Glucose Monitoring Research Articles

Fabrication of a novel non-enzymatic glucose sensor based on ruthenium supported by a combination of PBA nanocubes and MOF nanosheets

The development of novel catalysts with high catalytic activity is of high importance of electrochemical non-enzymatic glucose sensing. Herein, a sensitive non-enzymatic electrochemical sensor (Ru@NiCo-MOF/NiFe-PBA) has been synthesized using a fast, simple and eco-friendly method. The high stability of PBA and the large specific surface area (two-dimensional nanosheet structure) of MOF support the excellent electrochemical performance, while the incorporation of the highly active metal Ru enhances the electrochemical activity even more. The results reveal that the sensitivities of the sensor are 1070.77 and 487.62 μA mM−1 cm−2, corresponding to linear ranges of 0.01–0.67 mM and 0.67–5.07 mM, respectively. Meanwhile, the sensor has a low detection limit (0.446 μM) and a short response time (2 s), making it a strong candidate for non-enzymatic glucose sensors. The successful detection of glucose in saliva further demonstrates its potential for practical applications. Therefore, Ru@NiCo-MOF/NiFe-PBA will make an impact in the field of glucose monitoring and detection.

The Effect of COVID-19 Lockdown Among Adolescents with Type 1 Diabetes: A Systematic Review and Meta-Analysis.

The aim of this study was to assess how the lockdown of the COVID-19 pandemic had affected the glycaemic control of adolescents aged 10-19 with type 1 diabetes. A comprehensive search of literature was performed in PubMed, Scopus, Web of Science, and ProQuest. Published articles up to September 2022 were included. The Glucose Monitoring Index (GMI) and HbA1c level were defined as outcome variables. Average glucose level was found to be a common variable in both HbA1c levels and GMI; therefore, HbA1c and GMI were converted to average glucose (mg/dL) using appropriate formulas. Studies reported the outcomes in two or three periods (pre-lockdown, lockdown, and post-lockdown) were included in the analysis. A paired wise meta-analysis was performed among the studies that reported all three periods. Homogeneity across studies was assessed using I2 statistic. Fourteen studies were included in the study. The pooled average glucose during the lockdown decreased to 166.9 mg/dL (95% CI, 153.78, 180.02) from 205.793 mg/dL (95% CI, 188.412, 223.173) during the pre-lockdown period, then it increased to 204.23 mg/dL (95% CI, 186.17, 222.29) during the post-lockdown period. A paired wise meta-analysis indicated a reduction in average glucose levels. However, it was not statistically significant, possibly due to the small number of studies that reported data from all three periods. Although the descriptive analysis of our study showed that the lockdown had affected (decreased) the average glucose level among adolescents with type 1 diabetes, this was not statistically significant in the pooled analysis.

The status of blood glucose monitoring and its influencing factors in Chinese patients with type 2 diabetes initiating premixed insulin: A prospective real-world study

ObjectiveThis study aimed to assess the current state of self-monitoring of blood glucose (SMBG) in Chinese patients initiating premixed insulin and its influential factors. Research Design and MethodsThis is a single-arm, multi-center, prospective real-world study enrolling a total of 8214 adult patients with type 2 diabetes mellitus (T2DM) newly initiated premixed insulin analogues. Each patient was followed up for 12 weeks, and the data related to SMBG was collected at week 1, week 4, week 8 and week 12, while data related to glycated hemoglobin were collected at week 1 and week 12. The primary outcome was the frequency of SMBG over 12 weeks. ResultsAt week 12, 83.3 % monitored blood glucose at least once, while 20.3 % of participants continued SMBG every week. The average monitoring frequency was 4.78 times/week over the first 4 weeks and 3.33 times/week over 12 weeks. The patients with a higher frequency of SMBG had better control of blood glucose. ConclusionsThis study found that most T2DM patients would take SMBG but the adherence decreased over time. The adherence to SMBG in Chinese T2DM patients was influenced by age, insulin dosage, education level, and diabetes duration. SMBG benefited the improvement of glycemic control.

Noninvasive Blood Glucose Monitoring Using Spatiotemporal ECG and PPG Feature Fusion and Weight-Based Choquet Integral Multimodel Approach.

change of blood glucose (BG) level stimulates the autonomic nervous system leading to variation in both human's electrocardiogram (ECG) and photoplethysmogram (PPG). In this article, we aimed to construct a novel multimodal framework based on ECG and PPG signal fusion to establish a universal BG monitoring model. This is proposed as a spatiotemporal decision fusion strategy that uses weight-based Choquet integral for BG monitoring. Specifically, the multimodal framework performs three-level fusion. First, ECG and PPG signals are collected and coupled into different pools. Second, the temporal statistical features and spatial morphological features in the ECG and PPG signals are extracted through numerical analysis and residual networks, respectively. Furthermore, the suitable temporal statistical features are determined with three feature selection techniques, and the spatial morphological features are compressed by deep neural networks (DNNs). Lastly, weight-based Choquet integral multimodel fusion is integrated for coupling different BG monitoring algorithms based on the temporal statistical features and spatial morphological features. To verify the feasibility of the model, a total of 103 days of ECG and PPG signals encompassing 21 participants were collected in this article. The BG levels of participants ranged between 2.2 and 21.8 mmol/L. The results obtained show that the proposed model has excellent BG monitoring performance with a root-mean-square error (RMSE) of 1.49 mmol/L, mean absolute relative difference (MARD) of 13.42%, and Zone A + B of 99.49% in tenfold cross-validation. Therefore, we conclude that the proposed fusion approach for BG monitoring has potentials in practical applications of diabetes management.

Electrochemical Glucose Sensors: Classification, Catalyst Innovation, and Sampling Mode Evolution.

Glucose sensors are essential tools for monitoring blood glucose concentration in diabetic patients. In recent years, with the increasing number of individuals suffering from diabetes, blood glucose monitoring has become extremely necessary, which expedites the iteration and upgrade of glucose sensors greatly. Currently, two main types of glucose sensors are available for blood glucose testing: enzyme-based glucose sensor (EBGS) and enzyme-free glucose sensor (EFGS). For EBGS, several progresses have been made to comprehensively improve detection performance, ranging from enhancing enzyme activity, thermostability, and electron transfer properties, to introducing new materials with superior properties. For EFGS, more and more new metallic materials and their oxides are being applied to further optimize its blood glucose monitoring. Here the latest progress of electrochemical glucose sensors, their manufacturing methods, electrode materials, electrochemical parameters, and applications were summarized, the development glucose sensors with various noninvasive sampling modes were also compared.

Impaired Awareness of Hypoglycemia and its Risk Factors among Diabetic Patients in Jazan, Saudi Arabia: A Cross-Sectional Study.

Impaired awareness of hypoglycemia (IAH) is related to a three- to sixfold increase in the risk of severe hypoglycemia in adults with type 1 diabetes mellitus (T1D). This study aimed to assess the prevalence of IAH and its risk factors and determine the frequency of hypoglycemic symptoms. This study aimed to assess the prevalence of IAH and its risk factors and determine the frequency of hypoglycemic symptoms. A cross-sectional study was conducted among T1D patients attending Jazan Endocrine and Diabetes Center in Jazan province, Saudi Arabia. A total of 151 patients participated, using the interview-based Clarke questionnaire, a validated eight-item questionnaire to evaluate IAH. Scoring four or more answers as reduced awareness categorizes the participant as having IAH. The prevalence of IAH was 25.2% among the T1D patients. IAH was significantly associated with body mass index (BMI; p = 0.034), occupation (p = 0.014), and blood glucose monitoring methods (p = 0.027). Shaking and sweating were the most commonly reported symptoms of hypoglycemia. A BMI of <25 kg/m2 was higher linked to hunger and speech difficulty compared to a BMI of ≥25 kg/m2 (p < 0.05). Changing the insulin injection site was associated with confusion, odd behavior, and speech difficulty (p < 0.05). Monitoring blood glucose four times daily was associated with sweating, odd behavior, and incoordination (p = 0.024) compared to monitoring less than four times daily (p < 0.05). A hemoglobin A1c (HbA1c) reading of ≥7 was linked to odd behavior compared to an HbA1c reading of <7 (p = 0.032). Patients committed to insulin injections were more likely to experience palpitations than non-committed patients (p = 0.038). Each one-unit increase in age, monitoring blood glucose, and income was associated with a decrease in the odds of IAH (OR of Age = 0.89, 95% CI: 0.83-0.95) (OR of income = 0.10, 95% CI: 0.01-0.55). Moreover, individuals with a Body Mass Index (BMI) greater than or equal to 25 (OR = 2.99, 95% CI: 1.13-8.25), employed individuals (OR = 18.2, 95% CI: 3.75-105), and diabetes duration of more than ten years (OR = 3.96, 95% CI: 1.31- 13.2) exhibited an increase in the higher risk of IAH. IAH was prevalent among T1D patients attending Jazan Endocrine and Diabetes Center. The main associated factors included BMI, blood glucose monitoring method, and occupation. Future research should investigate the underlying causes of the observed associations and explore strategies to enhance the awareness of hypoglycemia.

Development and Validation of a Nocturnal Hypoglycaemia Risk Model for Patients With Type 2 Diabetes Mellitus.

To develop and test different machine learning algorithms for predicting nocturnal hypoglycaemia in patients with type 2 diabetes mellitus. A retrospective study. We collected data from dynamic blood glucose monitoring of patients with T2DM admitted to the Department of Endocrinology and Metabolism at a hospital in Shanghai, China, from November 2020 to January 2022. Patients undergone the continuous glucose monitoring (CGM) for ≥ 24 h were included in this study. Logistic regression, random forest and light gradient boosting machine algorithms were employed, and the models were validated and compared using AUC, accuracy, specificity, recall rate, precision, F1 score and the Kolmogorov-Smirnov test. A total of 4015 continuous glucose-monitoring data points from 440 patients were included, and 28 variables were selected to build the risk prediction model. The 440 patients had an average age of 62.7 years. Approximately 48.2% of the patients were female and 51.8% were male. Nocturnal hypoglycaemia appeared in 573 (14.30%) of 4015 continuous glucose monitoring data. The light gradient boosting machine model demonstrated the highest predictive performances: AUC (0.869), specificity (0.802), accuracy (0.801), precision (0.409), recall rate (0.797), F1 score (0.255) and Kolmogorov (0.603). The selected predictive factors included time below the target glucose range, duration of diabetes, insulin use before bed and dynamic blood glucose monitoring parameters from the previous day. No Patient or Public Contribution.

Objetivos del tratamiento de la diabetes mellitus tipo 1

There are three ways to assess blood glucose control in type 1 diabetes (DM1): glycated hemoglobin (HbA1c, general target less than or equal to 7%), time in the 70–180mg/dl range (general target 70% or higher), and capillary blood glucose. Diabetes education is a fundamental element in achieving self-management of the disease. It is based on three fundamental pillars: nutrition, insulin therapy, and management of diabetes technology (sensors, insulin pumps, smart insulin pens, and mobile applications). Drug treatment for DM1 consists of intensive insulin therapy using multiple dose injection (MDI) or continuous subcutaneous insulin infusion (CSII). The choice of treatment regimen should be based on patient characteristics and other clinical considerations (e.g., pregnancy, presence of undetected hypoglycemia, or variable insulin requirements). There are currently no other insulin adjuvant therapies approved for use in DM1, although DM2 drugs such as GLP-1 analogs or SGLT2 inhibitors that may provide benefits in blood glucose control or weight loss have been used. Kidney-pancreas or pancreatic islet transplantation is considered a final option in patients with end-stage chronic kidney disease together with or following renal transplantation or in individuals with undetected or severe recurrent hypoglycemia that does not respond to optimized management.

Estrategia terapéutica en el paciente diabético (I). Empoderamiento del paciente y educación. Objetivos terapéuticos. Estilo de vida, alimentación y control de los factores de riesgo cardiovascular. Sistemas de control glucémico

Managing type 2 diabetes (DM2) requires a multifactorial approach. The foundation of treatment is lifestyle modification and diabetes education. Patients must be involved in decision making and be given tools to help them manage their disease. The four basic pillars of DM2 management are: a) glycemic control; b) blood pressure management; c) lipid management; and d) use of drugs with proven cardiovascular and renal benefits. In terms of lifestyle, it is recommended to combine a diet that contributes to weight maintenance and glycemic control as well as the incorporation of regular moderate physical activity (at least 150minutes per week). Cholesterol and blood pressure targets should be individualized for each patient with DM2 based on the presence of cardiovascular disease or cardiovascular risk. Smoking cessation is recommended. There are two main glycemic control systems: blood glucose monitoring (capillary glycemia) and continuous glucose monitoring.

Reusable gallium-based electrochemical sensor for efficient glucose detection

Among wearable sensing devices, electrochemical sensors are overwhelming in biochemical detection due to their simple design but high sensitivity. Most electrochemical sensors are disposable, which significantly impairs the service life. Here we present a reusable gallium (Ga)-based multi-layer electrochemical glucose biosensor to extend noninvasive monitoring of glucose in the interstitial fluid. This multilayer sensor includes Ga as a conductive interconnector, poly(3,4-ethylenedioxythiophene) as an electrode layer to prevent oxidation and metal leakage, a nano-Pt layer to enhance the electrochemical properties, and a nano-Prussian blue layer for reducing the hydrogen peroxide reduction potential. Most importantly, the biosensors can be renewed by automatically removing the modified nanocomposites via the electrolysis-induced bubbles and by electroplating without impairing the whole structure of the biosensor. The biosensor showed high sensitivity (24.6 μA mM-1 cm-2), wide linear range (0.01-26 mM), excellent stability (i.e., pH, long-term use) and superior selectivity, that is comparable to those of the current electrochemical tools for glucose detection. More importantly, incorporated with the reverse iontophoresis (RI), the Ga-based hybrid sensor was applied as a skin patch on mice for the in vivo noninvasive and continuous monitoring of ISF-borne glucose. The results showed a good correlation with that measured by blood glucometer. As a whole, this Ga-based electrochemical biosensor should endow new functions like biochemical analysis in biofluids for Ga-based soft bioelectronic sensing devices, in which almost are physical sensors. We believe that the reusability of electrochemically controllable processes may further inspire the development of more integrated and long-term stable gallium-based biosensing devices.

Clinical perspective on innovative insulin delivery technologies in diabetes management.

The primary objective of this study is to report the results of an online questionnaire and the in-person discussion sessions of physicians specializing in diabetes care in which their opinions about current diabetes management was obtained. The Diabetes Innovation Summit 2023 drew attendance from a diverse group of specialized physicians from multiple countries. A comprehensive literature review was conducted to examine the technologies and medical needs associated with diabetes management. Using the results of the review, a questionnaire was developed by three experts from the steering committee to solicit feedback from specialized physicians. The online survey was made accessible between 10th December 2022 and 10th January 2023. Following the online survey, six structured in-person discussion sessions were conducted with specialized physicians from the Middle East, Central-Eastern Europe, and North Africa regions. The study revealed that about 59% of survey requests were answered, with many participants being pediatric endocrinologists from North Africa. Around 60% of diabetes patients followed Multiple Daily Injections (MDI) according to specialized physicians. Among MDI users, 62% employed Blood Glucose Monitors (BGM), 31% used intermittent-scanning Continuous Glucose Monitors (isCGM), and 23% used CGM. In North Africa, nearly 90% of patients used MDI due to financial constraints. While physicians focused on both Time in Range (TIR) and HbA1c for MDI-treated patients, satisfaction with TIR achieved was expressed by 31%, while 74·1% believed Real-Time CGM (rtCGM) was effective. Concerns arose about potentially misleading HbA1c results and the relatively low patient achievement of target TIR despite CGM usage. The Smart MDI System was seen favorably compared to other applications. The system's affordability was a significant barrier, particularly in the Middle East and Africa. The present study highlights that physicians are generally supportive of utilizing new technology. The questionnaires and the open discussion revealed the expectation that the Smart MDI technology provides better control, primarily by identifying missed boluses, while expressing concerns on the use of the technology by teenagers and children, who might forget the device and be reluctant to use in public, and by the older population, who might be challenged by the technology.

Efficacy and safety of henagliflozin combined with continuous subcutaneous insulin infusion in the treatment of Chinese inpatients with type 2 diabetes mellitus based on a continuous glucose monitoring system: protocol of a multicentre, open-label, inpatient, randomised, controlled trial

IntroductionThe role of sodium-glucose cotransporter 2 inhibitors (SGLT2is) in diabetes treatment is expanding; however, few studies have investigated the efficacy and safety of combining SGLT2is with insulin pump therapy. Notably,...

Tecnología aplicada a la diabetes

Diabetes technology refers to devices and applications that people with diabetes use to help manage their disease. There are two main categories: blood glucose monitoring systems (including continuous glucose monitoring (CGM) systems) and insulin delivery devices (including insulin pumps and smart insulin pens). CGM devices measure interstitial blood glucose continuously or intermittently (flash measurements), showing alerts and trends that help predict glucose behavior. The data are represented by the ambulatory glucose profile, which provides measurements and a summary graph to aid in its interpretation. Insulin pumps or continuous subcutaneous insulin infusion systems are devices that infuse rapid-acting insulin continuously with the objective of maintaining glucose levels in range. Among these, hybrid closed loop systems are capable of adjusting insulin infusion according to CGM blood glucose figures. Smart insulin pens are linked to smartphone apps and have features that help patients manage their diabetes (dose reminder, bolus calculator, active insulin monitoring).

Advances in diabetes technology within the digital diabetes ecosystem.

Ongoing innovations in glucose monitoring, insulin delivery, and telehealth technologies have created a digital diabetes ecosystem populated by connected tools and technologies that have been shown to improve clinical outcomes, lower costs, and reduce the burden of diabetes. Advances in connected continuous glucose monitoring devices, insulin pumps, and insulin pens have led to the development of automated insulin delivery systems that modulate insulin infusion based on sensor glucose data. Similar integrations of continuous glucose monitoring and connected blood glucose meter data into "smart" pens have lessened the guesswork of intensive insulin management for individuals who prefer traditional injection therapy. A growing number of health apps that can be accessed through smartphones and wearable devices provide information and advice that support individuals in adopting healthier lifestyles. The differences in features and functionality give users the ability to select the devices that best meet their unique requirements and preferences. This article reviews the most current digital diabetes technologies and discusses how the connectivity of these tools can create an overarching architecture of feedback mechanisms that monitor an individual's health status, motivate and enhance adherence to self-management, and provide advice and decision-support tools to clinicians as well as other members of the health care team to make living with diabetes more manageable.

Comparison of feature learning methods for non-invasive interstitial glucose prediction using wearable sensors in healthy cohorts: a pilot study

BackgroundAlterations in glucose metabolism, especially the postprandial glucose response (PPGR), are crucial contributors to metabolic dysfunction, which underlies the pathogenesis of metabolic syndrome (MetSyn). Personalized low-glycemic diets (PLGD) have shown promise in reducing postprandial glucose spikes. However, current methods such as invasive continuous glucose monitoring (CGM) or multi-omics data integration to assess PPGR come with limitations, including cost and invasiveness, hindering widespread adoption in primary disease prevention. Our aim is to assess machine-learning algorithms for predicting individual PPGR using non-invasive wearable data, thereby circumventing the limitations associated with existing approaches. By identifying the most accurate model, we seek to provide a more accessible and efficient method for managing glucose metabolism. MethodsThis data-driven analysis utilized the experimental dataset from the SENSE (”Systemische Ernährungsmedizin”) study. Healthy participants used an Empatica E4 wristband and Abbott Freestyle Libre 3 CGM for ten days. Blood volume pulse (BVP), electrodermal activity (EDA), heart rate (HR), skin temperature (TEMP), and the corresponding CGM values were measured. Subsequently, four data-driven deep learning (DL) model-convolutional neural network (CNN), lightweight transformer (LTF), long short-term memory with attention (LSTM-A), and Bi-directional LSTM (BiLSTM)-were implemented and compared to determine the potential of DL in predicting interstitial glucose levels without involving food and activity logs. ResultsThe proposed BiLSTM achieved the best interstitial glucose prediction performance, with an average root mean squared error (RMSE) of 13.42 mg/dL, an average mean absolute percentage error (MAPE) of 0.12, and only 3.01% values falling within area D in Clarke error grid analysis (CEGA), incorporating the leave-one-out cross-validation (LOOCV) strategy for a five-minute prediction horizon. ConclusionThe findings of this study may demonstrate the feasibility of transferring knowledge gained from invasive glucose monitoring devices to non-invasive approaches. Furthermore, it emphasizes the promising prospects of combining deep learning with wearable technologies to predict glucose levels in healthy individuals.

Platform for precise, personalised glucose forecasting through continuous glucose and physical activity monitoring and deep learning

Emerging research has demonstrated the advantage of continuous glucose monitoring for use in artificial pancreas and diabetes management in general. Recent studies demonstrate that glucose level forecasting using deep learning can help avoid postprandial hyperglycemia (≥ 180 mg/dL) or hypoglycemia (≤70 mg/dL) from delayed or increased insulin dosing in artificial pancreas. In this paper, a novel hybrid deep learning framework with integration of content-based attention learning is presented, to effectively predict the glucose measurements with prediction horizons (PH) = 15, 30 and, 60 minutes for T1D and T2D patients based on past data. We also present a complete cloud-based system and mobile app used for collecting CGM sensor, physical activity data, CHO values and insulin measurements to perform glucose forecasts using the proposed model running on Cloud. This model was validated using clinical data of individual with Type 1 diabetes (OhioT1DM) and individual with Type 2 diabetes. The mean absolute relative difference (MARD) was 12.33±3.15, 7.14±1.76% for PH=60 and, 30 min respectively on OhioT1DM clinical Dataset. The root mean squared error (RMSE) was 29.41±5.92 mg/dL and 17.19±3.22 mg/dL and the mean absolute error (MAE) was 21.96±4.67 mg/dL and 12.58±2.34 mg/dL for PH=60 and, 30 min respectively on the same clinical dataset. It was observed that inclusion of physical activity leads to improved glucose forecasting accuracy. Furthermore, all these results were obtained by training the model on only 8 days of clinical data of a single patient, followed by testing on clinical data on the following days. The results indicate that training on a single patient data may lead to better personalisation and better glucose forecasting results compared to existing works.

Efficacy of continuous glucose monitoring for finding unusual causes of hyperglycemia: a clinical case

Diabetes is a chronic condition that requires constant blood glucose self-monitoring. The carbohydrate metabolism compensation is usually assessed by the level of glycated hemoglobin. But it does not always help reveal the true cause of poor glycemic control. The devices for continuous glucose monitoring allows to assess blood glucose level in real time and find unusual causes of hyper- and hypoglycemia. Achieving target glycemic levels is influen­ced by many factors: age of patients, duration of diabetes, eating habits and eating disorders, level of education, patient’s understan­ding of the diabetes course. One of the reasons for not reaching the target levels of glycemia can be hyperphagic eating disorders, such as night eating syndrome. According to the American Psychiatric Association classification, night eating syndrome belongs to the category of “Other specified eating disorders”. Diagnostic criteria in this syndrome include consumption of &gt; 25 % of food from the daily diet after dinner or at least 2 cases per week of food consumption at night; awareness of these episodes; and at least 3 of the following: morning anorexia, uncontrollable desire to eat between dinner and sleep or at night, conviction that eating will help you fall asleep or return to sleep, insomnia and/or bad mood in the morning. The prevalence of night eating syndrome in the general population is 1.1 %, and in those who refer to metabolic surgery specialists, it is 2–20 %. The presence of night eating syndrome in diabetes adversely affects metabolic control and complicates the management of these patients, in particular, they have higher levels of HbA1c, blood pressure and body mass index compared to individuals without such eating disorder. In this case report, we describe a patient with poor glycemic control who used continuous glucose monitor and was diagnosed with night eating syndrome that helped change management and achieve normoglycemia in the evening and nighttime.

HYGIENIC SUBSTANTIATION OF EARLY DETECTION AND PREVENTION OF RISK FACTORS FOR DIABETES IN PREGNANT WOMEN

Gestational Diabetes Mellitus (GDM) is a condition that develops during pregnancy and poses significant health risks to both mother and baby if not detected and managed early. This article explores the importance of hygienic practices in the early detection and prevention of GDM. It highlights key factors, including regular prenatal check-ups, the role of nutritional and personal hygiene, and the need for consistent blood glucose monitoring. Additionally, it emphasizes lifestyle modifications for at-risk women, such as maintaining a balanced diet and proper sleep hygiene. The article concludes with recommendations for overcoming common challenges in managing GDM through education and community awareness programs, underlining the critical role of proactive healthcare in ensuring healthy pregnancies.

Implementing mobile text messaging on glycemic control in patients with diabetes mellitus.

Uncontrolled blood glucose may lead to serious complications in patients with type two diabetes mellitus (T2DM). Patients may not have the support, motivation, or encouragement to adhere to the lifestyle changes necessary to control their T2DM. More than 75% of the primary care clinic's patients are diagnosed with T2DM, with most patients at the practice site having an average hemoglobin A1c (HbA1c) level of 8.5%. The primary care clinic did not use text messaging to disseminate diabetes self-management education and support (DSMES) as outlined in Standard 4 of the American Diabetic Association's (ADA) clinical practice guideline. This evidence-based quality improvement project was conducted in a rural outpatient primary care clinic to determine whether implementing the ADA's 2022 National Standards for DSMES using text messaging would affect HbA1C levels among adult patients with T2DM. Patients were sent weekly text messages over a 12-week period. Text messages contained information promoting self-care, tips about healthy diet, exercise reminders, instructions about proper blood glucose monitoring, and reminders about medication adherence. A total of 160 patients were included. A paired-sample t -test showed a reduction in HbA1c levels after the intervention from baseline (M = 7.53, SD = 1.72) to postimplementation (M = 6.91, SD = 0.89), t (159) = 11.88, p = .001. Based on the results, implementing the ADA's National Standards for DSMES Standard 4 may affect HbA1c levels in this population.

Electrochemical biosensor based on copper sulfide/reduced graphene oxide/glucose oxidase construct for glucose detection

Due to the current increase in the number of people suffering from diabetes worldwide, how to monitor the blood glucose level in the human body has become an urgent problem to be solved nowadays. The electrochemical sensor method can be used for real-time glucose monitoring due to its advantages of real-time monitoring capability and high sensitivity. Reduced graphene oxide (rGO) has great potential for application in the field of sensors due to its advantages of large specific surface area, high stability, and good electrical and thermal conductivity. Meanwhile, the synergistic effect between two-dimensional transition metal sulfides and graphene can improve the electrochemical performance of materials due to their similar mechanical flexibility and strength. This article uses flake graphite, copper sulfate, and glucose oxidase (GOx) as raw materials to prepare CuS/rGO/GOx/GCE electrodes, and explores the performance of electrode electrocatalysis for glucose. The results showed that the prepared sensor was characterized by a low detection limit (1.75 nM) and a wide linear range (0.1-100 mM) for glucose detection, displaying a good overall detection performance, and its sensing mechanism and dynamic process were also investigated. In addition, the sensor has outstanding selectivity, anti-interference, repeatability, reproducibility and practicality.