Commercial and Scientific Solutions for Blood Glucose Monitoring-A Review

Electrochemically Synthesized MIP Sensors: Applications in Healthcare Diagnostics

Early-stage detection and diagnosis of diseases is essential to the prompt commencement of treatment regimens, curbing the spread of the disease, and improving human health. Thus, the accurate detection of disease biomarkers through the development of robust, sensitive, and selective diagnostic tools has remained cutting-edge scientific research for decades. Due to their merits of being selective, stable, simple, and having a low preparation cost, molecularly imprinted polymers (MIPs) are increasingly becoming artificial substitutes for natural receptors in the design of state-of-the-art sensing devices. While there are different MIP preparation approaches, electrochemical synthesis presents a unique and outstanding method for chemical sensing applications, allowing the direct formation of the polymer on the transducer as well as simplicity in tuning the film properties, thus accelerating the trend in the design of commercial MIP-based sensors. This review evaluates recent achievements in the applications of electrosynthesized MIP sensors for clinical analysis of disease biomarkers, identifying major trends and highlighting interesting perspectives on the realization of commercial MIP-endowed testing devices for rapid determination of prevailing diseases.

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.

A Polarity-Sensitive Far-Red Fluorescent Probe for Glucose Sensing through Skin

The field of glucose biosensors for diabetes management has been of great interest over the past 60 years. Continuous glucose monitoring (CGM) is important to continuously track the glucose level to provide better management of the disease. Concanavalin A (ConA) can reversibly bind to glucose and mannose molecules and form a glucose biosensor via competitive binding. Here, we developed a glucose biosensor using ConA and a fluorescent probe, which generated a fluorescent intensity change based on solvatochromism, the reversible change in the emission spectrum dependent on the polarity of the solvent. The direction in which the wavelength shifts as the solvent polarity increases can be defined as positive (red-shift), negative (blue-shift), or a combination of the two, referred to as reverse. To translate this biosensor to a subcutaneously implanted format, Cyanine 5.5 (Cy5.5)-labeled small mannose molecules were used, which allows for the far-red excitation wavelength range to increase the skin penetration depth of the light source and returned emission. Three Cy5.5-labeled small mannose molecules were synthesized and compared when used as the competing ligand in the competitive binding biosensor. We explored the polarity-sensitive nature of the competing ligands and examined the biosensor's glucose response. Cy5.5-mannotetraose performed best as a biosensor, allowing for the detection of glucose from 25 to 400 mg/dL. Thus, this assay is responsive to glucose within the physiologic range when its concentration is increased to levels needed for an implantable design. The biosensor response is not statistically different when placed under different skin pigmentations when comparing the percent increase in fluorescence intensity. This shows the ability of the biosensor to produce a repeatable signal across the physiologic range for subcutaneous glucose monitoring under various skin tones.

The Applications of Electrochemical Immunosensors in the Detection of Disease Biomarkers: A Review

Disease-related biomarkers may serve as indicators of human disease. The clinical diagnosis of diseases may largely benefit from timely and accurate detection of biomarkers, which has been the subject of extensive investigations. Due to the specificity of antibody and antigen recognition, electrochemical immunosensors can accurately detect multiple disease biomarkers, including proteins, antigens, and enzymes. This review deals with the fundamentals and types of electrochemical immunosensors. The electrochemical immunosensors are developed using three different catalysts: redox couples, typical biological enzymes, and nanomimetic enzymes. This review also focuses on the applications of those immunosensors in the detection of cancer, Alzheimer's disease, novel coronavirus pneumonia and other diseases. Finally, the future trends in electrochemical immunosensors are addressed in terms of achieving lower detection limits, improving electrode modification capabilities and developing composite functional materials.

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.

Blood glucose estimation based on ECG signal

Successful self-management of diabetes requires Continuous Glucose Monitors (CGMs). These CGMs have several limitations such as being invasive, expensive and limited in terms of use. Many techniques, in vain, have been proposed to overcome these limitations. Nowadays, with the help of the Internet of Medical Things (IoMT) technologies, researchers are working to find alternative solutions. They succeed to predict hypoglycemia and hyperglycemia peaks using Electrocardiogram (ECG) signals. However, they failed to use it to estimate the Blood Glucose Concentration (BGC) directly and in real time. Three patients with 08 days of measurements from the D1namo dataset contributed to the study. A new technique has been proposed to estimate the BGC curves based on ECG signals. We used a convolutional neural network to segment the different regions of ECG signals as well as we extracted ECG features that were required for the next step. Then, five regression models have been employed to estimate BGC using as input sixth ECG parameters. We were able to segment the ECG signals with an accuracy of 94% using the convolutional neural network algorithm. The best performance among all simulated models was provided by Exponential Gaussian Process Regression (GPR) with Root Mean Squared Error (RMSE) values of 0.32, 0.41, 0.67 and R-squared (R²) values of 98%, 80%, and 70% for patients 01, 02 and 03 respectively. The method indicates the potential use of ECG wearable devices as non-invasive for continuous blood glucose monitoring, which is affordable and durable.

A glucose-blue light AND gate-controlled chemi-optogenetic cell-implanted therapy for treating type-1 diabetes in mice

Exogenous insulin therapy is the mainstay treatment for Type-1 diabetes (T1D) caused by insulin deficiency. A fine-tuned insulin supply system is important to maintain the glucose homeostasis. In this study, we present a designed cell system that produces insulin under an AND gate control, which is triggered only in the presence of both high glucose and blue light illumination. The glucose-sensitive GIP promoter induces the expression of GI-Gal4 protein, which forms a complex with LOV-VP16 in the presence of blue light. The GI-Gal4:LOV-VP16 complex then promotes the expression of UAS-promoter-driven insulin. We transfected these components into HEK293T cells, and demonstrated the insulin was secreted under the AND gate control. Furthermore, we showed the capacity of the engineered cells to improve the blood glucose homeostasis through implantation subcutaneously into Type-1 diabetes mice.

Terahertz Asymmetric S-Shaped Complementary Metasurface Biosensor for Glucose Concentration

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

Recent Advancement in Biofluid-Based Glucose Sensors Using Invasive, Minimally Invasive, and Non-Invasive Technologies: A Review

Biosensors have potentially revolutionized the biomedical field. Their portability, cost-effectiveness, and ease of operation have made the market for these biosensors to grow rapidly. Diabetes mellitus is the condition of having high glucose content in the body, and it has become one of the very common conditions that is leading to deaths worldwide. Although it still has no cure or prevention, if monitored and treated with appropriate medication, the complications can be hindered and mitigated. Glucose content in the body can be detected using various biological fluids, namely blood, sweat, urine, interstitial fluids, tears, breath, and saliva. In the past decade, there has been an influx of potential biosensor technologies for continuous glucose level estimation. This literature review provides a comprehensive update on the recent advances in the field of biofluid-based sensors for glucose level detection in terms of methods, methodology and materials used.