Estimation Of Glucose Concentration Research Articles

DEMIGOD: A Low-Cost Microcontroller-Based Closed-Loop System Integrating Nanoengineered Sweat-Based Glucose Monitoring and Controlled Transdermal Nanoemulsion Release of Hypoglycemic Treatment with a Software Application for Noninvasive Personalized Diabetes Care.

This study endeavored to design and develop an innovative closed-loop diagnostic and therapeutic system with the following objectives: (a) the noninvasive detection of glucose concentration in sweat utilizing nanonengineered screen-printed biosensors; (b) the management of measured data through a specialized computer system comprising both hardware and software components, thereby enabling the precise control of therapeutic responses via a patch-based nanomedicine delivery system. This initiative addresses the significant challenges inherent in the management of diabetes mellitus, including the imperative need for glucose-level monitoring to optimize glycemic control. Leveraging chronoamperometric results as a foundational dataset and the in vivo hypoglycemic activity of nanoemulsion formulations, this research underscores the efficacy and accuracy of glucose concentration estimation, decision-making mechanism responses, and transdermal hypoglycemic treatment effects, within the proposed system.

Colorimetric Glucose Biosensor Based on Chitosan Films and Its Application for Glucose Detection in Beverages Using a Smartphone Application.

Nowadays, biosensors are gaining increasing interest in foods' and beverages' quality control, owing to their economic production, enhanced sensitivity, specificity, and faster analysis. In particular, colorimetric biosensors can be combined with color recognition applications on smartphones for the detection of analytes, rendering the whole procedure more applicable in everyday life. Herein, chitosan (CS) films were prepared with the deep eutectic solvent (DES) choline chloride/urea/glycerol (ChCl:U:Gly). Glucose oxidase (GOx), a widely utilized enzyme in quality control, was immobilized within CS films through glutaraldehyde (GA), leading to the formation of CS/GOx films. The optimized GOx concentration and DES content were determined for the films. Moreover, the effect of the pH and temperature of the glucose oxidation reaction on the enzymatic activity of GOx was studied. The structure, stability, and specificity of the CS/GOx films as well as the Km values of free and immobilized GOx were also determined. Finally, the analytical performance of the films was studied by using both a spectrophotometer and a color recognition application on a smartphone. The results demonstrated that the films were highly accurate, specific to glucose, and stable when stored at 4 °C for 4 weeks and when reused 10 times, without evident activity loss. Furthermore, the films displayed a good linear response range (0.1-0.8 mM) and a good limit of detection (LOD, 33 μM), thus being appropriate for the estimation of glucose concentration in real samples through a smartphone application.

Skin-Penetrating Interfaces for Millimeter-Wave Non-Invasive Glucose Concentration Estimation: Numerical Analysis.

Millimeter-wave (MMW) glucose concentration estimation possesses a great advantage of non-invasiveness. The long history of investigation, however, has not yet reached practical applications because of its insufficient accuracy and stability. To solve these problems, this paper proposes two high skin-penetration interfaces, which we name equivalent quarter-wavelength interface and equivalent Brewster's-angle interface. We analyze their scattering characteristics in a frequency range of 60 - 90 GHz. Analysis results show that both the interfaces suppress the body-surface scattering, allowing the MMWs to penetrate through body surface into tissues to extract information on blood-glucose concentration with higher sensitivity, e.g., with 147-times enhancement of phase changes. These interfaces can be an important step toward realizing non-invasive blood glucose concentration estimation.

Effects of Carbamazepine on blood pressure, serum glucose concentration, lipid profile and prevalence of metabolic syndrome in epileptic patients

To investigate the effects of carbamazepine on serum glucose concentrations, lipid profile, blood pressure and prevalence of metabolic syndrome in epileptic patients. Seventy epileptic patients involved in this study. They have primary generalized and partial seizures with duration of treatment with carbamazepine of 11.5±8.37 years. Another 100 apparently healthy individuals were also participated in the study as a control group. Ten ml of venous blood samples were taken from each patient and control after 14 hours fasting. The obtained serum was separated and used for the estimation of glucose concentration, total cholesterol, triglycerides and HDL-cholesterol by using special Kits. LDL-cholesterol was determined by Friedewald equation. Metabolic syndrome was determined according to ATP III criteria for the diagnosis of metabolic syndrome. The patients and controls were matched regarding age and sex. Weight, BMI and waist circumference were statistically not different between patients and controls. Comparison between the measured variables shows significant elevations of the lipid parameters in the patient group as compared with the control group. Non significant differences obtained between serum glucose concentrations and significant low values of BP were obtained in the patient group as compared with the controls. The number of patients and control having metabolic syndrome according to ATP II diagnostic criteria for metabolic syndrome were 4 and 3, respectively. The difference is not significant. This study demonstrated that carbamazepine have no effects on serum glucose concentrations, but have elevating effects on lipid parameters and reducing effects on BP. It has no effects on the prevalence of metabolic syndrome in the epileptic patients

Insulin Resistance in relation to Hypertension and Dyslipidaemia among Men Clinically Diagnosed with Type 2 Diabetes.

Pathophysiologically, type 2 diabetes can result from insulin resistance or insulin insufficiency alone. It is unclear whether relative insulin shortage or pronounced insulin resistance is linked to poor cardiometabolic problems like obesity. Therefore, the objective of this study was to evaluate the relationship between insulin resistance (IR), hypertension, and dyslipidaemia, in men with type 2 diabetes mellitus. One hundred and twenty-one (121) type 2 diabetic men participated in this cross-sectional study, which was conducted between September 2018 and September 2019. Sociodemographic information was collected using a self-designed questionnaire. Anthropometric data were also taken and blood samples collected for estimation of insulin, glucose, and lipid concentrations. HOMA-IR was calculated from the fasting insulin and glucose values, and a HOMA - IR ≥ 2 was considered to indicate insulin resistance. Of the 121 participants, 39.7% were classified as insulin-resistant. Levels of total cholesterol (4.82 ± 1.2 mmol/L; p = 0.007 vs. 4.25 ± 1.1 mmol/L), LDL cholesterol (3.17 ± 0.9 mmol/L; p = 0.001 vs. 2.52 ± 0.8 mmol/L), and TC/HDL-C ratio (3.93 ± 0.9; p = 0.042 vs. 3.58 ± 0.9) and the prevalence of abnormal LDL-C (14.6%; p = 0.015 vs. 2.7%) and elevated BP (83.3%; p = 0.048 vs. 67.1%) were higher in the insulin-resistant group. LDL cholesterol (AUC = 0.670; p = 0.001) better classified subjects as being insulin-resistant compared to other lipid markers. The odds of insulin resistance in dyslipidaemia were not statistically significant after adjusting for obesity. The link between insulin resistance and dyslipidaemia and hypertension in male diabetics may thus be mediated by obesity.

Flexible, disposable, and portable self-powered glucose biosensors visible to the naked eye

Diabetic patients need to measure their blood glucose level ∼5 times a day using glucose sensor. However, typical glucose sensors require additional power supply and equipment to interpret glucose concentration. In this study, flexible, disposable, and portable self-powered glucose biosensor is suggested to quickly measure glucose concentration with the naked eye. The theory of enzymatic biofuel cell is adopted for its self-powered operation. This glucose biosensor includes glucose dehydrogenase (GDH) and Prussian blue (PB) as biocatalysts for anode and cathode, while glucose oxidation reaction and reduction reaction of PB are utilized as the main reactions. In electrochemical evaluations, maximum current density and onset potential of anodic and cathodic reactions are 0.39 and – 0.53 mA/cm2, and 0 and 0.3 V vs. Ag/AgCl. Furthermore, when many interfering compounds are included, anodic catalyst is only sensitive to glucose, while this shows linearity even at a low glucose range of 0.5 – 10 mM (R2 = 0.999). When 3, 5, and 10 mM glucose solutions corresponding to low, normal, and high glucose level are prepared and injected into glucose biosensor, each glucose is well recognized by the naked eye through the number of PB bars whose color is automatically changed after one min. Ultimately, this glucose biosensor, which can quickly estimate glucose concentration without external power source, offers strong potential as commercial glucose sensor

Low glucose concentration estimation based on reaction with 4,4'-biphenyl boronic acid using deep learning

Minimally invasive blood glucose level estimation with Raman spectroscopy is an important research field and attracts great attention. However, glucose concentration in blood is low and is difficult to be accurately measured. In this paper, we creatively proposed applying the 4,4'-biphenyl boronic acid to react with different concentrations of glucose to obtain the complex—(C36H40O18B4) n. We performed a regression of the Raman spectral data of (C36H40O18B4) n and the glucose solution separately to compare their estimation results. We applied a deep learning network, ResNet, and compared it with regression models of conventional machine learning, uniformly using ten-fold cross-validation. The experimental results show that the generated (C36H40O18B4) n can effectively improve the estimation performance of glucose. The results showed, the ResNet model does not require explicit feature extraction and can achieve fast and accurate estimation. Its performance is significantly better than the traditional linear analysis method, and the R square can reach 0.93. The method in the article can effectively improve the estimation effect of low-concentration glucose.

Nano/micro-scaled materials based optical biosensing of glucose

Diabetes is a chronic metabolic disease that lasts a person's life and the estimation of blood glucose concentration is one of the main diagnostic criteria for this disease. The past decade has witnessed a huge demand for new biocompatibility and high-performance glucose biosensors. Non-enzymatic optical glucose sensing using nano/microscaled materials has surged significantly as it provides a direct visualized and cost-effective way to measure glucose concentrations. This review, in a non-exhaustive way, considers some of the recent most important achievements in the glucose-sensitive optical biosensors based upon nano/microscaled materials. Such materials are capable of mimicking the activities of the enzymes which oxidize glucose to release H2O2. The overall framework of the review can be divided into three parts. The first part describes the colorimetric glucose sensing using nano/microscaled materials as enzyme mimics for the oxidation of different chromogenic substrates. In the second part, some recent developments in chromogenic substrate-free fluorescence glucose sensing facilitated by nano/microscaled materials are elaborated. The third part reviews the glucose-sensing through Förster resonance energy transfer (FRET) based assays. Finally, some current challenges, gaps in fundamental understanding and future improvements in the field of optical glucose biosensors are discussed.

Magnetic Microdimer as Mobile Meter for Measuring Plasma Glucose and Lipids.

With the development of designed materials and structures, a wide array of micro/nanomachines with versatile functionalities are employed for specific sensing applications. Here, we demonstrated a magnetic propelled microdimer-based point-of-care testing system, which can be used to provide the real-time data of plasma glucose and lipids relying on the motion feedback of mechanical properties. On-demand and programmable speed and direction of the microdimers can be achieved with the judicious adjustment of the external magnetic field, while their velocity and instantaneous postures provide estimation of glucose, cholesterol, and triglycerides concentrations with high temporal accuracy. Numerical simulations reveal the relationship between motility performance and surrounding liquid properties. Such technology presents a point-of-care testing (POCT) approach to adapt to biofluid measurement, which advances the development of microrobotic system in biomedical fields.

Estimation of Glucose Levels in Blood Sample using A Biosensor

This paper discusses about estimation of glucose concentration in blood using a Triple pole Complementary split ring resonator (TP-CSRR) antenna. Glucose concentration in blood is the direct indicator of Diabetes disease. The designed microstrip antenna operates in range of 2-5 Ghz and has a resonance frequency of 3.35 Ghz when simulated. When the antenna is excited, blood acts as dielectric load to it. Hence the glucose concentration of blood affects the resonant frequency and amplitude at resonant frequency of the s21 parameter of the antenna. Using this information, we can estimate the glucose concentration of blood sample. Debye model was used to model the blood. It is effective in detecting glucose concentration of Type-2 diabetes (70-120 mg/dL). The amplitude sensitivity is 0.58 dB(mg/ml) and frequency sensitivity is 583 Mhz/(mg/ml).

Effect of Sample Volume Variation and Delay in Analysis on Plasma Glucose Concentration in Sri Lankan Healthy Adults.

The correct volume of sample and time of storing prior to the analysis are important considerations in the estimation of plasma glucose concentration of patients. The present study was to determine the effect of sample volume variation and time delay in the analysis of plasma glucose results in healthy adults. A total of 30 individuals aged between 20 and 30 years were selected for the study. Blood samples were collected into three fluoride-oxalate collection tubes separately. The results revealed that the sample volume variation from 2.0 mL fluoride-oxalate tube to 1.0 mL and 3.0 mL did not significantly affect the plasma glucose concentration (p > 0.05). However, the plasma glucose concentration in the sample significantly decreased upon delaying the analysis. The mean fasting plasma glucose concentration of analysis after one hour of collection and analysis after three hours of collection was not significantly different (p > 0.05). The mean fasting plasma glucose concentrations between one hour and five hours timepoints after collection (p < 0.001) and between three hours and five hours after collection (p = 0.014) were significantly different. In conclusion, overfilling and underfilling (2.0 ± 1.0 mL) of fluoride-oxalate tubes did not affect the plasma glucose results significantly. If the samples are analyzed within three hours of collection, the time dependent change too is not statistically significant.

An OGI model for personalized estimation of glucose and insulin concentration in plasma.

Plasma glucose concentration (PGC) and plasma insulin concentration (PIC) are two essential metrics for diabetic regulation, but difficult to be measured directly. Often, PGC and PIC are estimated from continuous glucose monitoring and insulin delivery data. Nevertheless, the inter-individual variability and external disturbance (e.g. carbohydrate intake) bring challenges for accurate estimations. This study is to estimate PGC and PIC adaptively by identifying personalized parameters and external disturbances. An observable glucose-insulin (OGI) dynamic model is established to describe insulin absorption, glucose regulation, and glucose transport. The model parameters and disturbances can be extended to observable state variables and be identified dynamically by Bayesian filtering estimators. Two basic Gaussian noise based Bayesian filtering estimators, extended Kalman filtering (EKF) and unscented Kalman filtering (UKF), are implemented. Recognizing the prevalence of non-Gaussian noise, in this study, two new filtering estimators: particle filtering with Gaussian noise (PFG), and particle filtering with mixed non-Gaussian noise (PFM) are designed and implemented. The proposed OGI model in conjunction with the estimators is evaluated using the data from 30 in-silico subjects and 10 human participants. For in-silico subjects, the OGI with PFM estimator has the ability to estimate PIC and PGC adaptively, achieving RMSE of PIC 9.49±3.81 mU/L, and PGC 0.89±0.19 mmol/L. For human, the OGI with PFM has the promise to identify disturbances (95.46%±0.65% accurate rate of meal identification). OGI model provides a way to fully personalize the parameters and external disturbances in real time, and has potential clinical utility for artificial pancreas.

Parameter and state estimation of backers yeast cultivation with a gas sensor array and unscented Kalman filter.

Real‐time information about the concentrations of substrates and biomass is the key to accurate monitoring and control of bioprocess. However, on‐line measurement of these variables is a challenging task and new measurement systems are still required. An alternative are software sensors, which can be used for state and parameter estimation in bioprocesses. The software sensors predict the state of the process by using mathematical models as well as data from measured variables. The Kalman filter is a type of such sensors.In this paper, we have used the Unscented Kalman Filter (UKF) which is a nonlinear extension of the Kalman filter for on‐line estimation of biomass, glucose and ethanol concentration as well as for estimating the growth rate parameters in S. cerevisiae batch cultivation, based on infrequent ethanol measurements. The UKF algorithm was validated on three different cultivations with variability of the substrate concentrations and the estimated values were compared to the off‐line values.The results obtained showed that the UKF algorithm provides satisfactory results with respect to estimation of concentrations of substrates and biomass as well as the growth rate parameters during the batch cultivation.

Estimation of Blood Glucose Concentration During Endurance Sports

In this paper, we describe a new statistical approach to estimate blood glucose concentration along time during endurance sports based on measurements of glucose concentration in subcutaneous interstitial tissue. The final goal is the monitoring of glucose concentration in blood to maximize performance in endurance sports. Blood glucose concentration control during and after aerobic physical activity could also be useful to reduce the risk of hypoglycemia in type 1 diabetes mellitus subjects. By means of a low invasive technology known as "continuous glucose monitoring", glucose concentration in subcutaneous interstitial tissue can now be measured every five minutes. However, it can be expressed as function of blood glucose concentration along time by means of a convolution integral equation. In the training phase of the proposed approach, based on measurements of glucose concentration in both artery and subcutaneous interstitial tissue during physical activity, the parameters of the convolution kernel are estimated. Then, given a new subject performing aerobic physical activity, a deconvolution problem is solved to estimate glucose concentration in blood from continuous glucose monitoring measurements

Precise Noninvasive Estimation of Glucose Using UWB Microwave With Improved Neural Networks and Hybrid Optimization

A precise noninvasive estimation method for blood glucose concentration is proposed using an improved neural network model and hybrid optimization (INNHO) method. This method consists of a modified back propagation neural network model and a hybrid least squares-random sample consensus algorithm for accuracy optimization. The S-parameter and frequency data measured by ultrawide band (UWB) microwave are employed as the input data for glucose concentration estimation. Before model training, a preprocess technique is put forward to select the data range and generate relative S-parameters from a reference baseline. In this study, a three-layer earlobe model is built to theoretically analyze the wave propagation and elucidate the relationship between the S-parameters and the glucose concentration. Based on the model, the sensitivity and uncertainty of the system are investigated and the suitable frequency range is determined. During the measurement, the S-parameters of UWB signals in the 0.2-4 GHz range are measured and the concentration of glucose water solutions from 20 to 500 mg/dL is investigated. The results show that using the proposed preprocessing method and neural networks, the estimation accuracy can be significantly improved. Then, the hybrid optimization algorithm is able to further enhance the estimation accuracy. The final estimations for solutions of unknown concentrations agree well with the true concentration values. The relative error ranges from 0.31% to 4.64% and the root-mean-square error of the final results is 5.5237 mg/dL, demonstrating that the proposed method is effective and precise for glucose concentration estimation.

Influence of Raman Spectrometer Collection Efficiency on Performance of Noninvasive Blood Glucose Detection for Device Miniaturization.

Recently the world population with diabetes has increased significantly, and the market demand for noninvasive blood glucose monitoring has increased accordingly. Our previous study demonstrated the capability to detect glucose through the direct observation of glucose Raman fingerprint peaks from in vivo skin but using a benchtop device. From the perspective of commercialization, miniaturized devices are expected to make more impact on the market than bulky benchtop devices. In this study, as an effort for commercialization of noninvasive glucose sensing technology, we investigate the relationship between Raman spectrometer specification, especially collection efficiency, and glucose prediction performance. Raman spectra were synthesized at given spectrometer collection efficiencies in computer simulation, in which spectra are designed to contain glucose signal at specific concentrations. Then, we estimated glucose concentrations back using regression analysis and evaluated prediction performances. Finally, the relationship was analyzed between the collection efficiencies and glucose prediction performances. In order to mimic actual conditions with skin tissue, Monte-Carlo simulations were conducted to count the number of Raman photons escaping from the skin surface in a multi-layered skin model. As the collection efficiency decreased from 3.2 % to 0.2 %, the correlation coefficient between the actual and predicted glucose concentrations dropped from 0.91 to 0.35. The glucose Raman peaks at 1125 cm-1 was identified as the most important wavelength for glucose sensing. This study may help identify optimal Raman spectrometer specifications for transcutaneous blood glucose sensing in miniaturized devices and commercialize noninvasive blood glucose sensors in Raman spectroscopy.

690-P: Use of Physiological and Psychological States to Enhance Glucose Concentration Estimation

The accuracy of blood glucose concentration (BGC) estimation affects insulin dosing decisions. Wristband biosignals (Empatica E4) are used to estimate physical activity type and intensity, and psychological stress to enhance BGC prediction accuracy. Three cases are compared: (1) BGC prediction by using only continuous glucose monitor (CGM) data, which considers only meal effects and plasma insulin concentration (PIC); (2) Incorporating physical activity information to complement CGM data; and (3) Incorporating both physical activity and psychological stress information to complement CGM data. Twenty-eight experiments with seven subjects with T1D are conducted including various physical activities and acute psychological stress inducements. The subjects wore a CGM, insulin pump and E4 wristband during the experiments. Physical activities included treadmill running, stationary bicycling, resistance exercises and activities of daily living. Acute psychological stress inducements included the Trier Social Stress test, playing competitive videogames, solving arithmetic problems, and daily stressful events. The overall mean absolute errors (%) were 10.42, 8.69. and 8.42 for Cases 1, 2, and 3, respectively. The results illustrate that incorporating estimates of psychological and psychological effects improve the BGC prediction accuracy by 17.18% (Freidman’s ANOVA test, p-value &amp;lt; 0.01). Disclosure M. Sevil: None. N. Hobbs: None. M. Rashid: None. I. Hajizadeh: None. M. Askari: None. M. Park: None. L.T. Quinn: None. A. Cinar: Research Support; Self; Dexcom, Inc., JDRF. Stock/Shareholder; Self; Dexcom, Inc., Eli Lilly and Company, Insulet Corporation, Medtronic, Novo Nordisk A/S, Tandem Diabetes Care. Funding National Institutes of Health (1DP3DK101075-01, 1DP3DK101077-01); JDRF (A18-0036-001)

Study on data processing technique for estimation of glucose concentrations in tissues

Study on data processing technique for estimation of glucose concentrations in tissues

Study on data processing technique for estimation of glucose concentrations in tissues

Study on data processing technique for estimation of glucose concentrations in tissues

Evaluating the effect of artesunate on the pharmacokinetics of gliclazide in diabetic subjects

Therapeutic management of diabetic patients frequently involves polypharmacy which is likely to provoke drug-drug interactions (DDIs). This may require appropriate monitoring and most common clinically relevant DDIs that occur with antidiabetic medications often leads to variations in therapeutic response. Due to the wide use of artesunate combination in the treatment of malaria in diabetic patients, drug interactions with gliclazide is a possibility. This study was aimed at determining the effect of artesunate on the pharmacokinetics of gliclazide in diabetic subjects. Six freshly diagnosed diabetics subjects participated in the study. Written informed consent was sought and obtained from the volunteers. The study is a one-way single dose cross-over study in two phases. Phase 1 of the study involved the administration of a single oral dose of 80 mg of gliclazide after an overnight fast. After a wash out period of one week, 80 mg gliclazide and 100 mg artesunate were co-administered. Serial blood samples were collected over a period of 24 h during each phase into an EDTA vacutainer. High Performance Liquid Chromatography method was used in the estimation of plasma glucose concentration; while the glucose oxidase peroxidase method was used in the estimation of plasma glucose concentration. Results showed changes in the pharmacokinetic parameters and blood glucose concentration were not significant (p>0.05). This shows that artesunate does not alter the pharmacokinetics and of gliclazide in diabetic patients after single oral dose administration; and hence can be co-administered without dose modification.© 2019 International Formulae Group. All rights reserved.Keywords: Diabetes mellitus, gliclazide, artesunate, drug Interactions, pharmacokinetics