Modelling the effects of glucagon during glucose tolerance testing.

From Europe PMC via Jisc Publications RouterHistory: ppub 2019-12-01, epub 2019-12-12Publication status: PublishedBACKGROUND:Glucose tolerance testing is a tool used to estimate glucose effectiveness and insulin sensitivity in diabetic patients. The importance of such tests has prompted the development and utilisation of mathematical models that describe glucose kinetics as a function of insulin activity. The hormone glucagon, also plays a fundamental role in systemic plasma glucose regulation and is secreted reciprocally to insulin, stimulating catabolic glucose utilisation. However, regulation of glucagon secretion by α-cells is impaired in type-1 and type-2 diabetes through pancreatic islet dysfunction. Despite this, inclusion of glucagon activity when modelling the glucose kinetics during glucose tolerance testing is often overlooked. This study presents two mathematical models of a glucose tolerance test that incorporate glucose-insulin-glucagon dynamics. The first model describes a non-linear relationship between glucagon and glucose, whereas the second model assumes a linear relationship. RESULTS:Both models are validated against insulin-modified and glucose infusion intravenous glucose tolerance test (IVGTT) data, as well as insulin infusion data, and are capable of estimating patient glucose effectiveness (sG) and insulin sensitivity (sI). Inclusion of glucagon dynamics proves to provide a more detailed representation of the metabolic portrait, enabling estimation of two new diagnostic parameters: glucagon effectiveness (sE) and glucagon sensitivity (δ). CONCLUSIONS:The models are used to investigate how different degrees of pax'tient glucagon sensitivity and effectiveness affect the concentration of blood glucose and plasma glucagon during IVGTT and insulin infusion tests, providing a platform from which the role of glucagon dynamics during a glucose tolerance test may be investigated and predicted

A mathematical model of brain glucose homeostasis

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Smart Sensors and Virtual Physiology Human Approach as a Basis of Personalized Therapies in Diabetes Mellitus

Diabetes mellitus (DM) has a growing incidence and prevalence in modern societies, pushed by the aging and change of life styles. Despite the huge resources dedicated to improve their quality of life, mortality and morbidity rates, these are still very poor. In this work, DM pathology is revised from clinical and metabolic points of view, as well as mathematical models related to DM, with the aim of justifying an evolution of DM therapies towards the correction of the physiological metabolic loops involved. We analyze the reliability of mathematical models, under the perspective of virtual physiological human (VPH) initiatives, for generating and integrating customized knowledge about patients, which is needed for that evolution. Wearable smart sensors play a key role in this frame, as they provide patient’s information to the models

A critical review of mathematical models and data used in diabetology

The literature dealing with mathematical modelling for diabetes is abundant. During the last decades, a variety of models have been devoted to different aspects of diabetes, including glucose and insulin dynamics, management and complications prevention, cost and cost-effectiveness of strategies and epidemiology of diabetes in general. Several reviews are published regularly on mathematical models used for specific aspects of diabetes. In the present paper we propose a global overview of mathematical models dealing with many aspects of diabetes and using various tools. The review includes, side by side, models which are simple and/or comprehensive; deterministic and/or stochastic; continuous and/or discrete; using ordinary differential equations, partial differential equations, optimal control theory, integral equations, matrix analysis and computer algorithms

Studies on the Surface Properties and Catalytic Activity of Metal Chromites on Thermal Decomposition of Ammonium Perchlorate

Department of Applied Chemistry, Cochin University of Science and Technolog

Residual network-based supervised learning of remotely sensed fall incidents using ultra-wideband radar

Detecting falls using radar has many applications in smart health care. In this paper, a novel method for fall detection in human daily activities using an ultra wideband radar technology is proposed. A time series derived from the radar scattering matrix is used as input to the the residual network for automatic feature extraction. In contrast to other existing methods, the proposed method relies on multi-level feature learning directly from the radar time series signals. In particular, the proposed method utilizes a deep residual neural network for automating feature learning and enhancing model discriminability. The performance of the proposed method is compared with that of the other methods such as support vector machine, K-nearest neighbors, multi-layer perceptron and dynamic time warping techniques. The results show that the proposed fall detection method outperforms the other methods in terms of accuracy and sensitivity values

Localized nonlinear excitations in diffusive memristor-based neuronal networks.

We extend the existing ordinary differential equations modeling neural electrical activity to include the memory effect of electromagnetic induction through magnetic flux, used to describe time varying electromagnetic field. Through the multi-scale expansion in the semi-discrete approximation, we show that the neural network dynamical equations can be governed by the complex Ginzburg-Landau equation. The analytical and numerical envelop soliton of this equation are reported. The results obtained suggest the possibility of collective information processing and sharing in the nervous system, operating in both the spatial and temporal domains in the form of localized modulated waves. The effects of memristive synaptic electromagnetic induction coupling and perturbation on the modulated action potential dynamics examined. Large electromagnetic induction coupling strength may contribute to signal block as the amplitude of modulated waves are observed to decrease. This could help in the development of a chemical brain anaesthesia for some brain pathologies

Dose estimates to the public due to

210Po activity concentrations have been measured in Lolodorf high background radiation area in cocoa beans which are hand-processed into cocoa powder for breakfast purposes to estimate radiological dose to human. 210Po has been also measured in cocoa leaves and compared to the cocoa beans 210Po content. The analysis has been carried out by CANBERRA alpha spectrometry using ion-implanted silicon detectors. 210Po activity concentrations in cocoa beans varied from 2.31 ± 0.23 to 8.09 ± 0.56 Bq.kg−1, while these values varied from 21.7 ± 0.87 to 66.67 ± 1.58 Bq.kg−1 in cocoa leaves. The corresponding mean values are 4.96 ± 1.86 and 42.54 ± 16 Bq.kg−1 on a dry weight basis respectively. The obtained values confirm the fact that 210Po activity concentrations in cocoa leaves are high compared to the cocoa beans due to the deposition of 222Rn daughters in the atmosphere. The mean radiological doses to human were founded to be 0.227, 0.134, 0.083 and 0.062 mSv/year for children 2- to 7-year-olds, 7- to 12-year-olds, 12- to 17-year-olds and for adult respectively. Ingestion of cocoa powder by the most exposed group ages (children) might not exceed the recommended dose limit for members of the public, which is 1 mSv/year