The Progress of Glucose Monitoring-A Review of Invasive to Minimally and Non-Invasive Techniques, Devices and Sensors

Malaria: biochemical, physiological, diagnostic, and therapeutic updates

Background

Malaria has been appraised as a significant vector-borne parasitic disease with grave morbidity and high-rate mortality. Several challenges have been confronting the efficient diagnosis and treatment of malaria.

Method

Google Scholar, PubMed, Web of Science, and the Egyptian Knowledge Bank (EKB) were all used to gather articles.

Results

Diverse biochemical and physiological indices can mirror complicated malaria e.g., hypoglycemia, dyslipidemia, elevated renal and hepatic functions in addition to the lower antioxidant capacity that does not only destroy the parasite but also induces endothelial damage. Multiple trials have been conducted to improve recent points of care in malaria involving biosensors, lap on-chip, and microdevices technology. Regarding recent therapeutic trials, chemical falcipain inhibitors and plant extracts with anti-plasmodial activities are presented. Moreover, antimalaria nano-medicine and the emergence of nanocarrier (either active or passive) in drug transportation are promising. The combination therapeutic trials e.g., amodiaquine + artemether + lumefantrine are presented to safely counterbalance the emerging drug resistance in addition to the Tafenoquine as a new anti-relapse therapy.

Conclusion

Recognizing the pathophysiology indices potentiate diagnosis of malaria. The new points of care can smartly manipulate the biochemical and hematological alterations for a more sensitive and specific diagnosis of malaria. Nano-medicine appeared promising. Chemical and plant extracts remain points of research.

Technological Advances of Wearable Device for Continuous Monitoring of In Vivo Glucose

Managing diabetes is a chronic challenge today, requiring monitoring and timely insulin injections to maintain stable blood glucose levels. Traditional clinical testing relies on fingertip or venous blood collection, which has facilitated the emergence of continuous glucose monitoring (CGM) technology to address data limitations. Continuous glucose monitoring technology is recognized for tracking long-term blood glucose fluctuations, and its development, particularly in wearable devices, has given rise to compact and portable continuous glucose monitoring devices, which facilitates the measurement of blood glucose and adjustment of medication. This review introduces the development of wearable CGM-based technologies, including noninvasive methods using body fluids and invasive methods using implantable electrodes. The advantages and disadvantages of these approaches are discussed as well as the use of microneedle arrays in minimally invasive CGM. Microneedle arrays allow for painless transdermal puncture and are expected to facilitate the development of wearable CGM devices. Finally, we discuss the challenges and opportunities and look forward to the biomedical applications and future directions of wearable CGM-based technologies in biological research.

Artificial intelligence powered glucose monitoring and controlling system: Pumping module

BACKGROUND

Diabetes, a globally escalating health concern, necessitates innovative solutions for efficient detection and management. Blood glucose control is an essential aspect of managing diabetes and finding the most effective ways to control it. The latest findings suggest that a basal insulin administration rate and a single, high-concentration injection before a meal may not be sufficient to maintain healthy blood glucose levels. While the basal insulin rate treatment can stabilize blood glucose levels over the long term, it may not be enough to bring the levels below the post-meal limit after 60 min. The short-term impacts of meals can be greatly reduced by high-concentration injections, which can help stabilize blood glucose levels. Unfortunately, they cannot provide long-term stability to satisfy the post-meal or pre-meal restrictions. However, proportional-integral-derivative (PID) control with basal dose maintains the blood glucose levels within the range for a longer period.

AIM

To develop a closed-loop electronic system to pump required insulin into the patient's body automatically in synchronization with glucose sensor readings.

METHODS

The proposed system integrates a glucose sensor, decision unit, and pumping module to specifically address the pumping of insulin and enhance system effectiveness. Serving as the intelligence hub, the decision unit analyzes data from the glucose sensor to determine the optimal insulin dosage, guided by a pre-existing glucose and insulin level table. The artificial intelligence detection block processes this information, providing decision instructions to the pumping module. Equipped with communication antennas, the glucose sensor and micropump operate in a feedback loop, creating a closed-loop system that eliminates the need for manual intervention.

RESULTS

The incorporation of a PID controller to assess and regulate blood glucose and insulin levels in individuals with diabetes introduces a sophisticated and dynamic element to diabetes management. The simulation not only allows visualization of how the body responds to different inputs but also offers a valuable tool for predicting and testing the effects of various interventions over time. The PID controller's role in adjusting insulin dosage based on the discrepancy between desired setpoints and actual measurements showcases a proactive strategy for maintaining blood glucose levels within a healthy range. This dynamic feedback loop not only delays the onset of steady-state conditions but also effectively counteracts post-meal spikes in blood glucose.

CONCLUSION

The WiFi-controlled voltage controller and the PID controller simulation collectively underscore the ongoing efforts to enhance efficiency, safety, and personalized care within the realm of diabetes management. These technological advancements not only contribute to the optimization of insulin delivery systems but also have the potential to reshape our understanding of glucose and insulin dynamics, fostering a new era of precision medicine in the treatment of diabetes.

What makes a medical intervention invasive?

The classification of medical interventions as either invasive or non-invasive is commonly regarded to be morally important. On the most commonly endorsed account of invasiveness, a medical intervention is invasive if and only if it involves either breaking the skin ('incision') or inserting an object into the body ('insertion'). Building on recent discussions of the concept of invasiveness, we show that this standard account fails to capture three aspects of existing usage of the concept of invasiveness in relation to medical interventions-namely, (1) usage implying that invasiveness comes in degrees, (2) that the invasiveness of an intervention can depend on the characteristics of the salient alternative interventions, and (3) that medical interventions can be invasive in non-physical ways. We then offer the beginnings of a revised account that, we argue, is able to capture a wider range of existing usage. Central to our account is a distinction between two properties: basic invasiveness and threshold invasiveness We end by assessing what the standard account gets right, and what more needs to be done to complete our schematic account.

Blood glucose monitoring devices for type 1 diabetes: a journey from the food and drug administration approval to market availability

Blood glucose monitoring constitutes a pivotal element in the clinical management of Type 1 diabetes (T1D), a globally escalating metabolic disorder. Continuous glucose monitoring (CGM) devices have demonstrated efficacy in optimizing glycemic control, mitigating adverse health outcomes, and augmenting the overall quality of life for individuals afflicted with T1D. Recent progress in the field encompasses the refinement of electrochemical sensors, which enhances the effectiveness of blood glucose monitoring. This progress empowers patients to assume greater control over their health, alleviating the burdens associated with their condition, and contributing to the overall alleviation of the healthcare system. The introduction of novel medical devices, whether derived from existing prototypes or originating as innovative creations, necessitates adherence to a rigorous approval process regulated by the Food and Drug Administration (FDA). Diverse device classifications, stratified by their associated risks, dictate distinct approval pathways, each characterized by varying timelines. This review underscores recent advancements in blood glucose monitoring devices primarily based on electrochemical sensors and elucidates their regulatory journey towards FDA approval. The advent of innovative, non-invasive blood glucose monitoring devices holds promise for maintaining stringent glycemic control, thereby preventing T1D-associated comorbidities, and extending the life expectancy of affected individuals.

Bimetallic FeOx-TiO2@Carbon hybrid structure materials with notable peroxidase enzyme mimics applied to one-step colorimetric detection of glucose

FeOx-TiO2@Carbon hybrid structure materials (FeOx-TiO2@CHs) with high peroxidase (POD)-like activity have been prepared by one-pot hydrothermal method. Based on the excellent POD activity of FeOx-TiO2@CHs, one pot colorimetric detection for glucose was constructed by using TMB as substrate with the synergistic reaction of glucose oxidase; the linear range and the limit of detection (LOD) are 25 ~ 1000 and 1.77 µM, respectively. Using this method, the glucose in serum real samples was detected with satisfactory results, and the results are consistent with that of the glucometer method in the hospital. The recovery in diabetic and artificial urine samples was 95.71 ~ 104.67% and 99.01 ~ 103.16%, respectively. The mechanism of the catalytic colorimetric reaction was also investigated by multiple measurements, and the results indicated that superoxide anions (O2•-) between FeOx-TiO2@CHs and substrate play a main role, but a small quantity of hydroxyl radical •OH and singlet oxygen 1O2 is also generated simultaneously. The one-pot reaction method is simple and fast; the detection process only requires a simple mixing, which is suitable for application in special environment.

Reagentless Glucose Biosensor Based on Combination of Platinum Nanostructures and Polypyrrole Layer

Two types of low-cost reagentless electrochemical glucose biosensors based on graphite rod (GR) electrodes were developed. The electrodes modified with electrochemically synthesized platinum nanostructures (PtNS), 1,10-phenanthroline-5,6-dione (PD), glucose oxidase (GOx) without and with a polypyrrole (Ppy) layer-(i) GR/PtNS/PD/GOx and (ii) GR/PtNS/PD/GOx/Ppy, respectively, were prepared and tested. Glucose biosensors based on GR/PtNS/PD/GOx and GR/PtNS/PD/GOx/Ppy electrodes were characterized by the sensitivity of 10.1 and 5.31 μA/(mM cm2), linear range (LR) up to 16.5 and 39.0 mM, limit of detection (LOD) of 0.198 and 0.561 mM, good reproducibility, and storage stability. The developed glucose biosensors based on GR/PtNS/PD/GOx/Ppy electrodes showed exceptional resistance to interfering compounds and proved to be highly efficient for the determination of glucose levels in blood serum.

Noninvasive Glucose Measurements Through Transcutaneous Raman Spectroscopy: A Review

BACKGROUND

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

OBJECTIVE

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

RESULTS

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

CONCLUSIONS

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

Detection of Hypoglycemia and Hyperglycemia Using Noninvasive Wearable Sensors: Electrocardiograms and Accelerometry

BACKGROUND

Monitoring glucose excursions is important in diabetes management. This can be achieved using continuous glucose monitors (CGMs). However, CGMs are expensive and invasive. Thus, alternative low-cost noninvasive wearable sensors capable of predicting glycemic excursions could be a game changer to manage diabetes.

METHODS

In this article, we explore two noninvasive sensor modalities, electrocardiograms (ECGs) and accelerometers, collected on five healthy participants over two weeks, to predict both hypoglycemic and hyperglycemic excursions. We extract 29 features encompassing heart rate variability features from the ECG, and time- and frequency-domain features from the accelerometer. We evaluated two machine learning approaches to predict glycemic excursions: a classification model and a regression model.

RESULTS

The best model for both hypoglycemia and hyperglycemia detection was the regression model based on ECG and accelerometer data, yielding 76% sensitivity and specificity for hypoglycemia and 79% sensitivity and specificity for hyperglycemia. This had an improvement of 5% in sensitivity and specificity for both hypoglycemia and hyperglycemia when compared with using ECG data alone.

CONCLUSIONS

Electrocardiogram is a promising alternative not only to detect hypoglycemia but also to predict hyperglycemia. Supplementing ECG data with contextual information from accelerometer data can improve glucose prediction.

Biologics, theranostics, and personalized medicine in drug delivery systems

The progress in human disease treatment can be greatly advanced through the implementation of nanomedicine. This approach involves targeted and cell-specific therapy, controlled drug release, personalized dosage forms, wearable drug delivery, and companion diagnostics. By integrating cutting-edge technologies with drug delivery systems, greater precision can be achieved at the tissue and cellular levels through the use of stimuli-responsive nanoparticles, and the development of electrochemical sensor systems. This precision targeting - by virtue of nanotechnology - allows for therapy to be directed specifically to affected tissues while greatly reducing side effects on healthy tissues. As such, nanomedicine has the potential to transform the treatment of conditions such as cancer, genetic diseases, and chronic illnesses by facilitating precise and cell-specific drug delivery. Additionally, personalized dosage forms and wearable devices offer the ability to tailor treatment to the unique needs of each patient, thereby increasing therapeutic effectiveness and compliance. Companion diagnostics further enable efficient monitoring of treatment response, enabling customized adjustments to the treatment plan. The question of whether all the potential therapeutic approaches outlined here are viable alternatives to current treatments is also discussed. In general, the application of nanotechnology in the field of biomedicine may provide a strong alternative to existing treatments for several reasons. In this review, we aim to present evidence that, although in early stages, fully merging advanced technology with innovative drug delivery shows promise for successful implementation across various disease areas, including cancer and genetic or chronic diseases.

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

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

User requirements for non-invasive and minimally invasive glucose self-monitoring devices in low-income and middle-income countries: a qualitative study in Kyrgyzstan, Mali, Peru and Tanzania

AIMS

Development of non-invasive and minimally invasive glucose monitoring devices (NI-MI-GMDs) generally takes place in high-income countries (HICs), with HIC's attributes guiding product characteristics. However, people living with diabetes (PLWD) in low-income and middle-income countries (LMICs) encounter different challenges to those in HICs. This study aimed to define requirements for NI-MI-GMDs in LMICs to inform a target product profile to guide development and selection of suitable devices.

METHODS

This was a multiple-methods, exploratory, qualitative study conducted in Kyrgyzstan, Mali, Peru and Tanzania. Interviews and group discussions/activities were conducted with healthcare workers (HCWs), adults living with type 1 (PLWD1) or type 2 diabetes (PLWD2), adolescents living with diabetes and caregivers.

RESULTS

Among 383 informants (90 HCW, 100 PLWD1, 92 PLWD2, 24 adolescents, 77 caregivers), a range of differing user requirements were reported, including preferences for area of glucose measurement, device attachment, data display, alert type and temperature sensitivity. Willingness to pay varied across countries; common requirements included ease of use, a range of guiding functions, the possibility to attach to a body part of choice and a cost lower than or equal to current glucose self-monitoring.

CONCLUSIONS

Ease-of-use and affordability were consistently prioritised, with broad functionality required for alarms, measurements and attachment possibilities. Perspectives of PLWD are crucial in developing a target product profile to inform characteristics of NI-MI-GMDs in LMICs. Stakeholders must consider these requirements to guide development and selection of NI-MI-GMDs at country level, so that devices are fit for purpose and encourage frequent glucose monitoring among PLWD in these settings.

3D Carbon-Based Conductive Network Printed for Glucose Sensors on Curved and Flexible Substrates

The rising prevalence of diabetes has led to an increased focus on real-time glucose monitoring. Wearable glucose sensor patches allow noninvasive, real-time monitoring, reducing patient discomfort compared to invasive sensors. However, most existing glucose sensor patches rely on complex and contaminating metal vapor deposition technologies, which pose limitations in practical production. In this study, we propose a novel approach for preparing graphite/multiwall carbon nanotubes (MWCNT)/reduced graphene oxide (rGO) using a high-viscosity ink, which can be easily obtained through simple mechanical stirring. To create intricate patterns and enable printing on curved substrates, we employed a 3D printer equipped with an infrared laser ranging system. The ink served as a working electrode, and we developed a three-electrode system patch with a concentric circle structure. Subsequently, the working electrode underwent enzymatic modification with glucose dehydrogenase with flavin adenine dinucleotide (GDH-FAD) using a polymer embedding method. The resulting wearable glucose sensor exhibited a sensitivity of 2.42 μA mM-1 and a linear detection range of 1-12 mM. In addition, the glucose sensor has excellent anti-interference capability and demonstrates good repeatability in simulated real human wear scenarios, which meets the requirements for accurate human detection. These findings provide valuable insights into the development of human health monitoring technologies.

Green tea consumption and incidence of cardiovascular disease in type 2 diabetic patients with overweight/obesity: a community-based cohort study

BACKGROUND

Green tea has been reported to be potentially protective against the development of cardiovascular disease (CVD). This study aimed to investigate the association between green tea consumption and incident CVD in type 2 diabetes (T2D) patients with overweight/obesity.

METHODS

A total of 4756 Chinese overweight/obese T2D patients were recruited and followed up for 6.27 years. Information on green tea consumption was collected at baseline using interviewer-administered questionnaires. Hazard ratios (HRs) and 95% confidence intervals (CIs) for incident CVD according to green tea consumption were estimated using the Cox proportional hazards model.

RESULTS

Compared with non-habitual consumers, participants who consumed > 5 g/day of green tea leaves reduced the risk of CVD by 29% (95%CI: 0.55-0.92), stroke by 30% (95%CI: 0.51-0.95) and coronary heart disease (CHD) by 40% (95%CI: 0.40-0.89). Similarly, participants who consumed green tea for ≥ 40 years reduced the risk of CVD by 31% (95%CI: 0.54-0.88), stroke by 33% (95%CI: 0.50-0.90) and CHD by 39% (95%CI: 0.42-0.88). Among participants with < 5-year history of T2D, > 5 g/day of tea leaves and > 40 years of tea consumption were associated with 59% (95%CI: 0.23-0.72) and 57% (95%CI: 0.26-0.74) reduced risk of stroke, respectively. However, among participants with ≥ 5-year history of T2D, > 5 g/day of tea leaves and > 40 years of tea consumption were associated with a 50% (95%CI: 0.30-0.82) and 46% (95%CI: 0.35-0.85) reduced risk of CHD, respectively.

CONCLUSIONS

Green tea consumption is associated with reduced risk of CVD, stroke, and CHD in overweight/obese T2D patients.

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.

Screen-printed electrode designed with MXene/doped-polyindole and MWCNT/doped-polyindole for chronoamperometric enzymatic glucose sensor

The enzymatic glucose sensors as modified by MXene-dPIn and MWCNT-dPIn on a screen-printed carbon electrode (SPCE) were investigated. Herein, MXene was molybdenum carbide (Mo3C2) which has never been utilized and reported for glucose sensors. The biopolymer type to support the enzyme immobilization was examined and compared between chitosan (CHI) and κ-carrageenan (κC). MWCNT-dPIn obviously showed a larger electroactive surface area, lower charge transfer resistance and higher redox current than Mo3C2-dPIn, indicating that MWCNT-dPIn is superior to Mo3C2-dPIn. For the chitosan-based sensors, the sensitivity value of CHI-GOD/Mo3C2-dPIn is 3.53 μA mM-1 cm-2 in the linear range of 2.5-10 mM with the calculated LOD of 1.57 mM. The sensitivity value of CHI-GOD/MWCNT-dPIn is 18.85 μA mM-1 cm-2 in the linear range of 0.5-25 mM with the calculated LOD of 0.115 mM. For the κ-carrageenan based sensors, κC-GOD/MWCNT-dPIn exhibits the sensitivity of 15.80 μA mM-1 cm-2 and the widest linear range from 0.1 to 50 mM with the calculated LOD of 0.03 mM. The presently fabricated sensors exhibit excellent reproducibility, good selectivity, high stability, and disposal use. The fabricated glucose sensors are potential as practical glucose sensors as the detectable glucose ranges well cover the glucose levels found in blood, urine, and sweat for both healthy people and diabetic patients.

Portable glucose sensing analysis based on laser-induced graphene composite electrode

In this work, a portable electrochemical glucose sensor was studied based on a laser-induced graphene (LIG) composite electrode. A flexible graphene electrode was prepared using LIG technology. Poly(3,4-ethylene dioxythiophene) (PEDOT) and gold nanoparticles (Au NPs) were deposited on the electrode surface by potentiostatic deposition to obtain a composite electrode with good conductivity and stability. Glucose oxidase (GOx) was then immobilized using glutaraldehyde (GA) to create an LIG/PEDOT/Au/GOx micro-sensing interface. The concentration of glucose solution is directly related to the current value by chronoamperometry. Results show that the sensor based on the LIG/PEDOT/Au/GOx flexible electrode can detect glucose solutions within a concentration range of 0.5 × 10-5 to 2.5 × 10-3 mol L-1. The modified LIG electrode provides the resulting glucose sensor with an excellent sensitivity of 341.67 μA mM-1 cm-2 and an ultra-low limit of detection (S/N = 3) of 0.2 × 10-5 mol L-1. The prepared sensor exhibits high sensitivity, stability, and selectivity, making it suitable for analyzing biological fluid samples. The composite electrode is user-friendly, and can be built into a portable biosensor device through smartphone detection. Thus, the developed sensor has the potential to be applied in point-of-care platforms such as environmental monitoring, public health, and food safety.

Diagnosis of diabetes mellitus using high frequency ultrasound and convolutional neural network

The incidence of diabetes mellitus has been increasing, prompting the search for non-invasive diagnostic methods. Although current methods exist, these have certain limitations, such as low reliability and accuracy, difficulty in individual patient adjustment, and discomfort during use. This paper presents a novel approach for diagnosing diabetes using high-frequency ultrasound (HFU) and a convolutional neural network (CNN). This method is based on the observation that glucose in red blood cells (RBCs) forms glycated hemoglobin (HbA1c) and accumulates on its surface. The study incubated RBCs with different glucose concentrations, collected acoustic reflection signals from them using a custom-designed 90-MHz transducer, and analyzed the signals using a CNN. The CNN was applied to the frequency spectra and spectrograms of the signal to identify correlations between changes in RBC properties owing to glucose concentration and signal features. The results confirmed the efficacy of the CNN-based approach with a classification accuracy of 0.98. This non-invasive diagnostic technology using HFU and CNN holds promise for in vivo diagnosis without the need for blood collection.

Non-contact monitoring of glucose concentration and pH by integration of wearable and implantable hydrogel sensors with optical coherence tomography

Optical coherence tomography (OCT) is a noninvasive imaging technique with large penetration depth into the tissue, but limited chemical specificity. By incorporating functional co-monomers, hydrogels can be designed to respond to specific molecules and undergo reversible volume changes. In this study, we present implantable and wearable biocompatible hydrogel sensors combined with OCT to monitor their thickness change as a tool for continuous and real-time monitoring of glucose concentration and pH. The results demonstrate the potential of combining hydrogel biosensors with OCT for non-contact continuous in-vivo monitoring of physiological parameters.

Development of enzyme-inorganic hybrid nanoflower-modified electrodes and a smartphone-controlled electrochemical analyzer for point-of-care testing of salivary amylase in saliva

Quantitation of salivary alpha-amylase (sAA) plays a significant role in not only theoretical studies but also clinical practice. This study reports a quantitative point-of-care testing (POCT) system for sAA quantitation anywhere, anytime and by anyone, which consists of customized electrodes and a smartphone-controlled electrochemical analyzer. Organic-inorganic hybrid nanoflowers (NFs) encapsulating α-glucosidase (AG) and glucose dehydrogenase (GDH) have been synthesized and modified onto screen-printed electrodes (SPCEs) to fabricate the customized electrodes. The SPCEs integrated with the smartphone-controlled electrochemical analyzer exhibit good analytical performance for sAA with a low detection limit of 5.02 U mL-1 and a wide dynamic range of 100-2000 U mL-1 using chronoamperometry. The reported POCT system has been successfully demonstrated for quantitation of sAA in clinical saliva samples, and the quantitation results correlated well with those of the Bernfeld method which is extensively used in clinics. More importantly, this study reveals the great potential of sAA as an early warning indicator of abnormal glucose metabolism in obese individuals. Considering the non-invasive saliva sampling process as well as the easy-to-use and cost-effectiveness features of this quantitative POCT system, quantitation of salivary sAA at home by laypersons might become an appealing choice for obese individuals to monitor their glucose metabolism status anytime.

Non-invasive blood glucose monitoring technology in diabetes management: review

Diabetes is one of the leading non-communicable diseases globally, adversely impacting an individual's quality of life and adding a considerable burden to the healthcare systems. The necessity for frequent blood glucose (BG) monitoring and the inconveniences associated with self-monitoring of BG, such as pain and discomfort, has motivated the development of non-invasive BG approaches. However, the current research progress is slow, and only a few BG self-monitoring devices have made considerable progress. Hence, we evaluate the available non-invasive glucose monitoring technologies validated against BG recordings to provide future research direction to design, develop, and deploy self-monitoring of BG with integrated emerging technologies. We searched five databases, Embase, MEDLINE, Proquest, Scopus, and Web of Science, to assess the non-invasive technology's scope in the diabetes management paradigm published from 2000 to 2020. A total of three approaches to non-invasive screening, including saliva, skin, and breath, were identified and discussed. We observed a statistical relationship between BG measurements obtained from non-invasive methods and standard clinical measures. Opportunities exist for future research to advance research progress and facilitate early technology adoption for healthcare practice. The results promise clinical validity; however, formulating regulatory guidelines could foresee the deployment of approved non-invasive BG monitoring technologies in healthcare practice. Further, research prospects are there to design, develop, and deploy integrated diabetes management systems with mobile technologies, data analytics, and the internet of things (IoT) to deliver a personalised monitoring system.

High-precision prediction of blood glucose concentration utilizing Fourier transform Raman spectroscopy and an ensemble machine learning algorithm

Raman spectroscopy has gained popularity in analyzing blood glucose levels due to its non-invasive identification and minimal interference from water. However, the challenge lies in how to accurately predict blood glucose concentrations in human blood using Raman spectroscopy. This paper researches a novel integrated machine learning algorithm called Bagging-ABC-ELM. The optimal input weights and biases of extreme learning machine (ELM) model are obtained by artificial bee colony (ABC) algorithm. The bagging algorithm is used to obtain a better the stability of the model and higher performance than ELM algorithm. The results show that the mean value of coefficient of determination is 0.9928, and root mean square error is 0.1928. Compared to other regression models, the Bagging-ABC-ELM model exhibited superior prediction accuracy, robustness, and generalization capability. The Bagging-ABC-ELM model presents a promising alternative for analyzing blood glucose levels in clinical and research settings.

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

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

Field effect transistor based wearable biosensors for healthcare monitoring

The rapid advancement of wearable biosensors has revolutionized healthcare monitoring by screening in a non-invasive and continuous manner. Among various sensing techniques, field-effect transistor (FET)-based wearable biosensors attract increasing attention due to their advantages such as label-free detection, fast response, easy operation, and capability of integration. This review explores the innovative developments and applications of FET-based wearable biosensors for healthcare monitoring. Beginning with an introduction to the significance of wearable biosensors, the paper gives an overview of structural and operational principles of FETs, providing insights into their diverse classifications. Next, the paper discusses the fabrication methods, semiconductor surface modification techniques and gate surface functionalization strategies. This background lays the foundation for exploring specific FET-based biosensor designs, including enzyme, antibody and nanobody, aptamer, as well as ion-sensitive membrane sensors. Subsequently, the paper investigates the incorporation of FET-based biosensors in monitoring biomarkers present in physiological fluids such as sweat, tears, saliva, and skin interstitial fluid (ISF). Finally, we address challenges, technical issues, and opportunities related to FET-based biosensor applications. This comprehensive review underscores the transformative potential of FET-based wearable biosensors in healthcare monitoring. By offering a multidimensional perspective on device design, fabrication, functionalization and applications, this paper aims to serve as a valuable resource for researchers in the field of biosensing technology and personalized healthcare.

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.

Optical Integration in Wearable, Implantable and Swallowable Healthcare Devices

Recent advances in materials and semiconductor technologies have led to extensive research on optical integration in wearable, implantable, and swallowable health devices. These optical systems utilize the properties of light─intensity, wavelength, polarization, and phase─to monitor and potentially intervene in various biological events. The potential of these devices is greatly enhanced through the use of multifunctional optical materials, adaptable integration processes, advanced optical sensing principles, and optimized artificial intelligence algorithms. This synergy creates many possibilities for clinical applications. This Perspective discusses key opportunities, challenges, and future directions, particularly with respect to sensing modalities, multifunctionality, and the integration of miniaturized optoelectronic devices. We present fundamental insights and illustrative examples of such devices in wearable, implantable, and swallowable forms. The constant pursuit of innovation and the dedicated approach to critical challenges are poised to influence diverse fields.

Micro interstitial fluid extraction and detection device integrated with the optimal extraction conditions for noninvasive glucose monitoring

Interstitial fluid glucose sensors have promising prospects in noninvasive glucose monitoring. However, the commonly used method of extracting interstitial fluid, reverse iontophoresis (RI), still remains to be optimized to solve problems such as insufficient extraction flux and skin irritation. To find the optimal RI conditions, in this study we explored the effects of multiple factors such as current frequency, duration, duty cycle and their interactions on extraction with the design of experiments (DOE) method. A multifunctional extraction and detection device was designed to control extraction conditions and measure the surface water content of the extraction electrode in situ and real time. A micro glucose monitoring device (MicroTED) combined with a cheap and flexible paper-based electrode was developed under the determined optimal extraction conditions. In on-body continuous glucose monitoring tests carried out to verify the performance of the device, the optimized conditions can facilitate stable extraction of up to 1.0 mg without any skin discomfort. The mean Pearson correlation coefficient between the measurement results of MicroTED and commercial glucometer is above 0.9. In the Clarke error grid analysis, all data points fell within Clarke error grid areas A and B, demonstrating the feasibility of further clinical application of the device.

Narrative review of the role of technology in pediatric diabetes: from testing blood glucose to subcutaneous automated therapy and hope for cure

Background and Objective

Type 1 diabetes, the most common cause of diabetes in pediatrics, is defined by the hyperglycemia that results from the permanent autoimmune damage to the pancreas. The Diabetes Control and Complications Trial (DCCT) demonstrated that strict glycemic control targeting lower HbA1c goals can both delay the onset and progression of its complications that include diabetic neuropathy, nephropathy, retinopathy, and increased cardiovascular events. Our primary objective is to review the literature available regarding the technology applied for the treatment of diabetes, not only aiding patients' quality of life but addressing its effects on hypoglycemia and reduced risk of the long-term complications. It will synthesize the evolution of glucose monitoring devices; the development of insulin: from animal to recombinant engineering, smart insulin in the future; the development of algorithm-driven insulin delivery devices, the closed loop system/artificial pancreas; and the future utilization of technology to support islet cell transplant with the goal of a long-term cure. Emphasis will be made on what is known about the impact on its outcomes in children and adolescents.

Methods

A literature search was conducted using PubMed for publications from 1985 to present. Keywords used: type 1 diabetes, children, adolescents, pediatrics, continuous glucose monitoring (CGM), insulin pumps. Referenced articles include other reviews, current care guidelines as supported by cross sectional studies, cohort studies and randomized clinical trials.

Key Content and Findings

Understanding the pathophysiology of type 1 diabetes has led to the design of technology that facilitates glucose monitoring and insulin administration in a personalized manner. The current technology has improved outcomes and quality of life by decreasing hypoglycemic events and decreasing risk of long-term metabolic complications. Barriers remain, for children and adults, often driven by patient's preference as well as their understanding of the limitations of what they are wearing.

Conclusions

With the progressive evolution of this technology, it is now realistic to lower the burden of diabetes self-management while reducing hypoglycemia and risk of complications that otherwise impact daily life from academics, physical activity, career choices and even life expectancy.

Polyvinylpyrrolidone/TiO2 composites' preparation via sol-gel procedure furthered with non-enzymatic glucose sensing and antibacterial effectiveness

An amperometric non-enzymatic glucose sensing based on polyvinylpyrrolidone and titanium dioxide nanocomposites (PVP-TiO2 NCs) fabricated over stainless-steel (SS) electrode was experimented. The electrode was fabricated of PVP-TiO2 NCs onto SS surface through drop casting coating. The NCs and the electrode were analyzed using Fourier transform infrared (FTIR) and UV-visible (UV-Vis) spectroscopy, X-ray diffraction spectra (XRD), X-ray photoelectron spectra (XPS), particle analyzer, scanning electron microscopy (SEM), EDS and transmission electron microscopy (TEM) and Brunauer-Emmett-Teller (BET) analysis for surface. For electrochemical investigation, cyclic voltammetry, electrochemical impedance spectra, including Nyquist plots and Bode plots, and chronoamperometry were adopted. PVP-TiO2/SS-modified electrode manifested high sensitivity of 360.13 µA/mM.cm2, lower detection limit of 756.8 µM and 0 to 13 mM linear range with regression coefficient of R2 = 0.992. The electrode exhibited high stability with good anti-interference tendency against fructose, uric acid, ascorbic acid and sucrose. The modified electrode also performed well in real sample glucose detection. Further, PVP-TiO2 NCs performed appreciably toward antibacterial studies against some Gram-positive and Gram-negative deleterious bacteria, Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus, Shigella flexneri microbes.

A novel multipurpose device for dataset creation and on-device immediate estimation of blood glucose level from reflection ppg

We propose a completely non-invasive and highly accurate portable blood glucose estimator, which is simple hardware that anyone, regardless of their prior experience or knowledge, can use to obtain an immediate reading of their blood sugar level. Glucose levels can be monitored in real time and displayed on the device thanks to its infrared LED light source, sensor with built-in amplification unit, processing unit with blood glucose regression model, power management unit for autonomous operation, and display. The device was initially used to create a dataset of photoplethysmography (PPG) signals collected from the fingertip of 50 subjects. The extracted signal features were correlated with the subject's glucose level, which was measured at the same time using a commercial glucometer, and several regression models were constructed. The models were evaluated using signals from up to 110 subjects across three datasets, and the most optimized model was implemented in the device to predict the subject's blood glucose level solely based on PPG in real-time. The device with the built-in model has been subjected to extensive testing to gauge its efficacy. The device's clinical accuracy is encouraging. The pricey strips and needles that must be purchased along with the hardware in the conventional method will no longer be necessary with this device.

The role of continuous glucose monitoring in physical activity and nutrition management: perspectives on present and possible uses

PURPOSE

Continuous glucose monitoring (CGM) is on the rise as the prevalence of obesity and diabetes increases. This review aimed to explore the use of CGM and its potential novel applications in physical activity and nutrition management.

METHODS

We searched PubMed, Web of Science, and Wiley Online Library databases using the keywords 'continuous glucose monitor,' 'nutrition,' 'physical activity,' and 'numerical modeling.'

RESULTS

Continuous blood glucose measurement is useful for individuals with obesity and diabetes. Long-term blood glucose data allow for personalized planning of nutritional composition, meal timing, and physical activity type and intensity, as well as help prevent hypoglycemia and hyperglycemia. Thus, understanding the limitations of CGM is important for its effective use.

CONCLUSION

CGM systems are being increasingly used to monitor and identify appropriate blood glucose controlling interventions. Blood glucose level is influenced by various factors such as nutrient composition, meal timing, physical activity, circadian rhythm, and cortisol levels. Numerical modeling can be used to analyze the complex relationship between stress, sleep, nutrition, and physical activity, which affect blood glucose levels. In future, blood glucose, sleep, and stress data will be integrated to predict appropriate lifestyle levels for blood glucose management. This integrated approach improves glucose control and overall wellbeing, potentially reducing societal costs.

Fast and Durable Nanofiber Mat Channel Organic Electrochemical Transistors

Bioelectronic devices that offer real-time measurements, biological signal processing, and continuous monitoring while maintaining stable performance are in high demand. The materials used in organic electrochemical transistors (OECTs) demonstrate high transconductance (GM) and excellent biocompatibility, making them suitable for bioelectronics in a biological environment. However, ion migration in OECTs induces a delayed response time and low cut-off frequency, and the adverse biological environment causes OECT durability problems. Herein, we present OECTs with a faster response time and improved durability, made possible by using a nanofiber mat channel of a conventional OECT structure. Poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS)/polyacrylamide (PAAm) nanofiber mat channel OECTs are fabricated and subjected to various durability tests for the first time based on continuous measurements and mechanical stability assessments. The results indicate that the nanofiber mat channel OECTs have a faster response time and longer life spans compared to those of film channel OECTs. The improvements can be attributed to the increased surface area and fibrous structure of the nanofiber mat channel. Furthermore, the hydrogel helps to maintain the structure of the nanofiber, facilitates material exchange, and eliminates the need for a crosslinker.

Noninvasive Glucose Sensing In Vivo

Blood glucose monitoring is an essential aspect of disease management for individuals with diabetes. Unfortunately, traditional methods require collecting a blood sample and thus are invasive and inconvenient. Recent developments in minimally invasive continuous glucose monitors have provided a more convenient alternative for people with diabetes to track their glucose levels 24/7. Despite this progress, many challenges remain to establish a noninvasive monitoring technique that works accurately and reliably in the wild. This review encompasses the current advancements in noninvasive glucose sensing technology in vivo, delves into the common challenges faced by these systems, and offers an insightful outlook on existing and future solutions.

Patent analysis of digital sensors for continuous glucose monitoring

The high need for optimal diabetes management among an ever-increasing number of patients dictates the development and implementation of new digital sensors for continuous glucose monitoring. The purpose of this work is to systematize the global patenting trends of digital sensors for continuous glucose monitoring and analyze their effectiveness in controlling the treatment of diabetes patients of different ages and risk groups. The Lens database was used to build the patent landscape of sensors for continuous glucose monitoring. Retrospective analysis showed that the patenting of sensors for continuous glucose monitoring had positive trend over the analyzed period (2000-2022). Leading development companies are Dexcom Inc., Abbott Diabetes Care Inc., Medtronic Minimed Inc., Roche Diabetes Care Inc., Roche Diagnostics Operations Inc., Roche Diabetes Care Gmbh, and Ascensia Diabetes Care Holdings Ag, among others. Since 2006, a new approach has emerged where digital sensors are used for continuous glucose monitoring, and smartphones act as receivers for the data. Additionally, telemedicine communication is employed to facilitate this process. This opens up new opportunities for assessing the glycemic profile (glycemic curve information, quantitative assessment of the duration and amplitude of glucose fluctuations, and so on), which may contribute to improved diabetes management. A number of digital sensors for minimally invasive glucose monitoring are patented, have received FDA approval, and have been on the market for over 10 years. Their effectiveness in the clinic has been proven, and advantages and disadvantages have been clarified. Digital sensors offer a non-invasive option for monitoring blood glucose levels, providing an alternative to traditional invasive methods. This is particularly useful for patients with diabetes who require frequent monitoring, including before and after meals, during and after exercise, and in other scenarios where glucose levels can fluctuate. However, non-invasive glucose measurements can also benefit patients without diabetes, such as those following a dietary treatment plan, pregnant women, and individuals during fasting periods like Ramadan. The availability of non-invasive monitoring is especially valuable for patients in high-risk groups and across different age ranges. New world trends have been identified in the patenting of digital sensors for non-invasive glucose monitoring in interstitial skin fluid, saliva, sweat, tear fluid, and exhaled air. A number of non-invasive devices have received the CE mark approval, which confirms that the items meet European health, safety, and environmental protection standards (TensorTip Combo-Glucometer, Cnoga Medical Ltd.; SugarBEAT, Nemaura Medical; GlucoTrack, GlucoTrack Inc.), but are not FDA-approved yet. The above-mentioned sensors have characteristics that make them popular in the treatment of diabetes: they do not require implantation, do not cause an organism reaction to a foreign body, and are convenient to use. In the EU, in order to increase clinical safety and the level of transparency about medical devices, manufacturers must obtain certificates in accordance with Regulation (EU) 2017/745, taking into account the transition period. The development of systems, which include digital sensors for continuous glucose monitoring, mobile applications, and web platforms for professional analysis of glycemic control and implementation of unified glycemic assessment principles in mobile healthcare, represent promising approaches for controlling glycaemia in patients.

Improving the Accuracy of Continuous Blood Glucose Measurement Using Personalized Calibration and Machine Learning

Despite tremendous developments in continuous blood glucose measurement (CBGM) sensors, they are still not accurate for all patients with diabetes. As glucose concentration in the blood is <1% of the total blood volume, it is challenging to accurately measure glucose levels in the interstitial fluid using CBGM sensors due to within-patient and between-patient variations. To address this issue, we developed a novel data-driven approach to accurately predict CBGM values using personalized calibration and machine learning. First, we scientifically divided measured blood glucose into smaller groups, namely, hypoglycemia (<80 mg/dL), nondiabetic (81-115 mg/dL), prediabetes (116-150 mg/dL), diabetes (151-181 mg/dL), severe diabetes (181-250 mg/dL), and critical diabetes (>250 mg/dL). Second, we separately trained each group using different machine learning models based on patients' personalized parameters, such as physical activity, posture, heart rate, breath rate, skin temperature, and food intake. Lastly, we used multilayer perceptron (MLP) for the D1NAMO dataset (training to test ratio: 70:30) and grid search for hyperparameter optimization to predict accurate blood glucose concentrations. We successfully applied our proposed approach in nine patients with type 1 diabetes and observed that the mean absolute relative difference (MARD) decreased from 17.8% to 8.3%.

Continuous Glucose Monitoring (CGM) in Sports-A Comparison between a CGM Device and Lab-Based Glucose Analyser under Resting and Exercising Conditions in Athletes

The objective of this pilot study was to compare glucose concentrations in capillary blood (CB) samples analysed in a laboratory by a validated method and glucose concentrations measured in the interstitial fluid (ISF) by continuous glucose monitoring (CGM) under different physical activity levels in a postprandial state in healthy athletes without diabetes. As a physiological shift occurs between glucose concentration from the CB into the ISF, the applicability of CGM in sports, especially during exercise, as well as the comparability of CB and ISF data necessitate an in-depth assessment. Ten subjects (26 ± 4 years, 67 ± 11 kg bodyweight (BW), 11 ± 3 h) were included in the study. Within 14 days, they underwent six tests consisting of (a) two tests resting fasted (HC_Rest/Fast and LC_Rest/Fast), (b) two tests resting with intake of 1 g glucose/kg BW (HC_Rest/Glc and LC_Rest/Glc), (c) running for 60 min at moderate (ModExerc/Glc), and (d) high intensity after intake of 1 g glucose/kg BW (IntExerc/Glc). Data were collected in the morning, following a standardised dinner before test day. Sensor-based glucose concentrations were compared to those determined from capillary blood samples collected at the time of sensor-based analyses and subjected to laboratory glucose measurements. Pearson's r correlation coefficient was highest for Rest/Glc (0.92, p < 0.001) compared to Rest/Fast (0.45, p < 0.001), ModExerc/Glc (0.60, p < 0.001) and IntExerc/Glc (0.70, p < 0.001). Mean absolute relative deviation (MARD) and standard deviation (SD) was smallest for resting fasted and similar between all other conditions (Rest/Fast: 8 ± 6%, Rest/Glc: 17 ± 12%, ModExerc/Glc: 22 ± 24%, IntExerc/Glc: 18 ± 17%). However, Bland-Altman plot analysis showed a higher range between lower and upper limits of agreement (95% confidence interval) of paired data under exercising compared to resting conditions. Under resting fasted conditions, both methods produce similar outcomes. Under resting postprandial and exercising conditions, respectively, there are differences between both methods. Based on the results of this study, the application of CGM in healthy athletes is not recommended without concomitant nutritional or medical advice.

The Development and Evaluation of Reagentless Glucose Biosensors Using Dendritic Gold Nanostructures as a Promising Sensing Platform

Reagentless electrochemical glucose biosensors were developed and investigated. A graphite rod (GR) electrode modified with electrochemically synthesized dendritic gold nanostructures (DGNs) and redox mediators (Med) such as ferrocenecarboxylic acid (FCA), 1,10-phenathroline-5,6-dione (PD), N,N,N',N'-tetramethylbenzidine (TMB) or tetrathiafulvalene (TTF) in combination with glucose oxidase (GOx) (GR/DGNs/FCA/GOx, GR/DGNs/PD/GOx, GR/DGNs/TMB/GOx, or GR/DGNs/TTF/GOx) were developed and electrochemically investigated. A biosensor based on threefold-layer-by-layer-deposited PD and GOx (GR/DGNs/(PD/GOx)₃) was found to be the most suitable for the determination of glucose. To improve the performance of the developed biosensor, the surface of the GR/DGNs/(PD/GOx)₃ electrode was modified with polypyrrole (Ppy) for 5 h. A glucose biosensor based on a GR/DGNs/(PD/GOx)₃/Ppy_{(5 h)} electrode was characterized using a wide linear dynamic range of up to 39.0 mmol L^-1 of glucose, sensitivity of 3.03 µA mM^-1 cm^-2, limit of detection of 0.683 mmol L^-1, and repeatability of 9.03% for a 29.4 mmol L^-1 glucose concentration. The Ppy-based glucose biosensor was characterized by a good storage stability (τ_1/2 = 9.0 days). Additionally, the performance of the developed biosensor in blood serum was investigated.

Passive Wireless Sensor Utilizing an Interdigital Capacitor for Ongoing Monitoring of Glucose Concentration

The wireless glucose sensor represents a major step forward in continuous glucose monitoring. With its innovative interdigital capacitor and inductor combination, the sensor works without active components and can measure glucose levels by detecting changes in reflection magnitude of the surrounding environment. Experimental results validated the proposed passive sensor's capability in detecting glucose concentration in aqueous solution, demonstrating a linear relationship between reflection magnitude and glucose concentration ranging from 0 to 500 mg/dL with a sensitivity of 3×10-3 dB/(mg/dL). These findings make the proposed sensor a good option for continuous glucose monitoring, offering wireless measurement of blood glucose levels. Additionally, its compact size and absence of active components make it suitable for implantable applications, providing individuals with wireless invasive method of monitoring their glucose levels.

Mid-infrared photoacoustic spectroscopy based on ultrasound detection for blood component analysis

For the non-invasive measurement of biological tissue, a piezoelectric photoacoustic spectroscopy (PZT-PAS) system that detects a single frequency of ultrasound induced by the irradiation of pulse-modulated mid-infrared laser light was developed. PA spectra of the optical phantom and biological samples were obtained, and the relationship between the PA signal intensity and optical absorbance in the fingerprint region (930-1,200 cm^-1) was analyzed to estimate the optical absorbance. The resonance vibration of the induced ultrasound was utilized to further increase the signal strength for biological tissue measurement. Consequently, PA spectrum reflecting the absorption of components in biological tissues was obtained.

An Innovative Electrochemical Immuno-Platform for Monitoring Chronic Conditions Using the Biosensing of Hyaluronic Acid in Human Plasma Samples

Hyaluronic acid (HA) is the main non-sulfated glycosaminoglycan of the extracellular matrix that is synthesized by fibroblasts and other specialized connective tissue cells. The accumulation of HA on different tissues is a characteristic of disorders that are associated with progressive tissue fibrosis. HA is also known to play a critical role in tumorigenesis and tumor metastasis. It is overproduced by many types of tumors and promotes tumor progression and multidrug resistance. There is a great necessity for the development of an easy and cost-effective detection method for the monitoring of HA for both the diagnosis and efficient treatment of related disorders. In the present study, an innovative immune device was designed for the rapid and sensitive recognition of HA in human plasma samples. For this purpose, an efficient alloy (Pt@Au) was fabricated on the surface of the gold electrode. Thus, a novel substrate was used for the preparation of an efficient transducer, which is necessary for the immobilization of biotinylated antibodies. CHA was applied for the electrochemical deposition of Pt@Au nano-alloy on Au electrodes. Additionally, the morphological study of the used nanocomposite was assessed using FESEM at a working voltage of 3 kV, and the chemical structures of the electrode were analyzed using the EDS apparatus. For the first time, a biocompatible alloy-based substrate was prepared for the study of antigen–antibody identification. The developed immunosensor has a linear response within the range of 0.156–160 ng.mL−1 with a limit of detection of 0.039 ng.mL−1 in human plasma samples. This research study offers a novel promising technique for HA analyses and is anticipated to be used in the early diagnosis of some disorders related to abnormal levels of HA in human bio-fluids. Thus, a constructed (pt@Au) nano-alloy provides a useful interface for the dense loading of AB. This excellent design loads high sensations of the biosensor for the selective detection of HA in real samples (human bio-fluids).

The convergence of traditional and digital biomarkers through AI-assisted biosensing: A new era in translational diagnostics?

Advances in consumer electronics, alongside the fields of microfluidics and nanotechnology have brought to the fore low-cost wearable/portable smart devices. Although numerous smart devices that track digital biomarkers have been successfully translated from bench-to-bedside, only a few follow the same fate when it comes to track traditional biomarkers. Current practices still involve laboratory-based tests, followed by blood collection, conducted in a clinical setting as they require trained personnel and specialized equipment. In fact, real-time, passive/active and robust sensing of physiological and behavioural data from patients that can feed artificial intelligence (AI)-based models can significantly improve decision-making, diagnosis and treatment at the point-of-procedure, by circumventing conventional methods of sampling, and in person investigation by expert pathologists, who are scarce in developing countries. This review brings together conventional and digital biomarker sensing through portable and autonomous miniaturized devices. We first summarise the technological advances in each field vs the current clinical practices and we conclude by merging the two worlds of traditional and digital biomarkers through AI/ML technologies to improve patient diagnosis and treatment. The fundamental role, limitations and prospects of AI in realizing this potential and enhancing the existing technologies to facilitate the development and clinical translation of "point-of-care" (POC) diagnostics is finally showcased.

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.

Gingival Crevicular Blood Glucose as a Novel Method for Screening Diabetes Mellitus in Periodontally Compromised Patients

INTRODUCTION

Patients with periodontitis are significantly more likely to have undetected diabetes mellitus (DM). Self‑monitoring devices like glucometers provide a simple method for rapid monitoring of the glucose level in blood by utilizing a blood sample from the finger, but this method requires puncturing to obtain blood. Bleeding from the gingival sulcus, obtained during oral hygiene examination, can be utilized for screening DM patients. Therefore, this study was performed with the aim of determining the efficacy of gingival crevicular blood as a non-invasive screening method for DM patients, as well as correlating and comparing gingival crevicular blood glucose (GCBG) levels with finger capillary blood glucose (FCBG) and fasting blood glucose (FBG) among non-diabetic and diabetic group patients.

METHODS

In this cross-sectional comparative study, a total of 120 participants having moderate to severe gingivitis/periodontitis with an age range of 40 to 65 years were divided into two groups on the basis of FBG range taken from an antecubital vein: non-diabetic (≤126, n=60) and diabetic (≥126, n=60) groups. Blood oozing during the routine periodontal examination from the periodontal pocket was recorded using a test strip of a glucose self-monitoring device (AccuSure^®Simple) as GCBG. Concomitantly FCBG was collected from the fingertip. These three parameters were statistically analyzed using the Student's t-test and the one-way ANOVA test and correlated with Pearson's correlation coefficient for both groups.

RESULTS

The mean and standard deviation for the three parameters GCBG, FBG, and FCBG were 93.78±12.03, 89.98±13.22, and 93.08±15.56, respectively, for the non-diabetic group and 154.52±45.05, 159±47.00, and 162.23±50.60 subsequently for the diabetic group. Comparing glucose level parameters among the non-diabetic and diabetic groups suggests a significant difference with the p-value <0.001*(inter-group). ANOVA test was done for both groups suggesting no significant difference among these three methods of measuring blood glucose level, where the p-value found was 0.272 for the non-diabetic and 0.665 for the diabetic group (intra-group comparison). Pearson's correlation values suggested a good positive correlation for the non-diabetic group, with parameters GCBG and FBG (r=0.864), GCBG and FCBG (r=0.936), and FBG and FCBG (r=0.837). The diabetic group's Pearson's correlation suggested a highly significant positive correlation between three different methods in which GCBG and FBG (r=0.978), GCBG and FBG (r=0.977), and FBG and FCBG (r= 0.982).

CONCLUSION

Blood oozing from the periodontal pocket during routine oral hygiene examination can be utilized by dental healthcare professionals to screen pre-diabetic patients which can be used as a simple and less invasive method for DM patients.

Therapeutic Investigation of Palm Oil Mill Effluent-Derived Beta-Carotene in Streptozotocin-Induced Diabetic Retinopathy via the Regulation of Blood-Retina Barrier Functions

Diabetic retinopathy (DR) primarily progresses into retinal degeneration caused by microvascular dysfunction. The pathophysiology of DR progression is still uncertain. This study investigates the function of beta-carotene (PBC) originating from palm oil mill effluent in the treatment of diabetes in mice. An intraperitoneal injection of streptozotocin (35 mg/kg) was used to induce diabetes, which was then accelerated by an intravitreal (i.vit.) injection of STZ (20 µL on day 7). PBC (50 and 100 mg/kg) and dexamethasone (DEX: 10 mg/kg) were also administered orally (p.o.) for 21 days. At various time intervals, the optomotor response (OMR) and visual-cue function test (VCFT) responses were evaluated. Biomarkers, such as reduced glutathione (GSH), thiobarbituric acid reactive substances (TBARSs), and catalase activity were determined in retinal tissue samples. DR significantly lowers the spatial frequency threshold (SFT) and time spent in the target quadrant (TSTQ), increases the reaching time in the visual-cue platform (RVCP), lowers retinal GSH and catalase activity levels, and elevates TBARS levels. The treatments of PBC and DEX also ameliorate STZ-induced DR alterations. The potential ameliorative activity of PBC in DR is attributed to its anti-diabetic, anti-oxidative, and control of blood-retinal barrier layer properties.

Reducing the Cost of Diabetes Care with Telemedicine, Smartphone, and Home Monitoring

The effect of an increasing diabetes population has resulted in escalated costs and overburdened physicians. The increase in cost is not due to the disease per se, but because of its largely preventable complications. Patient-friendly technologies are proven to significantly reduce complications and thereby cost, but seldom practised. Telemedicine is increasingly being utilized in diabetology to improve access to health care, quality of care, and clinical/psychosocial outcomes in patients with diabetes (PWD). In PWD, patient-physician interactions are essential for improving health outcomes and preventing long-term complications. Smartphones are one of the basic modalities for telemedicine application. Mobile phone messaging applications, including text messaging and multimedia message service, could offer a convenient and cost-effective way to support desirable health behaviors. There are diabetes-related mobile apps mainly focusing on self-management of diabetes, lifestyle modification, and medication adherence motivation. With the widespread availability of high-speed Internet, remote monitoring has also become popular. Home monitoring of blood glucose and blood pressure, wearable devices, and continuous glucose monitoring also play a vital role in bringing down the long‑term vascular complications of diabetes and thereby reduce the overall cost and improve the quality of life of patients. There are hundreds of tech platforms for diabetes management, of which only a few with proven efficacy and safety are recommended by physicians.

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

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

Accurate Post-Calibration Predictions for Noninvasive Glucose Measurements in People Using Confocal Raman Spectroscopy

In diabetes prevention and care, invasiveness of glucose measurement impedes efficient therapy and hampers the identification of people at risk. Lack of calibration stability in non-invasive technology has confined the field to short-term proof of principle. Addressing this challenge, we demonstrate the first practical use of a Raman-based and portable non-invasive glucose monitoring device used for at least 15 days following calibration. In a home-based clinical study involving 160 subjects with diabetes, the largest of its kind to our knowledge, we find that the measurement accuracy is insensitive to age, sex, and skin color. A subset of subjects with type 2 diabetes highlights promising real-life results with 99.8% of measurements within A + B zones in the consensus error grid and a mean absolute relative difference of 14.3%. By overcoming the problem of calibration stability, we remove the lingering uncertainty about the practical use of non-invasive glucose monitoring, boding a new, non-invasive era in diabetes monitoring.

Non-invasive method for blood glucose monitoring using ECG signal

Introduction: Tight glucose monitoring is crucial for diabetic patients by using a Continuous Glucose Monitor (CGM). The existing CGMs measure the Blood Glucose Concentration (BGC) from the interstitial fluid. These technologies are quite expensive, and most of them are invasive. Previous studies have demonstrated that hypoglycemia and hyperglycemia episodes affect the electrophysiology of the heart. However, they did not determine a cohort relationship between BGC and ECG parameters.

Material and method: In this work, we propose a new method for determining the BGC using surface ECG signals. Recurrent Convolutional Neural Networks (RCNN) were applied to segment the ECG signals. Then, the extracted features were employed to determine the BGC using two mathematical equations. This method has been tested on 04 patients over multiple days from the D1namo dataset, using surface ECG signals instead of intracardiac signal.

Results: We were able to segment the ECG signals with an accuracy of 94% using the RCNN algorithm. According to the results, the proposed method was able to estimate the BGC with a Mean Absolute Error (MAE) of 0.0539, and a Mean Squared Error (MSE) of 0.1604. In addition, the linear relationship between BGC and ECG features has been confirmed in this paper.

Conclusion: In this paper, we propose the potential use of ECG features to determine the BGC. Additionally, we confirmed the linear relationship between BGC and ECG features. That fact will open new perspectives for further research, namely physiological models. Furthermore, the findings point to the possible application of ECG wearable devices for non-invasive continuous blood glucose monitoring via machine learning.

Evaluation of a Continuous Blood Glucose Monitor: A Novel and Non-Invasive Wearable Using Bioimpedance Technology

BACKGROUND

Frequent blood glucose level (BGL) monitoring is essential for effective diabetes management. Poor compliance is common due to the painful finger pricking or subcutaneous lancet implantation required from existing technologies. There are currently no commercially available non-invasive devices that can effectively measure BGL. In this real-world study, a prototype non-invasive continuous glucose monitoring system (NI-CGM) developed as a wearable ring was used to collect bioimpedance data. The aim was to develop a mathematical model that could use these bioimpedance data to estimate BGL in real time.

METHODS

The prototype NI-CGM was worn by 14 adult participants with type 2 diabetes for 14 days in an observational clinical study. Bioimpedance data were collected alongside paired BGL measurements taken with a Food and Drug Administration (FDA)-approved self-monitoring blood glucose (SMBG) meter and an FDA-approved CGM. The SMBG meter data were used to improve CGM accuracy, and CGM data to develop the mathematical model.

RESULTS

A gradient boosted model was developed using a randomized 80-20 training-test split of data. The estimated BGL from the model had a Mean Absolute Relative Difference (MARD) of 17.9%, with the Parkes error grid (PEG) analysis showing 99% of values in clinically acceptable zones A and B.

CONCLUSIONS

This study demonstrated the reliability of the prototype NI-CGM at collecting bioimpedance data in a real-world scenario. These data were used to train a model that could successfully estimate BGL with a promising MARD and clinically relevant PEG result. These results will enable continued development of the prototype NI-CGM as a wearable ring.