Various proteins play important roles in hypertension and a number of plants have been tested for their efficacy in modulating hypertension. Angiotensin 1-converting enzyme, renin and extracellular regulated kinase 2(ERK2) proteins, respectively, has major role in hypertension and therefore protein -ligand interaction studies were performed on 266 compounds from different parts of 7 plants (Allium sativum, Coriandrum sativum, Dacus carota, Murrayya koneigii, Eucalyptus globus, Calendula officinalis and Lycopersicon esculentum). Analysis was conducted using GOLD (Genetic Optimisation for Ligand Docking) software as docking program and the molecules drawn in ISIS Draw software are energy minimized using cosmic -optimize 3D module of Tsar (Tools for structure activity relationships) software. Before docking plant compounds, software validation was performed and found that all co-crystallized ligands are within 2.0 A°. Further, docking and re-scoring of 266 compounds with GOLD, Molegro and eHiTS followed by rank-sum technique revealed high binding affinity of compound 27, from Allium sativum, with Angiotensin converting enzyme, 1UZE and Renin, 2IKO. The docked pose of compound 27 (Phytic acid) exactly fits into the active site region and the ligand formed more number of H-bond interactions than the co-crystallized ligand. The best compound that exhibited high binding affinity with 3ERK was molecule 23 (Stigmasterol) from Lycopersicon esculentum.
The need for a general purpose Content Based Image Retrieval (CBIR) system for huge image databases has attracted information-technology researchers and institutions for CBIR techniques development. These techniques include image feature extraction, segmentation, feature mapping, representation, semantics, indexing and storage, image similarity-distance measurement and retrieval making CBIR system development a challenge. Since medical images are large in size running to megabits of data they are compressed to reduce their size for storage and transmission. This paper investigates medical image retrieval problem for compressed images. An improved image classification algorithm for CBIR is proposed. In the proposed method, RAW images are compressed using Haar wavelet. Features are extracted using Gabor filter and Sobel edge detector. The extracted features are classified using Partial Recurrent Neural Network (PRNN). Since training parameters in Neural Network are NP hard, a hybrid Particle Swarm Optimization (PSO)-Cuckoo Search algorithm (CS) is proposed to optimize the learning rate of the neural network.
The digital medical images are stored in large databases for easy accessibility and Content based image retrieval (CBIR) is used to retrieve diagnostic cases similar to the query medical image. Image compression condense the amount of data required to represent an image, it reduces the storage and transmission requirements. The medical image retrieval problem for compressed images is studied in this paper. The proposed method integrates image retrieval to retrieve diagnostic cases similar to the query medical image and image compression techniques to minimize the bandwidth utilization. Haar wavelet is used for image compression without losses. Edge and texture features are extracted from the medical compressed medical images using Sobel edge detector and Gabor transforms respectively. The classification accuracy of retrieval is evaluated using Naïve Bayes and Support Vector Machine.
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