An emerging protagonist: Sodium Glucose Co-transporters (SGLTs) as a burgeoning target for the treatment of diabetes mellitus

Contemporary therapies to rationalize the hyperglycaemia in type 2 diabetes mellitus (T2DM) generally involve insulin-dependent mechanisms and lose their effectiveness as pancreatic b-cell function decreases to a greater extent. The kidney emerges out as a novel and potential target to trim down the T2DM. The filtered glucose is reabsorbed principally through the sodium glucose co-transporter-2 (SGLT2), a low affinity transport system, which is present at the luminal surface cells that cover the first segment of proximal tubules. Competitive inhibition of SGLT2 therefore represents an innovative therapeutic strategy for the treatment of hyperglycaemia and/or obesity in patients with type 1 or type 2 diabetes by enhancing glucose and energy loss through the urine. Selective inhibitors of SGLT2 reduce glucose reabsorption, causing excess glucose to be eliminated in the urine; this decreases plasma glucose. SGLT2 inhibitors are coupled with osmotic dieresis and loss of weight which aid in reducing blood pressure. The observation that individuals with familial renal glycosuria maintain normal long-term kidney function provides some encouragement that this mode of action will not adversely affect renal function. This novel mechanism of targeting the kidney for the treatment of T2DM is reasonably valuable and is independent of insulin and clutch with the low risk of hypoglycemia

Effect of Bacillus spp. on Gerbera plant growth and control of Meloidogyne incognita

A greenhouse experiment was conducted to evaluate the efficacy of Bacillus spp. against Meloidogyne incognita (Kofoid and White) Chitwood infesting gerbera plants (Var. Valletta). Investigations were undertaken in pots filled with 5 kg of sterilized potting mixture consisting of red soil:sand:FYM (2:1:1 v/v) to assess the effect of liquid and talc formulations of Bacillus spp. viz., B. subtilis strain BG42, B. subtilis strain BG37 and B. amyloliquefaciens strain B4. The results indicated that Soil drenching of liquid formulation of B. subtilis strain BG42 @ 1%/m2 (1x108 colony forming units/g) gave maximum reduc-tion of juveniles per 250g soil (67.40%), number of adult females/5g root (73.46%), number of eggmass/5g root (69.44%), gall index (1.67) and increased flower yield/m2 (127.03%). Soil drenching of liquid formulation of B. subtilis strain BG 37 were next in line in efficacy. Further liquid formulation of B. subtilis strain BG42 had a positive influence on growth parameters viz., shoot length, root length, shoot and root weight, number of leaves / plant and flower yield/m2 and quality parameters viz., flower diameter, colour of the flower, length of flower stalk and vase life . The endophytic colonization potential of the Bacillus spp. introduced into the soil was confirmed by reisolating them from gerbera roots

BIG DATA ANALYTICS METHODS USING GPU : A COMPREHENSIVE SURVEY

Big data analytics is eventual discovery of knowledge from large set of data thus leading to business benefits. Its biggest challenge is the ability to provide information within reasonable time. The traditional analytics methods might fail to produce efficient result when data handled is of large size. As part of enhancing the performance, the researchers incorporated Graphical Processing Unit (GPU) on big data. GPU being the soul of computer delivers high performance by using its multi core parallel architecture. This paper investigates some methods of integrating GPU on analytics of big data that solely delivered high performance when compared to conventional schemes

A Novel Densenet-324 Densely Connected Convolution Neural Network for Medical Crop Classification using Remote Sensing Hyperspectral Satellite Images

In the past few decades, importance of the medicinal Crops is extending to a large extent due to its benefits in treating life-threatening diseases. Medicinal Crop has excellent medicinal properties on its roots, stem, and leaves to prevent human and animal health. Particularly detection and identification of the Crop classes are effectively carried out using hyperspectral images as discrimination of the target feature or objects is simple and it contains rich information containing the spatial and temporal details of underlying the land cover. However, Crop classification using machine learning architectures concerning spectral characteristics obtained on the anatomical features and morphological features. Extracted features towards classification lead to several challenges such as large spatial and temporal variability and spectral signatures similarity between different objects. A further hyperspectral image poses several difficulties with changes in illumination, environment, and atmospheric aspects. To tackle those non-trivial challenges, DenseNet-324 Densely Connected convolution neural network architecture has been designed in this work to discriminate the crop and medical Crop effectively in the interested areas.  Initially, the Hyperspectral image is pre-processed against a large number of noises through the employment of the noise removal technique and bad line replacement techniques. Pre-processed image is explored to image segmentation using the global thresholding method to segment it into various regions based on spatial pieces of information on grouping the neighboring similar pixels intensity or textures. Further regions of the image are processed using principle component analysis to extract spectral features of the image. That extracted feature is employed to ant colony optimization technique to obtain the optimal features. Computed optimal features are classified using Convolution Neural Network with a hyper parameter setup. The convolution Layer of the CNN architecture process spatial, temporal, and spectral feature and generates the feature map in various context, generated feature map is max pooled in the pooling layer and classified into crops and medicinal Crop in the SoftMax layer. Experimental analysis of the proposed architecture is carried out on the Indiana Pines dataset using cross-fold validation to analyze the representation ability to discriminate the features with large variance between the different classes. From the results, it is confirmed that the proposed architecture exhibits higher performance in classification accuracy of 98.43% in classifying the Crop species compared with conventional approaches.&nbsp

Modeling Spread of Polio with the Role of Vaccination

In this paper, we have proposed and analyzed a nonlinear mathematical model for the spread of Polio in a population with variable size structure including the role of vaccination. A threshold parameter, R , is found that completely determines the stability dynamics and outcome of the disease. It is found that if R 1, the disease free equilibrium is stable and the disease dies out. However, if R \u3e1, there exists a unique endemic equilibrium that is locally asymptotically stable. Conditions for the persistence of the disease are determined by means of Fonda’s theorem. Moreover, numerical simulation of the proposed model is also performed by using fourth order Runge - Kutta method. Numerically, it has been found that the system exhibits steady state bifurcation for some parameter values. It is concluded from our analysis that endemic level of infective population increases with the increase in rate of transmission of infection due to infective among susceptible class that further enhances because of transmission of infection due to latent hosts. A particular value of disease transmission coefficient r is found for which exposed and infective population dies out. It is found that periodic outbreak of the disease occurs when infection due to exposed and infective class occurs at the same rate. It is also observed from our analysis that although vaccination helps in eradicating polio by decreasing endemic equilibrium level yet careful administration of vaccination is desired because if vaccine is administered during incubation period, endemic equilibrium level increases and disease persists in the population

Mathematical Modeling and Analysis of Leukemia: Effect of External Engineered T Cells Infusion

In this paper, a nonlinear model is proposed and analyzed to study the spread of Leukemia by considering the effect of genetically engineered patients T cells to attack cancer cells. The model is governed by four dependent variables namely; naive or susceptible blood cells, infected or dysfunctional blood cells, cancer cells and immune cells. The model is analyzed by using the stability theory of differential equations and numerical simulation. We have observed that the system is stable in the local and global sense if antigenicity rate or rate of stimulation of immune cells is greater than a threshold value dependent on the density of immune cells. Further, external infusion of T cells (immune cells) reduces the concentration of cancer cells and infected cells in the blood. It is observed that the infected cells decrease with the increase in antigenicity rate or stimulation rate of immune response due to abnormal cancer cells present in the blood. This indicates that immune cells kill cancer cells on being stimulated and as antigenicity rate increases rate of destruction of cancer cells also increase leading to decrease in the concentration of cancer cells in the body. This decrease in cancer cells further causes decrease in the concentration of infected or dysfunctional cells in the body

A Robust Cardiovascular Disease Predictor Based on Genetic Feature Selection and Ensemble Learning Classification

Timely detection of heart diseases is crucial for treating cardiac patients prior to the occurrence of any fatality. Automated early detection of these diseases is a necessity in areas where specialized doctors are limited. Deep learning methods provided with a decent set of heart disease data can be used to achieve this. This article proposes a robust heart disease prediction strategy using genetic algorithms and ensemble deep learning techniques. The efficiency of genetic algorithms is utilized to select more significant features from a high-dimensional dataset, combined with deep learning techniques such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Multi-Layer Perceptron (MLP), and Radial Basis Function (RBF), to achieve the goal. The boosting algorithm, Logit Boost, is made use of as a meta-learning classifier for predicting heart disease. The Cleveland heart disease dataset found in the UCI repository yields an overall accuracy of 99.66%, which is higher than many of the most efficient approaches now in existence