DIAP – Double Intraperitoneal Artificial Pancreas

• Dirnena-Fusini, Ilze; Riaz, Misbah; Christiansen, Sverre Christian & Carlsen, Sven Magnus (2025). Nano-amounts of glucagon pre-mixed with fast-acting insulin lispro: Effect on insulin absorption in pigs. Current Therapeutic Research: Clinical and Experimental. ISSN 0011-393X. 103. doi: 10.1016/j.curtheres.2025.100803. Full text in Research Archive Show summary

  Background Glucagon leads to substantial but short-lived subcutaneous vasodilation. Using micro-amounts of glucagon at the insulin injection site increases insulin absorption. Objective We hypothesized that a premixed solution of insulin and nanogram doses of glucagon would improve the pharmacokinetic and pharmacodynamic properties of subcutaneously injected insulin. Methods In this series of proof-of-concept experiments, 17 anesthetized pigs were included. Nine pigs were included in the control groups; they received a subcutaneous injection of 10 U of insulin lispro (LyumjevⓇ or HumalogⓇ). Eight pigs were included in the glucagon groups; they received 10 U of a premixed insulin (LyumjevⓇ or HumalogⓇ)/glucagon solution (5 ng glucagon/unit of insulin). Arterial blood was frequently sampled for 210 minutes to assess insulin and glucose concentrations. The impact on glucose metabolism was evaluated through euglycemic clamp investigation. Results When premixed insulin LyumjevⓇ/glucagon was injected, insulin Tmax decreased from 33 to 20 minutes (SE = 6.6, P = 0.08), and Cmax was 2-fold higher than that in the control group (100 mU/L vs 46 mU/L, SE = 4.8, P = 0.007). When premixed insulin HumalogⓇ/glucagon was injected, Tmax and Cmax did not change significantly (P = 0.53 and P = 0.83, respectively). Insulin AUC for the first 15 minutes increased two-fold when insulin LyumjevⓇ/glucagon was injected (946 mU×min/L vs 337 mU×min/L, SE = 196, P = 0.02). A similar trend was observed when HumalogⓇ/glucagon was injected (306 mU×min/L vs 65 mU×min/L, SE = 125), although this difference did not reach statistical significance (P = 0.102) compared with the control groups. Conclusions This series of proof-of-concept experiments in anesthetized pigs indicate that premixing nanogram doses of glucagon in fast-acting insulin lispro formulations may speed up the absorption of subcutaneously injected insulin.

• Wolff, Miriam Kopperstad; Royston, Sam; Fougner, Anders Lyngvi; Schaathun, Hans Georg; Steinert, Martin & Volden, Rune (2025). A Perspective on Harmonizing Diabetes Management Datasets. Data in Brief. 59(111399). doi: 10.1016/j.dib.2025.111399. Full text in Research Archive Show summary

  Diabetes management datasets are often compiled from various sensors and devices, including diabetes technology, activity trackers, and other health-related equipment, resulting in heterogeneous data formats. Despite the abundance of available data, inconsistencies in dataset formats and data-sharing practices limit the ability to build on prior work and compare results across studies. Standardizing data-sharing formats can improve consistency, facilitate dataset consolidation, and reduce the data processing burden for researchers. This article explores the current state of data-sharing practices in diabetes management research and proposes guidelines for harmonizing datasets using a unified time-aligned tabular format. We demonstrate the application of these guidelines on three widely used datasets and highlight key challenges in achieving data harmonization. We call on the broader research community to develop and adopt detailed recommendations for standardized data-sharing practices.

• Wolff, Miriam Kopperstad; Schaathun, Hans Georg; Gros, Sebastien Nicolas; Volden, Rune; Steinert, Martin & Fougner, Anders Lyngvi (2025). Blood Glucose Prediction Algorithms Require Clinically Relevant Performance Criteria Beyond Accuracy. Diabetes Technology & Therapeutics. ISSN 1520-9156. 27(10), p. 858–870. doi: 10.1089/dia.2025.0074. Full text in Research Archive Show summary

  Background: The root mean squared error (RMSE) is commonly used to evaluate blood glucose prediction algorithms. However, it primarily measures how well predictions align with the most likely future values, rather than supporting optimal and proactive treatment decisions. Since diabetes management data predominantly features blood glucose values within the target range, RMSE tends to favor models that consistently predict target-range values, often at the expense of detecting clinically critical events such as rapid fluctuations, hypoglycemia, or hyperglycemia. This study examines how and why RMSE biases evaluations toward trivial models, highlighting the need for alternative performance criteria that better reflect clinical priorities. Methods: We developed the composite glucose prediction metric (CGPM) to integrate three components: RMSE, temporal gain and geometric mean (glycemic event prediction). A custom loss function was designed to emphasize clinically critical predictions during model training. Pareto frontier analysis was used to assess trade-offs among models with comparable performance. Results: CGPM was computed for five blood glucose prediction techniques (zero-order hold, naïve linear regression, ridge regression, ridge regression trained with a custom loss function, and a physiology-based model) applied to the OhioT1DM dataset. The data-driven model with the lowest RMSE performed poorly on glycemic event prediction, highlighting RMSE’s bias toward target-range predictions. In contrast, the ridge regressor trained with the custom loss function improved event prediction, showing that clinically weighted optimization mitigates biases. Conclusions: Blood glucose prediction algorithms require evaluation and optimization criteria beyond accuracy to better support optimal treatment decisions. This study introduced the CGPM as an alternative evaluation framework, along with a loss function designed for model optimization that emphasizes clinically critical but rare events. Further clinical validation is needed to refine these criteria and ensure they align more closely with the needs of diabetes management.

• Benam, Karim Davari; Dirdal, Hasti Khoshamadi; Åm, Marte Kierulf; Christiansen, Sverre Christian; Bösch, Patrick Christian & Hjelme, Dag Roar [Show all 10 contributors for this article] (2025). A Dual Hormone Predictive Controller for a Fully Automated Intraperitoneal Artificial Pancreas in Pigs. Journal of Process Control. ISSN 0959-1524. 154(103499), p. 1–21. doi: 10.1016/j.jprocont.2025.103499. Full text in Research Archive Show summary

  Fully automated regulation of blood glucose levels (BGL) has been the ultimate goal in the treatment of type 1 diabetes (T1D). In this context, full automation refers to a system that operates without requiring any patient interaction, such as meal or exercise announcements or manual insulin adjustments. However, achieving BGL control without such inputs remains a significant challenge for artificial pancreas (AP) systems, primarily due to the unfavorable mismatch between the time constants of meal absorption and the slower absorption kinetics of subcutaneously administered insulin. In this paper, we propose and test a dual-hormone intraperitoneal (IP) artificial pancreas system — delivering both insulin and glucagon — to explore the challenges and feasibility of achieving fully automated glucose regulation. To this, a predictive control approach was developed and tested in animal experiments. Experiments were conducted in six anesthetized pigs for 12–24 h and in an awake (unanaesthetized) pig for five days. The proposed method achieved a time-in-range (TIR, 3.9–10 mmol/L) of 73.1–94.2%, exceeding the average TIR reported for commercially available hybrid closed-loop systems. For comparison, the Medtronic MiniMed 670G reports a TIR of 70%, the Tandem t:slim X2 with Control-IQ achieves 72%, the Omnipod 5 with Horizon reports 70%, and the Diabeloop G7 achieves 74% TIR. The findings demonstrate that the full automation of BGL control using dual-hormone AP with IP injections is feasible. The paper also discusses the challenges and complexities associated with implementing the dual-hormone IP artificial pancreas system from the ground up. These challenges include addressing BGL measurement, estimation, prediction, and surgical considerations.

• Engell, Sarah Ellinor; Benam, Karim Davari; Bengtsson, Henrik; Jørgensen, John Bagterp & Fougner, Anders Lyngvi (2024). Optimal Experimental Design for System Identification in a Bi-Hormonal Intraperitoneal Artificial Pancreas. In Ferrara, Antonella (Eds.), Proceedings of the 2024 European Control Conference (ECC). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9783907144107. p. 856–861. doi: 10.23919/ECC64448.2024.10591137. Full text in Research Archive
• Wolff, Miriam Kopperstad; Royston, Sam & Volden, Rune (2024). GluPredKit: A Python Package for Blood Glucose Prediction and Evaluation. Journal of Open Source Software (JOSS). 9(101). doi: 10.21105/joss.06904. Full text in Research Archive Show summary

  Managing blood glucose levels is crucial for individuals with diabetes. Historically, non-linear physiological modeling of glucose dynamics laid the groundwork for automated insulin delivery. Blood glucose prediction can be used as decision support for patients or as a component in an automated insulin delivery control strategy. Today, machine learning and deep neural networks offer new pathways for improvement, and the literature is vast on proposed models. Yet, comparing these advanced models is challenging. Differences in the datasets used for testing and how results are evaluated can make comparisons from existing studies unreliable (Jacobs et al., 2023). Additionally, many research studies do not share their code, making it hard to build upon previous work. GluPredKit addresses these issues by standardizing the pipeline steps needed for any blood glucose prediction research (see Figure 1). This includes the collection, organization, and preparation of data, as well as the ability to easily compare different models and measure their effectiveness. Additionally, the software incorporates state of-the-art components, including the ability to integrate and standardize data from various sources, utilize existing prediction models, and apply established evaluation metrics. It also features automated generation of detailed model evaluation reports, guided by the consensus on blood glucose model evaluation (Jacobs et al., 2023).

• Wolff, Miriam Kopperstad; Royston, Sam; Fougner, Anders Lyngvi; Schaathun, Hans Georg; Steinert, Martin & Volden, Rune (2024). GluPredKit: Development and User Evaluation of a Standardization Software for Blood Glucose Prediction. NIKT: Norsk IKT-konferanse for forskning og utdanning. ISSN 1892-0713. Full text in Research Archive Show summary

  Blood glucose prediction is an important component of biomedical technology for managing diabetes with automated insulin delivery systems. Machine learning algorithms hold the potential to advance this technology. However, the lack of standardized methodologies impedes direct comparisons of emerging algorithms. The purpose of this study is to address this challenge by developing a software platform designed to standardize the training, testing and comparison of blood glucose prediction algorithms. First, we design and implement the software guided by the current literature. To ensure the platform's user-friendliness, we conducted preliminary testing and a user study. In this study, four participants interacted with the software and provided feedback through the System Usability Scale (SUS) and open-ended questions. The result of the study was the software GluPredKit, which features a modular, open-source architecture, complemented by a command-line interface, comprehensive documentation, and a video tutorial to enhance usability. The user study indicates that GluPredKit offers high usability, facilitating comparisons between different algorithms. Future directions include continuously enhancing the software based on user feedback. We also invite community contributions to further expand GluPredKit with state-of-the-art components and foster a collaborative effort in standardizing blood glucose prediction research.

• Wolff, Miriam Kopperstad; Steinert, Martin; Fougner, Anders Lyngvi; Oh, Doyoung; Årsand, Eirik & Volden, Rune (2024). Real-Time Blood Glucose Prediction Reveals a Discrepancy Between Performance Metrics and Real-World Evaluations. In Zhang, Houxiang & Shi, Qing (Ed.), Proceedings of the 2024 IEEE International Conference on Real-time Computing and Robotics (RCAR). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350372601. p. 570–575. doi: 10.1109/RCAR61438.2024.10671342. Full text in Research Archive Show summary

  This study evaluates machine learning (ML) algorithms for predicting blood glucose (BG) levels, essential in real-time robotic diabetes control systems that integrate insulin pumps, continuous glucose monitors, and potentially additional sensors. Our objective is to use real-time deployment insights to guide future algorithm design. While existing research presents algorithms with strong performance metrics, these often rely on repetitive datasets, limiting real-world applicability. We compared a Ridge Regressor and a Long-Short-Term Memory deep neural network, focusing on their real-time deployment and evaluation. Initially, we validated our algorithms against a benchmark dataset to ensure consistency with published studies by calculating the root mean square error (RMSE). We then assessed the same models using data from a study participant within a smartphone application, evaluating real-time predictions through a user questionnaire. Our findings revealed a discrepancy between the performance metrics and real-world evaluation, suggesting these metrics might neglect complex transformations within hidden layers and fail to reflect critical situations. This study underscores the need for future research to refine evaluation methods that consider model behavior in critical scenarios and to develop models rooted in domain-specific knowledge, incorporating physiological constraints like insulin effects, to ensure alignment with physical reality.

• Wolff, Miriam Kopperstad; Schaathun, Hans Georg; Fougner, Anders Lyngvi; Steinert, Martin & Volden, Rune (2024). Mobile Software Development Kit for Real Time Multivariate Blood Glucose Prediction. IEEE Access. 12, p. 5910–5919. doi: 10.1109/ACCESS.2024.3349496. Full text in Research Archive Show summary

  In the field of blood glucose prediction, the literature is abounded with algorithms that demonstrate potential in glucose management. However, these propositions face an issue common to many machine learning algorithms: the repeated reuse of datasets (overfitting) and a tendency to develop algorithms in isolation, detached from practical scenarios. Compounding these challenges is that many insulin pump vendors and continuous glucose monitor vendors use closed and proprietary protocols, restricting researchers’ data access and the ability to deploy complex, multivariate optimizers. This study seeks to bridge the gap between theoretical algorithms and their real-world applications by devising a software development kit. This kit collects real-time data from continuous glucose monitors, carbohydrate intake, insulin deliveries from insulin management systems, and metrics like physical activity, stress, and sleep from wearables. Our methodology leverages the open-source insulin management system, Loop, integrated with Apple Health and various wearable devices. Although navigating through diverse communication protocols to link these devices presented challenges, we succeeded in aggregating a comprehensive dataset for blood glucose predictions. To underscore the utility of our software development kit, we executed a technical proof-of-concept on this platform, illustrating real-time, individualized, data-driven multivariate blood glucose predictions. We hope that our platform can contribute to transforming machine learning algorithms from technical developments into actionable tools with real-world benefits in blood glucose management. It provides a foundation for researchers to refine their predictive algorithms and decision support systems within a more dynamic, data-rich environment.

• Teigen, Ingrid Anna; Åm, Marte Kierulf; Riaz, Misbah; Christiansen, Sverre Christian & Carlsen, Sven Magnus (2024). Effects of Low-Dose Glucagon on Subcutaneous Insulin Absorption in Pigs. Current Therapeutic Research: Clinical and Experimental. ISSN 0011-393X. 100. doi: 10.1016/j.curtheres.2024.100736. Full text in Research Archive Show summary

  Background Slow insulin absorption prevents the development of a fully automated artificial pancreas with subcutaneous insulin delivery. Objective We have hypothesized that glucagon could be used as a vasodilator to accelerate insulin absorption in a bihormonal subcutaneous artificial pancreas. The present proof-of-concept study is the first study to investigate the pharmacokinetics of insulin after subcutaneous administration of a low dose of glucagon at the site of subcutaneous insulin injection. Methods Twelve anesthetized pigs were randomized to receive a subcutaneous injection of 10 IU insulin aspart with either 100 µg glucagon or the equivalent volume of placebo (0.9% saline solution) injected at the same site. Arterial samples were collected for 180 minutes to determine insulin, glucagon, and glucose concentrations. Results Glucagon did not influence the insulin concentration Tmax in plasma. The plasma insulin AUC0–∞ was significantly larger after glucagon administration (P < 0.01). The glucagon group had significantly higher glucose concentrations in the first 30 minutes after insulin administration (P < 0.05). Conclusions This proof-of-concept study indicates that glucagon may increase the total absorption of a single dose of subcutaneously injected insulin. This is a novel observation. However, we did not observe any reduction in insulin concentration Tmax, as we had hypothesized. Further, glucagon induced a significant, undesirable increase in early blood glucose concentrations.

• Perez, Laura Lema; Aguirre-Zapata, Estefania; Fougner, Anders Lyngvi; Amicarelli, Adriana & Alvarez, Hernan (2024). Connecting sensitivity, identifiability and interpretability of a glucose minimal model, 2023 XX Workshop on Information Processing and Control (RPIC). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350329414. doi: 10.1109/RPIC59053.2023.10530857. Full text in Research Archive Show summary

  Mathematical models have increased their applications in physiology, control and systems science, and biomedical engineering because they offer the opportunity to examine the structure and behavior of complex physiological systems. In addition, they provide a concise description of complex dynamic processes, indicating ways to improve experimental design and allowing the testing of hypotheses related to physiological structure. When building a mathematical model, it is important to assess the impact of different factors on the overall behavior of the system being modeled and to determine which variables have the most significant influence on the system. Properties such as identifiability, sensitivity, and interpretability in mathematical models are crucial for representing real-world phenomena. In this paper, a sensitivity, structural identifiability, and qualitative parameter interpretability analysis is carried out in a new version of the minimal model of the glucose-insulin system proposed by Bergman. The aim was to evaluate the structure of this new mathematical model and the existing relationship among sensitivity, identifiability, and interpretability of the model parameters. The findings demonstrated the connection between the properties of sensitivity, structural identifiability, and parameter interpretability, which provide important details about the model structure.

• Langholz, Jana; Benam, Karim Davari; Sharan, Bindu; Gros, Sebastien Nicolas & Fougner, Anders Lyngvi (2023). Fully Automated Bi-Hormonal Intraperitoneal Artificial Pancreas Using a Two-Layer PID Control Scheme, Proceedings of 2023 European Control Conference (ECC). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9783907144084. doi: 10.23919/ECC57647.2023.10178295. Full text in Research Archive Show summary

  Treatment of type 1 diabetes mellitus is significantly improved by using commercially available hybrid closed-loop systems to deliver insulin. These systems, also called artificial pancreas (AP), use the subcutaneous (SC) route to deliver insulin. However, meal announcements are necessary due to the slow insulin absorption from the SC tissue. Thus due to the need for human intervention, it is called “hybrid closed loop” AP. In this work, a bi-hormonal AP with intraperitoneal (IP) infusion is designed to increase the time within the range of 3.9–10.0 mmol/l and alleviate the burden of meal announcements. A two-layer controller is designed to provide safe and effective insulin and glucagon delivery. The primary layer is based on classical PID controllers for insulin and glucagon, and the supervisory layer includes four parts: (A) Zone-based control settings, (B) Extrapolation of sensor data to compensate for sensor delay in SC tissue, (C) Auto-tuning of the PID parameters in the primary layer through simulation in an animal model, and (D) Safety barriers. The controller is designed to prevent hypoglycemia after meals and during physical activity, as well as prevent postprandial hyperglycemia. The designed AP achieved 92.5% of the time within the range of 3.9–10.0 mmol/l on a simulator trained on data from animal experiments. The results indicate that this two-layer control structure with IP infusions makes it feasible to achieve a fully automated artificial pancreas without the need for meal announcements, i.e. without human intervention.

• Åm, Marte Kierulf; Teigen, Ingrid Anna; Carlsen, Sven Magnus & Christiansen, Sverre Christian (2023). Severe bradycardia and hypotension in anaesthetized pigs: Possible interaction between octreotide, xylazine, and atropine: A case series. Scandinavian Journal of Laboratory Animal Science (SJLAS). 49(1), p. 1–5. Full text in Research Archive Show summary

  Pigs are common animal models in diabetes research. Streptozotocin-induced destruction of pancreatic β-cells is used to induce diabetes in conscious pigs. However, in short-term non-recovery experiments, suppression of endogenous insulin secretion can be faster and more easily achieved with somatostatin analogues. We report a series of severe and unexpected episodes of severe bradycardia in eight pigs during non-recovery experiments with the original aim of investigating the pharmacokinetics and pharmacodynamics of intraperitoneal hormone delivery. The adverse events occurred four to five hours into the experiments, and we believe that they were caused by an interaction between the somatostatin analogue octreotide, and the sedative drug xylazine and that atropine delayed the time of occurrence.

• Åm, Marte Kierulf; Teigen, Ingrid Anna; Riaz, Misbah; Fougner, Anders Lyngvi; Christiansen, Sverre Christian & Carlsen, Sven Magnus (2023). The artificial pancreas: two alternative approaches to achieve a fully closed-loop system with optimal glucose control. Journal of Endocrinological Investigation. ISSN 0391-4097. 46. doi: 10.1007/s40618-023-02193-2. Full text in Research Archive Show summary

  Introduction Diabetes mellitus type 1 is a chronic disease that implies mandatory external insulin delivery. The patients must monitor their blood glucose levels and administer appropriate insulin boluses to keep their blood glucose within the desired range. It requires a lot of time and endeavour, and many patients struggle with suboptimal glucose control despite all their efforts. Materials and methods This narrative review combines existing knowledge with new discoveries from animal experiments. Discussion In the last decade, artificial pancreas (AP) devices have been developed to improve glucose control and relieve patients of the constant burden of managing their disease. However, a feasible and fully automated AP is yet to be developed. The main challenges preventing the development of a true, subcutaneous (SC) AP system are the slow dynamics of SC glucose sensing and particularly the delay in effect on glucose levels after SC insulin infusions. We have previously published studies on using the intraperitoneal space for an AP; however, we further propose a novel and potentially disruptive way to utilize the vasodilative properties of glucagon in SC AP systems. Conclusion This narrative review presents two lesser-explored viable solutions for AP systems and discusses the potential for improvement toward a fully automated system: A) using the intraperitoneal approach for more rapid insulin absorption, and B) besides using glucagon to treat and prevent hypoglycemia, also administering micro-boluses of glucagon to increase the local SC blood flow, thereby accelerating SC insulin absorption and SC glucose sensor site dynamics.

• Engell, Sarah Ellinor; Bengtsson, Henrik; Benam, Karim Davari; Fougner, Anders Lyngvi & Jørgensen, John Bagterp (2023). Optimal Experimental Design to Estimate Insulin Response in Type 2 Diabetes. In Mastellone, Silvia (Eds.), Proceedings of the 2023 IEEE Conference on Control Technology and Applications (CCTA). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350335446. p. 540–545. doi: 10.1109/CCTA54093.2023.10252530. Full text in Research Archive Show summary

  In late-stage type 2 diabetes, automated titration algorithms provide a promising alternative to the current standard-of-care. Many published methods rely on personalized dose-response models to predict a safe and effective insulin dose. In this case study, we address the challenge of how to collect an informative data set to ensure practical identifiability of such models. We apply optimal experimental design to enhance the performance of a published titration algorithm. For a 24-hour experiment, we solve an optimization problem to select the size of three meals and the hourly fast-acting insulin infusion rate. In simulation, we demonstrate how the optimized protocol improves the safety of the algorithm’s dose-predictions. The results indicate that optimal experimental design has the potential to improve model-based algorithms and may be used as a qualitative tool when planning clinical experiments.

• Cheema, Muhammad Asaad; Patil, Pallavi; Siddiqui, Salman Ijaz; Rossi, Pierluigi Salvo; Stavdahl, Øyvind & Fougner, Anders Lyngvi (2023). Data-Driven Classifiers for Early Meal Detection Using ECG. IEEE Sensors Letters. 7(9), p. 1–4. doi: 10.1109/LSENS.2023.3307106. Full text in Research Archive Show summary

  This study investigates the potential of the electrocardiogram (ECG) to perform early meal detection, which is critical for developing a fully-functional automatic artificial pancreas. The study was conducted in a group of healthy subjects with different ages and genders. Two classifiers were trained: one based on neural networks (NNs) and working on features extracted from the signals; one based on convolutional NNs (CNNs) and working directly on raw data. During the test phase, both classifiers correctly detected all the meals, with the CNN outperforming the NN in terms of misdetected meals (MMs) and detection time (DT). Reliable meal onset detection with short detection time has significant practical implications: it reduces the risk of postprandial hyperglycemia and hypoglycemia, it reduces the mental burden of meal documentation for patients and related stress.

• Ahdab, Mohamad Al; Benam, Karim Davari; Khoshamadi, Hasti; Fougner, Anders Lyngvi & Gros, Sebastien Nicolas (2023). Sensor Fusion for Glucose Monitoring Systems. IFAC-PapersOnLine. ISSN 2405-8971. 56(2), p. 11527–11532. doi: 10.1016/j.ifacol.2023.10.444. Full text in Research Archive Show summary

  A fully automated artificial pancreas (AP) requires accurate blood glucose (BG) readings. However, many factors can affect the accuracy of commercially available sensors. These factors include sensor artifacts due to the pressure on surrounding tissues, connection loss, and poor calibration. The AP may administer an incorrect insulin bolus due to inaccurate sensor data when the patient is not supervising the system. The situation can be even worse in animal experiments because animals are eager to play with the sensor and apply pressure. In this study, we propose and derive a Multi-Model Kalman Filter with Forgetting Factor (MMKFF) for the problem of fusing information from redundant subcutaneous glucose sensors. The performance of the developed MMKFF was assessed by comparing it against other Kalman Filter (KF) strategies on experimental data obtained in two different animals. The developed MMKFF was shown to provide a reliable fused glucose reading. Additionally, compared to the other KF approaches, the MMKFF was shown to be better able to adjust to changes in the accuracy of the glucose sensors.

• Benam, Karim Davari; Gros, Sebastien Nicolas & Fougner, Anders Lyngvi (2023). Estimation and Prediction of Glucose Appearance Rate for Use in a Fully Closed-Loop Dual-Hormone Intraperitoneal Artificial Pancreas. IEEE Transactions on Biomedical Engineering. ISSN 0018-9294. 71(1), p. 343–354. doi: 10.1109/TBME.2023.3301730. Full text in Research Archive Show summary

  Objective: A fully automated artificial pancreas requires a meal estimator and predictions of blood glucose levels (BGL) to handle disturbances during meal times, all without relying on manual meal announcements and user interventions. This study introduces a technique for estimating the glucose appearance rate (GAR) and predicting BGL in people with type 1 diabetes and insulin and glucagon administration. It is demonstrated for intraperitoneal insulin and glucagon delivery but may be adapted to other delivery sites. Method: The estimator is designed based on the moving horizon estimation (MHE) approach, where the underlying cost function incorporates prior statistical information on the GAR in subjects over the course of a day. The proposed prediction scheme is developed to predict GAR using estimated states and an intestinal model, which is then used to predict BGL with the help of an animal glucose metabolic model. Results: The intraperitoneal dual-hormone estimator was evaluated on three anesthetized animals, achieving a 21.8% mean absolute percentage error (MAPE) for GAR estimation and a 10.0% MAPE for BGL prediction when the future GAR is known. For a 120-minute prediction horizon, the proposed predictor achieved an 18.0% MAPE for GAR and a 28.4% MAPE for BGL. Conclusion: The findings demonstrate the effectiveness and reliability of the proposed estimator and its potential for use in a fully automated artificial pancreas and reducing user interventions. Significance: This study represents advancements toward the development of a fully automated artificial pancreas, ultimately enhancing the quality of life for people with type 1 diabetes.

• Zazueta, Claudia Lopez; Stavdahl, Øyvind & Fougner, Anders Lyngvi (2022). Low-Order Nonlinear Animal Model of Glucose Dynamics for a Bihormonal Intraperitoneal Artificial Pancreas. IEEE Transactions on Biomedical Engineering. ISSN 0018-9294. 69(3), p. 1273–1280. doi: 10.1109/TBME.2021.3125839. Full text in Research Archive Show summary

  Objective: The design of an Artificial Pancreas (AP) to regulate blood glucose levels requires reliable control methods. Model Predictive Control has emerged as a promising approach for glycemia control. However, model–based control methods require computationally simple and identifiable mathematical models that represent glucose dynamics accurately, which is challenging due to the complexity of glucose homeostasis. Methods: In this work, a simple model is deduced to estimate blood glucose concentration in subjects with Type 1 Diabetes Mellitus (T1DM). Novel features in the model are power–law kinetics for intraperitoneal insulin absorption and a separate glucagon sensitivity state. Profile likelihood and a method based on singular value decomposition of the sensitivity matrix are carried out to assess parameter identifiability and guide a model reduction for improving the identification of parameters. Results: A reduced model with 10 parameters is obtained and calibrated, showing good fit to experimental data from pigs where insulin and glucagon boluses were delivered in the intraperitoneal cavity. Conclusion: A simple model with power–law kinetics can accurately represent glucose dynamics submitted to intraperitoneal insulin and glucagon injections. The reduced model was found to exhibit local practical as well as structural identifiability. Importance: The proposed model facilitates intraperitoneal bi-hormonal model-based closed-loop control in animal trials.

• Halvorsen, Martha; Benam, Karim Davari; Khoshamadi, Hasti & Fougner, Anders Lyngvi (2022). Blood Glucose Level Prediction Using Subcutaneous Sensors for in Vivo Study: Compensation for Measurement Method Slow Dynamics Using a Kalman Filter Approach. In Aghdam, Amir & IEEE, . (Ed.), IEEE 61st Conference on Decision and Control (CDC 2022). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665467612. p. 6034–6039. doi: 10.1109/CDC51059.2022.9992638. Full text in Research Archive Show summary

  The continuous glucose monitoring (CGM) system is the most common system used by people with type 1 diabetes to monitor blood glucose levels. However, it measures glucose in interstitial fluid in subcutaneous tissue rather than directly in plasma. Measuring blood glucose level in this method has slow dynamics and introduce a time lag in capturing the blood glucose level. This can reduce the quality of blood glucose regulation and result in hypo-or hyperglycemia. In this paper, a linear Kalman filter is developed to predict blood glucose concentration using CGM data to compensate for that slow dynamics. To this end, an observable input-less model describing the glucose diffusion from plasma to interstitial fluid is utilized. Notably, this model is physiology-based, and its parameters can be obtained from the literature. The designed structure is evaluated on data from two animal experiments conducted on anesthetized pigs. The data sets include CGM measurements every 1.2 seconds and sporadic blood sample analysis during experiments. Results show that the designed approach sufficiently can compensate for the slow dynamics of CGM measurements when compared to blood glucose samples, and the performance is measured using statistical accuracy scores. This compensation can improve the decision-making of control algorithms for glucose regulation during rapid changes in glucose concentration, e.g., during meals and exercise.

• Åm, Marte Kierulf; Munkerud, Erlend Yttersian; Berge, Mathilde Hallem; Christiansen, Sverre Christian & Carlsen, Sven Magnus (2022). The effect of glucagon on local subcutaneous blood flow in non-diabetic volunteers; a proof-of-concept study. European Journal of Pharmacology. ISSN 0014-2999. 926. doi: 10.1016/j.ejphar.2022.175045. Full text in Research Archive Show summary

  Introduction Glucagon and insulin are the two most important hormones for glucose metabolism and have been incorporated in the dual-hormonal artificial pancreas, a device for automated glucose regulation for people with diabetes type 1. Currently the subcutis is the preferred site of hormone delivery for insulin-only as well as dual-hormonal artificial pancreas systems. The delay in glucose-lowering effect after subcutaneous injection of insulin is substantial, in contrast to the elevation of blood glucose values after subcutaneously injected glucagon which is occurs shortly after injection. We hypothesize that this is caused by properties of glucagon and have investigated the vasodilative effect of glucagon on subcutaneous blood flow in this proof-of-concept study. Methods Twenty-two volunteers received subcutaneous injections of 0.1 mg and 0.01 mg glucagon, and saline on the abdomen. Blood flow was measured by a laser doppler blood flowmeter for 35 minutes after injections. Results Injection of 0.1 mg glucagon resulted in a significant increase in blood flow compared with baseline blood flow for all time intervals. Significant increase was also observed after the 0.01 mg glucagon injection, except between two- and five-minutes post injection. The inter-individual variance was large and a third of the subjects did not show an apparent increase in local subcutaneous blood flow after the 0.1 mg glucagon injection. Conclusion This proof-of-concept study shows that micro-boluses of glucagon increase local subcutaneous blood flow on the abdomen of non-diabetic subjects. However, the vasodilative effect of glucagon is not observed in all subjects. The trial was not registered to protect intellectual property rights.

• Teigen, Ingrid Anna; Riaz, Misbah; Åm, Marte Kierulf; Christiansen, Sverre Christian & Carlsen, Sven Magnus (2022). Vasodilatory effects of glucagon: A possible new approach to enhanced subcutaneous insulin absorption in artificial pancreas devices. Frontiers in Bioengineering and Biotechnology. 10. doi: 10.3389/fbioe.2022.986858. Full text in Research Archive Show summary

  Patients with diabetes mellitus type 1 depend on exogenous insulin to keep their blood glucose concentrations within the desired range. Subcutaneous bihormonal artificial pancreas devices that can measure glucose concentrations continuously and autonomously calculate and deliver insulin and glucagon infusions is a promising new treatment option for these patients. The slow absorption rate of insulin from subcutaneous tissue is perhaps the most important factor preventing the development of a fully automated artificial pancreas using subcutaneous insulin delivery. Subcutaneous insulin absorption is influenced by several factors, among which local subcutaneous blood flow is one of the most prominent. We have discovered that micro-doses of glucagon may cause a substantial increase in local subcutaneous blood flow. This paper discusses how the local vasodilative effects of micro-doses of glucagon might be utilised to improve the performance of subcutaneous bihormonal artificial pancreas devices. We map out the early stages of our hypothesis as a disruptive novel approach, where we propose to use glucagon as a vasodilator to accelerate the absorption of meal boluses of insulin, besides using it conventionally to treat hypoglycaemia.

• Benam, Karim Davari; Khoshamadi, Hasti; Pérez, Laura Lema; Gros, Sebastien & Fougner, Anders Lyngvi (2022). A Nonlinear State Observer for the Bi-Hormonal Intraperitoneal Artificial Pancreas. In Barbieri, Riccardo (Eds.), Proceedings of the 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC 2022). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728127835. p. 171–176. doi: 10.1109/EMBC48229.2022.9871264. Full text in Research Archive Show summary

  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 pancreas 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.

• Cheema, Muhammad Asaad; Siddiqui, Salman Ijaz & Rossi, Pierluigi Salvo (2022). Comparison of Different Classifiers for Early Meal Detection Using Abdominal Sounds. Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop. ISSN 1551-2282. doi: 10.1109/SAM53842.2022.9827841. Full text in Research Archive
• Teigen, Ingrid Anna; Åm, Marte Kierulf; Carlsen, Sven Magnus & Christiansen, Sverre Christian (2022). Pharmacokinetics of glucagon after intravenous, intraperitoneal and subcutaneous administration in a pig model. Basic & Clinical Pharmacology & Toxicology. ISSN 1742-7835. 130(6), p. 623–631. doi: 10.1111/bcpt.13731. Full text in Research Archive
• Schwermer, Silje Fuglerud; Noh, Jong Wook; Aksnes, Astrid & Hjelme, Dag Roar (2022). Performance improvement in a supercontinuum fiber-coupled system for near infrared absorption spectroscopy. Applied Optics. ISSN 1559-128X. 61(9), p. 2371–2381. doi: 10.1364/AO.449908. Full text in Research Archive Show summary

  Accurate, in-field-compatible, sensing based on near infrared spectroscopy (NIRS) requires development of instrumentation with low noise and long-term stability. Here, we present a fully fiber-optic spectroscopy setup using a supercontinuum source in the long-pulse regime (2 ns) and a balanced detector scheme to demonstrate high-accuracy NIRS-based sensing. The noise sources of the system are studied theoretically and experimentally. The relative intensity noise was reduced from typical values up to 6% to less than 0.1% by deploying a balanced detector and averaging. At well-balanced wavelengths, the system without transmission cells achieved a signal to noise ratio (SNR) above 70 dB, approaching the shot noise limit. With transmission cells and long-term measurements, the overall SNR was 55 dB. Glucose in physiological concentrations was measured as a model system, yielding a root mean square error of 4.8 mM, approaching the needed accuracy for physiological glucose monitoring.

• Benam, Karim Davari; Khoshamadi, Hasti; Åm, Marte Kierulf; Stavdahl, Øyvind; Gros, Sebastien & Fougner, Anders Lyngvi (2022). Identifiable Prediction Animal Model for the Bi-Hormonal Intraperitoneal Artificial Pancreas. Journal of Process Control. ISSN 0959-1524. 121, p. 13–29. doi: 10.1016/j.jprocont.2022.11.008. Full text in Research Archive Show summary

  To achieve a fully automatic artificial pancreas (AP), i.e., an AP without the need for meal announcements, the intraperitoneal (IP) route is explored. This route has faster dynamics than the typical subcutaneous (SC) route. Model predictive control (MPC) is the most promising control algorithm, but it requires a predictive and identifiable model. This paper presents the design of such a model for MPC-based dual hormone IP APs. This model is trained and tested on recorded data from anesthetized pigs. Animal experiments show that the saturation of the hepatic first-pass effect is essential in how IP insulin and IP glucagon affect glucose levels. These physiological phenomena must be modeled to estimate the system behavior for various conditions. This, in turn, increases the number of parameters and complicates system identification. The availability of rich experimental data from 26 animal trials motivated the design of a technique to exploit this prior information to ensure the identifiability of our model. Through this technique, most parameters were either modeled as body weight functions or common among animals. The correlation between parameter values and body weight is discovered utilizing prior data from various animal experiments, such as blood glucose, plasma insulin, and glucagon levels, in which hormones were administered intraperitoneally or intravenously. This method simplifies the system identification for every new subject while keeping the model’s essential details that improve the prediction capability relative to comparable models. The model can be exploited in MPC or any other model-based controller of a bi-hormonal IP AP. It can also be used as a simulator to develop control approaches for single and bi-hormonal IP APs.

• Moser, Othmar; Sternad, Christoph; Eckstein, Max L; Szadkowska, Agnieszka; Michalak, Arkadiusz & Mader, Julia K [Show all 19 contributors for this article] (2021). Performance of intermittently scanned continuous glucose monitoring systems in people with type 1 diabetes: A pooled analysis. Diabetes, obesity and metabolism. ISSN 1462-8902. 24(3), p. 522–529. doi: 10.1111/dom.14609. Full text in Research Archive Show summary

  Aims: To conduct a pooled analysis to assess the performance of intermittently scanned continuous glucose monitoring (isCGM) in association with the rate of change in sensor glucose in a cohort of children, adolescents, and adults with type 1 diabetes. Material and methods: In this pooled analysis, isCGM system accuracy was assessed depending on the rate of change in sensor glucose. Clinical studies that have been investigating isCGM accuracy against blood glucose, accompanied with collection time points were included in this analysis. isCGM performance was assessed by means of median absolute relative difference (MedARD), Parkes error grid (PEG) and Bland-Altman plot analyses. Results: Twelve studies comprising 311 participants were included, with a total of 15 837 paired measurements. The overall MedARD (interquartile range) was 12.7% (5.9-23.5) and MedARD differed significantly based on the rate of change in glucose (P < 0.001). An absolute difference of -22 mg/dL (-1.2 mmol/L) (95% limits of agreement [LoA] 60 mg/dL (3.3 mmol/L), -103 mg/dL (-5.7 mmol/L)) was found when glucose was rapidly increasing (isCGM glucose minus reference blood glucose), while a -32 mg/dL (1.8 mmol/L) (95% LoA 116 mg/dL (6.4 mmol/L), -51 mg/dL (-2.8 mmol/L)) absolute difference was observed in periods of rapidly decreasing glucose. Conclusions: The performance of isCGM was good when compared to reference blood glucose measurements. The rate of change in glucose for both increasing and decreasing glucose levels diminished isCGM performance, showing lower accuracy during high rates of glucose change. Keywords: continuous glucose monitoring (CGM); type 1 diabetes.

• Teigen, Ingrid Anna; Åm, Marte Kierulf; Carlsen, Sven Magnus & Christiansen, Sverre Christian (2021). Pharmacokinetics of Intraperitoneally Delivered Glucagon in Pigs: A Hypothesis of First Pass Metabolism. European journal of drug metabolism and pharmacokinetics. ISSN 0378-7966. doi: 10.1007/s13318-021-00692-2. Full text in Research Archive Show summary

  Background and Objective Artificial pancreases administering low-dose glucagon in addition to insulin have the scope to improve glucose control in patients with diabetes mellitus type 1. If such a device were to deliver both hormones intraperitoneally, it would mimic normal physiology, which may be beneficial. However, the pharmacokinetic properties of glucagon after intraperitoneal administration are not well known. Hence, the current study aims to evaluate the relationship between the amount of intraperitoneally delivered glucagon and pharmacokinetic variables in a pig model. Methods Pharmacokinetic data was retrieved from experiments on 19 anaesthetised pigs and analysed post hoc. The animals received a single intraperitoneal bolus of glucagon ranging from 0.30 to 4.46 µg/kg. Plasma glucagon was measured every 2–10 min for 50 min. Results Peak plasma concentration and area under the time–plasma concentration curve of glucagon correlated positively with the administered dose, and larger boluses provided a relatively greater increase. The mean (standard deviation) time to maximum glucagon concentration in plasma was 11 (5) min, and the mean elimination half-life of glucagon in plasma was 19 (7) min. Conclusions Maximum plasma concentration and area under the time–plasma concentration curve of glucagon increase nonlinearly in relation to the intraperitoneally administered glucagon dose. We hypothesise that the results are compatible with a satiable first-pass metabolism in the liver. Time to maximum glucagon concentration in plasma and the elimination half-life of glucagon in plasma seem independent of the drug dose.

• Formo, Mads Wikmark; Stavdahl, Øyvind & Fougner, Anders Lyngvi (2021). Modelling and simulation of occlusions in insulin pumps. In Barbieri, Riccardo (Eds.), Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC 2021). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728111797. p. 1499–1503. doi: 10.1109/EMBC46164.2021.9630219. Full text in Research Archive Show summary

  Abstract — An open source simulation model of the mechanical properties of a fully functional insulin pump was made in Matlab Simscape. The model simulates realistic behavior of an insulin pump, parts of which are validated against real-world systems. Simulations include mechanical forces and internal pressures, and the following fluid dynamics. Failure modes, such as occlusions, can be simulated and the resulting simulations can give new insights on how these failures affect the pump and how to detect them. Clinical relevance — Realistic pump simulations can be used to analyze how pump failures affect the system and in turn how to most effectively detect them before posing a hazard to the user, increasing the safety and reliability of the system.

• Fuglerud, Silje Skeide; Ellingsen, Reinold; Aksnes, Astrid & Hjelme, Dag Roar (2021). Investigation of the effect of clinically relevant interferents on glucose monitoring using near‐infrared spectroscopy. Journal of Biophotonics. ISSN 1864-063X. doi: 10.1002/jbio.202000450. Full text in Research Archive Show summary

  Near infrared spectroscopy (NIR) is a promising technique for continuous blood glucose monitoring for diabetic patients. Four interferents, at physiological concentrations, were introduced to study how the glucose predictions varied with a standard multivariate calibration model. Lactate and ethanol were found to interfere strongly with the glucose predictions unless theywere included in the calibration models. Lactate was mistaken for glucose and gave erroneously high glucose predictions, with a dose response of 0.46 mM/mM. The presence of ethanol resulted in too low glucose predictions, with a dose response of -0.43 mM/mM. Acetaminophen (APAP), a known interferent in the glucose monitoring devices used for diabetes management today, was not found to be an interferent in NIR spectroscopy, nor was caffeine. Thus, interferents that may appear in high concentrations, such as ethanol and lactate, must be included in the calibration or model building of future NIR-based glucose measurement devices for diabetes monitoring.

• Dirnena-Fusini, Ilze; Åm, Marte Kierulf; Fougner, Anders Lyngvi; Carlsen, Sven Magnus & Christiansen, Sverre Christian (2021). Intraperitoneal insulin administration in pigs: Effect on circulating insulin and glucose levels. BMJ Open Diabetes Research & Care. 9(e001929), p. 1–7. doi: 10.1136/bmjdrc-2020-001929. Full text in Research Archive Show summary

  Introduction The effect of intraperitoneal insulin infusion has limited evidence in the literature. Therefore, the aim of the study was to investigate the pharmacokinetics and pharmacodynamics of different intraperitoneal insulin boluses. There is a lack of studies comparing the insulin appearance in the systemic circulation after intraperitoneal compared with subcutaneous insulin delivery. Thus, we also aimed for a comparison with the subcutaneous route. Research design and methods Eight anesthetized, non-diabetic pigs were given three different intraperitoneal insulin boluses (2, 5 and 10 U). The order of boluses for the last six pigs was randomized. Endogenous insulin and glucagon release were suppressed by repeated somatostatin analog injections. The first pig was used to identify the infusion rate of glucose to maintain stable glucose values throughout the experiment. The estimated difference between insulin boluses was compared using two-way analysis of variance (GraphPad Prism V.8). In addition, a trial of three pigs which received subcutaneous insulin boluses was included for comparison with intraperitoneal insulin boluses. Results Decreased mean blood glucose levels were observed after 5 and 10 U intraperitoneal insulin boluses compared with the 2 U boluses. No changes in circulating insulin levels were observed after the 2 and 5 U intraperitoneal boluses, while increased circulating insulin levels were observed after the 10 U intraperitoneal boluses. Subcutaneously injected insulin resulted in higher values of circulating insulin compared with the corresponding intraperitoneal boluses. Conclusions Smaller intraperitoneal boluses of insulin have an effect on circulating glucose levels without increasing insulin levels in the systemic circulation. By increasing the insulin bolus, a major increase in circulating insulin was observed, with a minor additive effect on circulating glucose levels. This is compatible with a close to 100% first-pass effect in the liver after smaller intraperitoneal boluses. Subcutaneous insulin boluses markedly increased circulating insulin levels.

• Dirnena-Fusini, Ilze; Åm, Marte Kierulf; Fougner, Anders Lyngvi; Carlsen, Sven Magnus & Christiansen, Sverre Christian (2021). Physiological effects of intraperitoneal versus subcutaneous insulin infusion in patients with diabetes mellitus type1: A systematic review and meta-analysis. PLOS ONE. 16(4). doi: 10.1371/journal.pone.0249611. Full text in Research Archive Show summary

  The intraperitoneal route of administration accounts for less than 1% of insulin treatment regimes in patients with diabetes mellitus type 1 (DM1). Despite being used for decades, a systematic review of various physiological effects of this route of insulin administration is lacking. Thus, the aim of this systematic review was to identify the physiological effects of continuous intraperitoneal insulin infusion (CIPII) compared to those of continuous subcutaneous insulin infusion (CSII) in patients with DM1. Four databases (EMBASE, PubMed, Scopus and CENTRAL) were searched beginning from the inception date of each database to 10th of July 2020, using search terms related to intraperitoneal and subcutaneous insulin administration. Only studies comparing CIPII treatment (≥ 1 month) with CSII treatment were included. Primary outcomes were long-term glycaemic control (after ≥ 3 months of CIPII inferred from glycated haemoglobin (HbA1c) levels) and short-term (≥ 1 day for each intervention) measurements of insulin dynamics in the systematic circulation. Secondary outcomes included all reported parameters other than the primary outcomes. The search identified a total of 2242 records; 39 reports from 32 studies met the eligibility criteria. This meta-analysis focused on the most relevant clinical end points; the mean difference (MD) in HbA1c levels during CIPII was significantly lower than during CSII (MD = -6.7 mmol/mol, [95% CI: -10.3 –-3.1]; in percentage: MD = -0.61%, [95% CI: -0.94 –- 0.28], p = 0.0002), whereas fasting blood glucose levels were similar (MD = 0.20 mmol/L, [95% CI: -0.34–0.74], p = 0.47; in mg/dL: MD = 3.6 mg/dL, [95% CI: -6.1–13.3], p = 0.47). The frequencies of severe hypo- and hyper-glycaemia were reduced. The fasting insulin levels were significantly lower during CIPII than during CSII (MD = 16.70 pmol/L, [95% CI: -23.62 –-9.77], p < 0.0001). Compared to CSII treatment, CIPII treatment improved overall glucose control and reduced fasting insulin levels in patients with DM1.

• Jernelv, Ine Larsen; Hjelme, Dag Roar & Aksnes, Astrid (2020). Infrared measurements of glucose in peritoneal fluid with a tuneable quantum cascade laser. Biomedical Optics Express. 11(7), p. 3818–3829. doi: 10.1364/BOE.393617. Full text in Research Archive Show summary

  Fast and accurate continuous glucose monitoring is needed in future systems for control of blood glucose levels in type 1 diabetes patients. Direct spectroscopic measurement of glucose in the peritoneal cavity is an attractive alternative to conventional electrochemical sensors placed subcutaneously. We demonstrate the feasibility of fast glucose measurements in peritoneal fluid using a fibre-coupled tuneable mid-infrared quantum cascade laser. Mid-infrared spectra (1200–925 cm−1) of peritoneal fluid samples from pigs with physiological glucose levels (32–426 mg/dL, or 1.8–23.7 mmol/L) were acquired with a tuneable quantum cascade laser employing both transmission and attenuated total reflection (ATR) spectroscopy. Using partial least-squares regression, glucose concentrations were predicted with mean absolute percentage errors (MAPEs) of 8.7% and 12.2% in the transmission and ATR configurations, respectively. These results show that highly accurate concentration predictions are possible with mid-infrared spectroscopy of peritoneal fluid, and represent a first step towards a miniaturised optical sensor for intraperitoneal continuous glucose monitoring.

• Jernelv, Ine Larsen; Hjelme, Dag Roar; Matsuura, Yuji & Aksnes, Astrid (2020). Convolutional neural networks for classification and regression analysis of one-dimensional spectral data. arXiv.org. ISSN 2331-8422. Full text in Research Archive
• Jernelv, Ine Larsen; Høvik, Jens; Hjelme, Dag Roar & Aksnes, Astrid (2020). Signal enhancement in microstructured silicon attenuated total reflection elements with quantum cascade laser-based spectroscopy. Proceedings of SPIE, the International Society for Optical Engineering. ISSN 0277-786X. doi: 10.1117/12.2554528. Full text in Research Archive Show summary

  Sensors in mid-infrared spectroscopy based on attenuated total reflection (ATR) sensing with internal reflection elements (IREs) facilitate easier measurements of aqueous solutions or other opaque analytes. Micromachined silicon (Si) elements are an attractive alternative to conventional IREs, as they can be produced cheaply with silicon processing. Techniques for surface modifications are also easily integrated into the wafer process, and surface structures such as micropillars or nanoparticles can thereby be used for signal enhancement. Replacing the classic Fourier transform infrared (FTIR) spectrometers with tuneable quantum cascade lasers (QCLs) also opens up new avenues for sensing. In this study, the performance of basic and signal-enhanced Si IREs has been compared for measurements in a spectroscopy setup with a fibre-coupled tuneable QCL source. These IREs had V-shaped microgrooves etched on the underside for more efficient in-coupling of light, while the signal enhanced IREs also had micropillars on the top surface. The results are also contrasted with measurements done in a standard ATR-FTIR spectrometer, using an Alpha II spectrometer with a single-reflection diamond ATR crystal. Various concentrations of glucose (0-5000 mg/dl) in aqueous solutions were used to characterise the system performance. The quality of the signal enhancement was evaluated with regard to sensitivity and noise level in the acquired spectra. The microstructured Si IREs gave a signal enhancement of up to a factor of 3.8 compared to a basic Si element, with some concomitant increase in noise. The absorbance was higher for both types of Si IREs as compared to the diamond ATR crystal. The effective enhancement and the limit of quantification improved by a factor up to 3.1 in the signal-enhanced IREs compared to the basic Si element.

• Kölle, Konstanze; Biester, Torben; Christiansen, Sverre; Fougner, Anders Lyngvi & Stavdahl, Øyvind (2020). Pattern recognition reveals characteristic postprandial glucose changes: Non-individualized meal detection in diabetes mellitus type 1. IEEE Journal of Biomedical and Health Informatics. ISSN 2168-2194. 24(2), p. 594–602. doi: 10.1109/JBHI.2019.2908897. Full text in Research Archive Show summary

  Accurate continuous glucose monitoring (CGM) is essential for fully automated glucose control in diabetes mellitus type 1. State-of-the-art glucose control systems automatically regulate the basal insulin infusion. Users still need to manually announce meals to dose the prandial insulin boluses. An automated meal detection could release the user and improve the glucose regulation. In this study, patterns in the postprandial CGM data are exploited for meal detection. Binary classifiers are trained to recognize the postprandial pattern in horizons of the estimated glucose rate of appearance and in CGM data. The appearance rate is determined by moving horizon estimation (MHE) based on a simple model. Linear discriminant analysis (LDA) is used for classification. The proposed method is compared to methods that detect meals when thresholds are violated. Diabetes care data from twelve free-living pediatric patients was downloaded during regular screening. Experts identified meals and their start by retrospective evaluation. The classification was tested by cross-validation. Compared to the threshold-based methods, LDA showed higher sensitivity to meals with a low rate of false alarms. Classifying horizons outperformed the other methods also with respect to time of detection. The onset of meals can be detected by pattern recognition based on estimated model states and consecutive CGM measurements. No individual tuning is necessary. This makes the method easily adopted in the clinical practice.

• Milenko, Karolina Barbara; Fuglerud, Silje Skeide; Aksnes, Astrid; Ellingsen, Reinold & Hjelme, Dag Roar (2020). Optimization of SERS Sensing With Micro-Lensed Optical Fibers and Au Nano-Film. Journal of Lightwave Technology. ISSN 0733-8724. 38(7). doi: 10.1109/JLT.2019.2958128. Full text in Research Archive Show summary

  We present the fabrication and characterization of optical fiber reflection probes based on surface-enhanced Raman scattering (SERS) and micro-lensed multimode fibers. For the SERS substrate, a nano-sphere lithography method is used. Comparison of SERS measurements with three different fiber probe configurations is presented. The proposed optimized structure shows a five times increase in SERS signal for dried Rhodamine 6G (R6G) and two times for aqueous R6G in comparison to a standard reflection configuration. Measurements of different concentrations of R6G in a water solution with an limit of detection (LOD) equal to 10 -7 M are demonstrated.

• Fuglerud, Silje Skeide; Milenko, Karolina Barbara; Aksnes, Astrid & Hjelme, Dag Roar (2020). Surface-Enhanced Absorption Spectroscopy for Optical Fiber Sensing. Materials. 13(34). doi: 10.3390/ma13010034. Full text in Research Archive Show summary

  Visible and near-infrared spectroscopy are widely used for sensing applications but suffer from poor signal-to-noise ratios for the detection of compounds with low concentrations. Enhancement by surface plasmon resonance is a popular technique that can be utilized to increase the signal of absorption spectroscopy due to the increased near-field created close to the plasmons. Despite interest in surface-enhanced infrared absorption spectroscopy (SEIRAS), the method is usually applied in lab setups rather than real-life sensing situations. This study aimed to achieve enhanced absorption from plasmons on a fiber-optic probe and thus move closer to applications of SEIRAS. A tapered coreless fiber coated with a 100 nm Au film supported signal enhancement at visible wavelengths. An increase in absorption was shown for two dyes spanning concentrations from 5 × 10−8 mol/L to 8 × 10−4 mol/L: Rhodamine 6G and Crystal Violet. In the presence of the Au film, the absorbance signal was 2–3 times higher than from an identically tapered uncoated fiber. The results confirm that the concept of SEIRAS can be implemented on an optical fiber probe, enabling enhanced signal detection in remote sensing applications.

• Jernelv, Ine Larsen; Strøm, Karina; Hjelme, Dag Roar & Aksnes, Astrid (2020). Mid-infrared spectroscopy with a fiber-coupled tuneable quantum cascade laser for glucose sensing. Proceedings of SPIE, the International Society for Optical Engineering. ISSN 0277-786X. 11233. doi: 10.1117/12.2543984. Full text in Research Archive Show summary

  A fiber-coupled transmission spectroscopy setup using a pulsed external-cavity quantum cascade laser (EC-QCL, 1200-900 cm−1 ) has been developed and demonstrated for measurements of aqueous solutions. The system has been characterised with regard to the laser noise and optimal optical pathlength. Solutions with glucose were used to further test the setup, and glucose concentrations down to physiologically relevant levels (0-600 mg/dl) were investigated. Albumin, lactate, urea, and fructose in various concentrations were added as interfering substances as their absorption bands overlap with those of glucose, and because they may be of interest in a clinical setting. Analyte concentrations were predicted using partial least-squares (PLS) regression, and the root-mean-square error of cross-validation for glucose was 10.7 mg/dl. The advantages of using a convolutional neural network (CNN) for regression analysis in comparison to the PLS regression were also shown. The application of a CNN gave an improved prediction error (8.3 mg/dl), and was used to identify important spectral regions. These results are comparable to state-of-the-art enzymatic glucose sensors, and are encouraging for further research on optics-based glucose sensors.

• Setti, Sunilkumar Telagam; Søiland, Elise; Stavdahl, Øyvind & Fougner, Anders Lyngvi (2020). Pilot study of Early Meal Onset Detection from Abdominal Sounds. In Costin, Hariton (Eds.), IEEE International Conference on e-Health and Bioengineering (EHB 2019), Proceedings of the. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728126036. doi: 10.1109/EHB47216.2019.8969901. Full text in Research Archive Show summary

  A typical artificial pancreas depends only on the continuous glucose monitoring (CGM) value for insulin dosing. However, both the insulin infusion and the glucose sensing are subject to time delays and slow dynamics. An automated and reliable meal onset information could enhance the control outcome of artificial pancreas by making it possible to infuse insulin earlier and thereby avoid large postprandial glucose excursions. In this study we employ abdominal sounds recorded in two healthy volunteers with a condenser microphone and propose an automated approach for meal onset detection from abdominal sounds. We use the Mel-frequency cepstral coefficients (MFCCs) and wavelet entropy extracted from the abdominal sounds as features. These features are fed to a simple feed forward neural network for discriminating meal from no-meal abdominal sounds. This approach detects meal onset with an average delay of 4.3 minutes in our limited number of subjects. More importantly, it provides lesser response delay than the state-of-the-art CGM based approach, which achieved a response delay ranging from 30- 40 minutes. The preliminary results indicate that the proposed abdominal sound-based approach may provide early meal onset information. This can be exploited in an artificial pancreas through allowable earlier meal insulin boluses, resulting in improved glycemic control.

• Åm, Marte Kierulf; Dirnena-Fusini, Ilze; Fougner, Anders Lyngvi; Carlsen, Sven Magnus & Christiansen, Sverre (2020). Intraperitoneal and subcutaneous glucagon delivery in anaesthetized pigs: effects on circulating glucagon and glucose levels. Scientific Reports. 10. doi: 10.1038/s41598-020-70813-5. Full text in Research Archive Show summary

  Glucagon is a pancreatic hormone and increases the blood glucose levels. It may be incorporated in a dual hormone artificial pancreas, a device to automatically and continuously control blood glucose levels of individuals with diabetes. Artificial pancreas systems have been developed for use in the subcutaneous tissue; however, the systems are not fully automated due to slow dynamics. The intraperitoneal space is therefore investigated as an alternative location for an artificial pancreas. Glucose dynamics after subcutaneous and intraperitoneal glucagon delivery in ten anaesthetized pigs were investigated. The pigs received intraperitoneal boluses of 0.3 µg/kg and 0.6 µg/kg and a subcutaneous bolus of 0.6 µg/kg in randomized order. They also received an intraperitoneal bolus of 1 mg given at the end of the experiments to test the remaining capacity of rapid glucose release. Six pigs were included in the statistical analysis. The intraperitoneal glucagon bolus of 0.6 µg/kg gave a significantly higher glucose response from 14 to 30 min compared with the subcutaneous bolus. The results indicate that glucagon induces a larger glucose response after intraperitoneal delivery compared with subcutaneous delivery and is encouraging for the incorporation of glucagon in an intraperitoneal artificial pancreas.

• Fuglerud, Silje Skeide; Milenko, Karolina Barbara; Ellingsen, Reinold; Aksnes, Astrid & Hjelme, Dag Roar (2019). Feasibility of supercontinuum sources for use in glucose sensing by absorption spectroscopy. Proceedings of SPIE, the International Society for Optical Engineering. ISSN 0277-786X. 11073. doi: 10.1117/12.2524005. Full text in Research Archive Show summary

  Diabetes mellitus 1 requires tight control of the blood glucose levels to avoid harmful effects of either too high (hyperglycemia) or too low (hypoglycemia) blood sugar. Due to the availability of low-cost components, fiber- coupled near infrared (NIR) absorption spectroscopy could be a feasible measurement method. From the molar absorptivity of glucose, it is shown that to achieve high accuracy using near infrared spectroscopy for glucose sensing, relative noise levels should not exceed 0:003 %. Two supercontinuum (SC) sources and one broadband lamp were investigated with a low-cost portable spectrometer. The SNR of the two SC sources was limited by amplitude fluctuations and could be improved by averaging. The SNR of the broadband source was found to be largely limited by the detector noise due to the weak intensity. 16 aqueous glucose samples ranging from 0 to 500mm were measured with the broadband source and an SC laser. A partial least squares regression (PLSR) model was built for both measurement sets, yielding root mean square errors of 49 and 54mm, illustrating how the limit of detection is restrained by the high relative intensity noise. A reference arm setup was built and could account for much of the variability of the SC source. A glucose measurement series using this setup and five samples (100 to 500mm) yielded a root mean square error of 10:6mm. The results indicate that an SC source can be feasible for absorption spectroscopy in a reference arm setup.

• Fuglerud, Silje Skeide; Milenko, Karolina Barbara; Ellingsen, Reinold; Aksnes, Astrid & Hjelme, Dag Roar (2019). Glucose sensing by absorption spectroscopy using lensed optical fibers. Applied Optics. ISSN 1559-128X. 58(10), p. 2456–2462. doi: 10.1364/AO.58.002456. Full text in Research Archive Show summary

  The bulkiness of common transmission spectroscopy probes prevents applicability at remote locations such as within the body. We present the fabrication and characterization of lensed fibers for transmission spectroscopy in the near infrared. Eigenmode simulations and measurements of the coupling efficiency are presented and applied to design the setup corresponding to the sample absorption. Sensing capabilities are demonstrated on aqueous glucose samples ranged 80 to 500 mM obtaining a mean absolute percentage error of calibration of 4.3%. With increased flexibility, transmission spectroscopic sensors at remote locations may be achievable, for example applied to in vivo continuous glucose monitoring.

• Zazueta, Claudia Lopez; Stavdahl, Øyvind & Fougner, Anders Lyngvi (2019). Simple Nonlinear Models for Glucose-Insulin Dynamics: Application to Intraperitoneal Insulin Infusion. IFAC-PapersOnLine. ISSN 2405-8971. 52(26), p. 219–224. doi: 10.1016/j.ifacol.2019.12.261. Full text in Research Archive Show summary

  The design of a model-based control method for an Artificial Pancreas requires a relatively simple and identifiable mathematical model to control glucose levels through hormone delivery. In this work we introduce new, simple nonlinear models to simulate data from experiments where insulin boluses are administrated in the peritoneal cavity. The models account for the delay between insulin administration and its nonlinear transport to other compartments. They were calibrated using experimental data from pigs. The results show that the suggested models are able to describe the data well, with average BIC value of 145. Moreover, the new models were compared with a common linear model which was not able to describe the data well, with BIC value of 920. They were also compared with a common nonlinear model which failed to represent insulin increases in the data and had BIC value of 637. Finally, profile likelihoods were applied for assessing the identifiability of one of the new models.

• Staal, Odd Martin; Fougner, Anders Lyngvi; Sælid, Steinar & Stavdahl, Øyvind (2019). Glucose-insulin metabolism model reduction and parameter selection using sensitivity analysis. American Control Conference (ACC). ISSN 0743-1619. 2019-July, p. 4104–4111. doi: 10.23919/ACC.2019.8814949. Full text in Research Archive Show summary

  Glucose-insulin metabolism models are useful tools for research on diabetes, in development of diabetes-related medical devices like artificial pancreas systems, insulin pumps and continuous glucose monitors, and may also play a role in personalized decision support tools for people with diabetes. Such models are often highly nonlinear with many parameters that are person dependent. An example is the model used in the UVa/Padova T1DM simulator, which has a large number of states and parameters. It is desirable to be able to personalize such models through parameter identification based on limited glucose, meal and insulin data obtainable from free-living settings, as opposed to clinical research settings that have traditionally been required. In this paper we use the UVa-Padova T1DM simulator model in a case study to investigate observability of the model under different measurements, and the identifiability of its parameters as a function of the model's inputs and outputs. Structural identifiability is discussed and briefly investigated using the nonlinear Observability Rank Condition. Practical identifiability is discussed and investigated using sensitivity and Fisher information matrix analysis. We show how such analyses can be used to guide model reduction for improved identifiability, or to select the most proper subset of parameters to estimate.

View all works in NVA

• Carlsen, Sven Magnus; Berg, Hilde Kjeldstad; Stavdahl, Øyvind; Ellingsen, Reinold; Hjelme, Dag Roar & Christiansen, Sverre Christian [Show all 8 contributors for this article] (2025). Gir pasienter med diabetes type 1 et enklere liv. [Internet]. Helse Midt-Norge: Nyheter. Full text in Research Archive
• Engell, Sarah Ellinor; Benam, Karim Davari; Bengtsson, Henrik; Jørgensen, John Bagterp & Fougner, Anders Lyngvi (2024). Optimal Experimental Design for System Identification in a Bi-Hormonal Intraperitoneal Artificial Pancreas. Full text in Research Archive
• Normannsen, Sølvi Waterloo; Carlsen, Sven Magnus; Berg, Hilde Kjeldstad; Stavdahl, Øyvind; Ellingsen, Reinold & Hjelme, Dag Roar [Show all 8 contributors for this article] (2024). Ny insulin-miks kan gi pasienter med diabetes type 1 et lettere liv. [Internet]. Forskning.no. Full text in Research Archive
• Khoshamadi, Hasti; Perez, Laura Lema; Riaz, Misbah; Christiansen, Sverre Christian; Stavdahl, Øyvind & Carlsen, Sven Magnus [Show all 7 contributors for this article] (2024). Modelling the Relationship Between Insulin Action and Insulin Clearance - Where Do We Have Saturation? Full text in Research Archive
• Wolff, Miriam Kopperstad (2024). The Ice Cream Experiment - A Real-Life Experiment of Machine Learning-Driven Blood Glucose Prediction. Full text in Research Archive
• Riaz, Misbah; Carlsen, Sven Magnus; Dirnena-Fusini, Ilze; Åm, Marte Kierulf & Christiansen, Sverre Christian (2024). Effects of Micro-Dose Glucagon on Subcutaneous Insulin Absorption in Pigs. Full text in Research Archive
• Wolff, Miriam Kopperstad; Steinert, Martin; Fougner, Anders Lyngvi; Oh, Doyoung; Årsand, Eirik & Volden, Rune (2024). Real-Time Blood Glucose Prediction Reveals a Discrepancy Between Performance Metrics and Real-World Evaluations. Full text in Research Archive
• Carlsen, Sven Magnus; Berg, Hilde Kjeldstad; Stavdahl, Øyvind; Ellingsen, Reinold; Hjelme, Dag Roar & Christiansen, Sverre Christian [Show all 8 contributors for this article] (2024). Norsk forskning: Ny medisin kan forenkle livet til type 1-diabetikere. [Internet]. VG Pluss. Full text in Research Archive
• Benam, Karim Davari; Gros, Sebastien Nicolas; Carlsen, Sven Magnus; Christiansen, Sverre Christian & Fougner, Anders Lyngvi (2024). A Method for Estimation of Glucose Appearance Rate and Prediction of Blood Glucose Level Without Meal Announcements in Animal Studies. Full text in Research Archive
• Wolff, Miriam Kopperstad; Royston, Sam; Fougner, Anders Lyngvi; Schaathun, Hans Georg; Steinert, Martin & Volden, Rune (2024). GluPredKit: Development and User Evaluation of a Standardization Software for Blood Glucose Prediction. Full text in Research Archive
• Normannsen, Sølvi Waterloo; Carlsen, Sven Magnus; Berg, Hilde Kjeldstad; Stavdahl, Øyvind; Ellingsen, Reinold & Hjelme, Dag Roar [Show all 8 contributors for this article] (2024). A new insulin mixture could make life easier for patients with type 1 diabetes. [Journal]. Norwegian SciTech News. Full text in Research Archive
• Normannsen, Sølvi Waterloo; Carlsen, Sven Magnus; Berg, Hilde Kjeldstad; Stavdahl, Øyvind; Ellingsen, Reinold & Hjelme, Dag Roar [Show all 8 contributors for this article] (2024). Ny insulin-miks kan gi pasienter med diabetes type 1 et lettere liv. [Journal]. Gemini. Full text in Research Archive
• Carlsen, Sven Magnus; Berg, Hilde Kjeldstad; Stavdahl, Øyvind; Ellingsen, Reinold; Hjelme, Dag Roar & Christiansen, Sverre Christian [Show all 8 contributors for this article] (2024). New formulation of insulin may speed up its action for people with type 1 diabetes. [Internet]. Breakthrough T1D Research News. Full text in Research Archive
• Carlsen, Sven Magnus & Christiansen, Sverre Christian (2023). Effects of Low-Dose Glucagon on Subcutaneous Insulin Absorption in Pigs. Full text in Research Archive
• Patil, Pallavi; Perez, Laura Lema; Siddiqui, Salman Ijaz; Stavdahl, Øyvind & Fougner, Anders Lyngvi (2023). Electrocardiogram as a Predictor for Early and Robust Meal Onset Detection in Artificial Pancreas. Full text in Research Archive
• Langholz, Jana; Benam, Karim Davari; Sharan, Bindu; Gros, Sebastien Nicolas & Fougner, Anders Lyngvi (2023). Fully Automated Bi-Hormonal Intraperitoneal Artificial Pancreas Using a Two-Layer PID Control Scheme. Full text in Research Archive
• Carlsen, Sven Magnus (2023). Kunstig pankreas, - en oppdatering. Full text in Research Archive
• Carlsen, Sven Magnus & Christiansen, Sverre Christian (2023). Glucagon enhanced insulin absorption. The road to a fully automated artificial pancreas? Full text in Research Archive
• Setti, Sunilkumar Telagam; Holten, Andrea; Patil, Pallavi; Perez, Laura Lema; Stavdahl, Øyvind & Fougner, Anders Lyngvi (2023). Feasibility of Meal Onset Detection Using Electrocardiograms. Full text in Research Archive
• Engell, Sarah Ellinor; Bengtsson, Henrik; Benam, Karim Davari; Fougner, Anders Lyngvi & Jørgensen, John Bagterp (2023). Optimal Experimental Design to Estimate Insulin Response in Type 2 Diabetes. Full text in Research Archive
• Al, Ahdab Mohamad; Benam, Karim Davari; Khoshamadi, Hasti; Fougner, Anders Lyngvi & Gros, Sebastien Nicolas (2023). Sensor Fusion for Glucose Monitoring Systems. Full text in Research Archive
• Benam, Karim Davari; Khoshamadi, Hasti; Gros, Sebastien & Fougner, Anders Lyngvi (2022). The Bihormonal Intraperitoneal Artificial Pancreas Achieves Fully Closed Loop Control in Anaesthetized Animals. Full text in Research Archive
• Halvorsen, Martha; Benam, Karim Davari; Khoshamadi, Hasti & Fougner, Anders Lyngvi (2022). Blood Glucose Level Prediction Using Subcutaneous Sensors for in Vivo Study: Compensation for Measurement Method Slow Dynamics Using a Kalman Filter Approach. Full text in Research Archive
• Tabella, Gianluca; Siddiqui, Salman Ijaz & Rossi, Pierluigi Salvo (2022). Quickest Meal Detection Using Statistical Machine Learning. Full text in Research Archive
• Khoshamadi, Hasti; Benam, Karim Davari; Pérez, Laura Lema; Gros, Sebastien & Fougner, Anders Lyngvi (2022). A Nonlinear State Observer for the Bi-Hormonal Intraperitoneal Artificial Pancreas. Full text in Research Archive Show summary

  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 pancreas 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.

• Siddiqui, Salman Ijaz; Isifan, Ahmed Khalil Omar; Klavins, Davis & Fougner, Anders Lyngvi (2022). Early Meal Detection Based on Different Body Sounds. Full text in Research Archive
• Teigen, Ingrid Anna; Åm, Marte Kierulf; Carlsen, Sven Magnus & Christiansen, Sverre Christian (2022). Glucagon Pharmacokinetics in a Pig Model. Full text in Research Archive
• Åm, Marte Kierulf; Munkerud, Erlend Yttersian; Berge, Mathilde Hallem; Christiansen, Sverre Christian & Carlsen, Sven Magnus (2022). Glucagon and local subcutaneous blood flow; a proof-of-concept study. Full text in Research Archive
• Fuglerud, Silje Skeide & Hjelme, Dag Roar (2021). Reducing the SNR for Fiber-Coupled Supercontinuum Absorption Spectroscopy Applied to Glucose Sensing. Full text in Research Archive
• Formo, Mads Wikmark; Stavdahl, Øyvind & Fougner, Anders Lyngvi (2021). Modelling and simulation of occlusions in insulin pumps. Full text in Research Archive Show summary

  Abstract — An open source simulation model of the mechanical properties of a fully functional insulin pump was made in Matlab Simscape. The model simulates realistic behavior of an insulin pump, parts of which are validated against real-world systems. Simulations include mechanical forces and internal pressures, and the following fluid dynamics. Failure modes, such as occlusions, can be simulated and the resulting simulations can give new insights on how these failures affect the pump and how to detect them. Clinical relevance — Realistic pump simulations can be used to analyze how pump failures affect the system and in turn how to most effectively detect them before posing a hazard to the user, increasing the safety and reliability of the system.

• Fuglerud, Silje Skeide; Ellingsen, Reinold; Aksnes, Astrid & Hjelme, Dag Roar (2021). Interfering substances for glucose sensing by near-infrared spectroscopy. Full text in Research Archive Show summary

  Near-Infrared (NIR) Spectroscopy could be an alternative method to measure blood glucose concentration, with access to low-cost components and potential for non-invasive use. Possible interferents to this method are not widely investigated. We aimed to identify lactate, ethanol, acetaminophen and caffeine for possible interference. Glucose and the four interferents were mixed in varying physiological concentrations and measured by NIR spectroscopy in vitro. A calibration model was built by partial least squares regression. The full set (273 spectra) was divided into subsets (59-73 spectra) where one of the interferents were left out and new models were built without knowledge of the interferent. Subsets with random samples were used as a reference to the smaller calibration sets of the interferents. All models were applied to a validation set containing all interferents. The models built without ethanol and caffeine had high errors and several points outside of zone A and B in the Clarke Error grid. The model without lactate mistook lactate for glucose and gave false high predictions. The model without ethanol gave lower glucose predictions with increasing ethanol concentration. The models without acetaminophen and caffeine were comparable to the random subsets. Lactate and ethanol interfere with glucose prediction by NIR spectroscopy if not included in the model calibration. Acetaminophen and caffeine are not interferents to glucose using NIR spectroscopy.

• Fuglerud, Silje Skeide & Hjelme, Dag Roar (2021). Towards Remote Fiber-Based In Vivo Sensing of Glucose by Near Infrared Spectroscopy. Full text in Research Archive
• Åm, Marte Kierulf (2021). HEALTH Tech Dives. [Internet]. Technoport. Full text in Research Archive Show summary

  Presentation of the research group Artificial Pancreas Trondheim and the work on developing an intraperitoneal artificial pancreas.

• Fuglerud, Silje Skeide & Hjelme, Dag Roar (2021). Fiber-based glucose sensing for an artificial pancreas. Full text in Research Archive
• Jernelv, Ine Larsen; Høvik, Jens; Hjelme, Dag Roar & Aksnes, Astrid (2020). Signal enhancement in microstructured silicon attenuated total reflection elements with quantum cascade laser-based spectroscopy. Full text in Research Archive
• Åm, Marte Kierulf; Dirnena-Fusini, Ilze; Fougner, Anders Lyngvi; Carlsen, Sven Magnus & Christiansen, Sverre (2020). Intraperitoneal and subcutaneous glucagon delivery in anaesthetized pigs: Effects on circulating glucagon and glucose levels. Full text in Research Archive
• Setti, Sunilkumar Telagam; Søiland, Elise; Stavdahl, Øyvind & Fougner, Anders Lyngvi (2020). Pilot Study of Exploiting Abdominal Sound for Early Meal Onset Detection. Full text in Research Archive
• Jernelv, Ine Larsen; Strøm, Karina; Hjelme, Dag Roar & Aksnes, Astrid (2020). Mid-infrared spectroscopy with a fiber-coupled tuneable quantum cascade laser for glucose sensing. Full text in Research Archive
• Dirnena-Fusini, Ilze; Åm, Marte Kierulf; Carlsen, Sven Magnus; Fougner, Anders Lyngvi & Christiansen, Sverre (2020). The metabolic effects of continuous intra-peritoneal insulin infusion, a systematic review. Full text in Research Archive
• Zazueta, Claudia Lopez; Stavdahl, Øyvind & Fougner, Anders Lyngvi (2019). Simple Nonlinear Models for Glucose-Insulin Dynamics: Application to Intraperitoneal Insulin Infusion. Full text in Research Archive
• Fougner, Anders Lyngvi; Åm, Marte Kierulf; Bösch, Patrick Christian; Christiansen, Sverre; Ellingsen, Reinold & Hjelme, Dag Roar [Show all 8 contributors for this article] (2019). Why intraperitoneal glucose sensing is sometimes surprisingly rapid and sometimes slow: A hypothesis. Full text in Research Archive
• Setti, Sunilkumar Telagam; Søiland, Elise; Stavdahl, Øyvind & Fougner, Anders Lyngvi (2019). Pilot study of Early Meal Onset Detection from Abdominal Sounds. Full text in Research Archive
• Dirnena-Fusini, Ilze; Åm, Marte Kierulf; Carlsen, Sven Magnus; Fougner, Anders Lyngvi & Christiansen, Sverre (2019). Intraperitoneal insulin administration in pigs: Effect on circulating insulin and glucose levels. Full text in Research Archive
• Åm, Marte Kierulf; Dirnena-Fusini, Ilze; Carlsen, Sven Magnus; Fougner, Anders Lyngvi & Christiansen, Sverre (2019). Intraperitoneal and subcutaneous glucagon delivery in pigs: Effects on circulating glucagon and glucose levels. Full text in Research Archive
• Jernelv, Ine Larsen; Hjelme, Dag Roar; Ellingsen, Reinold & Aksnes, Astrid (2019). Quantum Cascade Lasers in Biomedicine and Nanotechnology. Full text in Research Archive
• Fuglerud, Silje Skeide; Milenko, Karolina Barbara; Ellingsen, Reinold; Aksnes, Astrid; Martens, Harald Aagaard & Hjelme, Dag Roar (2019). Aqueous glucose sensing by fiber-based near-infrared spectroscopy. Full text in Research Archive
• Fuglerud, Silje Skeide; Milenko, Karolina Barbara; Ellingsen, Reinold; Aksnes, Astrid & Hjelme, Dag Roar (2019). Feasibility of supercontinuum sources for use in glucose sensing by absorption spectroscopy. Full text in Research Archive
• Staal, Odd Martin; Fougner, Anders Lyngvi; Sælid, Steinar & Stavdahl, Øyvind (2019). Glucose-insulin metabolism model reduction and parameter selection using sensitivity analysis. Full text in Research Archive
• Wolff, Miriam Kopperstad; Volden, Rune; Fougner, Anders Lyngvi; Schaathun, Hans Georg & Steinert, Martin (2025). Research Foundations for Blood Glucose Prediction: Methodologies and Software Tools for Real-Time Deployment, Benchmarking, and Model Evaluation. Norges teknisk-naturvitenskapelige universitet. ISSN 9788232689910. Full text in Research Archive Show summary

  Blood glucose prediction plays an important role in artificial pancreas systems and automated insulin delivery by addressing the challenges posed by delays in insulin absorption. Accurate predictions have the potential to improve glycemic control and optimize diabetes management. While numerous datadriven models that utilize sensor data have been proposed, their evaluation is often confined to controlled environments or repetitive datasets, which may not adequately capture the variability encountered in real-world situations. This thesis aims to reinforce the methodological foundations of blood glucose prediction through insights gained from real-world model evaluation. In the initial segment of the thesis, a smartphone real-time Software Development Kit (SDK) was developed to facilitate the deployment of various prediction models, as outlined in PaperA. This achievementwas realized despite facing considerable challenges associated with real-time data access, including issues related to API usage and regulatory constraints, which are discussed in Paper C. A pilot study was conducted to evaluate existing algorithms using the SDK, revealing that literature-based models struggled to predict rare yet critical scenarios in diabetes management, as noted in Paper B. Furthermore, a discrepancy between theoretical evaluations and in-situ analyses was identified, as detailed in Paper D. This finding led to a shift in research focus from model deployment to enhancing the alignment of blood glucose prediction evaluations with real-world priorities. Subsequently, an open-source blood glucose prediction benchmarking tool, GluPredKit, was developed to enable experiments on well-defined datasets, models, and metrics. The introduction of this software is provided in Paper E, while its development and user evaluation are presented in Paper F. Utilizing this tool, limitations in the most commonly used evaluation metric were identified, which resulted in the formulation of a compound metric presented in Paper G. Challenges in data access and sharing practices were also addressed, leading to the perspective paper suggesting guidelines for data harmonization in Paper H. Overall, this thesis highlights the gaps between theoretical model evaluations and real-world applications, offering open-source tools and recommendations for enhanced evaluation practices.

• Benam, Karim Davari; Fougner, Anders Lyngvi & Gros, Sebastien Nicolas (2023). Design and Implementation of the Dual-Hormone Artificial Pancreas in Animal Studies – A Model Predictive Control Approach with Intraperitoneal Hormone Injections. Norges teknisk-naturvitenskapelige universitet. ISSN 9788232675777. Full text in Research Archive Show summary

  In this thesis, the exploration of new ways to help people with Type 1 Diabetes better control their condition is described. In this research, our primary focus was on the feasibility and benefits of utilizing the intraperitoneal (IP) route for insulin and glucagon injections in Type 1 DiabetesMellitus (T1DM), chosen due to its significantly faster absorption and more rapid effects on glucose levels compared to the subcutaneous (SC) route. The core contribution of this research lies in the development of a fully automated dual hormone AP system and testing in various animal experiments. In the first major part of this work, a new model is introduced, designed with a minimal number of parameters and states, exclusively intended for control applications within dual-hormone AP systems. Demonstrating remarkable prediction accuracy in over 30 animal experiments, this model represents a significant advancement that has the potential to facilitate future advancements in diabetes management. Subsequently, an estimator based on the Moving Horizon Estimation (MHE) method is designed, incorporating embedded prior knowledge to effectively estimate non-measurable states of the model, as well as meals and exercises. The experimental evaluation showcases the high accuracy of the estimator, further validating its potential as a valuable tool in diabetes care future. The work proceeds with the development of an MPC-based controller, adeptly incorporating practical considerations. Extensively tested in both in vivo and in silico experiments, the controller demonstrates high performance, surpassing existing Hybrid Closed-Loop AP systems in the market. Importantly, the proposed controller does not necessitate the meals and exercise announcements, enhancing its user-friendliness and autonomy compared to the commercial devices which all require meal announcements. Beyond the primary research target, this study delves into various other areas within diabetes management. The investigation includes testing a two-layer PID controller scheme, developing a method to compensate for CGM sensor time lag, exploring sensor fusion techniques to enhance glucose measurements, and studying experimental design strategies to increase model parameter identification accuracy. The findings of this research contribute to the advancement of diabetes research, which in turn may result in advances in diabetes care. The proposed model, estimator, and controller collectively offer a comprehensive and efficient solution for achieving reliable glycemic control in T1DM patients with IP injections. Ultimately, this work represents a vital step forward in personalized care and opens new avenues for future research and technological innovations.

• Teigen, Ingrid Anna; Carlsen, Sven Magnus & Christiansen, Sverre Christian (2023). The Bihormonal Artificial Pancreas: New Perspectives on the Pharmacokinetics and Pharmacodynamics of Glucagon. Norges teknisk-naturvitenskapelige universitet. ISSN 9788232668779. Full text in Research Archive Show summary

  Diabetes mellitus type 1 is an autoimmune disease that causes selective destruction of the insulin-producing β-cells in the pancreatic islet of Langerhans. Insulin induces glucose storage and reduces blood glucose concentrations. As it is essential for normal glucose regulation, patients with diabetes mellitus type 1 require lifelong treatment with exogenous insulin. The treatment of diabetes mellitus type 1 has evolved significantly since insulin was discovered in 1921. Modern treatment comprises continuous self-monitoring of glucose concentrations and self-administration of appropriate insulin dosages. Most patients cover their basal and prandial insulin needs through multiple manual injections or infusions via a pump into subcutaneous tissue. Hypoglycaemic episodes are a common side-effect of insulin treatment, and can be reversed with glucagon, which mobilises glucose from the liver and increases blood glucose concentrations. Lately, artificial pancreas technology has emerged as a promising new treatment option for patients with diabetes mellitus type 1. An artificial pancreas is a device that automatically calculates the demand for insulin based on continuous glucose feedback. Both unihormonal devices, delivering only insulin, and bihormonal devices, delivering insulin and glucagon, have been developed. However, only hybrid (partially automated), unihormonal devices that require meal announcements from the user are commercially available. Developing a fully automated subcutaneous artificial pancreas that can achieve excellent glucose control without meal announcements is challenging. One major barrier is the slow absorption rate of insulin from the subcutaneous tissue, which makes timing and adjustments of meal boluses of insulin difficult. As such, measures that can promote insulin absorption are needed. Alternatively, a more efficient route of insulin administration could be chosen. Intraperitoneal insulin administration mimics normal physiology and has a favourable pharmacokinetic profile compared to subcutaneous insulin administration. The first aim of this thesis was to investigate if the absorption, elimination, and utilisation of glucagon may also be improved with intraperitoneal drug delivery, as this could support developing a bihormonal intraperitoneal artificial pancreas, that would deliver both insulin and glucagon into the intraperitoneal space. The second aim was to investigate if subcutaneous insulin absorption may be accelerated by simultaneous subcutaneous glucagon administration, as glucagon is a potent vasodilator. This thesis demonstrates that intraperitoneal delivery of glucagon results in lower systemic glucagon concentrations than subcutaneous administration, probably because of a large first-pass metabolism of glucagon in the liver. However, the effects on glucose metabolism are equal after intraperitoneal and subcutaneous glucagon administration. Because of the increased risk of complications with intraperitoneal drug delivery, we conclude that the subcutaneous route for glucagon delivery is preferable for most patients. Simultaneous administration of glucagon did not accelerate subcutaneous insulin absorption. However, the total amount of insulin absorbed after a single bolus was significantly increased over an observation period of three hours. Clinical trials are needed to investigate if micro-doses of glucagon can be used to enhance insulin absorption and improve postprandial glucose control in subjects with diabetes mellitus type 1.

• Schwermer, Silje Fuglerud; Hjelme, Dag Roar; Aksnes, Astrid & Milenko, Karolina Barbara (2022). Near Infrared Spectroscopy for Continuous Glucose Monitoring. Norges teknisk-naturvitenskapelige universitet. ISSN 9788232652068. Full text in Research Archive Show summary

  Millions of people are affected by diabetes. The disease can be managed by measuring the blood glucose levels and administering appropriate insulin doses. However, diabetes management is difficult and time consuming for the patient and the medical community works to achieve a fully automated artificial pancreas (AP) that can manage the disease for the patients so that they can live normal lives and experience better health. Towards the goal of a fully automated AP for diabetes management, there is a need for an accurate long-term continuous glucose monitoring (CGM) device. One possible approach is to measure the glucose by near infrared (NIR) spectroscopy. Although a noninvasive CGM device would be the preferred solution for the patients, it has been difficult to achieve despite many commercialization efforts throughout the past 20 years. Additionally, the glucose concentration measured noninvasively might be delayed for several minutes compared to the blood glucose value, which can diminish the effect of an AP system. As an alternative, measurements in the intraperitoneal (IP) fluid surrounding the inner organs might be an option where the dynamics are faster. The aim of this thesis is to investigate the viability for a CGM sensor based on NIR spectroscopy for measurements in fluids, such as the IP fluid. To avoid the potential risk of placing active devices in the IP space, the thesis focuses on optical fiber-based sensing. Optical fiber components are widely used and developed for the telecommunication range around the 1550nm wavelength, overlapping with the first overtone band of glucose absorption. The thesis contributions are divided into three sections. The first section evaluates existing technology and is comprised of Papers I and II. The first paper reviewed the status of optical technologies for CGM sensing for diabetes. The second paper investigated the effects of interfering substances on calibration models that had not encountered the interfering substances before. The measurements were acquired from a benchtop lab spectrometer. Lactate and ethanol were found to interfere with glucose predictions if they were not part of the calibration set for model building. The paper concluded that the calibration of a near infrared spectroscopy (NIRS) model for CGM must be based on samples with large variance and from different physiological situations. The second section is the main part of the thesis where experimental NIRS setups were built and optimized and span Papers III-V. The third paper developed and characterized lensed fibers to increase the transmission in the interaction region without bulky components. The fourth paper investigated the needed signal to noise ratio (SNR) levels to achieve accurate NIRS glucose sensing. It showed that the detector noise was a limiting factor when applying a broadband lamp as a light source, whereas the relative intensity noise (RIN) of the source was the limiting factor when employing brighter supercontinuum (SC) sources that could overcome the detector noise. The RIN could be reduced by referencing every pulse from one of the SC lasers. Paper V further developed this SC-based system and added a more stable probe and a balanced detection scheme where also the large and varying water absorption could be canceled at well-balanced wavelengths. The system was approaching the fundamental shot noise limit at well-balanced wavelengths, but the noise increased due to instability of the transmission cell. Limiting instrumental factors were investigated and possible solutions to realize a fiber and NIRS-based CGM sensor were discussed. The third section focuses on overcoming the low absorption cross-section in the first overtone band by signal enhancement on an optical fiber and includes Papers VI and VII. This research on enhanced absorption spectroscopy is at an earlier stage than traditional NIR spectroscopy and was conducted stepwise with the ultimate goal of surface enhanced near-infrared absorption spectroscopy (SENIRAS) for glucose sensing. Paper VI realized absorption enhancement on an optical fiber in the visible band on model dyes. Paper VII demonstrated plasmon resonance on optical fibers in the NIR range and around wavelengths where glucose absorbs. In summary, this thesis investigates limitations and challenges for instrumentation, analysis and methods, followed by solutions and strategies to accomplish NIRSbased CGM using optical fibers for the IP space.

• Dirnena-Fusini, Ilze; Christiansen, Sverre Christian; Carlsen, Sven Magnus & Fougner, Anders Lyngvi (2021). Intraperitoneal insulin administration - superior choice for an artificial pancreas? Norges teknisk-naturvitenskapelige universitet. ISSN 9788232657346. Full text in Research Archive Show summary

  The main focus of this thesis is to explore the intraperitoneal (IP) cavity as the location for insulin administration. We hypothesized that IP insulin administration is more physiologic as it mimics endogenous insulin secretion and theoretically should achieve superior glycaemic control compared to subcutaneous (SC) delivered insulin. An additional focus was on IP glucagon administration, as glucagon is administered in a case of severe hypoglycaemia and is explored for use in a bi-hormonal artificial pancreas (AP). We hypothesized that IP glucagon administration, as IP insulin administration, mimics endogenous glucagon secretion closer than SC glucagon administration. In the work of the thesis, we performed a systematic review where we explored a variety of physiological effects in a comparison between continuous IP insulin infusion (CIPII) and continuous SC insulin infusion (CSII) in patients with diabetes mellitus type 1 (Paper I). In the second paper, we contrasted the pharmacokinetics and pharmacodynamics of various IP insulin boluses in anaesthetised pigs to that of SC insulin (Paper II). In the third paper, we investigated pharmacodynamics of glucagon after IP and SC injection in rats (Paper III). The thesis involves the two most important pancreatic hormones affecting glucose homeostasis, insulin and glucagon. Therefore, in the chapter 2 ʻBackgroundʼ, endogenous insulin and glucagon synthesis, secretion and effects are described. Relevant available exogenous analogues are also described. Additionally, somatostatin is also mentioned as somatostatin analogues were used in the animal trials to supress endogenous insulin and glucagon secretion. The Artificial Pancreas Trondheim (APT) research group has its main focus towards the development of bi-hormonal AP system, i.e., a closed-loop IP insulin infusion system. In this thesis we obtained information of insulin pharmacodynamics and pharmacokinetics and glucagon pharmacodynamics that will be used to develop an algorithm for an IP AP. Therefore, chapter 2 includes an evaluation of potential benefits and disadvantages of CIPII compared to CSII. Also, available information on possible advantages and disadvantages of IP AP and bi-hormonal AP development are summarized. This thesis demonstrates the potential benefits of IP insulin and glucagon delivery as part of an AP. My hope is that this thesis will be one more step toward an AP that normalizes or close to normalizes glucose levels in patients with DM1.

• Åm, Marte Kierulf; Christiansen, Sverre; Carlsen, Sven Magnus & Fougner, Anders Lyngvi (2020). The intraperitoneal artificial pancreas: glucose sensing and glucagon delivery. Norges teknisk-naturvitenskapelige universitet. ISSN 9788232648832. Full text in Research Archive Show summary

  Summary Diabetes is a heterogeneous disease affecting millions of people worldwide. Diabetes mellitus type 1 is characterised with no, or very little, production of insulin and was a fatal disease until 1922, when the first successful treatment with insulin was conducted. Although the treatment of diabetes mellitus type 1 has had a revolutionary development in the last century, and many patients invest a lot of time and effort into the management of their disease, side effects due to suboptimal glucose control are still common. The artificial pancreas is an automated glucose regulatory device to keep the blood glucose close to normal levels and thus avoid the side effects of the disease. The artificial pancreas system should also relieve the user of the constant management of the disease. The first commercially available AP system, approved by the United States Food and Drug Administration in 2016, is an insulin-only double subcutaneous system, i.e., subcutaneous continuous glucose monitoring, and subcutaneous continuous insulin infusion. The system automatically adjusts the basal insulin infusion but still relies on user intervention to control post-prandial glucose elevations. The artificial pancreas system is a closed-loop system comprised of a glucose sensor, a hormone pump (or pumps for dual-hormone artificial pancreas) and a controller. The controller decides on the appropriate action of hormone delivery based on the detected glucose levels, and the success of the system relies on minimal delay in every part of the loop. There are physiological delays in both glucose sensing and hormone effect, and the delay in insulin effect is currently the biggest challenge to any double subcutaneous artificial pancreas system. The intraperitoneal space is, therefore, being investigated as an alternative site for an artificial pancreas system because of the fast dynamics of hormone effect and glucose sensing. The main aim of this thesis was to investigate the intraperitoneal space as a site for glucose sensing and glucagon delivery as a part of an intraperitoneal artificial pancreas system. The first paper investigated potential spatial differences in glucose dynamics within the peritoneal space of anaesthetized pigs. There does not seem to be any clinically significant differences in glucose dynamics between the four quadrants of the intraperitoneal space. The second paper in the dissertation discusses the importance of measuring glucose as close to the peritoneal lining as possible in order to detect changes in blood glucose as fast as possible. Investigating the effect on the blood glucose level after intraperitoneal delivery of glucagon showed that the glucose response was faster after intraperitoneal delivery in rats and gave a higher glucose response in anaesthetized pigs compared to subcutaneous delivery. It seems also possible that sufficient glucose elevations might be achieved with smaller glucagon doses by IP delivery compared with subcutaneous delivery, possible avoiding side effects of glucagon treatment. This thesis demonstrates how the intraperitoneal space is a promising site for glucose sensing and glucagon delivery in an artificial pancreas system, however, development of appropriate sensor technology and further animal experiments are needed to fully evaluate the performance of an intraperitoneal artificial pancreas.

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