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

A Nanotechnology-Based Approach to Biosensor Application in Current Diabetes Management Practices

Diabetes mellitus is linked to both short-term and long-term health problems. Therefore, its detection at a very basic stage is of utmost importance. Research institutes and medical organizations are increasingly using cost-effective biosensors to monitor human biological processes and provide precise health diagnoses. Biosensors aid in accurate diabetes diagnosis and monitoring for efficient treatment and management. Recent attention to nanotechnology in the fast-evolving area of biosensing has facilitated the advancement of new sensors and sensing processes and improved the performance and sensitivity of current biosensors. Nanotechnology biosensors detect disease and track therapy response. Clinically efficient biosensors are user-friendly, efficient, cheap, and scalable in nanomaterial-based production processes and thus can transform diabetes outcomes. This article is more focused on biosensors and their substantial medical applications. The highlights of the article consist of the different types of biosensing units, the role of biosensors in diabetes, the evolution of glucose sensors, and printed biosensors and biosensing systems. Later on, we were engrossed in the glucose sensors based on biofluids, employing minimally invasive, invasive, and noninvasive technologies to find out the impact of nanotechnology on the biosensors to produce a novel device as a nano-biosensor. In this approach, this article documents major advances in nanotechnology-based biosensors for medical applications, as well as the hurdles they must overcome in clinical practice.

Non-Invasive Classification of Blood Glucose Level Based on Photoplethysmography Using Time–Frequency Analysis

Diabetes monitoring systems are crucial for avoiding potentially significant medical expenses. At this time, the only commercially viable monitoring methods that exist are invasive ones. Since patients are uncomfortable while blood samples are being taken, these techniques have significant disadvantages. The drawbacks of invasive treatments might be overcome by a painless, inexpensive, non-invasive approach to blood glucose level (BGL) monitoring. Photoplethysmography (PPG) signals obtained from sensor leads placed on specific organ tissues are collected using photodiodes and nearby infrared LEDs. Cardiovascular disease can be detected via photoplethysmography. These characteristics can be used to directly affect BGL monitoring in diabetic patients if PPG signals are used. The Guilin People’s Hospital’s open database was used to produce the data collection. The dataset was gathered from 219 adult respondents spanning an age range from 21 to 86 of which 48 percent were male. There were 2100 sampling points total for each PPG data segment. The methodology of feature extraction from data may assist in increasing the effectiveness of classifier training and testing. PPG data information is modified in the frequency domain by the instantaneous frequency (IF) and spectral entropy (SE) moments using the time–frequency (TF) analysis. Three different forms of raw data were used as inputs, and we investigated the original PPG signal, the PPG signal with instantaneous frequency, and the PPG signal with spectral entropy. According to the results of the model testing, the PPG signal with spectral entropy generated the best outcomes. Compared to decision trees, subspace k-nearest neighbor, and k-nearest neighbor, our suggested approach with the super vector machine obtains a greater level of accuracy. The super vector machine, with 91.3% accuracy and a training duration of 9 s, was the best classifier.

Sensor-on-Microtips: Design and Development of Hydrothermally Grown ZnO on Micropipette Tips as a Modified Working Electrode for Detection of Glucose

Miniaturization of electrochemical components has become less common in the last decade, with the focus predominantly being the design and development of state-of-the-art microelectrodes for achieving small volume analysis of samples. However, such microelectrodes involve cumbersome processing procedures to convert the base material for the required application. A potential paradigm shift in such miniaturization could be achieved by using cheaper alternatives such as plastics to build electrochemical components, such as micropipette tips made of polypropylene, which are commercially available at ease. Hence, this work presents the design of an electrochemical working electrode based upon a micropipette tip, involving minimal processing procedures. Furthermore, such a working electrode was realized by sputtering silver onto a bare micropipette tip using a radio-frequency sputtering technique, to obtain electrical contacts on the tip, followed by hydrothermal growth of ZnO, which acted as the active electrode material. The ZnO nanostructures grown on the micropipette tip were characterized for their morphology and surface properties using a scanning electron microscope (SEM), laser microscope, Raman spectrometer, and X-ray photoelectron spectrometer (XPS). The developed micropipette tip-based electrode was then used as the working electrode in a three-electrode system, wherein its electrochemical stability and properties were analyzed using cyclic voltammetry (CV). Furthermore, the above system was used to detect glucose concentrations of 10-200 µM, to evaluate its sensing properties using amperometry. The developed working electrode exhibited a sensitivity of 69.02 µA/µM cm-2 and limit of detection of 67.5 µM, indicating the potential for using such modified micropipette tips as low-cost miniaturized sensors to detect various bio-analytes in sample solutions.

Detection of Glucose Based on Noble Metal Nanozymes: Mechanism, Activity Regulation, and Enantioselective Recognition

Glucose monitoring is essential to evaluate the degree of glucose metabolism disorders. The enzymatic determination has been the most widely used method in glucose detection because of its high efficiency, accuracy, and sensitivity. Noble metal nanomaterials (NMs, i.e., Au, Ag, Pt, and Pd), inheriting their excellent electronic, optical, and enzyme-like properties, are classified as noble metal nanozymes (NMNZs). As the NMNZs are often involved in two series of reactions, the oxidation of glucose and the chromogenic reaction of peroxide, here the chemical mechanism by employing NMNZs with glucose oxidase (GOx) and peroxidase (POD) mimicking activities is briefly summarized first. Subsequently, the regulation strategies of the GOx-like, POD-like and tandem enzyme-like activities of NMNZs are presented in detail, including the materials, size, morphology, composition, and the reaction condition of the representative NMs. In addition, in order to further mimic the enantioselectivity of enzyme, the design of NMNZs with enantioselective recognition of d-glucose and l-glucose by using different chiral compounds (DNA, amino acids, and cyclodextrins) and molecular imprinting is further described in this review. Finally, the feasible solutions to the existing challenges and a vision for future development possibilities are discussed.

Comparative assessment of blood glucose monitoring techniques: a review

Monitoring blood glucose levels is a vital indicator of diabetes mellitus management. The mainstream techniques of glucometers are invasive, painful, expensive, intermittent, and time-consuming. The ever-increasing number of global diabetic patients urges the development of alternative non-invasive glucose monitoring techniques. Recent advances in electrochemical biosensors, biomaterials, wearable sensors, biomedical signal processing, and microfabrication technologies have led to significant research and ideas in elevating the patient's life quality. This review provides up-to-date information about the available technologies and compares the advantages and limitations of invasive and non-invasive monitoring techniques. The scope of measuring glucose concentration in other bio-fluids such as interstitial fluid (ISF), tears, saliva, and sweat are also discussed. The high accuracy level of invasive methods in measuring blood glucose concentrations gives them superiority over other methods due to lower average absolute error between the detected glucose concentration and reference values. Whereas minimally invasive, and non-invasive techniques have the advantages of continuous and pain-free monitoring. Various blood glucose monitoring techniques have been evaluated based on their correlation to blood, patient-friendly, time efficiency, cost efficiency, and accuracy. Finally, this review also compares the currently available glucose monitoring devices in the market.

Evaluation of first and second trimester maternal thyroid profile on the prediction of gestational diabetes mellitus and post load glycemia

Maternal thyroid alterations have been widely associated with the risk of gestational diabetes mellitus (GDM). This study aims to 1) test the first and the second trimester full maternal thyroid profile on the prediction of GDM, both alone and combined with non-thyroid data; and 2) make that prediction independent of the diagnostic criteria, by evaluating the effectiveness of the different maternal variables on the prediction of oral glucose tolerance test (OGTT) post load glycemia. Pregnant women were recruited in Concepción, Chile. GDM diagnosis was performed at 24-28 weeks of pregnancy by an OGTT (n = 54 for normal glucose tolerance, n = 12 for GDM). 75 maternal thyroid and non-thyroid parameters were recorded in the first and the second trimester of pregnancy. Various combinations of variables were assessed for GDM and post load glycemia prediction through different classification and regression machine learning techniques. The best predictive models were simplified by variable selection. Every model was subjected to leave-one-out cross-validation. Our results indicate that thyroid markers are useful for the prediction of GDM and post load glycemia, especially at the second trimester of pregnancy. Thus, they could be used as an alternative screening tool for GDM, independently of the diagnostic criteria used. The final classification models predict GDM with cross-validation areas under the receiver operating characteristic curve of 0.867 (p<0.001) and 0.920 (p<0.001) in the first and the second trimester of pregnancy, respectively. The final regression models predict post load glycemia with cross-validation Spearman r correlation coefficients of 0.259 (p = 0.036) and 0.457 (p<0.001) in the first and the second trimester of pregnancy, respectively. This investigation constitutes the first attempt to test the performance of the whole maternal thyroid profile on GDM and OGTT post load glycemia prediction. Future external validation studies are needed to confirm these findings in larger cohorts and different populations.

Design of Smart Aggregates: Toward Rapid Clinical Diagnosis of Hyperlipidemia in Human Blood

Molecular aggregates with environmental responsive properties are desired for their wide practical applications such as bioprobes. Here, a series of smart near-infrared (NIR) luminogens for hyperlipidemia (HLP) diagnosis is reported. The aggregates of these molecules exhibit a twisted intramolecular charge-transfer effect in aqueous media, but aggregation-induced emission in highly viscous media due to the restriction of the intramolecular motion. These aggregates, which can autonomously respond to different environments via switching the aggregation state without changing their chemical structures are described, as "smart aggregates". Intriguingly, these luminogens demonstrate NIR-II and NIR-III luminescence with ultralarge Stokes shifts (>950 nm). Both in vitro detection and in vivo imaging of HLP can be realized in a mouse model. Linear relationships exist between the emission intensity and multiple pathological parameters in blood samples of HLP patients. Thus, the design of smart aggregate facilitates rapid and accurate detection of HLP and provides a promising attempt in aggregate science.

What is holding back glucometer use? -A comparative study of rural and urban India

BACKGROUND

Self-monitoring of blood glucose (SMBG) is associated with better glycemic control. There have been significant technological advances in blood glucose monitoring in the recent past, but the wider acceptance of these technologies is still debatable.

AIM

This study investigates the adoption of glucometers and the extent of the use of features in rural and urban India.

METHODS

The study uses Bass Model to predict the diffusion of innovation (DOI). Mathematical modeling was used to determine the stage of adoption (using 5-stage of DOI) and the Chi-Square test was used for examining the relationship between depth of implementation (extent of use) and place of residence.

RESULTS

The result of the study suggests that glucometer diffusion of innovation has passed the initial hurdle (chasm) in the urban population and is in the late majority. However, for rural respondents, the adoption of glucometers has just passed the chasm and falls under the early majority stage. The diffusion of innovation of combined rural and urban populations has just entered the late majority zone in 2022. The study suggests that a significant number of people with diabetes are still not using a glucometer and discusses the socio-economic issue related to this phenomenon. The utilization of the potential of a glucometer in SMBG falls under the localized shallow implementation category (not using advanced features).

CONCLUSIONS

The study suggests that the adoption of glucometer has just passed the initial hurdle in rural India, where two-thirds of the Indian population lives. Furthermore, the adoption of advanced glucometers is low among users.

A regression-based machine learning approach for pH and glucose detection with redox-sensitive colorimetric paper sensors

Colorimetric paper sensors are used in various fields due to their convenience and intuitive manner. However, these sensors present low accuracy in practical use because it is difficult to distinguish color changes for a minute amount of analyte with the naked eye. Herein, we demonstrate that a machine learning (ML)-based paper sensor platform accurately determines the color changes. We fabricated a colorimetric paper sensor by adsorbing polyaniline nanoparticles (PAni-NPs), whose color changes from blue to green when the ambient pH decreases. Adding glucose oxidase (GOx) to the paper sensor enables colorimetric glucose detection. Target analytes (10 μL) were aliquoted onto the paper sensors, and their images were taken with a smartphone under the same conditions in a darkroom. The red-green-blue (RGB) data from the images were extracted and used to train and test three regression models: support vector regression (SVR), decision tree regression (DTR), and random forest regression (RFR). Of the three regression models, RFR performed the best at estimating pH levels (R² = 0.957) ranging from pH 2 to 10 and glucose concentrations (R² = 0.922) ranging from 0 to 10 mg mL^-1.

A Mediated Enzymatic Electrochemical Sensor Using Paper-Based Laser-Induced Graphene

Laser-induced graphene (LIG) has been applied in many different sensing devices, from mechanical sensors to biochemical sensors. In particular, LIG fabricated on paper (PaperLIG) shows great promise for preparing cheap, flexible, and disposable biosensors. Distinct from the fabrication of LIG on polyimide, a two-step process is used for the fabrication of PaperLIG. In this study, firstly, a highly conductive PaperLIG is fabricated. Further characterization of PaperLIG confirmed that it was suitable for developing biosensors. Subsequently, the PaperLIG was used to construct a biosensor by immobilizing glucose oxidase, aminoferrocene, and Nafion on the surface. The developed glucose biosensor could be operated at a low applied potential (-90 mV) for amperometric measurements. The as-prepared biosensor demonstrated a limit of detection of (50-75 µM) and a linear range from 100 µM to 3 mM. The influence of the concentration of the Nafion casting solution on the performance of the developed biosensor was also investigated. Potential interfering species in saliva did not have a noticeable effect on the detection of glucose. Based on the experimental results, the simple-to-prepare PaperLIG-based saliva glucose biosensor shows great promise for application in future diabetes management.

Harnessing machine learning models for non-invasive pre-diabetes screening in children and adolescents

BACKGROUND AND OBJECTIVES

Pre-diabetes has been identified as an intermediate diagnosis and a sign of a relatively high chance of developing diabetes in the future. Diabetes has become one of the most frequent chronic disorders in children and adolescents around the world; therefore, predicting the onset of pre-diabetes allows a person at risk to make efforts to avoid or restrict disease progression. This research aims to create and implement a cross-validated machine learning model that can predict pre-diabetes using non-invasive methods.

METHODS

We have analysed the national representative dataset of children and adolescents (5-19 years) to develop a machine learning model for non-invasive pre-diabetes screening. Based on HbA1c levels the data (n = 26,567) was segregated into normal (n = 23,777) and pre-diabetes (n = 2790). We have considered eight features, six hyper-tuned machine learning models and different metrics for model evaluation. The final model was selected based on the area under the receiver operator curve (AUC), Cohen's kappa and cross-validation score. The selected model was integrated into the screening tool for automated pre-diabetes prediction.

RESULTS

The XG boost classifier was the best model, including all eight features. The 10-fold cross-validation score was highest for the XG boost model (90.13%) and least for the support vector machine (61.17%). The AUC was highest for RF (0.970), followed by GB (0.968), XGB (0.959), ETC (0.918), DT (0.908), and SVM (0.574) models. The XGB model was used to develop the screening tool.

CONCLUSION

We have developed and deployed a machine learning model for automated real-time pre-diabetes screening. The screening tool can be used over computers and can be transformed into software for easy usage. The detection of pre-diabetes in the pediatric age may help avoid its enhancement. Machine learning can also show great competence in determining important features in pre-diabetes.

An overview of advancements in closed-loop artificial pancreas system

Type 1 diabetes (T1D) is one of the world's health problems with a prevalence of 1.1 million for children and young adults under the age of 20. T1D is a health problem characterized by autoimmunity and the destruction of pancreatic cells that produce insulin. The available treatment is to maintain blood glucose within the desired normal range. To meet bolus and basal requirements, T1D patients may receive multiple daily injections (MDI) of fast-acting and long-acting insulin once or twice daily. In addition, insulin pumps can deliver multiple doses a day without causing injection discomfort in individuals with T1D. T1D patients have also monitored their blood glucose levels along with insulin replacement treatment using a continuous glucose monitor (CGM). However, this CGM has some drawbacks, like the sensor needs to be replaced after being inserted under the skin for seven days and needs to be calibrated (for some CGMs). The treatments and monitoring devices mentioned creating a lot of workloads to maintain blood glucose levels in individuals with T1D. Therefore, to overcome these problems, closed-loop artificial pancreas (APD) devices are widely used to manage blood glucose in T1D patients. Closed-loop APD consists of a glucose sensor, an insulin infusion device, and a control algorithm. This study reviews the progress of closed-loop artificial pancreas systems from the perspective of device properties, uses, testing procedures, regulations, and current market conditions.

Is Raman the best strategy towards the development of non-invasive continuous glucose monitoring devices for diabetes management?

Diabetes has no well-established cure; thus, its management is critical for avoiding severe health complications involving multiple organs. This requires frequent glycaemia monitoring, and the gold standards for this are fingerstick tests. During the last decades, several blood-withdrawal-free platforms have been being studied to replace this test and to improve significantly the quality of life of people with diabetes (PWD). Devices estimating glycaemia level targeting blood or biofluids such as tears, saliva, breath and sweat, are gaining attention; however, most are not reliable, user-friendly and/or cheap. Given the complexity of the topic and the rise of diabetes, a careful analysis is essential to track scientific and industrial progresses in developing diabetes management systems. Here, we summarize the emerging blood glucose level (BGL) measurement methods and report some examples of devices which have been under development in the last decades, discussing the reasons for them not reaching the market or not being really non-invasive and continuous. After discussing more in depth the history of Raman spectroscopy-based researches and devices for BGL measurements, we will examine if this technique could have the potential for the development of a user-friendly, miniaturized, non-invasive and continuous blood glucose-monitoring device, which can operate reliably, without inter-patient variability, over sustained periods.

Nanozyme-based colorimetric biosensor with a systemic quantification algorithm for noninvasive glucose monitoring

Diabetes mellitus accompanies an abnormally high glucose level in the bloodstream. Early diagnosis and proper glycemic management of blood glucose are essential to prevent further progression and complications. Biosensor-based colorimetric detection has progressed and shown potential in portable and inexpensive daily assessment of glucose levels because of its simplicity, low-cost, and convenient operation without sophisticated instrumentation. Colorimetric glucose biosensors commonly use natural enzymes that recognize glucose and chromophores that detect enzymatic reaction products. However, many natural enzymes have inherent defects, limiting their extensive application. Recently, nanozyme-based colorimetric detection has drawn attention due to its merits including high sensitivity, stability under strict reaction conditions, flexible structural design with low-cost materials, and adjustable catalytic activities. This review discusses various nanozyme materials, colorimetric analytic methods and mechanisms, recent machine learning based analytic methods, quantification systems, applications and future directions for monitoring and managing diabetes.

Electrospun nanofiber-based glucose sensors for glucose detection

Diabetes is a chronic, systemic metabolic disease that leads to multiple complications, even death. Meanwhile, the number of people with diabetes worldwide is increasing year by year. Sensors play an important role in the development of biomedical devices. The development of efficient, stable, and inexpensive glucose sensors for the continuous monitoring of blood glucose levels has received widespread attention because they can provide reliable data for diabetes prevention and diagnosis. Electrospun nanofibers are new kinds of functional nanocomposites that show incredible capabilities for high-level biosensing. This article reviews glucose sensors based on electrospun nanofibers. The principles of the glucose sensor, the types of glucose measurement, and the glucose detection methods are briefly discussed. The principle of electrospinning and its applications and advantages in glucose sensors are then introduced. This article provides a comprehensive summary of the applications and advantages of polymers and nanomaterials in electrospun nanofiber-based glucose sensors. The relevant applications and comparisons of enzymatic and non-enzymatic nanofiber-based glucose sensors are discussed in detail. The main advantages and disadvantages of glucose sensors based on electrospun nanofibers are evaluated, and some solutions are proposed. Finally, potential commercial development and improved methods for glucose sensors based on electrospinning nanofibers are discussed.

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.

Chemical Sensor Nanotechnology in Pharmaceutical Drug Research

The increase in demand for pharmaceutical treatments due to pandemic-related illnesses has created a need for improved quality control in drug manufacturing. Understanding the physical, biological, and chemical properties of APIs is an important area of health-related research. As such, research into enhanced chemical sensing and analysis of pharmaceutical ingredients (APIs) for drug development, delivery and monitoring has become immensely popular in the nanotechnology space. Nanomaterial-based chemical sensors have been used to detect and analyze APIs related to the treatment of various illnesses pre and post administration. Furthermore, electrical and optical techniques are often coupled with nano-chemical sensors to produce data for various applications which relate to the efficiencies of the APIs. In this review, we focus on the latest nanotechnology applied to probing the chemical and biochemical properties of pharmaceutical drugs, placing specific interest on several types of nanomaterial-based chemical sensors, their characteristics, detection methods, and applications. This study offers insight into the progress in drug development and monitoring research for designing improved quality control methods for pharmaceutical and health-related research.

A Reinforcement Learning Based System for Blood Glucose Control without Carbohydrate Estimation in Type 1 Diabetes: In Silico Validation

Type 1 Diabetes (T1D) is a chronic autoimmune disease, which requires the use of exogenous insulin for glucose regulation. In current hybrid closed-loop systems, meal entry is manual which adds cognitive burden to the persons living with T1D. In this study, we proposed a control system based on Proximal Policy Optimisation (PPO) that controls both basal and bolus insulin infusion and only requires meal announcement, thus eliminating the need for carbohydrate estimation. We evaluated the system on a challenging meal scenario, using an open-source simulator based on the UVA/Padova 2008 model and achieved a mean Time in Range value of 65% for the adult subject cohort, while maintaining a moderate hypoglycemic and hyperglycemic risk profile. The approach shows promise and welcomes further research towards the translation to a real-life artificial pancreas. Clinical relevance- This was an in-silico analysis towards the development of an autonomous artificial pancreas system for glucose control. The proposed system show promise in eliminating the need for estimating the carbohydrate content in meals.

Technologies for Diabetes Self-Monitoring: A Scoping Review and Assessment Using the REASSURED Criteria

BACKGROUND

Self-management is an important pillar for diabetes control and to achieve it, glucose self-monitoring devices are needed. Currently, there exist several different devices in the market and many others are being developed. However, whether these devices are suitable to be used in resource constrained settings is yet to be evaluated.

AIMS

To assess existing glucose monitoring tools and also those in development against the REASSURED which have been previously used to evaluate diagnostic tools for communicable diseases.

METHODS

We conducted a scoping review by searching PubMed for peer-review articles published in either English, Spanish or Portuguese in the last 5 years. We selected papers including information about devices used for self-monitoring and tested on humans with diabetes; then, the REASSURED criteria were used to assess them.

RESULTS

We found a total of 7 continuous glucose monitoring device groups, 6 non-continuous, and 6 devices in development. Accuracy varied between devices and most of them were either invasive or minimally invasive. Little to no evidence is published around robustness, affordability and delivery to those in need. However, when reviewing publicly available prices, none of the devices would be affordable for people living in low- and middle-income countries.

CONCLUSIONS

Available devices cannot be considered adapted for use in self-monitoring in resource constraints settings. Further studies should aim to develop less-invasive devices that do not require a large set of components. Additionally, we suggest some improvement in the REASSURED criteria such as the inclusion of patient-important outcomes to increase its appropriateness to assess non-communicable diseases devices.

Majid H, Ashyap AYI, Nebhen J, Kamarudin MR, See CH, Abd-Alhameed RA. Glucose level detection using millimetre-wave metamaterial-inspired resonator

Millimetre-wave frequencies are promising for sensitive detection of glucose levels in the blood, where the temperature effect is insignificant. All these features provide the feasibility of continuous, portable, and accurate monitoring of glucose levels. This paper presents a metamaterial-inspired resonator comprising five split-rings to detect glucose levels at 24.9 GHz. The plexiglass case containing blood is modelled on the sensor’s surface and the structure is simulated for the glucose levels in blood from 50 mg/dl to 120 mg/dl. The novelty of the sensor is demonstrated by the capability to sense the normal glucose levels at millimetre-wave frequencies. The dielectric characteristics of the blood are modelled by using the Debye parameters. The proposed design can detect small changes in the dielectric properties of blood caused by varying glucose levels. The variation in the transmission coefficient for each glucose level tested in this study is determined by the quality factor and resonant frequency. The sensor presented can detect the change in the quality factor of transmission response up to 2.71/mg/dl. The sensor’s performance has also been tested to detect diabetic hyperosmolar syndrome. The sensor showed a linear shift in resonant frequency with the change in glucose levels, and an R² of 0.9976 was obtained by applying regression analysis. Thus, the sensor can be used to monitor glucose in a normal range as well as at extreme levels.

Non-Invasive Blood Glucose Estimation System Based on a Neural Network with Dual-Wavelength Photoplethysmography and Bioelectrical Impedance Measuring

This study proposed a noninvasive blood glucose estimation system based on dual-wavelength photoplethysmography (PPG) and bioelectrical impedance measuring technology that can avoid the discomfort created by conventional invasive blood glucose measurement methods while accurately estimating blood glucose. The measured PPG signals are converted into mean, variance, skewness, kurtosis, standard deviation, and information entropy. The data obtained by bioelectrical impedance measuring consist of the real part, imaginary part, phase, and amplitude size of 11 types of frequencies, which are converted into features through principal component analyses. After combining the input of seven physiological features, the blood glucose value is finally obtained as the input of the back-propagation neural network (BPNN). To confirm the robustness of the system operation, this study collected data from 40 volunteers and established a database. From the experimental results, the system has a mean squared error of 40.736, a root mean squared error of 6.3824, a mean absolute error of 5.0896, a mean absolute relative difference of 4.4321%, and a coefficient of determination (R Squared, R²) of 0.997, all of which fall within the clinically accurate region A in the Clarke error grid analyses.

A Contactless Glucose Solution Concentration Measurement System Based on Improved High Accurate FMCW Radar Algorithm

To reduce the pain and the probability of cross-infection caused by the invasive blood glucose testing instruments, the ex vivo glucose measurement is of high significance. The electrical property of blood varies with the density of the glucose, which can be sensed by measuring its reflected coefficient in millimeter-wave. In this article, we built a contactless glucose solution concentration measurement system based on 77-GHz FMCW radar. Several preliminary signal processing algorithms are cascaded with a deep neural network to improve the accuracy of glucose solution concentration measurement. Our experiment shows that the resolution of this ex vivo glucose measurement can achieve up to 0.1 mg/mL.

Optimized multilayer structure for sensitive THz characterization of thin-film glucose solutions

Terahertz time-domain spectroscopy (THz-TDS) has shown promise in biomedical sample characterization and high characterization sensitivity is in demand due to the thin-film (TF) feature of the sample. This paper proposes an optimized multilayer structure for sensitive characterization of TF aqueous solutions in reflection THz-TDS. Theoretical simulations are conducted for structural optimization and the 75 µm window-sample-mirror structure displays the best sensitivity compared to other sandwich structures and traditional THz measurement geometries. 0-20% TF glucose solutions are then measured; and a spectral peak introduced by the proposed structure is observed to result in the high sensitivity. Our work provides a new way of customizing multilayer structure for THz thin-film characterization.

Selection of Noninvasive Features in Wrist-Based Wearable Sensors to Predict Blood Glucose Concentrations Using Machine Learning Algorithms

Glucose monitoring technologies allow users to monitor glycemic fluctuations (e.g., blood glucose levels). This is particularly important for individuals who have diabetes mellitus (DM). Traditional self-monitoring blood glucose (SMBG) devices require the user to prick their finger and extract a blood drop to measure the blood glucose based on chemical reactions with the blood. Unlike traditional glucometer devices, noninvasive continuous glucose monitoring (NICGM) devices aim to solve these issues by consistently monitoring users' blood glucose levels (BGLs) without invasively acquiring a sample. In this work, we investigated the feasibility of a novel approach to NICGM using multiple off-the-shelf wearable sensors and learning-based models (i.e., machine learning) to predict blood glucose. Two datasets were used for this study: (1) the OhioT1DM dataset, provided by the Ohio University; and (2) the UofM dataset, created by our research team. The UofM dataset consists of fourteen features provided by six sensors for studying possible relationships between glucose and noninvasive biometric measurements. Both datasets are passed through a machine learning (ML) pipeline that tests linear and nonlinear models to predict BGLs from the set of noninvasive features. The results of this pilot study show that the combination of fourteen noninvasive biometric measurements with ML algorithms could lead to accurate BGL predictions within the clinical range; however, a larger dataset is required to make conclusions about the feasibility of this approach.

Current Status and Emerging Options for Automated Insulin Delivery Systems

Combining technologies including rapid insulin analogs, insulin pumps, continuous glucose monitors, and control algorithms has allowed for the creation of automated insulin delivery (AID) systems. These systems have proven to be the most effective technology for optimizing metabolic control and could hold the key to broadly achieving goal-level glycemic control for people with type 1 diabetes. The use of AID has exploded in the past several years with several options available in the United States and even more in Europe. In this article, we review the largest studies involving these AID systems, and then examine future directions for AID with an emphasis on usability.

A Novel Approach for Non-Invasive Continuous In-Line Control of Perfusion Cell Cultivations by Raman Spectroscopy

Continuous manufacturing is becoming more important in the biopharmaceutical industry. This processing strategy is favorable, as it is more efficient, flexible, and has the potential to produce higher and more consistent product quality. At the same time, it faces some challenges, especially in cell culture. As a steady state has to be maintained over a prolonged time, it is unavoidable to implement advanced process analytical technologies to control the relevant process parameters in a fast and precise manner. One such analytical technology is Raman spectroscopy, which has proven its advantages for process monitoring and control mostly in (fed-) batch cultivations. In this study, an in-line flow cell for Raman spectroscopy is included in the cell-free harvest stream of a perfusion process. Quantitative models for glucose and lactate were generated based on five cultivations originating from varying bioreactor scales. After successfully validating the glucose model (Root Mean Square Error of Prediction (RMSEP) of ∼0.2 g/L), it was employed for control of an external glucose feed in cultivation with a glucose-free perfusion medium. The generated model was successfully applied to perform process control at 4 g/L and 1.5 g/L glucose over several days, respectively, with variability of ±0.4 g/L. The results demonstrate the high potential of Raman spectroscopy for advanced process monitoring and control of a perfusion process with a bioreactor and scale-independent measurement method.

What Is the Optimal Method for Cleaning Screen-Printed Electrodes?

Screen-printed electrodes-based sensors can be successfully used to determine all kinds of analytes with great precision and specificity. However, obtaining a high-quality sensor can be difficult due to factors such as lack of reproducibility, surface contamination or other manufacturing challenges. An important step in ensuring reproducible results is the cleaning step. The aim of the current work is to help researchers around the world who struggle with finding the most suitable method for cleaning screen-printed electrodes. We evaluated the cleaning efficiency of different chemical compounds and cleaning methods using cyclic voltammetry and electrochemical impedance spectroscopy. The percentage differences in polarization resistance (Rp) before and after cleaning were as follows: acetone—35.33% for gold and 49.94 for platinum; ethanol—44.50% for gold and 81.68% for platinum; H2O2—47.34% for gold and 92.78% for platinum; electrochemical method—3.70% for gold and 67.96% for platinum. Thus, we concluded that all the evaluated cleaning methods seem to improve the surface of both gold and platinum electrodes; however, the most important reduction in the polarization resistance (Rp) was obtained after treating them with a solution of H2O2 and multiple CV cycles with a low scanning speed (10 mV/s).

Clinical and economic outcomes of continuous glucose monitoring system (CGMS) in patients with diabetes mellitus: A systematic literature review

OBJECTIVE

Poor glycemic management increases the risk of cardiac, microvascular, and other complications. Therefore, timely assessment and control of glycemic levels is paramount in diabetes. Recent advancements in automated management methods is the use of a continuous glucose monitoring system (CGMS). The objective was to study its clinical and economic impact in the glucose level monitoring and how it can be effectively used or reimbursed for wider population.

METHODS

Comprehensive search was done using multiple databases to capture relevant and most recent evidence. All steps were conducted by two independent researchers and discrepancies resolved by a third reviewer. Quality appraisal was performed by relevant scale depending on study design.

RESULTS

Twenty-six and 12 studies were included for clinical and economic outcomes, respectively. Clinical outcomes like HbA1c and glucose variation, time in range, accuracy, etc. were captured. Comparison of different CGMS types was also reported. Major economic outcomes were direct cost, healthcare resource utilization, and work absenteeism.

CONCLUSIONS

CGMS in patients with diabetes is associated with a valuable clinical implications in reducing hypoglycemic events, glucose and HbA1c level. Additionally, it has an impact on direct and indirect costs of management. Further, quantitative analysis would be required to produce concrete evidence.

Effectiveness of continuous glucose monitoring in maintaining glycaemic control among people with type 1 diabetes mellitus: a systematic review of randomised controlled trials and meta-analysis

AIMS/HYPOTHESIS

The aim of this work was to assess the effectiveness of continuous glucose monitoring (CGM) vs self-monitoring of blood glucose (SMBG) in maintaining glycaemic control among people with type 1 diabetes mellitus.

METHODS

Cochrane Library, PubMed, Embase, CINAHL, Scopus, trial registries and grey literature were searched from 9 June 2011 until 22 December 2020 for RCTs comparing CGM intervention against SMBG control among the non-pregnant individuals with type 1 diabetes mellitus of all ages and both sexes on multiple daily injections or continuous subcutaneous insulin infusion with HbA1c levels, severe hypoglycaemia and diabetic ketoacidosis (DKA) as outcomes. Studies also included any individual or caregiver-led CGM systems. Studies involving GlucoWatch were excluded. Risk of bias was appraised with Cochrane risk of bias tool. Meta-analysis and meta-regression were performed using Review Manager software and R software, respectively. Heterogeneity was evaluated using χ2 and I2 statistics. Overall effects and certainty of evidence were evaluated using Z statistic and GRADE (Grading of Recommendations, Assessment, Development and Evaluation) software.

RESULTS

Twenty-two studies, involving 2188 individuals with type 1 diabetes, were identified. Most studies had low risk of bias. Meta-analysis of 21 studies involving 2149 individuals revealed that CGM significantly decreased HbA1c levels compared with SMBG (mean difference -2.46 mmol/mol [-0.23%] [95% CI -3.83, -1.08], Z = 3.50, p=0.0005), with larger effects experienced among higher baseline HbA1c >64 mmol/mol (>8%) individuals (mean difference -4.67 mmol/mol [-0.43%] [95% CI -6.04, -3.30], Z = 6.69, p<0.00001). However, CGM had no influence on the number of severe hypoglycaemia (p=0.13) and DKA events (p=0.88). Certainty of evidence was moderate.

CONCLUSIONS/INTERPRETATION

CGM is superior to SMBG in improving glycaemic control among individuals with type 1 diabetes in the community, especially in those with uncontrolled glycaemia. Individuals with type 1 diabetes with HbA1c >64 mmol/mol (>8%) are most likely to benefit from CGM. Current findings could not confer a concrete conclusion on the effectiveness of CGM on DKA outcome as DKA incidences were rare. Current evidence is also limited to outpatient settings. Future research should evaluate the accuracy of CGM and the effectiveness of CGM across different age groups and insulin regimens as these remain unclear in this paper.

PROSPERO REGISTRATION

Registration no. CRD42020207042.

FUNDING

This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

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

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

Glucose and Ethanol Detection with an Affinity-Switchable Lateral Flow Assay

The lateral flow assay (LFA) is one of the most successful analytical platforms for rapid on-site detection of target substances. This type of assay has been used in many rapid diagnoses, for example, pregnancy tests and infectious disease prevention. However, applications of LFAs for very small molecules remain a demanding challenge due to the problem of obtaining the corresponding binding partners to form sandwich complexes. In this paper, we report an affinity-switchable (AS) LFA (ASLFA) for the rapid and selective detection of hydrogen peroxide (H₂O₂), glucose, and ethanol in blood serum and urine samples. Unlike classical LFAs, which rely on the "always on" interaction between the antigen and the antibody, the working principle of ASLFA is based on the gold nanoparticle-conjugated AS biotin probe Au@H₂O₂-ASB, which can be activated by H₂O₂ for binding with the streptavidin (SA) protein. In the presence of glucose and ethanol, glucose oxidase and alcohol oxidase can react with the substrate to generate H₂O₂ and thereby activate Au@H₂O₂-ASB for binding with SA. Therefore, this ASLFA approach can be an alternative for classical glucose and ethanol detection methods in a wide variety of samples, where simple and rapid on-site detection is essential.

Continuous Glucose Monitoring in Healthy Adults-Possible Applications in Health Care, Wellness, and Sports

INTRODUCTION

Continuous glucose monitoring (CGM) systems were primarily developed for patients with diabetes mellitus. However, these systems are increasingly being used by individuals who do not have diabetes mellitus. This mini review describes possible applications of CGM systems in healthy adults in health care, wellness, and sports.

RESULTS

CGM systems can be used for early detection of abnormal glucose regulation. Learning from CGM data how the intake of foods with different glycemic loads and physical activity affect glucose responses can be helpful in improving nutritional and/or physical activity behavior. Furthermore, states of stress that affect glucose dynamics could be made visible. Physical performance and/or regeneration can be improved as CGM systems can provide information on glucose values and dynamics that may help optimize nutritional strategies pre-, during, and post-exercise.

CONCLUSIONS

CGM has a high potential for health benefits and self-optimization. More scientific studies are needed to improve the interpretation of CGM data. The interaction with other wearables and combined data collection and analysis in one single device would contribute to developing more precise recommendations for users.

Semiconducting Carbon Nanotube-Based Nanodevices for Monitoring the Effects of Chlorphenamine on the Activities of Intracellular Ca2+ Stores

We report a flexible and noninvasive method based on field-effect transistors hybridizing semiconducting single-walled carbon nanotubes for monitoring the effects of histamine on Ca²⁺ release from the intracellular stores of a nonexcitable cell. These nanodevices allowed us to evaluate the real-time electrophysiological activities of HeLa cells under the stimulation of histamine via the recording of the conductance changes of the devices. These changes resulted from the binding of histamine to its receptor type 1 on the HeLa cell membrane. Moreover, the effects of chlorphenamine, an antihistamine, on the electrophysiological activities of a single HeLa cell were also evaluated, indicating that the pretreatment of the cell with chlorpheniramine decreased intracellular Ca²⁺ release. Significantly, we only utilized a single nanodevice to perform the measurements for multiple cells pretreated with various concentrations of chlorphenamine. This enabled the statistically meaningful analysis of drug effects on cells without errors from device variations. Obtained results indicated the novel advantages of our method such as real-time monitoring and quantitative capability. Our devices, therefore, can be efficient tools for biomedical applications such as electrophysiology research and drug screening.

Nanomaterial-Based Biosensors using Field-Effect Transistors: A Review

Field-effect transistor biosensors (Bio-FET) have attracted great interest in recent years owing to their distinctive properties like high sensitivity, good selectivity, and easy integration into portable and wearable electronic devices. Bio-FET performance mainly relies on the constituent components such as the bio-recognition layer and the transducer, which ensures device stability, sensitivity, and lifetime. Nanomaterial-based Bio-FETs are excellent candidates for biosensing applications. This review discusses the basic concepts, function, and working principles of Bio-FETs, and focuses on the progress of recent research in Bio-FETs in the sensing of neurotransmitters, glucose, nucleic acids, proteins, viruses, and cancer biomarkers using nanomaterials. Finally, challenges in the development of Bio-FETs, as well as an outlook on the prospects of nano Bio-FET-based sensing in various fields, are discussed.

Integrating Multiple Inputs Into an Artificial Pancreas System: Narrative Literature Review

BACKGROUND

Type 1 diabetes (T1D) is a chronic autoimmune disease in which a deficiency in insulin production impairs the glucose homeostasis of the body. Continuous subcutaneous infusion of insulin is a commonly used treatment method. Artificial pancreas systems (APS) use continuous glucose level monitoring and continuous subcutaneous infusion of insulin in a closed-loop mode incorporating a controller (or control algorithm). However, the operation of APS is challenging because of complexities arising during meals, exercise, stress, sleep, illnesses, glucose sensing and insulin action delays, and the cognitive burden. To overcome these challenges, options to augment APS through integration of additional inputs, creating multi-input APS (MAPS), are being investigated.

OBJECTIVE

The aim of this survey is to identify and analyze input data, control architectures, and validation methods of MAPS to better understand the complexities and current state of such systems. This is expected to be valuable in developing improved systems to enhance the quality of life of people with T1D.

METHODS

A literature survey was conducted using the Scopus, PubMed, and IEEE Xplore databases for the period January 1, 2005, to February 10, 2020. On the basis of the search criteria, 1092 articles were initially shortlisted, of which 11 (1.01%) were selected for an in-depth narrative analysis. In addition, 6 clinical studies associated with the selected studies were also analyzed.

RESULTS

Signals such as heart rate, accelerometer readings, energy expenditure, and galvanic skin response captured by wearable devices were the most frequently used additional inputs. The use of invasive (blood or other body fluid analytes) inputs such as lactate and adrenaline were also simulated. These inputs were incorporated to switch the mode of the controller through activity detection, directly incorporated for decision-making and for the development of intermediate modules for the controller. The validation of the MAPS was carried out through the use of simulators based on different physiological models and clinical trials.

CONCLUSIONS

The integration of additional physiological signals with continuous glucose level monitoring has the potential to optimize glucose control in people with T1D through addressing the identified limitations of APS. Most of the identified additional inputs are related to wearable devices. The rapid growth in wearable technologies can be seen as a key motivator regarding MAPS. However, it is important to further evaluate the practical complexities and psychosocial aspects associated with such systems in real life.

Skin Patchable Sensor Surveillance for Continuous Glucose Monitoring

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

Review of Microdevices for Hemozoin-Based Malaria Detection

Despite being preventable and treatable, malaria still puts almost half of the world's population at risk. Thus, prompt, accurate and sensitive malaria diagnosis is crucial for disease control and elimination. Optical microscopy and immuno-rapid tests are the standard malaria diagnostic methods in the field. However, these are time-consuming and fail to detect low-level parasitemia. Biosensors and lab-on-a-chip devices, as reported to different applications, usually offer high sensitivity, specificity, and ease of use at the point of care. Thus, these can be explored as an alternative for malaria diagnosis. Alongside malaria infection inside the human red blood cells, parasites consume host hemoglobin generating the hemozoin crystal as a by-product. Hemozoin is produced in all parasite species either in symptomatic and asymptomatic individuals. Furthermore, hemozoin crystals are produced as the parasites invade the red blood cells and their content relates to disease progression. Hemozoin is, therefore, a unique indicator of infection, being used as a malaria biomarker. Herein, the so-far developed biosensors and lab-on-a-chip devices aiming for malaria detection by targeting hemozoin as a biomarker are reviewed and discussed to fulfil all the medical demands for malaria management towards elimination.

Agricultural Potentials of Molecular Spectroscopy and Advances for Food Authentication: An Overview

Meat, fish, coffee, tea, mushroom, and spices are foods that have been acknowledged for their nutritional benefits but are also reportedly targets of fraud and tampering due to their economic value. Conventional methods often take precedence for monitoring these foods, but rapid advanced instruments employing molecular spectroscopic techniques are gradually claiming dominance due to their numerous advantages such as low cost, little to no sample preparation, and, above all, their ability to fingerprint and detect a deviation from quality. This review aims to provide a detailed overview of common molecular spectroscopic techniques and their use for agricultural and food quality management. Using multiple databases including ScienceDirect, Scopus, Web of Science, and Google Scholar, 171 research publications including research articles, review papers, and book chapters were thoroughly reviewed and discussed to highlight new trends, accomplishments, challenges, and benefits of using molecular spectroscopic methods for studying food matrices. It was observed that Near infrared spectroscopy (NIRS), Infrared spectroscopy (IR), Hyperspectral imaging (his), and Nuclear magnetic resonance spectroscopy (NMR) stand out in particular for the identification of geographical origin, compositional analysis, authentication, and the detection of adulteration of meat, fish, coffee, tea, mushroom, and spices; however, the potential of UV/Vis, 1H-NMR, and Raman spectroscopy (RS) for similar purposes is not negligible. The methods rely heavily on preprocessing and chemometric methods, but their reliance on conventional reference data which can sometimes be unreliable, for quantitative analysis, is perhaps one of their dominant challenges. Nonetheless, the emergence of handheld versions of these techniques is an area that is continuously being explored for digitalized remote analysis.

Non-Invasive Classification of Blood Glucose Level for Early Detection Diabetes Based on Photoplethysmography Signal

Monitoring systems for the early detection of diabetes are essential to avoid potential expensive medical costs. Currently, only invasive monitoring methods are commercially available. These methods have significant disadvantages as patients experience discomfort while obtaining blood samples. A non-invasive method of blood glucose level (BGL) monitoring that is painless and low-cost would address the limitations of invasive techniques. Photoplethysmography (PPG) collects a signal from a finger sensor using a photodiode, and a nearby infrared LED light. The combination of the PPG electronic circuit with artificial intelligence makes it possible to implement the classification of BGL. However, one major constraint of deep learning is the long training phase. We try to overcome this limitation and offer a concept for classifying type 2 diabetes (T2D) using a machine learning algorithm based on PPG. We gathered 400 raw datasets of BGL measured with PPG and divided these points into two classification levels, according to the National Institute for Clinical Excellence, namely, “normal” and “diabetes”. Based on the results for testing between the models, the ensemble bagged trees algorithm achieved the best results with an accuracy of 98%.

Colorimetric and Electrochemical Screening for Early Detection of Diabetes Mellitus and Diabetic Retinopathy-Application of Sensor Arrays and Machine Learning

In this review, a selection of works on the sensing of biomarkers related to diabetes mellitus (DM) and diabetic retinopathy (DR) are presented, with the scope of helping and encouraging researchers to design sensor-array machine-learning (ML)-supported devices for robust, fast, and cost-effective early detection of these devastating diseases. First, we highlight the social relevance of developing systematic screening programs for such diseases and how sensor-arrays and ML approaches could ease their early diagnosis. Then, we present diverse works related to the colorimetric and electrochemical sensing of biomarkers related to DM and DR with non-invasive sampling (e.g., urine, saliva, breath, tears, and sweat samples), with a special mention to some already-existing sensor arrays and ML approaches. We finally highlight the great potential of the latter approaches for the fast and reliable early diagnosis of DM and DR.