Non-Invasive Real-Time Monitoring of Glucose Level Using Novel Microwave Biosensor Based on Triple-Pole CSRR

Recent advances in bio-microsystem integration and Lab-on-PCB technology

The concept of micro-total analysis systems (µTAS) introduced in the early 1990s revolutionized the development of lab-on-a-chip (LoC) technologies by miniaturizing and automating complex laboratory processes. Despite their potential in diagnostics, drug development, and environmental monitoring, the widespread adoption of LoC systems has been hindered by challenges in scalability, integration, and cost-effective mass production. Traditional substrates like silicon, glass, and polymers struggle to meet the multifunctional requirements of practical applications. Lab-on-Printed Circuit Board (Lab-on-PCB) technology has emerged as a transformative solution, leveraging the cost-efficiency, scalability, and precision of PCB fabrication techniques. This platform facilitates the seamless integration of microfluidics, sensors, and actuators within a single device, enabling complex, multifunctional systems suitable for real-world deployment. Recent advancements have demonstrated Lab-on-PCB's versatility across biomedical applications, such as point-of-care diagnostics, electrochemical biosensing, and molecular detection, as well as drug development and environmental monitoring. This review examines the evolution of Lab-on-PCB technology over the past eight years, focusing on its applications and impact within the research community. By analyzing recent progress in PCB-based microfluidics and biosensing, this work highlights how Lab-on-PCB systems address key technical barriers, paving the way for scalable and practical lab-on-chip solutions. The growing academic and industrial interest in Lab-on-PCB is underscored by a notable increase in publications and patents, signaling its potential for commercialization and broader adoption.

Analysis of Personalized Cardiovascular Drug Therapy: From Monitoring Technologies to Data Integration and Future Perspectives

Cardiovascular diseases have long been a major challenge to human health, and the treatment differences caused by individual variability remain unresolved. In recent years, personalized cardiovascular drug therapy has attracted widespread attention. This paper reviews the strategies for achieving personalized cardiovascular drug therapy through traditional dynamic monitoring and multidimensional data integration and analysis. It focuses on key technologies for dynamic monitoring, dynamic monitoring based on individual differences, and multidimensional data integration and analysis. By systematically reviewing the relevant literature, the main challenges in current research and the proposed potential directions for future studies were summarized.

A comprehensive review of non-invasive optical and microwave biosensors for glucose monitoring

Frequent glucose monitoring is essential for effective diabetes management. Currently, glucose monitoring is done using invasive methods such as finger-pricking and subcutaneous sensing. However, these methods can cause discomfort, heighten the risk of infection, and some sensing devices need frequent calibration. Non-invasive glucose monitoring technologies have attracted significant attention due to their potential to overcome the limitations of their invasive counterparts by offering painless and convenient alternatives. This review focuses on two prominent approaches to non-invasive glucose sensing: optical- and microwave-based methods. On one hand, optical techniques, including Raman and near-infrared (NIR) spectroscopy, leverage the unique spectral properties of glucose molecules to measure their concentration in tissues and biofluids. On the other hand, microwave sensing leverages the dielectric properties of glucose to detect concentration changes based on impedance measurements. Despite their promise, optical- and microwave-based technologies face challenges such as signal interference and high variability due to tissue heterogeneity, which impact their accuracy and reliability. This review provides a comprehensive overview of the advancements of these non-invasive methods, highlighting their technical implementation, limitations, and their future potential in revolutionizing glucose monitoring for diabetes care.

A multi-band high-sensitivity microwave sensor for simultaneous detection of two dielectric materials

A multi-band high-sensitivity microwave sensor is reported. The two resonance units are based on complementary square spiral resonators (CSSRs) and produce four measurement bands through parasitic resonances. The four frequency bands are 2.001, 2.988, 5.438, and 7.755 GHz, respectively. Through an analysis of the coupling effects between the two CSSR resonance units, it is shown that the sensor is capable of simultaneously characterizing two samples with distinct dielectric properties. By adding a metal patch to the microstrip line, the overall quality factor of the sensor is enhanced, leading to higher sensitivity and accuracy in both real and imaginary part measurements. The measurement results demonstrate that this sensor provides an accurate and efficient solution for solid permittivity measurements.

Secure and privacy-preserving automated machine learning operations into end-to-end integrated IoT-edge-artificial intelligence-blockchain monitoring system for diabetes mellitus prediction

Diabetes Mellitus, one of the leading causes of death worldwide, has no cure to date and can lead to severe health complications, such as retinopathy, limb amputation, cardiovascular diseases, and neuronal disease, if left untreated. Consequently, it becomes crucial to be able to monitor and predict the incidence of diabetes. Machine learning approaches have been proposed and evaluated in the literature for diabetes prediction. This paper proposes an IoT-edge-Artificial Intelligence (AI)-blockchain system for diabetes prediction based on risk factors. The proposed system is underpinned by blockchain to obtain a cohesive view of the risk factors data from patients across different hospitals and ensure security and privacy of the user's data. We provide a comparative analysis of different medical sensors, devices, and methods to measure and collect the risk factors values in the system. Numerical experiments and comparative analysis were carried out within our proposed system, using the most accurate random forest (RF) model, and the two most used state-of-the-art machine learning approaches, Logistic Regression (LR) and Support Vector Machine (SVM), using three real-life diabetes datasets. The results show that the proposed system predicts diabetes using RF with 4.57% more accuracy on average in comparison with the other models LR and SVM, with 2.87 times more execution time. Data balancing without feature selection does not show significant improvement. When using feature selection, the performance is improved by 1.14% for PIMA Indian and 0.02% for Sylhet datasets, while it is reduced by 0.89% for MIMIC III.

Development of optical and electrochemical immunodevices for dengue virus detection

Dengue virus (DENV) is the most prevalent global arbovirus, exhibiting a high worldwide incidence with intensified severity of symptoms and alarming mortality rates. Faced with the limitations of diagnostic methods, an optical and electrochemical biosystem was developed for the detection of DENV genotypes 1 and 2, using cysteine (Cys), cadmium telluride (CdTe) quantum dots, and anti-DENV antibodies. Cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), surface plasmon resonance (SPR), atomic force microscopy (AFM), and Fourier transform infrared spectroscopy (FTIR) were employed to characterize the immunosensor. The AFM and SPR results demonstrated discernible topographic and angular changes confirming the biomolecular recognition. Different concentrations of DENV-1 and DENV-2 were evaluated (0.05 × 106 to 2.0 × 106 PFU mL-1), resulting in a maximum anodic shift (ΔI%) of 263.67% ± 12.54 for DENV-1 and 63.36% ± 3.68 for DENV-2. The detection strategies exhibited a linear response to the increase in viral concentration. Excellent linear correlations, with R2 values of 0.95391 for DENV-1 and 0.97773 for DENV-2, were obtained across a broad concentration range. Data analysis demonstrated high reproducibility, displaying relative standard deviation values of 3.42% and 3.62% for Cys-CdTe-antibodyDENV-1-BSA and Cys-CdTe-antibodyDENV-2-BSA systems. The detection limits were 0.34 × 106 PFU mL-1 and 0.02 × 106 PFU mL-1, while the quantification limits were set at 1.49 × 106 PFU mL-1 and 0.06 × 106 PFU mL-1 for DENV-1 and DENV-2, respectively. Therefore, the biosensing apparatus demonstrates analytical effectiveness in viral screening and can be considered an innovative solution for early dengue diagnosis, contributing to global public health.

A selective LSPR biosensor for molecular-level glycated albumin detection

A biosensor specifically engineered to detect glycated albumin (GA), a critical biomarker for diabetes monitoring, is presented. Unlike conventional GA monitoring methods, the biosensor herein uniquely employs localised surface plasmon resonance (LSPR) for signal transduction, leveraging a novel fabrication process where gold nanoparticles are deposited on a quartz substrate using flame spray pyrolysis. This enables the biosensor to provide mean glucose levels over a three-week period, correlating with the glycation status of diabetes patients. The sensor's DNA aptamer conjugation selectively binds GA, inducing a plasmonic wavelength shift; resulting in a detection limit of 0.1 μM, well within the human GA range of 20-240 μM. Selectivity experiments with diverse molecules and an exploration of sensor reusability were carried out with positive results. The novelty of the biosensor presented includes specificity, sensitivity and practical applicability; which is promising for enhanced diabetes diagnosis using a rapid and inexpensive process.

Non-Invasive Glucose Sensing Technologies and Products: A Comprehensive Review for Researchers and Clinicians

Diabetes Mellitus incidence and its negative outcomes have dramatically increased worldwide and are expected to further increase in the future due to a combination of environmental and social factors. Several methods of measuring glucose concentration in various body compartments have been described in the literature over the years. Continuous advances in technology open the road to novel measuring methods and innovative measurement sites. The aim of this comprehensive review is to report all the methods and products for non-invasive glucose measurement described in the literature over the past five years that have been tested on both human subjects/samples and tissue models. A literature review was performed in the MDPI database, with 243 articles reviewed and 124 included in a narrative summary. Different comparisons of techniques focused on the mechanism of action, measurement site, and machine learning application, outlining the main advantages and disadvantages described/expected so far. This review represents a comprehensive guide for clinicians and industrial designers to sum the most recent results in non-invasive glucose sensing techniques' research and production to aid the progress in this promising field.

Review of Noninvasive Continuous Glucose Monitoring in Diabetics

For diabetics, taking regular blood glucose measurements is crucial. However, traditional blood glucose monitoring methods are invasive and unfriendly to diabetics. Recent studies have proposed a biofluid-based glucose sensing technique that creatively combines wearable devices with noninvasive glucose monitoring technology to enhance diabetes management. This is a revolutionary advance in the diagnosis and management of diabetes, reflects the thoughtful modernization of medicine, and promotes the development of digital medicine. This paper reviews the research progress of noninvasive continuous blood glucose monitoring (CGM), with a focus on the biological liquids that replace blood in monitoring systems, the technical principles of continuous noninvasive glucose detection, and the output and calibration of sensor signals. In addition, the existing limits of noninvasive CGM systems and prospects for the future are discussed. This work serves as a resource for further promoting the development of noninvasive CGM systems.

Sensing High 17β-Estradiol Concentrations Using a Planar Microwave Sensor Integrated with a Microfluidic Channel

The global issue of pollution caused by endocrine-disrupting chemicals (EDCs) has been gaining increasing attention. Among the EDCs of environmental concern, 17β-estradiol (E2) can produce the strongest estrogenic effects when it enters the organism exogenously through various routes and has the potential to cause harm, including malfunctions of the endocrine system and development of growth and reproductive disorders in humans and animals. Additionally, in humans, supraphysiological levels of E2 have been associated with a range of E2-dependent disorders and cancers. To ensure environmental safety and prevent potential risks of E2 to human and animal health, it is crucial to develop rapid, sensitive, low cost and simple approaches for detecting E2 contamination in the environment. A planar microwave sensor for E2 sensing is presented based on the integration of a microstrip transmission line (TL) loaded with a Peano fractal geometry with a narrow slot complementary split-ring resonator (PF-NSCSRR) and a microfluidic channel. The proposed technique offers a wide linear range for detecting E2, ranging from 0.001 to 10 mM, and can achieve high sensitivity with small sample volumes and simple operation methods. The proposed microwave sensor was validated through simulations and empirical measurements within a frequency range of 0.5-3.5 GHz. The E2 solution was delivered to the sensitive area of the sensor device via a microfluidic polydimethylsiloxane (PDMS) channel with an area of 2.7 mm² and sample value of 1.37 µL and measured by a proposed sensor. The injection of E2 into the channel resulted in changes in the transmission coefficient (S₂₁) and resonance frequency (F_r), which can be used as an indicator of E₂ levels in solution. The maximum quality factor of 114.89 and the maximum sensitivity based on S₂₁ and F_r at a concentration of 0.01 mM were 1746.98 dB/mM and 40 GHz/mM, respectively. Upon comparing the proposed sensor with the original Peano fractal geometry with complementary split-ring (PF-CSRR) sensors without a narrow slot, several parameters were evaluated, including sensitivity, quality factor, operating frequency, active area, and sample volume. The results showed that the proposed sensor exhibited an increased sensitivity of 6.08% and had a 40.72% higher quality factor, while the operating frequency, active area, and sample volume showed decreases of 1.71%, 25%, and 28.27%, respectively. The materials under tests (MUTs) were analyzed and categorized into groups using principal component analysis (PCA) with a K-mean clustering algorithm. The proposed E2 sensor has a compact size and simple structure that can be easily fabricated with low-cost materials. With the small sample volume requirement, fast measurement with a wide dynamic range, and a simple protocol, this proposed sensor can also be applied to measure high E2 levels in environmental, human, and animal samples.

A Concise and Systematic Review on Non-Invasive Glucose Monitoring for Potential Diabetes Management

The current standard of diabetes management depends upon the invasive blood pricking techniques. In recent times, the availability of minimally invasive continuous glucose monitoring devices have made some improvements in the life of diabetic patients however it has its own limitations which include painful insertion, excessive cost, discomfort and an active risk due to the presence of a foreign body under the skin. Due to all these factors, the non-invasive glucose monitoring has remain a subject of research for the last two decades and multiple techniques of non-invasive glucose monitoring have been proposed. These proposed techniques have the potential to be evolved into a wearable device for non-invasive diabetes management. This paper reviews research advances and major challenges of such techniques or methods in recent years and broadly classifies them into four types based on their detection principles. These four methods are: optical spectroscopy, photoacoustic spectroscopy, electromagnetic sensing and nanomaterial based sensing. The paper primarily focuses on the evolution of non-invasive technology from bench-top equipment to smart wearable devices for personalized non-invasive continuous glucose monitoring in these four methods. With the rapid evolve of wearable technology, all these four methods of non-invasive blood glucose monitoring independently or in combination of two or more have the potential to become a reality in the near future for efficient, affordable, accurate and pain-free diabetes management.

Majid H, Ashyap AYI, Nebhen J, Kamarudin MR, See CH, Abd-Alhameed RA. Glucose level detection using millimetre-wave metamaterial-inspired resonator

Millimetre-wave frequencies are promising for sensitive detection of glucose levels in the blood, where the temperature effect is insignificant. All these features provide the feasibility of continuous, portable, and accurate monitoring of glucose levels. This paper presents a metamaterial-inspired resonator comprising five split-rings to detect glucose levels at 24.9 GHz. The plexiglass case containing blood is modelled on the sensor’s surface and the structure is simulated for the glucose levels in blood from 50 mg/dl to 120 mg/dl. The novelty of the sensor is demonstrated by the capability to sense the normal glucose levels at millimetre-wave frequencies. The dielectric characteristics of the blood are modelled by using the Debye parameters. The proposed design can detect small changes in the dielectric properties of blood caused by varying glucose levels. The variation in the transmission coefficient for each glucose level tested in this study is determined by the quality factor and resonant frequency. The sensor presented can detect the change in the quality factor of transmission response up to 2.71/mg/dl. The sensor’s performance has also been tested to detect diabetic hyperosmolar syndrome. The sensor showed a linear shift in resonant frequency with the change in glucose levels, and an R² of 0.9976 was obtained by applying regression analysis. Thus, the sensor can be used to monitor glucose in a normal range as well as at extreme levels.

IoMT aware data collective quadratic ensembled cat boost module classification algorithm for non-invasive blood glucose monitoring in VLSI design

Internet of Medical Things (IoMT) is the combination of medical devices and utilization by networking technologies. But, the response time and cost were not reduced. In order to address these issues, IoMT Aware Data Collective Quadratic Ensembled Cat Boost Module Classification (IoMT-DCQECBMC) Method is introduced. Initially, IoMT Aware Data Collection is used for gathering data from medical devices. After the data collection process, Quadratic Ensembled Cat Boost Module Classification (QECBM) is carried out in IoMT-DCQECBMC Method to design an efficient VLSI architecture with minimal cost and area. The quadratic classifier is considered the weak learner that categorizes the module for efficient VLSI design. Finally, the weak learners are joined to form the strong classifier to perform non-invasive blood glucose monitoring efficiently. Experimental evaluation is carried out on the factors such as computation cost, area, and accuracy with respect to a number of modules in VLSI circuits. The accuracy of the IoMT-DCQECBMC method is increased by 4% than conventional methods. In addition, the area consumption and computation cost of the proposed IoMT-DCQECBMC method are reduced by 13% to 30% other than existing methods.

Microwave Microfluidic Sensor for Detection of High Equol Concentrations in Aqueous Solution

This paper presents a Peano fractal geometry complementary split ring resonator (PFCSRR) loaded microstrip transmission line with a microfluidic channel for equol (EQ) sensing in a high and wide range of concentrations in aqueous solution. The proposed sensor was designed based on a CSRR loaded microstrip line with a Peano fractal in the center of a CSRR and validated through simulation and experiment. The microfluidic channel was fabricated using polydimethylsiloxane (PDMS) and installed to cover the sensing area. The free space, empty microfluidic channels, deionized (DI) water, dimethyl sulfoxide (DMSO), and various concentrations of EQ were measured by a microwave sensor through sample-filled microfluidic channels. Detection of high levels of EQ was in the concentration range of 0.01 mM - 100 mM. The materials under test (MUTs) were measured in the frequency range of 1.0 GHz-3.5 GHz based on the magnitude of the transmission coefficient (S₂₁) and resonance frequency (F_r) at room temperature. The S₂₁ and F_r were recorded and analyzed by logarithmic concentrations of EQ for the determinant of the correlations between EQ concentration and S₂₁ and F_r. Principal component analysis (PCA) and K-means clustering were used to analyze and classify groups of MUTs.

Non-invasive Lactate Monitoring System Using Wearable Chipless Microwave Sensors with Enhanced Sensitivity and Zero Power Consumption

AbstractMonitoring lactate levels is an established method for determining hyperlactatemia in critically ill patients and assessing aerobic fitness. It is a widely used gold-standard technique in both professional and serious amateur sports. Non-invasive real-time lactate monitoring offers significant advantages over the current technology of finger-prick blood sampling. Possible candidate technology for developing non-invasive real-time lactate monitoring should be highly sensitive, flexible, and capable of real-time monitoring of lactate levels in interstitial fluid or within specific working muscle groups depending on the type of sport. Herein we describe a planar, flexible, passive, chipless tag resonator that is electromagnetically coupled to a reader placed in proximity to the lactate sensor tag. The tag resonator is a thin metallic tracing that can be taped on the skin. The resonance frequency of the tag fluctuates proportionately with changing lactate concentrations in a solution mimicking human interstitial fluid with very high sensitivity. The spectrum of the tag is reflected in the spectrum of the reader, which is a planar microwave resonator designed at a different frequency. The reader could be embedded in a cellphone or an application-specific wearable device for data communication and processing. The tag can accurately and reproducibly measure lactate concentrations in the range of 1 to 10 mM, which is in the physiological range of lactate observed at rest and during intense physical activity. Furthermore, the chrematistics of this technology will allow monitoring of lactate in specific working muscle groups.

Recent advancement in noninvasive glucose monitoring and closed-loop management system for diabetes

Diabetes can cause many complications, which has become one of the most common diseases that may lead to death. Currently, the number of diabetics continues increasing year by year. Thus,...

Machine Learning Methods of Regression for Plasmonic Nanoantenna Glucose Sensing

The measurement and quantification of glucose concentrations is a field of major interest, whether motivated by potential clinical applications or as a prime example of biosensing in basic research. In recent years, optical sensing methods have emerged as promising glucose measurement techniques in the literature, with surface-enhanced infrared absorption (SEIRA) spectroscopy combining the sensitivity of plasmonic systems and the specificity of standard infrared spectroscopy. The challenge addressed in this paper is to determine the best method to estimate the glucose concentration in aqueous solutions in the presence of fructose from the measured reflectance spectra. This is referred to as the inverse problem of sensing and usually solved via linear regression. Here, instead, several advanced machine learning regression algorithms are proposed and compared, while the sensor data are subject to a pre-processing routine aiming to isolate key patterns from which to extract the relevant information. The most accurate and reliable predictions were finally made by a Gaussian process regression model which improves by more than 60% on previous approaches. Our findings give insight into the applicability of machine learning methods of regression for sensor calibration and explore the limitations of SEIRA glucose sensing.

RFID-Based Microwave Biosensor for Non-Contact Detection of Glucose Solution

Due to the increasing number of diabetic patients, early monitoring of glucose levels is particularly important; therefore, glucose biosensors have attracted enormous attention from researchers. In this paper, we propose a glucose microwave biosensor based on RFID and achieve a non-contact measurement of the concentration of glucose solutions. The Reader is a complementary split-ring resonator (CSRR), and the Tag is comprised of a squared spiral capacitor (SSC). A polydimethylsiloxane microfluidic quantitative cavity with a volume of 1.56 μL is integrated on the Tag to ensure that the glucose solution can be accurately set to the sensitive area and fully contacted with the electromagnetic flux. Because the SSC exhibits different capacitances when it contacts glucose solutions of different concentrations, changing the resonant frequency of the CSRR, we can use the relationship to characterize the biosensing response. Measurement results show that bare CSRR and RFID-based biosensors have achieved sensitivities of 0.31 MHz/mg·dL⁻¹ and 10.27 kHz/mg·dL⁻¹, and detection limits of 13.79 mg/dL and 1.19 mg/dL, respectively, and both realize a response time of less than 1 s. Linear regression analysis of the abovementioned biosensors showed an excellent linear relationship. The proposed design provides a feasible solution for microwave biosensors aiming for the non-contact measurement of glucose concentration.

Microwave Planar Resonant Solutions for Glucose Concentration Sensing: A Systematic Review

The measurement of glucose concentration finds interesting potential applications in both industry and biomedical contexts. Among the proposed solutions, the use of microwave planar resonant sensors has led to remarkable scientific activity during the last years. These sensors rely on the changes in the dielectric properties of the medium due to variations in the glucose concentration. These devices show electrical responses dependent on the surrounding dielectric properties, and therefore the changes in their response can be related to variations in the glucose content. This work shows an up-to-date review of this sensing approach after more than one decade of research and development. The attempts involved are sorted by the sensing parameter, and the computation of a common relative sensitivity to glucose is proposed as general comparison tool. The manuscript also discusses the key points of each sensor category and the possible future lines and challenges of the sensing approach.

Textile Electromagnetic Brace for Knee Imaging

A wearable textile brace is introduced as an electromagnetic imaging system that breaks hospital boundaries to real-time onsite scanning for knee injuries. The proposed brace consists of a 12-element textile slot loop antenna array, which is designed to match the human knee for enhanced electromagnetic wave penetration. Wool felt and conductive fabric are used to fabricate the antenna array thanks to their flexibility and proper dielectric properties. Each antenna element has a compact footprint of 42 ×24 ×3.22 mm³ and achieves unidirectional radiation, high front-to-back ratio of 14 dB, wide bandwidth of 81% at 0.7-1.7 GHz, and safe SAR levels. A modified double-stage delay, multiply, and sum (DS-DMAS) algorithm is used to process the collected signals from the antenna array based on differential left/right knee imaging. The reconstructed images numerically and experimentally on realistic phantoms demonstrate the potential of the brace system for onsite detection of different types of ligaments/tendon tears.

Towards Development of a Non-Intrusive and Label-Free THz Sensor for Rapid Detection of Aqueous Bio-Samples Using Microfluidic Approach

As most of the bio-molecules sizes are comparable to the terahertz (THz) wavelength, this frequency range has spurred great attention for bio-medical and bio-sensing applications. Utilizing such capabilities of THz electromagnetic wave, this paper presents the design and analysis of a new non-intrusive and label-free THz bio-sensor for aqueous bio-samples using the microfluidic approach with real-time monitoring. The proposed THz sensor unit utilizes the highly confined feature of the localized spoof surface plasmon (LSSP) resonator to get high sensitivity for any minute change in the dielectric value near it's surface. The proposed sensor, which is designed at 1 THz, exploits the reflection behavior (S₁₁) of the LSSP resonator as the sensing response. The proposed sensor has been designed with a high-quality factor of 192 to obtain a high sensitivity of 13.5 MHz/mgml^-1. To validate the proposed concept, a similar sensor unit has been designed and implemented at microwave frequency owing to the geometry dependent characteristics of the LSSP. The developed sensor has got a highly sensitive response at microwave frequency with a sensitivity of 1.2771e-4 MHz/mgml^-1. A customized read-out circuitry is also designed and developed to get the sensor response in terms of DC-voltage and to provide a proof of concept for the low-cost point of care (PoC) detection solution using the proposed sensor. It is anticipated that the proposed design of highly sensitive sensor will pave a path to develop lab-on-chip systems for bio-sensing applications.