Article
Automation & Control Systems
Abhinoy Kumar Singh, Mihailo V. Rebec, Ahmad Haidar
Summary: A continuous glucose monitoring system consists of a glucose sensor and an estimation algorithm, which can monitor blood glucose levels in real-time. The sensor generates electrical current when inserted under the skin, while the algorithm infers glucose levels in the blood. The study proposes a Kalman filter-based estimation algorithm, which provides more accurate results compared to alternative algorithms.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2021)
Article
Nutrition & Dietetics
Souptik Barua, Raven A. Wierzchowska-McNew, Nicolaas E. P. Deutz, Ashutosh Sabharwal
Summary: This study found discordance between plasma glucose measurements made using continuous glucose monitoring (CGM) and fingerstick meter after meals. CGM underestimated the rise in glucose levels and time spent in the normal range, while the fingerstick meter was more accurate. These discordances may have implications for applications such as precision nutrition and early detection of diabetes.
AMERICAN JOURNAL OF CLINICAL NUTRITION
(2022)
Article
Automation & Control Systems
Peihu Duan, Jiachen Qian, Qishao Wang, Zhisheng Duan, Ling Shi
Summary: This article investigates the problem of distributed state estimation for a continuous-time linear system with a sensor network, where each sensor can only communicate with its neighbors and contains time-correlated measurement noise. A novel augmented leader-following information fusion strategy is proposed to collect measurements and system matrices. A class of distributed state estimators is developed with bounded estimation error covariances, and a closed-form relation between the designed distributed estimator and the centralized estimator is established. The proposed estimator converges to the estimation performance of the centralized estimator when the consensus gain tends to infinity. The estimator is extended to the fully distributed case by introducing an adaptive law for the consensus gain without using any global information. It is shown that the designed estimator is applicable for systems with deterministic noise. Comparative numerical simulations are provided to demonstrate the effectiveness and superiority of the theoretical results.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Chemistry, Analytical
Kai Sun, Siyang Liu, Jing Liu, Zhaoyang Ding, Yifei Jiang, Jicheng Zhang, Haobin Chen, Jiangbo Yu, Changfeng Wu, Daniel T. Chiu
Summary: The study introduces an external ratiometric calibration method to improve measurement accuracy of Pdot glucose transducers in implanted CGM systems. This method uses the ratio of oxygen concentrations to correct for signal deviations caused by tissue oxygen fluctuations, achieving higher accuracy glucose measurements.
ANALYTICAL CHEMISTRY
(2021)
Article
Chemistry, Analytical
Sudip Paul, Rohit Sharma, Prashant Tathireddy, Ricardo Gutierrez-Osuna
Summary: In this paper, a multi-calibration ensemble approach is proposed to compensate for sensor drift in long-term application of chemical sensor arrays. The method utilizes past sensor measurements and known ground-truth data to build a regression model for predicting the concentration of target analytes. Experimental and simulation results demonstrate the superiority of the proposed approach compared to existing methods under various conditions.
SENSORS AND ACTUATORS B-CHEMICAL
(2022)
Article
Chemistry, Analytical
Yiqun Liu, Li Yang, Yue Cui
Summary: Diabetes is a prevalent and chronic metabolic disease, and continuous monitoring is crucial for its management. Transdermal glucose biosensors offer accurate monitoring, but face challenges in biosensing principles, device configuration, and material integration. This review aims to address these questions and explore the practical applications of transdermal glucose biosensors.
Article
Chemistry, Analytical
Ananthakrishnan Soundaram Jeevarathinam, Waqas Saleem, Nya Martin, Connie Hu, Michael J. McShane
Summary: A sensitive and scalable phosphorescent oxygen sensor formulation using ethyl cellulose (EC) and polystyrene (PS) nanoparticles stabilized with various surfactants was evaluated. The EC nanoparticles showed higher sensitivity and narrow size distribution compared to PS nanoparticles. The preferred formulation of EC nanoparticles protected with PF68 exhibited high oxygen sensitivity, wide phosphorescence lifetime response, and low cytotoxicity. These EC-PF68 nanoparticles were then encapsulated in alginate microspheres to create phosphorescent nanoparticles-in-microparticle (NIMs) for glucose and lactate sensing.
Article
Biophysics
Yuanyuan Zou, Zhengkang Chu, Jiuchuan Guo, Shan Liu, Xing Ma, Jinhong Guo
Summary: Diabetes and its complications are a major threat to the health and well-being of millions of people. Continuous glucose monitoring (CGM) technology, particularly based on electrochemical sensing principles, has great potential to improve the quality of life of diabetics by overcoming the limitations of self-monitoring blood glucose (SMBG). However, the application of minimally invasive electrochemical CGM sensors is still limited due to issues such as invasiveness, short lifespan, biocompatibility, and calibration and prediction. Recent developments in materials and technologies have significantly improved the performance of these sensors.
BIOSENSORS & BIOELECTRONICS
(2023)
Article
Thermodynamics
Behnam Mobaraki, Francisco Javier Castilla Pascual, Fidel Lozano-Galant, Jose Antonio Lozano-Galant, Rocio Porras Soriano
Summary: Accurate characterization of the U-value is crucial in assessing energy demand or consumption of buildings. Commercial devices used for estimating the U-value have drawbacks like high cost, contact sensors, and difficulty meeting ideal measuring conditions. To overcome these issues, the authors proposed a low-cost Hyper Efficient Arduino Transmittance-meter (HEAT) for monitoring the U-value. The system now includes recording time series data using IoT protocols and non-contact temperature sensors. In this case study, HEAT was used to characterize the walls and ceiling of a building room, and its performance was compared to standards and conventional modeling tools. The results show that the developed system is reliable, with an acceptable range of uncertainty compared to literature values.
CASE STUDIES IN THERMAL ENGINEERING
(2023)
Article
Chemistry, Physical
Jiyong Kim, Salman Khan, Eun Kyu Kim, Hye-Jun Kil, Bo Min Kang, Hyo Geon Lee, Jin-Woo Park, Jun Young Yoon, Woochul Kim
Summary: Continuous monitoring and timely treatment are crucial for wearable and implantable healthcare systems, but they require continuous power supply. We developed a continuous healthcare system for type 1 diabetes by combining a low-energy micropump, self-powered glucose sensor, and body heat conversion to electricity. This system can provide true continuous healthcare for patients.
Article
Chemistry, Multidisciplinary
Jian Yang, Xia Gong, Shuijin Chen, Ying Zheng, Lelun Peng, Bin Liu, Zhipeng Chen, Xi Xie, Changqing Yi, Lelun Jiang
Summary: Continuous glucose monitoring (CGM) is highly desirable for diabetes management to track blood glucose fluctuation and reduce the risk of hyperglycemia and hypoglycemia. In this study, a smartphone-controlled and microneedle-based wearable CGM system was developed for long-term glucose monitoring. The system, modified with a sandwich-type enzyme immobilization strategy, demonstrated good performance, costing less than $15 and correlating well with commercial glucometers and FDA-approved CGM systems. The intelligently wearable CGM system accurately monitored glucose fluctuations and provided valuable clinical information, offering an alternative solution for home-care diabetes management.
Article
Biophysics
Wan-lu Zheng, Ya-nan Zhang, Li-ke Li, Xue-gang Li, Yong Zhao
Summary: A plug-and-play SPR dual-parameter optical fiber biosensor was developed for simultaneous detection of glucose and cholesterol concentrations. The sensor utilized Au film and Au nanoparticles to excite SPR and generate double SPR resonance valleys. By coating modified PMBA and I3-CD on the sensor probe surface, the sensor could detect glucose and cholesterol through the interaction between the biomolecules and the coatings. The sensor demonstrated ultra-low detection limits and good selectivity.
BIOSENSORS & BIOELECTRONICS
(2022)
Article
Computer Science, Interdisciplinary Applications
Martina Vettoretti, Martina Drecogna, Simone Del Favero, Andrea Facchinetti, Giovanni Sparacino
Summary: This study aims to develop a mathematical model of CGM gaps, which are occasional portions of missing data generated by temporary sensor errors. The model achieved good performance and can be used to realistically simulate CGM gaps in ISCTs, enabling the development of more effective and robust diabetes management strategies.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Article
Biophysics
Inyoung Lee, David Probst, David Klonoff, Koji Sode
Summary: Diabetes mellitus is a chronic illness in the United States with approximately 120 million affected adults. Tight glycemic control is essential for managing diabetes and reducing the risk of complications. Continuous glucose monitoring systems are recognized as the ideal monitoring systems for diabetic patients.
BIOSENSORS & BIOELECTRONICS
(2021)
Article
Engineering, Electrical & Electronic
Antos Cheeramban Varghese, Anamitra Pal, Gautam Dasarathy
Summary: Accurate knowledge of transmission line parameters is crucial for power system monitoring, protection, and control applications. This study proposes a novel approach for transmission line parameter estimation (TLPE) using phasor measurement unit (PMU) data with non-Gaussian noise. The measurement noise is modeled as a Gaussian mixture model (GMM) and identified using the expectation-maximization (EM) algorithm. The proposed approach demonstrates superior performance compared to traditional methods and recently proposed alternatives, as evidenced by simulations on the IEEE 118-bus system and proprietary PMU data from a U.S. power utility.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Endocrinology & Metabolism
Nunzio Camerlingo, Martina Vettoretti, Giovanni Sparacino, Andrea Facchinetti, Julia K. Mader, Pratik Choudhary, Simone Del Favero
Summary: This study compares correlation-based and equation-based approaches for reliable estimation of time spent in different glycaemic ranges. Results show that the equation-based approach is more robust and consistent, suggesting specific monitoring durations based on population characteristics to achieve desired precision in time-in-ranges estimates.
Article
Engineering, Biomedical
Faccioli Simone, Facchinetti Andrea, Sparacino Giovanni, Pillonetto Gianluigi, Del Favero Simone
Summary: This study investigates different techniques for learning individualized linear models of glucose response to insulin and meal, and compares the performance of non-parametric approach with the state-of-the-art parametric approach. The non-parametric technique shows better prediction performance and significant improvement compared to the parametric approach.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
S. Faccioli, I. Sala-Mira, J. L. Diez, A. Facchinetti, G. Sparacino, S. Del Favero, J. Bondia
Summary: Hybrid automated insulin delivery systems rely on carbohydrate counting for postprandial control in type 1 diabetes, which can be burdensome and prone to errors. This study proposes an automated meal detection algorithm and evaluates its performance on real-life data. The results show a high recall and precision for the algorithm, but there are still false positives and false negatives, which are associated with low-risk situations.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Article
Computer Science, Interdisciplinary Applications
N. Camerlingo, M. Vettoretti, S. Del Favero, A. Facchinetti, P. Choudhary, G. Sparacino
Summary: This study developed a simple and interpretable model to simulate the timing variability of correction boluses observed in real data, providing a more realistic representation of patient behavior in taking correction boluses and their post-prandial blood glucose response. The model showed better performance compared to traditional classifiers and exhibited good interpretability.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Article
Computer Science, Interdisciplinary Applications
J. Pavan, C. Dalla Man, D. Herzig, L. Bally, S. Del Favero
Summary: This study introduces an open-source software called Gluclas to support the modulation of glucose infusion rate (GIR) in glucose clamp experiments. The software utilizes a proportional-integrative-derivative controller to provide infusion rate suggestions based on blood glucose measurements. The preliminary validation of Gluclas shows satisfactory control in simulated and in-vivo experiments.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Article
Endocrinology & Metabolism
Afroditi Tripyla, David Herzig, Gemma Reverter-Branchat, Jacopo Pavan, Michele Schiavon, Philippe J. Eugster, Eric Grouzmann, Christos T. Nakas, Valerie Sauvinet, Laure Meiller, Joerg Zehetner, Daniel Giachino, Philipp Nett, Joanna Gawinecka, Simone Del Favero, Andreas Thomas, Mario Thevis, Chiara Dalla Man, Lia Bally
Summary: This study compared the glucagon response to insulin-induced postprandial hypoglycemia between post-bariatric surgery individuals and non-surgical control individuals. The results showed that the glucagon response was significantly lower in the post-bariatric surgery group, regardless of the surgical modality, compared to the non-surgical control group. There were no significant differences between patients with post-bariatric hypoglycemia and surgical control individuals, suggesting that impaired counter-regulation is not the root cause of post-bariatric hypoglycemia.
Article
Chemistry, Analytical
Francesco Prendin, Jose-Luis Diez, Simone Del Favero, Giovanni Sparacino, Andrea Facchinetti, Jorge Bondia
Summary: Accurate blood glucose forecasting is essential in diabetes management. This study introduces a methodology called C-SARIMA, which utilizes seasonal stochastic models to predict blood glucose levels in real-time. The results show that C-SARIMA performs well compared to other linear and nonlinear black-box methods.
Article
Automation & Control Systems
Jacopo Pavan, Domenico Salvagnin, Andrea Facchinetti, Giovanni Sparacino, Simone Del Favero
Summary: This article introduces a new model predictive control algorithm that improves blood glucose control by providing suggestions of carbohydrate intake while administering insulin. The algorithm significantly increases the time spent in the safe physiological range and reduces the occurrence of hypoglycemia, with minimal manual interventions.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
(2023)
Article
Computer Science, Interdisciplinary Applications
Martina Vettoretti, Martina Drecogna, Simone Del Favero, Andrea Facchinetti, Giovanni Sparacino
Summary: This study aims to develop a mathematical model of CGM gaps, which are occasional portions of missing data generated by temporary sensor errors. The model achieved good performance and can be used to realistically simulate CGM gaps in ISCTs, enabling the development of more effective and robust diabetes management strategies.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2023)
Article
Endocrinology & Metabolism
Jacopo Pavan, David Herzig, Afrodity Tripyla, Chiara Dalla Man, Lia Bally, Simone Del Favero
Summary: This study evaluated the accuracy and safety of Gluclas decision support system in a hypoglycemic clamp study and found that it achieved high accuracy and minimal safety risks in a population with differences in glucose-insulin dynamics, demonstrating its applicability to various patient groups.
DIABETES OBESITY & METABOLISM
(2023)
Article
Engineering, Biomedical
Giacomo Cappon, Francesco Prendin, Andrea Facchinetti, Giovanni Sparacino, Simone Del Favero
Summary: Accurate blood glucose prediction is crucial for the development of T1D management tools. This study develops a personalized physiological model for blood glucose prediction and compares the performance of white-box and black-box prediction techniques.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2023)
Article
Engineering, Biomedical
Edoardo Faggionato, Alessandro Guazzo, Elena Pegolo, Ruggero Carli, Mattia Bruschetta, Simone Del Favero
Summary: This study proposes an adaptive closed-loop control algorithm for warfarin therapy management. In silico validation shows that the algorithm significantly improves the effectiveness of warfarin therapy compared to existing medical guidelines and controllers.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2023)
Article
Engineering, Biomedical
Giacomo Cappon, Martina Vettoretti, Giovanni Sparacino, Simone Del Favero, Andrea Facchinetti
Summary: The ReplayBG simulation methodology proposed in this study enables the design and assessment of new therapies for T1D management through in silico simulations. It accurately simulates the effects of insulin and carbohydrate treatment alterations and proves to be a reliable and robust tool for studying the glucose dynamics in T1D.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2023)
Article
Endocrinology & Metabolism
Lorenzo Meneghetti, Eyal Dassau, Francis J. Doyle, Simone Del Favero
Summary: The study introduces a novel method for detecting real-time infusion site failures using machine learning algorithms. The algorithm showed potential in improving the safety of patients treated with personal insulin pumps by detecting failures early and predicting replacement time accurately.
JOURNAL OF DIABETES SCIENCE AND TECHNOLOGY
(2022)
Article
Endocrinology & Metabolism
Chiara Roversi, Martina Vettoretti, Simone Del Favero, Andrea Facchinetti, Pratik Choudhary, Giovanni Sparacino
Summary: This study utilized in silico trials to quantify the impact of carb-counting errors on glycemic control in type 1 diabetes management. The results showed that random errors worsen glycemic control, while systematic underestimations lead to more time above the target range and systematic overestimations result in more time below the target range. Linear regression models were developed to mathematically describe the relationship between error mean, standard deviation, and changes in glycemic metrics.
JOURNAL OF DIABETES SCIENCE AND TECHNOLOGY
(2022)