Chemical composition and antibacterial activity of bee venom against multi-drug resistant pathogens.

Bee venom with an antimicrobial effect is a powerful natural product. One of the most important areas where new antimicrobials are needed is in the prevention and control of multi-drug resistant pathogens. Today, antibacterial products used to treat multi-drug resistant pathogen infections in hospitals and healthcare facilities are insufficient to prevent colonisation and spread, and new products are needed. The aim of the study is to investigate the antibacterial effect of the bee venom (BV), a natural substance, on the species of Methicillin resistant Staphylococcus aureus, Vancomycin resistant Enterococcus faecalis, Carbapenem resistant Escherichia coli, Carbapenem resistant Klebsiella pneumoniae and Carbapenem resistant Acinetobacter baumannii. As a result of this study, it was found that MIC90 and MBC90 values ranged from 6.25 µg/mL - 12.5 µg/mL and numbers of bacteria decreased by 4-6 logs within 1-24 h for multi-drug resistant pathogens. In particular, Vancomycin resistant Enterococcus faecalis isolate decreased 6 log cfu/mL at 50 µg/mL and 100 µg/mL concentrations in the first hour. The effective bacterial inhibition rate of bee venom suggests that it could be a potential antibacterial agent for multi-drug resistant pathogens.Contribution: The treatment options of antibiotic-resistant pathogens are a major problem in both veterinary and human medicine fields. We have detected a high antibacterial effect against these agents in this bee venom study, which is a natural product. Apitherapy is a fashionable treatment method all over the world and is used in many areas of health. Bee venom is also a product that can be used as a drug or disinfectant raw material and can fill the natural product gap that can be used against resistant bacteria.

Approximation and compression with sparse orthonormal transforms.

We propose a new transform design method that targets the generation of compression-optimized transforms for next-generation multimedia applications. The fundamental idea behind transform compression is to exploit regularity within signals such that redundancy is minimized subject to a fidelity cost. Multimedia signals, in particular images and video, are well known to contain a diverse set of localized structures, leading to many different types of regularity and to nonstationary signal statistics. The proposed method designs sparse orthonormal transforms (SOTs) that automatically exploit regularity over different signal structures and provides an adaptation method that determines the best representation over localized regions. Unlike earlier work that is motivated by linear approximation constructs and model-based designs that are limited to specific types of signal regularity, our work uses general nonlinear approximation ideas and a data-driven setup to significantly broaden its reach. We show that our SOT designs provide a safe and principled extension of the Karhunen-Loeve transform (KLT) by reducing to the KLT on Gaussian processes and by automatically exploiting non-Gaussian statistics to significantly improve over the KLT on more general processes. We provide an algebraic optimization framework that generates optimized designs for any desired transform structure (multiresolution, block, lapped, and so on) with significantly better n -term approximation performance. For each structure, we propose a new prototype codec and test over a database of images. Simulation results show consistent increase in compression and approximation performance compared with conventional methods.