Blood Glucose Detection Research Articles

Preparation of Self‐Driven Triboelectric Nanosensor Based on Chitosan‐Coated Ti Foil for Detecting Blood Glucose Concentration

The clinical diagnosis of diabetes mellitus typically depends on blood glucose measurements. Many laboratory studies have focused on enzyme–electrode biosensors that require an external power source. In this study, the potential of triboelectric nanogenerators (TENGs) as self‐driven sensors for glucose detection is investigated by utilizing the glucose specificity of glucose oxidase. When immersed in glucose solutions with concentrations ranging from 3.9 to 13.1 mmol L−1, the TENGs exhibit a linear relationship between the glucose concentration and current and voltage changes. These findings suggest that the TENGs developed in this study can effectively function as glucose sensors, providing a foundation for future human blood glucose monitoring applications and demonstrating a promising new approach for biomedical sensor technology.

Deep Learning‐Coupled Metabolic Heat Integrated Sensing System for Noninvasive Continuous Monitoring of Blood Glucose

The dynamic and continuous monitoring of blood glucose (BG) concentration is crucial for the health management of diabetic patients. Despite its importance, significant challenges remain in the development of effective BG monitoring technologies. Metabolic heat conformation (MHC) offers a promising solution due to its noninvasiveness and reliability. However, progress in MHC technology is hindered by the complexities of multisensor integration and the intricate correlation between MHC and BG. Herein, a wearable BG continuous monitoring device based on metabolic heat integrated sensing (MHIS) is developed, combined with a deep learning network to enable continuous BG detection using MHC principles. The MHIS device integrates a miniaturized circuit and intelligent secondary signal processing, allowing for the simultaneous acquisition and integrated operation of various physiological parameters, including metabolic heat production. By integrating a gate recurrent unit neural network, a model is established to facilitate continuous BG monitoring. The wearable device has a certain accuracy, and when analyzed in comparison with commercial noninvasive glucose meters, the mean absolute relative error meets international standards. The deep learning‐enhanced MHIS system proposed in this work enables noninvasive BG monitoring, paving the way for advancements in personalized healthcare management and offering new opportunities in digital healthcare consultation.

A novel trident shaped microstrip antenna sensor for glucose detection in human blood

A novel trident-shaped microstrip patch antenna sensor with great sensitivity is developed to estimate the amounts of glucose in blood samples of human. The suggested sensor is built on a 30 × 24 × 1.6 mm3 FR-4 substrate layer with a quality factor of 64 and a dielectric constant value of 4.3 resonating at 4.5 GHz. A finger phantom is the replica of the human finger and it is originated in the electromagnetic simulator in order to forecast the glucose content. By positioning the phantom replica at various localities on the constructed antenna sensor, the frequency shifts are observed for various levels of glucose concentration in different degrees, ranging from 0 to 1000 mg/dL are detected. The proposed sensor can identify diabetic conditions in patients as it has a finger phantom positioned parallel to the feed at the top of the antenna giving a 156 MHz paramount shift in frequency, sensitivity of 156 kHz/(mg/dL), and a minimum frequency shift of 24 MHz and sensitivity of 24 kHz/(mg/dL). To confirm the suggested sensor's functionality in a real-time environment, its performance is examined for various actual human finger postures, and the resulting resonance frequencies are observed. In order to detect the levels of glucose concentration, the suggested sensor's error performance is calculated and are found to be near to 1%.

Deep-Learning-Based Blood Glucose Detection Device Using Acetone Exhaled Breath Sensing Features of α-Fe2O3-MWCNT Nanocomposites.

Owing to the correlation between acetone in human's exhaled breath (EB) and blood glucose, the development of EB acetone gas-sensing devices is important for early diagnosis of diabetes diseases. In this article, a noninvasive blood glucose detection device through acetone sensing in EB, based on an α-Fe2O3-multiwalled carbon nanotube (MWCNT) nanocomposite, was successfully developed. Different amounts of α-Fe2O3 were added to the MWCNTs by a simple solution method. The optimized acetone gas sensor showed a response of 5.15 to 10 ppm acetone gas at 200 °C. Also, the fabricated sensor showed very good sensing properties even in an atmosphere with high relative humidity. Since the EB has high humidity, the proposed sensor is a promising device to exactly detect the amount of acetone in EB with high humidity. The sensor was powered by a 3200 mAh battery with the possibility of charging using mains electricity. To increase the reliability and calibration of the sensing device, a practical test was taken to detect acetone EB from 50 volunteers, and a deep learning algorithm (DLA) was used to detect the effect of various factors on the amount of acetone in each person's acetone EB. The proposed device with ±15 errors had almost 85% correct responses. Also, the proposed device had excellent response, short response time, good selectivity, and good repeatability, leading it to be a suitable candidate for noninvasive blood glucose sensing.

A microfluidic chip with integrated plasma separation for sample-to-answer detection of multiple chronic disease biomarkers in whole blood

Point-of-care testing of multiple chronic disease biomarkers is crucial for timely intervention and management of chronic diseases. Here, a “sample-to-answer” microfluidic chip was developed for simultaneous detection of multiple chronic disease biomarkers in whole blood by integrating a plasma separation module. The whole detection process is very convenient, i.e., just add whole blood and get the results. The chip successfully achieved the simultaneous detection of total cholesterol, triglycerides, uric acid, and glucose in undiluted whole blood within 21 min, including 6 min for plasma separation and 15 min for enzymatic chromogenic reactions. Moreover, the sensitivity levels of on-chip detection of chronic disease biomarkers can also meet clinically relevant thresholds. The chip is easy to use and has significant potential to improve home self-management of chronic diseases and enhance healthcare outcomes.

The Impact of Different Regions and Dietary Structures on Blood Glucose and Lipid Levels

<i>Objective: </i>To explore the effects of different regions and dietary structures on the blood glucose and lipid levels of residents. <i>Method: </i>1220 examinees from Fenyang City, Shanxi Province were selected as the research subjects and included in the study group. In addition, 1225 examinees from Xinjiang Communist Youth League Farm were selected as the control group 1, and 1200 examinees from Wujiaqu City, Xinjiang Uygur Autonomous Region (limited to regular physical labor and light diet) were selected as the control group 2. Collect general information such as the regional situation and dietary structure of personnel in each group, and use a fully automated biochemical analyzer to detect blood glucose (GLU) and lipid indicators (including total cholesterol (TC) and triglycerides (TG)). Compare the levels of the above indicators and the differences in the abnormal detection rates of each indicator in each group. <i>The results: </i>The levels of GLU, TC, and TG in the study group were significantly higher than those in control group 1 and control group 2 [GLU (mmol/L): 5.30±1.45 compared to 5.03±1.50, 4.18±0.39; TC (mmol/L): 1.67± 1.20 compared to 1.58±1.01, 0.69±0.36; TG (mmol/L): 5.02±1.71 compared to 4.72±1.17, 3.19±0.89], and the differences were statistically significant (all P<0.01). The abnormal detection rates of GLU, TC, and TG in the study group were significantly higher than those in the control group 1 and control group 2 (GLU: 18.43% compared to 13.64% and 2.00%, TC: 35.43% compared to 28.42% and 0.00%, TG: 14.05% compared to 5.63% and 0.00%), and the differences were statistically significant (all P<0.01). <i>Conclusion: </i>Regional differences and dietary structures are important factors affecting the blood sugar and lipid levels of residents. Balanced diet is beneficial for the stability of blood indicators and physical health of residents.

Design and analysis of a metamaterial based biosensor to determine blood glucose concentration

In this paper, a biosensor utilizing metamaterials is designed and simulated to detect blood glucose concentration. The proposed sensor comprised of a microstrip patch antenna designed on a Rogers RT5880 substrate. A circular-shaped complementary split ring resonator (CSRR) cell is integrated onto the patch of the antenna which acts as the sensing region. The sensor is analyzed in order to ascertain the blood glucose concentration ranging from 50-300 mg/dL in a human finger model. The sensing parameter is amplitude of reflection coefficient, which exhibits variation in response to alterations in the dielectric characteristics of the sample being tested. The Cole-Cole relaxation model is employed to predict the dielectric properties of different finger tissues. An analysis of the characteristics of the CSRR was conducted to illustrate its significance in the realm of glucose detection. The glucose level is determined through the utilization of a linear regression model that describes the relationship between the reflection coefficient of the sensor and glucose level. The sensor demonstrates an impressive sensitivity of 1.792 dB per (mgdL<sup>-1</sup>) and has the ability of determining glucose levels with a good accuracy, as verified by the application of Clarke error grid. This sensor exhibits enhanced performance compared to some other recent glucose sensors.

Paper-Based Microfluidic Analytical Device Patterned by Label Printer for Point-of-Care Blood Glucose and Hematocrit Detection Using 3D-Printed Smartphone Cassette.

This study presents a portable, low-cost, point-of-care (POC) system for the simultaneous detection of blood glucose and hematocrit. The system consists of a disposable origami microfluidic paper-based analytical device (μPAD) for plasma separation, filtration, and reaction functions and a 3D-printed cassette for hematocrit and blood glucose detection using a smartphone. The origami μPAD is patterned using a cost-effective label printing technique instead of the conventional wax printing method. The 3D-printed cassette incorporates an array of LED lights, which mitigates the effects of intensity variations in the ambient light and hence improves the accuracy of the blood glucose and hematocrit concentration measurements. The hematocrit concentration is determined quantitatively by measuring the distance of plasma wicking along the upper layer of the origami μPAD, which is pretreated with sodium chloride and Tween 20 to induce dehydration and aggregation of the red blood cells. The filtered plasma also penetrates to the lower layer of the origami μPAD, where it reacts with embedded colorimetric assay reagents to produce a yellowish-brown complex. A color image of the reaction complex is captured using a smartphone inserted into the 3D-printed cassette. The image is analyzed using self-written RGB software to quantify the blood glucose concentration. The calibration results indicate that the proposed detection platform provides an accurate assessment of the blood glucose level over the range of 45-630 mg/dL (R2 = 0.9958). The practical feasibility of the proposed platform is demonstrated by measuring the blood glucose and hematocrit concentrations in 13 human whole blood samples. Taking the measurements obtained from commercial glucose and hematocrit meters as a benchmark, the proposed system has a differential of no more than 6.4% for blood glucose detection and 9.1% for hematocrit detection. Overall, the results confirm that the proposed μPAD is a promising solution for cost-effective and reliable POC health monitoring.

A flexible glucose biosensor modified by reduced-swelling and conductive zwitterionic hydrogel enzyme membrane.

This paper reports a flexible glucose biosensor which is modified by a reduced-swelling and conductive zwitterionic hydrogel enzyme membrane that contains two forms of chemical cross-links. One chemical cross-linking is induced by thermal initiators and forms the basal network of the hydrogel. Another cross-linking is achieved by the coordination interactions between the multivalent metal ion Al3+ and anionic group -COO- of zwitterionic poly-carboxy betaine (pCBMA), which significantly increase the cross-linking density of the zwitterionic hydrogel, improving the reduced-swelling property and reducing the pore size. The better reduced-swelling property and reduced diameters of pores within the zwitterionic hydrogel make less glucose oxidase (GOx) leakage, thus significantly improving the enzyme membrane's service life. By introducing the Al3+ and Cl-, the conductivity of the zwitterionic hydrogel is enhanced approximately 10.4-fold. According to the enhanced conductivity, the reduced-swelling property, and the high GOx loading capacity of the zwitterionic hydrogel, the sensitivity of the biosensor with GOx/pCBMA-Al3+ is significantly improved by 5 times and has a long service life. Finally, the proposed GOx/pCBMA-Al3+ biosensor was applied in non-invasive blood glucose detection on the human body, verifying the capability in practice.

Detection and intelligent optimization of blood glucose signals based on MXene-assisted sensors

Two-dimensional materials exhibit unique properties that make them ideal for electronic sensing applications, particularly in the field of biosensors. In this study, a Ti3C2Tx MXene/graphene composite was investigated for its potential use in non-invasive blood glucose detection. The strong electrostatic interaction between MXene and graphene nanosheets effectively reduces the self-stacking of both components, resulting in the formation of a porous interlayer structure with enlarged interlayer spacing. This unique structure creates an efficient nanoscale transport channel, which increases the number of active sites and shortens ion diffusion paths. Consequently, the electrochemical properties of the composite membrane are enhanced, making it a promising candidate for sensing element applications. As the human blood glucose level is primarily determined by the glucose concentration in the blood, the concentration of glucose solution serves as the foundation for noninvasive blood glucose concentration detection. In this work, a prediction model was established using the partial least squares fitting method to correlate the electrochemical fingerprinting data with the glucose aqueous solution concentration, and the reliability of the model was verified. To address the limitations of the partial least squares method, which lacks nonlinear fitting capabilities, an RBF (radial basis function) network model was employed. Finally, a genetic algorithm was utilized to perform global optimization of the training parameters of the RBF network, and the quantitative relationship between the NIR spectral data and glucose concentration was validated. The findings of this study provide a theoretical basis for the development of equipment and the improvement of measurement accuracy in the field of human noninvasive glucose detection.

Non-Destructive Sensor for Glucose Solution Concentration Detection Using Electromagnetic Technology.

In this paper, a sensor using a complementary split ring resonator (CSRR) is proposed for non-destructive testing of blood glucose. By depicting the complementary split ring structure on the ground, the electromagnetic field strength between the split rings can be enhanced effectively. The structure size of the sensor by CSRR is determined by simulation, so that the insertion loss curve of the device has a resonance point at the frequency of 3.419 GHz. With a special holder created by three-dimensional (3D) printing technology, the test platform was established when the concentration of the solution varied from 0 mg/mL to 20 mg/mL. The experimental results indicate that there is an obvious linear relationship between the insertion loss S21 and the glucose concentration at the resonant frequency. Similarly, the measured real part and imaginary part of the S21 both vary with glucose concentration linearly. Based on the above experimental results, the feasibility of the sensor using a CSRR proposed in this paper for non-destructive detection of blood glucose is preliminarily verified.

A cross-sectional observational study to assess socio-demographic factors in newly diagnosed TB DM comorbidity

Background Tuberculosis (TB) and diabetes mellitus (DM) co-morbidity is one of the rising public health problems. There is growing evidence that DM is an important risk factor for TB. This study was carried out to know the prevalence of DM among newly detected sputum positive pulmonary TB patients registered in District Tuberculosis Centre and to assess the risk factors of DM among TB patients. Methods In a cross-sectional study, newly detected sputum positive pulmonary TB patients were screened for DM (those having symptoms of DM). Furthermore, they were diagnosed by detecting blood glucose levels (≥200 mg/dL). Mean, standard deviation (SD), Chi-squared and Fisher-Freeman-Halton exact tests were used to determine the significant associations. P-values less than 0.05 were considered to be statistically significant. Results A total of 215 TB patients were included in this study. The prevalence of DM among TB patients was found to be 23.7% (2.8% known and 97.8% new cases). Significant associations were found between age (>46 years old), educational status, smoking habits, alcohol consumption, physical activity, presence of DM symptoms and family history of DM. Conclusions Routine screening for DM is mandatory due to its increasing prevalence, which may help in early diagnosis and to reduce complications by proper management that in turn helps in the successful outcome of TB treatment.

Noninvasive Blood Glucose Detection System With Infrared Pulse Sensor and Hybrid Feature Neural Network

Noninvasive Blood Glucose Detection System With Infrared Pulse Sensor and Hybrid Feature Neural Network

Augmenting authenticity for non-invasive in vivo detection of random blood glucose with photoacoustic spectroscopy using Kernel-based ridge regression

Photoacoustic Spectroscopy (PAS) is a potential method for the noninvasive detection of blood glucose. However random blood glucose testing can help to diagnose diabetes at an early stage and is crucial for managing and preventing complications with diabetes. In order to improve the diagnosis, control, and treatment of Diabetes Mellitus, an appropriate approach of noninvasive random blood glucose is required for glucose monitoring. A polynomial kernel-based ridge regression is proposed in this paper to detect random blood glucose accurately using PAS. Additionally, we explored the impact of the biological parameter BMI on the regulation of blood glucose, as it serves as the primary source of energy for the body’s cells. The kernel function plays a pivotal role in kernel ridge regression as it enables the algorithm to capture intricate non-linear associations between input and output variables. Using a Pulsed Laser source with a wavelength of 905 nm, a noninvasive portable device has been developed to collect the Photoacoustic (PA) signal from a finger. A collection of 105 individual random blood glucose samples was obtained and their accuracy was assessed using three metrics: Root Mean Square Error (RMSE), Mean Absolute Difference (MAD), and Mean Absolute Relative Difference (MARD). The respective values for these metrics were found to be 10.94 (mg/dl), 10.15 (mg/dl), and 8.86%. The performance of the readings was evaluated through Clarke Error Grid Analysis and Bland Altman Plot, demonstrating that the obtained readings outperformed the previously reported state-of-the-art approaches. To conclude the proposed IoT-based PAS random blood glucose monitoring system using kernel-based ridge regression is reported for the first time with more accuracy.

Quantitative blood glucose detection influenced by various factors based on the fusion of photoacoustic temporal spectroscopy with deep convolutional neural networks

In order to efficiently and accurately monitor blood glucose concentration (BGC) synthetically influenced by various factors, quantitative blood glucose in vitro detection was studied using photoacoustic temporal spectroscopy (PTS) combined with a fusion deep neural network (fDNN). Meanwhile, a photoacoustic detection system influenced by five factors was set up, and 625 time-resolved photoacoustic signals of rabbit blood were collected under different influencing factors.In view of the sequence property for temporal signals, a dimension convolutional neural network (1DCNN) was established to extract features containing BGC. Through the parameters optimization and adjusting, the mean square error (MSE) of BGC was 0.51001 mmol/L for 125 testing sets. Then, due to the long-term dependence on temporal signals, a long short-term memory (LSTM) module was connected to enhance the prediction accuracy of BGC. With the optimal LSTM layers, the MSE of BGC decreased to 0.32104 mmol/L. To further improve prediction accuracy, a self-attention mechanism (SAM) module was coupled into and formed an fDNN model, i.e., 1DCNN-SAM-LSTM. The fDNN model not only combines the advantages of temporal expansion of 1DCNN and data long-term memory of LSTM, but also focuses on the learning of more important features of BGC. Comparison results show that the fDNN model outperforms the other six models. The determination coefficient of BGC for the testing set was 0.990, and the MSE reached 0.1432 mmol/L. Results demonstrate that PTS combined with 1DCNN-SAM-LSTM ensures higher accuracy of BGC under the synthetical influence of various factors, as well as greatly enhances the detection efficiency.

A Novel Synthesis of Nickel Carbide Modified Glassy Carbon Electrode for Electrochemical Investigation of Archetypal Diabetes Biomarker in Human Serum and Urine Samples

We began with an exploration of a novel method for non-enzymatic glucose sensing through the direct electrochemical oxidation process using an annealed Nickel carbide (Ni3C) modified glassy carbon electrode (GCE). We cover the synthesis and detailed characterization of Ni3C, the modification process of the electrode, and its application in the electrocatalytic detection of glucose in human blood and urine samples. Ni3C, known for its high charge transfer efficiency, exceptional stability in harsh environments, and outstanding electrochemical activity, was prepared through an annealing method. The produced Ni3C, characterized by a nanoplate structure ranging from 20 to 50 nanometers, was applied to a GCE to benefit from its extensive surface area and structural robustness. Electrochemical impedance spectroscopy and cyclic voltammetry confirmed the superior electrocatalytic properties and charge transfer capabilities of Ni3C/GCE over the unmodified GCE. The glucose detection was achieved by the direct electrochemical oxidation of glucose on the modified electrode, showcasing a linear detection range from 0.05 to 2236 μM and an impressively low detection limit of 0.0186 μM. This research underscores the effectiveness of Ni3C/GCE as durable, efficient, and reliable tools for the non-enzymatic electrochemical sensing of glucose, providing new prospects for diabetes monitoring.

Non-invasive glucose prediction and classification using NIR technology with machine learning

In this paper, a dual wavelength short near-infrared system is described for the detection of glucose levels. The system aims to improve the accuracy of blood glucose detection in a cost-effective and non-invasive way. The accuracy of the method is evaluated using real-time samples collected with the reference finger prick glucose device. A feed forward neural network (FFNN) regression method is employed to predict glucose levels based on the input data obtained from NIR technology. The system calculates glucose evaluation metrics and performs Surveillance error grid (SEG) analysis. The coefficient of determination R2 and mean absolute error are observed 0.99 and 2.49 mg/dl, respectively. Additionally, the system determines the root mean square error (RMSE) as 3.02 mg/dl. It also shows that the mean absolute percentage error (MAPE) is 1.94% and mean squared error (MSE) is 9.16 (mg/dl)2 for FFNN. The SEG analysis shows that the glucose values measured by the system fall within the clinically acceptable range when compared to the reference method. Finally, the system uses the multi-class classification method of the multilayer perceptron (MLP) and K-nearest neighbors (KNN) classifier to classify glucose levels with an accuracy of 99%.

A High-Accuracy Blood Glucose Detection Sensor Using Tunable Bandpass Filter and MLP and RBF Artificial Neural Network Algorithms

A High-Accuracy Blood Glucose Detection Sensor Using Tunable Bandpass Filter and MLP and RBF Artificial Neural Network Algorithms

Low-cost microstrip sensor based on coupled step line (CSL) resonator for blood plasma glucose determination

In this paper, a microstrip sensor structure was designed and implemented to detect blood glucose levels (BGL) based on changes in resonant frequencies, achieved by creating couplings at sensitive regions. A coupled step line was used to create a sensitive region on the sensor suitable for BGL testing. Transmission matrix theory was employed for the mathematical analysis to obtain the resonant frequency. Received blood samples from 50 different patients were centrifuged, and their serums were extracted to study the performance of the microchip sensor. By placing individual serum samples (50 µl) at the sensitive region of the sensor, the scattering parameters of the sensor were measured to investigate the resonant frequency variation. In addition, the effects of basic blood parameters were analyzed based on the amount of frequency shift. High sensitivity, suitable quality (Q) factor, compact size, and acceptable reproducibility of the measured results are important features of the developed sensor.

Self-powered optical fiber biosensor integrated with enzymes for non-invasive glucose sensing

To alleviate the discomfort associated with frequent blood glucose detection in diabetic patients, a novel non-invasive tear glucose biosensor has been developed. This involved the design and preparation of a photoelectrochemical probe based on an optical fiber and biological enzymes. One end of the optical fiber connects to a light source, acting as an energy source and imparting, self-powered capability to the biosensor. The opposite end is loaded with nanomaterials and glucose oxidase, designed for insertion into the sample to realize photoelectrochemical sensing. This innovative configuration not only improves the integration of the biosensor but is also suitable for analyzing minuscule voluminal samples. The results show that the proposed biosensor exhibits a linear range from 10 nM to 100 μM, possesses a low detection limit of 4.1 nM and a short response time of 0.7 s. Benefiting from the high selectivity of the enzyme, the proposed biosensor demonstrates excellent resistance to the interference of common tear components. In summary, this work provides a more effective method for non-invasive glucose detection and affords valuable ideas for the design and fabrication of non-invasive and self-powered biosensors.