interpretability, training, and data ethics.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Neuroengineering

Background:

  • Modern neuroscience discoveries increasingly depend on quantitative analysis of complex datasets.
  • Data-intensive modeling approaches offer significant potential for advancing brain understanding and neuroengineering.

Purpose of the Study:

  • To provide an accessible overview of contemporary modeling techniques in neuroscience.
  • To highlight recent data-driven advancements across key neuroscience domains.
  • To identify critical challenges for future progress in the field.

Main Methods:

  • Review of modern modeling approaches in neuroscience.
  • Analysis of recent data-driven discoveries in neuroimaging.
  • Examination of findings in single-neuron and neuronal population responses.
  • Assessment of progress in device neuroengineering.

Main Results:

  • Data-intensive modeling is crucial for understanding the brain.
  • Recent discoveries span neuroimaging, neural activity, and neuroengineering applications.
  • Key challenges include model interpretability, generalizability, and data ethics.

Conclusions:

  • Advancing neuroscience requires robust, interpretable, and generalizable models.
  • Developing data-fluent neuroscientists is essential.
  • Ethical considerations must be integrated into data-driven neuroscience research.