Search Results (114)

Non-invasive glucose monitoring has become a critical area of research for diabetes management, offering a less intrusive and more patient-friendly alternative to traditional methods such as finger-prick tests. This study presents a novel approach using a semi-solid tissue-mimicking phantom designed to replicate the dielectric properties of human skin and blood vessels. The phantom was simplified to focus solely on the skin layer, with embedded channels representing veins to achieve realistic glucose monitoring conditions. These channels were filled with D-(+)-Glucose solutions at varying concentrations (60 mg/dL to 200 mg/dL) to simulate physiological changes in blood glucose levels. A miniature patch antenna optimized to operate at 14 GHz with a penetration depth of approximately 1.5 mm was designed and fabricated. The antenna was tested in direct contact with the skin phantom, allowing for precise measurements of the changes in glucose concentration without interference from deeper tissue layers. Simulations and experiments demonstrated the antenna’s sensitivity to variations in glucose concentration, as evidenced by measurable shifts in the dielectric properties of the phantom. Importantly, the system enabled stationary measurements by injecting glucose solutions into the same blood vessels, eliminating the need to reposition the sensor while ensuring reliable and repeatable results. This work highlights the importance of shallow penetration depth in targeting close vessels for noninvasive glucose monitoring, and emphasizes the potential of microwave-based sensing systems as a practical solution for continuous glucose management. Full article

Advances in continuous glucose monitoring (CGM) technologies and wearable devices are enabling the enhancement of automated insulin delivery systems (AIDs) towards fully automated closed-loop systems, aiming to achieve secure, personalized, and optimal blood glucose concentration (BGC) management for individuals with diabetes. While model predictive control provides a flexible framework for developing AIDs control algorithms, models that capture inter- and intra-patient variability and perturbation uncertainty are needed for accurate and effective regulation of BGC. Advances in artificial intelligence present new opportunities for developing data-driven, fully closed-loop AIDs. Among them, deep reinforcement learning (DRL) has attracted much attention due to its potential resistance to perturbations. To this end, this paper conducts a literature review on DRL-based BGC control algorithms for AIDs. First, this paper systematically analyzes the benefits of utilizing DRL algorithms in AIDs. Then, a comprehensive review of various DRL techniques and extensions that have been proposed to address challenges arising from their integration with AIDs, including considerations related to low sample availability, personalization, and security are discussed. Additionally, the paper provides an application-oriented investigation of DRL-based AIDs control algorithms, emphasizing significant challenges in practical implementations. Finally, the paper discusses solutions to relevant BGC control problems, outlines prospects for practical applications, and suggests future research directions. Full article

Diabetes is a growing global health concern, affecting millions and leading to severe complications if not properly managed. The primary challenge in diabetes management is maintaining blood glucose levels (BGLs) within a safe range to prevent complications such as renal failure, cardiovascular disease, and neuropathy. Traditional methods, such as finger-prick testing, often result in low patient adherence due to discomfort, invasiveness, and inconvenience. Consequently, there is an increasing need for non-invasive techniques that provide accurate BGL measurements. Photoplethysmography (PPG), a photosensitive method that detects blood volume variations, has shown promise for non-invasive glucose monitoring. Deep neural networks (DNNs) applied to PPG signals can predict BGLs with high accuracy. However, training DNN models requires large and diverse datasets, which are typically distributed across multiple healthcare institutions. Privacy concerns and regulatory restrictions further limit data sharing, making conventional centralized machine learning (ML) approaches less effective. To address these challenges, this study proposes a federated learning (FL)-based solution that enables multiple healthcare organizations to collaboratively train a global model without sharing raw patient data, thereby enhancing model performance while ensuring data privacy and security. In the data preprocessing stage, continuous wavelet transform (CWT) is applied to smooth PPG signals and remove baseline drift. Adaptive cycle-based segmentation (ACBS) is then used for signal segmentation, followed by particle swarm optimization (PSO) for feature selection, optimizing classification accuracy. The proposed system was evaluated on diverse datasets, including VitalDB and MUST, under various conditions with data collected during surgery and anesthesia. The model achieved a root mean square error (RMSE) of 19.1 mg/dL, demonstrating superior predictive accuracy. Clarke error grid analysis (CEGA) confirmed the model’s clinical reliability, with 99.31% of predictions falling within clinically acceptable limits. The FL-based approach outperformed conventional deep learning models, making it a promising method for non-invasive, privacy-preserving glucose monitoring. Full article

Background: Limited data exist on managing type 1 diabetes mellitus (T1DM) during long-distance endurance events such as marathons. This study aimed to assess glycemic control and participant safety during a marathon. Methods: Five men with T1DM, participating in the 22nd Poznan Marathon, were recruited. They completed health questionnaires and received training on glycemic management. Their physical capacity was assessed (including maximal oxygen uptake on a cycle ergometer). Participants reduced their insulin doses and consumed breakfast 2.5–3 h before the race. During the marathon, self-monitoring blood glucose (SMBG) and ketone levels were measured at five checkpoints (start, 10 km, 19 km, 30 km, and finish). The medical team followed a pre-approved protocol, providing carbohydrate and fluid supplementation as needed. Glycemia was monitored by two continuous glucose monitoring (CGM) systems (FreeStyle Libre 2 and Dexcom G6) and SMBG. Results: The participants’ median age was 44 years (34–48), with a diabetes duration of 10 years (6–14), and a BMI of 22.5 kg/m² (22.0–23.3). All finished the marathon in an average time of 4:02:56 (±00:43:11). Mean SMBG was 125.6 (±43.5) mg/dL, while CGM readings were 149.6 (±17.9) mg/dL (FreeStyle Libre 2) and 155.4 (±12.9) mg/dL (Dexcom G6). One participant experienced prolonged hypoglycemia undetected by CGM, whereas another developed symptomatic hypoglycemia between SMBG measurements. Conclusions: Safe marathon completion in people with T1DM requires individualized insulin dose adjustments, appropriate carbohydrate supplementation, and dedicated medical support at checkpoints. Combining CGM with periodic SMBG measurements further enhances safety and helps to detect potential glycemic excursions. Full article

Background/Objectives: Glycemic variability (GV) is a novel concept in the assessment of the quality of glycemic control in patients with diabetes, with its importance emphasized in patients with type 1 diabetes. Its adoption in clinical practice emerged with the increased availability of continuous glycemic monitoring systems. The aim of this study is to evaluate the GV in patients with type 1 diabetes mellitus (T1DM) and to assess its associations with other parameters used to evaluate the glycemic control. Methods: GV indexes and classical glycemic control markers were analyzed for 147 adult patients with T1DM in a multicentric cross-sectional study. Results: Stable glycemia was associated with a higher time in range (TIR) (78% vs. 63%; p < 0.001) and a lower HbA1c (6.8% vs. 7.1%; p = 0.006). The coefficient of variation (CV) was reversely correlated with TIR (Spearman’s r = −0.513; p < 0.001) and positively correlated with hemoglobin A1c (HbA1c) (Spearman’s r = 0.349; p < 0.001), while TIR was reversely correlated with HbA1c (Spearman’s r = −0.637; p < 0.001). The composite GV and metabolic outcome was achieved by 28.6% of the patients. Conclusions: Stable glycemia was associated with a lower HbA1c, average and SD of blood glucose, and a higher TIR. A TIR higher than 70% was associated with a lower HbA1c, and SD and average blood glucose. Only 28.6% of the patients with T1DM achieved the composite GV and metabolic outcome, despite 53.7% of them achieving the HbA1c target, emphasizing thus the role of GV in the assessment of the glycemic control. Full article

Background/Objectives: Continuous and flash glucose monitoring (CGM and FGM) may enhance glucose management by providing real-time glucose data. Furthermore, growing evidence is linking altered blood glucose concentrations and worse short-term outcomes in critically ill patients. While hyperglycemia is more common in these patients and is associated with an increased risk of adverse events, hypoglycemia is particularly concerning and significantly raises the risk of fatal outcomes. This exploratory study investigated the link between FGM variables and cardiogenic shock in critically ill Coronary Care Unit (CCU) patients. Methods: Twenty-eight CCU patients (1 May 2021–31 January 2022) were monitored using a Libre FreeStyle system. Analyzed data included patient demographic and laboratory data, left ventricular ejection fraction, standard glucose monitoring, APACHE IV scores, and cardiogenic shock occurrence. Analysis was performed using the χ² test, Mann–Whitney U test, and logistic regression. Results: Among the patients, 13 (46.43%) developed cardiogenic shock. FGM detected hypoglycemia in 18 (64.29%) patients, while standard methods in 6 (21.43%) patients. FGM-detected hypoglycemia was more frequent in patients who developed cardiogenic shock (p = 0.0129, χ² test) with a significantly higher time below range reading (p = 0.0093, Mann Withney U test), despite no differences in mean glucose values. In addition, hypoglycemia detected by FGM was an independent predictor of shock (p = 0.0390, logistic regression). Conclusions: FGM identified more hypoglycemic events compared to standard glucose monitoring in the CCU. Frequent FGM-detected hypoglycemic events were associated with cardiogenic shock, regardless of a history of diabetes. Due to a limited sample size, these results should be interpreted cautiously and further research in this area is justified. Full article

This study aimed to evaluate the acute effects of consuming in a full-course meal manner over one hour, with intervals between courses, on glycemic and insulin parameters in young healthy women, with a randomized controlled crossover study design. Experiment 1: Fifteen participants consumed a test meal under two eating conditions: fast eating manner for 10 min, and eating in a full-course meal manner for 60 min. In both conditions, the food order was standardized: vegetables first, followed by the main dish, and carbohydrates last. Blood glucose and insulin concentrations were measured at 0, 40, 80, 120, and 180 min on two separate days. Postprandial blood glucose and insulin levels at 40 min, as well as the incremental area under the curve (IAUC) at 40 min for glucose and the IAUC at both 40 and 80 min for insulin, were significantly lower for the full-course meal manner compared to the fast eating manner, due to delayed consumption of the carbohydrate dish in the former condition at these time points. To continuously monitor postprandial blood glucose responses over a 24 h period, Experiment 2 was conducted using an intermittent continuous glucose monitoring system (isCGM). Eighteen participants wore isCGM devices and consumed the same test meals under the two different eating conditions as in Experiment 1. The mean amplitude of glycemic excursions (MAGE; p < 0.05) and IAUC for glucose were significantly lower for the full-course meal manner compared to the fast eating manner. These findings suggest that consuming meals in a full-course meal manner, with intervals between courses, is associated with a reduced MAGE in young healthy women. Full article

Continuous glucose monitoring (CGM) and flash glucose monitoring (FGM) systems have revolutionized diabetes management by delivering real-time, dynamic insights into blood glucose levels. This article provides a concise overview of the evolution of CGM technology, highlights emerging innovations in the field and explores current and potential future applications (including insulin management, early diagnostics, predictive modeling, diabetes education and integration into automated insulin delivery (AID) systems) of CGM in healthcare. Full article

Background: Insulin pumps coupled with continuous glucose monitoring sensors use algorithms to analyze real-time blood glucose levels. This allows for the suspension of insulin administration before hypoglycemic thresholds are reached or for adaptive tuning in hybrid closed-loop systems. This longitudinal retrospective study aims to analyze real-world glycemic outcomes in a pediatric population transitioning to such devices. Methods: We evaluated children with type 1 diabetes mellitus (T1D) admitted to the Pediatric Diabetes Department from a major University Hospital in Bucharest, Romania, who transitioned to hybrid closed-loop or predictive low-glucose suspend system from either non-automated insulin pumps or multiple daily injections. The primary outcome was assessing the change in glycated hemoglobin (HbA1c) after initiating these devices. Secondary outcomes analyzed changes in glucose metrics from the 90 days prior to the baseline and follow-up visit. Results: 51 children were included (58.8% girls), the mean age was 10.3 ± 3.7 years, and the mean follow-up duration was 13.2 ± 4.5 months. The analyzed parameters, such as HbA1c (6.9 ± 0.7% vs. 6.7 ± 0.6%, p = 0.023), time in range (69.3 ± 11.2% vs. 76 ± 9.9%, p < 0.001), time in tight range (47.4 ± 10.9% vs. 53.7 ± 10.7%, p < 0.001), time below range (5.6 ± 2.9% vs. 3.5 ± 1.9%, p < 0.001), time above range (25 ± 11.2% vs. 20.4 ± 9.4%, p = 0.001), and coefficient of variation (37.9 ± 4.8% vs. 35.6 ± 4.6%, p = 0.001), showed significant improvements. Conclusions: The application of these sensor-integrated insulin pumps can significantly enhance metabolic control in pediatric populations, minimizing glycemic variations to mitigate complications and enrich the quality of life. Full article

Hyaluronic acid-based hydrogels are emerging as highly versatile materials for cost-effective biosensors, capable of sensitive chemical and biological detection. These hydrogels, functionalized with specific groups, exhibit sensitivity modulated by factors such as temperature, pH, and analyte concentration, allowing for a broad spectrum of applications. This study presents a patent-centered overview of recent advancements in hyaluronic acid hydrogel biosensors from 2003 to 2023. A total of 50 patent documents—including 41 patent applications and 9 granted patents—reveal a growing interest, primarily driven by United States-based institutions, which account for approximately 54% of all filings. This trend reflects the strong collaboration between universities, industry, and foundations in pushing this technology forward. Most patented technologies focus on biosensors for in vivo blood analysis, measuring critical parameters such as gas concentration and pH, with particular emphasis on glucose monitoring via tissue impedance using enzyme-immobilized oxidase electrodes. Additionally, the 9 granted patents collectively showcase key innovations, highlighting applications from continuous glucose monitors to implantable vascular devices and sweat analyte detection systems. These patents underscore the adaptability and biocompatibility of hyaluronic acid hydrogels, reinforcing their role in enhancing biosensor performance for real-time health monitoring. In summary, this overview highlights the importance of patent analysis in tracking and directing research and development, helping to clarify the field’s evolution and identify innovation gaps for hyaluronic acid-based hydrogel biosensors. Full article

Diabetes mellitus, a chronic condition affecting millions worldwide, necessitates continuous monitoring of blood glucose level (BGL). The increasing prevalence of diabetes has driven the development of non-invasive methods, such as electronic noses (e-noses), for analyzing exhaled breath and detecting biomarkers in volatile organic compounds (VOCs). Effective machine learning models require extensive patient data to ensure accurate BGL predictions, but previous studies have been limited by small sample sizes. This study addresses this limitation by employing conditional generative adversarial networks (CTGAN) to generate synthetic data from real-world tests involving 29 healthy and 29 diabetic participants, resulting in over 14,000 new synthetic samples. These data were used to validate machine learning models for diabetes detection and BGL prediction, integrated into a Tiny Machine Learning (TinyML) e-nose system for real-time analysis. The proposed models achieved an 86% accuracy in BGL identification using LightGBM (Light Gradient Boosting Machine) and a 94.14% accuracy in diabetes detection using Random Forest. These results demonstrate the efficacy of enhancing machine learning models with both real and synthetic data, particularly in non-invasive systems integrating e-noses with TinyML. This study signifies a major advancement in non-invasive diabetes monitoring, underscoring the transformative potential of TinyML-powered e-nose systems in healthcare applications. Full article

Background/Objectives: Managing blood glucose levels effectively remains a significant challenge for individuals with diabetes. Traditional methods often lack the flexibility needed for personalized care. This study explores the potential of reinforcement learning-based approaches, which mimic human learning and adapt strategies through ongoing interactions, in creating dynamic and personalized blood glucose management plans. Methods: We developed a mathematical model specifically for patients with type IVP diabetes, validated with data from 10 patients and 17 key parameters. The model includes continuous glucose monitoring (CGM) noise and random carbohydrate intake to simulate real-life conditions. A closed-loop system was designed to enable the application of reinforcement learning algorithms. Results: By implementing a Policy Optimization (PPO) branch, we achieved an average Time in Range (TIR) metric of 73%, indicating improved blood glucose control. Conclusions: This study presents a personalized insulin therapy solution using reinforcement learning. Our closed-loop model offers a promising approach for improving blood glucose regulation, with potential applications in personalized diabetes management. Full article

Non-invasive/minimally invasive continuous monitoring of blood glucose and blood glucose administration have a high impact on chronic disease management in diabetic patients, but the existing technology is yet to achieve the above two purposes at the same time. Therefore, this study proposes a microfluidic microneedle patch based on 3D printing technology and an integrated control system design for blood glucose measurement, and a drug delivery control circuit based on a 555 chip. The proposed method provides an improved preparation of a PVA-PEG-MoS₂ nanosheet hydrogel, making use of its dielectric properties to fabricate a microcapacitor and then embedding it in a microfluidic chip. When MoS₂ nanosheets react with interstitial liquid glucose (and during the calibration process), the permittivity of the hydrogel is changed, resulting in changes in the capacitance of the capacitor. By converting the capacitance change into the square-wave period change in the output of the 555 chip with the control circuit design accordingly, the minimally invasive continuous measurement of blood glucose and the controlled release of hypoglycemic drugs are realized. In this study, the cross-linking structure of MoS₂ nanosheets in hydrogel was examined using infrared spectroscopy and scanning electron microscopy (SEM) methods. Moreover, the critical doping mass fraction of MoS₂ nanosheets was determined to be 2% via the measurement of the dielectric constant. Meanwhile, the circuit design and the relationship between the pulse cycle and glucose concentration is validated. The results show that, compared with capacitors in series, the microcapacitors embedded in microfluidic channels can be connected in parallel to obtain better linearized blood glucose measurement results. Full article

Type 1 diabetes (T1D) severely impairs metabolic control and can lead to erectile dysfunction (ED) through hyperglycemia-induced vascular damage, autonomic neuropathy, and psychological distress. This review examines the role of continuous glucose monitoring (CGM) in ameliorating ED by addressing glucose variability and inflammation. A comprehensive analysis of studies and clinical trials was conducted to evaluate the impact of CGM on metabolic control, inflammatory responses, and vascular health in patients with T1D. Evidence suggests that CGM systems significantly stabilize blood glucose levels and reduce hyper- and hypoglycemic episodes that contribute to endothelial dysfunction and ED. CGM’s real-time feedback helps patients optimize metabolic control, improve vascular health, and reduce inflammation. CGM has the potential to redefine ED management in patients with T1D by improving glycemic control and reducing the physiological stressors that cause ED, potentially improving quality of life and sexual health. Further research is warranted to explore the specific benefits of CGM for ED management. Full article

Continuous Glucose Monitoring (CGM) not only can be used for glycemic control in chronic diseases (e.g., diabetes), but is increasingly being utilized by individuals and athletes to monitor fluctuations in training and everyday life. However, it is not clear how accurately CGM reflects plasma glucose concentration in a healthy population in the absence of chronic diseases. In an oral glucose tolerance test (OGTT) with forty-four healthy male subjects (25.5 ± 4.5 years), the interstitial fluid glucose (ISFG) concentration obtained by a CGM sensor was compared against finger-prick capillary plasma glucose (CPG) concentration at fasting baseline (T0) and 30 (T30), 60 (T60), 90 (T90), and 120 (T120) min post OGTT to investigate differences in measurement accuracy. The overall mean absolute relative difference (MARD) was 12.9% (95%-CI: 11.8–14.0%). Approximately 100% of the ISFG values were within zones A and B in the Consensus Error Grid, indicating clinical accuracy. A paired t-test revealed statistically significant differences between CPG and ISFG at all time points (T0: 97.3 mg/dL vs. 89.7 mg/dL, T30: 159.9 mg/dL vs. 144.3 mg/dL, T60: 134.8 mg/dL vs. 126.2 mg/dL, T90: 113.7 mg/dL vs. 99.3 mg/dL, and T120: 91.8 mg/dL vs. 82.6 mg/dL; p < 0.001) with medium to large effect sizes (d = 0.57–1.02) and with ISFG systematically under-reporting the reference system CPG. CGM sensors provide a convenient and reliable method for monitoring blood glucose in the everyday lives of healthy adults. Nonetheless, their use in clinical settings wherein implications are drawn from CGM readings should be handled carefully. Full article