Search Results (1,057)

We present a novel disposable electrochemical biosensor for highly sensitive and selective glucose detection, employing gold-modified screen-printed electrodes combined with square wave (SWV) and linear sweep voltammetry (LSV). The sensor integrates recombinant corn-derived manganese peroxidase with glucose oxidase, bovine serum albumin, and gold nanoparticles to enhance stability and signal transduction. Glucose detection by LSV covered 0.001–6.5 mM (R² = 0.9913; LOD = 0.50 µM), while SWV achieved a broader range of 0.0006–6.5 mM (R² = 0.998; LOD = 0.29 µM). The sensor demonstrated excellent selectivity, showing minimal interference from common electroactive species including caffeine, aspartame, and ascorbic acid, and provided rapid responses, making it ideal for point-of-care and food monitoring applications. Full article

Pregnancy in women with type 1 diabetes mellitus (T1DM) is associated with a high risk of maternal and perinatal complications, and achieving optimal glycaemic control remains a clinical challenge. This article presents a narrative review of the evidence on advanced hybrid closed loop (AHCL) insulin delivery systems in pregnancy, with a focus on maternal glycaemic outcomes, neonatal outcomes, and psychosocial aspects. The relevant literature was identified through a structured search of PubMed, Scopus, and Web of Science (2010–2025), supplemented by guideline documents and reference screening. Eligible studies included randomised controlled trials, observational studies, and qualitative investigations. Data were synthesised thematically. Findings from key trials, including CONCEPTT, AiDAPT, and CRISTAL, demonstrate that AHCL systems improve time in range, lower mean glucose, and reduce hyperglycaemia without increasing hypoglycaemia. Some evidence also suggests improved neonatal outcomes, though statistical significance varies. Qualitative studies highlight reduced anxiety, improved sleep, and enhanced quality of life for women using AHCL during pregnancy. In conclusion, AHCL systems show strong promise in optimising maternal glycaemic control and potentially improving perinatal outcomes. However, larger, unbiased studies and real-world evaluations are needed to confirm their benefits and support broader clinical implementation. Full article

Background: Continuous glucose monitoring (CGM) has changed the clinical practice in diabetes management during pregnancy; however, its application during caesarean section remains understudied. This feasibility study evaluates the performance, reliability, and clinical utility of two CGM systems—FreeStyle Libre 2 and Medtronic Guardian Connect—during caesarean delivery and the early postpartum period in a patient with pregestational diabetes mellitus (PGDM). Methods: A prospective, single-patient study was conducted. A 32-year-old woman with type 1 diabetes underwent elective caesarean section at 38 weeks of gestation. Both CGM systems were applied over 18 h prior to surgery and monitored continuously through the intraoperative and five-day postpartum period. Glucose data, device performance, and usability were assessed. Results: Both CGM systems provided uninterrupted, high-quality glucose data throughout the perioperative period, including during spinal anaesthesia, surgical manipulation, and postoperative recovery. No sensor displacement nor signal loss occurred. Glycaemic readings remained within the normoglycaemic range (90–100 mg/dL) during surgery, with mild elevations observed during anaesthesia initiation. Postoperatively, both systems showed comparable glucose trends, with slightly lower readings from FreeStyle Libre 2. Conclusions: CGM is feasible and reliable during caesarean section in PGDM patients. These findings support the integration of CGM into obstetric surgical care and highlight the need for larger studies to validate clinical benefits. Full article

In optical one-dimensional grating-on-layer planar structures, an optical resonance occurs when the incident light wave becomes phase-matched to a leaky waveguide mode excited in the layer underneath the grating by an appropriate tuning of the grating periodicity. Changing the refractive indices of the grating’s constituents, and/or thickness, changes the resonance frequency. In the case of a two-dimensional grating atop such a smooth layer, a similar and also cavity-mode resonance can occur. This idea has straightforward usage in diverse optical sensor applications. In this study, a novel guided-mode resonance sensor design for detecting glucose and hemoglobin in minute concentrations at a wide range of incidence angles is presented. In this design, materials of the grating, such as a polymer and cesium-lead halide with a perovskite crystal structure, are examined, which will allow flexible, low-cost fabrication by soft-lithography/imprint-lithography methods. The sensitivity, figure of merit, and quality factor are reported for one- and two-dimensional grating structures. The simulations performed are based on rigorous coupled-wave analysis. Optical resonance quality factor of ∼$5·{10}^5$ is achieved at oblique incidence for a structure comprising a one-dimensional grating etched in a poly-vinylidene chloride layer atop a silicon nitride waveguide layer on a substrate. Record values of the above-noted characteristics are achieved with a synergetic interplay of the materials, structural dimensions, incidence angle, polarization, and grating geometry. Full article

Three-dimensional printing technology is fundamentally reshaping the design and fabrication of health monitoring sensors. While it holds great promise for achieving miniaturization, multi-material integration, and personalized customization, the lack of a clear selection framework hinders the optimal matching of printing technologies to specific sensor requirements. This review presents a classification framework based on existing standards and specifically designed to address sensor-related requirements, categorizing 3D printing technologies into point-based, line-based, and area-based modalities according to their fundamental fabrication unit. This framework directly bridges the capabilities of each modality, such as nanoscale resolution, multi-material versatility, and high-throughput production, with the critical demands of modern health monitoring sensors. We systematically demonstrate how this approach guides technology selection: Point-based methods (e.g., stereolithography, inkjet) enable micron-scale features for ultra-sensitive detection; line-based techniques (e.g., Direct Ink Writing, Fused Filament Fabrication) excel in multi-material integration for creating complex functional devices such as sweat-sensing patches; and area-based approaches (e.g., Digital Light Processing) facilitate rapid production of sensor arrays and intricate structures for applications like continuous glucose monitoring. The point–line–area paradigm offers a powerful heuristic for designing and manufacturing next-generation health monitoring sensors. We also discuss strategies to overcome existing challenges, including material biocompatibility and cross-scale manufacturing, through the integration of AI-driven design and stimuli-responsive materials. This framework not only clarifies the current research landscape but also accelerates the development of intelligent, personalized, and sustainable health monitoring systems. Full article

Diabetes, a chronic metabolic disease marked by elevated blood glucose levels, affects millions of people globally. A lower quality of life and a markedly higher chance of potentially deadly consequences, such as heart disease, renal failure, and other organ dysfunctions, are closely linked to it. In order to effectively manage diabetes and avoid serious consequences, early detection and ongoing monitoring are essential. Remote health monitoring has emerged as a viable and promising option for proactive healthcare due to the development of contemporary technology, particularly in the areas of wearables and mobile computing. In this work, we suggest a thorough and sophisticated framework for remote monitoring that is intended to automatically predict, identify, and manage diabetes risks. To facilitate real-time data collection analysis and tailored feedback, the system makes use of the integration of smartphones, wearable sensors, and specialized medical equipment. In addition to enhancing patient engagement and lowering the strain on conventional healthcare infrastructures, our suggested model aims to assist patients and healthcare providers in maintaining improved glycemic control. We employed a tenfold stratified cross-validation approach to assess the efficacy of our framework and the results showed remarkable performance metrics. A score of 79.00 percent for clarity (specificity) 87.20 percent for sensitivity, and 83.20 percent for accuracy were all attained by the system. The outcomes show how our framework can be a dependable and scalable remote diabetes management solution, opening the door to more intelligent and easily accessible healthcare systems around the world. Full article

Paper-based electrochemical biosensors have emerged as a revolutionary technology in healthcare diagnostics due to their affordability, portability, ease of use, and environmental sustainability. These biosensors utilize paper as the primary material, capitalizing on its unique properties such as high porosity, flexibility, and capillary action, which make it an ideal candidate for low-cost, functional, and reliable diagnostic devices. The simplicity and cost-effectiveness of paper-based biosensors make them especially suitable for point-of-care (POC) applications, particularly in resource-limited settings where traditional diagnostic tools may be inaccessible. Their lightweight nature and ease of operation allow non-specialized users to perform diagnostic tests without the need for complex laboratory equipment, making them suitable for emergency, field, and remote applications. Technological advancements in paper-based biosensors have significantly enhanced their capabilities. Integration with microfluidic systems has improved fluid handling and reagent storage, resulting in enhanced sensor performance, including greater sensitivity and specificity for target biomarkers. The use of nanomaterials in electrode fabrication, such as reduced graphene oxide and gold nanoparticles, has further elevated their sensitivity, allowing for the precise detection of low-concentration biomarkers. Moreover, the development of multiplexed sensor arrays has enabled the simultaneous detection of multiple biomarkers from a single sample, facilitating comprehensive and rapid diagnostics in clinical settings. These biosensors have found applications in diagnosing a wide range of diseases, including infectious diseases, cancer, and metabolic disorders. They are also effective in genetic analysis and metabolic monitoring, such as tracking glucose, lactate, and uric acid levels, which are crucial for managing chronic conditions like diabetes and kidney diseases. In this review, the latest advancements in paper-based electrochemical biosensors are explored, with a focus on their applications, technological innovations, challenges, and future directions. Full article

This paper presents a fully passive and wireless glucose concentration sensor that integrates a capacitively coupled complementary split-ring resonator (CSRR) with an H-slot UHF RFID antenna. The CSRR serves as the primary sensing element, where changes in glucose concentration alter the effective permittivity of the surrounding solution, thereby modifying the resonator capacitance and shifting its resonance behavior. Through near-field capacitive coupling, these dielectric variations affect the antenna input impedance and backscatter response, enabling wireless sensing by modulating the maximum read range. The proposed sensor operates within the 902–928 MHz UHF RFID band and is interrogated using commercial RFID readers, eliminating the need for specialized laboratory equipment such as vector network analyzers. Full-wave electromagnetic simulations and experimental measurements validate the sensor performance, demonstrating a variation in the read range from 6.23 m to 4.67 m as glucose concentration increases from 50 to 200 mg/dL. Moreover, the sensor exhibits excellent linearity, with a high coefficient of determination ($R^2=0.986$) based on the curve-fitted data. These results underscore the feasibility of the proposed sensor as a low-cost and fully portable platform for concentration monitoring, with potential applications in liquid characterization and chemical sensing. Full article

Integration of machine learning (ML) and artificial intelligence (AI) into point-of-care (POC) sensor systems represents a transformative advancement in healthcare. This integration enables sophisticated data analysis and real-time decision-making in emergency and intensive care settings. AI and ML algorithms can process complex biomedical data, improve diagnostic accuracy, and enable early disease detection for better patient outcomes. Predictive analytics in POC devices supports proactive healthcare by analyzing data to forecast health issues and facilitating early intervention and personalized treatment. This review covers the key areas of ML and AI integration in POC devices, including data analysis, pattern recognition, real-time decision support, predictive analytics, personalization, automation, and workflow optimization. Examples of current POC devices that use ML and AI include AI-powered blood glucose monitors, portable imaging devices, wearable cardiac monitors, AI-enhanced infectious disease detection, and smart wound care sensors are also discussed. The review further explores new directions for POC sensors and ML integration, including mental health monitoring, nutritional monitoring, metabolic health tracking, and decentralized clinical trials (DCTs). We also examined the impact of integrating ML and AI into POC devices on healthcare accessibility, efficiency, and patient outcomes. Full article

Effective glucose monitoring is paramount for patients with diabetes to effectively manage their condition and prevent health complications. Electrochemical sensors for glucose monitoring have key advantages over other systems, including cost-effectiveness, miniaturisation and portability, enabling the design of compact and wearable devices. Typically, enzymes are used in these sensors, with the limitations of poor stability and high cost. In alternative, this study reports the development of a gold and platinum composite nanostructured electrode and its testing as an abiotic (enzyme-free) electrocatalyst for glucose oxidation. The electrode consists of a film of highly porous gold electrodeposited onto gold-plated electrodes on a printed circuit board (PCB), which is coated with polyaniline decorated with platinum nanoparticles. The resulting nanocomposite structure shows a sensitivity towards glucose as high as 95.12 ± 2.54 µA mM⁻¹ cm⁻², nearly twice that of the highly porous gold electrodes, and excellent stability in synthetic interstitial fluid over extended testing, thus demonstrating robustness. Accordingly, this study lays the groundwork for the next generation of durable, selective, and affordable abiotic glucose biosensors. Full article

High sugar intake from commercial beverages is a public health concern, motivating rapid, user-friendly tools for sugar quantification. We present a compact planar microwave metamaterial sensor that estimates sugar concentration by monitoring resonant frequency shifts induced by dielectric loading. Tests with aqueous glucose solutions demonstrated a wide dynamic range (0 to 12,000 mg/dL), perfect linearity (R² = 1), and high repeatability. Validation on two commercial beverages showed sensor-predicted sugar contents consistent with their nutrition labels. The method is reagent-free, tolerates opaque samples, and operates under ambient conditions, making it suitable for on-site consumer use as well as regulatory inspection and quality-control applications. Full article

Conductive hydrogels are attractive for flexible electronics; however, achieving high mechanical strength and conductivity simultaneously remains challenging. Herein, we present a facile strategy to fabricate a tough and conductive hydrogel by immersing a physically crosslinked gelatin/glucose hydrogel in an aqueous sodium citrate. The introduction of sodium citrate induced multiple physical interactions via the Hofmeister effect, which synergistically reinforced the hydrogel network. The resulting hydrogel exhibited excellent mechanical properties, with a fracture strength of 2.7 MPa, a fracture strain of 932%, and a toughness of 9.5 MJ/m³. Moreover, the incorporation of free ions imparted excellent ionic conductivity of 0.97 S/m. A resistive strain sensor based on this hydrogel showed a linear and sensitive response over a wide strain range and stable performance under repeated loading–unloading cycles. These features enabled accurate and reliable monitoring of various human movements. This work offers an effective strategy for designing hydrogels with both high strength and conductivity for flexible and wearable electronics. Full article

Postprandial hyperglycemia, marked by the blood glucose level exceeding the normal range after consuming a meal, is a critical indicator of progression toward type 2 diabetes in people with prediabetes and in healthy individuals. A key metric for understanding blood glucose dynamics after eating is the postprandial Area Under the Curve (AUC). Predicting postprandial AUC in advance based on a person’s lifestyle factors, such as diet and physical activity level, and explaining the factors that affect postprandial blood glucose could allow an individual to adjust their behavioral choices accordingly to maintain normal glucose levels. In this work, we develop an explainable machine learning solution, GlucoLens, that takes sensor-driven inputs and utilizes advanced data processing, large language models, and trainable machine learning models to estimate postprandial AUC and predict hyperglycemia from diet, physical activity, and recent glucose patterns. We use data obtained using wearables in a five-week clinical trial of 10 adults who worked full-time to develop and evaluate the proposed computational model that integrates wearable sensing, multimodal data, and machine learning. Our machine learning model takes multimodal data from wearable activity and glucose monitoring sensors, along with food and work logs, and provides an interpretable prediction of the postprandial glucose patterns. GlucoLens achieves a normalized root mean squared error (NRMSE) of 0.123 in its best configuration. On average, the proposed technology provides a 16% better predictive performance compared to the comparison models. Additionally, our technique predicts hyperglycemia with an accuracy of 79% and an F1 score of 0.749 and recommends different treatment options to help avoid hyperglycemia through diverse counterfactual explanations. With systematic experiments and discussion supported by established prior research, we show that our method is generalizable and consistent with clinical understanding. Full article

Enzyme-based biosensors have emerged as a transformative technology, leveraging the specificity and catalytic efficiency of enzymes across various domains, including medical diagnostics, environmental monitoring, food safety, and industrial processes. These biosensors integrate biological recognition elements with advanced transduction mechanisms to provide highly sensitive, selective, and portable solutions for real-time analysis. This review explores the key components, detection mechanisms, applications, and future trends in enzyme-based biosensors. Artificial enzymes, such as nanozymes, play a crucial role in enhancing enzyme-based biosensors by mimicking natural enzyme activity while offering improved stability, cost-effectiveness, and scalability. Their integration can significantly boost sensor performance by increasing the catalytic efficiency and durability. Additionally, lab-on-a-chip and microfluidic devices enable the miniaturization of biosensors, allowing for the development of compact, portable devices that require minimal sample volumes for complex diagnostic tests. The functionality of enzyme-based biosensors is built on three essential components: enzymes as biocatalysts, transducers, and immobilization techniques. Enzymes serve as the biological recognition elements, catalyzing specific reactions with target molecules to produce detectable signals. Transducers, including electrochemical, optical, thermal, and mass-sensitive types, convert these biochemical reactions into measurable outputs. Effective immobilization strategies, such as physical adsorption, covalent bonding, and entrapment, enhance the enzyme stability and reusability, enabling consistent performance. In medical diagnostics, they are widely used for glucose monitoring, cholesterol detection, and biomarker identification. Environmental monitoring benefits from these biosensors by detecting pollutants like pesticides, heavy metals, and nerve agents. The food industry employs them for quality control and contamination monitoring. Their advantages include high sensitivity, rapid response times, cost-effectiveness, and adaptability to field applications. Enzyme-based biosensors face challenges such as enzyme instability, interference from biological matrices, and limited operational lifespans. Addressing these issues involves innovations like the use of synthetic enzymes, advanced immobilization techniques, and the integration of nanomaterials, such as graphene and carbon nanotubes. These advancements enhance the enzyme stability, improve sensitivity, and reduce detection limits, making the technology more robust and scalable. Full article

Currently, flexible sensors based on electrochemical principles are predominantly limited to single-parameter detection, making it challenging to meet the demand for synchronous monitoring of multiple analytes in complex physiological environments. This study presents a 3D-printed flexible sensor for synchronous glucose/pH detection. Glucose was quantified via H₂O₂ oxidation current (GOD-catalyzed reaction), while pH was measured through polyaniline (PANI) resistance changes. The ionogel-based microneedle electrode ensures mechanical robustness. At 0.2 V, optimal signal decoupling was achieved: glucose oxidation current dominates, while PANI’s polarization effect is minimized. Neutral pH minimally affected glucose oxidase (GOD) activity, and low glucose concentrations induced negligible pH interference, ensuring orthogonality. In artificial interstitial fluid, the sensor showed glucose: linear response (0.5–2.5 g·L⁻¹, 0.288 μA·mM⁻¹·cm⁻²); pH: piecewise-linear sensitivity (0.155 Ω/pH·cm² for pH > 7; 0.135 Ω/pH·cm² for pH < 7). The design enables real-time multiparameter monitoring with high selectivity, addressing current limitations in flexible electrochemical sensors. Full article