Search results

The purpose of this study was to investigate diabetes self-care behaviors, stigmatization and A1C as predictors of a negative perception of insulin treatment in insulin-treated type 2 diabetic patients.

A descriptive cross-sectional and relational design was used. The study was carried out in the Diabetes Training Centre and Endocrine and Metabolism Clinic of a university hospital in the southeast of Turkey between May and October 2017. The research sample consisted of 100 type 2 diabetic patients determined by using a convenience sampling method. An introductory information form for type 2 diabetic patients, the Insulin Treatment Appraisal Scale (ITAS), Diabetes Self-Care Activities Survey (DSCAS) and Barriers to Insulin Treatment Scale (BIT) were used to collect the research data. The data were analyzed using descriptive statistics, correlations and step wise multi-linear regression.

The number of daily insulin injections, training received about insulin and stigmatization was significant predictors of a negative perception of insulin treatment.

Strategies to decrease diabetic individuals' fear of stigmatization should be utilized to minimize their negative insulin treatment perception (giving diabetic individuals training about diabetes, planning public training to inform society and using mass media tools). Diabetes educators should know that diabetic individuals' perception of the severity of the illness could influence the daily number of injections applied and decrease the negative perception regarding insulin.

Renal failure is an end-stage consequence after persistent hyperglycemia during diabetic nephropathy (DN), and the etiology of DN has been linked to oxidative stress. The purpose of this research was to determine the beneficial synergistic effects of S-Allyl Cysteine (SAC) and Taurine (TAU) on oxidative damage in the kidneys of type 2 diabetic rats induced by hyperglycemia.

Experimental diabetes was developed by administering intraperitoneal single dose of streptozotocin (STZ; 65 mg/kg) with nicotinamide (NA; 230 mg/kg) in adult rats. Diabetic and control rats were treated with SAC (150 mg/kg), TAU (200 mg/kg) or SAC and TAU combination (75 + 100 mg/kg) for four weeks. The estimation of body weight, fasting blood glucose (FBG), oral glucose tolerance test (OGTT), oxidative stress markers along with kidney histopathology was done to investigate the antidiabetic potential of SAC/TAU in the NA/STZ diabetic group.

The following results were obtained for the therapeutic efficacy of SAC/TAU: decrease in blood glucose level, decreased level of thiobarbituric acid reactive substances (TBARS) and increased levels of GSH, glutathione-s-transferase (GST) and catalase (CAT). SAC/TAU significantly modulated diabetes-induced histological changes in the kidney of rats.

SAC/TAU combination therapy modulated the oxidative stress markers in the kidney in diabetic rat model and also prevented oxidative damage as observed through histopathological findings.

Diabetes is a major metabolic disorder which can affect entire body system adversely. Undiagnosed diabetes can increase the risk of cardiac stroke, diabetic nephropathy and other disorders. All over the world millions of people are affected by this disease. Early detection of diabetes is very important to maintain a healthy life. This disease is a reason of global concern as the cases of diabetes are rising rapidly. Machine learning (ML) is a computational method for automatic learning from experience and improves the performance to make more accurate predictions. In the current research we have utilized machine learning technique in Pima Indian diabetes dataset to develop trends and detect patterns with risk factors using R data manipulation tool. To classify the patients into diabetic and non-diabetic we have developed and analyzed five different predictive models using R data manipulation tool. For this purpose we used supervised machine learning algorithms namely linear kernel support vector machine (SVM-linear), radial basis function (RBF) kernel support vector machine, k-nearest neighbour (k-NN), artificial neural network (ANN) and multifactor dimensionality reduction (MDR).

The purpose of this paper is to investigate the health and medicinal importance of bush tea (Athrixia phylicoides DC) and special tea (Monsonia burkeana Planch. ex Harv), two of Southern African indigenous herbal teas.

The two herbal teas, A. phylicoides and M. burkeana were extracted individually and in combined ratios for analysis. The phenolic content was determined and the different phenolic compounds were identified using thin-layer chromatography (TLC) and high-performance liquid chromatography (HPLC). The anti-diabetic activity of the teas was determined by evaluating the inhibition of both α-amylase and α-glucosidase in vitro. The anti-proliferative activity was measured on human cervical cancer (HeLa) cell line using the MTT (3-(4,5-dimethylthiazol-2-yl)2,5-diphenyltetrazolium) assay.

Gallic acid, chlorogenic acid and quercetin were identified to be present in significant quantities by TLC. The HPLC quantified the presence of catechin (1.567 mg/g) and chlorogenic acid (1.862 mg/g) in special tea while chlorogenic acid (1.288 mg/g) was present in bush tea. Bush tea and special tea expressed significant levels of phenolic content and high antioxidant activities. Special tea (S100) expressed high inhibition of α-amylase, α-glucosidase and HeLa cell line proliferation when compared to bush tea (B100).

Both bush tea and special tea could provide an alternative for treatment and management of both diabetes and cervical cancer. However, future studies are needed to investigate their synergistic effect with a wide range of other commercial herbal teas.

Machine Learning is an intelligent methodology used for prediction and has shown promising results in predictive classifications. One of the critical areas in which machine learning can save lives is diabetes prediction. Diabetes is a chronic disease and one of the 10 causes of death worldwide. It is expected that the total number of diabetes will be 700 million in 2045; a 51.18% increase compared to 2019. These are alarming figures, and therefore, it becomes an emergency to provide an accurate diabetes prediction.

Health professionals and stakeholders are striving for classification models to support prognosis of diabetes and formulate strategies for prevention. The authors conduct literature review of machine models and propose an intelligent framework for diabetes prediction.

The authors provide critical analysis of machine learning models, propose and evaluate an intelligent machine learning-based architecture for diabetes prediction. The authors implement and evaluate the decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction as the mostly used approaches in the literature using our framework.

This paper provides novel intelligent diabetes mellitus prediction framework (IDMPF) using machine learning. The framework is the result of a critical examination of prediction models in the literature and their application to diabetes. The authors identify the training methodologies, models evaluation strategies, the challenges in diabetes prediction and propose solutions within the framework. The research results can be used by health professionals, stakeholders, students and researchers working in the diabetes prediction area.

Diabetic retinopathy (DR) is one of the dangerous complications of diabetes. Its grade level must be tracked to manage its progress and to start the appropriate decision for treatment in time. Effective automated methods for the detection of DR and the classification of its severity stage are necessary to reduce the burden on ophthalmologists and diagnostic contradictions among manual readers.

In this research, convolutional neural network (CNN) was used based on colored retinal fundus images for the detection of DR and classification of its stages. CNN can recognize sophisticated features on the retina and provides an automatic diagnosis. The pre-trained VGG-16 CNN model was applied using a transfer learning (TL) approach to utilize the already learned parameters in the detection.

By conducting different experiments set up with different severity groupings, the achieved results are promising. The best-achieved accuracies for 2-class, 3-class, 4-class and 5-class classifications are 86.5, 80.5, 63.5 and 73.7, respectively.

In this research, VGG-16 was used to detect and classify DR stages using the TL approach. Different combinations of classes were used in the classification of DR severity stages to illustrate the ability of the model to differentiate between the classes and verify the effect of these changes on the performance of the model.

This paper aims to propose a solution for detecting and grading diabetic retinopathy (DR) in retinal images using a convolutional neural network (CNN)-based approach. It could classify input retinal images into a normal class or an abnormal class, which would be further split into four stages of abnormalities automatically.

The proposed solution is developed based on a newly proposed CNN architecture, namely, DeepRoot. It consists of one main branch, which is connected by two side branches. The main branch is responsible for the primary feature extractor of both high-level and low-level features of retinal images. Then, the side branches further extract more complex and detailed features from the features outputted from the main branch. They are designed to capture details of small traces of DR in retinal images, using modified zoom-in/zoom-out and attention layers.

The proposed method is trained, validated and tested on the Kaggle dataset. The regularization of the trained model is evaluated using unseen data samples, which were self-collected from a real scenario from a hospital. It achieves a promising performance with a sensitivity of 98.18% under the two classes scenario.

The new CNN-based architecture (i.e. DeepRoot) is introduced with the concept of a multi-branch network. It could assist in solving a problem of an unbalanced dataset, especially when there are common characteristics across different classes (i.e. four stages of DR). Different classes could be outputted at different depths of the network.