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Akshey, Singh TR. Analysis of the human liver model through semi-analytical and numerical techniques with non-singular kernel. Comput Methods Biomech Biomed Engin 2024:1-13. [PMID: 38556900 DOI: 10.1080/10255842.2024.2332370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 03/13/2024] [Indexed: 04/02/2024]
Abstract
This work consists of the study of the time-fractional human liver model with the Caputo-Fabrizio fractional derivative. The existence and uniqueness of the proposed model are shown using fixed point theory. Also, the stability of the considered model is shown using the Ulam Hyres theorem and the Lyapunov function. The solution of the proposed model is obtained using a semi-analytical and numerical scheme. The series solution obtained from the semi-analytical method gives the proper result at any time, similarly, the numerical scheme gives the solution for a long time. The obtained numerical results are compared with real clinical data and earlier published work and found to be very close to real data than earlier published work. Results in the graphs and tables show that the proposed fractional-order model is superior to the traditional model.
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Affiliation(s)
- Akshey
- Department of Mathematics, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India
| | - Twinkle R Singh
- Department of Mathematics, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India
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Benam KD, Khoshamadi H, Lema-Parez L, Gros S, Fougner AL. A Nonlinear State Observer for the Bi-Hormonal Intraperitoneal Artificial Pancreas. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:171-176. [PMID: 36086155 DOI: 10.1109/embc48229.2022.9871264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Currently, continuous glucose monitoring sensors are used in the artificial pancreas to monitor blood glucose levels. However, insulin and glucagon concentrations in different parts of the body cannot be measured in real-time, and determining body glucagon sensitivity is not feasible. Estimating these states provides more information about the current system status, facilitating improved decision-making by the model-based controller. In this regard, the aim of this paper is to design a nonlinear high-gain observer for a bi-hormonal artificial pancreas in the presence of measurement noises, model uncertainties, and disturbances. The model used in the observer is based on an existing intraperitoneal nonlinear animal model in the literature. This model is modified by assuming that insulin can directly transfer from the peritoneal cavity to the bloodstream. Based on a set of realistic assumptions, one model is considered after each hormone infusion, and two observers are separately designed. The model is divided into the insulin-phase and glucagon-phase models based on a set of realistic assumptions. Thereafter, two high-gain observers are designed separately for these phases contributing to estimating the non-measurable states. The observer error is proven to be locally uniformly ultimately bounded, and it is verified that any asymptotically stable control laws remain stable in the presence of the observer. The performance of the observers with different gains is evaluated for a scenario with multiple insulin and glucagon infusions. The proposed observer converges to a finite error, according to the results. Clinical relevance- In Type 1 diabetic patients, the developed observer can be employed in a closed-loop artificial pan-creas to improve the performance of model-based controllers. It estimates the key states, which are necessary for forecasting the body's response to insulin and glucagon boluses.
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Olçomendy L, Cassany L, Pirog A, Franco R, Puginier E, Jaffredo M, Gucik-Derigny D, Ríos H, Ferreira de Loza A, Gaitan J, Raoux M, Bornat Y, Catargi B, Lang J, Henry D, Renaud S, Cieslak J. Towards the Integration of an Islet-Based Biosensor in Closed-Loop Therapies for Patients With Type 1 Diabetes. Front Endocrinol (Lausanne) 2022; 13:795225. [PMID: 35528003 PMCID: PMC9072637 DOI: 10.3389/fendo.2022.795225] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 02/25/2022] [Indexed: 01/01/2023] Open
Abstract
In diabetes mellitus (DM) treatment, Continuous Glucose Monitoring (CGM) linked with insulin delivery becomes the main strategy to improve therapeutic outcomes and quality of patients' lives. However, Blood Glucose (BG) regulation with CGM is still hampered by limitations of algorithms and glucose sensors. Regarding sensor technology, current electrochemical glucose sensors do not capture the full spectrum of other physiological signals, i.e., lipids, amino acids or hormones, relaying the general body status. Regarding algorithms, variability between and within patients remains the main challenge for optimal BG regulation in closed-loop therapies. This work highlights the simulation benefits to test new sensing and control paradigms which address the previous shortcomings for Type 1 Diabetes (T1D) closed-loop therapies. The UVA/Padova T1DM Simulator is the core element here, which is a computer model of the human metabolic system based on glucose-insulin dynamics in T1D patients. That simulator is approved by the US Food and Drug Administration (FDA) as an alternative for pre-clinical testing of new devices and closed-loop algorithms. To overcome the limitation of standard glucose sensors, the concept of an islet-based biosensor, which could integrate multiple physiological signals through electrical activity measurement, is assessed here in a closed-loop insulin therapy. This investigation has been addressed by an interdisciplinary consortium, from endocrinology to biology, electrophysiology, bio-electronics and control theory. In parallel to the development of an islet-based closed-loop, it also investigates the benefits of robust control theory against the natural variability within a patient population. Using 4 meal scenarios, numerous simulation campaigns were conducted. The analysis of their results then introduces a discussion on the potential benefits of an Artificial Pancreas (AP) system associating the islet-based biosensor with robust algorithms.
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Affiliation(s)
- Loïc Olçomendy
- Univ. Bordeaux, CNRS, Bordeaux INP, IMS, UMR 5218, Talence, France
| | - Louis Cassany
- Univ. Bordeaux, CNRS, Bordeaux INP, IMS, UMR 5218, Talence, France
| | - Antoine Pirog
- Univ. Bordeaux, CNRS, Bordeaux INP, IMS, UMR 5218, Talence, France
| | - Roberto Franco
- Tecnológico Nacional de México/I.T. La Laguna, Torreón, Mexico
| | | | | | | | - Héctor Ríos
- Tecnológico Nacional de México/I.T. La Laguna, Torreón, Mexico
- Cátedras CONACYT, Ciudad de México, Mexico
| | | | - Julien Gaitan
- Univ. Bordeaux, CNRS, CBMN, UMR 5248, Pessac, France
| | | | - Yannick Bornat
- Univ. Bordeaux, CNRS, Bordeaux INP, IMS, UMR 5218, Talence, France
| | - Bogdan Catargi
- Univ. Bordeaux, CNRS, CBMN, UMR 5248, Pessac, France
- Bordeaux Hospitals, Endocrinology and Metabolic Diseases Unit, Bordeaux, France
| | - Jochen Lang
- Univ. Bordeaux, CNRS, CBMN, UMR 5248, Pessac, France
| | - David Henry
- Univ. Bordeaux, CNRS, Bordeaux INP, IMS, UMR 5218, Talence, France
| | - Sylvie Renaud
- Univ. Bordeaux, CNRS, Bordeaux INP, IMS, UMR 5218, Talence, France
| | - Jérôme Cieslak
- Univ. Bordeaux, CNRS, Bordeaux INP, IMS, UMR 5218, Talence, France
- *Correspondence: Jérôme Cieslak,
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A Hybrid Automata Approach for Monitoring the Patient in the Loop in Artificial Pancreas Systems. SENSORS 2021; 21:s21217117. [PMID: 34770425 PMCID: PMC8587755 DOI: 10.3390/s21217117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 10/18/2021] [Accepted: 10/23/2021] [Indexed: 11/16/2022]
Abstract
The use of automated insulin delivery systems has become a reality for people with type 1 diabetes (T1D), with several hybrid systems already on the market. One of the particularities of this technology is that the patient is in the loop. People with T1D are the plant to control and also a plant operator, because they may have to provide information to the control loop. The most immediate information provided by patients that affects performance and safety are the announcement of meals and exercise. Therefore, to ensure safety and performance, the human factor impact needs to be addressed by designing fault monitoring strategies. In this paper, a monitoring system is developed to diagnose potential patient modes and faults. The monitoring system is based on the residual generation of a bank of observers. To that aim, a linear parameter varying (LPV) polytopic representation of the system is adopted and a bank of Kalman filters is designed using linear matrix inequalities (LMI). The system uncertainty is propagated using a zonotopic-set representation, which allows determining confidence bounds for each of the observer outputs and residuals. For the detection of modes, a hybrid automaton model is generated and diagnosis is performed by interpreting the events and transitions within the automaton. The developed system is tested in simulation, showing the potential benefits of using the proposed approach for artificial pancreas systems.
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Olcomendy L, Pirog A, Lebreton F, Jaffredo M, Cassany L, Gucik Derigny D, Cieslak J, Henry D, Lang J, Catargi B, Raoux M, Bornat Y, Renaud S. Integrating an Islet-Based Biosensor in the Artificial Pancreas: In Silico Proof-of-Concept. IEEE Trans Biomed Eng 2021; 69:899-909. [PMID: 34469288 DOI: 10.1109/tbme.2021.3109096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE Current treatment of type 1 diabetes by closed-loop approaches depends on continuous glucose monitoring. However, glucose readings alone are insufficient for an artificial pancreas to truthfully restore glucose homeostasis where additional physiological regulators of insulin secretion play a considerable role. Previously, we have developed an electrophysiological biosensor of pancreatic islet activity, which integrates these additional regulators through electrical measurement. This work aims at investigating the performance of the biosensor in a blood glucose control loop, to establish an in silico proof-of-concept. METHODS Two islet algorithm models were identified on experimental data recorded with the biosensor. First, we validated electrical measurement as a means to exploit the inner regulation capabilities of islets for intravenous glucose measurement and insulin infusion. Then, an artificial pancreas integrating the islet-based biosensor was compared to standard treatment approaches using subcutaneous routes. The closed-loop simulations were performed in the UVA/Padova T1DM Simulator where a series of realistic meal scenarios were applied to virtual diabetic patients. RESULTS With intravenous routes, the endogenous islet algorithms successfully restored glucose homeostasis for all patient categories (mean time in range exceeds 90%) while mitigating the risk of adverse glycaemic events (mean BGI < 2). Using subcutaneous routes, the biosensor-based artificial pancreas was as performing as standard treatments, and outperformed them under challenging conditions. CONCLUSION This work validates the concept of using pancreatic islets algorithms in an artificial pancreas in silico. SIGNIFICANCE Pancreatic islet endogenous algorithms obtained via an electrophysiological biosensor successfully regulate blood glucose levels of virtual type 1 diabetic patients.
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Zhu T, Li K, Herrero P, Georgiou P. Basal Glucose Control in Type 1 Diabetes Using Deep Reinforcement Learning: An In Silico Validation. IEEE J Biomed Health Inform 2021; 25:1223-1232. [PMID: 32755873 DOI: 10.1109/jbhi.2020.3014556] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
People with Type 1 diabetes (T1D) require regular exogenous infusion of insulin to maintain their blood glucose concentration in a therapeutically adequate target range. Although the artificial pancreas and continuous glucose monitoring have been proven to be effective in achieving closed-loop control, significant challenges still remain due to the high complexity of glucose dynamics and limitations in the technology. In this work, we propose a novel deep reinforcement learning model for single-hormone (insulin) and dual-hormone (insulin and glucagon) delivery. In particular, the delivery strategies are developed by double Q-learning with dilated recurrent neural networks. For designing and testing purposes, the FDA-accepted UVA/Padova Type 1 simulator was employed. First, we performed long-term generalized training to obtain a population model. Then, this model was personalized with a small data-set of subject-specific data. In silico results show that the single and dual-hormone delivery strategies achieve good glucose control when compared to a standard basal-bolus therapy with low-glucose insulin suspension. Specifically, in the adult cohort (n = 10), percentage time in target range 70, 180 mg/dL improved from 77.6% to 80.9% with single-hormone control, and to 85.6% with dual-hormone control. In the adolescent cohort (n = 10), percentage time in target range improved from 55.5% to [Formula: see text] with single-hormone control, and to 78.8% with dual-hormone control. In all scenarios, a significant decrease in hypoglycemia was observed. These results show that the use of deep reinforcement learning is a viable approach for closed-loop glucose control in T1D.
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Moscardó V, Díez JL, Bondia J. Parallel Control of an Artificial Pancreas with Coordinated Insulin, Glucagon, and Rescue Carbohydrate Control Actions. J Diabetes Sci Technol 2019; 13:1026-1034. [PMID: 31631688 PMCID: PMC6835176 DOI: 10.1177/1932296819879093] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND An artificial pancreas with insulin and glucagon delivery has the potential to reduce the risk of hypo- and hyperglycemia in people with type 1 diabetes. However, a maximum dose of glucagon of 1 mg/d is recommended, potentially still requiring rescue carbohydrates in some situations. This work presents a parallel control structure with intrinsic insulin, glucagon, and rescue carbohydrates coordination to overcome glucagon limitations when needed. METHODS The coordinated controller that combines insulin, glucagon, and rescue carbohydrate suggestions (DH-CC-CHO) was compared with the insulin and glucagon delivery coordinated controller (DH-CC). The impact of carbohydrate quantization for practical delivery was also assessed. An in silico study using the UVA-Padova simulator, extended to include exercise and various sources of variability, was performed. RESULTS DH-CC and DH-CC-CHO performed similarly with regard to mean glucose (126.25 [123.43; 130.73] vs 127.92 [123.99; 132.97] mg/dL, P = .088), time in range (93.04 [90.00; 95.92] vs 92.91 [90.05; 95.75]%, P = .508), time above 180 mg/dL (4.94 [2.72; 7.53] vs 4.99 [2.93; 7.24]%, P = .966), time below 70 mg/dL (0.61 [0.09; 1.75] vs 0.96 [0.23; 2.17]%, P = .1364), insulin delivery (43.50 [38.68; 51.75] vs 42.86 [38.58; 51.36] U/d, P = .383), and glucagon delivery (0.75 [0.40; 1.83] vs 0.76 [0.43; 0.99] mg/d, P = .407). Time below 54 mg/dL was different (0.00 [0.00; 0.05] vs 0.00 [0.00; 0.16]%, P = .036), although non-clinically significant. This was due to the carbs quantization effect in a specific patient, as no statistical difference was found when carbs were not quantized (0.00 [0.00; 0.05] vs 0.00 [0.00; 0.00]%, P = .265). CONCLUSIONS The new strategy of automatic rescue carbohydrates suggestion in coordination with insulin and glucagon delivery to overcome constraints on daily glucagon delivery was successfully evaluated in an in silico proof of concept.
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Affiliation(s)
- Vanessa Moscardó
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, Valencia, Spain
| | - José Luis Díez
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, Valencia, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Av. Monforte de Lemos, Madrid, Spain
| | - Jorge Bondia
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, Valencia, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Av. Monforte de Lemos, Madrid, Spain
- Jorge Bondia, Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, C/Camino de Vera, s/n, 46022 Valencia, Spain.
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Moscardó V, Herrero P, Díez JL, Giménez M, Rossetti P, Georgiou P, Bondia J. Coordinated dual-hormone artificial pancreas with parallel control structure. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2019.06.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Beato-Víbora PI, Arroyo-Díez FJ. New uses and formulations of glucagon for hypoglycaemia. Drugs Context 2019; 8:212599. [PMID: 31402931 PMCID: PMC6675539 DOI: 10.7573/dic.212599] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 06/23/2019] [Accepted: 06/26/2019] [Indexed: 12/11/2022] Open
Abstract
Hypoglycaemia is the more frequent complication of insulin therapy and the main barrier to tight glycaemic control. Injectable glucagon and oral intake of carbohydrates are the recommended treatments for severe and non-severe hypoglycaemia episodes, respectively. Nasal glucagon is currently being developed as a ready-to-use device, to simplify severe hypoglycaemia rescue. Stable forms of liquid glucagon could open the field for different approaches for mild to moderate hypoglycaemia treatment, such as mini-doses of glucagon or continuous subcutaneous glucagon infusion as a part of dual-hormone closed-loop systems. Pharmaceutical companies are developing stable forms of native glucagon or glucagon analogues for that purpose.
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Affiliation(s)
- Pilar I Beato-Víbora
- Department of Endocrinology and Nutrition, Department of Paediatrics, Badajoz University Hospital, Badajoz, Spain
| | - Francisco J Arroyo-Díez
- Department of Endocrinology and Nutrition, Department of Paediatrics, Badajoz University Hospital, Badajoz, Spain
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Shirin A, Della Rossa F, Klickstein I, Russell J, Sorrentino F. Optimal regulation of blood glucose level in Type I diabetes using insulin and glucagon. PLoS One 2019; 14:e0213665. [PMID: 30893335 PMCID: PMC6426249 DOI: 10.1371/journal.pone.0213665] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 02/26/2019] [Indexed: 12/11/2022] Open
Abstract
The Glucose-Insulin-Glucagon nonlinear model accurately describes how the body responds to exogenously supplied insulin and glucagon in patients affected by Type I diabetes. Based on this model, we design infusion rates of either insulin (monotherapy) or insulin and glucagon (dual therapy) that can optimally maintain the blood glucose level within desired limits after consumption of a meal and prevent the onset of both hypoglycemia and hyperglycemia. This problem is formulated as a nonlinear optimal control problem, which we solve using the numerical optimal control package PSOPT. Interestingly, in the case of monotherapy, we find the optimal solution is close to the standard method of insulin based glucose regulation, which is to assume a variable amount of insulin half an hour before each meal. We also find that the optimal dual therapy (that uses both insulin and glucagon) is better able to regulate glucose as compared to using insulin alone. We also propose an ad-hoc rule for both the dosage and the time of delivery of insulin and glucagon.
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Affiliation(s)
- Afroza Shirin
- Mechanical Engineering Department, University of New Mexico, Albuquerque, NM 87131, United States of America
- * E-mail:
| | - Fabio Della Rossa
- Mechanical Engineering Department, University of New Mexico, Albuquerque, NM 87131, United States of America
| | - Isaac Klickstein
- Mechanical Engineering Department, University of New Mexico, Albuquerque, NM 87131, United States of America
| | - John Russell
- Mechanical Engineering Department, University of New Mexico, Albuquerque, NM 87131, United States of America
| | - Francesco Sorrentino
- Mechanical Engineering Department, University of New Mexico, Albuquerque, NM 87131, United States of America
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Kim DM. New anti-diabetic agents. JOURNAL OF THE KOREAN MEDICAL ASSOCIATION 2017. [DOI: 10.5124/jkma.2017.60.12.992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Affiliation(s)
- Doo-Man Kim
- Department of Endocrinology and Metabolism, Gangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
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