Search

This paper presents a Proportional-Integral-Derivative (PID) type controllers design-based Type-1 Diabetes Mellitus (T1DM). These controllers are designed using PIDTuner MATLAB tool. The T1DM is a critical illness in human beings and it is affected human daily life activities. So, to control the level of insulin and glucose in the human body, these controllers are applied and utilized as artificial pancreases for these diseases. From simulation responses, it is predicted that the PID-type controller regulates the glucose level at the pre-specified levels specified by practitioners. [ABSTRACT FROM AUTHOR]

Hashimoto's Thyroiditis is prevalent among type 1 diabetic patients. Prevalence rate can be dependent on many factors like geographical area, level of HbA1C, duration of diabetes, age & BMI of the patient. This study aimed to investigate the prevalence of Hashimoto's Thyroiditis among insulin dependent diabetes mellitus patients in the Jaipur district. This prospective, single-centered & clinical-based study includes a total of a hundred and twenty-five subjects (87 males & 38 females) with type 1 diabetes among them 10.4% were children, 41.6% were Adolescents, and 48% were Adults. The age ranged from 4 to 65 years (mean age 21.42 ± 11.02) selected from Endocrine OPD of Sawai Man Singh (S.M.S) Medical College and Hospital, jaipur, rajasthan between June 2017 and August 2017. Mean age, extent of diabetes, and BMI of subjects were determined according to the physical examination and information collected through Performa. Detailed clinical examination was done to detect Hashimoto's thyroiditis followed by appropriate hematological and biochemical investigations like Fasting Blood Sugar (FBS), Glycosylated Hemoglobin (HbA1c), Thyroid Stimulating Hormone (TSH), and Anti-Thyroid Peroxidase Autoantibodies (Anti-TPO). Categorical data were analyzed by Chi-Square test & continuous data was analyzed by Pearson Correlational test & p-value was calculated. After screening, 44% of patients had sera-positivity for Anti-TPO (55 out of 125) & 56% (70/125) of patients had negative for Anti-TPO. Among fifty-five autoimmune thyroiditis patients, 12 (0% Children, 16.7% Adolescents, & 83.3% Adults) were women & 43 (7% Children, 32.5% Adolescents, & 60.5% Adults) were men. Patients with higher anti-TPO titer were comparatively older than those who had anti-TPO levels within the normal range (mean age of the subjects respectively 24.4 ± 11.61, 19.07 ± 10.13). Patients with positive anti-TPO had a higher BMI (21.26 ± 9.69) than patients who were negative (17.9 ± 2.79) for autoimmune thyroiditis. It is also observed that the duration of diabetes has a positive correlation with the level of anti-TPO, r value – 0.3204. In conclusion, Thyroiditis is more prevalent in adults than children & adolescents with type 1 diabetes. Regular screening of thyroid antibodies may be suggested in all type 1 diabetic patients. [ABSTRACT FROM AUTHOR]

Diabetes is a chronic disease that can be prevented through early detection. It is caused by an increase in the level of sugar in the blood. Over 380 million people are expected to have diabetes during pregnancy in 2025 due to the increasing number of cases. It increases the risk of developing various conditions such as stillbirth, miscarriage, and severe congenital malformations. Data mining is commonly utilized in the field of medicine. This work aims to predict the algorithms that will be used to predict diabetes mellitus. Type 1 diabetes is usually caused by a deficiency of insulin. In other cases, it can be identified by genetic markers and serological evidence. Type 2 diabetes is a disease caused by resistance to insulin action and a lack of insulin secretion. In the other hand, hyperglycemia is a condition that can cause various target tissues to become hypersensitive. This asymptomatic period can be used to diagnose carbohydrate metabolism disorder. It can be observed by measuring plasma glucose and after a challenge with a glucose load. [ABSTRACT FROM AUTHOR]

Diabetes is known as a non-communicable disease, but it is one of the deadliest diseases because the number of diabetic patients continues to increase every year. In general, diabetes is divided into two types, namely type 1 diabetes (autoimmune) and type 2 diabetes (melitus). Research also shows that there is a type of diabetes that can be completely cured and is only found in pregnant women called gestational. However, in pregnant women the symptoms are different and there are several accompanying risk factors. This study built a model with C50 algorithm and indicate the most influential variable which affecting diabetes in women. The research process began with pre-processing the data. After the data was ready for use, the next step was to diagnose diabetes using the C50 algorithm. The diagnostic model built using the C50 algorithm shows an accuracy value of 81.5%. Furthermore, five variables that affect diabetic patients consist of glucose levels, body mass index, pregnancy history, family history of diabetes, and insulin levels. [ABSTRACT FROM AUTHOR]

The aim of the study is to identify the prevalence of diabetic Cataract in type I diabetic patients aged 10 years – 30 years, within the Russian Federation, and the stage of cataract at the time of identification. The specialized center for Endocrinology & diabetes – Baghdad... Al Rasafa city was taken 100 patients from 1000 patients with type 1 diabetes about (24) of males and (76 ,) of females were selected for the study. The patients detected by blood testing FBS, Cholestrol ,HbAIC, and Triglyceride. In addition that Diagnostic scans of the lens of the eye in Subcortical, Cortical, Nuclear. Aldose reductase , Endoplasmic reticulum. Results are revealed that a significant increase in FBS and HbAIC for injured by cataract, comparing with cholesterol and Triglyceride especially the nuclear type and cortical cataract. In addition, that cataract is treatable and potentially sight-saving for young children and adolescents. [ABSTRACT FROM AUTHOR]

Type 1 diabetes (T1D) is an autoimmune disease with chronic nature resulting from a combination of both factors genetic and environmental. The genetic contributors of T1D among Iraqis are unexplored enough. The study aimed to shed a light on the contribution between genetic variation of interleukin2 (IL2) gene to T1D as a risk influencer in a sample of Iraqi patients. The association between IL2−330 polymorphism (rs2069762) was investigated in 322 Iraqis (78 T1D patients and 244 volunteers as controls). Genotyping for the haplotypes using polymerase chain reaction test – specific sequence primer (PCR-SSP) for (GG, GT, and TT) genotypes corresponding to (G and T) alleles were performed. A significant association revealed a decreased frequency between the observed and expected TT genotype between patients compared to control subjects (10.26% vs 25.89%; P= 0.004); however, in term of alleles, the T allele was non significantly decreased (24.36% vs. 53.13%; P = 1.000) and the related PF values were (0.33 and 0.28 respectively). The current study demonstrated that IL2−330 SNP; TT genotype / T allele is associated with a risk of developing T1D in this sample of the Iraqi population, and that IL2−330 genetic variation confers a risk factor for T1D pathology. [ABSTRACT FROM AUTHOR]

Diabetes is a chronic disease when there is insufficient insulin produced by the pancreas or ineffective use of the insulin by the body, causing high blood sugar. There are three types of diabetes known as type 1, type 2 and, Gestational. The cause of this metabolic disease is not only the heredity or family history, but also due to the lack of knowledge, unawareness and carelessness that lead to the unhealthy diet practices and lifestyles. In this study, Mamdani fuzzy inference system is used to diagnose this disease among women in helping them to control and prevent from getting diabetes mellitus disease. This study aims to examine the symptoms of positive diabetes or negative diabetes and to ascertain the accuracy of the fuzzy system in diagnosing diabetes mellitus. All female patients represented in the dataset diagnosed with diabetes mellitus aged at least 21 years old from Pima Indian heritage living near Phoenix, Arizona. Graphical User Interface (GUI) application was developed using Fuzzy Logic Toolbox software and MATLAB to implement the models. The results from diagnosis of diabetes mellitus by using Mamdani Fuzzy Inference System method obtained an accuracy rate of 90% where 260 women with positive diagnoses and 76 with negative diagnoses. This demonstrates that Mamdani Fuzzy Inference System has the capacity and adaptability in managing this issue as well as a good alternative to diagnose diabetes. [ABSTRACT FROM AUTHOR]

Diabetes mellitus is a chronic disease that occurs because the pancreas does not produce enough insulin and the body cells cannot use the insulin effectively. Type 1 diabetes mellitus requires insulin therapy, whereas in type 2 diabetes mellitus insulin resistance occurs, so additional insulin therapy may or may not be necessary. Problems related to insulin therapy arise from repeated daily injections that cause pain and damage or trauma to the skin. To overcome problems related to insulin therapy, innovative insulin delivery systems have been developed, such as insulin patches. In the present study, scientific data on pectin insulin patch will be reviewed. This review is based on a search of Medline and citation lists of relevant publications. Several in vitro and in vivo studies have found that pectin can deliver insulin through transdermal patches. The application of a pectin-insulin patch can attenuate hyperglycemia in diabetes, attenuate cardiovascular complications, support hemodynamic control, and attenuate diabetes-associated kidney damage. Overall, the results from multiple studies suggest that a pectin-insulin patch may provide a new valuable tool in diabetes mellitus treatment. [ABSTRACT FROM AUTHOR]

Diabetes in the population is one of the world's main diseases. Diabetes is a chronic condition if there is either insufficient insulin in the pancreas or insulin cannot be used properly in the body. Long-term risks such as cardiac disease and liver cancer could lead to diabetes if not treated. Therefore, a timely prevalence of diabetes is very important to people around the world. In particular, the challenges to newer people and the workforce were identified as diabetes. Diabetes is tracked if patient changes in diet and lifestyle in the early days could be identified. Type 1 and type 2 diabetes were one of the most common forms of diseases; however, other types of diabetes, such as gestational diabetes pregnancy and other forms of diabetes. This paper outlines a method for predicting early diabetes, taking important risk effects into consideration. This approach uses the strategy to display a feature selection procedure. In order to construct a good subset to improve predictive performance, the selected features are assessed through the rating of design models. Ultimately, these characteristics are learned from the neural network classifier and defined. [ABSTRACT FROM AUTHOR]

Type 1 diabetes mellitus patients on intensive insulin therapy use advanced carbohydrate counting to proper dose prandial insulin. Therefore, the patient's ability to accurately estimate the meal's carbohydrate content is paramount. However, despite its significance, several studies show that the patient's ability to estimates the meal's carbohydrate content is far from ideal and identify the need for continuous education on carbohydrate counting. In this context, the authors have proposed in previous works an analytic method to determine the maximum error to the carbohydrate counting regarding each patient's insulin-to-carb ratio and the insulin sensitivity factor. This maximum can be of great significance to design patient-specific educational programs and to define learning outcomes according to the specific characteristics of each patient. This work presents a methodology and conditions to assess the previously proposed method, using the FDA-approved University of Virginia(UVA)/Padova Type 1 Diabetes Simulator. [ABSTRACT FROM AUTHOR]

In India the age group of 20+population, it is estimated that 65 million people are diagnosed with DR and retinitis pigmentosa in 2016. On assumption that around a quarter of these people have any related diseases like Diabetic Retinopathy or the retinitis pigmentosa, in these people a very minor proportion are subjected to the factor of eyesight loss or are already suffering from it. Diabetic Retinopathy is generally the sub disease of diabetes which leads to problems in the eyes. This is mainly because of the harm caused to the light absorbing part called the retina behind the eye, the blood vessels present there get damaged due to DR which further leads to these problems. In the initial stages, diabetic retinopathy has zero symptoms or the affect individuals may experience very minor vision problems. As it develops in the retina, it gradually causes entire vision loss as worsens; the development of this problem is usually noticed in people who are affected with either type one of diabetes of even noted in the later stages of diabetes. The development and impact of this disease is directly proportional to the longer the you have diabetes and the reduced control you have over your sugar levels. Retinitis pigmentosa is an inherited degenerative disease. It gradually affects the retinal part of the eye and causes night blindness and loss of side vision. In this project, we will be effectively classifying the retinal vessel in an eye based on the scanned images of the eye using deep learning algorithms to effectively identify the occurrence of diabetic retinopathy in a patient. A dataset consisting of the scanned images of the eye is trained using the resnet deep learning algorithm. Thus, this project gives an innovative solution to effectively identify the occurrence of diabetic retinopathy & retinitis pigmentosa in a patient based on the classification of the retinal vessels. [ABSTRACT FROM AUTHOR]

Diabetes is referred to a group of metabolic disorder that is caused by irregular amount of insulin produced in body. There are two types of diabetes in major clinical cases that is type 1 diabetes (T1D) and type 2 diabetes (T2D). Most of the cases reported is T2D and this is caused by a number of people that are unaware of their possibility in having T2D and lack of knowledge regarding its risks. It is important to perform an early diagnosis of T2D to ensure that people will become more aware and also reduce the chances of them getting high risk or complications such as stroke, blindness and damage to body and nerves. To do a classification task, a tool namely machine learning will be used. This tool is used by researcher to analyze a large amount of data to obtain a data pattern. This pattern then will be interpreted to get a knowledge that can help in decision making process. For the medical diagnosis, there are two things that is important which is accuracy and comprehensibility. Ant Colony Optimization (ACO) is a meta-heuristic method that is branch of swarm intelligence and it has been used to solve many complex problems and generate comprehensible result. Hence, this paper will introduce the new proposed algorithm that will hybrid between ACO with Gini Index. Gini Index is a heuristic measure that is widely used in Classification and Regression Trees (CART). The proposed algorithm was execute using the BlueJ application that is written in Java language. The aim of this paper is to investigate the performance of the new proposed algorithm in terms of accuracy and computation time. Experimental results on Pima Indian Diabetes data set show that the proposed algorithm can improve the predictive accuracy and computation time compared to the original ACO. In addition, it also leads to a smaller number of rules and terms. [ABSTRACT FROM AUTHOR]

Apps have been attested to empower patients regarding disease self-management through numerous studies. However, it is still unclear what factors determine the perception of patients whether an app is a useful tool for this purpose. A multiple regression model that was informed by the Technology Acceptance Model (TAM 2) was tested based on the answers of 235 app users with Diabetes type 1 or 2. The model accounted for 59.2% of the variance of the perceived degree of self-management. Factors belonging to the relevance-usefulness-quality complex as well as factors reflecting the patient's self-control were found to be significant in the model. Patient demographics, i.e. age, gender, app experience and type of Diabetes did not play any significant role. In conclusion, this study raises the question whether apps should be designed to strengthen self-management in the sense of self-control (e.g. own measurements, diary) as opposed to guiding and advice giving. [ABSTRACT FROM AUTHOR]

Glucose responsive nanoparticles are a promising technology for the treatment of insulin dependent diabetes. The construction of nanoparticles with different sizes, characteristics, and compositions allow for various functions associated with drug delivery. Combined with a glucose sensing unit, this technology can be used to deliver exogenous insulin in a more reliable and less painful manner than current treatment. The advantages of using such nanoparticles is their ability to mimic pancreatic β-cells by encapsulating large amounts of insulin and releasing them into a diabetic patient's body through various bodily systems. However, these systems are still being developed to be completely reliable and commercially available. This paper will review recent developments of several different types of glucose responsive nanoparticles, including their composition and working mechanisms. [ABSTRACT FROM AUTHOR]

Objective The molecular mechanism of catalpol for the treatment of diabetes mellitus based on network pharmacology and molecular docking. Methods TCMSP was used to obtain information on catalponol. GeneCards, Uniprot and other databases were used to obtain diabetes targets. The spatial structure of proteins was downloaded from the PDB database and molecular docking was also carried out using AutoDock Vina. The targets with good results were screened out and the PPI network was constructed using string. GeneOntology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were carried out on the targets using WebGestalt database. Results Get 77 targets with good docking effect, including ALB, ADIPOQ, VEGFA and other key at the node; GO functional enrichment analysis 53 GO entries, KEGG by enrichment analysis 27 entries, including maturity onset diabetes of the young pathway, type I diabetes pathway, type II diabetes pathway and so on. Conclusion Catalpol has a good effect on the treatment of diabetes, while providing evidence for the molecular mechanism of catalpol for the treatment of diabetes and target prediction for some unreported factors, which is expected to become a new target for the treatment of diabetes in the future. [ABSTRACT FROM AUTHOR]

To mitigate the adverse consequences of chronic hyperglycemia, patients with type 1 diabetes mellitus must provide their bodies with insulin to control their blood glucose. In most cases, insulin therapy consists of a combination of basal insulin and bolus insulin, the so-called basal-bolus insulin therapy. To determine the bolus insulin, patients must know not only the carbohydrate content of each meal but also the values of the insulin-to-carbohydrate ratio and the insulin sensitivity factor. Although important, the blood glucose complex dynamics make determining these parameters a difficult and error-prone task, usually performed by experienced diabetologists using high-quality data. Moreover, the insulin-to-carbohydrate ratio and the insulin sensitivity factor vary over the day due to several factors. Thus, daily, patients use approximate values to determine their prandial bolus. In this paper, we propose an analytic method to find the safe maximum interval for the error in the estimates of the insulin-to-carbohydrate ratio and, therefore, avoid dysglycemia. Our study suggests that slimmer patients with smaller insulin-to-carbohydrate ratios need to be more careful when estimating it. Another significant finding of our work is that in such cases, having small meals reduces the adverse effect of inaccurate insulin-to-carbohydrate ratio estimates in the postprandial blood glucose. [ABSTRACT FROM AUTHOR]

Patients with type 1 diabetes mellitus use intensive insulin therapy to suppress their insulin needs and avoid the adverse consequences of chronic hyperglycemia. Intensive insulin therapy consists of a combination of basal insulin and bolus insulin. While the basal insulin dose is periodically adjusted in collaboration with the healthcare team, patients have to estimate the bolus insulin dose by themselves, before each meal. To accurately estimate the bolus insulin dose, patients must know the carbohydrates content of each meal and their insulin to carbohydrate ratio. The insulin to carbohydrate ratio is initially calculated by experienced diabetologists using high-quality data. However, regarding the glucose complex metabolism, it varies over the day due to several factors. Consequently, daily, patients use approximate values to estimate their bolus insulin. Thus, depending on the error of the insulin to carbohydrate ratio estimates, the patient could experience hypo or hyperglycemic events. Therefore, to avoid the consequences of inaccurate bolus insulin and to improve the patient’s glycemic control, this work presents an adaptive insulin bolus calculator that uses the patient’s glycemic data to dynamically adjust the mealtime bolus and compensate for the adverse effects of inaccurate insulin to carbohydrate ratio estimates. [ABSTRACT FROM AUTHOR]

Insulin-dependent diabetes mellitus (IDDM) is an autoimmune disease that is thought to be triggered by environmental factors in genetically susceptible individuals. IDDM includes two types, first type 1daibetes (MD1), and the second type latent autoimmune diabetes in adults (LADA). Enteroviruses have been mentioned as the most probable induction component of the disease. Nevertheless, the literature is controversial regarding the association of IDDM with viral infection and first-line antiviral defense component, such as cytokines. This study was carried out in the department of Biology, College of Science, University of Wasit, from December 2018 to December 2019. The current study aimed to evaluate the role of some cytokines (CXCL9 and IFIH1) in initiate the IDDM as an autoimmune disorder. The patients were included 68 with IDDM (40 male and 28 female) that divided into 53 MD1 patients and 15 from LADA patients, their age from <10 years to 50 years old. Total RNAs were extracted from all patients and control groups by using Acuazol kit then converted to complementary DNA. Real Time qPCR was used to detect gene expression of CXCL9 and IFH1 by using Eva green kit. By using a relative gene expression method, a high significant of CXCL9 (6.34 folding) was found in patients with type 1(DM1) diabetes than the control groups, while LADA patients showed significant upregulation (9.22 folding) than DM1 and control group, however adults diabetic patients type I appeared a highly significant expression (12.52 folding) than kids patients (1.71 folding) with IDDM. On the other hand, our findings showed slightly overexpression regarding of interferon-induced helicase 1 (IFIH1) (1.41 folding) in the blood cells of IDDM subjects comparing to the control groups. In the opposite direction, the IFIH1 showed downexpression (0.13 folding) in LADA patients comparing to the DM1 and the control groups. In addition kids DM1 patients showed upregulation (2.72 folding) than adults patients (0.84 folding) and control groups. [ABSTRACT FROM AUTHOR]

Blood glucose level in patients with diabetes mellitus type 1 is controlled by external administration of insulin. Physical activity is a disturbance in blood glucose control, as it influences the glucoregulatory system significantly. In this paper, strategies for improving blood glucose control during physical activity are examined and compared using an extended version of the Sorensen glucoregulatory model. Investigated control strategies for open loop control are a feedforward compensator, which is informed about the intensity of performed activity, and an adaption of insulin infusion rate according to a fixed pattern. Control strategies for closed loop control are a PID controller, a combination of a PID controller and a feedforward compensator and a PID controller, whose setpoint is increased during activity. Results show that best blood glucose control is achieved with strategies including the feedforward compensator due to the achieved reduction of the deviation from the basal value, reduction of time in hypoglycemia and increase in minimal blood glucose level. [ABSTRACT FROM AUTHOR]

Stress research has established the pathways through which life events predict varying mental health outcomes for individuals, but has given less attention to how individuals come to interpret these life events as stressors. Using data from 18 in-depth, open-ended interviews with individuals that have been diagnosed with either type 1 diabetes or type 2 diabetes, this paper uses diagnosis with a chronic illness as an empirical case to study how individuals interpret and give meaning to stressors they face. This paper also aims to fulfill a need in stress research to uncover the structural and cultural origins of the meanings that individuals give to stressors. Using the "Sort and Sift, Think and Shift" method of qualitative analysis, I find that the meanings given to diagnosis with a chronic illness as a stressor originate from three source levels: the individual level (e.g., biography, personality), the interpersonal level (e.g., social support, social comparisons), and the cultural level (i.e., cultural understandings, norms, and expectations about encountering and managing a given stressor). Organizing the origins of the meanings given to stressors by individuals into these three levels could allow scholars to understand what types of objective life circumstances lead to differential interpretations of a given stressor, as well as pinpoint at what level interventions can be implemented to alleviate stress. Empirically, findings indicate that the type of chronic illness and age at which an individual are diagnosed have significant influence on how an individual might interpret being diagnosed. This paper also suggest that life events might not be inherently strenuous, but are rather interpreted as more or less so based on other life circumstances. [ABSTRACT FROM AUTHOR]

Within a mobile-technology enhanced educational framework, this study investigates needs and expectations of children (aged 8-14) with type 1 diabetes, and their parents, to define the functionalities of PAL Inform, a web-based monitoring module. The aim is to design it as a tool for parents to be informed on the educational progresses, as well on diabetes-related data, of their children without violating their values (privacy, trust, autonomy). Through a set of co-design activities, carried out with 39 children and 45 parents, it was possible to define which kind of information parents would monitor and children (especially pre-adolescents), in turn, would share, without compromising family dynamics: i.e. glycaemic values and trends, insulin doses, carbohydrates intake, generic emotional status, educational progresses on diabetes-related knowledge. Leveraging on these insights, a set of functional requirements was elicited for the future monitoring module implementation. [ABSTRACT FROM AUTHOR]

Copyright of Proceedings of the International Scientific Conference of Daugavpils University / Daugavpils Universitates Starptautiskas Zinatniskas Konferences Materiali is the property of Daugavpils University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Managing type-1 diabetes (T1D) is complex, and diagnosis often occurs in children when they are too young to manage their own care. This requires the parents to take on the demanding responsibility. As a result of these demands, parents are at risk of feelings of depression, loneliness, isolation, guilt, loss of control, anxiety, and increased marital strain. Education, support, and problem solving often help relieve some of the difficulties. The objective of this study was to conduct a formative analysis of parents' caregiving needs in order to inductively identify an appropriate theory to be used as a lens in developing a website, MyT1DHope. A focus group, semi-structured phone interviews, and a survey with parents of children with T1D were conducted. When analyzing results, we found that the parent responses mapped onto the Transactional Model of Stress and Coping (TMSC). This model will be used as a lens to develop the website. [ABSTRACT FROM AUTHOR]

Type 1diabetes (T1D) is a chronic disease (CD) affecting people of different ages including children. The growing number of diabetics has placed a burden not only on healthcare providers, but also on caregivers at home including parents, close relatives and friends. Information and communication technologies (ICTs) have been employed to support the self-management of CDs. However, little is known about how these ICTs help patients' parents to manage their children's disease. This study aimed to identify the challenges experienced by parents, which may inhibit the use and effectiveness of ICTs. Employing a qualitative approach, 58 semi-structured in-depth interviews were conducted with 37 parents of T1D children and 20 allied health professionals. Data was analysed using Atlas software. Initial coding identified two major themes namely that ICTs are ineffective in facilitate the acquisition of T1D information and not suitable for parents' preferences and culture. Results from this study can help various organisations such as ministries, and ICT companies, individuals and groups including nurses, doctors and web designers to provide recommendations for the diverse ICT tools that could empower parents' use of ICT and facilitate the adoption of ICT tools for other stakeholders. [ABSTRACT FROM AUTHOR]

The purpose of this study is to explore the coping with disease in adolescents with type 1 diabetes in its complexity - from the viewpoint of emotional distress, perception of the disease, and equally that of preferred coping strategies in the context of differing levels of metabolic compensation. Set of questionnaires consisting of Big Five Inventory (BFI), Brief-Cope, Problem Areas in Diabetes Questionnaire (PAID), Brief Illness Perception Questionnaire (BIPQ) and Diabetes Adolescent Problem Solving Questionnaire (DAPSQ) was used. Differences between different groups of adolescents with type 1 diabetes were detected in the usage of coping strategies in utilizing the strategy of giving up. Difference in usage of problem solving was confirmed in "awareness" and "revision". Significant correlation between perception of the disease and utilization of the strategy focused on problem solving was found as well as correlation between emotional distress and problem-solving. Different aspects of coping with the disease in patients with type 1 diabetes significantly influence each other; patients with insufficient compensation are at risk of serious complications, those with excellent compensation, are exposed to high levels of emotional distress and the risk of burnout. Reinforcement of competences through education contributes to an increase in awareness of treatment, realization and saturation of psycho-social and cultural needs, as well as to a greater extent of taking over the responsibility of treatment and self-care that has a positive impact on shortterm and long-term results alike. Therefore, it is very important that specialists from different fields cooperate with individuals with diabetes and their parents. [ABSTRACT FROM AUTHOR]

Diabetes type I is one of the most important medical and social problem of health in all countries of the world. Treatment of diabetes type I diabetes is based on insulin therapy. Introduction a-2ß interferon in addition to insulin therapy for patients with diabetes type I at the beginning of the disease can slow the progression of the disease and even achieve remission. Therefore, the purpose of this work was to develop a method of producing nanoparticles loaded with interferon for oral delivery systems of drugs. The optimum conditions for obtaining the biodegradable nanoparticles loaded with a-2- interferon with a size of 600-800 nm on the basis of the natural polymers alginate and chitosan wich provides pH-dependent release of immobilized protein were selected. It was shown that the nanoparticles release the encapsulated a-2ß-interferon in an acidic environment of the stomach within 60 minutes no more than 15%. The main quality (78%) of the protein is released into the small intestine. It was shown by ELISA that immobilized in chitosan-alginate nanoparticles a-2ß interferon retains its activity not less than 85%. [ABSTRACT FROM AUTHOR]

In this paper, the research is focusing on Type 1 Diabetes Mellitus (T1DM). Since this disease requires a full attention on the blood glucose concentration with the help of insulin injection, it is important to have a tool that able to predict that level when consume a certain amount of carbohydrate during meal time. Therefore, to make it realizable, a Hovorka model which is aiming towards T1DM is chosen in this research. A high-level language is chosen that is C++ to construct the mathematical model of the Hovorka model. Later, this constructed code is converted into intellectual property (IP) which is also known as a hardware accelerator by using of high-level synthesis (HLS) approach which able to improve in terms of design and performance for glucose-insulin analysis tool later as will be explained further in this paper. This is the first step in this research before implementing the design into system-on-chip (SoC) to achieve a highperformance system for the glucose-insulin analysis tool. [ABSTRACT FROM AUTHOR]

This project introduces a simulation of Proportional-Derivative (PD) and Proportional-Integral-Derivative (PID) controller based on a virtual Type 1 Diabetes Mellitus (T1DM) patient: Hovorka diabetic model using MATLABSimulink software. The results of these simulations are based on three tuning responses for each controller which are fast, slow and oscillation responses. The main purpose of this simulation is to achieve an acceptable stability and fastness response towards the regulation of glucose concentration using PD and PID controller response with insulin infusion rate. Therefore, in order to analyze and compare the responses of both controller performances, one-day simulations of the insulin-glucose dynamic have been conducted using a typical day meal plan that contains five meals of different bolus size. It is found that the PID closed-loop control with a short rise time is required to retrieve a satisfactory glucose regulation. [ABSTRACT FROM AUTHOR]

Type 1 diabetes is a unique chronic disease where successful control is predicated on selfmanagement. For adolescents and young adults, however, self-management is a daunting task and many struggle to control their disease. This two-phase study uses a mixed-method approach, intended as formative research, to develop effective management messages targeting adolescents and young adults who have Type 1 diabetes. The first phase consisted of three small focus groups (N=11) with questions guided by the theory of planned behavior (TPB). Findings included a preference for attitude- and norms-based messages presented with exemplars. For the second phase, a 2 message style (exemplar/directive) X 3 TPB-based appeal (attitude, norms, PBC) between-subjects factorial design experiment with a control condition was conducted with Type 1 diabetics between the ages of 12-22 (N=142). A key result showed that norm-based messages had a significantly greater impact on diabetics' attitude toward their disease than attitude-based messages. [ABSTRACT FROM AUTHOR]

Inpatient management of insulin-dependent diabetes (IDD) is a complex task that requires clinicians to cognitively process information across distinct domains in different locations of the electronic medical record (EMR). Current data displays are not optimized to support insulin management by end users. We sought to develop a set of user-centered displays of capillary glucose data and insulin dose to improve inpatient management of IDD. Our proposed conceptual data display prototype is designed to simplify the presentation and visualization of key information needed for treatment decisions. The goal is also to enhance clinician's ability to identify opportunities to optimize insulin dosing and decrease end user's cognitive load and error rates. [ABSTRACT FROM AUTHOR]

People with diabetes experience elevated blood glucose (BG) levels at the time of an infection. We propose to utilize patientgathered information in an Electronic Disease Surveillance Monitoring Network (EDMON), which may support the identification of a cluster of infected people with elevated BG levels on a spatiotemporal basis. The system incorporates data gathered from diabetes apps, continuous glucose monitoring (CGM) devices and other appropriate physiological indicators from people with type 1 diabetes. This paper presents a novel approach towards modeling of the individual's BG dynamics, a mechanism to track and detect deviations of elevated BG readings. The models were developed and validated using self-recorded data in the non-infection status using Dexcom CGM devices, from two type 1 diabetes individuals over a 1-month period. The models were also tested using simulated datasets, which resemble the individual's BG evolution during infections. The models accurately simulated the individual's normal BG fluctuations and further detected statistically significant BG elevations. [ABSTRACT FROM AUTHOR]

In this paper, the research is focusing on Type 1 Diabetes Mellitus (T1DM). Since this disease requires a full attention on the blood glucose concentration with the help of insulin injection, it is important to have a tool that able to predict that level when consume a certain amount of carbohydrate during meal time. Therefore, to make it realizable, a Hovorka model which is aiming towards T1DM is chosen in this research. A high-level language is chosen that is C++ to construct the mathematical model of the Hovorka model. Later, this constructed code is converted into intellectual property (IP) which is also known as a hardware accelerator by using of high-level synthesis (HLS) approach which able to improve in terms of design and performance for glucose-insulin analysis tool later as will be explained further in this paper. This is the first step in this research before implementing the design into system-on-chip (SoC) to achieve a highperformance system for the glucose-insulin analysis tool. [ABSTRACT FROM AUTHOR]

This project introduces a simulation of Proportional-Derivative (PD) and Proportional-Integral-Derivative (PID) controller based on a virtual Type 1 Diabetes Mellitus (T1DM) patient: Hovorka diabetic model using MATLABSimulink software. The results of these simulations are based on three tuning responses for each controller which are fast, slow and oscillation responses. The main purpose of this simulation is to achieve an acceptable stability and fastness response towards the regulation of glucose concentration using PD and PID controller response with insulin infusion rate. Therefore, in order to analyze and compare the responses of both controller performances, one-day simulations of the insulin-glucose dynamic have been conducted using a typical day meal plan that contains five meals of different bolus size. It is found that the PID closed-loop control with a short rise time is required to retrieve a satisfactory glucose regulation. [ABSTRACT FROM AUTHOR]

Copyright of CISTI (Iberian Conference on Information Systems & Technologies / Conferência Ibérica de Sistemas e Tecnologias de Informação) Proceedings is the property of Conferencia Iberica de Sistemas Tecnologia de Informacao and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper we present 2 patient-specific models (PSM) developed by 3D Visual Membrane Petri Nets (VMPN) application. [ABSTRACT FROM PUBLISHER]

This work describes the architecture of an agent based computer model that simulates the autoimmune response in Type 1 diabetes in a three-dimensional cubic volume representing a fragment of pancreas of a mouse. Immune cells and β-cells are modeled as agents, making the abstraction of one cell - one agent. We present our preliminary results for the interactions between β-cell agents and macrophage agents and a model of cell motility in the continuous three-dimensional volume. This agent-based approach allows implementing multi-scale computational modeling capturing subcellular characteristics into cell agents and obtaining outcomes from agent populations at tissue and systemic levels. [ABSTRACT FROM AUTHOR]

The analysis of time duration between consecutive R waves of electrocardiogram (ECG) is a standard method to evaluate the variations in heart rate. The physiological literature reveals that blood glucose levels modulate the autonomic nervous system (ANS) activity and heart rate variability (HRV) is representative of the cardiovascular autonomic function. In the research described here, a pilot investigation was carried out to investigate the relationship between HRV signal measures derived from ECG and arterial blood glucose changes in a female subject with type 1 diabetes mellitus (T1DM) subject during normoglycemic and mildly hyperglycemic conditions. A CleveLabs BioCapture wireless device was used to acquire ECG signals from a 160 Kg, 59.6 year old female volunteer with type 1 diabetes. The PhysioToolkit Software was used to extract the HRV signal and the Kubios software package was deployed to perform comprehensive HRV signal analysis. This software has an easy-to-use graphical user interface that displays the HRV signal and provides three options to calculate: Time-domain, Frequency-domain and Nonlinear Dynamics parameters from raw HRV signals. In its Frequency-domain analysis section, it provides frequency bands such as VLF (Hz), LF (Hz), and HF (Hz), with LF/HF as an index that reflects the sympathovagal balance of the ANS. ECG data were acquired for 30 minutes during normoglycemic condition and for another 30 minutes during mildly hyperglycemic conditions, while blood glucose levels were measured manually by the subject using a glucometer every 5 minutes. ECG signal segments of 5 minute durations were then processed to extract HRV signals and these in turn were analyzed to provide frequency-domain measures. The results indicated that blood glucose changes were inversely related to LF/HF. For this dataset, it was observed that mean ± std of the LF/HF decreased from 6.0 ± 1.04 to 0.91 ± 0.17 when blood glucose levels increased from 156 ± 22 mg/dl to 202 ± 29 mg/dl. Further investigation is underway to recruit more diabetic subjects to acquire a large dataset and explore the relationships between different HRV signal parameters and blood glucose changes under different gylcemic conditions in a comprehensive way. [ABSTRACT FROM PUBLISHER]

An abstract of the article "Cardiomyocyte contractile function and structural remodelling in the left ventricle of streptozotocin-induced type 1 diabetic rat" by T. Iqbal, A. D'Souza, F. Howarth, E. Adeghate, K. Bidasse, and J. Singh is presented.

Knowing what the conversation on Twitter regarding type 1 diabetes (T1D) is about can help in understanding the kind of information relevant to the individuals affected by the disease. The profile of Twitter users posting on T1D was collected and classified. The number of re-tweets was also registered. The tweets posted by non-governmental organizations (NGOs), communication media, and individuals affected by T1D had higher number of potential readers. More than a half of the tweets were posted by individuals affected by T1D, and their tweets were the most re-tweeted. The next most active users were NGOs and healthcare professionals. However, while tweets soliciting for research funds posted by the NGOs were the next most re-tweeted messages, tweets posted by healthcare professionals were the least re-tweeted. Twitter could be used more actively by healthcare professionals to disseminate correct information about T1D. [ABSTRACT FROM AUTHOR]