Worldwide, breast cancer has become the second most common cancer in women. The disease has currently been named the most deadly cancer in women but little is known on what causes the disease. We present the effects of estrogen as a risk factor on the dynamics of breast cancer. We develop a deterministic mathematical model showing general dynamics of breast cancer with immune response. This is a four-population model that includes tumor cells, host cells, immune cells, and estrogen. The effects of estrogen are then incorporated in the model. The results show that the presence of extra estrogen increases the risk of developing breast cancer.

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

Enhancement of the Human Immunodeficiency Virus (HIV) specific cytotoxic Tcells mechanisms in an HIV-1 and Mycobacterium tuberculosis (Mtb) co-infected individual seems to improve the clinical picture of an individual by reducing Acquired Immuno Deficiency Syndrome (AIDS) state progression rate. In this paper, we develop a system of deterministic differential equations representing the immune cells involved in an HIV-1 and Mtb co-infected individual. Results show that although the non-lytic arm of the HIV-1 cytotoxic T-cells affects the co-infection dynamics more than the lytic factors, a combination of both factors results in a more positive reduced progression to the AIDS state. This is due to the increased protection of the CD4 + T-cells by the CTL mechanisms by further reducing infections and replications by the HIV. Thus, HIV-1 specific CTLs mechanisms' involvement is here recommended to be part of a solution to the HIV and Mtb co-infection problems.

Aims/Objectives: Effective and efficient heart disease prediction via nonparametric mixture regression models. Data Source: Data used in this paper is from the UCI database of the Cleveland Clinic Foundation for heart disease. The original data source contains 76 raw attributes with 303 observations each. For the purpose of this paper only 14 attributes were used as explained in section 4. Methodology: Cluster analysis was applied via mixture models in the form of Nonparametric Density-based models. The clusters were identified using a graph theory based technique. Voronoi diagrams were used and and their distributions were estimated nonparametrically through a mixture model with Gaussian kernels. The optimal number of clusters and components of the