Search PubMed⌕ Search

PubMed · 16091245

Thymic function in HIV infection.

Abstract

Current models hold that CD4+ depletion occurs as a result of direct and indirect effects of HIV, which both kill peripheral CD4+ cells and prevent adequate regeneration. Although age-associated involution diminishes thymic reserve and HIV is clearly thymotoxic, clinical trials have nonetheless shown that large proportions of patients who sustain adequate control of viral replication with highly active antiretroviral therapy (HAART) will demonstrate some evidence for thymic-dependent immune reconstitution, which is associated with improved immune competence. Furthermore, patients with insufficient or absent immune reconstitution following HAART generally lack evidence for thymopoiesis. Current studies are focused on improving our understanding of the causes for thymic failure in HIV infection. Recent work has demonstrated that some HIV strains, especially those that are CXCR4 trophic, are more thymotoxic and may contribute to irreversible thymic damage in this population.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Rohan Hazra, Crystal Mackall. 2005. Thymic function in HIV infection.. https://doi.org/10.1007/s11904-996-0005-2

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Random changepoint modelling of HIV immunologic responses.

We propose a changepoint model for the analysis of longitudinal CD4 T-cell counts for HIV infected subjects following highly active antiretroviral treatment. The profile of CD4 counts for each subject follows a simple, 'broken stick' changepoint model, with random subject-specific parameters, including the changepoint. The model accounts for baseline covariates. The longitudinal CD4 records are censored at the time of the subject going off-study-treatment. This is a potentially informative drop-out mechanism, which we address by modelling it jointly with the CD4 count outcome. The drop-out model incorporates terms from the CD4 model, including the changepoint. The estimation is done in a Bayesian framework, with implementation via Markov chain Monte Carlo methods in the WinBUGS software. Model selection using DIC indicates that the data support the complex random changepoint and informative censoring model.

Antiretroviral Therapy, Highly Active↗

Estimation and prediction with HIV-treatment interruption data.

We consider longitudinal clinical data for HIV patients undergoing treatment interruptions. We use a nonlinear dynamical mathematical model in attempts to fit individual patient data. A statistically-based censored data method is combined with inverse problem techniques to estimate dynamic parameters. The predictive capabilities of this approach are demonstrated by comparing simulations based on estimation of parameters using only half of the longitudinal observations to the full longitudinal data sets.

Antiretroviral Therapy, Highly Active↗