A case-control regression analysis of liver enzymes in obesity-induced metabolic disorders in South Asian females.

Excessive body weight may disrupt hepatic enzymes that may be aggravated by obesity-related comorbidities. The current case-control study was designed to evaluate the extent of liver enzyme alteration in obesity-related metabolic disorders. Obese females with BMI ≥ 30 suffering from metabolic disorders were grouped according to existing co-morbidity and their hepatic enzymes were compared with non-obese healthy females. The resultant data was subjected to analysis of variance and mean difference in liver enzymes were calculated at P = 0.05. Analysis of variance indicated that obese diabetic and obese hypertensive females had almost 96% and 67% increase in the concentration of gamma-glutamyl transferase than control, respectively (P<0.0001). The obese females suffering from diabetes and hypertension exhibited nearly 54% enhancement in alanine transaminase level (P<0.0001) and a 17% increase in aspartate aminotransferase concentration (P = 0.0028). Obesity along with infertility decline liver enzyme production and a 31% significant decline in aspartate aminotransferase was observed while other enzyme concentrations were not significantly altered. Regression analysis was performed on the resultant data to understand the association between liver enzyme alteration and the development of metabolic diseases. Regression analysis indicated that obese diabetic and obese diabetic hypertensive women had 20% production of normal liver enzymes and 80% enzymes produced abnormally. Obese hypertensive and obese infertile females had only 5% and 6% normal production of liver enzymes, respectively. This research leads to the conclusion that the ability of the liver to function normally is reduced in obesity-related diabetes and hypertension. This may be due to inflamed and injured liver and poses a serious threat to developing fatty liver disease and ultimately liver cirrhosis.

Deep Learning Using Isotroping, Laplacing, Eigenvalues Interpolative Binding, and Convolved Determinants with Normed Mapping for Large-Scale Image Retrieval.

Convolutional neural networks (CNN) are relational with grid-structures and spatial dependencies for two-dimensional images to exploit location adjacencies, color values, and hidden patterns. Convolutional neural networks use sparse connections at high-level sensitivity with layered connection complying indiscriminative disciplines with local spatial mapping footprints. This fact varies with architectural dependencies, insight inputs, number and types of layers and its fusion with derived signatures. This research focuses this gap by incorporating GoogLeNet, VGG-19, and ResNet-50 architectures with maximum response based Eigenvalues textured and convolutional Laplacian scaled object features with mapped colored channels to obtain the highest image retrieval rates over millions of images from versatile semantic groups and benchmarks. Time and computation efficient formulation of the presented model is a step forward in deep learning fusion and smart signature capsulation for innovative descriptor creation. Remarkable results on challenging benchmarks are presented with a thorough contextualization to provide insight CNN effects with anchor bindings. The presented method is tested on well-known datasets including ALOT (250), Corel-1000, Cifar-10, Corel-10000, Cifar-100, Oxford Buildings, FTVL Tropical Fruits, 17-Flowers, Fashion (15), Caltech-256, and reported outstanding performance. The presented work is compared with state-of-the-art methods and experimented over tiny, large, complex, overlay, texture, color, object, shape, mimicked, plain and occupied background, multiple objected foreground images, and marked significant accuracies.