ST131 Escherichia coli is an emergent clonal group that has achieved successful worldwide spread through a combination of virulence and antimicrobial resistance. Our aim was to develop a mathematical model, based on current knowledge of the epidemiology of ESBL-producing and non-ESBL-producing ST131 E. coli, to provide a framework enabling a better understanding of its spread within the community, in hospitals and long-term care facilities, and the potential impact of specific interventions on the rates of infection. A model belonging to the SEIS (Susceptible-Exposed-Infected-Susceptible) class of compartmental models, with specific modifications, was developed. Quantification of the model is based on the law of mass preservation, which helps determine the relationships between flows of individuals and different compartments. Quantification is deterministic or probabilistic depending on subpopulation size. The assumptions for the model are based on several developed epidemiological studies. Based on the assumptions of the model, an intervention capable of sustaining a 25% reduction in person-to-person transmission shows a significant reduction in the rate of infections caused by ST131; the impact is higher for non-ESBL-producing ST131 isolates than for ESBL producers. On the other hand, an isolated intervention reducing exposure to antimicrobial agents has much more limited impact on the rate of ST131 infection. Our results suggest that interventions achieving a continuous reduction in the transmission of ST131 in households, nursing homes and hospitals offer the best chance of reducing the burden of the infections caused by these isolates.
The design and implementation of a framework that facilitates the development of Mobile Health applications to manage the communications with biomedical sensors in compliance with the CEN ISO/IEEE 11073 standard family are presented. The framework includes a set of functional modules that are responsible, among other tasks, of the communication of sensors and the processing and storage of data. The mobile terminal acts as an intermediary or hub, collecting and presenting the data received in a standardised way, regardless of the sensor type used. In this context, as proof of concept it is presented a mobile app built on the top of the framework to manage the communications with a smart fall detector.
Obesity and metabolic syndrome represent an increasing epidemiological challenge for society given the associated social and health implications. Obesity is related to different metabolic disorders, like diabetes mellitus type 2, which has been subject to study in the last decades. Nowadays, most of the related research focuses on endocrine aspects, especially related to adipose tissue. This is due to the fact that some adipocytokines are proven to be of great relevance as therapeutic agents both for obesity and mellitus type 2 diabetes. This work integrates some aspects of the knowledge generated under these research studies. In this context, it is proposed the design and development of a computational model that provides a better dynamic view of the existing interactions between endocrine aspects of the adipose tissue and glucose control mechanisms in people with obesity. The model behaves as an observer that estimates the dynamics of internal state variables, not easy to be measured in the clinical practice, and which helps to understand the dynamic behaviour of measurable variables. The work analyses the effects of the external energy intake and exercises over the obesity control and glycemia. The model has been validated by using other authors’ data. Predictions of the influence over the measurable variables of the behaviours considered when the virtual patient follows different diets and physical exercise are shown as results.
Computational methods and modeling are widely used in many fields to study the dynamic behaviour of different phenomena. Currently, the use of these models is an accepted practice in the biomedical field. One of the most significant efforts in this direction is applied to the simulation and prediction of pathophysiological conditions that can affect different systems of the human body. In this work, the design and development of a computational model of the human cardiovascular system is proposed. The structure of the model has been built from a physiological base, considering some of the mechanisms associated to the cardiovascular system. Thus, the aim of the model is the prediction, heartbeat by heartbeat, of some hemodynamic variables from the cardiovascular system, in different pathophysiological cardiac situations. A modular approach to development of the model has been considered in order to include new knowledge that could force the model's hemodynamic. The model has been validated comparing the results obtained with hemodynamic values published by other authors. The results show the usefulness and applicability of the model developed. Thus, different simulations of some cardiac pathologies and physical exercise situations are presented, together with the dynamic behaviors of the different variables considered in the model.
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