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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.
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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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Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

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Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Autotrophic nitrate uptake in river networks: A modeling approach using continuous high-frequency data.

Xiaoqiang Yang1, Seifeddine Jomaa1, Olaf Büttner1

  • 1Department of Aquatic Ecosystem Analysis and Management, Helmholtz Centre for Environmental Research - UFZ, Brückstrasße 3a, 39114, Magdeburg, Germany.

Water Research
|April 9, 2019
PubMed
Summary

High-frequency sensors track autotrophic nitrate uptake, crucial for understanding riverine ecosystems. This study developed a transferable method to model nitrate uptake across river networks, revealing seasonal patterns and factors influencing efficiency.

Keywords:
High-frequency monitoringNetwork upscalingRegionalizationStream metabolismThe fully distributed mHM-Nitrate modelWater quality

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Area of Science:

  • Environmental Science
  • Hydrology
  • Biogeochemistry

Background:

  • Continuous high-frequency sensor data offer new insights into aquatic ecosystem processes.
  • Autotrophic nitrate uptake is intrinsically linked to Gross Primary Production (GPP), but regionalizing this rate across river networks remains challenging.

Purpose of the Study:

  • To develop and validate a parsimonious, transferable approach for regionalizing continuous autotrophic nitrate uptake rates.
  • To integrate this approach into a hydrological model for networked nitrate transport and uptake analysis.

Main Methods:

  • Utilized 15-min sensor data (2011-2015) from forest and agricultural river reaches.
  • Developed a regionalization approach for autotrophic nitrate uptake considering global radiation and riparian shading.
  • Integrated the approach into the mesoscale hydrological nitrate model (mHM-Nitrate) for networked modeling.

Main Results:

  • Calculated daily GPP-based nitrate uptake rates, showing distinct seasonal patterns and differences between agricultural (mean 80.9 mgNm-2d-1) and forest (mean 15.5 mgNm-2d-1) streams.
  • Validated the regionalization approach with acceptable performance (R2 = 0.47 and 0.45) and demonstrated spatial transferability.
  • Networked modeling revealed high spatiotemporal variability in nitrate transport and uptake, with increased gross uptake but decreased efficiency along stream order.

Conclusions:

  • The study provides a parsimonious and transferable method for regionalizing in-stream autotrophic nitrate uptake using high-frequency data.
  • Integration into the mHM-Nitrate model enables detailed investigation of nitrate dynamics throughout river networks.
  • Riparian shading and hydrochemical variations significantly influence daily uptake efficiency, highlighting the complexity of riverine nitrate cycling.