Abstract
Introduction: Pseudomonas aeruginosa is an opportunistic pathogen leading to severe, acute and chronic infections in burn patients. In the process of wound healing, a significant delay in the closure of excisional wound sites is due to the bacterial skin infection, so anti-infection is important in wound healing.
Objective: Abuse of antibiotics leads to the development of multi drug resistance of P. aeruginosa strains which requires a rapid intervention to find a solution focusing on new natural antibacterial pathways. Punica granatum (pomegranate) has emerged as a medicinal plant with potential antimicrobial activity. The present study was planned to evaluate this activity against P. aeruginosa collected from burn wound cultures.
Methods: Aqueous extract of sweet pomegranate peels from Safita, a mountainous region west of Syria was prepared. Minimum inhibitory concentration (MIC) and antivirulence efficacy were determined for the -prepared extract.
Results: MIC was 1.4 mg/ml and bacterial gelatinase and lecithinase activities were reduced by 40.28±2.35% and 53.84±4.89%, respectively.
Conclusion: The extracts from pomegranate fruit peel possess strong antimicrobial and antivirulence activities against P. aeruginosa. Therefore, this plant could be an important source of new antimicrobial compounds.
Keywords: Antibacterial, gelatinase, lecithinase, MIC, pomegranate peels, pseudomonas aeruginosa.
Graphical Abstract
Related Books
Aromatherapy: The Science of Essential Oils
In Vitro Propagation and Secondary Metabolite Production from Medicinal Plants: Current Trends (Part 2)
Micropropagation of Medicinal Plants
Software and Programming Tools in Pharmaceutical Research
Biotechnology and Drug Development for Targeting Human Diseases
In Vitro Propagation and Secondary Metabolite Production from Medicinal Plants: Current Trends (Part 1)
Molecular and Physiological Insights into Plant Stress Tolerance and Applications in Agriculture- Part 2
Micropropagation of Medicinal Plants
Genome Editing in Bacteria (Part 1)
Data Science for Agricultural Innovation and Productivity
18
3