Objective:Charybdotoxin-C (ChTx-C), from the scorpion Leiurus, quinquestriatus hebraeus blocks the calcium-activated potassium channels and causes hyper excitability of the nervous system. Detailed understanding the structure of ChTx-C, conformational stability, and intermolecular interactions are required to select the potential inhibitors of the toxin.Materials and Methods:The structure of ChTx-C was modeled using Modeller 9v7. The amino acid residues lining the binding site were predicted and used for toxin-ligand docking studies, further, selected toxin-inhibitor complexes were studied using molecular dynamics (MD) simulations.Results:The predicted structure has 91.7% of amino acids in the core and allowed regions of Ramachandran plot. A total of 133 analog compounds of existing drugs for scorpion bites were used for docking. As a result of docking, a list of compounds was shown good inhibiting properties with target protein. By analyzing the interactions, Ser 15, Lys 32 had significant interactions with selected ligand molecules and Val5, which may have hydrophobic interaction with the cyclic group of the ligand. MD simulation studies revealed that the conformation and intermolecular interactions of all selected toxin-inhibitor complexes were stable.Conclusion:The interactions of the ligand and active site amino acids were found out for the best-docked poses in turn helpful in designing potential antitoxins which may further be exploited in toxin based therapies.
Background/Objectives: Image denoising is an important step in image processing applications. Usually noise is added to the original image during transmission, acquisition and storage process and is considered as noisy image. For precise analysis and extraction of image features, the noisy image is denoised without losing the original image details. This study aims to introduce a novel denoising method to obtain denoised image(s) such that it has fewer artifacts and is more efficient at higher noise levels. Method: The proposed novel denoising method introduces Adaptive Non Local Means (ANL) along with Method Noise Thresholding (MNT) technique to improve the image quality of the denoised image. Method Noise (MN) image obtained by taking the difference of image details between noisy image and pre-filtered mage. Recovered value from the MN through thresholding includes some of the important components of the original image. These values computed added to pre-filtered image to recover image features of the original image. Findings: The standard image, denoised with noise standard (σ =10) using bior6.8 wavelet when filtered using existing Gaussian Bilateral Filter along with Method-Noise Thresholding filtering technique and Wiener Filter along with Residual Thresholding show improvement in quality of the denoised image in terms of PSNR and ISSN values as compared to the proposed filtering technique. The proposed filter technique results in higher PSNR and ISSN values (PSNR =33.80 and SSIN =0.9994). Novelty: It is known that ANLM results in improved denoised parameters compared with NLM filter; however, when MNT is blended with ANLM shows further improvement in quality of denoised image. Hence, in the proposed method, MNT is incorporated along with ANLM for improvement in denoising process. Image Quality Index (IQI) of the different standard images using ANLMT filtering technique is also studied.
Machine learning techniques are associated with diagnostics systems to apply methods that enable computers to link patient data to earlier data and give instructions to correct the disease.In recent years, researchers have promoted two or three data mining based techniques for disease diagnosis. Each function in machine learning and data mining techniques is built through characteristics and features.As a part of prognosis, information must be separated from patient data and information retrieved in stored databases and comparative records. For any disease, early diagnosis or diagnosis will determine the chances of a correct recovery. Disease prediction therefore becomes a more important task to support physicians in delivering efficient treatment to people.In health care, data is being created and disposed of at an extraordinary rate compared to the health care sectors. Data for medical profiling is often found in a variety of sources such as electronic health records, lab and imaging systems, doctor notes and accounts. The medical records database will then contain irrelevant data sourced from multiple sources. Preprocessing data and eliminating irrelevant data then immediately opening it up for predictive analysis is one of the significant difficulties of the health care industry.
Background:The alpha-delta bungartoxin-4 (α-δ-Bgt-4) is a potent neurotoxin produced by highly venomous snake species, Bungarus caeruleus, mainly targeting neuronal acetylcholine receptors (nAchRs) and producing adverse biological malfunctions leading to respiratory paralysis and mortality. Objective: In this study, we predicted the three-dimensional structure of α-δ-Bgt-4 using homology modeling and investigated the conformational changes and the key residues responsible for nAchRs inhibiting activity.Materials and Methods:From the selected plants, which are traditionally used for snake bites, the active compounds are taken and performed molecular interaction studies and also used for modern techniques like pharmacophore modeling and mapping and absorption, distribution, metabolism, elimination and toxicity analysis which may increase the possibility of success.Results:Moreover, 100's of drug-like compounds were retrieved and analyzed through computational virtual screening and allowed for pharmacokinetic profiling, molecular docking and dynamics simulation.Conclusion:Finally the top five drug-like compounds having competing level of inhibition toward α-δ-Bgt-4 toxin were suggested based on their interaction with α-δ-Bgt-4 toxin.
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