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Prospective Study of the Performance of Vibrational Spectroscopies for Rapid Identification of Bacterial and Fungal Pathogens Recovered from Blood Cultures

机译:振动光谱法用于快速鉴定从血液培养物中回收的细菌和真菌病原体性能的前瞻性研究

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摘要

Rapid identification of microbial pathogens reduces infection-related morbidity and mortality of hospitalized patients. Raman spectra and Fourier transform infrared (IR) spectra constitute highly specific spectroscopic fingerprints of microorganisms by which they can be identified. Little biomass is required, so that spectra of microcolonies can be obtained. A prospective clinical study was carried out in which the causative pathogens of bloodstream infections in hospitalized patients were identified. Reference libraries of Raman and IR spectra of bacterial and yeast pathogens highly prevalent in bloodstream infections were created. They were used to develop identification models based on linear discriminant analysis and artificial neural networks. These models were tested by carrying out vibrational spectroscopic identification in parallel with routine diagnostic phenotypic identification. Whereas routine identification has a typical turnaround time of 1 to 2 days, Raman and IR spectra of microcolonies were collected 6 to 8 h after microbial growth was detected by an automated blood culture system. One hundred fifteen samples were analyzed by Raman spectroscopy, of which 109 contained bacteria and 6 contained yeasts. One hundred twenty-one samples were analyzed by IR spectroscopy. Of these, 114 yielded bacteria and 7 were positive for yeasts. High identification accuracy was achieved in both the Raman (92.2%, 106 of 115) and IR (98.3%, 119 of 121) studies. Vibrational spectroscopic techniques enable simple, rapid, and accurate microbial identification. These advantages can be easily transferred to other applications in diagnostic microbiology, e.g., to accelerate identification of fastidious microorganisms.
机译:快速鉴定微生物病原体可降低与感染有关的发病率和住院患者的死亡率。拉曼光谱和傅立叶变换红外(IR)光谱构成了微生物的高特异性光谱指纹,可以通过它们进行鉴定。几乎不需要生物质,因此可以获得微菌落的光谱。进行了一项前瞻性临床研究,其中确定了住院患者血液感染的病原体。创建了在血液感染中高度流行的细菌和酵母菌病原体的拉曼光谱和红外光谱参考库。它们被用来开发基于线性判别分析和人工神经网络的识别模型。通过与常规诊断表型识别同时进行振动光谱识别来测试这些模型。常规鉴定通常需要1-2天的周转时间,而在通过自动血液培养系统检测到微生物生长后6至8 h收集了微菌落的拉曼光谱和IR光谱。用拉曼光谱法分析了115个样品,其中109个细菌和6个酵母。通过红外光谱分析了112个样品。在这些细菌中,有114种产生细菌,而7种是酵母阳性的。拉曼(92.2%,115的106)和红外(98.3%,121的119)研究均实现了较高的识别准确性。振动光谱技术可实现简单,快速和准确的微生物鉴定。这些优点可以很容易地转移到诊断微生物学的其他应用中,例如,以加速对难辨微生物的鉴定。

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