Managing Challenges of Non Communicable Diseases during Pregnancy: An Innovative Approach

Le sfide lanciate dalle malattie non trasmissibili sono accolte da tecnologie sempre più all'avanguardia. Nonostante questo, ancora oggi gestire e monitorare gravidanze a rischio rimane un problema. La simulazione di condizioni come quella data dal diabete gestazionale, può aiutare a capire quali sono i principali fattori che influenzano l'andamento della malattia in modo da poterne evitare l'insorgenza e, in questo modo, migliorare la salute di madri e generazioni future. Questa tesi ha come obietto lo studio e il miglioramento di un sistema Agent-Based pensato per il trattamento del diabete di tipo 1 e la modellazione di una sua estensione per il diabete gestazionale. Al termine della tesi è stato migliorato il sistema rendendolo più fedele ai cambiamenti fisiologici che avvengono durante il metabolismo del glucosio e la modellazione della placenta e relativamente delle modifiche che apporta all'intero sistema getta le basi per nuovi sviluppi legati al trattamento di malattie durante il periodo di gestazione

Gestational Diabetes Mellitus

The incidence of gestational diabetes mellitus (GDM) is increasing, and this pathological condition is strongly associated with some serious adverse pregnancy outcomes and important miscellaneous long-term complications. Therefore, it is important that GDM is timely recognized and adequately managed. Although much knowledge has been acquired regarding the prevention, diagnosis, implications, and management of GDM, the exact mechanisms of its genesis are still under investigation. This book provides a comprehensive overview of recent advances in gestational diabetes mellitus. It includes three major sections directing the reader’s attention to the etiology, management, and consequences of the disorder

Gramáticas evolutivas para la predicción de hipoglucemias en diabetes

Trabajo de Fin de Grado en Ingeniería Informática, Facultad de Informática UCM, Departamento Ingeniería del Software e Inteligencia Artificial, Curso 2021/2022. https://github.com/ABSysGroup/jeco/tree/TFG_GE4HYPODiabetic patients have to manage their blood sugar correctly to prevent complications. One such complication is hypoglycemia or low blood sugar, which occurs when the blood glucose concentration goes below a certain threshold. A hypoglycemic episode needs to be rectified before it becomes harmful and can be a very distressing situation for the patient. The main goal of this study is to program Structured Grammatical Evolution and Dynamic Structured Grammatical Evolution algorithms and use them to generate models for the prediction of hypoglycemic episodes in patients with diabetes. The algorithms will be used to obtain a white-box model made up of if-then-else statements that given some input data, comprised of the blood glucose and exercise readings of the patients from the previous 2 hours, optimizes a logical relation between these variables. The resulting formula will be able to determine if the patient is going to have a hypoglycemic episode in a 30, 60, 90 and 120 minutes prediction horizon.Los pacientes con diabetes deben controlar correctamente su nivel de azúcar en sangre para evitar complicaciones. Una de estas complicaciones es la hipoglucemia o nivel bajo de azúcar en sangre, que ocurre cuando la concentración de glucosa en la sangre cae por debajo de cierto umbral. Un episodio de hipoglucemia debe corregirse antes de que se vuelva dañino y puede ser una situación muy angustiosa para el paciente. El objetivo principal de este estudio es programar los algoritmos de Gramáticas Evolutivas Estructuradas y Gramáticas Evolutivas Estructuradas Dinámicas, y utilizarlos para generar modelos de predicción de episodios hipoglucémicos en pacientes con diabetes. Los algoritmos se utilizarán para obtener un modelo de caja blanca formado por sentencias if-then-else que, dados unos datos de entrada, compuestos por el nivel de la glucosa en sangre y las lecturas de datos de ejercicio de los pacientes de las 2 horas anteriores, optimiza una relación lógica entre estas variables. La fórmula resultante se usa para determinar si el paciente va a tener un episodio de hipoglucemia en un plazo de 30, 60, 90 y 120 minutos.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 385)

This bibliography lists 536 reports, articles and other documents introduced into the NASA Scientific and Technical Information System Database. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

Biosensors

A biosensor is defined as a detecting device that combines a transducer with a biologically sensitive and selective component. When a specific target molecule interacts with the biological component, a signal is produced, at transducer level, proportional to the concentration of the substance. Therefore biosensors can measure compounds present in the environment, chemical processes, food and human body at low cost if compared with traditional analytical techniques. This book covers a wide range of aspects and issues related to biosensor technology, bringing together researchers from 11 different countries. The book consists of 16 chapters written by 53 authors. The first four chapters describe several aspects of nanotechnology applied to biosensors. The subsequent section, including three chapters, is devoted to biosensor applications in the fields of drug discovery, diagnostics and bacteria detection. The principles behind optical biosensors and some of their application are discussed in chapters from 8 to 11. The last five chapters treat of microelectronics, interfacing circuits, signal transmission, biotelemetry and algorithms applied to biosensing

P5 eHealth: An Agenda for the Health Technologies of the Future

This open access volume focuses on the development of a P5 eHealth, or better, a methodological resource for developing the health technologies of the future, based on patients’ personal characteristics and needs as the fundamental guidelines for design. It provides practical guidelines and evidence based examples on how to design, implement, use and elevate new technologies for healthcare to support the management of incurable, chronic conditions. The volume further discusses the criticalities of eHealth, why it is difficult to employ eHealth from an organizational point of view or why patients do not always accept the technology, and how eHealth interventions can be improved in the future. By dealing with the state-of-the-art in eHealth technologies, this volume is of great interest to researchers in the field of physical and mental healthcare, psychologists, stakeholders and policymakers as well as technology developers working in the healthcare sector