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(1 - 3 of 3)
- Title
- AGENT-BASED MODELING OF IMMUNE RESPONSE IN THE DEVELOPMENT OF TYPE 1 DIABETES
- Creator
- Xu, Qian
- Date
- 2020
- Description
-
Diabetes is a chronic disease that affects a large number of people around the world and cause many co-morbidities ranging from cardiovascular...
Show moreDiabetes is a chronic disease that affects a large number of people around the world and cause many co-morbidities ranging from cardiovascular diseases, neuropathy, retinopathy and blindness and kidney failure. The economic burden induced by diabetes is not only caused by the wage loss and medical burden, but also with the cost of treatment of diabetes and co-morbidities caused by diabetes. Clinical research for treatment and cure of diabetes is costly. Computer modeling and simulation studies provide an economical alternative to conduct preliminary evaluation of new hypotheses and alternatives in new therapies. The most promising results obtained from simulations can then be investigates experimentally, improving the efficiency of experiments and clinical studies. This work focuses on the development of an agent-based model to describe the destruction of islets and β cells and the development of Type 1 diabetes. The whole process of inflammation related to diabetes takes place in pancreatic lymph node, circulation, and pancreatic tissue with islets. The infiltration to islets and insulin-producing β cell damage happens in the pancreatic tissue with islets; the lymphocytes activation and antigen presentation majorly happened in the pancreatic lymph node. Therefore, the model described activities taking place in the islets in the pancreatic tissue section and pancreatic lymph nodes, the interactions among T cells, α/β cells, antigen presentation cells and immunosuppression cells. Cell behavior was obtained from the literature that published experiment results and used to develop the rules followed by the agents representing various types of cells and their interactions. The agent-based model provides a framework to describe relationship between lymphocytes and β cell through the trends of cell variations in the inflammation and demonstrates the effects of these cells in the disease development. Two different systems, a mouse model and a human model have been developed. The simulation results with the mouse model indicate that the different types of regulatory cells play different roles in suppressing inflammation. Among them, the regulatory T cells play the most important role in suppressing inflammation, but the B regulatory cell conversion is the key to induce the cascade of regulatory cell generation in inflammatory environment when there are no regulatory cytokines in the environment. The simulation results with the human model are mostly similar with mouse model, however, their effect of potential therapies such as addition of Tregs did not do as well as that in mouse model. The treatment method might be adjusted by combining other cytokines or immunosuppression cells in human assays.
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- Title
- Modeling the Glycemic Response to Physical Activity and Athletic Competition Anxiety in People with Type 1 Diabetes
- Creator
- Hobbs, Nicole B.
- Date
- 2021
- Description
-
The first observational study of recreational athletes with T1D during a meaningful athletic competition and a non-competitive exercise...
Show moreThe first observational study of recreational athletes with T1D during a meaningful athletic competition and a non-competitive exercise session was conducted. Non-invasive wearable devices and surveys are used to identify the presence or absence of competition stress during physical activity and to estimate physical activity intensity. An elevated glycemic trend on the day of an athletic competition is a frequent complaint among people with T1D and this increase was consistently observed in our study population. The elevation in glycemia is impacted by the individual behavior related to diabetes management and this behavioral change is impacted by the individual’s duration of diabetes and other demographic traits. A physical activity-intensity dependent model of glucose-insulin dynamics was developed for a type 1 diabetes simulator as a basis for the development of multivariable artificial pancreas systems. Several potential model structures were compared to assess the influence of model terms related to endogenous glucose production, glucose utilization, and glucose transfer. The model including all three terms accurately describes the relation of plasma insulin and physical activity intensity upon glucose production and glucose utilization to generate the appropriate glucose response for each physical activity condition ranging from low to maximal intensity efforts. All artificial pancreas performance metrics have been determined based upon physician-defined metrics for success. An online survey was conducted to assess individual goals for diabetes management, and for many individuals, the ability to achieve personalized metrics is unnecessary as their goals match the general metrics. As individual targets may be set by the individual or their doctor, the ability to achieve those are still of interest. A framework to target the individual management goals with the multivariable artificial pancreas system is developed which increased the percentage of time spent in each individual target range in simulations.
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- Title
- Assessment of Sleep Characteristics and Their Effects in People with Type 1 Diabetes for the Development of a Sleep Module for the Multivariable Artificial Pancreas System
- Creator
- Brandt, Rachel
- Date
- 2021
- Description
-
his work is focused on the relationship between sleep and blood glucose control in people with Type 1 Diabetes and on the development of a...
Show morehis work is focused on the relationship between sleep and blood glucose control in people with Type 1 Diabetes and on the development of a sleep module incorporating new variables and rules for use in automated insulin delivery and advisory systems. Through this research, sleep effects were identified, quantified and incorporated into a multivariable artificial pancreas system (mvAP) that is currently being developed. The mvAP uses different physiological signals acquired through non-invasive wearable sensors along with a continuous glucose monitor (CGM) to detect the state of the user to predict future blood glucose values to aid in insulin dosing decisions. The overall objective of the research was to develop and add a module to further improve the successful mvAP by incorporating sleep related information while retaining the functionality and safety of the system and improving the effectiveness in maintaining good glycemic control. Two types of sleep effects were studied: effects of sleep characteristics and stages in real-time (during sleep) and effects of sleep on glucose metabolism the next day. It was found that poor sleep quality was related to higher glycemic variability overnight in adults with Type 1 Diabetes. However, in adults without diabetes, there were no consistent relationships found between sleep stages and changes in blood glucose levels overnight. For adults with Type 1 Diabetes, it was determined that Sleep Quality, Total Sleep Time, Wake After Sleep Onset (WASO), Number of Awakenings >5 minutes, and amount of Deep sleep could be used in conjunction with insulin on board and the amount of time that has passed since the user has woken up to predict how much more insulin may be needed at the first meal of the day. This Insulin Multiplier Algorithm was tested and validated in replay simulations. Finally, in order to incorporate these relationships into the mvAP, a sleep stage detection algorithm was developed using the Empatica E4 wristband.
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