ArticlePloS one2012
HIV model parameter estimates from interruption trial data including drug efficacy and reservoir dynamics.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
42 citing papers in PubMed.
- Modelling HIV-1 2-LTR dynamics following raltegravir intensification.Journal of the Royal Society, Interface · 2013Trial
- A Nonparametric Approach to Practical Identifiability of Nonlinear Mixed Effects Models.Bulletin of mathematical biology · 2026Article
- Clonal heterogeneity and antigenic stimulation shape persistence of the latent reservoir of HIV.PLoS computational biology · 2025Article
- Evaluating the long-term effects of combination antiretroviral therapy of HIV infection: a modeling study.Journal of mathematical biology · 2025Article
- Modelling Role of Protective and Nonprotective HLA Allele Inducing Different HIV Infection Outcomes.Bulletin of mathematical biology · 2024Article
- Ongoing HIV replication in lymph node sanctuary sites in treated individuals contributes to the total latent HIV at a very slow rate.Journal of theoretical biology · 2023Article
- Article
- Machine Learning Bolsters Evidence That D1, Nef, and Tat Influence HIV Reservoir Dynamics.Pathogens & immunity · 2023Article
- Computational systems biology in disease modeling and control, review and perspectives.NPJ systems biology and applications · 2022Review
- Healthy dynamics of CD4 T cells may drive HIV resurgence in perinatally-infected infants on antiretroviral therapy.PLoS pathogens · 2022Article
- Estimation of the in vivo neutralization potency of eCD4Ig and conditions for AAV-mediated production for SHIV long-term remission.Science advances · 2022Article
- Modeling HIV-1 Within-Host Dynamics After Passive Infusion of the Broadly Neutralizing Antibody VRC01.Frontiers in immunology · 2021Article
- Impact of fluctuation in frequency of human immunodeficiency virus/simian immunodeficiency virus reactivation during antiretroviral therapy interruption.Proceedings. Biological sciences · 2020Article
- Significant Unresolved Questions and Opportunities for Bioengineering in Understanding and Treating COVID-19 Disease Progression.Cellular and molecular bioengineering · 2020Article
- Mathematical modeling to reveal breakthrough mechanisms in the HIV Antibody Mediated Prevention (AMP) trials.PLoS computational biology · 2020Article
- An Integrated Spatial Dynamics-Pharmacokinetic Model Explaining Poor Penetration of Anti-retroviral Drugs in Lymph Nodes.Frontiers in bioengineering and biotechnology · 2020Article
- Optimal control modulation of HIV reservoir formation rate by antigen infusion.Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control · 2019Article
- Positive feedback through inflammation creates bistable behavior in HIV tissue sanctuaries.Proceedings of the ... American Control Conference. American Control Conference · 2019Article
- Robust control of HIV infection by antiretroviral therapy: a super-twisting sliding mode control approach.IET systems biology · 2019Article
- A majority of HIV persistence during antiretroviral therapy is due to infected cell proliferation.Nature communications · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Mathematical models based on ordinary differential equations (ODE) have had significant impact on understanding HIV disease dynamics and optimizing patient treatment. A model that characterizes the essential disease dynamics can be used for prediction only if the model parameters are identifiable from clinical data. Most previous parameter identification studies for HIV have used sparsely sampled data from the decay phase following the introduction of therapy. In this paper, model parameters are identified from frequently sampled viral-load data taken from ten patients enrolled in the previously published AutoVac HAART interruption study, providing between 69 and 114 viral load measurements from 3-5 phases of viral decay and rebound for each patient. This dataset is considerably larger than those used in previously published parameter estimation studies. Furthermore, the measurements come from two separate experimental conditions, which allows for the direct estimation of drug efficacy and reservoir contribution rates, two parameters that cannot be identified from decay-phase data alone. A Markov-Chain Monte-Carlo method is used to estimate the model parameter values, with initial estimates obtained using nonlinear least-squares methods. The posterior distributions of the parameter estimates are reported and compared for all patients.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.