Search

Leukemia is a malignant cancer of the blood. It is the most common type of cancer in children. In this paper, a mathematical model of leukemia in terms of ordinary differential equations has been developed. We propose a model to study the spread of leukemia by considering the effect of adoptive T cell therapy. The disease dynamics are given by a system of ordinary nonlinear differential equations that describe the interaction among susceptible blood cells, infected blood cells, cancer cells, and immune cells. The model is analyzed by using the stability theory of nonlinear differential equations and numerical simulations. A major goal of this work is to determine the spread of leukemia after applying the adoptive T cell therapy. We have observed that the system is stable locally and globally if stimulation rate or antigenicity rate of immune cells is greater than a threshold value dependent on the density of immune cells in the blood. We have also observed that the external reinfusion of immune cells by adoptive T cell therapy reduces the concentration of cancer cells and infected cells in the blood, which implies that immune cells kill cancer cells after being stimulated in the body. [ABSTRACT FROM AUTHOR]

This paper deals with stability and Hopf bifurcation analyses of a mathematical model of HIV infection of CD4+ T-cells. The model is based on a system of delay differential equations with logistic growth term and antiretroviral treatment with a discrete time delay, which plays a main role in changing the stability of each steady state. By fixing the time delay as a bifurcation parameter, we get a limit cycle bifurcation about the infected steady state. We study the effect of the time delay on the stability of the endemically infected equilibrium. We derive explicit formulae to determine the stability and direction of the limit cycles by using center manifold theory and normal form method. Numerical simulations are presented to illustrate the results. [ABSTRACT FROM AUTHOR]

In silico T-cell epitope prediction plays an important role in immunization experimental design and vaccine preparation. Currently, most epitope prediction research focuses on peptide processing and presentation, e.g., proteasomal cleavage, transporter associated with antigen processing (TAP), and major histocompatibility complex (MHC) combination. To date, however, the mechanism for the immunogenicity of epitopes remains unclear. It is generally agreed upon that T-cell immunogenicity may be influenced by the foreignness, accessibility, molecular weight, molecular structure, molecular conformation, chemical properties, and physical properties of target peptides to different degrees. In this work, we tried to combine these factors. Firstly, we collected significant experimental HLA-I T-cell immunogenic peptide data, as well as the potential immunogenic amino acid properties. Several characteristics were extracted, including the amino acid physicochemical property of the epitope sequence, peptide entropy, eluted ligand likelihood percentile rank (EL rank(%)) score, and frequency score for an immunogenic peptide. Subsequently, a random forest classifier for T-cell immunogenic HLA-I presenting antigen epitopes and neoantigens was constructed. The classification results for the antigen epitopes outperformed the previous research (the optimal AUC = 0.81 , external validation data set AUC = 0.77). As mutational epitopes generated by the coding region contain only the alterations of one or two amino acids, we assume that these characteristics might also be applied to the classification of the endogenic mutational neoepitopes also called "neoantigens." Based on mutation information and sequence-related amino acid characteristics, a prediction model of a neoantigen was established as well (the optimal AUC = 0.78). Further, an easy-to-use web-based tool "INeo-Epp" was developed for the prediction of human immunogenic antigen epitopes and neoantigen epitopes. [ABSTRACT FROM AUTHOR]