Identification of virulence genes in Pseudomonas aeruginosa isolated from human and animal samples by multiplex-PCR and their antibiotic resistance pattern
Publish place: Iranian Journal of Medical Microbiology، Vol: 13، Issue: 4
Publish Year: 1398
نوع سند: مقاله ژورنالی
زبان: English
View: 191
This Paper With 8 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_IJMM-13-4_006
تاریخ نمایه سازی: 10 اسفند 1399
Abstract:
Background and Aims: A major problem in the treatment of Pseudomonas aeruginosa infections is the emergence of strains with multiple resistances (MDR). The aim of this study was to identify virulence genes lasB, toxA, algD, exos in P. aeruginosa isolated from human and animal using Multiplex-PCR method and determination of antibiotic resistance.
Materials and Methods: This cross-sectional study was performed on the 120 non-repetitive samples, including, 60 clinical samples of human and 60 animal samples collected from Tehran, Iran. Antibiotic susceptibility test was performed by disk diffusion test. The multiplex - PCR method was performed to identify various virulence genes.
Results & Conclusion: The highest resistance rate was related to amoxicillin and amikacin in the both types of samples. lasB and exoA genes were the most prevalent virulence determinants in the human and animals samples, respectively. The results of antibiotic susceptibility test showed that the resistance in human strains was far higher than animal and this could be the result of arbitrary administration of the drug. The lasB gene in human specimens and the exoA gene in animals play an important role in the development of the disease.
Keywords:
Authors
Khatereh Mokhtari
MSc in Microbiology, Faculty of Sciences, Saveh Branch, Islamic Azad University, Saveh, Iran
Kumarss Amini
Associate Professor, Faculty of Life Sciences, Saveh Branch, Islamic Azad University, Saveh, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :