Convergence of a Diabetes Mellitus, Protein Energy Malnutrition, and TB Epidemic: the Neglected Elderly Population

On a global scale, nearly two billion persons are infected with Mycobacterium tuberculosis. From this vast reservoir of latent tuberculosis (TB) infection, a substantial number will develop active TB during their lifetime, with some being able to transmit TB or Multi-drug- resistant (MDR) TB to others. There is clinical evidence pointing to a higher prevalence of infectious diseases including TB among individuals with Diabetes Mellitus (DM). Furthermore, ageing and diabetes mellitus may further aggravate protein-energy malnutrition (PEM), which in turn impairs T-lymphocyte mediated immunologic defenses, thereby increasing the risk of developing active TB and compromising TB treatment. This article aims to a) highlight synergistic mechanisms associated with immunosenescence, DM and PEM in relation to the development of active TB and b) identify nutritional, clinical and epidemiological research gaps

Discrete and continuum models for the evolutionary and spatial dynamics of cancer:a very short introduction through two case studies

We give a very short introduction to discrete and continuum models for the evolutionary and spatial dynamics of cancer through two case studies: a model for the evolutionary dynamics of cancer cells under cytotoxic therapy and a model for the mechanical interaction between healthy and cancer cells during tumour growth. First we develop the discrete models, whereby the dynamics of single cells are described through a set of rules that result in branching random walks. Then we present the corresponding continuum models, which are formulated in terms of non-local and nonlinear partial differential equations, and we summarise the key properties of their solutions. Finally, we carry out numerical simulations of the discrete models and we construct numerical solutions of the corresponding continuum models. The biological implications of the results obtained are briefly discussed.Comment: 21 pages, 5 figures, BIOMAT2019 (19th International Symposium on Mathematical and Computational Biology