Prospects and limitations of non-invasive blood glucose monitoring using near-infrared spectroscopy

Optical blood glucose non-invasive detection and its research progress

Blood glucose concentration is an important index for the diagnosis of diabetes, its self-monitoring technology is the method for scientific diabetes management. Currently, the typical household blood glucose meters have achieved great success in diabetes management, but they are discrete detection methods, and involve invasive blood sampling procedures. Optical detection technologies, which use the physical properties of light to detect the glucose concentration in body fluids non-invasively, have shown great potential in non-invasive blood glucose detection. This article summarized and analyzed the basic principles, research status, existing problems, and application prospects of different optical glucose detection technologies. In addition, this article also discusses the problems of optical detection technology in wearable sensors and perspectives on the future of non-invasive blood glucose detection technology to improve blood glucose monitoring in diabetic patients.

Equivalent Electrical Circuit Approach to Enhance a Transducer for Insulin Bioavailability Assessment

The equivalent electrical circuit approach is explored to improve a bioimpedance-based transducer for measuring the bioavailability of synthetic insulin already presented in previous studies. In particular, the electrical parameter most sensitive to the variation of insulin amount injected was identified. Eggplants were used to emulate human electrical behavior under a quasi-static assumption guaranteed by a very low measurement time compared to the estimated insulin absorption time. Measurements were conducted with the EVAL-AD5940BIOZ by applying a sinusoidal voltage signal with an amplitude of 100 mV and acquiring impedance spectra in the range [1-100] kHz. 14 units of insulin were gradually administered using a Lilly's Insulin Pen having a 0.4 cm long needle. Modified Hayden's model was adopted as a reference circuit and the electrical component modeling the extracellular fluids was found to be the most insulin-sensitive parameter. The trnasducer achieves a state-of-the-art sensitivity of 225.90 ml1. An improvement of 223 % in sensitivity, 44 % in deterministic error, 7 % in nonlinearity, and 42 % in reproducibility was achieved compared to previous experimental studies. The clinical impact of the transducer was evaluated by projecting its impact on a Smart Insulin Pen for real-time measurement of insulin bioavailability. The wide gain in sensitivity of the bioimpedance-based transducer results in a significant reduction of the uncertainty of the Smart Insulin Pen. Considering the same improvement in in-vivo applications, the uncertainty of the Smart Insulin Pen is decreased from [Formula: see text]l to [Formula: see text]l.Clinical and Translational Impact Statement: A Smart Insulin Pen based on impedance spectroscopy and equivalent electrical circuit approach could be an effective solution for the non-invasive and real-time measurement of synthetic insulin uptake after subcutaneous administration.

Design and Development of a Non-invasive Opto-Electronic Sensor for Blood Glucose Monitoring Using a Visible Light Source

Background The management of diabetes is critically dependent on the continuous monitoring of blood glucose levels. Contemporary approaches primarily utilize invasive methods, which often prove to be uncomfortable and can deter patient adherence. There is a pressing need for the development of novel strategies that improve patient compliance and simplify the process of glucose monitoring. Aim and objectives The primary objective of this research is to develop a non-invasive blood glucose monitoring system (NIBGMS) that offers a convenient alternative to conventional invasive methods. This study aims to demonstrate the feasibility and accuracy of using visible laser light at a wavelength of 650 nm for glucose monitoring and to address physiological and technical challenges associated with in vivo measurements. Methods Our approach involved the design of a device that exploits the quantitative relationship between glucose concentration and the refraction phenomena of laser light. The system was initially calibrated and tested using glucose solutions across a range of concentrations (25-500 mg/dL). To get around the problems that come up when people's skin and bodies are different, we combined an infrared (IR) transmitter (800 nm) and receiver that checks for changes in voltage, which are indicative of glucose levels. Results The prototype device was compared with a commercially available blood glucose monitor (Accu-Chek active machine; Roche Diabetes Care, Inc., Mumbai, India). The results demonstrated an average linearity of 95.7% relative to the Accu-Chek machine, indicating a high level of accuracy in the non-invasive measurement of glucose levels. Conclusions The findings suggest that our NIBGMS holds significant promise for clinical application. It reduces the discomfort associated with blood sampling and provides reliable measurements that are comparable to those of existing invasive methods. The successful development of this device paves the way for further commercial translation and could significantly improve the quality of life for individuals with diabetes, by facilitating easier and more frequent monitoring.

Development of sensor system and data analytic framework for non-invasive blood glucose prediction

Periodic quantification of blood glucose levels is performed using painful, invasive methods. The proposed work presents the development of a noninvasive glucose-monitoring device with two sensors, i.e., finger and wrist bands. The sensor system was designed with a near-infrared (NIR) wavelength of 940 nm emitter and a 900-1700 nm detector. This study included 101 diabetic and non-diabetic volunteers. The obtained dataset was subjected to pre-processing, exploratory data analysis (EDA), data visualization, and integration methods. Ambiguities such as the effects of skin color, ambient light, and finger pressure on the sensor were overcome in the proposed 'niGLUC-2.0v'. niGLUC-2.0v was validated with performance metrics where accuracy of 99.02%, mean absolute error (MAE) of 0.15, mean square error (MSE) of 0.22 for finger, and accuracy of 99.96%, MAE of 0.06, MSE of 0.006 for wrist prototype with ridge regression (RR) were achieved. Bland-Altman analysis was performed, where 98% of the data points were within ± 1.96 standard deviation (SD), 100% were under zone A of the Clarke Error Grid (CEG), and statistical analysis showed p < 0.05 on evaluated accuracy. Thus, niGLUC-2.0v is suitable in the medical and personal care fields for continuous real-time blood glucose monitoring.

Augmenting authenticity for non-invasive in vivo detection of random blood glucose with photoacoustic spectroscopy using Kernel-based ridge regression

Photoacoustic Spectroscopy (PAS) is a potential method for the noninvasive detection of blood glucose. However random blood glucose testing can help to diagnose diabetes at an early stage and is crucial for managing and preventing complications with diabetes. In order to improve the diagnosis, control, and treatment of Diabetes Mellitus, an appropriate approach of noninvasive random blood glucose is required for glucose monitoring. A polynomial kernel-based ridge regression is proposed in this paper to detect random blood glucose accurately using PAS. Additionally, we explored the impact of the biological parameter BMI on the regulation of blood glucose, as it serves as the primary source of energy for the body's cells. The kernel function plays a pivotal role in kernel ridge regression as it enables the algorithm to capture intricate non-linear associations between input and output variables. Using a Pulsed Laser source with a wavelength of 905 nm, a noninvasive portable device has been developed to collect the Photoacoustic (PA) signal from a finger. A collection of 105 individual random blood glucose samples was obtained and their accuracy was assessed using three metrics: Root Mean Square Error (RMSE), Mean Absolute Difference (MAD), and Mean Absolute Relative Difference (MARD). The respective values for these metrics were found to be 10.94 (mg/dl), 10.15 (mg/dl), and 8.86%. The performance of the readings was evaluated through Clarke Error Grid Analysis and Bland Altman Plot, demonstrating that the obtained readings outperformed the previously reported state-of-the-art approaches. To conclude the proposed IoT-based PAS random blood glucose monitoring system using kernel-based ridge regression is reported for the first time with more accuracy.

A three-in-one point-of-care electrochemical sensing platform for accurate monitoring of diabetes

A three-in-one electrochemical sensing platform was designed for the simultaneous detection of total hemoglobin (tHb), glycated hemoglobin (HbA1c) and HbA1c% by using a dual-aptamer sensing strategy. The developed sensing platform exhibits excellent sensitivity, selectivity, repeatability and long-term stability, and holds promising prospects in the early diagnosis and long-term monitoring of diabetes.

High-accuracy noninvasive continuous glucose monitoring using OCT angiography-purified blood scattering signals in human skin

The accuracy of noninvasive continuous glucose monitoring (CGM) through near-infrared scattering is challenged by mixed scattering signals from different compartments, where glucose has a positive correlation with a blood scattering coefficient but a negative correlation with a tissue scattering coefficient. In this study, we developed a high-accuracy noninvasive CGM based on OCT angiography (OCTA)-purified blood scattering signals. The blood optical scattering coefficient (BOC) was initially extracted from the depth attenuation of backscattered light in OCT and then purified by eliminating the scattering signals from the surrounding tissues under the guidance of a 3D OCTA vascular map in human skin. The purified BOC was used to estimate the optical blood glucose concentration (BGC) through a linear calibration. The optical and reference BGC measurements were highly correlated (R = 0.94) without apparent time delay. The mean absolute relative difference was 6.09%. All optical BGC measurements were within the clinically acceptable Zones A + B, with 96.69% falling in Zone A on Parke's error grids. The blood glucose response during OGTT was mapped with a high spatiotemporal resolution of the single vessel and 5 seconds. This noninvasive OCTA-based CGM shows promising accuracy for clinical use. Future research will involve larger sample sizes and diabetic participants to confirm these preliminary findings.

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.

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.

Implicit HbA1c Achieving 87% Accuracy within 90 Days in Non-Invasive Fasting Blood Glucose Measurements Using Photoplethysmography

To reduce the error induced by overfitting or underfitting in predicting non-invasive fasting blood glucose (NIBG) levels using photoplethysmography (PPG) data alone, we previously demonstrated that incorporating HbA1c led to a notable 10% improvement in NIBG prediction accuracy (the ratio in zone A of Clarke's error grid). However, this enhancement came at the cost of requiring an additional HbA1c measurement, thus being unfriendly to users. In this study, the enhanced HbA1c NIBG deep learning model (blood glucose level predicted from PPG and HbA1c) was trained with 1494 measurements, and we replaced the HbA1c measurement (explicit HbA1c) with "implicit HbA1c" which is reversely derived from pretested PPG and finger-pricked blood glucose levels. The implicit HbA1c is then evaluated across intervals up to 90 days since the pretest, achieving an impressive 87% accuracy, while the remaining 13% falls near the CEG zone A boundary. The implicit HbA1c approach exhibits a remarkable 16% improvement over the explicit HbA1c method by covering personal correction items automatically. This improvement not only refines the accuracy of the model but also enhances the practicality of the previously proposed model that relied on an HbA1c input. The nonparametric Wilcoxon paired test conducted on the percentage error of implicit and explicit HbA1c prediction results reveals a substantial difference, with a p-value of 2.75 × 10-7.

Optoacoustic classification of diabetes mellitus with the synthetic impacts via optimized neural networks

A highly accurate classification of diabetes mellitus (DM) with the synthetic impacts of several variables is first studied via optoacoustic technology in this work. For this purpose, an optoacoustic measurement apparatus of blood glucose is built, and the optoacoustic signals and peak-peak values for 625 cases of in vitro rabbit blood are obtained. The results show that although the single impact of five variables are obtained, the precise classification of DM is limited because of the synthetic impacts. Based on clinical standards, different levels of blood glucose corresponding to hypoglycaemia, normal, slight diabetes, moderate diabetes and severe diabetes are employed. Then, a wavelet neural network (WNN) is utilized to establish a classification model of DM severity. The classification accuracy is 94.4 % for the testing blood samples. To enhance the classification accuracy, particle swarm optimization (PSO) and quantum-behaved particle swarm optimization (QPSO) are successively utilized to optimize WNN, and accuracy is enhanced to 98.4 % and 100 %, respectively. It is demonstrated from comparison between several algorithms that optoacoustic technology united with the QPSO-optimized WNN algorithm can achieve precise classification of DM with synthetic impacts.

Ultra-broadband spectrometer on a chip of picometer scale resolution

A reconfigurable photonic integrated circuit was developed to operate as an ultra-broadband spectrometer on SiN chip resolving spectral lines with picometer precision and thermal stability of ±2 °C.

Broadband picometer-scale resolution on-chip spectrometer with reconfigurable photonics

Miniaturization of optical spectrometers is important to enable spectroscopic analysis to play a role in in situ, or even in vitro and in vivo characterization systems. However, scaled-down spectrometers generally exhibit a strong trade-off between spectral resolution and operating bandwidth, and are often engineered to identify signature spectral peaks only for specific applications. In this paper, we propose and demonstrate a novel global sampling strategy with distributed filters for generating ultra-broadband pseudo-random spectral responses. The geometry of all-pass ring filters is tailored to ensure small self- and cross-correlation for effective information acquisition across the whole spectrum, which dramatically reduces the requirement on sampling channels. We employ the power of reconfigurable photonics in spectrum shaping by embedding the engineered distributed filters. Using a moderate mesh of MZIs, we create 256 diverse spectral responses on a single chip and demonstrate a resolution of 20 pm for single spectral lines and 30 pm for dual spectral lines over a broad bandwidth of 115 nm, to the best of our knowledge achieving a new record of bandwidth-to-resolution ratio. Rigorous simulations reveal that this design will readily be able to achieve single-picometer-scale resolution. We further show that the reconfigurable photonics provides an extra degree of programmability, enabling user-defined features on resolution, computation complexity, and relative error. The use of SiN integration platform enables the spectrometer to exhibit excellent thermal stability of ±2.0 °C, effectively tackling the challenge of temperature variations at picometer-scale resolutions.

Diagnostic and monitoring applications using Near infrared (NIR) Spectroscopy in cancer and other diseases

Early cancer diagnosis plays a critical role in improving treatment outcomes and increasing survival rates for certain cancers. NIR spectroscopy offers a rapid and cost-effective approach to evaluate the optical properties of tissues at the microvessel level and provides valuable molecular insights. The integration of NIR spectroscopy with advanced data-driven algorithms in portable instruments has made it a cutting-edge technology for medical applications. NIR spectroscopy is a simple, non-invasive and affordable analytical tool that complements expensive imaging modalities such as functional magnetic resonance imaging, positron emission tomography and computed tomography. By examining tissue absorption, scattering, and concentrations of oxygen, water, and lipids, NIR spectroscopy can reveal inherent differences between tumor and normal tissue, often revealing specific patterns that help stratify disease. In addition, the ability of NIR spectroscopy to assess tumor blood flow, oxygenation, and oxygen metabolism provides a key paradigm for its application in cancer diagnosis. This review evaluates the effectiveness of NIR spectroscopy in the detection and characterization of disease, particularly in cancer, with or without the incorporation of chemometrics and machine learning algorithms. The report highlights the potential of NIR spectroscopy technology to significantly improve discrimination between benign and malignant tumors and accurately predict treatment outcomes. In addition, as more medical applications are studied in large patient cohorts, consistent advances in clinical implementation can be expected, making NIR spectroscopy a valuable adjunct technology for cancer therapy management. Ultimately, the integration of NIR spectroscopy into cancer diagnostics promises to improve prognosis by providing critical new insights into cancer patterns and physiology.

Fourier-transform infrared spectroscopy: a universal optical sensing technique with auspicious application prospects in the diagnosis and management of autoimmune diseases

Autoimmune diseases (AIDs) are poorly understood clinical syndromes due to breakdown of immune tolerance towards specific types of self-antigens. They are generally associated with an inflammatory response mediated by lymphocytes T, autoantibodies or both. Ultimately, chronic inflammation culminates in tissue damages and clinical manifestations. AIDs affect 5% of the world population, and they represent the main cause of fatality in young to middle-aged females. In addition, the chronic nature of AIDs has a devastating impact on the patient's quality of life. It also places a heavy burden on the health care system. Establishing a rapid and accurate diagnosis is considered vital for an ideal medical management of these autoimmune disorders. However, for some AIDs, this task might be challenging. Vibrational spectroscopies, and more particularly Fourier-transform infrared (FTIR) spectroscopy, have emerged as universal analytical techniques with promising applications in the diagnosis of various types of malignancies and metabolic and infectious diseases. The high sensitivity of these optical sensing techniques and their minimal requirements for test reagents qualify them to be ideal analytical techniques. The aim of the current review is to explore the potential applications of FTIR spectroscopy in the diagnosis and management of most common AIDs. It also aims to demonstrate how this technique has contributed to deciphering the biochemical and physiopathological aspects of these chronic inflammatory diseases. The advantages that can be offered by this optical sensing technique over the traditional and gold standard methods used in the diagnosis of these autoimmune disorders have also been extensively discussed.

An extreme learning machine optimized by differential evolution and artificial bee colony for predicting the concentration of whole blood with Fourier Transform Raman spectroscopy

Raman spectroscopy, with its advantages of non-contact nature, rapid detection, and minimum water interference, is promising for non-invasive blood detection or diagnosis in clinic applications. However, there is a critical issue that how to accurately analyze blood composition by Raman spectroscopy. In this study, we apply extreme learning machine (ELM) algorithm and a multivariate calibration regression model to analyze the results from Raman spectroscopy and determine the component's concentrations in blood samples, including glucose, cholesterol, and triglyceride. Self-adaption differential evolution artificial bee colony (SADEABC) algorithm was further applied to increase the data's accuracy and robustness. The obtained data for coefficient of determination, root mean square error of calibration, root mean square error of prediction, and relative percent deviation, were 0.9822, 0.3993, 0.3827, and 6.6679 for glucose, 0.9786, 0.2104, 0.2088 and 5.9533 for cholesterol, and 0.9921, 0.2744, 0.3433 and 10.5075 for triglyceride, respectively. Results demonstrated that the model based on SADEABC-ELM show much better prediction data than those models based on the ELM and ABC-ELM.

An In-Ear PPG-Based Blood Glucose Monitor: A Proof-of-Concept Study

Monitoring diabetes saves lives. To this end, we introduce a novel, unobtrusive, and readily deployable in-ear device for the continuous and non-invasive measurement of blood glucose levels (BGLs). The device is equipped with a low-cost commercially available pulse oximeter whose infrared wavelength (880 nm) is used for the acquisition of photoplethysmography (PPG). For rigor, we considered a full range of diabetic conditions (non-diabetic, pre-diabetic, type I diabetic, and type II diabetic). Recordings spanned nine different days, starting in the morning while fasting, up to a minimum of a two-hour period after eating a carbohydrate-rich breakfast. The BGLs from PPG were estimated using a suite of regression-based machine learning models, which were trained on characteristic features of PPG cycles pertaining to high and low BGLs. The analysis shows that, as desired, an average of 82% of the BGLs estimated from PPG lie in region A of the Clarke error grid (CEG) plot, with 100% of the estimated BGLs in the clinically acceptable CEG regions A and B. These results demonstrate the potential of the ear canal as a site for non-invasive blood glucose monitoring.

Explainable diabetes classification using hybrid Bayesian-optimized TabNet architecture

Diabetes is a deadly chronic disease that occurs when the pancreas is not able to produce ample insulin or when the body cannot use insulin effectively. If undetected, it may lead to a host of health complications. Hence, accurate and explainable early-stage detection of diabetes is essential for the proper administration of treatment options in leading a healthy and productive life. For this, we developed an interpretable TabNet model tuned via Bayesian optimization (BO). To achieve model-specific interpretability, the attention mechanism of TabNet architecture was used, which offered the local and global model explanations on the influence of the attributes on the outcomes. The model was further explained locally and globally using more robust model-agnostic LIME and SHAP eXplainable Artificial Intelligence (XAI) tools. The proposed model outperformed all benchmarked models by obtaining high accuracy of 92.2% and 99.4% using the Pima Indians diabetes dataset (PIDD) and the early-stage diabetes risk prediction dataset (ESDRPD), respectively. Based on the XAI results, it was clear that the most influential attribute for diabetes classification using PIDD and ESDRPD were Insulin and Polyuria, respectively. The feature importance values registered for insulin was 0.301 (PIDD) and for polyuria 0.206 was registered (ESDRPD). The high accuracy and ancillary interpretability of our objective model is expected to increase end-users trust and confidence in early-stage detection of diabetes.

Glucose emission spectra through mid-infrared passive spectroscopic imaging of the wrist for non-invasive glucose sensing

Non-invasive blood glucose sensing can be achieved using mid-infrared spectroscopy, although no practical device based on this method has yet been developed. Here, we propose mid-infrared passive spectroscopic imaging for glucose measurements from a distance. Spectroscopic imaging of thermal radiation from the human body enabled, for the first time in the world, the detection of glucose-induced luminescence from a distance. In addition, glucose emission spectra of the wrist acquired at regular intervals up to 60 min showed that there was a strong correlation between the glucose emission intensity and blood glucose level measured using an invasive sensor. Thus, the new technology proposed here is expected to be applied to real-time monitoring of diabetic patients to detect hypoglycemic attacks during sleep and to detect hyperglycemia in a population. Moreover, this technology could lead to innovations that would make it possible to remotely measure a variety of substances.

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.

Deduction learning for precise noninvasive measurements of blood glucose with a dozen rounds of data for model training

Personalized modeling has long been anticipated to approach precise noninvasive blood glucose measurements, but challenged by limited data for training personal model and its unavoidable outlier predictions. To overcome these long-standing problems, we largely enhanced the training efficiency with the limited personal data by an innovative Deduction Learning (DL), instead of the conventional Induction Learning (IL). The domain theory of our deductive method, DL, made use of accumulated comparison of paired inputs leading to corrections to preceded measured blood glucose to construct our deep neural network architecture. DL method involves the use of paired adjacent rounds of finger pulsation Photoplethysmography signal recordings as the input to a convolutional-neural-network (CNN) based deep learning model. Our study reveals that CNN filters of DL model generated extra and non-uniform feature patterns than that of IL models, which suggests DL is superior to IL in terms of learning efficiency under limited training data. Among 30 diabetic patients as our recruited volunteers, DL model achieved 80% of test prediction in zone A of Clarke Error Grid (CEG) for model training with 12 rounds of data, which was 20% improvement over IL method. Furthermore, we developed an automatic screening algorithm to delete low confidence outlier predictions. With only a dozen rounds of training data, DL with automatic screening achieved a correlation coefficient ([Formula: see text]) of 0.81, an accuracy score ([Formula: see text]) of 93.5, a root mean squared error of 13.93 mg/dl, a mean absolute error of 12.07 mg/dl, and 100% predictions in zone A of CEG. The nonparametric Wilcoxon paired test on [Formula: see text] for DL versus IL revealed near significant difference with p-value 0.06. These significant improvements indicate that a very simple and precise noninvasive measurement of blood glucose concentration is achievable.

A Review of Electrochemical Sensors for the Detection of Glycated Hemoglobin

Glycated hemoglobin (HbA1c) is the gold standard for measuring glucose levels in the diagnosis of diabetes due to the excellent stability and reliability of this biomarker. HbA1c is a stable glycated protein formed by the reaction of glucose with hemoglobin (Hb) in red blood cells, which reflects average glucose levels over a period of two to three months without suffering from the disturbance of the outside environment. A number of simple, high-efficiency, and sensitive electrochemical sensors have been developed for the detection of HbA1c. This review aims to highlight current methods and trends in electrochemistry for HbA1c monitoring. The target analytes of electrochemical HbA1c sensors are usually HbA1c or fructosyl valine/fructosyl valine histidine (FV/FVH, the hydrolyzed product of HbA1c). When HbA1c is the target analyte, a sensor works to selectively bind to specific HbA1c regions and then determines the concentration of HbA1c through the quantitative transformation of weak electrical signals such as current, potential, and impedance. When FV/FVH is the target analyte, a sensor is used to indirectly determine HbA1c by detecting FV/FVH when it is hydrolyzed by fructosyl amino acid oxidase (FAO), fructosyl peptide oxidase (FPOX), or a molecularly imprinted catalyst (MIC). Then, a current proportional to the concentration of HbA1c can be produced. In this paper, we review a variety of representative electrochemical HbA1c sensors developed in recent years and elaborate on their operational principles, performance, and promising future clinical applications.

Near Infrared LEDs-Based Non-Invasive Blood Sugar Testing for Detecting Blood Sugar Levels on Diabetic Care

Diabetes Mellitus, with its rapid development and various complications that have caused it, has become one of the deadliest diseases in the world. Early detection efforts to raise blood sugar levels can help to avoid a variety of complications. Measuring devices are needed to find out blood sugar levels detect how much sugar is in the blood. The blood sugar measuring device is invasive by taking blood from capillaries tested both in the lab and using portable testing instruments. The use of this tool results in discomfort, pain, and trauma for the patient. The purpose of this study was to determine the degree of sensitivity of the NIR LED sensor on the thumb to the little finger to the reading of light reflections coming out of body tissues.. Currently, the index finger is often used as a medium to find out how much blood sugar is in non-invasive blood sugar measurements. The other four fingers' sensitivity is unknown at this time. Because the use of the index finger, which is located in the middle, can make activities difficult at times, information on the sensitivity level of the other fingers is required. This paper discusses the sensitivity of placing the NIR LED sensor on the five fingers to determine the most sensitive finger with the best response. Based on the testing results of 15 samples, Although the index finger receives the most significant stress, the correlation and linear regression tests show that the thumb has the closest relationship with the R2 = 0.6841. With this research, a test instrument with higher sensitivity for Diabetes can be developed by placing the sensor in a comfortable area. The implication is that the results of this study can be recommended to use the thumb as an alternative to the placement of the NIR LED sensor to measure blood sugar levels non-invasively in DM patients.

Skin Patchable Sensor Surveillance for Continuous Glucose Monitoring

Diabetes mellitus is a physiological and metabolic disorder affecting millions of people worldwide, associated with global morbidity, mortality, and financial expenses. Long-term complications can be avoided by frequent, continuous self-monitoring of blood glucose. Therefore, this review summarizes the current state-of-art glycemic control regimes involving measurement approaches and basic concepts. Following an introduction to the significance of continuous glucose sensing, we have tracked the evolution of glucose monitoring devices from minimally invasive to non-invasive methods to present an overview of the spectrum of continuous glucose monitoring (CGM) technologies. The conveniences, accuracy, and cost-effectiveness of the real-time CGM systems (rt-CGMs) are the factors considered for discussion. Transdermal biosensing and drug delivery routes have recently emerged as an innovative approach to substitute hypodermal needles. This work reviews skin-patchable glucose monitoring sensors for the first time, providing specifics of all the major findings in the past 6 years. Skin patch sensors and their progressive form, i.e., microneedle (MN) array sensory and delivery systems, are elaborated, covering self-powered, enzymatic, and non-enzymatic devices. The critical aspects reviewed are material design and assembly techniques focusing on flexibility, sensitivity, selectivity, biocompatibility, and user-end comfort. The review highlights the advantages of patchable MNs' multi-sensor technology designed to maintain precise blood glucose levels and administer diabetes drugs or insulin through a "sense and act" feedback loop. Subsequently, the limitations and potential challenges encountered from the MN array as rt-CGMs are listed. Furthermore, the current statuses of working prototype glucose-responsive "closed-loop" insulin delivery systems are discussed. Finally, the expected future developments and outlooks in clinical applications are discussed.

Comprehensive Review on Wearable Sweat-Glucose Sensors for Continuous Glucose Monitoring

The incidence of diabetes is increasing at an alarming rate, and regular glucose monitoring is critical in order to manage diabetes. Currently, glucose in the body is measured by an invasive method of blood sugar testing. Blood glucose (BG) monitoring devices measure the amount of sugar in a small sample of blood, usually drawn from pricking the fingertip, and placed on a disposable test strip. Therefore, there is a need for non-invasive continuous glucose monitoring, which is possible using a sweat sensor-based approach. As sweat sensors have garnered much interest in recent years, this study attempts to summarize recent developments in non-invasive continuous glucose monitoring using sweat sensors based on different approaches with an emphasis on the devices that can potentially be integrated into a wearable platform. Numerous research entities have been developing wearable sensors for continuous blood glucose monitoring, however, there are no commercially viable, non-invasive glucose monitors on the market at the moment. This review article provides the state-of-the-art in sweat glucose monitoring, particularly keeping in sight the prospect of its commercialization. The challenges relating to sweat collection, sweat sample degradation, person to person sweat amount variation, various detection methods, and their glucose detection sensitivity, and also the commercial viability are thoroughly covered.

Simultaneous monitoring of sweat lactate content and sweat secretion rate by wearable remote biosensors

We report on the simultaneous monitoring of sweat lactate concentration and sweat secretion rate. For this aim lactate oxidase-Prussian Blue enzyme-nanozyme type lactate biosensors were elaborated. The use of siloxane-perfluorosulfonated ionomer composite membrane for enzyme-nanozyme immobilization results in the biosensor displaying flux independence in the whole range of physiological sweat secretion rates (0.025-2 μl cm^-2 min^-1). On the contrary, current response of the biosensor based on solely siloxane membranes becomes saturated at physiological sweat lactate concentration, depending mostly on the flow rate. Accordingly, for simultaneous monitoring of sweat lactate concentration and its secretion rate both flow-through biosensors were integrated with high-accuracy wearable electronic devices allowing real-time remote monitoring. As found, during exhaustive physical exercise sweat secretion rate and lactate content are independent of each other, thus, confirming that this excretory liquid is suitable for non-invasive diagnostics.

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.

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.

An interrelated CataFlower enzyme system for sensitively monitoring sweat glucose

An accurate measurement of sweat glucose is a promising alternative to invasive finger prick blood test, and may provide effective self-monitoring of blood glucose with good patient compliance. Herein, an interrelated catalytic enzyme system has been developed, termed as CataFlower, which is composed of nanoflower MoS₂ (peroxidase) decorated with GOx (glucose oxidase) and MnO₂ (oxygen generator), and exhibits synergistic oxidative capability for sensitively monitoring sweat glucose. CataFlower can not only generate oxygen in situ to maximize GOx activity, but promote peroxidase-triggered H₂O₂ oxidation of methylene blue, resulting in sensitive colorimetric detection of glucose. We identify that CataFlower can precisely detect glucose with a detection limit of 10 μM, allowing for measuring glucose levels in different biological samples, such as blood and urine. Particularly, CataFlower is capable of monitoring dynamic changes in sweat glucose with high sensitivity and accuracy during exercise. Therefore, CataFlower provides a stepping stone to eliminate invasive blood tests, significantly improving the diagnosis and management of diabetes mellitus.

90% Accuracy for Photoplethysmography-Based Non-Invasive Blood Glucose Prediction by Deep Learning with Cohort Arrangement and Quarterly Measured HbA1c

Previously published photoplethysmography-(PPG) based non-invasive blood glucose (NIBG) measurements have not yet been validated over 500 subjects. As illustrated in this work, we increased the number subjects recruited to 2538 and found that the prediction accuracy (the ratio in zone A of Clarke's error grid) reduced to undesirable 60.6%. We suspect the low prediction accuracy induced by larger sample size might arise from the physiological diversity of subjects, and one possibility is that the diversity might originate from medication. Therefore, we split the subjects into two cohorts for deep learning: with and without medication (1682 and 856 recruited subjects, respectively). In comparison, the cohort training for subjects without any medication had approximately 30% higher prediction accuracy over the cohort training for those with medication. Furthermore, by adding quarterly (every 3 months) measured glycohemoglobin (HbA1c), we were able to significantly boost the prediction accuracy by approximately 10%. For subjects without medication, the best performing model with quarterly measured HbA1c achieved 94.3% prediction accuracy, RMSE of 12.4 mg/dL, MAE of 8.9 mg/dL, and MAPE of 0.08, which demonstrates a very promising solution for NIBG prediction via deep learning. Regarding subjects with medication, a personalized model could be a viable means of further investigation.

Optical Assessment of Tear Glucose by Smart Biosensor Based on Nanoparticle Embedded Contact Lens

Diabetes is a disease condition characterized by a prolonged, high blood glucose level, which may lead to devastating outcomes unless properly managed. Here, we introduce a simple camera-based optical monitoring system (OMS) utilizing the nanoparticle embedded contact lens that produces color changes matching the tear glucose level without any complicated electronic components. Additionally, we propose an image processing algorithm that automatically optimizes the measurement accuracy even in the presence of image blurring, possibly caused by breathing, subtle movements, and eye blinking. As a result, using in vivo mouse models and human tear samples we successfully demonstrated robust correlations across the glucose concentrations measured by three different independent techniques, validating the quantitative efficacy of the proposed OMS. For its methodological simplicity and accessibility, our findings strongly support that the innovation offered by the OMS and processing algorithm would greatly facilitate the glucose monitoring procedure and improve the overall welfare of diabetes patients.

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

The prevalence of diabetes is increasing globally. More than 690 million cases of diabetes are expected worldwide by 2045. Continuous blood glucose monitoring is essential to control the disease and avoid long-term complications. Diabetics suffer on a daily basis with the traditional glucose monitors currently in use, which are invasive, painful, and cost-intensive. Therefore, the demand for non-invasive, painless, economical, and reliable approaches to monitor glucose levels is increasing. Since the last decades, many glucose sensing technologies have been developed. Researchers and scientists have been working on the enhancement of these technologies to achieve better results. This paper provides an updated review of some of the pioneering non-invasive optical techniques for monitoring blood glucose levels that have been proposed in the last six years, including a summary of state-of-the-art error analysis and validation techniques.

Effects of hemoencephalographic biofeedback with virtual reality on selected aspects of attention in children with ADHD

For children with attention deficit/hyperactivity disorder (ADHD), a reduction of inattention by biofeedback has been shown in several studies. As evidenced by previous reports, biofeedback (BFB) with virtual reality (VR) allows for controlling distractors, providing an environment that captures participants' attention. The purpose of this study was to evaluate the effects of hemoencephalographic (HEG) BFB with VR in treating deficits in vigilance (assessed using the short form of the Mackworth Clock Task), visual search (the Visual Search Task), and divided attention (Multitasking Test) among children with ADHD. Data subjected to analysis were collected from 87 participants aged 9-15 years. Children were assigned to one of three groups (standard 2D BFB in the lab, VR BFB with a limited visual scene, VR BFB with a complex visual scene) and were subjected to ten HEG BFB sessions. Children in the VR BFB groups exhibited a bigger regional cerebral blood oxygenation slope during BFB and better performance in cognitive tests following the experiment compared to children in the 2D BFB group. The data obtained suggest that HEG BFB with VR may have a more beneficial effect in treating attention deficits compared to standard 2D HEG BFB. We believe that the strong effects of HEG BFB with VR stem from the increased commitment and motivation in individuals, rather than from manipulation with regard to visual scene complexity.

Toward Non-Invasive Estimation of Blood Glucose Concentration: A Comparative Performance

The present study comprises a comparison of the Mel Frequency Cepstral Coefficients (MFCC), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) as feature extraction methods using ten different regression algorithms (AdaBoost, Bayesian Ridge, Decision Tree, Elastic Net, k-NN, Linear Regression, MLP, Random Forest, Ridge Regression and Support Vector Regression) to quantify the blood glucose concentration. A total of 122 participants—healthy and diagnosed with type 2 diabetes—were invited to be part of this study. The entire set of participants was divided into two partitions: a training subset of 72 participants, which was intended for model selection, and a validation subset comprising the remaining 50 participants, to test the selected model. A 3D-printed chamber for providing a light-controlled environment and a low-cost microcontroller unit were used to acquire optical measurements. The MFCC, PCA and ICA were calculated by an open-hardware computing platform. The glucose levels estimated by the system were compared to actual glucose concentrations measured by venipuncture in a laboratory test, using the mean absolute error, the mean absolute percentage error and the Clarke error grid for this purpose. The best results were obtained for MCCF with AdaBoost and Random Forest (MAE = 11.6 for both).

A Novel Luminescent "Nanochip" as a Tandem Catalytic System for Chemiluminescent Detection of Sweat Glucose

Accurate sweat glucose detection is a promising alternative to invasive finger-prick blood tests, allowing for self-monitoring of blood glucose with good patient compliance. In this study, we have developed a tandem catalytic system, termed as a luminescent "nanochip" (LAON), which was composed of gold nanoparticles (AuNPs) and N-(aminobutyl)-N-(ethylisoluminol) (ABEI)-engineered oxygen-doped carbon nitride (O-g-C₃N₄), for chemiluminescent detection of sweat glucose. The LAON exhibits dual catalytic activity of glucose oxidase and peroxidase and can not only oxidize glucose to generate H₂O₂ but catalyze H₂O₂-mediated luminol chemiluminescence, resulting in sensitive detection of glucose. We identify that the LAON can precisely detect glucose with a detection limit of 0.1 μM, enabling us to measure glucose levels in different biological samples. Particularly, the LAON is capable of sensitively and accurately monitoring dynamic changes in sweat glucose during exercise. Therefore, the LAON provides an alternative approach to supersede invasive blood tests and may improve the management of diabetes mellitus.

Optical Glucose Sensor Using Pressure Sensitive Paint

A glucose sensor is used as an essential tool for diagnosing and treating diabetic patients and controlling processes during cell culture. Since the development of an electrochemical-based glucose sensor, an optical glucose sensor has been devised to overcome its shortcomings, but this also poses a problem because it requires a complicated manufacturing process. This study aimed to develop an optical glucose sensor film that could be fabricated with a simple process using commercial pressure sensitive paints. The sensor manufacturing technology developed in this work could simplify the complex production process of the existing electrochemical or optical glucose sensors. In addition, a photometric method for glucose concentration analysis was developed using the color image of the sensor. By developing this sensor and analysis technology, the basis for glucose measurement was established that enables two-dimensional, online, and continuous measurement. The proposed sensor showed good linearity at 0–4 mM glucose in an aqueous sample solution, its limit of detection was 0.37 mM, and the response time was 2 min.

The Use of Supercontinuum Laser Sources in Biomedical Diffuse Optics: Unlocking the Power of Multispectral Imaging

Optical techniques based on diffuse optics have been around for decades now and are making their way into the day-to-day medical applications. Even though the physics foundations of these techniques have been known for many years, practical implementation of these technique were hindered by technological limitations, mainly from the light sources and/or detection electronics. In the past 20 years, the developments of supercontinuum laser (SCL) enabled to unlock some of these limitations, enabling the development of system and methodologies relevant for medical use, notably in terms of spectral monitoring. In this review, we focus on the use of SCL in biomedical diffuse optics, from instrumentation and methods developments to their use for medical applications. A total of 95 publications were identified, from 1993 to 2021. We discuss the advantages of the SCL to cover a large spectral bandwidth with a high spectral power and fast switching against the disadvantages of cost, bulkiness, and long warm up times. Finally, we summarize the utility of using such light sources in the development and application of diffuse optics in biomedical sciences and clinical applications.

Enhancing the Accuracy of Non-Invasive Glucose Sensing in Aqueous Solutions Using Combined Millimeter Wave and Near Infrared Transmission

We reported measurement results relating to non-invasive glucose sensing using a novel multiwavelength approach that combines radio frequency and near infrared signals in transmission through aqueous glucose-loaded solutions. Data were collected simultaneously in the 37–39 GHz and 900–1800 nm electromagnetic bands. We successfully detected changes in the glucose solutions with varying glucose concentrations between 80 and 5000 mg/dl. The measurements showed for the first time that, compared to single modality systems, greater accuracy on glucose level prediction can be achieved when combining transmission data from these distinct electromagnetic bands, boosted by machine learning algorithms.

Partial sulfidation for constructing Cu2O–CuS heterostructures realizing enhanced electrochemical glucose sensing

A Cu₂O–CuS heterostructure was constructed to elucidate the relationship between heterojunctions and electrochemical glucose sensing.

Evaluation of Opportunities and Limitations of Mid-Infrared Skin Spectroscopy for Noninvasive Blood Glucose Monitoring

BACKGROUND

A wide range of optical techniques has recently been presented for the development of noninvasive methods for blood glucose sensing based on multivariate skin spectrum analysis, and most recent studies are reviewed in short by us. The vibrational spectral fingerprints of glucose, as especially found in the mid-infrared or Raman spectrum, have been suggested for achieving largest selectivity for the development of noninvasive blood glucose methods.

METHODS

Here, the different aspects on integral skin measurements are presented, which are much dependent on the absorption characteristics of water as the main skin constituent. In particular, different mid-infrared measurement techniques as realized recently are discussed. The limitations of the use of the attenuated total reflection technique in particular are elaborated, and confounding skin or saliva spectral features are illustrated and discussed in the light of recently published works, claiming that the attenuated total reflection technique can be utilized for noninvasive measurements.

RESULTS

It will be shown that the penetration depth of the infrared radiation with wavelengths around 10 µm is the essential parameter, which can be modulated by different measurement techniques as with photothermal or diffuse reflection. However, the law of physics is limiting the option of using the attenuated total reflection technique with waveguides from diamond or similar optical materials.

CONCLUSIONS

There are confounding features from mucosa, stratum corneum, or saliva, which have been misinterpreted for glucose measurements. Results of an earlier study with multivariate evaluation based on glucose fingerprint features are again referred to as a negative experimental proof.

Colorimetric Nanoplasmonics to Spot Hyperglycemia From Saliva

Early diagnostics and point-of-care (POC) devices can save people's lives or drastically improve their quality. In particular, millions of diabetic patients worldwide benefit from POC devices for frequent self-monitoring of blood glucose. Yet, this still involves invasive sampling processes, which are quite discomforting for frequent measurements, or implantable devices dedicated to selected chronic patients, thus precluding large-scale monitoring of the globally increasing diabetic disorders. Here, we report a non-invasive colorimetric sensing platform to identify hyperglycemia from saliva. We designed plasmonic multibranched gold nanostructures, able to rapidly change their shape and color (naked-eye detection) in the presence of hyperglycemic conditions. This "reshaping approach" provides a fast visual response and high sensitivity, overcoming common detection issues related to signal (color intensity) losses and bio-matrix interferences. Notably, optimal performances of the assay were achieved in real biological samples, where the biomolecular environment was found to play a key role. Finally, we developed a dipstick prototype as a rapid home-testing kit.

Low-cost portable microwave sensor for non-invasive monitoring of blood glucose level: novel design utilizing a four-cell CSRR hexagonal configuration

This article presents a novel design of portable planar microwave sensor for fast, accurate, and non-invasive monitoring of the blood glucose level as an effective technique for diabetes control and prevention. The proposed sensor design incorporates four cells of hexagonal-shaped complementary split ring resonators (CSRRs), arranged in a honey-cell configuration, and fabricated on a thin sheet of an FR4 dielectric substrate.The CSRR sensing elements are coupled via a planar microstrip-line to a radar board operating in the ISM band 2.4-2.5 GHz. The integrated sensor shows an impressive detection capability and a remarkable sensitivity of blood glucose levels (BGLs). The superior detection capability is attributed to the enhanced design of the CSRR sensing elements that expose the glucose samples to an intense interaction with the electromagnetic fields highly concentrated around the sensing region at the induced resonances. This feature enables the developed sensor to detect extremely delicate variations in the electromagnetic properties that characterize the varying-level glucose samples. The desired performance of the fabricated sensor is practically validated through in-vitro measurements using a convenient setup of Vector Network Analyzer (VNA) that records notable traces of frequency-shift responses when the sensor is loaded with samples of 70-120 mg/dL glucose concentrations. This is also demonstrated in the radar-driven prototype where the raw data collected at the radar receiving channel shows obvious patterns that reflect glucose-level variations. Furthermore, the differences in the sensor responses for tested glucose samples are quantified by applying the Principal Component Analysis (PCA) machine learning algorithm. The proposed sensor, beside its impressive detection capability of the diabetes-spectrum glucose levels, has several other favorable attributes including compact size, simple fabrication, affordable cost, non-ionizing nature, and minimum health risk or impact. Such attractive features promote the proposed sensor as a possible candidate for non-invasive glucose levels monitoring for diabetes as evidenced by the preliminary results from a proof-of-concept in-vivo experiment of tracking an individual's BGL by placing his fingertip onto the sensor. The presented system is a developmental platform towards radar-driven wearable continuous BGL monitors.

Screen-Printed Glucose Sensors Modified with Cellulose Nanocrystals (CNCs) for Cell Culture Monitoring

Glucose sensors are potentially useful tools for monitoring the glucose concentration in cell culture medium. Here, we present a new, low-cost, and reproducible sensor based on a cellulose-based material, 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO) oxidized-cellulose nanocrystals (CNCs). This novel biocompatible and inert nanomaterial is employed as a polymeric matrix to immobilize and stabilize glucose oxidase in the fabrication of a reproducible, operationally stable, highly selective, cost-effective, screen-printed glucose sensor. The sensors have a linear range of 0.1–2 mM (R² = 0.999) and a sensitivity of 5.7 ± 0.3 µA cm⁻²∙mM⁻¹. The limit of detection is 0.004 mM, and the limit of quantification is 0.015 mM. The sensor maintains 92.3 % of the initial current response after 30 consecutive measurements in a 1 mM standard glucose solution, and has a shelf life of 1 month while maintaining high selectivity. We demonstrate the practical application of the sensor by monitoring the glucose consumption of a fibroblast cell culture over the course of several days.