Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

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

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

223
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...
223
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

123
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.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
123
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

704
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
704
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

121
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...
121
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

100
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
100

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A high-efficiency cryogenic neutron spin flipper for IMAGINE-X at the high flux isotope reactor.

The Review of scientific instruments·2026
Same author

Studies of Water Films and Carbonation via Neutron Scattering and Infrared Adsorption: In Situ Studies of Mg(OH)<sub>2</sub> and Ca(OH)<sub>2</sub>.

The journal of physical chemistry. C, Nanomaterials and interfaces·2026
Same author

Comparison of time-of-flight and MIEZE neutron spectroscopy of H<sub>2</sub>O.

Journal of applied crystallography·2025
Same author

Oriented composition fluctuation domains of a two-dimensionally confined critical Ising fluid.

The Journal of chemical physics·2025
Same author

Room-Temperature Multiferroic Liquids: Ferroelectric and Ferromagnetic Order in a Hybrid Nanoparticle-Liquid Crystal System.

Advanced materials (Deerfield Beach, Fla.)·2025
Same author

Association of Pluronics at silica surfaces and accompanying evolutions of inter particle interactions in conjugate nano-suspensions.

Soft matter·2025
Same journal

Quantitative analysis of light-induced ion segregation in mixed-halide perovskites.

Journal of applied crystallography·2026
Same journal

Towards machine-learning-based on-the-fly analysis of neutron reflectometry.

Journal of applied crystallography·2026
Same journal

<i>mcstas_gisans</i>: combining ray tracing with the distorted-wave Born approximation using <i>McStas</i> and <i>BornAgain</i> for virtual GISANS experiments.

Journal of applied crystallography·2026
Same journal

Computational methods for automated center determination in electron diffraction patterns.

Journal of applied crystallography·2026
Same journal

Epitaxy of ultrathin Fe<sub>3</sub>O<sub>4</sub> films on SrTiO<sub>3</sub>(001): influence of growth parameters on the formation of coexisting (111)- and (001)-oriented phases.

Journal of applied crystallography·2026
Same journal

Spin excitations near the pressure-induced antiferromagnetic transition in SrCu<sub>2</sub>(BO<sub>3</sub>)<sub>2</sub>.

Journal of applied crystallography·2026
See all related articles

Related Experiment Video

Updated: Sep 7, 2025

Structural Studies of Macromolecules in Solution using Small Angle X-Ray Scattering
07:19

Structural Studies of Macromolecules in Solution using Small Angle X-Ray Scattering

Published on: November 5, 2018

12.8K

Robust approaches for model-free small-angle scattering data analysis.

Philipp Bender1, Dirk Honecker2, Mathias Bersweiler3

  • 1Heinz Maier-Leibnitz Zentrum (MLZ), Technische Universität München, D-85748 Garching, Germany.

Journal of Applied Crystallography
|June 20, 2022
PubMed
Summary
This summary is machine-generated.

Analyzing magnetic nanoparticles requires robust data processing. This study compares three methods—indirect Fourier transform, singular value decomposition, and iterative algorithms—for deriving correlation functions from small-angle neutron scattering data, recommending a combined approach for reliable results.

Keywords:
Fourier transformMIEZERESEDAcorrelation functionsmagnetic nanoparticlesmodulation of intensity with zero effortsmall-angle scattering

More Related Videos

Analysis of SEC-SAXS data via EFA deconvolution and Scatter
10:59

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Published on: January 28, 2021

9.2K
Contrast-Matching Detergent in Small-Angle Neutron Scattering Experiments for Membrane Protein Structural Analysis and Ab Initio Modeling
10:27

Contrast-Matching Detergent in Small-Angle Neutron Scattering Experiments for Membrane Protein Structural Analysis and Ab Initio Modeling

Published on: October 21, 2018

12.5K

Related Experiment Videos

Last Updated: Sep 7, 2025

Structural Studies of Macromolecules in Solution using Small Angle X-Ray Scattering
07:19

Structural Studies of Macromolecules in Solution using Small Angle X-Ray Scattering

Published on: November 5, 2018

12.8K
Analysis of SEC-SAXS data via EFA deconvolution and Scatter
10:59

Analysis of SEC-SAXS data via EFA deconvolution and Scatter

Published on: January 28, 2021

9.2K
Contrast-Matching Detergent in Small-Angle Neutron Scattering Experiments for Membrane Protein Structural Analysis and Ab Initio Modeling
10:27

Contrast-Matching Detergent in Small-Angle Neutron Scattering Experiments for Membrane Protein Structural Analysis and Ab Initio Modeling

Published on: October 21, 2018

12.5K

Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Neutron Scattering

Background:

  • Small-angle neutron scattering (SANS) data reveals chemical and magnetic properties of nanostructured magnetic materials.
  • Model-free analysis is often the initial step to determine magnetic and structural length scales.
  • Direct Fourier transforms of SANS data can be ambiguous due to measurement uncertainties and limited q-ranges.

Purpose of the Study:

  • To compare the effectiveness of different regularization methods for deriving correlation functions from SANS data.
  • To evaluate the indirect Fourier transform, singular value decomposition, and an iterative algorithm for analyzing magnetic SANS data.
  • To determine the most robust approach for obtaining correlation functions from nanostructured magnetic samples.

Main Methods:

  • Magnetic small-angle neutron scattering (SANS) experiments were performed on iron oxide nanoparticle powder samples.
  • The indirect Fourier transform (IFT) method was applied to derive the correlation function.
  • Singular value decomposition (SVD) and an iterative algorithm were also employed for comparative analysis.

Main Results:

  • All three methods (IFT, SVD, iterative algorithm) successfully derived comparable correlation functions from the magnetic SANS data.
  • Each method demonstrated unique advantages and limitations in processing the data.
  • The derived correlation functions provided insights into the characteristic length scales of the nanostructured magnetic sample.

Conclusions:

  • The indirect Fourier transform, singular value decomposition, and iterative algorithms are all viable methods for analyzing magnetic SANS data.
  • Combining these three approaches is recommended to achieve more stable and reliable correlation function results.
  • This comprehensive analysis enhances the understanding of nanostructured magnetic materials through SANS data interpretation.