A Review of Non-Invasive Optical Systems for Continuous Blood Glucose Monitoring

Non-invasive blood glucose estimation method based on the phase delay between oxy- and deoxyhemoglobin using visible and near-infrared spectroscopy

Significance

Many researchers have attempted to estimate blood glucose levels (BGLs) noninvasively using near-infrared (NIR) spectroscopy. However, the optical absorption change induced by blood glucose is weak in the NIR region and often masked by interference from other components such as water and hemoglobin.

Aim

Instead of using direct optical absorption by glucose, this study proposes an index calculated from oxy- and deoxyhemoglobin signals that shows a good correlation with BGLs while using conventional visible and NIR spectroscopy.

Approach

The metabolic index, which is based on tissue oxygen consumption, was derived through analytical methods and further verified and reproduced in a series of glucose challenge experiments. Blood glucose estimation units were prototyped by utilizing commercially available smart devices.

Results

Our experimental results showed that the phase delay between the oxy- and deoxyhemoglobin signals in near-infrared spectroscopy correlates with BGL measured by a conventional continuous glucose monitor. The proposed method was also confirmed to work well with visible spectroscopy systems based on smartphone cameras. The proposed method also demonstrated excellent repeatability in results from a total of 19 oral challenge tests.

Conclusions

This study demonstrated the feasibility of non-invasive glucose monitoring using existing photoplethysmography sensors for pulse oximeters and smartwatches. Evaluating the proposed method in diabetic or unhealthy individuals may serve to further increase its practicality.

Nifuroxazide repurposing for protection from diabetes-induced retinal injury in rats: Implication of oxidative stress and JAK/STAT3 axis

The prevalence of diabetes mellitus (DM) is alarmingly increasing worldwide. Diabetic retinopathy (DR) is a prevailing DM microvascular complication, representing the major cause of blindness in working-age population. Inflammation is a crucial player in DR pathogenesis. JAK/STAT3 axis is a pleotropic cascade that modulates diverse inflammatory events. Nifuroxazide (Nifu) is a commonly used oral antibiotic with reported JAK/STAT3 inhibition activity. The present study investigated the potential protective effect of Nifu against diabetes-induced retinal injury. Effect of Nifu on oxidative stress, JAK/STAT3 axis and downstream inflammatory mediators has been also studied. Diabetes was induced in Sprague Dawley rats by single intraperitoneal injection of streptozotocin (50 mg/kg). Animals were assigned into four groups: normal, Nifu control, DM, and DM + Nifu. Nifu was orally administrated at 25 mg/kg/day for 8 weeks. The effects of Nifu on oxidative stress, JAK/STAT3 axis proteins, inflammatory factors, tight junction proteins, histological, and ultrastructural alterations were evaluated using spectrophotometry, gene and protein analyses, and histological studies. Nifu administration to diabetic rats attenuated histopathological and signs of retinal injury. Additionally, Nifu attenuated retinal oxidative stress, inhibited JAK and STAT3 phosphorylation, augmented the expression of STAT3 signaling inhibitor SOCS3, dampened the expression of transcription factor of inflammation NF-κB, and inflammatory cytokine TNF-α. Collectively, the current study indicated that Nifu alleviated DR progression in diabetic rats, suggesting beneficial retino-protective effect. This can be attributed to blocking JAK/STAT3 axis in retinal tissues with subsequent amelioration of oxidative stress and inflammation.

Noninvasive Glucose Measurements Through Transcutaneous Raman Spectroscopy: A Review

BACKGROUND

People living with diabetes need constant glucose monitoring to avoid health complications. However, they do not monitor their glucose levels as often as recommended, probably because glucose measurement devices can be painful, costly, need testing strips or sensors, require lancing the finger or inserting a sensor with risk of infection, and can be inaccurate or have failures. Therefore, developing new alternatives for noninvasive glucose measurements that overcome these disadvantages is necessary, being Raman spectroscopy (RS) a solution.

OBJECTIVE

This review aims to provide an overview of the current glucose-monitoring technologies and the uses and advantages of RS to improve noninvasive transcutaneously glucose-monitoring devices.

RESULTS

The skin has been used to assess glucose levels noninvasively because it is an accessible tissue where glucose can be measured in the interstitial fluid (ISF) in the epidermis (especially in the stratum corneum). The most selected skin sites to apply RS for noninvasive glucose measurements were the nailfold, finger, and forearm because, in these sites, the penetration depth of the excitation light can reach the stratum corneum (10-20 µm) and the ISF. Studies found that RS is a good optical technique to measure glucose noninvasively by comparing glucose levels obtained by RS with those from invasive methods such as glucose meters with testing strips during an oral glucose tolerance test (OGTT).

CONCLUSIONS

New alternatives for noninvasive glucose measurements that overcome the disadvantages of current devices is necessary, and RS is a possible solution. However, more research is needed to evaluate the stability, accuracy, costs, and acceptance.

Machine Learning Method and Hyperspectral Imaging for Precise Determination of Glucose and Silicon Levels

This article introduces an algorithm for detecting glucose and silicon levels in solution. The research focuses on addressing the critical need for accurate and efficient glucose monitoring, particularly in the context of diabetic management. Understanding and monitoring silicon levels in the body is crucial due to its significant role in various physiological processes. Silicon, while often overshadowed by other minerals, plays a vital role in bone health, collagen formation, and connective tissue integrity. Moreover, recent research suggests its potential involvement in neurological health and the prevention of certain degenerative diseases. Investigating silicon levels becomes essential for a comprehensive understanding of its impact on overall health and well-being and paves the way for targeted interventions and personalized healthcare strategies. The approach presented in this paper is based on the integration of hyperspectral data and artificial intelligence techniques. The algorithm investigates the effectiveness of two distinct models utilizing SVMR and a perceptron independently. SVMR is employed to establish a robust regression model that maps input features to continuous glucose and silicon values. The study outlines the methodology, including feature selection, model training, and evaluation metrics. Experimental results demonstrate the algorithm's effectiveness at accurately predicting glucose and silicon concentrations and showcases its potential for real-world application in continuous glucose and silicon monitoring systems.

Advances and Opportunities of Mobile Health in the Postpandemic Era: Smartphonization of Wearable Devices and Wearable Deviceization of Smartphones

Mobile health (mHealth) with continuous real-time monitoring is leading the era of digital medical convergence. Wearable devices and smartphones optimized as personalized health management platforms enable disease prediction, prevention, diagnosis, and even treatment. Ubiquitous and accessible medical services offered through mHealth strengthen universal health coverage to facilitate service use without discrimination. This viewpoint investigates the latest trends in mHealth technology, which are comprehensive in terms of form factors and detection targets according to body attachment location and type. Insights and breakthroughs from the perspective of mHealth sensing through a new form factor and sensor-integrated display overcome the problems of existing mHealth by proposing a solution of smartphonization of wearable devices and the wearable deviceization of smartphones. This approach maximizes the infinite potential of stagnant mHealth technology and will present a new milestone leading to the popularization of mHealth. In the postpandemic era, innovative mHealth solutions through the smartphonization of wearable devices and the wearable deviceization of smartphones could become the standard for a new paradigm in the field of digital medicine.

Hybrid CNN-LSTM for Predicting Diabetes: A Review

BACKGROUND

Diabetes is a common and deadly chronic disease caused by high blood glucose levels that can cause heart problems, neurological damage, and other illnesses. Through the early detection of diabetes, patients can live healthier lives. Many machine learning and deep learning techniques have been applied for noninvasive diabetes prediction. The results of some studies have shown that the CNN-LSTM method, a combination of CNN and LSTM, has good performance for predicting diabetes compared to other deep learning methods.

METHOD

This paper reviews CNN-LSTM-based studies for diabetes prediction. In the CNNLSTM model, the CNN includes convolution and max pooling layers and is applied for feature extraction. The output of the max-pooling layer was fed into the LSTM layer for classification.

DISCUSSION

The CNN-LSTM model performed well in extracting hidden features and correlations between physiological variables. Thus, it can be used to predict diabetes. The CNNLSTM model, like other deep neural network architectures, faces challenges such as training on large datasets and biological factors. Using large datasets can further improve the accuracy of detection.

CONCLUSION

The CNN-LSTM model is a promising method for diabetes prediction, and compared with other deep-learning models, it is a reliable method.

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.

Mobile health technology integrated care in atrial fibrillation patients with diabetes mellitus in China: A subgroup analysis of the mAFA-II cluster randomized clinical trial

BACKGROUND

The Mobile Health Technology for Improved Screening and Optimized Integrated Care in AF (mAFA-II) prospective randomized trial showed the efficacy of a mobile health (mHealth) implemented 'Atrial fibrillation Better Care' (ABC) pathway for the integrated care management of patients with atrial fibrillation (AF). In this ancillary analysis, we evaluated the effect of mAFA intervention according to the history of diabetes mellitus (DM).

METHODS

The mAFA-II trial enrolled 3324 AF patients across 40 centres in China, between June 2018 and August 2019. In this analysis, we assessed the interaction between history of DM and the effect of mAFA intervention on the risk of the primary composite outcome of stroke, thromboembolism, all-cause death and rehospitalizations. Results were expressed as adjusted hazard ratio (aHR) and 95% confidence intervals (95%CI). The effect of mAFA intervention on exploratory secondary outcomes was also assessed.

RESULTS

Overall, 747 (22.5%) patients had DM (mean age: 72.7 ± 12.3, 39.6% females; 381 allocated to mAFA intervention). mAFA intervention was associated with a significant risk reduction for the primary composite outcome both in patients with and without DM (aHR [95%CI]: .36 [.18-.73] and .37 [.23-.61], respectively, p for interaction = .941). A significant interaction was found only for the composite of recurrent AF, heart failure and acute coronary syndromes (pint  =.025), with lower effect of mAFA intervention in patients with DM.

CONCLUSIONS

A mHealth-technology implemented ABC pathway showed a consistent effect in reducing the risk of the primary composite outcome in AF patients with and without DM.

TRIAL REGISTRATION

WHO International Clinical Trials Registry Platform (ICTRP) Registration number: ChiCTR-OOC-17014138.

Progress and Perspectives of Mid-Infrared Photoacoustic Spectroscopy for Non-Invasive Glucose Detection

The prevalence of diabetes is rapidly increasing worldwide and can lead to a range of severe health complications that have the potential to be life-threatening. Patients need to monitor and control blood glucose levels as it has no cure. The development of non-invasive techniques for the measurement of blood glucose based on photoacoustic spectroscopy (PAS) has advanced tremendously in the last couple of years. Among them, PAS in the mid-infrared (MIR) region shows great promise as it shows the distinct fingerprint region for glucose. However, two problems are generally encountered when it is applied to monitor real samples for in vivo measurements in this MIR spectral range: (i) low penetration depth of MIR light into the human skin, and (ii) the effect of other interfering components in blood, which affects the selectivity of the detection system. This review paper systematically describes the basics of PAS in the MIR region, along with recent developments, technical challenges, and data analysis strategies, and proposes improvements for the detection sensitivity of glucose concentration in human bodies. It also highlights the recent trends of incorporating machine learning (ML) to enhance the detection sensitivity of the overall system. With further optimization of the experimental setup and incorporation of ML, this PAS in the MIR spectral region could be a viable solution for the non-invasive measurement of blood glucose in the near future.

Minimally invasive electrochemical continuous glucose monitoring sensors: Recent progress and perspective

Diabetes and its complications are seriously threatening the health and well-being of hundreds of millions of people. Glucose levels are essential indicators of the health conditions of diabetics. Over the past decade, concerted efforts in various fields have led to significant advances in glucose monitoring technology. In particular, the rapid development of continuous glucose monitoring (CGM) based on electrochemical sensing principles has great potential to overcome the limitations of self-monitoring blood glucose (SMBG) in continuously tracking glucose trends, evaluating diabetes treatment options, and improving the quality of life of diabetics. However, the applications of minimally invasive electrochemical CGM sensors are still limited owing to the following aspects: i) invasiveness, ii) short lifespan, iii) biocompatibility, and iv) calibration and prediction. In recent years, the performance of minimally invasive electrochemical CGM systems (CGMSs) has been significantly improved owing to breakthrough developments in new materials and key technologies. In this review, we summarize the history of commercial CGMSs, the development of sensing principles, and the research progress of minimally invasive electrochemical CGM sensors in reducing the invasiveness of implanted probes, maintaining enzyme activity, and improving the biocompatibility of the sensor interface. In addition, this review also introduces calibration algorithms and prediction algorithms applied to CGMSs and describes the application of machine learning algorithms for glucose prediction.

Non-Invasive Classification of Blood Glucose Level Based on Photoplethysmography Using Time–Frequency Analysis

Diabetes monitoring systems are crucial for avoiding potentially significant medical expenses. At this time, the only commercially viable monitoring methods that exist are invasive ones. Since patients are uncomfortable while blood samples are being taken, these techniques have significant disadvantages. The drawbacks of invasive treatments might be overcome by a painless, inexpensive, non-invasive approach to blood glucose level (BGL) monitoring. Photoplethysmography (PPG) signals obtained from sensor leads placed on specific organ tissues are collected using photodiodes and nearby infrared LEDs. Cardiovascular disease can be detected via photoplethysmography. These characteristics can be used to directly affect BGL monitoring in diabetic patients if PPG signals are used. The Guilin People’s Hospital’s open database was used to produce the data collection. The dataset was gathered from 219 adult respondents spanning an age range from 21 to 86 of which 48 percent were male. There were 2100 sampling points total for each PPG data segment. The methodology of feature extraction from data may assist in increasing the effectiveness of classifier training and testing. PPG data information is modified in the frequency domain by the instantaneous frequency (IF) and spectral entropy (SE) moments using the time–frequency (TF) analysis. Three different forms of raw data were used as inputs, and we investigated the original PPG signal, the PPG signal with instantaneous frequency, and the PPG signal with spectral entropy. According to the results of the model testing, the PPG signal with spectral entropy generated the best outcomes. Compared to decision trees, subspace k-nearest neighbor, and k-nearest neighbor, our suggested approach with the super vector machine obtains a greater level of accuracy. The super vector machine, with 91.3% accuracy and a training duration of 9 s, was the best classifier.

Diboronic-Acid-Based Electrochemical Sensor for Enzyme-Free Selective and Sensitive Glucose Detection

A diboronic acid anthracene-based fluorescent system for detecting blood glucose could be used for 180 days. However, there has not yet been a boronic acid immobilized electrode to selectively detect glucose in a signal-increased way. Considering malfunctions of sensors at high sugar levels, the electrochemical signal should be increased proportionally to the glucose concentration. Therefore, we synthesized a new diboronic acid derivative and fabricated the derivative-immobilized electrodes for the selective detection of glucose. We performed cyclic voltammetry and electrochemical impedance spectroscopy with an Fe(CN)₆^3-/4- redox pair for detecting glucose in the range of 0-500 mg/dL. The analysis revealed increased electron-transfer kinetics such as increased peak current and decreased semicircle radius of Nyquist plots as the glucose concentration increased. The cyclic voltammetry and impedance spectroscopy showed that the linear detection range of glucose was 40 to 500 mg/dL with limits of detection of 31.2 mg/dL and 21.5 mg/dL, respectively. We applied the fabricated electrode to detect glucose in artificial sweat and obtained 90% of the performance of the electrodes in PBS. Cyclic voltammetry measurements of other sugars such as galactose, fructose, and mannitol also showed linear increased peak currents proportional to the concentrations of the tested sugars. However, the slopes of the sugars were lower than that of glucose, indicating selectivity for glucose. These results proved the newly synthesized diboronic acid is a promising synthetic receptor for developing a long-term usable electrochemical sensor system.

Estimation of the Differential Pathlength Factor for Human Skin Using Monte Carlo Simulations

Near-infrared technology is an emerging non-invasive technique utilized for various medical applications. Recently, there have been many attempts to utilize NIR technology for the continues monitoring of blood glucose levels through the skin. Different approaches and designs have been proposed for non-invasive blood glucose measurements. Light photons penetrating the skin can undergo multiple scattering events, and the actual optical pathlength becomes larger than the source-to-detector separation (optode spacing) in the reflection-mode configuration. Thus, the differential pathlength factor (DPF) must be incorporated into the modified Beer-Lambert law. The accurate estimation of the DPF values will lead to an accurate quantification of the physiological variations within the tissue. In this work, the aim was to systematically estimate the DPF for human skin for a range of source-to-detector separations and wavelengths. The Monte Carlo (MC) method was utilized to mimic the different layers of human skin with different optical properties and blood and water volume fractions. This work could help improve the accuracy of the near-infrared technique in the measurement of physiological variations within skin tissue.

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.

A Review of Recent Advances in Vital Signals Monitoring of Sports and Health via Flexible Wearable Sensors

In recent years, vital signals monitoring in sports and health have been considered the research focus in the field of wearable sensing technologies. Typical signals include bioelectrical signals, biophysical signals, and biochemical signals, which have applications in the fields of athletic training, medical diagnosis and prevention, and rehabilitation. In particular, since the COVID-19 pandemic, there has been a dramatic increase in real-time interest in personal health. This has created an urgent need for flexible, wearable, portable, and real-time monitoring sensors to remotely monitor these signals in response to health management. To this end, the paper reviews recent advances in flexible wearable sensors for monitoring vital signals in sports and health. More precisely, emerging wearable devices and systems for health and exercise-related vital signals (e.g., ECG, EEG, EMG, inertia, body movements, heart rate, blood, sweat, and interstitial fluid) are reviewed first. Then, the paper creatively presents multidimensional and multimodal wearable sensors and systems. The paper also summarizes the current challenges and limitations and future directions of wearable sensors for vital typical signal detection. Through the review, the paper finds that these signals can be effectively monitored and used for health management (e.g., disease prediction) thanks to advanced manufacturing, flexible electronics, IoT, and artificial intelligence algorithms; however, wearable sensors and systems with multidimensional and multimodal are more compliant.

In Silico Investigation of SNR and Dermis Sensitivity for Optimum Dual-Channel Near-Infrared Glucose Sensor Designs for Different Skin Colors

Diabetes is a serious health condition that requires patients to regularly monitor their blood glucose level, making the development of practical, compact, and non-invasive techniques essential. Optical glucose sensors-and, specifically, NIR sensors-have the advantages of being non-invasive, compact, inexpensive, and user-friendly devices. However, these sensors have low accuracy and are yet to be adopted by healthcare providers. In our previous work, we introduced a non-invasive dual-channel technique for NIR sensors, in which a long channel is utilized to measure the glucose level in the inner skin (dermis) layer, while a short channel is used to measure the noise signal of the superficial skin (epidermis) layer. In this work, we investigated the use of dual-NIR channels for patients with different skin colors (i.e., having different melanin concentrations). We also adopted a Monte Carlo simulation model that takes into consideration the differences between different skin layers, in terms of blood content, water content, melanin concentration in the epidermis layer, and skin optical proprieties. On the basis of the signal-to-noise ratio, as well as the sensitivities of both the epidermis and dermis layers, we suggest the selection of wavelengths and source-to-detector separation for optimal NIR channels under different skin melanin concentrations. This work facilitates the improved design of a compact and non-invasive NIR glucose sensor that can be utilized by patients with different skin colors.

Terahertz Asymmetric S-Shaped Complementary Metasurface Biosensor for Glucose Concentration

In this article, we present a free-standing terahertz metasurface based on asymmetric S-shaped complementary resonators under normal incidence in transmission mode configuration. Each unit cell of the metasurface consists of two arms of mirrored S-shaped slots. We investigate the frequency response at different geometrical asymmetry via modifying the dimensions of one arm of the resonator. This configuration enables the excitation of asymmetric quasi-bound states in the continuum resonance and, hence, features very good field confinement that is very important for biosensing applications. Moreover, the performance of this configuration as a biosensor was examined for glucose concentration levels from 54 mg/dL to 342 mg/dL. This range covers hypoglycemia, normal, and hyperglycemia diabetes mellitus conditions. Two sample coating scenarios were considered, namely the top layer when the sample covers the metasurface and the top and bottom layers when the metasurface is sandwiched between the two layers. This strategy enabled very large resonance frequency redshifts of 236.1 and 286.6 GHz that were observed for the two scenarios for a 342 mg/dL concentration level and a layer thickness of 20 μm. Furthermore, for the second scenario and the same thickness, a wavelength sensitivity of 322,749 nm/RIU was found, which represents a factor of 2.3 enhancement compared to previous studies. The suggested terahertz metasurface biosensor in this paper could be used in the future for identifying hypoglycaemia and hyperglycemia conditions.

Noninvasive Blood Glucose Monitoring Systems Using Near-Infrared Technology—A Review

The past few decades have seen ongoing development of continuous glucose monitoring (CGM) systems that are noninvasive and accurately measure blood glucose levels. The conventional finger-prick method, though accurate, is not feasible for use multiple times a day, as it is painful and test strips are expensive. Although minimally invasive and noninvasive CGM systems have been introduced into the market, they are expensive and require finger-prick calibrations. As the diabetes trend is high in low- and middle-income countries, a cost-effective and easy-to-use noninvasive glucose monitoring device is the need of the hour. This review paper briefly discusses the noninvasive glucose measuring technologies and their related research work. The technologies discussed are optical, transdermal, and enzymatic. The paper focuses on Near Infrared (NIR) technology and NIR Photoplethysmography (PPG) for blood glucose prediction. Feature extraction from PPG signals and glucose prediction with machine learning methods are discussed. The review concludes with key points and insights for future development of PPG NIR-based blood glucose monitoring systems.

Potentio-tunable FET sensor having a redox-polarizable single electrode for the implementation of a wearable, continuous multi-analyte monitoring device

The emerging field of wearable devices for monitoring bioanalytes calls for the miniaturization of biochemical sensors. The only commercially available electrochemical wearable monitoring medical devices for bioanalytes are the amperometric continuous glucose monitoring (CGM) systems. The use of such amperometric methods to monitor glucose levels requires a relatively large electrode surface area for sufficient redox species collection, allowing accurate measurements to be made. Consequently, miniaturization of such sensors bearing large electrodes is challenging. Furthermore, it is difficult to introduce and deploy more than one electrode-based sensor per device, thereby limiting the number of analytes that can be monitored in parallel. To address these limitations, we have employed a non-referenced, single polarizable electrode coupled to a fin-shaped field-effect transistor (Fin-FET). We have discovered that by passivating the FET area by a relatively thick oxide and/or polytetrafluoroethylene (PTFE) polymer, leaving only the polarizable working electrode (WE) exposed, we can monitor redox analytes at the micromolar to millimolar concentration range. We attribute this effect to the WE polarization by the solution redox species. We have exploited the superior sensitivity of the adjacent silicon-based Fin-FET to detect changes in sensor electrode potentials induced by the redox species. Furthermore, we demonstrated the correlation between a specific analyte and the biasing WE potential on the accumulation/depletion of the coupled Fin-FET channel as manifested by the transistor source-drain current. Moreover, we utilized the analyte-electrode potential interaction, which is analyte-specific, to tune the specificity of the sensor towards an analyte of choice. In addition, we demonstrated the use of a single-electrode potentiometric sweep to assist in identifying the accumulation/depletion as a result of analyte-WE state. Collectively, the tiny potentio-tunable electrochemical sensor (PTEchem sensor) area is ~50 × 50 µm, and dedicated wireless transducer facilitates the use of this sensor for wearable continuous, multi-metabolite monitoring.

Commercial and Scientific Solutions for Blood Glucose Monitoring-A Review

Diabetes is a chronic and, according to the state of the art, an incurable disease. Therefore, to treat diabetes, regular blood glucose monitoring is crucial since it is mandatory to mitigate the risk and incidence of hyperglycemia and hypoglycemia. Nowadays, it is common to use blood glucose meters or continuous glucose monitoring via stinging the skin, which is classified as invasive monitoring. In recent decades, non-invasive monitoring has been regarded as a dominant research field. In this paper, electrochemical and electromagnetic non-invasive blood glucose monitoring approaches will be discussed. Thereby, scientific sensor systems are compared to commercial devices by validating the sensor principle and investigating their performance utilizing the Clarke error grid. Additionally, the opportunities to enhance the overall accuracy and stability of non-invasive glucose sensing and even predict blood glucose development to avoid hyperglycemia and hypoglycemia using post-processing and sensor fusion are presented. Overall, the scientific approaches show a comparable accuracy in the Clarke error grid to that of the commercial ones. However, they are in different stages of development and, therefore, need improvement regarding parameter optimization, temperature dependency, or testing with blood under real conditions. Moreover, the size of scientific sensing solutions must be further reduced for a wearable monitoring system.

Sensing Glucose Concentration Using Symmetric Metasurfaces under Oblique Incident Terahertz Waves

In this article, a planar metamaterial sensor designed at terahertz (THz) frequencies is utilized to sense glucose concentration levels that cover hypoglycemia, normal, and hyperglycemia conditions that vary from 54 to 342 mg/dL. The sensor was developed using a symmetric complementary split rectangular resonator at an oblique incidence angle. The resonance frequency shift was used as a measure of the changes in the glucose level of the samples. The increase in the glucose concentration level exhibited clear and noticeable redshifts in the resonance frequency. For instance, a 67.5 GHz redshift has been observed for a concentration level of 54 mg/dL and increased up to 122 GHz for the 342 mg/dL concentration level. Moreover, a high sensitivity level of 75,700 nm/RIU was observed for this design. In the future, the proposed THz sensors may have potential applications in diagnosing hypocalcemia and hyperglycemia cases.