Monitoring Of Blood Glucose Concentration Research Articles

MOF-based Sensing Materials for Non-enzymatic Glucose Sensors

<p>Diabetes mellitus is one of the common chronic diseases, seriously threating to human health. The continuous monitoring of blood glucose concentration can effectively prevent diabetic diseases. The sensing performance of glucose non-enzymatic sensors is mainly determined by working electrode materials. Metal–organic frameworks (MOFs) are recognized as promising candidate for glucose sensor application, due to its large surface areas, ordered porous structure and nearly infinite designability. In this review, the sensing performance, research progress and future challenge of non-enzymatic glucose sensors based on MOF-based materials in recent years are presented. We hope that this review would provide valuable technology guidance for high performance non-enzymatic glucose sensors based on MOFs.</p>

Biosensors Precision in Healthcare: A Scientific Odyssey

Biosensors have been the beacon of precision in the ever-evolving landscape of health care and offer a profound impact on diagnostics and patient care. These devices are designed to detect specific biological markers and convert then into measurable signals, seamlessly blending biology with cutting-edge technology. The diverse tapestry of biosensors encompass enzyme based sensors, immuno-assays, photo-biosensors, tissue-based sensors, DNA biosensors and nano-biosensors. Enzyme-based biosensors showcase unparalleled catalytic precision exemplified by glucose biosensors. Glucose oxidase serves as the biological catalyst and converts glucose into gluconic acid and hydrogen peroxide. Electrodes of the sensor then transduce this reaction into measurable signals which enables the precise quantification of glucose levels in diabetes management. Similarly, immunoassays rely on the specificity of antigen-antibody interactions. Most widely used immunoassay, Enzyme-linked immunosorbent assay (ELISA) exemplify this category, which finds applications in a number of diagnostics and provide a window into immunological responses and diseases such as HIV. Advancing the field of biosensing are the photobiosensors which utilize diverse technologies such as fluorescence resonance energy transfer (FRET) to illuminate biological processes. Genetically encoded biosensors that employ proteins like green fluorescent protein (GFP), enable non-invasive monitoring of cellular dynamics and offer real-time insights. Another cateogory, tissue-based biosensors converge the technology with the physiological environments. For instance, implantable glucose sensors that continuously monitor blood glucose concentrations and provide real-time data for personalized treatment approaches. At the core of precision diagnostics lie DNA biosensors such as DNA microarrays. They offer parallel analysis of gen expression, influencing decisions in cancer diagnostics and pharmacogenomics. Nanobiosensors, represented by nanoparticle-based sensors, navigate the nano-frontiers with exceptional sensitivity. For example, quantum dots exhibit unique optical properties for sensitive detection. Likewise, gold nanoparticles based biosensors enable real-time monitoring of drug release in drug delivery systems and emphasize their role in precision medicine. As biosensors advance, ethical considerations come to the forefront. Issues such as data privacy, consent, and equitable access to these technologies demand careful attention. It becomes imperative to ensure that biosensors are developed and deployed responsibly with a keen awareness of potential societal implications in navigating the ethical landscape. In the symphony of precision that biosensors bring to healthcare, their types and applications reflect not only technological prowess but also the ethical responsibility inherent in their development and deployment. As these devices continue to evolve, the healthcare landscape stands on the brink of a new era, where precision and ethics intertwine for the betterment of patient outcomes.

931. Host PFKFB3-dependent glycolytic reprogramming as a broad-spectrum antiviral strategy

Abstract Background The current therapeutic arsenal against viral infections remains poor, with limited spectrum of coverage and appears inadequate to face the emergence of pandemic and antiviral resistance. This study aims to develop a broad-spectrum antiviral strategy for rapid epidemic control and early treatment with better clinical outcome. Methods To investigate the in vivo change of glucose homeostasis under infectious circumstances, we carried out a time-course monitoring of blood glucose concentration in pdmH1N1-infected BALB/c mice and revealed that impairment of glucose tolerance and insulin sensitivity were caused when compared with the mock-infection control. To explore the change of gene expression file upon virus infection, we first plotted the pdmH1N1 virus replication kinetics in human primary bronchial/tracheal epithelial cells (hBTECs), followed by mRNA gene expression profiling of the key glycolytic enzymes at 0hpi, 6hpi and 24hpi, respectively. To discover potential enzymatic inhibitors/modulators of host glycolysis for antiviral treatment, we screened a small molecular compound library with 344 glycolysis compounds on pdmH1N1 infected hTBECs, followed by secondary validation to confirm the dose-dependent virus inhibition at non-toxic concentrations. Results Significant higher blood glucose level was detected in infected-mice with either glucose or insulin supplement, which suggested that host glucose metabolism, in particular the glycolysis homeostasis, were disrupted upon influenza virus infection. We also identified Glucose transporter 4 (GLUT4), enolase 2 (ENO2), monocarboxylate transporter 2 (MCT2), MCT4 and 6-phosphofructo-2-kinase (PFK2, also named PFKFB3) the most perturbed genes at 24hpi. KAN0438757, a potent and selective inhibitor of the metabolic kinase PFKFB3, was shown to be antiviral effective against both H1N1 and EV-A71 viruses in human primary cells. Moreover, Kan exhibited broad-spectrum antiviral activity in ZIKV-infected Huh7 cells and SARS-CoV-2 infected Caco-2 cells. Metabolic profiling reveals significant perturbations of host glucose homeostasis after influenza A virus infection. Conclusion Host PFKFB3-dependent glycolysis pathway with broad-relevance to multiple virus replications, which might be a vulnerable host target for antiviral intervention. Disclosures All Authors: No reported disclosures

Exploring Copper Oxide and Copper Sulfide for Non-Enzymatic Glucose Sensors: Current Progress and Future Directions.

Millions of people worldwide are affected by diabetes, a chronic disease that continuously grows due to abnormal glucose concentration levels present in the blood. Monitoring blood glucose concentrations is therefore an essential diabetes indicator to aid in the management of the disease. Enzymatic electrochemical glucose sensors presently account for the bulk of glucose sensors on the market. However, their disadvantages are that they are expensive and dependent on environmental conditions, hence affecting their performance and sensitivity. To meet the increasing demand, non-enzymatic glucose sensors based on chemically modified electrodes for the direct electrocatalytic oxidation of glucose are a good alternative to the costly enzymatic-based sensors currently on the market, and the research thereof continues to grow. Nanotechnology-based biosensors have been explored for their electronic and mechanical properties, resulting in enhanced biological signaling through the direct oxidation of glucose. Copper oxide and copper sulfide exhibit attractive attributes for sensor applications, due to their non-toxic nature, abundance, and unique properties. Thus, in this review, copper oxide and copper sulfide-based materials are evaluated based on their chemical structure, morphology, and fast electron mobility as suitable electrode materials for non-enzymatic glucose sensors. The review highlights the present challenges of non-enzymatic glucose sensors that have limited their deployment into the market.

Microwave substrate integrated waveguide resonator sensor for non-invasive monitoring of blood glucose concentration: Low cost and painless tool for diabetics

In this study, a microwave resonant sensor with an operation frequency of 9 GHz and a quality factor of 240 has been designed, fabricated and tested by fingertips of the users to control blood glucose levels. The proposed method includes the implementation of a band-pass filter in the substrate integrated waveguide cavity, which has been carried out using a modified split ring resonator. In this research, 4 volunteers with glucose level changes ranging from 92 to 141 mg dL−1 have been tested invasive (by a glucometer) and non-invasive (by the designed sensor). For this range of glucose level changes, the frequency detection resolution is 7.67 MHz (mg dL−1)−1.

Night home enteral nutrition as a novel enforced and physiologically effective nutrition therapy following total gastrectomy for gastric cancer

Enteral nutrition has been reported to be safe and effective in malnourished patients undergoing upper gastrointestinal surgery. In this study, we devised night home enteral nutrition (N-HEN) as a novel nutritional strategy and evaluated the efficacy in gastric cancer patients following total gastrectomy. Between January 2017 and March 2021, 24 patients were prospectively included in the protocol and supported by N-HEN for three postoperative months through a jejunostomy during the night (Elental:1200 kcal/day), and 22 patients without N-HEN were followed as a control group (CG). Body weight loss, nutritional indicators and tolerance to chemotherapy were evaluated. After 3 and 6 months, patients with N-HEN had significantly less body weight loss than CG (3 months P < 0.0001: N-HEN 4.0% vs. CG 15.2%, 6 months P < 0.0001: N-HEN 7.7% vs. CG 17.7%). Prealbumin was significantly higher in patients with N-HEN than CG after 3 and 6 months (3 months P < 0.0001, 6 months P = 0.0037). Albumin, total protein and hemoglobin, tended to be higher after 3 and 6 months in patients with N-HEN than CG, and total cholesterol after 6 months. Concerning the tolerance to adjuvant chemotherapy in Stage II–III patients, patients with N-HEN significantly had a higher completion rate (P = 0.0420: N-HEN 70% vs. CG 29%) and longer duration (P = 0.0313: N-HEN 458 days vs. CG 261 days) as planned. Continuous monitoring of blood glucose concentration in patients with N-HEN did not show nocturnal hypoglycemia or hyperglycemia. N-HEN could be a novel enforced and physiologically effective nutritional strategy to support potentially malnourished patients following total gastrectomy.

Development of a Noninvasive Blood Glucose Monitoring System Prototype: Pilot Study.

BackgroundDiabetes mellitus is a severe disease characterized by high blood glucose levels resulting from dysregulation of the hormone insulin. Diabetes is managed through physical activity and dietary modification and requires careful monitoring of blood glucose concentration. Blood glucose concentration is typically monitored throughout the day by analyzing a sample of blood drawn from a finger prick using a commercially available glucometer. However, this process is invasive and painful, and leads to a risk of infection. Therefore, there is an urgent need for noninvasive, inexpensive, novel platforms for continuous blood sugar monitoring.ObjectiveOur study aimed to describe a pilot test to test the accuracy of a noninvasive glucose monitoring prototype that uses laser technology based on near-infrared spectroscopy.MethodsOur system is based on Raspberry Pi, a portable camera (Raspberry Pi camera), and a visible light laser. The Raspberry Pi camera captures a set of images when a visible light laser passes through skin tissue. The glucose concentration is estimated by an artificial neural network model using the absorption and scattering of light in the skin tissue. This prototype was developed using TensorFlow, Keras, and Python code. A pilot study was run with 8 volunteers that used the prototype on their fingers and ears. Blood glucose values obtained by the prototype were compared with commercially available glucometers to estimate accuracy.ResultsWhen using images from the finger, the accuracy of the prototype is 79%. Taken from the ear, the accuracy is attenuated to 62%. Though the current data set is limited, these results are encouraging. However, three main limitations need to be addressed in future studies of the prototype: (1) increase the size of the database to improve the robustness of the artificial neural network model; (2) analyze the impact of external factors such as skin color, skin thickness, and ambient temperature in the current prototype; and (3) improve the prototype enclosure to make it suitable for easy finger and ear placement.ConclusionsOur pilot study demonstrates that blood glucose concentration can be estimated using a small hardware prototype that uses infrared images of human tissue. Although more studies need to be conducted to overcome limitations, this pilot study shows that an affordable device can be used to avoid the use of blood and multiple finger pricks for blood glucose monitoring in the diabetic population.

A Miniaturized Optofluidic Glucose Monitoring System Based on Enzyme Colorimetry

Continuous glucose monitoring is of great significance in the diagnosis and treatment of diabetes. In this paper, a miniaturized and automatic optofluidic system was designed for continuous glucose monitoring. Interstitial fluid (ISF) was first obtained by an ISF transdermal extracting chip. Then, the glucose concentration in the ISF was detected by enzyme colorimetry. For accurate detection of the glucose concentration, the volume of transdermally extracted ISF was measured, and the volumes of ISF and colorimetric reagent in the mixed sample were controlled and mixed uniformly. To test the precision and stability of the system, different concentrations of glucose solutions (30–200 mg/dL) were used to simulate ISF extracted from skin. The results revealed that the measured glucose concentration correlated well with the reference concentration (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.9976). In addition, the system achieved high-precision measurement of a low glucose concentration (30 mg/dL) even as the ISF volume fluctuated widely (0.5– <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.5 ~\mu \text{L}$ </tex-math></inline-formula> ), and the average absolute error was only 1.27%. Therefore, the system realized high precision for automatic and continuous monitoring of glucose in microvolume ISF samples, which would be a promising tool for the continuous monitoring of blood glucose concentration.

The Impact of Glucose Oxidase Immobilization on Dendritic Gold Nanostructures on the Performance of Glucose Biosensors.

In recent years, many efforts have been made to develop rapid, sensitive and user-friendly glucose biosensors for monitoring blood glucose concentration in patients. In this study, the electrochemical glucose biosensors based on graphite rod (GR) electrode electrochemically modified with dendritic gold nanostructures (DGNs) and glucose oxidase (GOx) were developed. Phenazine methosulfate was used as a soluble redox mediator. Three GOx immobilization methods: adsorption on DGNs and cross-linking with glutaraldehyde (GA) vapour (GA-GOx/DGNs/GR), covalent immobilization on DGNs modified with 11-mercaptoundecanoic acid self-assembled monolayer (SAM) (GOx-SAM/DGNs/GR) and covalent immobilization on SAM with additional cross-linking with GA vapour (GA-GOx-SAM/DGNs/GR), were used. It was determined that GA significantly improved the stability of the enzyme layer. The difference of maximal current generated during the enzymatic reaction (ΔImax) equal to 272.06 ± 8.69 µA was obtained using a biosensor based on GA-GOx/DGNs/GR electrodes. However, the highest ΔImax equal to 384.20 ± 16.06 µA was obtained using GA-GOx-SAM/DGNs/GR electrode. ΔImax for biosensors based on the GA-GOx-SAM/DGNs/GR electrode was 1.41 times higher than for the GA-GOx/DGNs/GR, whereas the linear dynamic range from 0.1 to 10 mM was the same using all three GOx immobilization methods. The limit of detection using GA-GOx-SAM/DGNs/GR and GA-GOx/DGNs/GR electrodes was 0.019 and 0.022 mM, respectively. The ability to detect glucose in the serum by developed biosensors was evaluated.

Diffuse Imaging Approach for Universal Noninvasive Blood Glucose Measurements

We proposed a diffuse imaging approach for universal noninvasive blood glucose measurements based on visible light, which can predict the blood glucose concentration without personal calibration. The proposed approach used a CCD to obtain diffuse images from human index finger pulp. The denoising autoencoder algorithm adopted effectively extracted the scattering information highly related to blood glucose concentration from the diffuse images, and the gradient boosting regression algorithm enabled an accurate calculation of blood glucose concentration without prior personalized calibration.In vivoexperimental results showed that the proposed approach had a mean absolute error of 19.44 mg/dl, with all the predicted results observed within the clinically acceptable region (Region A: 78.9%) in the Clarke error grid analysis. Compared to other blood glucose concentration measurement methods of scattering coefficient, this new method does not require individual calibration, therefore it is easier to implement and popularize, which is critical for the noninvasive monitoring of blood glucose concentration.

A New Decision Support System for Type 1 Diabetes Management.

Type 1 diabetes (T1D) is a chronic life-threatening metabolic condition which needs to be accurately and continuously managed with care by multiple daily exogenous insulin injections, frequent blood glucose concentration monitoring, ad-hoc diet, and physical activity. In the last decades, new technologies, such as continuous glucose monitoring sensors, eased the burden for T1D patients and opened new therapy perspectives by fostering the development of decision support systems (DSS). A DSS for T1D should be able to provide patients with advice aimed at improving metabolic control and reducing the number of actions related to therapy handling. Major challenges are the vast intra-/inter-subject physiological variability and the many factors that impact glucose metabolism. The present work illustrates a new DSS for T1D management. The algorithmic core includes a module for optimal, personalized, insulin dose calculation and a module that triggers the assumption of rescue carbohydrates to avoid/mitigate impending hypoglycemic events. The algorithms are integrated within a prototype communication platform that comprises a mobile app, a real-time telemonitoring interface, and a cloud server to safely store patients' data. Tests made in silico show that the use of the new algorithms lead to metabolic control indices significantly better than those obtained by the standard care for T1D. The preliminary test of the prototype platform suggests that it is robust, performant, and well-accepted by both patients and clinicians. Future work will focus on the refinement of the communication platform and the design of a clinical trial to assess the system effectiveness in real-life conditions.Clinical Relevance- The presented DSS is a promising tool to facilitate T1D daily management and improve therapy efficacy.

Noninvasive In Vivo Estimation of Blood-Glucose Concentration by Monte Carlo Simulation.

Continuous monitoring of blood-glucose concentrations is essential for both diabetic and nondiabetic patients to plan a healthy lifestyle. Noninvasive in vivo blood-glucose measurements help reduce the pain of piercing human fingertips to collect blood. To facilitate noninvasive measurements, this work proposes a Monte Carlo photon simulation-based model to estimate blood-glucose concentration via photoplethysmography (PPG) on the fingertip. A heterogeneous finger model was exposed to light at 660 nm and 940 nm in the reflectance mode of PPG via Monte Carlo photon propagation. The bio-optical properties of the finger model were also deduced to design the photon simulation model for the finger layers. The intensities of the detected photons after simulation with the model were used to estimate the blood-glucose concentrations using a supervised machine-learning model, XGBoost. The XGBoost model was trained with synthetic data obtained from the Monte Carlo simulations and tested with both synthetic and real data (n = 35). For testing with synthetic data, the Pearson correlation coefficient (Pearson’s r) of the model was found to be 0.91, and the coefficient of determination (R2) was found to be 0.83. On the other hand, for tests with real data, the Pearson’s r of the model was 0.85, and R2 was 0.68. Error grid analysis and Bland–Altman analysis were also performed to confirm the accuracy. The results presented herein provide the necessary steps for noninvasive in vivo blood-glucose concentration estimation.

The Continuous 3 Month Operation of Open Circuit Potential Based Glucose Sensor Employing Direct Electron Transfer Type Fad Dependent Glucose Dehydrogenase

Introduction To control glycemic levels of patients with type 1 diabetes, continuous monitoring of blood glucose concentrations is inevitable for the subsequent infusion of insulin. Since the amount of insulin being supplied is calculated based on the continuous glucose monitoring (CGM) results, CGM is considered as the core component for the continuous and accurate diabetes management. With the increase of the demands on convenient and user-friendly system, much accurate and stable sensing systems are required. Current commercially available CGM sensors employing amperometric enzyme sensor principles, are operated under continuous potential application to the glucose oxidoreductases and redox mediators, to keep their continuous electrochemical-catalytic reaction. Under such harsh conditions, inactivation of enzymes and mediators is unavoidable during continuous operation. Additionally, potential application to the system may cause oxidation of interfering substances directly on the electrode surface. Consequently, these disadvantage of amperometric enzyme sensor principle are the inherent reason for the limitation of current CGM stability and accuracy.We are currently engaged in the development of open circuit potential (OCP) based enzyme glucose sensing principle, which can solve the current issues in the glucose sensors based on amperometric principle. We previously reported OCP based glucose sensor employing direct electron transfer type FAD-dependent glucose dehydrogenase complex (FADGDH) derived from Burkholderia cepacia 1-5. By combining direct electron transfer type FADGDH with OCP principle, the sensor signal could be obtained without being affected by ascorbic acid and acetaminophen. In this study, we demonstrated a long-term operation, over 3 months, using OCP based CGM sensor employing direct electron transfer type FADGDH. Methods Recombinantly prepared bacterial FADGDH, capable of direct electron transfer, was used in this study, and the enzyme was immobilized onto gold electrode by self-assembled monolayer. OCP of the enzyme electrode was monitored continuously toward the sample containing various concentration of glucose, against Ag/AgCl reference electrode. We calculated the ∆OCP as difference between the OCP value at each glucose concentration and the OCP value in presence of 0.1 mM glucose, and then calibration curve was prepared based on plotting ∆OCP versus glucose concentration. Continuous operation was carried out in 100 mM phosphate buffer (pH 7.0) including 20 mM glucose for 108 days. Results and Discussion ∆OCP increased depending on glucose concentration (0.1 mM~20 mM) without being affected by ascorbic acid and acetaminophen which are representative interference of glucose sensor as we previously observed. This sensor could be operated continuously for 108 days without showing any significant decrease on the slope of calibration curve. Since there were no current flow during open circuit state, the turnover number of enzyme reaction could be significantly minimized. Consequently, stable OCP signal could be obtained for 108 days without significant decrease of OCP signal. These results revealed that OCP principle could prevent enzyme inactivation during continuous operation, thereby lead to improve long-term stability of sensor. This result indicated that the OCP based glucose sensor employing direct electron transfer type FADGDH could measure glucose concentration continuously, accurately and stably over 3 months, and this long-term stability could be achieved by employing OCP measurement principle.

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PbFG: Physique-based fuzzy granular modeling for non-invasive blood glucose monitoring

ObjectiveContinuous monitoring of blood glucose concentrations (BGC) is a crucial and integrated part of diabetes management. The most widely used glucose monitoring devices are blood glucose meters based on minimally-invasive finger-stick tests. The recent trend, however, is shifting towards non-invasive glucose monitoring (NGM) technology, as it alleviates the pain of frequently prick on patients’ skin. In NGM technology, it is difficult to establish a universal model that meets the clinical requirements in accuracy due to the individual differences in granularity, e.g., skin thickness, blood volume, body fat, etc. How to reduce the influence of individual differences on NGM is a technical difficulty. This study aims to propose a framework and a model-level solution for NGM while improving accuracy. MethodsAn optical NGM prototype is developed, and 4012 samples from 89 patients are collected by clinical trials. By clustering these samples using a fuzzy c-means algorithm, we found that human pulse waveforms can be roughly divided into four types, corresponding to four different physique characteristics, which reflect the individual differences to some extent. Based on this discovery, we propose a Physique-based Fuzzy Granular modeling (PbFG) framework, in which four granular glucose estimators are used to predict BGC values, and a fuzzy physique classifier is applied to classify user’s physique. The final BGC value is then obtained by fusing the four granular BGC values with the fuzzy physique classification results. ResultsUsing four practical machine learning algorithms as the BGC estimators, the PbFG framework is clinically evaluated and compared to the universal modeling framework. Our experimental results show that the PbFG raises the squared correlation coefficient (R2) between the NGM prototype and other invasive reference devices to 0.851 from 0.812. Following Clarke Error Grid Analysis (EGA), more than 97.9% of the measurement points are in region A (74.1%) and B (23.8%). In individual-customized modeling analysis, the PbFG can make the R2 reach to 0.9 after merely 30 times of calibration. ConclusionsBoth the accuracy and the EGA experimental results indicate that our proposed PbFG solution can reduce the influence of individual differences to a certain extent, and improve the performance of NGM to a clinically acceptable level.

Development of a Novel Enhanced Biosensor System for Real-Time Monitoring of Fish Stress Using a Self-Assembled Monolayer.

Wireless biosensor systems were developed in our lab for monitoring blood glucose concentrations in fish as an indicator of fish stress. However, uniform immobilization of the enzyme on the surface of the electrode is difficult, so the sensor response is typically reduced at a range of high glucose concentrations during the stress monitoring. In this study, we attempted to enhance sensor response by using a self-assembled monolayer-immobilized enzyme. Glucose oxidase was immobilized on a working electrode modified with a self-assembled monolayer. The proposed biosensor showed a good correlation between the output current and the glucose concentration range of 10–3500 mg dL−1 under an optimized working condition. The dynamic measurement range of this newly developed sensor is significantly improved, especially over a high concentration range, which helps the sensor to achieve better performance in dramatic changes in the stress response of fish. In addition, we used biological samples from test fish and obtained a good correlation coefficient between the sensor output current and the glucose concentration using a conventional method. The proposed wireless biosensor system was also applied to monitor fish stress responses in real time through different stressors and to obtain some precise data that reflect real fish stress responses.

Band‐stop filter sensor based on SIW cavity for the non‐invasive measuring of blood glucose

Design and realisation of a substrate integrated waveguide (SIW) planar sensor for the non-invasive monitoring of blood glucose concentration (BGC) is described. The structure of the presented sensor is similar to a conventional band-stop filter. To produce a substantial and localised field enhancement in the sensing region by the SIW technology, the slots and interdigital arms on the upper conductor of the SIW cavity are utilised. Further, the fingertip is used as materials under test and its displacement and fingerprints effects are studied. The introduced sensor is then fabricated and measured. The evaluated results indicate that the developed sensor features improvement in both the fingertip positioning and fingerprints effects compared to the other work. Also, the frequency resonance shift of the proposed sensor observing the valuable enhancement of non-invasive BGC detection sensitivity is much more than the previous study.

Effect of Methylphenidate on Blood Glucose Profile in an Adolescent Male with Type 1 Diabetes Mellitus, A Case Report

Introduction: Attention deficit/hyperactivity disorder (ADHD) is the commonest childhood neuro-developmental disorder. Methylphenidate (MPH) is a psychostimulant commonly prescribed in clinical practice to treat patients with ADHD. Comorbidity of Type 1Diabetes mellitus (T1D) and ADHD in children can be encountered in clinical practice. However, the impact of MPH on the blood glucose profile is scarcely addressed in the literature. Methods: Multiple blood glucose concentrations (BGCs) obtained from a 12 years old male with T1D (under treatment with continuous insulin infusion) and comorbid ADHD have been evaluated after having received 2 dose levels (5 mg and 7.5 mg) methylphenidate (MPH). BGCs were assessed at 6 time points, over 3 weeks period. During the assessment periods, the patient had similar daily feeding en activity conditions and unchanged background insulin infusion The means of the values obtained after each dose level were compared with each other and with those obtained before commencement of MPH treatment. Results: Three clinically relevant findings were encountered. The mean values of fasting Blood glucose levels (BGLs) obtained during MPH treatment periods were lower than that obtained before MPH use, respectively -28% and -23% below BL for the 5 and 7.5 mg dose levels. The means of other post-MPH treatment values obtained, through the day, were higher than that obtained before treatment, respectively +39% and +40% above Base line (BL). In addition, increasing MPH dose level was associated with increased BGL at all assessment time points. Conclusion: Methylphenidate seems to dysregulate blood glucose levels in patients with type 1 diabetes mellitus. This might lead to serious consequences, including both microvascular and macrovascular diseases, particularly in vulnerable subjects such as those who receive medication for a long period of time. Commencement of treatment with and up-titration of MPH dose in ADHD patients with comorbid T1D requires close monitoring of blood glucose concentrations to avoid potential blood glucose excursions that might lead to unwanted complications.

Split trehalase as a versatile reporter for a wide range of biological analytes.

In health care, biosensors are envisioned as universal diagnostic devices with AAAA characteristics (i.e., available for anything, anywhere, anytime, to anyone). Despite numerous attempts to develop such a diagnostic device, none have managed to fulfill all four criteria and be commercialized. Glucometers, the most successful class of biosensor currently marketed monitor blood glucose concentrations. Their performance in clinical samples, including sensitivity and specificity, has been optimized and they are small and relatively inexpensive. We aimed to develop a technology that uses this existing biosensor, but adds versatility in detection of a wide range of analytes. Herein, we report the periplasmic trehalase of E. coli as a novel split enzyme reporter capable of converting a wide variety of analytes into glucose. Conditional complementation of trehalase fragments induced by detection of analytes, resulting in trehalose hydrolysis and glucose production, was used to detect antibodies and bacterial cells. We also demonstrated retention of split TreA activity in undiluted clinical samples. In conclusion, a trehalase-based biosensor platform offers a versatile and convenient method for point-of-care applications as it does not require sample preparation or handling and can be integrated with existing glucometers or sensors.

A Hydrothermally Processed Maize Starch and Its Effects on Blood Glucose Levels During High-Intensity Interval Exercise.

Hetrick, MM, Naquin, MR, Gillan, WW, Williams, BM, and Kraemer, RR. A hydrothermally processed maize starch and its effects on blood glucose levels during high-intensity interval exercise. J Strength Cond Res 32(1): 3-12, 2018-A hydrothermally processed maize starch (HPMS) has been shown to blunt initial blood glucose and insulin response during endurance activity at 70% maximal oxygen uptake (V[Combining Dot Above]O2max). High-intensity interval training (HIIT) is a form of exercise that has many health benefits although it is only performed for short periods of time with interspersed rest periods. The purpose of this study was to compare the blood glucose and associated metabolic stress responses to a sprint interval cycling (SIC) exercise protocol (a form of HIIT) with and without an HPMS in a healthy population. Fourteen subjects completed a total of 4 sessions: a preliminary session, an SIC session with HPMS, an SIC session without HPMS, and a control session in which only HPMS was ingested. Blood glucose, blood lactate, respiratory exchange ratio, oxygen consumption, and rating of perceived exertion responses were recorded during the sessions. There was a significant and progressive rise in blood glucose levels during each of the cycle sprints of both exercise sessions, but not a significant difference between treatment or nontreatment SIC sessions. This is the first study to determine blood glucose responses to SIC after each sprint interval and to report that ingestion of HPMS does not affect glucose responses to SIC. The findings provide some preliminary evidence suggesting subjects at risk for glucose excursions could use SIC to improve health through monitoring blood glucose concentrations during SIC and if necessary, modifying number, intensity, and duration of sprints completed.