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Reliance on deep learning techniques has become an important trend in several science domains including biological science, due to its proven efficiency in manipulating big data that are often characterized by their non-linear processes and complicated relationships. In this study, Convolutional Neural Networks (CNN) has been recruited, as one of the deep learning techniques, to be used in classifying and predicting the biological activities of the essential oil-producing plant/s through their chemical compositions. The model is established based on the available chemical composition's information of a set of endemic Egyptian plants and their biological activities. Another type of machine learning algorithms, Multiclass Neural Network (MNN), has been applied on the same Essential Oils (EO) dataset. This aims to fairly evaluate the performance of the proposed CNN model. The recorded accuracy in the testing process for both CNN and MNN is 98.13% and 81.88%, respectively. Finally, the CNN technique has been adopted as a reliable model for classifying and predicting the bioactivities of the Egyptian EO-containing plants. The overall accuracy for the final prediction process is reported as approximately 97%. Hereby, the proposed deep learning model could be utilized as an efficient model in predicting the bioactivities of, at least Egyptian, EOs-producing plants. [ABSTRACT FROM AUTHOR]

The aim of this study was to assess the practices of health care workers during gasterointestinal endoscope reprocessing, evaluate their knowledge about reprocessing, and verify their compliance with laboratory and microbiological tests in endoscopy units at Zagazig University and Fayoum University hospitals. All nursing staff on duty from 10 endoscopy units, with 16 flexible endoscopes, were included. Knowledge and practice were assessed by a questionnaire and a checklist. The mean knowledge score was 7.5 (SD 1.9), which was poor. Compliance was 90% for disinfection and 74% for endoscope processing after disinfection. Before reuse after cleaning, no organisms were detected in 5 endoscopes, while 8 colony forming units were found in 2. Pseudomonas aeruginosa was the most common organism isolated. Strict implementation of the reprocessing guidelines are needed, especially the pre-cleaning stage and leak testing. Repeating high level disinfection after storage and before use must be followed.