• Journal of Biomedical Engineering Research
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• v.15 no.3
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• pp.295-304
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• 1994

This paper describes some preliminary attempts to formulate simple mathematical models of the transmission dynamics of HIV infection in homosexual communities. In conjunction with a survey of the available epidemiological data on HIV infection and the incidence of AIDS, the model is used to assess how various processes influence the course of the initial epidemic following the introduction of the virus. Models of the early stages of viral spread provide crude methods for estimating the basic reproductive rate of the virus, given a knowledge of the incubation period of AIDS and the initial doubling time of the epidemic. More complex models are formulated to assess the influence of heterogeneity in sexual activity. This latter factor is shown to have a major effect on the predicted pattern of the epidemic.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.22 no.1
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• pp.29-62
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• 2018

We investigate a class of HIV infection models with two kinds of target cells: $CD4^+$ T cells and macrophages. We incorporate three distributed time delays into the models. Moreover, we consider the effect of humoral immunity on the dynamical behavior of the HIV. The viruses are produced from four types of infected cells: short-lived infected $CD4^+$T cells, long-lived chronically infected $CD4^+$T cells, short-lived infected macrophages and long-lived chronically infected macrophages. The drug efficacy is assumed to be different for the two types of target cells. The HIV-target incidence rate is given by bilinear and saturation functional response while, for the third model, both HIV-target incidence rate and neutralization rate of viruses are given by nonlinear general functions. We show that the solutions of the proposed models are nonnegative and ultimately bounded. We derive two threshold parameters which fully determine the positivity and stability of the three steady states of the models. Using Lyapunov functionals, we established the global stability of the steady states of the models. The theoretical results are confirmed by numerical simulations.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.135-137
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• 2009

Mathematical models for describing the Human Immunodeficiency Virus(HIV) infection can be devised to better understand how the HIV causes Acquired Immune Deficiency Syndrome(AIDS). The HIV models can then be used to find clues to curing AIDS from a control theoretical point of view. Some models take Cytotoxic T Lymphocytes(CTL) response to HIV infection into account, and others consider mutants against the drugs. However, to the best of our knowledge, there has been no model developed, which describes CTL response and mutant HIV together. Hence we propose a unified model to consider both of these. On the basis of the resulting model, we also present a Model Predictive Control(MPC) scheme to find an optimal treatment strategy. The optimization is performed under the assumption that the Structured Treatment Interruption(STI) policy is employed.

• Journal of the Korean Mathematical Society
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• v.49 no.4
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• pp.779-794
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• 2012

In this paper, we study the global stability of two mathematical models for human immunodeficiency virus (HIV) infection with intra-cellular delays. The first model is a 5-dimensional nonlinear delay ODEs that describes the interaction of the HIV with two classes of target cells, $CD4^+$ T cells and macrophages taking into account the saturation infection rate. The second model generalizes the first one by assuming that the infection rate is given by Beddington-DeAngelis functional response. Two time delays are used to describe the time periods between viral entry the two classes of target cells and the production of new virus particles. Lyapunov functionals are constructed and LaSalle-type theorem for delay differential equation is used to establish the global asymptotic stability of the uninfected and infected steady states of the HIV infection models. We have proven that if the basic reproduction number $R_0$ is less than unity, then the uninfected steady state is globally asymptotically stable, and if the infected steady state exists, then it is globally asymptotically stable for all time delays.

• Bulletin of the Korean Mathematical Society
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• v.48 no.3
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• pp.555-574
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• 2011

It is well known that the mathematical models provide very important information for the research of human immunodeciency virus type. However, the infection rate of almost all mathematical models is linear. The linearity shows the simple interaction between the T-cells and the viral particles. In this paper, a differential equation model of HIV infection of $CD4^+$ T-cells with Crowley-Martin function response is studied. We prove that if the basic reproduction number $R_0$ < 1, the HIV infection is cleared from the T-cell population and the disease dies out; if $R_0$ > 1, the HIV infection persists in the host. We find that the chronic disease steady state is globally asymptotically stable if $R_0$ > 1. Numerical simulations are presented to illustrate the results.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.815-822
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• 2001

The aim of this study is to estimate the seroconversion date of the human immunodeficiency virus(HIV) infection for the HIV infected patients in Korea. Data are collected from two cohorts. The first cohort is a group of "seroprevalent" patients who were seropositive and AIDS-free at entry. The other is a group of "seroincident" patients who were initially seronegative but later converted to HIV antibody-positive. The seroconversion dates of the seroincident cohort are available while those of the seroprevalent cohort are not. Estimation of seroconversion date is important because it can be used to calculate the incubation period of AIDS which is defined as the elapsed time between the HIV infection and the development of AIDS. In this paper, a Weibull regression model Is fitted for the seroincident cohort using information about the elapsed time since seroconversion and the CD4$^{+}$ cell count.The seroconversion dates for the seroprevalent cohort are imputed on the basis of the marker of maturity of HIV infection percent of CD4$^{+}$cell count.unt.

• Journal of Microbiology and Biotechnology
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• v.32 no.12
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• pp.1515-1526
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• 2022

Eukaryotic chromatin is highly organized in the 3D nuclear space and dynamically regulated in response to environmental stimuli. This genomic organization is arranged in a hierarchical fashion to support various cellular functions, including transcriptional regulation of gene expression. Like other host cellular mechanisms, viral pathogens utilize and modulate host chromatin architecture and its regulatory machinery to control features of their life cycle, such as lytic versus latent status. Combined with previous research focusing on individual loci, recent global genomic studies employing conformational assays coupled with high-throughput sequencing technology have informed models for host and, in some cases, viral 3D chromosomal structure re-organization during infection and the contribution of these alterations to virus-mediated diseases. Here, we review recent discoveries and progress in host and viral chromatin structural dynamics during infection, focusing on a subset of DNA (human herpesviruses and HPV) as well as RNA (HIV, influenza virus and SARS-CoV-2) viruses. An understanding of how host and viral genomic structure affect gene expression in both contexts and ultimately viral pathogenesis can facilitate the development of novel therapeutic strategies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3519-3533
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• 2020

Tuberculosis is a chronic and delayed infection which is easily experienced by young people. According to the statistics of the World Health Organization (WHO), there are nearly ten million fell ill with tuberculosis and a total of 1.5 million people died from tuberculosis in 2018 (including 251000 people with HIV). Tuberculosis is the largest single infectious pathogen that leads to death. In order to help doctors with tuberculosis diagnosis, we compare the tuberculosis classification abilities of six popular convolutional neural network (CNN) models in the same data set to find the best model. Before training, we optimize three parts of CNN to achieve better results. We employ sigmoid function to replace the step function as the activation function. What's more, we use binary cross entropy function as the cost function to replace traditional quadratic cost function. Finally, we choose stochastic gradient descent (SGD) as gradient descent algorithm. From the results of our experiments, we find that Densenet121 is most suitable for tuberculosis diagnosis and achieve a highest accuracy of 0.835. The optimization and expansion depend on the increase of data set and the improvements of Densenet121.