Evolutionary algorithm for the optimization of meal intake and insulin administration in patients with type 2 diabetes

The optimal management of type 2 diabetes (T2DM) is complex and involves an appropriate combination of diet, exercise, and different pharmacological treatments. Artificial intelligence-based tools have been shown to be very useful for the diagnosis and treatment of diverse pathologies, including diabetes. In the present study, we present a proof of concept of the potential of an evolutionary algorithm to optimize the meal size, timing and insulin dose for the control of glycemia. We found that an appropriate distribution of food intake throughout the day permits a reduction in the insulin dose required to maintain glycemia within the range recommended by the American Diabetes Association for patients with T2DM of a range of severities. Furthermore, the effects of restrictions to both the timing and amount of food ingested were assessed, and we found that an increase in the amount of insulin was required to control glycemia as dietary intake became more restricted. In the near future, the use of these computational tools should permit patients with T2DM to optimize their personal meal schedule and insulin dose, according to the severity of their diabetes

Development and evaluation of culture media based on extracts of the cyanobacterium Arthrospira platensis

Continuous advances in the fields of industrial biotechnology and pharmacy require the development of new formulations of culture media based on new nutrient sources. These new sources must be sustainable, high yielding, and non-animal-based, with minimal environmental impact. Thus, culture media prepared from cyanobacterial extracts can be an interesting alternative to the current formulations. In this study, we prepared various minimal formulations of culture media using the extracts of Arthrospira platensis, and analyzed the efficiency of these formulations, based on their effect on the production of biomass and molecules of industrial interest, using different types of bacteria. All media formulations prepared in this study showed better performance than conventional media, including those based on animal ingredients. Thus, based on their versatility and high-yielding capacity, we conclude that culture media prepared from cyanobacterial extracts are a good alternative to conventional media for meeting the current demands of the cosmetic and pharmaceutical industries

Evolutionary algorithm for the optimization of meal intake and insulin administration in patients with type 2 diabetes

The optimal management of type 2 diabetes (T2DM) is complex and involves an appropriate combination of diet, exercise, and different pharmacological treatments. Artificial intelligence-based tools have been shown to be very useful for the diagnosis and treatment of diverse pathologies, including diabetes. In the present study, we present a proof of concept of the potential of an evolutionary algorithm to optimize the meal size, timing and insulin dose for the control of glycemia. We found that an appropriate distribution of food intake throughout the day permits a reduction in the insulin dose required to maintain glycemia within the range recommended by the American Diabetes Association for patients with T2DM of a range of severities. Furthermore, the effects of restrictions to both the timing and amount of food ingested were assessed, and we found that an increase in the amount of insulin was required to control glycemia as dietary intake became more restricted. In the near future, the use of these computational tools should permit patients with T2DM to optimize their personal meal schedule and insulin dose, according to the severity of their diabetes