Prevalence of extended-spectrum β-lactamase (ESBL) and molecular detection of blaTEM, blaSHV and blaCTX-M genotypes among Enterobacteriaceae isolates from patients in Khartoum, Sudan.

来自 PUBMED

作者:

Dirar MHBilal NEIbrahim MEHamid ME

展开

摘要:

the emergence of antibiotic resistance pathogens is an important health risk. Usually Gram negative bacteria acquire resistance to beta-lactam antibiotics by beta-lactamase production. The objectives of this study was to assess the prevalence of ESBL and to detect the frequency of blaTEM, blaSHV and blaCTX-M genotypes among ESBL producing Enterobacteriaceae isolates from patients in Khartoum, Sudan. a total of 171 isolates of Enterobacteriaceae were recovered from hospitals in Khartoum, Sudan (2014 -2015) were used to detect ESBL production using disc diffusion method. blaTEM, blaSHV and blaCTX-M genes were investigated by PCR based methods using gene-specific primers. the high resistance among Enterobacteriaceae was noticed in ciprofloxacin (72%) and ofloxacin (73%). ESBL production was mainly in Escherichia Coli (38%) and Klebsiella pneumonia (34%). Prevalent genotypes were blaTEM (86%), blaCTX-M (78%) and blaSHV (28%). These were found mainly in Escherichia Coli (38%, 37%, 2%) and K. pneumonia (34%, 31%, 26.1%). The majority of ESBL producing isolates possess more than one ESBL genes. the ESBL production in Enterobacteriaceae was high, with blaTEM and blaCTX-M genotypes more prevalent. Public health and laboratory standard of excellence is needed to reducing the spread of resistant pathogens.

收起

展开

DOI:

10.11604/pamj.2020.37.213.24988

被引量:

22

年份:

1970

SCI-Hub (全网免费下载) 发表链接

通过 文献互助 平台发起求助,成功后即可免费获取论文全文。

查看求助

求助方法1:

知识发现用户

每天可免费求助50篇

求助

求助方法1:

关注微信公众号

每天可免费求助2篇

求助方法2:

求助需要支付5个财富值

您现在财富值不足

您可以通过 应助全文 获取财富值

求助方法2:

完成求助需要支付5财富值

您目前有 1000 财富值

求助

我们已与文献出版商建立了直接购买合作。

你可以通过身份认证进行实名认证,认证成功后本次下载的费用将由您所在的图书馆支付

您可以直接购买此文献,1~5分钟即可下载全文,部分资源由于网络原因可能需要更长时间,请您耐心等待哦~

身份认证 全文购买

相似文献(379)

参考文献(22)

引证文献(22)

来源期刊

-

影响因子:暂无数据

JCR分区: 暂无

中科院分区:暂无

研究点推荐

关于我们

zlive学术集成海量学术资源,融合人工智能、深度学习、大数据分析等技术,为科研工作者提供全面快捷的学术服务。在这里我们不忘初心,砥砺前行。

友情链接

联系我们

合作与服务

©2024 zlive学术声明使用前必读