Katoziyan Ahmadreza, Imani Fooladi Abbas Ali, Taheri Ramezan Ali, Vatanpour Saba
Center of Excellence in Phylogeny of Living Organisms, School of Biology, University of Tehran, Iran.
Applied Microbiology Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Iran J Pharm Res. 2020 Summer;19(3):321-328. doi: 10.22037/ijpr.2020.112966.14042.
The aim of the study was to evaluate the drug-resistance patterns of infections in Baqiyatallah hospital within 2010-2019 and to present a novel monitoring and detection system making use of molecular laboratory methods teamed with molecular delimitation analyses. This in turn is a primary step to establishment of a digital health system within Baqiyatallah hospital as a perfect pilot instance for other hospitals to follow upon. Totally, 100 patients of Baqiyatallah hospital suspicious of infections were sampled. Bacterial identity confirmations were done using routine biochemical test. Antibiograms were made for all the patients in this study. Consequently, bacterial total DNA was extracted and 16S rDNA gene amplified and sequenced for all patients. To uncover any cryptic strain grouping within the samples, a molecular delimitation method, i.e. automated barcode gap discovery (ABGD), was done. Our results showed Ceftaroline to be the most and Erythromycin and Oxacillin the least effective drugs. Delimitation uncovered 19 groups out of which group 19 seemed to have location-specific genetic signals in regards to susceptibility of Erythromycin and Oxacillin. Our results indicate the importance of genetic identification of bacteria with respect to their genetic patterns before antibiotic administration in order to both reduce unnecessary medicine use and to biomonitor the bacterial patterns in respect to their behavior towards general antibiotics.
本研究的目的是评估2010年至2019年期间巴基耶塔拉医院感染的耐药模式，并提出一种利用分子实验室方法与分子界定分析相结合的新型监测和检测系统。这反过来又是在巴基耶塔拉医院建立数字健康系统的第一步，作为其他医院效仿的完美试点案例。总共对巴基耶塔拉医院100名疑似感染的患者进行了采样。使用常规生化试验进行细菌鉴定确认。对本研究中的所有患者进行了抗菌谱分析。因此，提取了所有患者的细菌总DNA，并对16S rDNA基因进行了扩增和测序。为了揭示样本中任何潜在的菌株分组，采用了一种分子界定方法，即自动条形码间隙发现（ABGD）。我们的结果表明，头孢洛林是最有效的药物，而红霉素和苯唑西林是最无效的药物。界定分析发现了19个组，其中第19组在红霉素和苯唑西林敏感性方面似乎具有特定位置的遗传信号。我们的结果表明，在使用抗生素之前，根据细菌的遗传模式对其进行基因鉴定非常重要，这样既能减少不必要的药物使用，又能对细菌对一般抗生素的反应模式进行生物监测。
