Nutrients in Infancy

Infants are not just small adults; they have a complex set of nutrient requirements and interactions to account for the high metabolic rate, growth, immunological and cognitive development, etc. Each year there are 138 million births, including 30 million from the least developed countries, where health is most at risk. Nutrition during infancy establishes growth patterns and development which moderate short term and long term health and life expectancy. There is continuing progress in understanding the nutrient composition of breastmilk and the importance nutritious and timely complementary foods. Nutrients at particular risk in the first years of life are iron, iodine, and vitamins A and D. There are still gaps in our knowledge on the influence of maternal diet, body composition and nutritional status on breastmilk nutrients. Interaction of nutrients with the human microbiome and gastro-intestinal tract hormonal secretion are developing fields. While breastmilk remains the “gold standard of pediatric nutrition”, the provisions of nutrients providing for optimal health in infant formulae, including nutrients and probiotics remains an area of research. The assessment of body composition for research and clinical practice has progressed rapidly and is important in understanding later obesity. After six months of age, there are new issues of the interaction of weaning foods and later family foods with the maturing gastro-intestinal tract to promote health and growth. Infant nutrition is made more difficult (and interesting) by the many cultural beliefs related to feeding practices. Developments in all of these fields of research into pediatric nutrition will be explored in this special issue together with state of the art reviews

Mathematical model of interactions immune system with Micobacterium tuberculosis

Tuberculosis (TB) remains a public health problem in the world, because of the increasing prevalence and treatment outcomes are less satisfactory. About 3 million people die each year and an estimated one third of the world's population infected with Mycobacterium Tuberculosis (M.tb) is latent. This is apparently related to incomplete understanding of the immune system in infection M.tb. When this has been known that immune responses that play a role in controlling the development of M.tb is Macrophages, T Lymphocytes and Cytokines as mediators. However, how the interaction between the two populations and a variety of cytokines in suppressing the growth of Mycobacterium tuberculosis germ is still unclear. To be able to better understand the dynamics of infection with M tuberculosis host immune response is required of a model.One interesting study on the interaction of the immune system with M.tb mulalui mathematical model approach. Mathematical model is a good tool in understanding the dynamic behavior of a system. With the mediation of mathematical models are expected to know what variables are most responsible for suppressing the growth of Mycobacterium tuberculosis germ that can be a more appropriate approach to treatment and prevention target is to develop a vaccine. This research aims to create dynamic models of interaction between macrophages (Macrophages resting, macrophages activated and macrophages infected), T lymphocytes (CD4 + T cells and T cells CD8 +) and cytokine (IL-2, IL-4, IL-10,IL-12,IFN-dan TNF-) on TB infection in the lung. To see the changes in each variable used parameter values derived from experimental literature. With the understanding that the variable most responsible for defense against Mycobacterium tuberculosis germs, it can be used as the basis for the development of a vaccine or drug delivery targeted so hopefully will improve the management of patients with tuberculosis. Mathematical models used in building Ordinary Differential Equations (ODE) in the form of differential equation systems Non-linear first order, the equation contains the functions used in biological systems such as the Hill function, Monod function, Menten- Kinetic Function. To validate the system used 4th order Runge Kutta method with the help of software in making the program Matlab or Maple to view the behavior and the quantity of cells of each population