Blood Glucose Concentration Levels Research Articles

Alterations of whole body glucose metabolism in a feline SARS-CoV-2 infection model.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been especially devastating to patients with comorbidities, including metabolic and cardiovascular diseases. Elevated blood glucose during SARS-CoV-2 infection increased mortality of patients with COVID-19, although the mechanisms are not well understood. It has been previously demonstrated that glucose transport and utilization is a crucial pathway for other highly infectious RNA viruses. Thus, we hypothesized that SARS-CoV-2 infection could lead to alterations in cellular and whole body glucose metabolism. Specific pathogen-free domestic cats were intratracheally inoculated with USA-WA1/2020 (wild-type) SARS-CoV-2 or vehicle-inoculated, then euthanized at 4- and 8-days postinoculation (dpi). Blood glucose and cortisol concentrations were elevated at 4 and 8 dpi. Blood ketones, insulin, and angiotensin II concentrations remained unchanged throughout the experimental timeline. SARS-CoV-2 RNA was detected in the lung and heart, without changes in angiotensin-converting enzyme 2 (ACE2) RNA expression. In the lung, SARS-CoV-2 infection increased glucose transporter 1 (GLUT1) protein levels at 4 and 8 dpi, whereas GLUT4 level was only upregulated at 8 dpi. In the heart, GLUT-1 and -4 protein levels remained unchanged. Furthermore, GLUT1 level was upregulated in the skeletal muscle at 8 dpi, and AMPK was activated in the hearts of infected cats. SARS-CoV-2 infection increased blood glucose concentration and pulmonary GLUT protein levels. These findings suggest that SARS-CoV-2 infection induces metabolic reprogramming primarily in the lung to support viral replication. Furthermore, this translational feline model mimicked human COVID-19 and could be used to explore novel therapeutic targets to treat metabolic disease during SARS-CoV-2 infection.NEW & NOTEWORTHY Our study on a feline model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, mirroring human COVID-19, revealed alterations in whole body and cellular glucose metabolism. Infected cats developed mild hyperglycemia, increased protein levels of glucose transporters in the lung, and AMPK activation in the heart. These findings suggest that SARS-CoV-2 infection induces metabolic reprogramming in the cardiorespiratory system to support viral replication. Understanding these mechanisms could lead to novel antiviral therapeutic strategies.

Comparison between mathematical methods to estimate blood glucose levels from ECG signals

Diabetes mellitus, known as diabetes, is a chronic disease that affects the control of blood glucose concentration levels, it is a disease that mostly affects adults (type 2 diabetes), but it can also occur in children (type 1 or childhood diabetes), as well as in pregnant women (gestational diabetes). Diabetes is one of the diseases with the highest prevalence and high mortality worldwide. Diabetes has no cure, but continuous monitoring to maintain blood glucose levels in normal ranges reduces the possibility of suffering from gastrointestinal problems, vision loss, limb amputations (such as diabetic foot) and damage to vital organs such as the heart and kidneys, among other associated complications. This article compares the results in glucose estimation by using a linear, quadratic and cubic regression considering the electrical characteristics generated in the cardiac conduction (HR, HRV, T-wave peak, and QT interval) recorded on a single-lead electrocardiogram (VII), used as a non-invasive blood glucose estimation model. The best estimate was obtained using a cubic regression. The validation was performed using the Clarke grid having 77.78 % of data in the A zone and 22.22 % in the B zone and a Pearson correlation value of 0.94103 in the cubic regression.

Influence of acute fasting on pain tolerance in healthy subjects: a randomised crossover study.

Although chronic pain and obesity are global health crises with substantial healthcare costs, little is known about the relationship between pain perception and eating behaviours. Food consumption has been reported to provide an analgesic effect by the release of neurotransmitters modulating the pain network. However, whether short-term (acute) fasting affects pain perception remains unclear. This study aimed to investigate the effect of acute fasting on pain perception and whether attention and mood changes drove the observed changes. The cold pressor test (CPT) was used to investigate the pain tolerance of 25 healthy participants in both non-fasting and 12-h fasting sessions. They were randomised to either session with a crossover to the other after at least 24 h, with the experimenter blinded to the sessions. The pain tolerance was measured using a Stroop task in both attentive and distracted states. The Profile of Mood States (POMS) questionnaire was used to capture the mood, and a 10-point hunger scale was used to measure hunger. Mixed-effects models were used to investigate the influence of fasting and distraction on pain perception, accounting for the repeated measures. Fasting reduced CPT pain tolerance, with fasting participants twice as likely to withdraw their hands early (hazard ratio = 2.4, 95% CI: 1.3-4.5). Though men tolerated CPT pain longer than women, there was no evidence that men responded to fasting differently than women (p = 0.9). In addition, no evidence supporting that fasting affected attention or mood was found. Nonetheless, it increased hunger scores by 2.7 points on a 10-point scale (95% CI: 1.2-4.2) and decreased blood glucose concentration levels by 0.51 mmol/L (95% CI: 0.19-0.84). Acute fasting reduces pain tolerance in the healthy participants, and this effect is independent of gender and attention or mood changes.

600-P: Higher Hydration Status Is Indicative of Better Glucose Tolerance

Introduction: Water intake is an important dietary behavior to replace high-caloric beverages and to enhance metabolic health. Type 2 diabetes is a metabolic disorder that has been shown to be more prevalent in Hispanics, a population shown to consume relatively less water compared to non-Hispanic Whites. The underlying cause of type 2 diabetes is insulin resistance, characterized by hyperglycemia and impaired glycemic control. However, whether a relationship exists between dehydration and the development of diabetes remains to be elucidated. Purpose: To evaluate if hydration status indicates glucose tolerance among a dominant Mexican American population. Methods: Sixty-one participants without diabetes (Age: 26.5 ± 9.1 years; BMI: 27.5 ± 6.0 kg/m2) participated in the study. Fasting blood glucose level was determined, and a 2-hour glucose tolerance test was performed following the ingestion of a 75g glucose drink. Blood glucose concentration levels were determined at 15−, 30−, 60−, 90−, and 120-minute time intervals post ingestion and glucose area under the curve was analyzed as well. Blood works were performed to determine blood urea nitrogen (BUN) and creatinine concentrations. Hydration status was estimated using the ratio of BUN to creatinine values. Results: Hydration status measured by BUN/Creatinine was positively associated with glucose intolerance measured at 15−, 30−, 60−, and 90-minute post glucose load and by glucose area under the curve (r=0.33; r=0.43; r=0.37; r=0.27; and r=0.41 respectively; p<0.05). Conclusion: Adequate fluid consumption should be encouraged for glycemic control, especially among people with high diabetes risks. Disclosure J.Apaflo: None. J.Labadah: None. G.Narvaez: None. V.M.Rocha: None. A.Mossayebi: None. S.Bajpeyi: None.

Prevalence and clinical profile of abnormal glucose in first-episode and drug-naïve patients with major depressive disorder with comorbid abnormal thyroid function: a large-scale cross-sectional study

BackgroundThe associated factors of abnormal glucose in patients with major depressive disorder (MDD) with comorbid abnormal thyroid function (ATF) remain unclear. To the best of our knowledge, this is the first study with a large sample size that examines the risk factors of abnormal glucose in first-episode drug-naïve (FEDN) MDD patients comorbid with ATF and includes clinical correlates and thyroid hormone levels.MethodsA total of 1718 FEDN MDD patients were recruited. The Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale (HAMA), and Positive and Negative Syndrome Scale (PANSS) positive subscale were used to evaluate the symptoms of patients. Fasting blood glucose concentration and thyroid hormone levels were measured.ResultsThe prevalence of abnormal glucose in MDD patients with comorbid ATF was 47.3%, which was 4.25 times higher than that in MDD patients without ATF (17.4%). Compared to those ATF patients without abnormal glucose, ATF patients with abnormal glucose scored higher on HAMD, HAMA and PANSS positive subscale, had a higher rate of suicide attempts, severe anxiety and psychotic symptoms, and had higher levels of thyroid-stimulating hormone (TSH), and thyroid peroxidases antibody (TPOAb) which were also correlated with abnormal glucose in MDD patients comorbid ATF (all P < 0.05). The combination of HAMD score and TSH could differentiate abnormal glucose from ATF. Further, TSH was independence-related with the concentration of fasting blood glucose in MDD patients with comorbid ATF.ConclusionOur results demonstrate a high prevalence of abnormal glucose in MDD patients with comorbid ATF. Some clinical and thyroid function-related variables may be associated with abnormal glucose in MDD patients with comorbid ATF.

Serpentine Enhances Insulin Regulation of Blood Glucose through Insulin Receptor Signaling Pathway

Insulin sensitizers targeting insulin receptors (IR) are a potential drug for the treatment of diabetes. Serpentine is an alkaloid component in the root of Catharanthus roseus (L.) G. Don. Serpentine screened by surface plasmon resonance (SPR) technology has the ability to target IR. The objective of this study was to investigate whether serpentine could modulate the role of insulin in regulating blood glucose through insulin receptors in cells and in animal models of diabetes. SPR technology was used to detect the affinity of different concentrations of serpentine with insulin receptors. The Western blotting method was used to detect the expression levels of key proteins of the insulin signaling pathway in C2C12 cells and 3T3-L1 cells as well as in muscle and subcutaneous adipose tissue of diabetic mice after serpentine and insulin treatment. Diabetic mice were divided into four groups and simultaneously injected with insulin or serpentine, and the blood glucose concentration and serum levels of insulin, glucagon, and C-peptide were measured 150 min later. mRNA levels of genes related to lipid metabolism and glucose metabolism in liver, muscle, and subcutaneous adipose tissue were detected by RT-PCR. Serpentine was able to bind to the extracellular domain of IR with an affinity of 2.883 × 10-6 M. Serpentine combined with insulin significantly enhanced the ability of insulin to activate the insulin signaling pathway and significantly enhanced the glucose uptake capacity of C2C12 cells. Serpentine enhanced the ability of low-dose insulin (1 nM) and normal-dose insulin (100 nM) to activate the insulin signaling pathway. Serpentine also independently activated AMPK phosphorylation, thus stimulating glucose uptake by C2C12 cells. In high-fat-diet/streptozotocin (HFD/STZ)-induced diabetic mice, serpentine significantly prolonged the hypoglycemic time of insulin, significantly reduced the use of exogenous insulin, and inhibited endogenous insulin secretion. In addition, serpentine alone significantly increased the expression of GSK-3β mRNA in muscle tissue, thus enhancing glucose uptake, and at the same time, serpentine significantly increased glucagon secretion and liver gluconeogenesis. Serpentine enhances the ability of insulin to regulate blood glucose through the insulin receptor, and can also regulate blood glucose alone, but it has a negative regulation mechanism and cannot produce a hypoglycemic effect. Therefore, serpentine may be useful as an insulin sensitizer to assist insulin to lower blood glucose.

Plant bioactive compounds and their mechanistic approaches in the treatment of diabetes: a review

Abstract Background Diabetes mellitus (DM) is a growing disease across the world; diabetes is a complex metabolic disorder in which blood glucose concentration level increases and continue for a prolonged period due to a decrease secretion of insulin or action, resulting in the disorder of carbohydrate, lipid, and protein metabolism. The plant-related bioactive compounds have proven their efficacy with least toxicities and can be utilized for the disease treatment. Our objective is to elucidate the mechanism of action of plant bioactive compounds which can give future direction in diabetes treatment. Main body In this review paper, we briefly study more than 200 research papers related to disease and bioactive compounds that have therapeutic applicability in treatment. The plant contains many bio-active compounds which possess in vitro and in vivo anti-diabetic effect which may be responsible for the hypoglycaemic property by inhibiting the digestive enzyme i.e. alpha-amylase and alpha-glucosidase, by producing mimetic action of insulin, by reducing the oxidative stress, by showing antihyperglycemic activity and hypolipidemic activity, by inhibition of aldose reductase, and by increasing or enhancing glucose uptake and insulin secretion. Conclusion Our study revealed that terpenes, tannin, flavonoids, saponin, and alkaloids are important bioactive constituents for anti-diabetic activity. The mechanistic approach on alpha-glucosidase and alpha-amylase, hypolipidemic activity, and AR inhibitory action clear-cut explain the therapeutic applicability of these bioactive compounds in disease. Plants that contain these bioactive compounds can be good drug candidates for future research on diabetes treatment.

Implementation of Non-Invasive Blood Glucose Monitoring System

In the case of diabetes, measuring blood sugar levels is made challenging by the need to pick one's finger. Laboratory testing and one-touch glucometers are intrusive procedures that increase the risk of blood-related illnesses. In the current work, we propose a novel edge device with IoMT (Internet-of-Medical-Things) capabilities for precise, non-invasive blood glucose monitoring to solve this crucial issue. In this study, a NIR (Near-Infrared) spectroscopy method is used to identify the glucose molecule in human blood at two wavelengths (940 nm, and 1300 nm). The cutting-edge gadget known as iGLU is based on high-accuracy ML (Machine Learning) models and NIR spectroscopy. For accurate measurement, a DNN (Deep Neural Network) model and an ideal multiple polynomial regression model have been provided. An open IoT platform is used to evaluate the proposed gadget, and blood glucose levels are then saved there for endocrinologist remote monitoring. For device validation, the blood glucose measurements obtained from the invasive device and the projected blood glucose levels have been compared. The AvgE (Average Error) &amp; MARD (Mean Absolute Relative Difference) of the predicted blood glucose concentration levels were determined to be 4.66 percent and 4.61 percent, respectively. There is a 0.81 regression coefficient. An accurate and economical solution for smart healthcare is offered by the suggested spectroscopic non-invasive gadget.

Positive input observer‐based controller design for blood glucose regulation for type 1 diabetic patients: A backstepping approach

In practice, there are many physical systems that can have only positive inputs, such as physiological systems. Most conventional control methods cannot ensure that the main system input is positive. A positive input observer‐based controller is designed for an intravenous glucose tolerance test model of type 1 diabetes mellitus (T1DM). The backstepping (BS) approach is employed to design the feedback controller for artificial pancreas (AP) systems, based on the Extended Bergman's Minimal Model (EBMM). The EBMM represents the T1DM in terms of the blood glucose concentration (BGC), insulin concentration, and plasma level and the disturbance of insulin during medication due to either meal intake or burning sugar by doing some physical exercise. The insulin concentration and plasma level are estimated using observers, and these estimations are applied as feedback to the controller. The asymptotic stability of the observer‐based controller is proved using the Lyapunov theorem. Moreover, it is proved that the system is bounded input‐bounded output (BIBO) stable in the presence of uncertainties generated by uncertain parameters and external disturbance. For realistic situations, we consider only the BGC to be available for measurement and additionally inter‐and intra‐patient variability of system parameters is considered.

Pulegone ameliorates inflammation and oxidative stress in L-arginine-induced acute pancreatitis in mice by regulating the activation of p38 MAPK pathway

Purpose: To evaluate the effects of pulegone (PLG) on inflammation and oxidative stress in L-arginineinduced acute pancreatitis (AP), and determine its molecular mechanism.Methods: Hematoxylin-Eosin (H &amp; E) staining assay were performed, and histopathological score, blood glucose concentration, and serum amylase level were evaluated in order to assess the effects of PLG on pancreatic injury in L-arginine-induced AP mice. Enzyme linked immunosorbent assay (ELISA) was conducted to assess the levels of myeloperoxidase (MPO), malonaldehyde (MDA) and inflammatory factors (IL-6, TNF-α and IL-1β) in L-arginine-induced AP mice. Serum lactate dehydrogenase (LDH) and relative levels of ROS generation in L-arginine-induced AP mice were determined using an LDH kit and immunofluorescence assay, respectively. The effect of pulegone (PLG) on the activation of p38 MAPK pathway in L-arginine-induced AP mice was evaluated by Western blot.Results: Significant pancreatic tissue injury occurred in L-arginine-induced AP mice was revealed. PLG alleviated the pathological injury of pancreatitis, and decreased the blood glucose concentration and serum amylase level in L-arginine-induced AP mice. In addition, PLG inhibited oxidative stress and inflammatory responses, and was enabled to inhibit the activation of p38 MAPK pathway in L-arginineinduced AP mice. Furthermore, PLG exhibited protective effect against the development of pancreatic injury in L-arginine-induced AP mice.Conclusion: PLG ameliorates L-arginine-induced inflammation and oxidative stress in AP mice in vivo by inhibiting p38 MAPK pathway, and therefore, is a potential therapeutic agent for the management of acute pancreatitis.

Examining Type 1 Diabetes Mathematical Models Using Experimental Data

Type 1 diabetes requires treatment with insulin injections and monitoring glucose levels in affected individuals. We explored the utility of two mathematical models in predicting glucose concentration levels in type 1 diabetic mice and determined disease pathways. We adapted two mathematical models, one with -cells and the other with no -cell component to determine their capability in predicting glucose concentration and determine type 1 diabetes pathways using published glucose concentration data for four groups of experimental mice. The groups of mice were numbered Mice Group 1–4, depending on the diabetes severity of each group, with severity increasing from group 1–4. A Markov Chain Monte Carlo method based on a Bayesian framework was used to fit the model to determine the best model structure. Akaike information criteria (AIC) and Bayesian information criteria (BIC) approaches were used to assess the best model structure for type 1 diabetes. In fitting the model with no -cells to glucose level data, we varied insulin absorption rate and insulin clearance rate. However, the model with -cells required more parameters to match the data and we fitted the -cell glucose tolerance factor, whole body insulin clearance rate, glucose production rate, and glucose clearance rate. Fitting the models to the blood glucose concentration level gave the least difference in AIC of , and a difference in BIC of for Mice Group 4. The estimated AIC and BIC values were highest for Mice Group 1 than all other mice groups. The models gave substantial differences in AIC and BIC values for Mice Groups 1–3 ranging from to . Our results suggest that the model without -cells provides a more suitable structure for modelling type 1 diabetes and predicting blood glucose concentration for hypoglycaemic episodes.

LncRNA Dlx6os1 Accelerates Diabetic Nephropathy Progression by Epigenetically Repressing SOX6 via Recruiting EZH2

Introduction: Diabetic nephropathy (DN) is the leading cause of kidney failure worldwide. To explore the pathogenesis and effective biological target of DN is beneficial to seeking novel treatment strategies. Objective: This study aimed to investigate the role of the lncRNA Dlx6os1/SOX6/EZH2 axis in DN progression. Methods: PAS staining was performed to evaluate extracellular matrix accumulation; ELISA was carried out to assess the levels of urine microalbumin and blood glucose concentration; RT-qPCR was carried out to detect the levels of lncRNA Dlx6os1, TNF-α, IL-1β, IL-6, SOX6, and EZH2. Western blot was performed to assess the levels of Col-IV, FN, TGF-β1, and SOX6 proteins. RIP assay was carried out to verify the interaction between lncRNA Dlx6os1 and EZH2. ChIP-qPCR was conducted to verify the interaction between EZH2 and SOX6 promoter. Results: Our results illustrated that lncRNA Dlx6os1 was highly expressed in DN mice and HG-induced SV40 MES13 cells. LncRNA Dlx6os1 knockdown inhibited HG-induced SV40 MES13 cell proliferation, fibrosis, and inflammatory cytokine release. LncRNA Dlx6os1 inhibited SOX6 expression by recruiting EZH2 in HG-SV40 MES13 cells, and SOX6 mediated the effects of lncRNA Dlx6os1 on proliferation, fibrosis, and inflammatory factor release of HG-induced SV40 MES13 cells. Conclusion: LncRNA Dlx6os1 accelerates the progression of DN by epigenetically repressing SOX6 via recruiting EZH2.

Hyperglycemia Identification Using ECG in Deep Learning Era.

A growing number of smart wearable biosensors are operating in the medical IoT environment and those that capture physiological signals have received special attention. Electrocardiogram (ECG) is one of the physiological signals used in the cardiovascular and medical fields that has encouraged researchers to discover new non-invasive methods to diagnose hyperglycemia as a personal variable. Over the years, researchers have proposed different techniques to detect hyperglycemia using ECG. In this paper, we propose a novel deep learning architecture that can identify hyperglycemia using heartbeats from ECG signals. In addition, we introduce a new fiducial feature extraction technique that improves the performance of the deep learning classifier. We evaluate the proposed method with ECG data from 1119 different subjects to assess the efficiency of hyperglycemia detection of the proposed work. The result indicates that the proposed algorithm is effective in detecting hyperglycemia with a 94.53% area under the curve (AUC), 87.57% sensitivity, and 85.04% specificity. That performance represents an relative improvement of 53% versus the best model found in the literature. The high sensitivity and specificity achieved by the 10-layer deep neural network proposed in this work provide an excellent indication that ECG possesses intrinsic information that can indicate the level of blood glucose concentration.

Effects of Sitagliptin on myocardial remodeling and autophagy in diabetic mice and its mechanism

Objective: To investigate the effects of Sitagliptin on myocardial remodeling and autophagy in diabetic mice and its possible mechanisms. Methods: C57 mice aged ten weeks were treated with streptozocin (STZ) at the dose of 50 mg/(kg·d) by intraperitoneal injections for five consecutive days, and the level of fasting blood glucose concentration was higher than 16.7 mmol/l after seven days indicated that the diabetic model was established successfully. Mice were divided into four groups, including control group (n=10) which was intraperitoneally injected with the same volum of saline, the model group (n=8), Sitagliptin treatment group(diabetic mice were treated with Sitagliptin at the dose of 10 mg/(kg·d)by gavage, n=8) and the inhibitor group(diabetic mice were treated with Compound C, an AMPK inhibitor, at the dose of 10 mg/(kg·d) by intraperitoneal injection, n=8). After six weeks, all the mices were weighted and then put to death and the hearts were separated to caculate ventricular /body weight ratio. Hemaloxylin-Eosin (HE) staining was used to observe the cell morphology and masson staining was used to observe interstitial fibrosis. Western blot was used to test the heart protein expressions of Connexin43(Cx43), adenosine 5'-monophosphate -activated protein kinase (AMPK), brain natriuretic peptide(BNP), transforming growth factor(TGF-β) and LC3B. Results: After six weeks of treatment, compared with control group, the ventricular /body weight ratio was improved (P＜0.05), The cardiomyocyte hypertrophy and increased fibrosis were observed in the model group. The expression levels of BNP and TGF-β were increased, while the expression levels of Cx43,LC3B and AMPK were decreased significantly(P＜0.05). However, compared with model group, treatment with Sitagliptin decreased BNP, TGF-β protein levels and increased Cx43 and LC3B protein levels, while Compound C could inhibit the upregulation of Cx43, LC3B and AMPK protein (P＜0.05). Conclusion: Sitagliptin could improve cardiac hypertrophy and decrease interstitial fibrosis and AMPK-related signaling pathways was involved in the regulation of Cx43 and autophagy.

Detection of hemoglobin in blood and urine glucose level samples using a graphene-coated SPR based biosensor

In this paper, we have presented a design and simulation of a graphene-coated surface plasmon resonance (SPR) based biosensor for targeting specific biological components. We have explicitly shown the detection of the hemoglobin level in blood samples and the glucose concentration level in urine samples by using the finite element method (FEM) based numerical simulation. In the blood component, the 0.001 refractive index increment causes a 6.1025 g/l hemoglobin (HB) level increment, which has been detected using this SPR based sensor with 200 deg/RIU angular sensitivity. Moreover, we have also detected the presence or absence of diabetes using the glucose concentration level in urine samples with this SPR sensor. Therefore, the novelty of this paper is detecting the blood hemoglobin level and glucose concentration levels in urine samples more accurately than the previously proposed whispering gallery mode (WGM) and photonic crystal nanocavity based optical sensors.

An immunohistochemical and biochemical evidences of pancreatic β-cell regeneration in type 2 diabetic rats after treated with Aegle marmelos leaf extract and Aegeline

The study aims to investigate the Immunohistochemical changes in pancreatic beta cells in fructose fed, streptozocin (STZ) induced Type 2 Diabetes (T2DM) rats treated with various doses of leaf extract of Aegle marmelos (AAM) and Aegeline (AG). 42 adult male wistar albino rats were separated into 7 groups, including Vehicle Control (VC); T2DM; T2DM + AAM 250 mg/kg; T2DM + AAM 500 mg/kg; T2DM + AG 20mg/kg; T2DM + AG 50 mg/kg and T2DM + AG 100 mg/kg. Experimental T2DM was created by a single dose of 40 mg/kg STZ injection intra-peritoneally along with 10% of fructose solution given orally for 30 days. Calculated dosages of AAM and AG were given with oral gavage for 30 days. Pancreas was harvested and processed. Slides were stained using hematoxylin and eosin (H&amp;E) stains. Insulin expressing beta-cells was analyzed using immunohistochemistry. Fructose fed, STZ induced rats showed degenerative expressions in beta-cells. In STZ treated rats, AG reduced the blood glucose concentration and serum insulin levels at the maximum functional dose compared to AAM. The immunohistochemical information suggests that the AG at 100 mg/kg dose has the capability of making the dormant cells to reproduce to restore the lost cells of islets of Langerhans.

Effects of Type-2 Resistant Starch (High-Amylose Maize Starch) on the Glycemic Index of Chinese Steamed Buns and Its Influence on Glycemic Response in Healthy Human Subjects

The incorporation of resistant starch (RS) in food has gained importance to be a good replacement for carbohydrate. This study aimed to determine the effects of resistant starch (RS) known as high-amylose maize starch (HM) as wheat flour substitute in Chinese steamed bun (CSB) formulation on postprandial glycemic response in healthy human subjects. In the single-blind cross-over experimental trial, subjects (female, n=15) consuming 30% HM (HM30) composite CSB had significantly lower postprandial blood glucose response compared with control consuming CSB (without HM) within 2 hours after ingestion of both samples. Compared with control CSB, HM30 produced lower glycemic response with a mean iAUC of 105.2 mmol x min/L. The mean iAUC of control CSB was 186.1 mmol x min/L. The low GI property of HM30 stabilized the blood glucose concentration level and did not cause sudden rapid increase in blood glucose concentration similar to high-GI reference food and medium-GI control CSB. Funding Information: This study was supported through funding from the Ministry of Higher Education Malaysia (MoHE), project code: FRGS/1/2017/STG/UMS/02/5. Declaration of Interests: The authors declare no conflicts of interest. Ethics Approval Statement: The study was ethically approved by the Medical Research Ethics Committee of Universiti Malaysia Sabah [Refer to Appendix D; UMS/FPSK6.9/100-6/1/95; JKEtika 3/18(7)]. The informed consent form was signed by the subjects to ensure their voluntary enrolment to participate in the study.

Effects of a Preoperative Carbohydrate-Rich Drink Before Ambulatory Surgery: A Randomized Controlled, Double-Blinded Study.

BackgroundThe guidelines recommend oral carbohydrates up to 2 hr before elective surgery. The objective of this study was to explore the safety and feasibility of preoperative carbohydrate drink in patients undergoing ambulatory surgery.Material/MethodsPatients undergoing ambulatory surgery under general anesthesia were enrolled. They were fasted from midnight and randomly assigned to a study group (200 mL of a carbohydrate beverage) or the control group (pure water) and received the assigned drink 2 hr before surgery. Bedside ultrasonography was performed to monitor gastric emptying at T0 (before liquid intake), T1 (5 min after intake), T2 (1 hr after intake), and T3 (2 hr after intake). Subjective feelings of thirst, hunger, anxiety, and fatigue were assessed 1 hr after liquid intake using the visual analogue scale (VAS).ResultsIn both groups, gastric antrum cross-sectional area, gastric content volume, and weight-corrected gastric content volume increased at T1 and returned to baseline at T3. These parameters were significantly higher in the study group at T2 (6.28±1.38 vs. 4.98±0.78, 67.22±29.49 vs. 49.04±15.4, 1.10±0.51 vs. 0.85±0.37, P<0.05). Thirst and hunger VAS scores were reduced in both groups. The study group suffered significantly less hunger (28.44±10.41 vs. 36.03±14.42, P<0.05). Blood electrolytes (sodium, potassium, calcium) and glucose concentration levels were similar in both groups at T2. No gastric regurgitation or pulmonary aspiration was recorded.ConclusionsAdministration of 200 mL of oral carbohydrate beverage 2 hr before ambulatory surgery is safe, effective, and can be used for preoperative management of fasting patients.

Software module development for non-invasive blood glucose measurement using an ultra-wide band and machine learning

Diabetes is a chronic disease and in uprising trend worldwide. There is no remedy, hence, blood glucose management is essential by screening blood glucose concentration levels (BGCL) regularly to maintain a healthy life. However, the present way of measuring BGCL is invasive by using a glucometer and drawing a blood sample directly from the human body. To overcome this discomfort-problem, a non-invasive device to measure BGCL is in demand. This paper presents an autonomous software module with a user-friendly graphical user interface (GUI) based on digital signal processing (DSP) and artificial neural network (ANN) to process, classify and recognize the BGL signature from captured ultra-wideband (UWB) signal through human blood medium. To capture the signal, a pair of UWB bio-antenna is placed in between the human earlobe. Received signals are captured and processed through GUI and undergo signal processing, ANN training, testing, and validation. An interface is developed to integrate the hardware (UWB transceiver, bio-antenna, etc.) and the developed software module to make a system. The initial system showed a consistent result with reliability and demonstrated 90.6% accuracy to detect the BGCL. The detection accuracy is 9.6% improved compared to existing work. Besides, this proposed system is cost-effective, user-friendly and suitable to be used by both doctors and home users.

Non-invasive blood glucose concentration level estimation accuracy using ultra-wide band and artificial intelligence

Diabetes becomes a rapidly increasing global epidemic and getting serious health concern worldwide. There is no remedy except systematic management to keep blood glucose level under control. To achieve that regular glucose level monitoring is a routine task for a patient. This involves collection of blood physically from body with some discomfort and measuring using some device. To overcome this disadvantages and distress, non-invasive blood glucose measurement system is in demand. This article presents an ultra-wide band (UWB) microwave imaging and artificial intelligence based prospective solution to detect blood glucose concentration level non-invasively (without physical blood). The system consists of a pair of small UWB biomedical planar antenna, UWB transceiver as hardware and an artificial neural network with signal acquisition and processing interface as software module. The UWB signal with center frequency of 4.7 GHz was transmitted through ear lobe and forward scattering signals were received from other side. Characteristics features of received signal were extracted for pattern recognition and detection through deep artificial neural network. The system exhibits around 88% accuracy to detect glucose concentration in blood plasma. Besides, it is affordable, safe, user friendly and can be used with comfort in near future.