Several lines of evidence suggest that HIV-1 is present in the thymus during HIV-1 infection. Precursors to mature CD4+ T lymphocytes develop in the thymus, which suggests that thymic infection may play a role in the CD4+ T-cell decline observed during the course of pediatric HIV-1 infection. We illustrate, through mathematical modeling, the potential effects of thymic infection on the course of pediatric AIDS disease progression. We find that infection in the thymus not only can supplement peripheral infection but can help explain the faster progression in pediatric cases, as well as the early and high viral burden.
"Significant efforts have been made in order to understand and to characterize the underlying mechanism of the disease. Earlier mathematical framework has been considered to model HIV/AIDS dynamics focusing on the viral and CD4 T-cells dynamics [3–11]and the references therein. Recently, there has been some work to explore the impact of the immune response in the HIV dynamics [12–15]. "
[Show abstract][Hide abstract] ABSTRACT: Objectives
Mathematical models can be helpful to understand the complex dynamics of human immunodeficiency virus infection within a host. Most of work has studied the interactions of host responses and virus in the presence of active cytotoxic immune cells, which decay to zero when there is no virus. However, recent research highlights that cytotoxic immune cells can be inactive but never be depleted.
We propose a mathematical model to investigate the human immunodeficiency virus dynamics in the presence of both active and inactive cytotoxic immune cells within a host. We explore the impact of the immune responses on the dynamics of human immunodeficiency virus infection under different disease stages.
Standard mathematical and numerical analyses are presented for this new model. Specifically, the basic reproduction number is computed and local and global stability analyses are discussed.
Our results can give helpful insights when designing more effective drug schedules in the presence of active and inactive immune responses.
"A number of previous works have examined some aspects of HIV infection, for example (Hazenberg et al., 2003; Ye et al., 2004; Wang, 1997; Turville et al., 2002; Letvin and Walker, 2003; Chun et al., 1997; Cloyd et al., 2000; Kirschner et al., 1998; Grossman et al., 2002; Yates et al., 2007; Hougue et al., 2008; Ferreira et al., 2011). These and other works present a basic relation between CD4þT cells, infected CD4 þT cells and viral load (Kirschner et al., 1998; Hougue et al., 2008; Nowak and May, 2000; Kirschner, 1996; Callaway and Perelson, 2002; Kirschner and Perelson, 1995; Perelson and Nelson, 1999; Xia, 2007; Tan and Wu, 1998; Dalal et al., 2008; Zorzenon dos Santos, 2001; Burkheada et al., 2009). A significant effort has been made in understanding the interaction of the immune response with HIV (Campello, 1999; Adams et al., 2004; Stan et al., 2007; Wodarz, 2001; Zurakowski and Teel, 2006). "
[Show abstract][Hide abstract] ABSTRACT: A typical HIV infection response consists of three stages: an initial acute infection, a long asymptomatic period and a final increase in viral load with simultaneous collapse in healthy CD4+T cell counts. The majority of existing mathematical models give a good representation of either the first two stages or the last stage of the infection. Using macrophages as a long-term active reservoir, a deterministic model is proposed to explain the three stages of the infection including the progression to AIDS. Simulation results illustrate how chronic infected macrophages can explain the progression to AIDS provoking viral explosion. Further simulation studies suggest that the proposed model retains its key properties even under moderately large parameter variations. This model provides important insights on how macrophages might play a crucial role in the long term behaviour of HIV infection.
"Traditional epidemiology focuses on between-host infection dynamics, either between individual hosts within well-mixed host populations, or among spatially segregated populations . In recent years, there has been increasing attention given to an important arena of infection embedded within the host population scale, namely that of within-host infection dynamics (e.g.,         ). Following successful infection by a virus, bacterium, or fungus, a population of the pathogen is established within an individual host, which in effect is a " patch " being colonized by that pathogen. "
[Show abstract][Hide abstract] ABSTRACT: The ecology and evolution of infectious disease occur at multi-ple spatial scales. In this paper, we explore some consequences of transient dynamics of pathogens within individual hosts. If infected hosts die quickly, relative to internal equilibration in pathogen dynamics, within-host transients may influence between-host transmission and spread. We develop a formula-tion for characterizing the overall growth rate of an infectious disease, which includes both within-host dynamics and between-host transmission, when the disease is sufficiently rare that the supply of available hosts can be viewed as a constant. This formulation is analogous to the familiar Euler equation in age-structured demography. We suggest that the pathogen growth rate esti-mated this way may be a better measure of pathogen fitness than is R 0 . We point out that even simple models of within-host pathogen dynamics can have phases in which numbers overshoot the final equilibrium, and that such phases may influence pathogen evolution. We touch on the potential importance of within-host spatial heterogeneities in pathogen dynamics, and suggest that an interesting question for future work is understanding the interplay of spatial structure and transient dynamics in the within-host infection process.
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