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Neuro modulated diabetes control (NeuMeDiC) - decoding the neural pathways to achieve a better control of diabetes.

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Title: Neuro modulated diabetes control (NeuMeDiC) - decoding the neural pathways to achieve a better control of diabetes.
Authors: Guemes Gonzalez, Amparo
Item Type: Thesis or dissertation
Abstract: Diabetes is a disease caused by a breakdown in the glucose metabolism resulting in abnormal blood glucose fluctuations. Traditional therapies involve external insulin injection in response to elevated blood glucose, which faces limitations such as delays in insulin action and the non-physiological absorption of insulin into the systemic circulation. Recently, bioelectronic medicine has emerged as a powerful strategy for treating chronic diseases, such as diabetes, by electrically interfacing with peripheral nerves and organs. The research conducted in this thesis combines different strategies to investigate the development of novel neuromodulation-pharmaceutical therapeutic technology for people with diabetes. Following a scientific-based approach, the thesis presents studies that allowed increasing our understanding of the physiological mechanisms underlying the neural glycemic regulation. In vivo experiments in rodents provided new insights into the impact of vagus nerve stimulation (VNS) frequency on blood glucose, insulin and glucagon concentrations. To further characterize this neuro-metabolic interaction, the first mathematical model describing the physiological metabolic events after cervicalVNSwas developed and validated. Using an application-based strategy, the thesis investigates the opportunities for incorporating bioelectronic medicine to traditional diabetes technology in clinical scenarios. In particular, one study presents the proof of concept of a novel closed-loop glucose controller that regulates the insulin and glucagon dose delivery, and drive the insulin sensitivity (SI) of virtual patients using neuromodulation. In silico experiments demonstrated improved safety and efficacy compared to traditional controllers. An additional study introduces an original data-driven approach to predict the quality of overnight glycaemia based on the application of binary classifiers on metabolic data. To fully exploit bioelectronic medicine as an alternative or complementary therapy for diabetes, advances in minimally invasive technology and promotion of our knowledge of the field are needed. The work presented in this thesis opens the door to further research that promises to transform the current healthcare scenario in diabetes.
Content Version: Open Access
Issue Date: Nov-2020
Date Awarded: Feb-2021
URI: http://hdl.handle.net/10044/1/87391
DOI: https://doi.org/10.25560/87391
Copyright Statement: Creative Commons Attribution NonCommercial Licence
Supervisor: Georgiou, Pantelakis
Sponsor/Funder: Rafael del Pino Foundation Excellence Scholarship
Broadcom Foundation
Engineering and Physical Sciences Research Council
Department: Electrical and Electronic Engineering
Publisher: Imperial College London
Qualification Level: Doctoral
Qualification Name: Doctor of Philosophy (PhD)
Appears in Collections:Electrical and Electronic Engineering PhD theses

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