Non-invasive Glucose Research Articles

The evolution of continuous glucose monitoring and advancing monitors

The evolution of continuous glucose monitoring and advancing monitors Afon Technology Ltd explains how its continuous glucose monitoring (CGM) device provides a non-invasive and pain-free solution for people with diabetes. A small team in South Wales are working hard to be the first to develop a genuinely non-invasive continuous glucose monitor, which will be a huge game-changer in the world of diabetes and beyond. Diabetes is a global health issue, with 537 million adults living with the disease worldwide, and nearly 80% of those living in low- and middle-income countries. (1) This number is predicted to rise as high as 643 million by 2030, (1) making diabetes arguably one of the world’s biggest health crises.

In situ synthesis copper phosphate-protein hybrid nanoflower on nickel foam for the sensitive detection of glucose in body fluids

We have synthesized copper phosphate-protein hybrid heterostructure nanoflowers (Cu(II)-HNFs) in situ on nickel foam for the first time. The interaction between protein and copper ions facilitated the nucleation growth of copper phosphate crystals and self-assembly of hybrid nanosheets into exquisite flower-like nanostructures. The unique high-specific surface structure of Cu(II)-HNFs provided high-density catalytically active sites for copper phosphate. Also, the direct growth of Cu(II)-HNFs on the NiF surface enhanced the electron transfer efficiency during catalysis. As a result, Cu(II)-HNFs exhibit an excellent electrocatalytic efficacy in glucose sensing. Including wide linear range of 0.1-3000 μM, high sensitivity of 2497.1 μA mM−1 cm−2, ultra-low detection limit of 0.03 μM, good selectivity, and stability. Moreover, Cu(II)-HNFs@NiF has been validated as a reliable tool for detecting glucose in urine samples, with recoveries ranging from 92.9 % to 101.9 %. These findings demonstrate that the developed Cu(II)-HNFs@NiF has immense potential for non-invasive glucose monitoring through body fluids.

Noninvasive Glucose Sensing in Dielectrically Equivalent Multilayer Skin Phantoms.

The interstitial fluid of the skin contains glucose levels comparable to those of blood. Noninvasive glucose sensing by microwaves has great potential to relieve diabetics from the burden of daily blood sampling, but improving the selectivity of this method remains a challenge. This study reports a dielectrically equivalent multilayer skin phantom and provides insight into the criteria for noninvasive glucose sensing by conducting dielectric analysis. The skin phantom was a hydrogel composed of gelatin, glucose, sodium chloride, and water covered by paraffin-impregnated paper. Investigations conducted on a wide range of component concentrations revealed characteristic relative permittivity and dielectric loss determined by the amount of electrolyte and solution that was independent of the amount of glucose. Since the microwave response due to glucose tends to be buried in noise, we developed a flowchart that first identifies the amounts of electrolytes and proteins, which are the major components other than glucose, and then quantifies the remaining glucose content. This noninvasive glucose sensing method would not be limited to the medical healthcare field; it could potentially be used in food manufacturing processes, livestock farming, and plant cultivation management.

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.

On-body non-invasive glucose monitoring sensor based on high figure of merit (FoM) surface plasmonic microwave resonator

High-figure of merit (FoM) plasmonic microwave resonator is researched as a non-invasive on-body sensor to monitor the human body's blood glucose variation rate in adults for biomedical applications, e.g., diabetic patients. The resonance frequencies of the proposed sensor are measured to be around {f}_{01}=3.25 GHz and {f}_{01}=4.67 GHz over the frequency band of DC to 6GHz which are suitable for monitoring interstitial fluid (ISF) changing rate. The 40times 40 {mathrm{mm}}^{2} sensor is experimentally wrapped on the human body arm to monitor the blood glucose changing rate via amplitude and frequency variations of the sensor. Amplitude variation and frequency shift are measured to be around 7 dB and 30 MHz, respectively. The measured results demonstrate the high precision of the proposed approach to depict a valid diagram for glucose changing rate due to good impedance matching of the designed microwave sensor and human body. The sensor is shown to enhance the sensitivity by a factor of 5 compared to the conventional ones.

Uniformly dispersing and anchoring graphene on GaN nanowire substrate: Application to electrochemical detection of glucose in sweat

For deposition of two-dimensional materials (e.g., graphene) on a substrate, self-aggregation and poor anchor strength are still issues. Herein, the GaN nanowire (NW) substrate was employed for electrochemical deposition of reduced graphene oxide (rGO) with satisfying dispersion uniformity and anchor strength. The deposited rGO exhibited flake morphology without agglomeration. Moreover, PtAu and rGO can be simultaneously and uniformly deposited on the GaN NW substrate to realize a PtAu–rGO/GaN electrochemical sensor for glucose detection. In comparison with deposition of PtAu–rGO on a stainless steel (SS) substrate (i.e., PtAu–rGO/SS), PtAu–rGO/GaN demonstrated much higher sensitivity and long-term stability, owing to better dispersion and anchor strength on GaN NW. In addition, with decoration of glucose oxidase (GOx), the GOx/PtAu–rGO/GaN sensor can be used for detecting glucose in human sweat with a low limit of detection of 5 μM, a wide linear detection range of 5 μM–12 mM, and high long-term stability, which indicates that GOx/PtAu–rGO/GaN sensor is promising for noninvasive glucose detection.

The effect of temperature on permittivity measurements of aqueous solutions of glucose for the development of non-invasive glucose sensors based on electromagnetic waves

This article presents for the first time an empirical study that shows the importance of considering temperature when analyzing the permittivity (dielectric constant) of aqueous glucose solutions of various concentrations. The permittivity is a parameter that is investigated by researchers as a biomarker for non-invasive measurement of glucose without drawing blood. The development of this technology will allow personalized healthcare diagnostics to monitor and prevent diabetes. Since human glucose levels in the blood vary in the range of a few milligrams per decilitre, estimating such small variations of glucose will require a highly accurate and repeatable sensing technology. Electromagnetic (EM) waves, specifically in the microwave and terahertz frequency ranges, have shown promise in detecting changes in the electrical properties of blood plasma as they relate to glucose concentration. However, it's important to note that while this technology shows promise, it is still in the research and development phase. It is shown here that the body temperature can affect the accuracy of the blood glucose measurements. Experiments were conducted with different glucose concentration solutions under various temperatures and the complex permittivity of the glucose was studied across a wide frequency range from 400 MHz to 11 GHz. The rise in thermal energy normally causes dipolar liquids like water to vibrate and rotate disrupting the alignment of the dipoles in response to an electric field thereby reducing its permittivity. Empirical results however show that for aqueous solution of glucose the permittivity increases with rise in temperature from 16 °C to 37 °C. This is attributed to the polar nature of the water and glucose molecules that becomes more pronounced with increased thermal energy. Based on the experimental results an accurate analytical expression is derived that considers the temperature of the aqueous glucose solution. The accuracy of the analytical expression is shown experimentally to be above 99%. The findings from the study should enable the design of accurate non-invasive glucose monitoring devices based on electromagnetic sensing techniques.

Beware of buying online noninvasive continuous glucose monitoring devices: danger is just around the corner!

Beware of buying online noninvasive continuous glucose monitoring devices: danger is just around the corner!

Telemedicine and AI in Remote Patient Monitoring

This article explores how telemedicine, especially with the help of artificial intelligence (AI), is transforming healthcare. It covers its applications in monitoring patients, managing chronic diseases like diabetes, and improving cardiovascular care. The importance of wearable devices and non-invasive blood glucose monitoring is highlighted. The article emphasizes how AI-driven remote patient monitoring can enhance healthcare by providing early intervention, reducing hospitalizations, and offering personalized care

Overview of modern sensors for continuous glucose monitoring

The incidence of diabetes is increasing in all age groups. The use of technological devices for the treatment of diabetes, such as continuous glucose monitoring (CGM), is expanding and is associated with improved control of blood glucose levels in order to prevent complications of this disease. Reducing glycemic variability and maintaining optimal glycemic control is critical to the management of patients with type 1 diabetes. The usefulness of glycemic monitoring devices has also been shown for patients with type 2 diabetes. CGM technology is constantly being improved in terms of analytical performance, biocompatibility, wear duration, safety and clinical performance. However, commonly used minimally invasive CGMs do not measure blood glucose directly, but instead measure the glucose concentration in the interstitial fluid (IF), so changes in IF glucose occur with a delay of 5 to 15 minutes compared to blood glucose. In addition, the lifetime of minimally invasive CGM sensors is relatively short, up to 14 days. Therefore, the introduction into clinical practice of devices for non-invasive glucose measurement in people with diabetes, which overcome the above-mentioned limitations of minimally invasive CGM, will expand the possibilities of glucose monitoring among patients with diabetes. The purpose of this review was to present the technologies of CGM system sensors approved for medical use in Russia and other countries.

Generative adversarial networks‐based super‐resolution algorithm enables high signal‐to‐noise ratio spatial heterodyne Raman spectra

AbstractHigh‐resolution interference pattern images are vital for spatial heterodyne Raman spectroscopy to produce quality Raman spectra with a good signal‐to‐noise ratio. A sought‐after super‐resolution algorithm can enhance Raman interference pattern images, reducing reliance on expensive imaging sensors. This enables miniaturization and portability for point‐of‐care testing. This study proposes a generative adversarial network (GAN)‐based super‐resolution reconstruction algorithm explicitly designed for Raman interference patterns. Employing GAN adversarial training, the algorithm effectively reconstructs interference pattern images at higher resolution, improving Raman spectra signal‐to‐noise ratio. Considering Raman spectra analysis mainly focuses on characteristic peaks, a Raman characteristic peak‐focused network training scheme is used. For instance, acetaminophen is studied with two selected Raman characteristic peaks centered at 388 and 858 cm−1. These peaks are observed in Fourier transformed Raman spectra from corresponding low‐resolution interference pattern images obtained by down‐sampling high‐resolution ones by twofold and fourfold. The proposed GAN‐based algorithm successfully reconstructs low‐resolution interference patterns into high‐resolution ones, achieving high R‐square values (96.05% for twofold and 91.1% for fourfold). This innovation holds potential for point‐of‐care applications, like noninvasive blood glucose concentration measurements, enabling cost‐effective, portable Raman spectrometers with improved capabilities.

Personalized diabetes monitoring platform leveraging IoMT and AI for non-invasive estimation

Non-invasive blood glucose estimation is an extensively researched area since current gold-standard invasive glucose monitoring methods present numerous inconveniences and challenges in terms of comfort and cost. We present the design, development, and validation of an Internet of Medical Things (IoMT) based wearable device for non-invasive and real-time measurement of blood glucose. This paper presents a diabetic health monitoring platform architecture that consists of (a) a user-worn photoplethysmography (PPG) device, (b) a smart analytics cloud that deploys models for blood glucose estimation, and (c) an end-to-end mobile/web application for monitoring diabetes patients. Blood glucose computation is achieved using a novel light-weight 1-dimensional input-reinforced deep neural network architecture, which we call as GlucoNet. This captures both long and short, temporal and spatial features from the PPG signal. The training and validation of the model were conducted on a dataset of 283 participants which demonstrated a mean absolute percentage error (MAPE) of 17.8% (± 12.8%) wherein 100% of predictions fall in the clinically acceptable zones A and B of the Clarke-error grid. The lightweight model is also deployed on edge devices for real-time and offline blood glucose measurement. We report a clinical outcome deployment study and insights from 20,000＋ glucose measurements obtained from another 600 patients. To our knowledge, this is the largest reported work employing a non-calibrated, non-invasive, demography, and time-of-food agnostic IoMT glucose monitoring system that does not require any feature engineering and is capable of running on edge devices.

Non-invasive glucose measurement using 950 nm reflective short wave NIR technique

Non-invasive glucose measurement using 950 nm reflective short wave NIR technique

OPTICAL BASED NON INVASIVE GLUCOMETER WITH IOT

Diabetes mellitus (DM) nicknamed as sugar is becoming one of incurable and critical challenge to medical field. Serious illness such as premature mortality even cause of death, which can be identified by continuous monitoring from a remote location. The traditional and conventional method such as finger pricking's demerits such as pain and damage to tissues that cause infection. Such type of infections can be overcome by non-invasive technique. The objective of this paper is to monitor glucose level using non-invasive technique by optical and IoT technology. The proposed sensor circuit consists of IR LED's of wavelength 650–2500nm for optical blood glucose measurement and NIR photodiodes (InGaAs) to receive the reflected light from body parts to determine the glucose level. The Beer-Lambert law is used for signal processing along with WIFI based IoT real time information transmission. The project is implemented using Arduino IDE for finding the performance matrix of the system as well as various analytical studies. With the rapid development of wearable technology and transdermal biosensors, non-invasive blood glucose monitoring will become more efficient, affordable, robust, and more competitive on the market. Key Words: Diabetes mellitus, blood glucose measurement, WIFI based IoT, wearable technology

Development of mobile biosensor reader for wearable sweat glucose biosensor application

Sweat contains numerous biomarkers, including glucose, which can provide valuable insights into an individual’s metabolic state. Noninvasive glucose monitoring eliminates the need for frequent finger pricks or blood samples, offering greater convenience and reducing discomfort for individuals with diabetes. These biosensors are designed to detect and quantify glucose levels through various sensing mechanisms, such as enzymatic reactions or electrochemical measurements. The integration of biosensors into wearable devices, such as smartwatches, patches, or flexible electronics, allows for mobile glucose monitoring in sweat. Accurate glucose measurements require calibration and validation against reference measurements, such as blood glucose levels. Research focuses on developing calibration algorithms and improving the accuracy and reliability of mobile glucose biosensors in sweat.

Random Forest Analysis of Combined Millimeter-Wave and Near-Infrared Sensing for Noninvasive Glucose Detection

Random Forest Analysis of Combined Millimeter-Wave and Near-Infrared Sensing for Noninvasive Glucose Detection

Non-invasive glucose sensing via the fingernail bed using THz radiation

Abstract The possibility of establishing a novel technique for reliably accessing glycemic information in a non-invasive, easy to implement method at THz frequencies via the fingernail bed is investigated. The nail bed’s major content is blood at its various glucose levels and is also partially protected from environmental conditions by the nail plate, making it a desirable platform for non-invasive glucose sensing. The study is based on a 2D computational electromagnetics (EM) model of the layered fingernail in COMSOL Multiphysics, where the required dielectric function (i.e. permittivity and conductivity) of the fingernail plate is measured using THz-Time-Domain- Spectroscopy (THz-TDS), and the glucose-dependend dielectric functions of the fingernail bed are taken from available experimental data in the literature. From this data a material model using a multipole Cole-Cole model is established for both, the nail plate and the nail bed, where the glucose content in the latter is varied from 3.0-19.0 mmol/l. A numerical analysis of the THz reflectometry at the fingernail in the frequency range of 0.1-2.0 THz revealed that the reflectance spectra are sensitive to the glucose content in the nail bed proving that accurate glucose sensing via the fingernail bed may become feasible around 0.2-0.4 THz.

Surface-Enhanced Raman Spectroscopy Combined with External Electric Fields for Non-Invasive Glucose Sensing

Blood glucose detection has been a critical issue in the medical field all over the world for a long time. Especially for diabetic patients, regular monitoring of blood glucose is necessary in their daily life. Nowadays, blood glucose detection mainly relies on fingertip blood glucose meters, which can provide the current blood glucose level in a short period of time. Meanwhile, there are several inconveniences and concerns, including the pain and discomfort of needle-prick blood collection, and the risk of infection at the needle-prick wound. Therefore, many researchers have devoted themselves to the development of non-invasive glucose detection in recent years. Till now, the practical glucose measurement in vivo is still faced with lots of difficulties. To get the signal or response of the molecules in vivo without penetration through the skin layers required two main properties. First of all, the biologic blockage and noise need to be avoided or overcame during the detection. Secondly, it’s necessary to enhance or amplify the signal due to relative low concentration of the target molecule under the skin. Here we introduce a surface-enhanced Raman spectroscopy (SERS) combined with external electric fields to achieve the goal of the detection in vivo without any invasive procedure. In the SERS system, we select 785 nm laser to reach deeper area through the skin layers while preventing the heat effect for tissue damaging. Next, with the signal differences from encountered with various concentration of glucose, 4-mercaptophenylboronic acid (4-MPBA) on silver nanoparticles (AgNPs) is able to quantify the glucose concentrations from the characteristic peak of the Raman shift. Furthermore, the combination with external electric fields induces coagulation of the sensing nanoparticles and provides many hotspots for SERS detection in vivo. We believed that this work is a great stepping stone of real time non-invasive glucose sensing in practical application.

(Invited) wearable Hydrogel Patch with Noninvasive, Electrochemical Glucose Sensor for Natural Sweat Detection

Recent advances in microelectronics and electrochemical sensing platforms have preceded the development of devices for personal monitoring and managing physiological and metabolic information that exploit sweat as a noninvasive, convenient approach for providing information about underlying health conditions, such as glucose level monitoring. Although most sweat glucose sensors have targeted applications during exercise and other active stimulation induced-sweat, natural sweating offers an attractive alternative with minimal effect on users that can be accessed during routine and sedentary activities without impeding personal lifestyle and preserves the correlation between blood and sweat glucose. Here, we present a noninvasive sweat glucose sensor with convenient hydrogel patches for rapid sampling of natural perspiration without external activities that stimulate sweating. The wearable hydrogel patch rapidly takes up natural sweat from the hand and serves as a medium for electrochemical sensing. A prussian blue-doped poly(3,4-ethylenedioxythiophene nanocomposite (PB-PEDOT NC) electrode provides cost-effective, stable and excellent electrocatalytic activity in sweat glucose measurements. We demonstrated sweat glucose sensor functionality by long-term measurements of glucose in sweat from human subjects consuming food and drinks. By enabling the analysis of sweat glucose during routine and sedentary activities, the sweat glucose sensor shows great promise for clinical-grade glucose management and enlarges the scope of next-generation noninvasive sensing systems.