Salmonella enterica serovar Typhimurium (S. Typhimurium) is a highly adaptive pathogenic bacteria with a serious public health concern due to its increasing resistance to antibiotics. Therefore, identification of novel drug targets for S. Typhimurium is crucial. Here, we first created a pathogen-host integrated genome-scale metabolic network by combining the metabolic models of human and S. Typhimurium, which we further tailored to the pathogenic state by the integration of dual transcriptome data. The integrated metabolic model enabled simultaneous investigation of metabolic alterations in human cells and S. Typhimurium during infection. Then, we used the tailored pathogen-host integrated genome-scale metabolic network to predict essential genes in the pathogen, which are candidate novel drug targets to inhibit infection. Drug target prioritization procedure was applied to these targets, and pabB was chosen as a putative drug target. It has an essential role in 4-aminobenzoic acid (PABA) synthesis, which is an essential biomolecule for many pathogens. A structure based virtual screening was applied through docking simulations to predict candidate compounds that eliminate S. Typhimurium infection by inhibiting pabB. To our knowledge, this is the first comprehensive study for predicting drug targets and drug like molecules by using pathogen-host integrated genome-scale models, dual RNA-seq data and structure-based virtual screening protocols. This framework will be useful in proposing novel drug targets and drugs for antibiotic-resistant pathogens.

Now a days the obtainability of smart Phones, cameras and sensors are highly increased and becomes the important part of our daily life. Due to the usage of these devices huge data is produced and is placed on local platform. Local platforms are not able to perform exhaustive calculations. Cloud services are used for storing huge data that is produced from mobiles, sensors and cameras.Advances in machine learning and computer vision provide huge cloud services with ability of content analysis and many other facilities. But suffers from unwanted privacy risks to users or individuals. In this paper our major focusing point are the privacy preserving techniques we proposed a hybrid framework also feature extractor and classification approaches for machine learning. Noise addition feature is also use to enhance security. Our proposed solution reduced the privacy ricks.

Background: Clostridioides difficile (CD) is a multi-drug resistant, enteric pathogenic bacterium. The CD associated infections are the leading cause of nosocomial diarrhea that can further lead to pseudomembranous colitis up to a toxic mega-colon or sepsis with greater mortality and morbidity risks. The CD infection possess higher rates of recurrence due to its greater resistance against antibiotics. Considering its higher rates of recurrence, it has become a major burden on the healthcare facilities. Therefore, there is a dire need to identify novel drug targets to combat with the antibiotic resistance of Clostridioides difficile. Objective: To identify and propose new and novel drug targets against the Clostridioides difficile. Methods: In the current study, a computational subtractive genomics approach was applied to obtain a set of potential drug targets that exists in the multi-drug resistant strain of Clostridioides difficile. Here, the uncharacterized proteins were studied as potential drug targets. The methodology involved several bioinformatics databases and tools. The druggable proteins sequences were retrieved based on non-homology with host proteome and essentiality for the survival of the pathogen. The uncharacterized proteins were functionally characterized using different computational tools and sub-cellular localization was also predicted. The metabolic pathways were analyzed using KEGG database. Eventually, the druggable proteome has been fetched using sequence similarity with the already available drug targets present in DrugBank database. These druggable proteins were further explored for the structural details to identify drug candidates. Results : A priority list of potential drug targets was provided with the help of the applied method on complete proteome set of the C. difficile. Moreover, the drug like compounds have been screened against the potential drug targets to prioritize potential drug candidates. To facilitate the need for drug targets and therapies, the study proposed five potential protein drug targets out of which three proposed drug targets were subjected to homology modeling to explore their structural and functional activities. Conclusion: In conclusion, we proposed three unique, unexplored drug targets against C. difficile. The structure-based methods were applied and resulted in a list of top scoring compounds as potential inhibitors to proposed drug targets.