Appropriate Amount Of Insulin Research Articles

IoT Based Expert System for Diabetes Diagnosis and Insulin Dosage Calculation.

High blood glucose levels are the defining characteristic of diabetes. Uncontrolled blood glucose levels in diabetic patients might result in mortality. As a result, there is a dire need to control blood glucose levels by constantly monitoring them and delivering the appropriate amount of insulin. However, insulin consumption is affected by several variables, including age, calorific intake, and body weight. The patient must see the doctor on a regular basis in order to determine the appropriate dose. Nonetheless, hospital facilities are finding it increasingly difficult to treat patients as the number of patients rises; thus, the healthcare industry is searching for an efficient method that can alleviate their burden by assisting patients with chronic conditions through remote patient care. In this work, we have developed an expert system to provide remote treatment for diabetic patients. Our expert system consists of two distinct components: one for the patient and one for the hospital. The sole requirement for the patient will be a wearable device that captures and transmits all relevant data to the cloud. On the hospital side, there should be a system in place to process that data in the cloud. The system employs a fuzzy system to handle data in two stages. A fuzzy system is initially employed to identify whether or not a patient is diabetic. In the second stage, a fuzzy system is utilized to determine the insulin dosage for a diabetic patient. Using sensors and the ESP8266 platform, we have developed a prototype of patient-side hardware. The MATLAB fuzzy toolbox is used for the processing part, which includes fuzzy systems, and the results of the MATLAB analysis are presented in the form of simulation results to demonstrate the accuracy of the proposed system in terms of determining insulin dosage. The results of the simulation using the fuzzy toolbox for the insulin dose of the diabetic patient are significantly close to the amount of dosage prescribed by the endocrinologist.

Algorithms for Automated Insulin Delivery: An Overview.

The technology needed to "close the loop," that is, a system for continuous glucose monitoring and a pump that infuses insulin, are only 2 of the 3 components needed for each system for automated insulin delivery (AID), the other is a "translation" of the glucose information into the appropriate amount of insulin to be applied at a given point in time to keep glucose levels in the body in the target range. It might look straightforward to calculate the required insulin dose and control the pump to apply this immediately; however, once a given amount of insulin is in the body, it will be absorbed and become metabolically active. To avoid lowering glucose levels toward too low levels, the algorithms used to calculate the insulin dose have to take a number of other factors into account. This is needed to make sure that AID systems are not only efficient but also safe, that is, not only Time-in-Range should be maximal, also Time-below-Range should be minimal. The review characterizes the different types of AID algorithms that were developed in the last decades. Using a structured approach, the different algorithms are classified. A systematic evaluation of the performance of the different algorithms is missing, not only during the clinical development of AID systems, but also in daily practice. However, it might very well be that other factors determine which AID algorithms will be used in practice.

A System for Blood Glucose Monitoring and Smart Insulin Prediction

Diabetes is the disease of the age; the blood glucose levels must be monitored at least three times per day for patients with diabetes mellitus. The method used currently for diabetes monitoring causes discomfort and distress because the patient ‘s finger needs pricking to get a blood sample. The amount of insulin required in the fumigation process is still determined manually using slow calculations. In this paper, the function of the pancreas in the body is implemented using a smart program. A machine learning (ML) based model is used to track the patient’s glucose level and predict the appropriate amount of insulin. We have used artificial neural network (ANN) in our final model since it provided the best prediction of the insulin pattern with mean square error (MSE) of 5.79. We have applied our model on 13 patients of different ages and genders, all with type 1 diabetes. Our final design was developed to be used as a simple and easy to use glucose meter that is compatible with Raspberry Pi to measure the blood glucose level and predict the optimal insulin level considering different conditions. The system basically does glucose level measurement with an average accuracy of 98.7%, patient’s data recording, and based on the recorded data the amount of required insulin is predicted with maximum accuracy. We mainly aim to reduce the damage caused by inaccuracy in glucose measurement and determine the appropriate insulin level using a method that is smarter, faster, and more accurate than the manual calculations.

Decision Support Systems for Insulin Treatment Adjustment in People with Type 1 Diabetes.

For people with type 1 diabetes, achieving optimal glycemic control requires use of intensive insulin therapy. To achieve this goal individuals are required to become proficient in accurately determining the appropriate amount of insulin needed to address a variety of situations throughout the day while considering numerous influencing factors. They also need to perform multiple tasks a day such as counting carbohydrates to accurately determine the required premeal bolus. There is also a need to periodically adjust insulin dosing as insulin sensitivity varies considerably over time. Sophisticated advanced technologies are being used by growing numbers of individuals to manage diabetes. However, despite innovations in glucose monitoring technologies and new insulin formulations, many of these individuals are not achieving their glycemic goals. The new technologies provide ample amount of valuable diabetes related data that may complicate even further the insulin dosing decision making for people with diabetes as well as for the health care providers. Mobile health, digital tools and decision support systems can help to increase accurate, timely insulin dosing and insulin titration and holds the potential to improve glycemic control to a wide range of individuals with diabetes. The emerging mobile toolbox for insulin dosing adjustments that will be reviewed in this paper includes insulin management Apps; diverse range of Apps to guide insulin adjustments such as in times of physical activity and eating; data management systems which enable visualization and analysis of insulin and glucose data of various devices; real-time alarms and glucose prediction Apps that help to prevent hypoglycemia and hyperglycemia events; various bolus calculators for meal time dosing; sophisticated decision support algorithms for people using insulin pump, multiple insulin injections and closed loop systems for real-time and retrospective insulin dosing adjustments.

Hyperglycemic hyperosmolar state in an adolescent with type 1 diabetes mellitus.

SummaryHyperglycemic hyperosmolar state (HHS) and diabetic ketoacidosis (DKA) are the most severe acute complications of diabetes mellitus (DM). HHS is characterized by severe hyperglycemia and hyperosmolality without significant ketosis and acidosis. A 14-year-old Japanese boy presented at the emergency room with lethargy, polyuria and polydipsia. He belonged to a baseball club team and habitually drank sugar-rich beverages daily. Three weeks earlier, he suffered from lassitude and developed polyuria and polydipsia 1 week later. He had been drinking more sugar-rich isotonic sports drinks (approximately 1000–1500 mL/day) than usual (approximately 500 mL/day). He presented with HHS (hyperglycemia (1010 mg/dL, HbA1c 12.3%) and mild hyperosmolality (313 mOsm/kg)) without acidosis (pH 7.360), severe ketosis (589 μmol/L) and ketonuria. He presented HHS in type 1 diabetes mellitus (T1DM) with elevated glutamate decarboxylase antibody and islet antigen 2 antibody. Consuming beverages with high sugar concentrations caused hyperglycemia and further exacerbates thirst, resulting in further beverage consumption. Although he recovered from HHS following intensive transfusion and insulin treatment, he was significantly sensitive to insulin therapy. Even the appropriate amount of insulin may result in dramatically decreasing blood sugar levels in patients with T1DM. We should therefore suspect T1DM in patients with HHS but not those with obesity. Moreover, age, clinical history and body type are helpful for identifying T1DM and HHS. Specifically, drinking an excess of beverages rich in sugars represents a risk of HHS in juvenile/adolescent T1DM patients.Learning points:Hyperglycemic hyperosmolar state (HHS) is characterized by severe hyperglycemia and hyperosmolality without significant ketosis and acidosis.The discrimination between HHS of type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM) in initial presentation is difficult.Pediatrician should suspect T1DM in patients with HHS but not obesity.Age, clinical history and body type are helpful for identifying T1DM and HHS.Children with T1DM are very sensitive to insulin treatment, and even appropriate amount of insulin may result in dramatically decreasing blood sugar levels.

The Official Journal of ATTD Advanced Technologies & Treatments for Diabetes Conference Berlin, Germany—February 20–23, 2019

The Official Journal of ATTD Advanced Technologies &amp; Treatments for Diabetes Conference <i>Berlin, Germany—February 20–23, 2019</i>

The pathogenetic role of β-cell mitochondria in type 2 diabetes.

Mitochondrial metabolism is a major determinant of insulin secretion from pancreatic β-cells. Type 2 diabetes evolves when β-cells fail to release appropriate amounts of insulin in response to glucose. This results in hyperglycemia and metabolic dysregulation. Evidence has recently been mounting that mitochondrial dysfunction plays an important role in these processes. Monogenic dysfunction of mitochondria is a rare condition but causes a type 2 diabetes-like syndrome owing to β-cell failure. Here, we describe novel advances in research on mitochondrial dysfunction in the β-cell in type 2 diabetes, with a focus on human studies. Relevant studies in animal and cell models of the disease are described. Transcriptional and translational regulation in mitochondria are particularly emphasized. The role of metabolic enzymes and pathways and their impact on β-cell function in type 2 diabetes pathophysiology are discussed. The role of genetic variation in mitochondrial function leading to type 2 diabetes is highlighted. We argue that alterations in mitochondria may be a culprit in the pathogenetic processes culminating in type 2 diabetes.

Differences between studying an islet β cell and studying whole pancreatic islets: immunological implications

The main role of the pancreatic islet β cell is to release the appropriate amount of insulin upon glucose stimulation. For this reason, islet transplantation has been advancing in the past few years as a therapeutic alternative for patients with diabetes, alongside the exciting field of manipulating β cell differentiation for the sake of β cell transplantation. However, do isolated β cells function the same as β cells within an intact islet? Within islets, β cells are surrounded by other cell types, including endocrine cells, endothelial cells and immune cells, a proximity which appears to be relevant for proper glucose homeostasis. Although insulin and glucose are the main regulators in this scenario, other factors, such as angiogenesis, local anti-inflammatory components and the activity profile of resident macrophages, have a profound effect on the function and fate of β cells. A paracrine interaction between β cells and α cell holds a dramatic effect on β cell function, which is additionally dependent on blood flow through the islet. Another important intercellular communication exists between β cells and endothelial cells, in this case a bidirectional interface. Moreover, β cell survival and proliferation is dependent on the potency of ECM proteins. Further parameters distinguish functionally between the isolated β cell and the intact islet, including the deposition of Zinc by β cells, synchronicity by electrical and calcium routes, the physical innervation of islets and more. In this review, we explore major parameters that relate to differences between the function of the isolated β cell and that of the β cell within an intact islet. These and some yet to be investigated aspects of β cell function should be included in the list of considerations when examining therapeutic targets for β cell–related pathologies and for the prospect of effective β cell replacement therapy.

Editing genetics, stem cells are prophetic, what’s the best way to model cells of diabetics?

The pancreas is composed of many different cell types, including the endocrine beta cells that are lost or dysfunctional in those afflicted with diabetes. Beta cells normally sense the level of glucose in the blood and secrete an appropriate amount of insulin in response, which binds to receptors on peripheral tissues like muscle and fat to induce glucose uptake. Current treatments for diabetics hinge on constant injections of external insulin, leaving large fluctuations in blood glucose levels and devastating consequences such as kidney failure and retinopathy.

Genetic Engineering of Surrogate &amp;lt;i&amp;gt;β&amp;lt;/i&amp;gt; Cells for Treatment of Type 1 Diabetes Mellitus

Type 1 diabetes mellitus (T1DM) is an autoimmune disease resulting from the destruction of the insulin-producing β cells of the pancreas. While treatment options like daily insulin injections or transplantation of whole-pancreas exist, they are associated with significant drawbacks. As a result, there has been great interest in engineering surrogate β cells, both ex vivo and in situ, to replace the function of those cells lost during the progression of the disease. However, the β cell is highly specialized and extraordinarily adept at synthesizing and rapidly secreting the appropriate amount of insulin in response to even small increases in blood glucose levels. Thus, genetic engineering of the “perfect” β cell may prove impossible. In this review, we will detail the features of β cells that make them so proficient at regulating blood glucose and highlight the key features that absolutely must be met by surrogate β cells if they are to be suitable for treatment of T1DM. Then, we will summarize the current approaches used to genetically engineer surrogate β cells, including the overexpression of β cell-specific transcription factors and insulin gene therapy. Along the way, we will discuss the advantages and disadvantages of each approach and review important studies in the field. Lastly, we will discuss important future directions necessary to genetically engineer surrogate β cells with the potential to treat T1DM.

Stem Cell-Derived Beta Cells for Treatment of Type 1 Diabetes?

Stem Cell-Derived Beta Cells for Treatment of Type 1 Diabetes?

The β-Cell/EC Axis: How Do Islet Cells Talk to Each Other?

Within the pancreatic islet, the β-cell represents the ultimate biosensor. Its central function is to accurately sense glucose levels in the blood and consequently release appropriate amounts of insulin. As the only cell type capable of insulin production, the β-cell must balance this crucial workload with self-preservation and, when required, regeneration. Evidence suggests that the β-cell has an important ally in intraislet endothelial cells (ECs). As well as providing a conduit for delivery of the primary input stimulus (glucose) and dissemination of its most important effector (insulin), intraislet blood vessels deliver oxygen to these dense clusters of metabolically active cells. Furthermore, it appears that ECs directly impact insulin gene expression and secretion and β-cell survival. This review discusses the molecules and pathways involved in the crosstalk between β-cells and intraislet ECs. The evidence supporting the intraislet EC as an important partner for β-cell function is examined to highlight the relevance of this axis in the context of type 1 and type 2 diabetes. Recent work that has established the potential of ECs or their progenitors to enhance the re-establishment of glycemic control following pancreatic islet transplantation in animal models is discussed.

The pancreatic beta cell surface proteome

The pancreatic beta cell is responsible for maintaining normoglycaemia by secreting an appropriate amount of insulin according to blood glucose levels. The accurate sensing of the beta cell extracellular environment is therefore crucial to this endocrine function and is transmitted via its cell surface proteome. Various surface proteins that mediate or affect beta cell endocrine function have been identified, including growth factor and cytokine receptors, transporters, ion channels and proteases, attributing important roles to surface proteins in the adaptive behaviour of beta cells in response to acute and chronic environmental changes. However, the largely unknown composition of the beta cell surface proteome is likely to harbour yet more information about these mechanisms and provide novel points of therapeutic intervention and diagnostic tools. This article will provide an overview of the functional complexity of the beta cell surface proteome and selected surface proteins, outline the mechanisms by which their activity may be modulated, discuss the methods and challenges of comprehensively mapping and studying the beta cell surface proteome, and address the potential of this interesting subproteome for diagnostic and therapeutic applications in human disease.Electronic supplementary materialThe online version of this article (doi:10.1007/s00125-012-2531-3) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

The hypoxia response pathway and β‐cell function

β-cells sense glucose and secrete appropriate amounts of insulin by coupling glucose uptake and glycolysis with quantitative ATP production via mitochondrial oxidative pathways. Therefore, oxidative phosphorylation is essential for normal β-cell function. Multiple cell types adapt to hypoxia by inducing a transcriptional programme coordinated by the transcription factor hypoxia-inducible factor (HIF). HIF activity is regulated by the von Hippel-Lindau (Vhl) protein, which targets the HIFα subunit for proteasomal degradation in the presence of oxygen. Several recent studies have shown that Vhl deletion in β-cells results in Hif1α activation, impaired glucose-stimulated insulin secretion (GSIS) and glucose intolerance. This was found to be because of alterations in β-cell gene expression inducing a switch from aerobic glucose metabolism to anaerobic glycolysis, thus disrupting the GSIS triggering pathway. Situations in which islets may become hypoxic are discussed, in particular islet transplantation which has been reported to cause islet hypoxia because of an inadequate blood supply post-transplant. Aside from this principal role for HIF in negatively regulating β-cell glucose sensing, other aspects of hypoxia signalling are discussed including β-cell differentiation, development and vascularization. In conclusion, recent studies clearly show that hypoxia response mechanisms can negatively impact on glucose sensing mechanisms in the β-cell and this has the potential to impair β-cell function in a number of physiological and clinical situations.

LKB1 Regulates Pancreatic β Cell Size, Polarity, and Function

Pancreatic beta cells, organized in the islets of Langerhans, sense glucose and secrete appropriate amounts of insulin. We have studied the roles of LKB1, a conserved kinase implicated in the control of cell polarity and energy metabolism, in adult beta cells. LKB1-deficient beta cells show a dramatic increase in insulin secretion in vivo. Histologically, LKB1-deficient beta cells have striking alterations in the localization of the nucleus and cilia relative to blood vessels, suggesting a shift from hepatocyte-like to columnar polarity. Additionally, LKB1 deficiency causes a 65% increase in beta cell volume. We show that distinct targets of LKB1 mediate these effects. LKB1 controls beta cell size, but not polarity, via the mTOR pathway. Conversely, the precise position of the beta cell nucleus, but not cell size, is controlled by the LKB1 target Par1b. Insulin secretion and content are restricted by LKB1, at least in part, via AMPK. These results expose a molecular mechanism, orchestrated by LKB1, for the coordinated maintenance of beta cell size, form, and function.

Multi-objective blood glucose control for type 1 diabetes

For people with type 1 diabetes, automatic controllers aim to maintain the blood glucose concentration within the desired range of 60-120 mg/dL by infusing the appropriate amount of insulin in the presence of meal and exercise disturbances. Blood glucose concentration outside the desired range can be harmful to an individual's health but concentration below 60 mg/dL, a state known as hypoglycemia, is considered to be more harmful than the concentration above 120 mg/dL, a state known as hyperglycemia. In this paper, two techniques to address this issue within a multi-parametric model based control framework are presented. The first technique introduces asymmetry into the objective function to penalize the deviation towards hypoglycemia more than the deviation towards hyperglycemia. The second technique is based upon placing higher priority on satisfaction of constraints on hypoglycemia than on satisfaction of constraints on hyperglycemia. The performance of both the control techniques is analyzed and compared in the presence of disturbances.

Mitochondrial dysfunction in pancreatic β-cells in Type 2 Diabetes

Mitochondrial metabolism controls insulin secretion from the pancreatic beta-cell. Type 2 Diabetes evolves when the beta-cells fail to release appropriate amounts of insulin, causing metabolic dysregulation and hyperglycemia. It is attractive to assume that mitochondrial dysfunction plays a decisive role in these processes. Indeed, while being a rare condition, genetically determined dysfunction of mitochondria causes a Type 2 Diabetes-like syndrome. Here, we review what is known about mitochondrial dysfunction in the beta-cell in Type 2 Diabetes. The focus is on observations in humans but relevant studies in animal models of the disease are discussed. A particular emphasis is placed on changes in metabolic enzymes and function in beta-cell mitochondria and how altered structure of the organelle appears to facilitate its function. These molecular processes are subject to tight genetic as well as epigenetic control. Variations and implications of these mechanisms are reviewed. The emerging picture is that alterations in mitochondria may be a culprit in the pathogenetic processes culminating in Type 2 Diabetes. Such processes may prove to be targets for therapeutic interventions in the disease.

Insulin-loaded PLGA/cyclodextrin large porous particles with improved aerosolization properties: In vivo deposition and hypoglycaemic activity after delivery to rat lungs

The aim of the present work is to develop large porous particles (LPP) of poly (lactide-co-glycolide) (PLGA) containing insulin with optimal aerodynamic properties and to test their in vivo potential, in pulmonary delivery. Insulin-loaded LPP were fabricated by a double emulsion method by aid of hydroxypropyl-β-cyclodextrin (HPβCD). Conceiving this system for the controlled release of insulin to the lungs, the aerosolization properties and the release features in simulated lung fluids of PLGA/HPβCD/insulin LPP were investigated in depth. The technological results show that the combination of appropriate amounts of insulin and HPβCD plays a crucial role to achieve PLGA/HPβCD/insulin LPP with the desired bulk and aerodynamic properties, that is a highly porous structure, a very low density (0.1 g/ml), an experimental mass mean aerodynamic diameter (MMAD exp) ranging from 4.01 to 7.00 and a fine particle fraction (FPF) estimated to be 26.9–89.6% at the different airflow rates tested (i.e. 30–90 l/min). Confocal microscopy studies, performed after administration of labeled PLGA/HPβCD/insulin LPP to the rat lung by means of a low-scale dry powder inhaler (DPI), suggest that particles reach alveoli and remain in situ after delivery. The pharmacological effect of PLGA/HPβCD/insulin LPP was confirmed by dose–response studies performed on both normoglycaemic and streptozotocin-induced diabetic rats. While insulin solutions administered via pulmonary route are unable to cause a significant hypoglycaemic effect, insulin delivered through PLGA/HPβCD/insulin LPP at the same doses (0.5–4.0 IU/kg) significantly reduces blood glucose level as a function of the administered dose in both animal models. The developed LPP, tested in hyperglycaemic rats at evident pathological conditions, exerts a very significant and longer hypoglycaemic effect even at insulin doses as low as 0.5 IU/kg (about 0.5 mg of PLGA/HPβCD/insulin LPP per rat) as compared to a insulin solution. Taken together, our results support the viability of a dry powder formulation based on biodegradable LPP for the controlled release of insulin to the lungs. In vivo data show that PLGA/HPβCD/insulin LPP are able to reach alveoli, release insulin, which is absorbed in its bioactive form.

Elevation of Global O-GlcNAc Levels in 3T3-L1 Adipocytes by Selective Inhibition of O-GlcNAcase Does Not Induce Insulin Resistance

The O-GlcNAc post-translational modification is considered to act as a sensor of nutrient flux through the hexosamine biosynthetic pathway. A cornerstone of this hypothesis is that global elevation of protein O-GlcNAc levels, typically induced with the non-selective O-GlcNAcase inhibitor PUGNAc (O-(2-acetamido-2-deoxy-D-glycopyranosylidene) amino-N-phenylcarbamate), causes insulin resistance in adipocytes. Here we address the potential link between elevated O-GlcNAc and insulin resistance by using a potent and selective inhibitor of O-GlcNAcase (NButGT (1,2-dideoxy-2'-propyl-alpha-D-glucopyranoso-[2,1-D]-Delta 2'-thiazoline), 1200-fold selectivity). A comparison of the structures of a bacterial homologue of O-GlcNAcase in complex with PUGNAc or NButGT reveals that these inhibitors bind to the same region of the active site, underscoring the competitive nature of their inhibition of O-GlcNAcase and the molecular basis of selectivity. Treating 3T3-L1 adipocytes with NButGT induces rapid increases in global O-GlcNAc levels, but strikingly, NButGT treatment does not replicate the insulin desensitizing effects of the non-selective O-GlcNAcase inhibitor PUGNAc. Consistent with these observations, NButGT also does not recapitulate the impaired insulin-mediated phosphorylation of Akt that is induced by treatment with PUGNAc. Collectively, these results suggest that increases in global levels of O-GlcNAc-modified proteins of cultured adipocytes do not, on their own, cause insulin resistance.

Peri‐operative iv fluids in diabetic patients – don’t forget the salt

Peri‐operative iv fluids in diabetic patients – don’t forget the salt