Abstract: The issue of high blood sugar level, the effects of which might end up as diabetes mellitus, is now becoming a rampant cardiovascular disorder in our community. In recent times, a lack of awareness among most people makes this disease a silent killer. The situation calls for urgency, hence the need to design a device that serves as a monitoring tool such as a wrist watch to give an alert of the danger a head of time to those living with high blood glucose, as well as to introduce a mechanism for checks and balances. The neural network architecture assumed 8-15-10 configuration with eight neurons at the input stage including a bias, 15 neurons at the hidden layer at the processing stage, and 10 neurons at the output stage indicating likely symptoms cases. The inputs are formed using the exclusive OR (XOR), with the expectation of getting an XOR output as the threshold value for diabetic symptom cases. The neural algorithm is coded in Java language with 1000 epoch runs to bring the errors into the barest minimum. The internal circuitry of the device comprises the compatible hardware requirement that matches the nature of each of the input neurons. The light emitting diodes (LED) of red, green, and yellow colors are used as the output for the neural network to show pattern recognition for severe cases, pre-hypertensive cases and normal without the traces of diabetes mellitus. The research concluded that neural network is an efficient Accu-Chek design tool for the proper monitoring of high glucose levels than the conventional methods of carrying out blood test.
Abstract: Diabetic Retinopathy (DR) is a severe retinal disease which is caused by diabetes mellitus. It leads to blindness when it progress to proliferative level. Early indications of DR are the appearance of microaneurysms, hemorrhages and hard exudates. In this paper, an automatic algorithm for detection of DR has been proposed. The algorithm is based on combination of several image processing techniques including Circular Hough Transform (CHT), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gabor filter and thresholding. Also, Support Vector Machine (SVM) Classifier is used to classify retinal images to normal or abnormal cases including non-proliferative or proliferative DR. The proposed method has been tested on images selected from Structured Analysis of the Retinal (STARE) database using MATLAB code. The method is perfectly able to detect DR. The sensitivity specificity and accuracy of this approach are 90%, 87.5%, and 91.4% respectively.
Abstract: Tannins are a unique category of plant phytochemicals
especially in terms of their vast potential health-benefiting properties.
Researchers have described the capacity of tannins to enhance
glucose uptake and inhibit adipogenesis, thus being potential drugs
for the treatment of non-insulin dependent diabetes mellitus. Thus,
the present research was conducted to find out tannin content of food
products. The percentage of tannin in various analyzed sources
ranged from 0.0 to 108.53%; highest in kathaa and lowest in ker and
mango bark. The percentage of tannins present in the plants,
however, varies. Numerous studies have confirmed that the naturally
occurring polyphenols are key factor for the beneficial effects of the
herbal medicines. Isolation and identification of active constituents
from plants, preparation of standardized dose & dosage regimen can
play a significant role in improving the hypoglycaemic action.
Abstract: Human beings have the ability to make logical
decisions. Although human decision - making is often optimal, it is
insufficient when huge amount of data is to be classified. Medical
dataset is a vital ingredient used in predicting patient’s health
condition. In other to have the best prediction, there calls for most
suitable machine learning algorithms. This work compared the
performance of Artificial Neural Network (ANN) and Decision Tree
Algorithms (DTA) as regards to some performance metrics using
diabetes data. WEKA software was used for the implementation of
the algorithms. Multilayer Perceptron (MLP) and Radial Basis
Function (RBF) were the two algorithms used for ANN, while
RegTree and LADTree algorithms were the DTA models used. From
the results obtained, DTA performed better than ANN. The Root
Mean Squared Error (RMSE) of MLP is 0.3913 that of RBF is
0.3625, that of RepTree is 0.3174 and that of LADTree is 0.3206
respectively.
Abstract: Some Chromium (III) complexes were synthesized
with three amino acids: L Glutamic Acid, Glycine, and L-cysteine as
the ligands, in order to provide a new supplement containing Cr(III)
for patients with type 2 diabetes mellitus. The complexes have been
prepared by refluxing a mixture of Chromium(III) chloride in
aqueous solution with L-glutamic acid, Glycine, and L-cysteine after
pH adjustment by sodium hydroxide. These complexes were
characterized by Infrared and Uv-Vis spectrophotometer and
Elemental analyzer. The product yields of four products were 87.50
and 56.76% for Cr-Glu complexes, 46.70% for Cr-Gly complex and
40.08% for Cr-Cys complex respectively. The predicted structure of
the complexes are [Cr(glu)2(H2O)2].xH2O, Cr(gly)3..xH2O and
Cr(cys)3.xH2O., respectively.
Abstract: Mushrooms are a group of fleshy macroscopic fungi.
They have been valued throughout the world as both edible and
medicine. They are highly nutritious with good amount of quality
proteins, vitamins and minerals. An edible mushroom, Calocybe
indica was selected to validate its nutritional and medicinal
properties. Since tissue damage in hyperglycemia has been related to
oxidative stress, we evaluated the enzymatic and non-enzymatic
antioxidant status in the serum, liver and kidney since they are the
target organs in diabetic complications. From the results, increased
oxidative stress and decreased antioxidants might be related to the
causation of diabetes mellitus. The treatment in the diabetic rats with
the Calocybe indica showed an increase in the antioxidant system
and decrease in the production of free radicals. The mushrooms
which contain antioxidant phytochemicals has potential free radical
scavenging capacity and hence can induce the antioxidant system in
the body significantly reduces the generated free radicals thereby
maintaining the normal levels of the antioxidants
Abstract: Sensory nerves in the foot play an important part in the diagnosis of various neuropathydisorders, especially in diabetes mellitus.However, a detailed description of the anatomical distribution of the nerves is currently lacking. A computationalmodel of the afferent nerves inthe foot may bea useful tool for the study of diabetic neuropathy. In this study, we present the development of an anatomically-based model of various major sensory nerves of the sole and dorsal sidesof the foot. In addition, we presentan algorithm for generating synthetic somatosensory nerve networks in the big-toe region of a right foot model. The algorithm was based on a modified version of the Monte Carlo algorithm, with the capability of being able to vary the intra-epidermal nerve fiber density in differentregionsof the foot model. Preliminary results from the combinedmodel show the realistic anatomical structure of the major nerves as well as the smaller somatosensory nerves of the foot. The model may now be developed to investigate the functional outcomes of structural neuropathyindiabetic patients.