Antibiotic resistance increment is a major problem for the human society nowadays which encourages the efforts to look for new therapeutic alternatives from natural defenses. Synergistic antibacterial activity of epidermin and staphylolysin LasA A against Staphylococcus aureus (Staph aureus), Escherichia coli (E. coli) and Pseudomonas aeruginosa (Ps. aeruginosa) was evaluated. The antibacterial activities of epidermin from Staphylococcus epidermidis (Staph epidermidis) and Staphylolysin (LasA) from Ps. aeruginosa using the agar well diffusion assay were evaluated, and then using the micro dilution method to evaluate the minimum inhibitory concentration (MIC) and the minimum bactericidal concentration (MBC). The checkerboard method and fractional inhibitory concentration (FIC) were used to evaluate the combination between epidermin and LasA toward targeted clinical isolates of Staph aureus, E. coli and Ps. aeruginosa. The results revealed a synergistic effect between epidermin and LasA on all clinical isolates growth. The highest MIC and MBC of epidermin were 36.04 µL/mL and 51.73 µL/mL against Staph aureus; meanwhile, the highest MIC and MBC of LasA were 44.38 µL/mL and 50 µL/mL against Staph aureus. The FICindex revealed synergistic interactions in combination of epidermin and LasA which recorded 0.286 for Staph aureus while for E. coli was 0.327 and for Ps. aeruginosa was 0.390 respectively showing a synergism effect. This study finds that combination of epidermin with LasA had inhibitory activity on the targeted clinical isolate growth, which can be useful for designing and developing alternative therapeutic strategies against pathogens causing wound and burn infections.
Feature selection (FS) constitutes a series of processes used to decide which relevant features/attributes to include and which irrelevant features to exclude for predictive modeling. It is a crucial task that aids machine learning classifiers in reducing error rates, computation time, overfitting, and improving classification accuracy. It has demonstrated its efficacy in myriads of domains, ranging from its use for text classification (TC), text mining, and image recognition. While there are many traditional FS methods, recent research efforts have been devoted to applying metaheuristic algorithms as FS techniques for the TC task. However, there are few literature reviews concerning TC. Therefore, a comprehensive overview was systematicall
... Show MoreThe evolution of the Internet of things (IoT) led to connect billions of heterogeneous physical devices together to improve the quality of human life by collecting data from their environment. However, there is a need to store huge data in big storage and high computational capabilities. Cloud computing can be used to store big data. The data of IoT devices is transferred using two types of protocols: Message Queuing Telemetry Transport (MQTT) and Hypertext Transfer Protocol (HTTP). This paper aims to make a high performance and more reliable system through efficient use of resources. Thus, load balancing in cloud computing is used to dynamically distribute the workload across nodes to avoid overloading any individual r
... Show MoreFlow-production systems whose pieces are connected in a row may not have maintenance scheduling procedures fixed because problems occur at different times (electricity plants, cement plants, water desalination plants). Contemporary software and artificial intelligence (AI) technologies are used to fulfill the research objectives by developing a predictive maintenance program. The data of the fifth thermal unit of the power station for the electricity of Al Dora/Baghdad are used in this study. Three stages of research were conducted. First, missing data without temporal sequences were processed. The data were filled using time series hour after hour and the times were filled as system working hours, making the volume of the data relativel
... Show MoreData scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for
This work includes the synthesis of new ester compounds containing two 1,3,4-oxadiazole rings, 15a-c and 16a-c. This was done over seven steps, starting with p-acetamido-phenol 1 and 2-mercaptobenzoimidazole 2. The structure of the products was determined using FT-IR, 1H NMR, and mass spectroscopy. The evaluation of the antimicrobial activities of some prepared compounds was achieved against four types of bacteria (two types of gram-positive bacteria; Staphylococcus aureus and Bacillus subtilis, and two types of gram-negative bacteria, Pseudomonas aeruginosa and E. Coli), as well as against one types of fungus (C. albino). The results show moderate activit against the study bacteria, and the theoretical analysis of the toxi
... Show Moreفي السنوات الأخيرة، أدى التقدم التكنولوجي في إنترنت الأشياء (IoT) وأجهزة الاستشعار الذكية إلى فتح اتجاهات جديدة وإعطاء حلول عملية في مختلف قطاعات الحياة. يتم التعرف على إنترنت الأشياء كتنولوجيا حديثة تربط بين مختلف انواع الشبكات. تم تحسين أنواع مختلفة من قطاعات الرعاية الصحية في المجال الطبي بناءً على هذه التكنولوجيا. أحد هذه القطاعات الهامة هو نظام مراقبة الصحة (HMS). تعتبر مراقبة المريض عن بعد لاسلكيًا وبت
... Show More