Adamus-Białek Wioletta, Lechowicz Łukasz, Kubiak-Szeligowska Anna B, Wawszczak Monika, Kamińska Ewelina, Chrapek Magdalena
Institute of Medical Sciences, Jan Kochanowski University, IX Wieków Kielc 19A Av., 25-317, Kielce, Poland.
Department of Microbiology, Institute of Biology, Jan Kochanowski University, 15 Swietokrzyska St., 25-406, Kielce, Poland.
Mol Biol Rep. 2017 Feb;44(1):191-202. doi: 10.1007/s11033-017-4099-y. Epub 2017 Jan 13.
Bacterial drug resistance and uropathogenic tract infections are among the most important issues of current medicine. Uropathogenic Escherichia coli strains are the primary factor of this issue. This article is the continuation of the previous study, where we used Kohonen relations to predict the direction of drug resistance. The characterized collection of uropathogenic E. coli strains was used for microbiological (the disc diffusion method for antimicrobial susceptibility testing), chemical (ATR/FT-IR) and mathematical (artificial neural networks, Ward's hierarchical clustering method, the analysis of distributions of inhibition zone diameters for antibiotics, Cohen's kappa measure of agreement) analysis. This study presents other potential tools for the epidemiological differentiation of E. coli strains. It is noteworthy that ATR/FT-IR technique has turned out to be useful for the quick and simple identification of MDR strains. Also, diameter zones of resistance of this E. coli population were compared to the population of E. coli strains published by EUCAST. We observed the bacterial behaviors toward particular antibiotics in comparison to EUCAST bacterial collections. Additionally, we used Cohen's kappa to show which antibiotics from the same class are closely related to each other and which are not. The presented associations between antibiotics may be helpful in selecting the proper therapy directions. Here we present an adaptation of interdisciplinary studies of drug resistance of E. coli strains for epidemiological and clinical investigations. The obtained results may be some indication in deciding on antibiotic therapy.
细菌耐药性和尿路致病性感染是当前医学中最重要的问题之一。尿路致病性大肠杆菌菌株是这一问题的主要因素。本文是先前研究的延续,在先前研究中我们使用Kohonen关系来预测耐药性的方向。对具有特征的尿路致病性大肠杆菌菌株进行了微生物学(抗菌药物敏感性试验的纸片扩散法)、化学(衰减全反射傅里叶变换红外光谱法)和数学(人工神经网络、沃德层次聚类法、抗生素抑菌圈直径分布分析、科恩一致性系数)分析。本研究提出了用于大肠杆菌菌株流行病学区分的其他潜在工具。值得注意的是,衰减全反射傅里叶变换红外光谱技术已被证明可用于快速、简单地鉴定多重耐药菌株。此外,将该大肠杆菌群体的耐药直径区域与欧洲抗菌药物敏感性试验委员会(EUCAST)公布的大肠杆菌菌株群体进行了比较。与EUCAST细菌库相比,我们观察了细菌对特定抗生素的反应。此外,我们使用科恩一致性系数来表明同一类抗生素中哪些彼此密切相关,哪些不相关。所呈现的抗生素之间的关联可能有助于选择合适的治疗方向。在此,我们展示了针对大肠杆菌菌株耐药性的跨学科研究在流行病学和临床研究中的应用。所得结果可能为决定抗生素治疗提供一些参考。