The process involved isolating E. faecium from the gut of honeybees, screening the bacterium for bacteriocin-like inhibitory substance (BLIS), evaluating its impact on the expression of the mexA gene in multidrug-resistant (MDR) P. aeruginosa, and determining the role of bacteriocin in treating infected wounds in mice through histopathological examination. After evaluating the best circumstances for producing BLIS, it was discovered that glucose was a superior carbon source and yeast extract was the best source of nitrogen. The pH was found to be 5, the ideal incubation time was 72 hours, and ammonium sulfate salt was used for partial purification at 80% saturation. The identification of MDR P. aeruginosa isolates from pus infections was a further focus of the study. The VITEK 2 system was used to perform the identification. The results of antibiotic susceptibility tests revealed that the greatest resistance rates were found against Meropenem (83.3%) and Gentamicin (73.3%), followed by beta-lactam antibiotics (Ticarcillin, Ticarcillin/Clavulanic Acid, Piperacillin, and Aztreonam), which showed resistance in about 66.6 and 36.6% of the study isolates, respectively. Followed by Imipenem (63.3%), Ceftazidime (36.6%), and Cefepime (36.6%). The mexA gene was detected in all nine strains. The study also investigated the impact of the bacteriocin of the chosen strain on the expression of the mexA gene. An in vivo study revealed that wound healing was enhanced by treating infected wounds with E. faecium bacteriocin. Conclusion: Down-regulation and up-regulation in the expression of the genes following exposure to Bacteriocin indicate the potential of E. faecium as an effective antimicrobial agent against MDR P. aeruginosa infections.
The goal of the research is to introduce new types of maps called semi totally Bc-continuous map and totally Bc-continuous map furthermore, study its properties. Additionally, we study the relationship of these functions and other known mappings are discussed.
Degradation is one of the key processes governing the impact of pharmaceuticals in the aquatic environment. Most studies on the degradation of pharmaceuticals have focused on soil and sludge, with fewer exploring persistence in aquatic sediments. We investigated the dissipation of 6 pharmaceuticals from different therapeutic classes in a range of sediment types. Dissipation of each pharmaceutical was found to follow first‐order exponential decay. Half‐lives in the sediments ranged from 9.5 (atenolol) to 78.8 (amitriptyline) d. Under sterile conditions, the persistence of pharmaceuticals was considerably longer. Stepwise multiple linear regression analysis was performed to
In vivo study revealed that ZnO nanoparticles treatment of Streptococcus SPP contaminated injured skin showed good prognosis and good healing process include complete regeneration of the epithelial cells of the epidermis and increase of cellulartiy of the dermal content compared with untreated group. In conclusion, treatment of S. pyogenes infected skin with Zinc oxide nanoparticles concentration (2 mg/ml) limit the skin damage and localized the lesion to the incision site with good healing process
The purpose of this paper is to statistically classify and categorize Building Information Modelling (BIM)-Facility Management (FM) publications in order to extract useful information related to the adoption and use of BIM in FM.
This study employs a quantitative approach using science mapping techniques to examine BIM-FM publications using Web of Science (WOS) database for the period between 2000 and April 2018.
The findi
In the current paper, the effect of fear in three species Beddington–DeAngelis food chain model is investigated. A three species food chain model incorporating Beddington-DeAngelis functional response is proposed, where the growth rate in the first and second level decreases due to existence of predator in the upper level. The existence, uniqueness and boundedness of the solution of the model are studied. All the possible equilibrium points are determined. The local as well as global stability of the system are investigated. The persistence conditions of the system are established. The local bifurcation analysis of the system is carried out. Finally, numerical simulations are used t
Over the last period, social media achieved a widespread use worldwide where the statistics indicate that more than three billion people are on social media, leading to large quantities of data online. To analyze these large quantities of data, a special classification method known as sentiment analysis, is used. This paper presents a new sentiment analysis system based on machine learning techniques, which aims to create a process to extract the polarity from social media texts. By using machine learning techniques, sentiment analysis achieved a great success around the world. This paper investigates this topic and proposes a sentiment analysis system built on Bayesian Rough Decision Tree (BRDT) algorithm. The experimental results show
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