Actual Blood Glucose Research Articles

ASSESSMENT OF BLOOD GLUCOSE LEVELS IN WISTAR RATS TREATED WITH COCCINIA GRANDIS AND DATA INTERPRETATION BASED ON NON-COMPARTMENT ANALYSIS AND RATE PROCESS

The study aims to examine the blood glucose-lowering effects of fresh Coccinia grandis fruit and assess how drying affects its antidiabetic potential. Additionally, the study seeks to determine the optimal administration procedure to maximize its antidiabetic efficacy. An oral glucose tolerance test was performed in female Albino Wistar rats to study the effects of C. grandis in three forms: fresh fruit slurry, dry fruit powder, and overnight soaked dry powder, with metformin as standard. The fresh fruit slurry (S-1) of C. grandis demonstrates excellent antidiabetic properties, showing a mean blood glucose level of 116.25±38 and a percentage blood glucose reduction of about 30%. This is more effective compared to the processed form of C. grandis and the standard drug metformin, with a mean actual blood glucose level of 44.5±35.9. The study indicates that C. grandis fresh form possesses significant hypoglycemic effects, reducing glucose absorption by peripheral tissues and enhancing glucose uptake into cells.

Accuracy enhancement of metabolic index-based blood glucose estimation with a screening process for low-quality data.

Many researchers have proposed various non-invasive glucose monitoring (NIGM) approaches using wearable or portable devices. However, due to the limited capacity of detectors for such compact devices and the movement of the body during measurement, the precision of the acquired data frequently diminishes, which can cause problems during actual use in daily life. In addition, intensive smoothing is often used in post-processing to mitigate the effects of erroneous values. However, this requires a considerable amount of data and results in a delay in the response to the actual blood glucose level (BGL). Instead of just applying data smoothing in the post-process of the data acquisition, we propose an active low-quality data screening method in the pre-process. In the proposal phase of the screening process, we employ an analytical approach to examine and formulate factors that might affect the BGL estimation accuracy. A signal quality index inspired by the standard deviation concept is introduced to detect visually apparent noise on signals. Furthermore, the total estimation error in the metabolic index (MI) is calculated based on potential perturbations defined by the signal-to-noise ratio (SNR) and the uncertainty due to discrete sampling. Thereafter, the acquired data were screened by these quality indices. By applying the proposed data screening process to the data obtained from a commercially available smartwatch device in the pre-process, the estimation accuracy of the MI-based BGL was improved significantly. Adopting the proposed screen process improves BGL estimation accuracy in the smartwatch-based prototype. Applying the proposed screen process will facilitate the integration of wearable and continuous BGL monitoring into size- and SNR-limited devices such as smartwatches and smart rings.

Differential signaling effects of blood glucose on delay discounting in individuals with and without type 1 diabetes.

Based on the signaling hypothesis of blood glucose (BG), a rise in BG levels signals a positive energy budget for healthy individuals but cellular starvation for individuals with type 1 diabetes. We examined this novel prediction and its intervention implications in the context of delay discounting, the degree to which delayed rewards are discounted, and the regulatory effects of insulin ingestion. We recruited 44 adults with type 1 diabetes (mean age 30.8 years, diabetes duration 15.4 years) and recorded their BG levels. The delay discounting rate was measured using the intertemporal choice task, where participants were required to choose between sets of smaller-and-sooner (SS) and larger-and-later (LL) rewards. In addition, 82 age-matched healthy participants were recruited to provide a baseline comparison on delay discounting. Random forest analysis showed that among many diagnostic factors, delay discounting was most dominating in differentiating the individuals with type 1 diabetes from the control participants. A hierarchical linear mixed model revealed that participants with type 1 diabetes had a stronger preference for SS rewards (p < .001) after controlling for covariates. Participants who had insulin delivered before the last meal exhibited a stronger preference for LL rewards compared to after-meal delivery. In contrast, subjective measures (e.g., self-reported hunger) failed to predict the participants' actual BG levels and delay discounting rates. In sum, individuals with type 1 diabetes tend to discount future rewards excessively compared to the control participants. Pre-meal insulin ingestion was associated with a higher LL preference for future rewards.

Exploratory insights into prefrontal cortex activity in continuous glucose monitoring: findings from a portable wearable functional near-infrared spectroscopy system.

The escalating global prevalence of diabetes highlights an urgent need for advancements in continuous glucose monitoring (CGM) technologies that are non-invasive, accurate, and user-friendly. Here, we introduce a groundbreaking portable wearable functional near-infrared spectroscopy (fNIRS) system designed to monitor glucose levels by assessing prefrontal cortex (PFC) activity. Our study delineates the development and application of this novel fNIRS system, emphasizing its potential to revolutionize diabetes management by providing a non-invasive, real-time monitoring solution. Fifteen healthy university students participated in a controlled study, where we monitored their PFC activity and blood glucose levels under fasting and glucose-loaded conditions. Our findings reveal a significant correlation between PFC activity, as measured by our fNIRS system, and blood glucose levels, suggesting the feasibility of fNIRS technology for CGM. The portable nature of our system overcomes the mobility limitations of traditional setups, enabling continuous, real-time monitoring in everyday settings. We identified 10 critical features related to blood glucose levels from extensive fNIRS data and successfully correlated PFC function with blood glucose levels by constructing predictive models. Results show a positive association between fNIRS data and blood glucose levels, with the PFC exhibiting a clear response to blood glucose. Furthermore, the improved regressive rule principal component analysis (PCA) method outperforms traditional PCA in model prediction. We propose a model validation approach based on leave-one-out cross-validation, demonstrating the unique advantages of K-nearest neighbor (KNN) models. Comparative analysis with existing CGM methods reveals that our paper's KNN model exhibits lower RMSE and MARD at 0.11 and 8.96%, respectively, and the fNIRS data were highly significant positive correlation with actual blood glucose levels (r = 0.995, p < 0.000). This study provides valuable insights into the relationship between metabolic states and brain activity, laying the foundation for innovative CGM solutions. Our portable wearable fNIRS system represents a significant advancement in effective diabetes management, offering a promising alternative to current technologies and paving the way for future advancements in health monitoring and personalized medicine.

Association of stress hyperglycemia ratio and mortality in patients with sepsis: results from 13,199 patients.

The stress hyperglycemia ratio (SHR), adjusted for average glycemic status, is suggested for assessing actual blood glucose levels. Its link with adverse outcomes is known in certain populations, yet its impact on sepsis patients' prognosis is unclear. This study explores the association between SHR and mortality in sepsis. We included 13,199 sepsis patients in this study and categorized SHR into distinct groups. Additionally, we utilized restricted cubic spline analysis to evaluate the correlation between SHR as a continuous variable and mortality. The primary outcome was 1-year all-cause mortality. Logistic regression and Cox proportional hazards models were employed to assess the associations between the SHR and both in-hospital mortality and 1-year mortality, respectively. Among the study participants, 4,690 (35.5%) patients died during the 1-year follow-up. After adjusting for confounding variables, we identified a U-shaped correlation between SHR and 1-year mortality. Using an SHR of 0.99 as the reference point, the hazard ratio for predicted 1-year mortality increased by 1.17 (95% CI 1.08 to 1.27) per standard deviation above 0.99, whereas each standard deviation increase predicted the hazard ratio of 0.52 (95% CI 0.39 to 0.69) below 0.99. Furthermore, we found that SHR could enhance the predictive performance of conventional severity scores. There exists a U shaped association between SHR and mortality in sepsis patients, where both low and high SHR values are associated with an increased risk of poor outcomes.

Wearable filamentary continuous sensor for interstitial glucose detection in diabetes management

The development of novel diabetes monitoring sensors is important for the diabetes management of millions of diabetic patients. This work reports a flexible filamentary continuous glucose monitoring (CGM) sensor. A multilayer CGM sensor has been constructed on titanium filament with low cost and ease of use. The sensor, made of flexible material, offers better adaptability and comfort than traditional rigid filament CGM sensors, allowing continuous monitoring of subcutaneous blood glucose levels to provide patients with treatment strategies. The performance and reliability of the sensor were verified through rat experiments. The trend of the increase and decrease of the detected current was generally consistent with the actual blood glucose, and the detected values were located in regions A and B of the Clarke error grid. The results show that the sensor has the advantages of high sensitivity, high accuracy and fast response speed, which is suitable for monitoring the blood glucose level for a long time and has a broad application prospect in diabetes monitoring, exercise monitoring, health management and clinical application.

A Machine Learning Web App to Predict Diabetic Blood Glucose Based on a Basic Noninvasive Health Checkup, Sociodemographic Characteristics, and Dietary Information: Case Study.

Over the past few decades, diabetes has become a serious public health concern worldwide, particularly in Bangladesh. The advancement of artificial intelligence can be reaped in the prediction of blood glucose levels for better health management. However, the practical validity of machine learning (ML) techniques for predicting health parameters using data from low- and middle-income countries, such as Bangladesh, is very low. Specifically, Bangladesh lacks research using ML techniques to predict blood glucose levels based on basic noninvasive clinical measurements and dietary and sociodemographic information. To formulate strategies for public health planning and the control of diabetes, this study aimed to develop a personalized ML model that predicts the blood glucose level of urban corporate workers in Bangladesh. Based on the basic noninvasive health checkup test results, dietary information, and sociodemographic characteristics of 271 employees of the Bangladeshi Grameen Bank complex, 5 well-known ML models, namely, linear regression, boosted decision tree regression, neural network, decision forest regression, and Bayesian linear regression, were used to predict blood glucose levels. Continuous blood glucose data were used in this study to train the model, which then used the trained data to predict new blood glucose values. Boosted decision tree regression demonstrated the greatest predictive performance of all evaluated models (root mean squared error=2.30). This means that, on average, our model's predicted blood glucose level deviated from the actual blood glucose level by around 2.30 mg/dL. The mean blood glucose value of the population studied was 128.02 mg/dL (SD 56.92), indicating a borderline result for the majority of the samples (normal value: 140 mg/dL). This suggests that the individuals should be monitoring their blood glucose levels regularly. This ML-enabled web application for blood glucose prediction helps individuals to self-monitor their health condition. The application was developed with communities in remote areas of low- and middle-income countries, such as Bangladesh, in mind. These areas typically lack health facilities and have an insufficient number of qualified doctors and nurses. The web-based application is a simple, practical, and effective solution that can be adopted by the community. Use of the web application can save money on medical expenses, time, and health management expenses. The created system also aids in achieving the Sustainable Development Goals, particularly in ensuring that everyone in the community enjoys good health and well-being and lowering total morbidity and mortality.

Fusion of various optimisation based feature smoothing methods for wearable and non-invasive blood glucose estimation.

The traditional blood glucose estimation method requires to take the invasive measurements several times a day. Therefore, it has a high infection risk and the users are suffered from the pain. Moreover, the long term consumable cost is high. Recently, the wearable and non-invasive blood glucose estimation approach has been proposed. However, due to the unreliability of the acquisition device, the presence of the noise and the variations of the acquisition environments, the obtained features and the reference blood glucose values are highly unreliable. Moreover, different subjects have different responses of the infrared light to the blood glucose. To address this issue, a polynomial fitting approach to smooth the obtained features or the reference blood glucose values has been proposed. In particular, the design of the coefficients in the polynomial is formulated as the various optimisation problems. First, the blood glucose values are estimated based on the individual optimisation approaches. Second, the absolute difference values between the estimated blood glucose values and the actual blood glucose values based on each optimisation approach are computed. Third, these absolute difference values for each optimisation approach are sorted in the ascending order. Fourth, for each sorted blood glucose value, the optimisation method corresponding to the minimum absolute difference value is selected. Fifth, the accumulate probability of each selected optimisation method is computed. If the accumulate probability of any selected optimisation method at a point is greater than a threshold value, then the accumulate probabilities of these three selected optimisation methods at that point are reset to zero. A range of the sorted blood glucose values are defined as that with the corresponding boundaries points being the previous reset point and this reset point. Hence, after performing the above procedures for all the sorted reference blood glucose values in the validation set, the regions of the sorted reference blood glucose values and the corresponding optimisation methods in these regions are determined. It is worth noting that the conventional lowpass denoising method was performed in the signal domain (either in the time domain or in the frequency domain), while the authors' proposed method is performed in the feature space or the reference blood glucose space. Hence, the authors' proposed method can further improve the reliability of the obtained feature values or the reference blood glucose values so as to improve the accuracy of the blood glucose estimation. Moreover, the individual modelling regression method has been employed here to suppress the effects of different users having different responses of the infrared light to the blood glucose values. The computer numerical simulation results show that the authors' proposed method yields the mean absolute relative deviation (MARD) at 0.0930 and the percentage of the test data falling in the zone A of the Clarke error grid at 94.1176%.

LBODP031 Evaluation Of Artifactual Hypoglycemia: A Case-based Review

Abstract Background Artifactual Hypoglycemia (AH), is defined as a discrepancy between the measured and actual blood glucose level. The absence of classical symptoms of hypoglycemia in patients with low measured glucose levels should raise the suspicion of AH. Here we describe a case of AH in a patient with chronic myeloid leukemia (CML). Clinical Case A 47-year-old woman was hospitalized for the evaluation of a four-month history of headache, anorexia, and weight loss. After further workup, she was diagnosed with CML; and upon evaluation, she was found to have a very low serum glucose level of 22 mg/dL. She reported no symptoms of hypoglycemia at the time and no personal or family history of diabetes. She was not using any glucose-lowering medication. Physical examination findings, including vital signs, were unremarkable. Laboratory studies revealed a serum glucose of 22 mg/dL with a simultaneous point-of-care (POC) capillary glucose level of 81 mg/dL. This discrepancy in glucose levels raised the suspicion of artifactual hypoglycemia. To further explore this, we drew three simultaneous venous blood samples and processed them differently to examine the impact of sample processing on serum glucose level in this patient. Two samples were drawn in tubes with no antiglycolytic agent (one sample was centrifuged immediately after the blood draw, and the second one was processed 5 hours after the blood draw). Whereas, the third sample was drawn in a tube containing sodium fluoride, an inhibitor of aerobic glycolysis, and was processed after one hour of the blood draw. Glucose concentrations in the three samples were as follows: 79 mg/dl, 41.4 mg/dl, and 77.4 mg/dl, respectively. The diagnosis of artifactual hypoglycemia due to excessive glucose consumption by leukocytes was established based on the findings of normal capillary blood glucose and normal plasma glucose levels when a tube prefilled with a glycolysis inhibitor is used. Conclusion Artifactual hypoglycemia should be considered in patients with asymptomatic hypoglycemia, especially when there is a discrepancy between the capillary and venous glucose levels. Patients with marked leukocytosis can have artifactually low serum glucose, an erroneous finding that can be mitigated by a) rapid processing of the blood sample or b) utilization of tubes pre-filled with a glycolysis inhibitor. Presentation: No date and time listed

187-LB: Leveling Health Disparities through Digital Health—Associations between Risk for Health Inequity and Diabetes App Satisfaction, Use, and Outcomes

Introduction: Disparities in health outcomes are well documented in the literature, with factors such as race, education and income increasing the risk of inequity. Mobile health (mHealth) may help bridge the health equity gap. This study investigates use and attitudes toward One Drop's mHealth program for type 2 diabetes (T2D) as it relates to cumulative risk for health inequity. Methods: Members with T2D using One Drop completed a health survey in exchange for a $20 gift card. We tested relationships between cumulative risk for inequity, calculated as a count of risks related to race/ethnicity, income, education, insurance and employment, and One Drop attitudes, use and outcomes. Results: Members (N=149) reported a median of 1 risk factor. Risk was positively associated with reported helpfulness of the One Drop app, educational topics, reminders and actual views of lessons, while negatively related to past-month glucose logging (p’s&amp;lt;.05) . No associations were found for helpfulness of glucose analysis or coaching, use of coaching, self-reported A1C and actual past-month blood glucose (p’s&amp;gt;.05) . Discussion: Higher risk for health inequity was associated with reporting greater helpfulness of One Drop's app and the use of some of its features. Combined with the absence of an association between risk for inequity and self-reported A1C and glucose, results suggest mHealth apps may be creating equitable outcomes by providing trusted content and greater accessibility. mHealth platforms should ensure this population has access to education emphasizing regular glucose monitoring as part of diabetes self-care. Disclosure S. D. Imrisek: Employee; One Drop. H. Nagra: Employee; One Drop, One Drop. J. R. Hoy-rosas: None. M. Lee: Employee; One Drop. D. Goldner: Employee; One Drop. J. Dachis: Board Member; One Drop, Employee; One Drop, Stock/Shareholder; One Drop. L. Sears: Employee; One Drop.

The impact of telehealth applications on pregnancy outcomes and costs in high-risk pregnancy: A systematic review and meta-analysis.

Telehealth is an applicable, acceptable, cost-effective, easily accessible, and speedy method for pregnant women. This study aimed to examine the impact of telehealth applications on pregnancy outcomes and costs in high-risk pregnancies. Studies were selected from PubMed, Science Direct, Web of Science, EBSCO, Scopus, and Clinical Key databases according to the inclusion and exclusion criteria from January to February 2021. Cochrane risk-of-bias tools were used in the quality assessment of the studies. Four observational and eight randomized controlled studies were included in this meta-analysis (telehealth: 135,875, control: 94,275). It was seen that the number of ultrasound (p < 0.01) and face-to-face visits (p < 0.01), fasting insulin (p < 0.01), hemoglobin A1C before delivery (p < 0.01), and emergency cesarean section rates (p = 0.05) were lower in the telehealth group. In the telehealth group, the women's use of antenatal corticosteroids (p = 0.03) and hypoglycemic medication at delivery (p = 0.03), the total of nursing interventions (p < 0.01), compliance with actual blood glucose measurements (p < 0.01), induction intervention at delivery (p = 0.003), and maternal mortality (p < 0.001) rates were higher. Two groups were similar in terms of the use of medical therapy, total gestational weight gain, health problems related to pregnancy, mode and complications of delivery, maternal intensive care unit admission, fetal-neonatal growth and development, neonatal health problems and mortality, follow-up, and care costs. Telehealth and routine care yielded similar maternal/neonatal health and cost outcomes. It can be said that telehealth is a safe technique to work with in the management of high-risk pregnancies.

Development of non-invasive blood glucose regression based on near-infrared spectroscopy combined with a deep-learning method

Extracting micro-scale spectral features from dynamic blood glucose concentrations is extremely difficult when using non-invasive measurement methods. This work proposes a new machine-learning method based on near-infrared spectroscopy, a deep belief network (DBN), and a support vector machine to improve prediction accuracy. First, the standard oral glucose tolerance test was used to collect near-infrared spectroscopy and actual blood glucose concentration values for specific wavelengths (1200, 1300, 1350, 1450, 1600, 1610, and 1650 nm); the blood glucose concentrations were within a clinical range of 70 ∼ 220 mg dl−1. Second, based on the DBN model, high-dimensional deep features of the non-invasive blood glucose spectrum were extracted. These were used to establish a support vector regression (SVR) model and to quantitatively analyze the influence of the spectral sample size and corresponding feature dimensions (i.e. DBN structure) on prediction accuracy. Finally, based on data from six volunteers, a comparative analysis of the SVR model’s prediction accuracy was performed both before and after using high-dimensional deep features. For volunteer 1, when the DBN-based high-dimensional deep features were used, the root mean square error of the SVR model was reduced by 71.67%, and the correlation coefficient (R 2) and the P value of the Clark grid analysis (P) were increased by 13.99% and 6.28%, respectively. Moreover, we had similar results when the proposed method was carried out on the data of other volunteers. The results show that the presented algorithm can play an important role in dynamic non-invasive blood glucose concentration prediction and can effectively improve the accuracy of the SVR model. Further, by applying the algorithm to six independent sets of data, this research also illustrates the high-precision regression and generalization capabilities of the DBN-SVR algorithm.

Non-Invasive Determination of Blood Glucose Levels by Optical Waveguide

Objective: Today, there are various non-invasive techniques available for the determination of blood glucose levels. In this study, the level of blood glucose was determined by developing a new device using near-infrared (NIR) wavelength, glass optical waveguide, and the phenomenon of evanescent waves. Materials and Methods: The body's interstitial fluid has made possible the development of new technology to measure the blood glucose. As a result of contacting the fingertip with the body of the borehole rod, where electromagnetic waves are reflected inside, evanescent waves penetrate from the borehole into the skin and are absorbed by the interstitial fluid. The electromagnetic wave rate absorption at the end of the borehole rod is investigated using a detection photodetector, and its relationship to the people's actual blood glucose level. Following precise optimization and design of the glucose monitoring device, a statistical population of 100 participants with a maximum blood glucose concentration of 200 mg/dL was chosen. Before measurements, participants put their index finger for 30 seconds on the device. Results: According to this experimental study, the values measured by the innovative device with Clark grid analysis were clinically acceptable in scales A and B. The Adjusted Coefficient of Determination of the data was estimated to be 0.9064. Conclusion: For future investigations, researchers are recommended to work with a larger statistical population and use error reduction trends to improve the accuracy and expand the range of measurements.

Multi-step ahead predictive model for blood glucose concentrations of type-1 diabetic patients

Continuous monitoring of blood glucose (BG) levels is a key aspect of diabetes management. Patients with Type-1 diabetes (T1D) require an effective tool to monitor these levels in order to make appropriate decisions regarding insulin administration and food intake to keep BG levels in target range. Effectively and accurately predicting future BG levels at multi-time steps ahead benefits a patient with diabetes by helping them decrease the risks of extremes in BG including hypo- and hyperglycemia. In this study, we present a novel multi-component deep learning model BG-Predict that predicts the BG levels in a multi-step look ahead fashion. The model is evaluated both quantitatively and qualitatively on actual blood glucose data for 97 patients. For the prediction horizon (PH) of 30 mins, the average values for root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and normalized mean squared error (NRMSE) are 23.22 pm 6.39 mg/dL, 16.77 ± 4.87 mg/dL, 12.84 pm 3.68 and 0.08 pm 0.01 respectively. When Clarke and Parkes error grid analyses were performed comparing predicted BG with actual BG, the results showed average percentage of points in Zone A of 80.17 pm 9.20 and 84.81 pm 6.11, respectively. We offer this tool as a mechanism to enhance the predictive capabilities of algorithms for patients with T1D.

Fingerstick artefactual hypoglycaemia in a patient suffering from Systemic Sclerosis and Raynaud's syndrome

Artefactual hypoglycaemia has been defined as a discrepancy between different laboratory measurements and actual blood glucose, regardless of symptoms. Our case illustrates this diagnosis trap. We report the case of a 59 year-old woman, whose glycaemia levels, measured by fingerstick blood glucose, were found to be low (between 34 and 70 mg/dl) on several samples contrasting with normal levels in control venous samples. The fortuitous discovery of low glycaemias was not associated with clinical hypoglycaemia symptoms. The patient had a history of Raynaud's syndrome related to Systemic Sclerosis. The result of cortisol investigations was normal. The ultrasound exam showed a normal pancreas and a fatty liver. The oral hyperglycaemia showed a slight functional hyperinsulinism. The fast test was not in favour of an insulinoma. The artefactual hypoglycaemia reported in this patient with Raynaud's syndrome and systemic sclerosis was due to a low capillary blood glucose level caused by a reduced capillary flow and capillary alterations. As venous access can be difficult in patients with systemic sclerosis, several teams propose the earlobe as an alternative site for the glucose measurements. The lack of clinical symptoms associated with hypoglycaemias measured by fingerstick should be interpreted with caution, depending on the clinical context. In case of doubt, hypoglycaemias should be confirmed by a venous measure, which remains the gold standard. It is important to take into account the patient's history, which can be a cause of artefactual hypoglycaemia.

Acacia Gum Is Well Tolerated While Increasing Satiety and Lowering Peak Blood Glucose Response in Healthy Human Subjects.

Acacia gum (AG) is a non-viscous soluble fiber that is easily incorporated into beverages and foods. To determine its physiological effects in healthy human subjects, we fed 0, 20, and 40 g of acacia gum in orange juice along with a bagel and cream cheese after a 12 h fast and compared satiety, glycemic response, gastrointestinal tolerance, and food intake among treatments. Subjects (n = 48) reported less hunger and greater fullness at 15 min (p = 0.019 and 0.003, respectively) and 240 min (p = 0.036 and 0.05, respectively) after breakfast with the 40 g fiber treatment. They also reported being more satisfied at 15 min (p = 0.011) and less hungry with the 40 g fiber treatment at 30 min (p = 0.012). Subjects reported more bloating, flatulence, and GI rumbling on the 40 g fiber treatment compared to control, although values for GI tolerance were all low with AG treatment. No significant differences were found in area under the curve (AUC) or change from baseline for blood glucose response, although actual blood glucose with 20 g fiber at 30 min was significantly less than control. Individuals varied greatly in their postprandial glucose response to all treatments. AG improves satiety response and may lower peak glucose response at certain timepoints, and it is well tolerated in healthy human subjects. AG can be added to beverages and foods in doses that can help meet fiber recommendations.

Pt Nanoparticles Confined by Zirconium Metal-Organic Frameworks with Enhanced Enzyme-like Activity for Glucose Detection.

Metal nanozymes hold promise for chemical and biological applications, and their implementation relies on high catalytic efficiency and stability. Using the metal–organic framework as an ideal carrier for well-dispersed ultra-small metal nanoparticles (NPs) is beneficial for improving the catalytic efficiency of nanozymes. In this study, a zirconium-based metal organic framework (UiO-66) with good chemical stability and high porosity was synthesized and used to construct Pt/UiO-66 nanocomposites. The percentage of Pt in UiO-66 can be tuned easily by adjusting the feeding amount of PtCl42–. Because of the confinement effect of mesopores, the Pt particles with an average diameter of 3.8 nm are formed and dispersed throughout the pores of the UiO-66 particle. The Pt/UiO-66 composites show efficient oxidase- and peroxidase-like activity. Both the oxidase- and peroxidase-like activities are dependent on the Pt percentage. Pt/UiO-66-6% exhibits enhanced peroxidase-like activity, ∼3.9 times higher than that of commercial Pt/C with 10 wt % Pt. We propose that the construction of Pt/UiO-66 increased the utilization efficiency and stability of Pt NPs and provided more active sites for catalytic reactions. Using the peroxidase-like activity of Pt/UiO-66, a colorimetric method that can be used for actual blood glucose detection was developed for the specific detection of glucose with a limit of detection of 0.033 mM.

A study on the stability and sensitivity of mediator-based enzymatic glucose sensor measured by catalyst consisting of multilayer stacked via layer-by-layer

A catalyst consisting of carbon nanotube, 2,2,6,6-tetramethylpiperidine 1-oxyl (TEMPO), polyacrylic acid (PAA), polyethylenimine (PEI), and glucose oxidase (GOx) (CNT/[TEMPO-PAA]/PEI/GOx) is developed to improve the stability and sensitivity of mediator based glucose sensor. In this catalyst, TEMPO is bonded with PAA to prevent the leaching out of TEMPO, which plays a crucial role in increasing the glucose sensitivity by instilling charges in it. The TEMPO-PAA composite is then linked to other components by electrostatic force. To confirm the chemical structure of the catalyst, its chemical bonds and surface charge are investigated by Fourier-transform infrared spectroscopy and zeta potential. When the stability and glucose sensitivity are measured, this catalyst preserves 80% of its initial catalytic activity for one week although other similar catalysts show far worse stability. In addition, the linear relationship of glucose concentration and reactivity of glucose oxidation shown in 1–10mM glucose range that corresponds to actual blood glucose concentration of human being indicates that this catalyst can induce excellent glucose sensitivity. In terms of selectivity, this catalyst reacts with blood glucose highly selectively, whereas this is not reacted with other eight different saccharides.

Applying Parkes Grid Method to Evaluate Impact of Variation in Blood Glucose Monitoring (BGM) Strip Accuracy Performance in Type 1 Diabetes Highlights the Potential for Amplification of Imprecision With Less Accurate BGM Strips.

The National Health Service spends £170 million on blood glucose monitoring (BGM) strips each year and there are pressures to use cheaper less accurate strips. Technology is also being used to increase test frequency with less focus on accuracy.Previous modeling/real-world data analysis highlighted that actual blood glucose variability can be more than twice blood glucose meter reported variability (BGMV). We applied those results to the Parkes error grid to highlight potential clinical impact. BGMV is defined as the percent of deviation from reference that contains 95% of results. Four categories were modeled: laboratory (<5%), high accuracy strips (<10%), ISO 2013 (<15%), and ISO 2003 (<20%) (includes some strips still used).The Parkes error grid model with its associated category of risk including "alter clinical decision" and "affect clinical outcomes" was used, with the profile of frequency of expected results fitted into each BGM accuracy category. Applying to single readings, almost all strip accuracy ranges derived in a controlled setting fell within the category: clinically accurate/no effect on outcomes areas.However modeling the possible blood glucose distribution in more detail, 30.6% of longer term results of the strips with current ISO accuracy would fall into the "alter clinical action" category. For previous ISO strips, this rose to 44.1%, and for the latest higher accuracy strips, this fell to 12.8%. There is a minimum standard of accuracy needed to ensure that clinical outcomes are not put at risk. This study highlights the potential for amplification of imprecision with less accurate BGM strips.

A Reinforcement Learning-Based Method for Management of Type 1 Diabetes: Exploratory Study.

BackgroundType 1 diabetes mellitus (T1DM) is characterized by chronic insulin deficiency and consequent hyperglycemia. Patients with T1DM require long-term exogenous insulin therapy to regulate blood glucose levels and prevent the long-term complications of the disease. Currently, there are no effective algorithms that consider the unique characteristics of T1DM patients to automatically recommend personalized insulin dosage levels.ObjectiveThe objective of this study was to develop and validate a general reinforcement learning (RL) framework for the personalized treatment of T1DM using clinical data.MethodsThis research presents a model-free data-driven RL algorithm, namely Q-learning, that recommends insulin doses to regulate the blood glucose level of a T1DM patient, considering his or her state defined by glycated hemoglobin (HbA1c) levels, body mass index, engagement in physical activity, and alcohol usage. In this approach, the RL agent identifies the different states of the patient by exploring the patient’s responses when he or she is subjected to varying insulin doses. On the basis of the result of a treatment action at time step t, the RL agent receives a numeric reward, positive or negative. The reward is calculated as a function of the difference between the actual blood glucose level achieved in response to the insulin dose and the targeted HbA1c level. The RL agent was trained on 10 years of clinical data of patients treated at the Mass General Hospital.ResultsA total of 87 patients were included in the training set. The mean age of these patients was 53 years, 59% (51/87) were male, 86% (75/87) were white, and 47% (41/87) were married. The performance of the RL agent was evaluated on 60 test cases. RL agent–recommended insulin dosage interval includes the actual dose prescribed by the physician in 53 out of 60 cases (53/60, 88%).ConclusionsThis exploratory study demonstrates that an RL algorithm can be used to recommend personalized insulin doses to achieve adequate glycemic control in patients with T1DM. However, further investigation in a larger sample of patients is needed to confirm these findings.