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

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

319
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
319
Nonlinear Pharmacokinetics: Causes of Nonlinearity01:22

Nonlinear Pharmacokinetics: Causes of Nonlinearity

736
Nonlinearity in drug pharmacokinetics is caused by various factors influencing how a drug is absorbed, distributed, metabolized, and excreted. Understanding these nonlinear processes is crucial for predicting drug behavior in the body and optimizing drug dosing regimens.
Nonlinear drug absorption can occur when the process is rate-limited by solubility, carrier-mediated transport systems, or saturation of the presystemic gut wall or hepatic metabolism. For instance, high doses of riboflavin...
736
Drug Distribution as One-Compartment Model and Elimination by Nonlinear Pharmacokinetics: Overview01:25

Drug Distribution as One-Compartment Model and Elimination by Nonlinear Pharmacokinetics: Overview

356
Drug administration can occur through various routes, each of which may result in a different process of elimination. This process is often mixed with nonlinear and linear processes. It's important to understand that a single drug can be metabolized into different metabolites through parallel processes.
For instance, consider the metabolism of sodium salicylate. This compound is metabolized into two distinct substances: a glucuronide and a glycine conjugate. The rate of conjugation depends...
356
Application of Nonlinear Inequalities01:29

Application of Nonlinear Inequalities

255
A nonlinear inequality describes a comparison involving an expression that curves or behaves more complexly than a straight line. These inequalities often appear in forms that include squares, products, or variables in the denominator.To solve such an inequality, one starts by rewriting it so that zero appears on one side. For example, the inequality:  can be factored as: This form makes it easier to identify the values that cause the expression to equal zero. In this case, the...
255
Introduction to Nonlinear Inequalities01:25

Introduction to Nonlinear Inequalities

228
Linear and nonlinear inequalities are fundamental for analyzing variable relationships and identifying ranges satisfying specific conditions. A linear inequality involves variables raised only to the first power, resulting in a straight-line graph. This line partitions the coordinate plane into two distinct regions: one that satisfies the inequality and one that does not. Each region represents a set of solutions where the linear relationship holds true under the specified constraint.Nonlinear...
228
Molecular Models02:00

Molecular Models

43.7K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
43.7K

You might also read

Related Articles

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

Sort by
Same author

Integrative learning of individualized treatment rules from multiple studies with partially overlapping treatments.

Biometrics·2026
Same author

DYNAMIC CLASSIFICATION OF LATENT DISEASE PROGRESSION WITH AUXILIARY SURROGATE LABELS.

The annals of applied statistics·2026
Same author

Learning optimal early decision treatment rules with multi-domain intermediate outcomes.

Biometrics·2026
Same author

Computationally efficient methods for estimating phenome-wide coheritability of multi-type phenotypes using biobank data.

Communications biology·2025
Same author

Simultaneous Feature Selection for Optimal Dynamic Treatment Regimens.

Statistics in medicine·2025
Same author

A hierarchical random effects state-space model for modeling brain activities from electroencephalogram data.

Biometrics·2024

Related Experiment Video

Updated: Feb 4, 2026

Olfactory Assays for Mouse Models of Neurodegenerative Disease
07:27

Olfactory Assays for Mouse Models of Neurodegenerative Disease

Published on: August 25, 2014

22.6K

Nonlinear model with random inflection points for modeling neurodegenerative disease progression.

Ming Sun1, Yuanjia Wang1,2

  • 1Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York.

Statistics in Medicine
|September 27, 2018
PubMed
Summary

Diagnosing neurological disorders is challenging due to reliance on late-stage symptoms. This study introduces a nonlinear model to track disease progression using biomarkers, revealing brain atrophy precedes motor and cognitive decline in Huntington's disease.

Keywords:
EM algorithmneuroimaging biomarkersnonlinear mixed effects modelrandom inflection pointsigmoid function

More Related Videos

Modeling Age-Associated Neurodegenerative Diseases in Caenorhabditis elegans
07:04

Modeling Age-Associated Neurodegenerative Diseases in Caenorhabditis elegans

Published on: August 15, 2020

5.8K
Author Spotlight: Exploring Non-Motor Symptoms in Parkinson's Disease
03:20

Author Spotlight: Exploring Non-Motor Symptoms in Parkinson's Disease

Published on: September 22, 2023

2.4K

Related Experiment Videos

Last Updated: Feb 4, 2026

Olfactory Assays for Mouse Models of Neurodegenerative Disease
07:27

Olfactory Assays for Mouse Models of Neurodegenerative Disease

Published on: August 25, 2014

22.6K
Modeling Age-Associated Neurodegenerative Diseases in Caenorhabditis elegans
07:04

Modeling Age-Associated Neurodegenerative Diseases in Caenorhabditis elegans

Published on: August 15, 2020

5.8K
Author Spotlight: Exploring Non-Motor Symptoms in Parkinson's Disease
03:20

Author Spotlight: Exploring Non-Motor Symptoms in Parkinson's Disease

Published on: September 22, 2023

2.4K

Area of Science:

  • Neurology
  • Biostatistics
  • Biomarker Research

Background:

  • Current neurological disorder diagnosis relies on late-stage clinical symptoms, leading to diagnostic variability.
  • Objective markers are lacking, hindering early prediction and personalized treatment strategies.
  • Modeling disease progression using biomarkers and subtle signs is crucial for improved accuracy and clinical trial design.

Purpose of the Study:

  • To develop a nonlinear model for jointly estimating marker trajectories and their relationship with disease mechanisms.
  • To establish a temporal order of disease progression across different marker domains.
  • To assess the impact of subject-specific characteristics on marker dynamics for personalized management.

Main Methods:

  • A nonlinear model with random inflection points was developed to jointly model marker trajectories.
  • Markers were scaled into comparable progression curves based on mean inflection points.
  • The model was applied to neuroimaging, cognitive, and motor data from a Huntington's disease study.

Main Results:

  • The model successfully established a temporal order of disease impairment across neuroimaging, motor, and cognitive domains.
  • Brain atrophy in specific regions was identified as an early indicator, preceding motor and cognitive decline.
  • Substantial brain atrophy was observed in average patients by the age of clinical diagnosis.

Conclusions:

  • The proposed model effectively integrates biomarkers to model neurological disease progression and temporal ordering.
  • Early detection of neurodegeneration through neuroimaging markers can be achieved before significant motor or cognitive symptoms manifest.
  • This approach supports the development of personalized therapeutics and disease management strategies for neurological disorders.