Blood Glucose Sensor Research Articles

Compact Quantum Cascade Laser-Based Noninvasive Glucose Sensor Upgraded with Direct Comb Data-Mining.

Mid-infrared spectral analysis has long been recognized as the most accurate noninvasive blood glucose measurement method, yet no practical compact mid-infrared blood glucose sensor has ever passed the accuracy benchmark set by the USA Food and Drug Administration (FDA): to substitute for the finger-pricking glucometers in the market, a new sensor must first show that 95% of their glucose measurements have errors below 15% of these glucometers. Although recent innovative exploitations of the well-established Fourier-transform infrared (FTIR) spectroscopy have reached such FDA accuracy benchmarks, an FTIR spectrometer is too bulky. The advancements of quantum cascade lasers (QCLs) can lead to FTIR spectrometers of reduced size, but compact QCL-based noninvasive blood glucose sensors are not yet available. This work reports on two compact sensor system designs, both reaching the FDA accuracy benchmark. Each design commonly comprises a mid-infrared QCL for emission, a multiple attenuation total reflection prism (MATR) for data acquisition, and a computer-controlled infrared detector for data analysis. The first design translates the comb-like signals into conventional spectra, and then data-mines the resultant spectra to yield blood glucose concentrations. When a pressure actuator is employed to press the patient's hypothenar against the MATR, the sensor accuracy is considered to reach the FDA accuracy benchmark. The second design abandons the data processing step of translating combs-to-spectra and directly data-mines the "first-hand" comb signal. Beyond increasing the measurement accuracy to the FDA accuracy benchmark, even without a pressure actuator, direct comb data-mining upgrades the sensor system with speed and data integrity, which can impact the healthcare of diabetic patients. Specifically, the sensor performance is validated with 492 glucose absorption scans in the time domain, each with 20 million datapoints measured from four subjects with glucose concentrations of 3.9-7.9 mM. The sensor data-mines 164 sets of critical singularity strengths, each comprising 4 critical singularity strengths directly from the 9840 million raw signal datapoints, and the 656 critical singularity strengths are subjected to a machine-learning regression model analysis, which yields 164 glucose concentrations. These concentrations are correlated with those measured with a standard finger-pricking glucometer. An accuracy of 99.6% is confirmed from the 164 measurements with errors not more than 15% from the reference of the standard glucometer.

Discovery and characterization of an FAD-dependent glucose 6-dehydrogenase (74 characters including spaces).

Many patients with diabetes use self-measurement devices for blood glucose to understand their blood glucose levels. Most of these devices utilize FAD-dependent glucose dehydrogenase (FAD-GDH) to determine blood glucose levels. For this purpose, FAD-GDHs specifically oxidizing glucose among the sugars present in blood is required. Many FAD-GDHs with high substrate specificity have been reported previously; however, their substrate specificity is insufficient as they also react with xylose. Therefore, we aimed to identify FAD-GDHs without xylose reactivity. We screened and obtained a new enzyme from Colletotrichum plurivorum (CpGDH). CpGDH showed high activity to glucose in the presence of electron mediators but low activity to xylose. We prepared the glucose oxidation products using CpGDH and subjected to TLC, HPLC, MS, and NMR analyses. The results demonstrated that CpGDH is a previously unknown FAD-dependent glucose 6-dehydrogenase (FAD-G6DH) that oxidizes glucose to glucuronic acid. The stoichiometric ratio of the substrate and electron mediator was 1:2, suggesting that CpGDH catalyzes two-step oxidation reactions, including oxidation of primary alcohols to aldehydes and of aldehydes to carboxylic acids. We concluded that CpGDH has the unique substrate-binding manner based on the result of docking simulation of CpGDH with a substrate glucose. We then constructed a phylogenetic tree of carbohydrate-related flavoproteins including FAD-G6DHs, indicating that FAD-G6DHs are different from the known FAD-dependent oxidoreductases. Overall, this study is the first to report FAD-G6DHs. These results will likely contribute to the development of more accurate blood glucose sensors and further research on the metabolisms of glucosides and their metabolites.

A Passive Perspiration Inspired Wearable Platform for Continuous Glucose Monitoring.

The demand for glucose monitoring devices has witnessed continuous growth from the rising diabetic population. The traditional approach of blood glucose (BG) sensor strip testing generates only intermittent glucose readings. Interstitial fluid-based devices measure glucose dynamically, but their sensing approaches remain either minimally invasive or prone to skin irritation. Here, a sweat glucose monitoring system is presented, which completely operates under rest with no sweat stimulation and can generate real-time BG dynamics. Osmotically driven hydrogels, capillary action with paper microfluidics, and self-powered enzymatic biochemical sensor are used for simultaneous sweat extraction, transport, and glucose monitoring, respectively. The osmotic forces facilitate greater flux inflow and minimize sweat rate fluctuations compared to natural perspiration-based sampling. The epidermal platform is tested on fingertip and forearm under varying physiological conditions. Personalized calibration models are developed and validated to obtain real-time BG information from sweat. The estimated BG concentration showed a good correlation with measured BG concentration, with all values lying in the A+B region of consensus error grid (MARD = 10.56% (fingertip) and 13.17% (forearm)). Overall, the successful execution of such osmotically driven continuous BG monitoring system from passive sweat can be a useful addition to the next-generation continuous sweat glucose monitors.

Frequency tunable non-invasive microwave blood glucose sensor based on negative impedance circuit

Abstract This article proposes a frequency adjustable non-invasive blood glucose microwave sensor based on negative impedance circuit. Firstly, this article establishes a skin-blood-skin human tissue model for the detection of non-invasive microwave sensors. Compared to traditional passive microwave blood glucose sensors, this design uses a negative impedance compensation circuit (NIC), greatly increasing microwave penetration and improving its detection sensitivity. In addition, this design achieves the goal of adjustable operating frequency by adding an additional dielectric layer at the detection end. This function is of great significance for microwave non-invasive blood glucose sensors. On the one hand, it can adjust to the optimal operating frequency band according to the characteristics of blood glucose changes; On the other hand, it is possible to measure changes in blood glucose concentration at multiple frequency points, thereby increasing the accuracy of detection. The relevant results show that adjusting the frequency band can reach 1–4 GHz, and the specific method of frequency adjustment is provided in the article; Meanwhile, four initial frequency points were selected in the article, at which the results show that the sensitivity reaches 1.356 × 10−3, 3.142 × 10−3, 2.071 × 10−3, 1.903 × 10−3. The above results demonstrate the superiority of our design, and blood glucose sensors based on this principle are expected to break through the limitations of current blood glucose sensors.

Psychometric validation of the hyperglycaemia avoidance scale UK (HAS-UK).

Hyperglycaemia aversion in type 1 diabetes can be associated with severe hypoglycaemia and impaired awareness of hypoglycaemia but is not routinely assessed clinically. This study aimed to undertake the first psychometric validation of the UK version of the Hyperglycaemia Avoidance Scale (HAS-UK). The HAS-UK was completed by adults with type 1 diabetes in three separate research studies. Psychometric properties were evaluated, using exploratory factor analysis, internal consistency, and convergent validity. Of the 431 participants who completed the HAS-UK in the three studies, mean age was 49.5 years, and 58.0% were women. Mean duration of diabetes was 29 years, with 192 (44.5%) using multiple daily injections and 229 (53.1%) using an insulin pump. Five participants were excluded from analyses due to incomplete HAS-UK responses. Exploratory factor analysis revealed a 3-factor solution, with acceptable internal consistency for 'worry' and 'blood glucose decisions' factors. HAS-UK total score was higher in those using insulin pumps versus multiple daily injections, and 'blood glucose decisions' score was higher in those using a continuous blood glucose sensor versus a meter. The HAS-UK is a reliable measure with acceptable structural validity and is likely to be useful for evaluating hyperglycaemia aversion in people with type 1 diabetes. Future research would benefit from investigating further psychometric properties including test-retest reliability, sensitivity to change, and clinical significance of scores.

New Class of Optical Blood Glucose Sensors Based on a PMMA Mach–Zehnder Interferometer

New Class of Optical Blood Glucose Sensors Based on a PMMA Mach–Zehnder Interferometer

Graphene‐Based Enzymatic and Non‐Enzymatic Electrochemical Glucose Sensors: Review of Current Research and Advances in Nanotechnology

AbstractThe determination of the body's glucose level is a difficult job that has served as the foundation for substantial research efforts in biosensing applications. Recent advances in material engineering and synthesis methodologies have had a significant influence on the development of enzymatic and non‐enzymatic sensors for glucose detection that may be used in managing diabetes, food technology, and clinical, and bio‐processing applications. The intricate fabrication, detecting principles and phenomena involved, material alterations, and design to improve sensor performance. There is a great need for high‐efficiency, low‐cost blood glucose sensors that may be used on industrial and domestic scales. This review is primarily intended to shed light on the current state of knowledge about the use of graphene. The performance of non‐enzymatic glucose sensors is comprehensively compared with that of enzymatic electrochemical sensors based on graphene. This study will go over the research efforts that have been conducted to build biosensors employing nanostructures such as carbon nanotubes, graphene, metal nanoparticles, metal oxides, and metal sulfides. The manufacturing of the very sensitive enzyme‐free glucose biosensor will be thoroughly discussed. A thorough examination of the use of graphene as a co‐catalyst to improve the sensitivity of electrochemical sensors for glucose detection is also provided.

Studying insulin needs in Type 1 Diabetes by analysing the OpenAPS Data Commons

Introduction & BackgroundType 1 Diabetes (T1D) is a chronic condition where the body produces too little insulin, a hormone required to regulate blood glucose (BG). Finding the correct insulin dose and time remains a complex and as yet unsolved control task. Many factors such as food, exercise, stress, menstrual cycle, etc. change how much insulin is required. Most of these factors remain unobserved and/or underexplored. The NHS national diabetes audit shows that in 2020-21 only 9.8% of people with T1D had a NICE recommended glycated haemoglobin (HbA1c) test result of 48 mmol/mol or less to avoid complications due to diabetes.
 Objectives & ApproachOur research aims to improve the understanding of underexplored factors that drive changes in insulin needs of people with T1D. We use unsupervised time series pattern detection methods such as time series k-means, heatmaps and matrix profile to identify underexplored patterns. These are times when insulin on board (IOB) does not rise with more carbohydrates on board (COB) and times when BG does not fall with more IOB and/or rise with more COB as expected. In the future, we aim to use causal feature selection methods to exclude COB as a causal driver for these patterns and identify other factors as causal drivers.
 Relevance to Digital FootprintsWe use a digital footprints dataset of n=187 people who use an open-source automated insulin delivery system and have donated their data via the OpenAPS Data Commons and the OpenHumans.org platform. The data has been collected in real-life conditions and contains system logs, BG sensor data and various user-entered annotations. The richness of the data provides a unique opportunity to study what happens in real-life conditions but also poses challenges for many methods. These include inconsistencies between users, irregular sampling between devices and missing data.
 ResultsOur pattern detection methods can successfully identify underexplored patterns in insulin needs that are not directly driven by COB. These include months that require more/less insulin without eating more/less carbohydrates and times of day when blood glucose does not rise with more COB and/or fall with more IOB.
 Conclusions & ImplicationsWhile our current methods can identify underexplored patterns in insulin needs, the principal pattern identified remains the well-known pattern of the main meal COB spikes. This is not surprising given the frequency and impact of this pattern. We are working on methods that can identify both frequent and less frequent patterns as well as patterns arising from the irregularity of the sampling. Digital footprints datasets like the OpenAPS Data Commons are promising to help increase understanding of complex conditions such as T1D. More of this type of multi-year data is needed. Especially data that includes multiple different sensor readings to help identify causal drivers behind underexplored patterns of insulin needs. And we need pattern-finding methods that deal well with missing and irregularly sampled data.

Cyclodextrin Host-Guest Recognition in Glucose-Monitoring Sensors.

Diabetes mellitus is a prevalent chronic health condition that has caused millions of deaths worldwide. Monitoring blood glucose levels is crucial in diabetes management, aiding in clinical decision making and reducing the incidence of hypoglycemic episodes, thereby decreasing morbidity and mortality rates. Despite advancements in glucose monitoring (GM), the development of noninvasive, rapid, accurate, sensitive, selective, and stable systems for continuous monitoring remains a challenge. Addressing these challenges is critical to improving the clinical utility of GM technologies in diabetes management. In this concept, cyclodextrins (CDs) can be instrumental in the development of GM systems due to their high supramolecular recognition capabilities based on the host-guest interaction. The introduction of CDs into GM systems not only impacts the sensitivity, selectivity, and detection limit of the monitoring process but also improves biocompatibility and stability. These findings motivated the current review to provide a comprehensive summary of CD-based blood glucose sensors and their chemistry of glucose detection, efficiency, and accuracy. We categorize CD-based sensors into four groups based on their modification strategies, including CD-modified boronic acid, CD-modified mediators, CD-modified nanoparticles, and CD-modified functionalized polymers. These findings shed light on the potential of CD-based sensors as a promising tool for continuous GM in diabetes mellitus management.

Legal issues of the use of continuous glucose monitoring in children with type 1 diabetes

Continuous glucose monitoring is an effective method of monitoring glycaemia, allowing to achieve optimal glycemic control and reduce the risk of complications of type 1 diabetes mellitus in children. After the approval of the new «Standard of medical care for children with type 1 diabetes (diagnosis and treatment)» (registered with the Ministry of Justice of Russia on 02/18/2021 No. 62543), hundreds of lawsuits were initiated in many regions of the country against regional public authorities in the field of healthcare and medical organizations demanding free supply of systems for continuous monitoring of blood glucose levels and/or sensors to them. This article analyzes the current legislation regarding the validity of the claims for free supply of disabled children with type 1 diabetes with continuous glucose monitoring.

Enhanced Acetone Sensing at Room Temperature using Tailored Co3O4 Nanostructures-Coated Optical Fibers: An Application towards Non-invasive Blood Glucose Sensor

Enhanced Acetone Sensing at Room Temperature using Tailored Co3O4 Nanostructures-Coated Optical Fibers: An Application towards Non-invasive Blood Glucose Sensor

Self-Supported 3D PtPdCu Nanowires Networks for Superior Glucose Electro-Oxidation Performance.

The development of non-enzymatic and highly active electrocatalysts for glucose oxidation with excellent durability for blood glucose sensors has aroused widespread concern. In this work, we report a fast, simple, and low-cost NaBH4 reduction method for preparing ultrafine ternary PtPdCu alloy nanowires (NWs) with a 3D network nanostructure. The PtPdCu NWs catalyst presents significant efficiency for glucose oxidation-reduction (GOR), reaching an oxidative peak-specific activity of 0.69 mA/cm2, 2.6 times that of the Pt/C catalyst (0.27 mA/cm2). Further reaction mechanism investigations show that the NWs have better conductivity and smaller electron transfer resistance. Density functional theory (DFT) calculations reveal that the alloying effect of PtPdCu could effectively enhance the adsorption energy of glucose and reduce the activation energy of GOR. The obtained NWs also show excellent stability over 3600 s through a chronoamperometry test. These self-supported ultrafine PtPdCu NWs with 3D networks provide a new functional material for building blood glucose sensors and direct glucose fuel cells.

Noninvasive blood glucose sensing by secondary speckle pattern artificial intelligence analyses.

Diabetes is a prevalent disease worldwide that can cause severe health problems. Accurate blood glucose detection is crucial for diabetes management, and noninvasive methods can be more convenient and less painful than traditional finger-prick methods. We aim to report a noncontact speckle-based blood glucose measurement system that utilizes artificial intelligence (AI) data processing to improve glucose detection accuracy. The study also explores the influence of an alternating current (AC) induced magnetic field on the sensitivity and selectivity of blood glucose detection. The proposed blood glucose sensor consists of a digital camera, an AC-generated magnetic field source, a laser illuminating the subject's finger, and a computer. A magnetic field is applied to the finger, and a camera records the speckle patterns generated by the laser light reflected from the finger. The acquired video data are preprocessed for machine learning (ML) and deep neural networks (DNNs) to classify blood plasma glucose levels. The standard finger-prick method is used as a reference for blood glucose level classification. The study found that the noncontact speckle-based blood glucose measurement system with AI data processing allows for the detection of blood plasma glucose levels with high accuracy. The ML approach gives better results than the tested DNNs as the proposed data preprocessing is highly selective and efficient. The proposed noncontact blood glucose sensing mechanism utilizing AI data processing and a magnetic field can potentially improve glucose detection accuracy, making it more convenient and less painful for patients. The system also allows for inexpensive blood glucose sensing mechanisms and fast blood glucose screening. The results suggest that noninvasive methods can improve blood glucose detection accuracy, which can have significant implications for diabetes management. Investigations involving representative sampling data, including subjects of different ages, gender, race, and health status, could allow for further improvement.

Predicting and monitoring blood glucose through nutritional factors in type 1 diabetes by artificial neural networks

The monitoring and management of Postprandial Glucose Response (PGR), by administering an insulin bolus before meals, is a crucial issue in Type 1 Diabetes (T1D) patients. Artificial Pancreas (AP), which combines autonomous insulin delivery and blood glucose sensor, is a promising solution; nevertheless, it still requires input from patients about meal carbohydrate intake for bolus administration. This is due to the limited knowledge of the factors that influence PGR. Even though meal carbohydrates are regarded as the major factor influencing PGR, medical experience suggests that other nutritional should be considered. To address this issue, in this work, we propose a Machine Learning (ML)-based approach for a more comprehensive analysis of the impact of nutritional factors (i.e., carbohydrates, protein, lipids, fiber, and energy intake) on the blood glucose levels (BGLs). In particular, the proposed ML-model takes into account BGLs, insulin doses, and nutritional factors in T1D patients to predict BGLs in 60-minute time windows after a meal. A Feed-Forward Neural Network was fed with different combinations of BGLs, insulin, and nutritional factors, providing a predicted glycaemia curve as output. The validity of the proposed system was demonstrated through tests on public data and on self-produced data, adopting intra- and inter-subject approach. Results anticipate that patient-specific data about nutritional factors of a meal have a major role in the prediction of postprandial BGLs.

Low Power RF Interface of the Near-field Communications Tag IC for Sensors

In this paper, we propose the RF interface of NFC tag IC which is necessary for transmitting the results obtained using blood glucose sensor, water quality sensor, gas sensor and radioactive sensor to portable devices such as mobile phones. The proposed RF interface complies with the ISO14443 type-A standard using 100% ASK and solves the power and clock generation difficulties by using the internal VTH canceling rectifier that is based on the high efficiency voltage doubler and the switching phase locked loop. In the measurement results, the internal VTH canceling rectifier in the RF interface shows more than 80% power efficiency from 100% ASK signal, and successfully generates 1.8 V / 2.5 V supply from the input signal power higher than 6 dBm through low loss voltage regulator. Additionally, we can verify that the proposed switching phase locked loop is locked at 8 dBm or higher input signal power and successfully demodulates ASK input signal.

Characterization of an Inter-digitated Sensor for Non-invasive Blood-glucose Sensing

A microwave sensor with an inter-digitated structure is proposed to estimate non-invasive blood glucose sensing. The proposed sensor is designed, fabricated and measured for the required specifications. The transmission electromagnetic response is investigated to predict the blood-glucose level. Initially, the effectiveness of the fabricated sensor is demonstrated with the help of controlled experiments, performed in fetal-bovine serum sample solutions. A novel multi-parameter-based sensitivity factor is determined to estimate the blood glucose level. The proposed work showed that a multi-parameter approach may be effectively utilized to determine the blood-glucose level non-invasively.

Nonenzymatic Glucose Biosensors: Latest Advances and Perspectives

Over the sixty years since glucose biosensor was introduced, development of glucose biosensor had experienced three generations. Considering the increasing demand of glucose biosensor, research and development has been carried out for more promising nonenzymatic glucose biosensor. Compared to the traditional enzyme-based glucose biosensor, nonenzymatic sensor is more capable to continuous glucose detection and has higher stability. Additionally, nonenzymatic glucose biosensor has a lower fabrication cost, which can be beneficial to the wide range of patients with the need for constant checking for blood glucose concentration. This paper introduces two proposed nonenzymatic glucose detection models: activated chemisorption and incipient hydrous oxide adatom mediator. As the developing trend of glucose biosensors is toward wearable and continuous glucose monitoring, two wearable sensors and working principles are introduced. This paper describes the application progress of different types of materials in enzyme-free blood glucose sensors, puts forward the existing problems in the application of enzyme-free blood glucose sensors, and looks forward to its future application prospects.

Signal Acquisition Methods for Vital and Nonvital Parameters in Electronic Health Devices: A Scoping Review

This paper presents the most common vital and nonvital parameters in current wearable electronic devices, describing the respective signal acquisition methods. We included complete studies in indexed sources and accessible online in the consulted databases between the years 2017 to 2022. Nine studies focused on capturing at least one vital sign. Five addressed HR, four FR, two BP, four SpO2, and seven body temperature. As for nonvital parameters, half of the total sample approached gait analysis, while one-third addressed non-invasive blood glucose sensors. The preparation of this material aimed to aid the scientific community by summarizing essential information to foster the development of new technologies capable of improving health decisions.

An Improved Lightweight User Authentication Scheme for the Internet of Medical Things.

The Internet of Medical Things (IoMT) is used in the medical ecosystem through medical IoT sensors, such as blood glucose, heart rate, temperature, and pulse sensors. To maintain a secure sensor network and a stable IoMT environment, it is important to protect the medical IoT sensors themselves and the patient medical data they collect from various security threats. Medical IoT sensors attached to the patient's body must be protected from security threats, such as being controlled by unauthorized persons or transmitting erroneous medical data. In IoMT authentication, it is necessary to be sensitive to the following attack techniques. (1) The offline password guessing attack easily predicts a healthcare administrator's password offline and allows for easy access to the healthcare worker's account. (2) Privileged-insider attacks executed through impersonation are an easy way for an attacker to gain access to a healthcare administrator's environment. Recently, previous research proposed a lightweight and anonymity preserving user authentication scheme for IoT-based healthcare. However, this scheme was vulnerable to offline password guessing, impersonation, and privileged insider attacks. These attacks expose not only the patients' medical data such as blood pressure, pulse, and body temperature but also the patients' registration number, phone number, and guardian. To overcome these weaknesses, in the present study we propose an improved lightweight user authentication scheme for the Internet of Medical Things (IoMT). In our scheme, the hash function and XOR operation are used for operation in low-spec healthcare IoT sensor. The automatic cryptographic protocol tool ProVerif confirmed the security of the proposed scheme. Finally, we show that the proposed scheme is more secure than other protocols and that it has 266.48% better performance than schemes that have been previously described in other studies.

Non-invasive blood glucose sensors based on electromagnetic wave

Non-invasive blood glucose sensors based on electromagnetic wave