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 Experiment Videos

Estimation of HIV dynamic parameters

H Wu1, A A Ding, V De Gruttola

  • 1Frontier Science & Technology Research Foundation, Chestnut Hill, MA 02167-2104, USA. wu@sdac.harvard.edu

Statistics in Medicine
|November 20, 1998
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Neural-network prediction with noisy predictors.

IEEE transactions on neural networks·2008
Same author

Backpropagation of pseudo-errors: neural networks that are adaptive to heterogeneous noise.

IEEE transactions on neural networks·2008
Same author

Assessing antiviral potency of anti-HIV therapies in vivo by comparing viral decay rates in viral dynamic models.

Biostatistics (Oxford, England)·2003
Same author

Population HIV-1 dynamics in vivo: applicable models and inferential tools for virological data from AIDS clinical trials.

Biometrics·2001
Same author

Estimate of the frequency of true incomplete exchanges in human lymphocytes exposed to 1 GeV/u Fe ions in vitro.

International journal of radiation biology·2001
Same author

Genetic deletion of the Pten tumor suppressor gene promotes cell motility by activation of Rac1 and Cdc42 GTPases.

Current biology : CB·2001

This study introduces advanced viral dynamic models for HIV, improving estimates of virus clearance and infected cell rates. The new hierarchical non-linear model offers better population dynamics analysis and parameter estimation in treatment studies.

Area of Science:

  • Virology
  • Mathematical Biology
  • Immunology

Background:

  • Understanding HIV pathogenesis and treatment requires accurate viral dynamic modeling.
  • Previous models by Perelson et al. estimated virus and infected CD4+ T-cell clearance rates using plasma HIV-RNA.
  • Existing models have limitations regarding drug activity assumptions.

Purpose of the Study:

  • To extend existing HIV viral dynamic models with less restrictive assumptions on drug activity.
  • To incorporate the production of infectious and non-infectious virions before and after treatment.
  • To propose a hierarchical non-linear model for population viral dynamics and parameter estimation.

Main Methods:

  • Developed extended viral dynamic models accounting for pre- and post-treatment virion production.

Related Experiment Videos

  • Utilized direct measurement of infectious virus load for antiretroviral drug efficacy parameter estimation.
  • Proposed and illustrated a hierarchical non-linear model for population-level viral dynamics.

    • Main Results:

    • The proposed models provide more accurate estimates by considering infectious and non-infectious virion production.
    • Direct measurement of infectious virus load is sufficient for estimating antiretroviral drug efficacy.
    • The hierarchical non-linear model enhances modeling of within- and between-patient variations and population dynamics.

    Conclusions:

    • The new models offer advantages over previous methods, including better handling of patient variations and data sparsity.
    • The hierarchical non-linear approach improves parameter estimation and model misspecification detection.
    • Findings support improved clinical trial design for HIV viral dynamic studies.