Characterization of bacteria causing acute otitis media using Raman microspectroscopy.
Characterization of bacteria causing acute otitis media using Raman microspectroscopy.
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DOI：
10.1039/c7ay00128b
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发表时间：
2017-03-28
期刊：
影响因子：
--
通讯作者：
Mahadevan-Jansen A
中科院分区：
文献类型：
--
作者：
Ayala OD;Wakeman CA;Pence IJ;O'Brien CM;Werkhaven JA;Skaar EP;Mahadevan-Jansen A
中耳炎(OM)是一种流行的疾病，是儿童就诊和开抗生素处方的最常见原因。目前诊断OM和区分两种主要类型OM的方法，急性中耳炎(AOM)和渗出性中耳炎(OME)，依赖于解释它们之间可能重叠的症状。由于AOM需要抗生素治疗，而OME不需要，因此临床上有必要区分AOM和OME，以确定是否需要抗生素治疗并指导未来的处方。我们使用光学光谱技术，拉曼光谱(RS)，在体外鉴定和表征了引起AOM的三种主要病原体的生化特征。使用雷尼绍Invia共焦拉曼显微镜在785 nm处对流感嗜血杆菌、卡他莫拉氏菌和肺炎链球菌的拉曼光谱进行了研究。生化特征或生物标志物对每种细菌的分类都很重要，识别准确率达到97%。为了在临床样本中测试基于拉曼的细菌分类的有效性，收集了复发性中耳积液患者的中耳积液，进行了培养，并使用RS进行了测量。从临床MEE样本中确定引起AOM的三种主要细菌中每一种细菌参与的可能性。这些结果表明，利用RS来帮助准确诊断AOM并为医生提供细菌鉴定以指导治疗是有潜力的。
Otitis media (OM) is a prevalent disease that is the most frequent cause of physician visits and prescription of antibiotics for children. Current methods to diagnose OM and differentiate between the two main types of OM, acute otitis media (AOM) and otitis media with effusion (OME), rely on interpreting symptoms that may overlap between them. Since AOM requires antibiotic treatment and OME does not, there is a clinical need to distinguish between AOM and OME to determine whether antibiotic treatment is necessary and guide future prescriptions. We used an optical spectroscopy technique, Raman spectroscopy (RS), to identify and characterize the biochemical features of the three main pathogens that cause AOM in vitro. A Renishaw inVia confocal Raman microscope at 785 nm was used to spectrally investigate the Raman signatures of Haemophilus influenzae, Moraxella catarrhalis, and Streptococcus pneumoniae. Biochemical features or biomarkers important for classification of each bacterial species were identified and yielded a 97% accuracy of discrimination. To test the effectiveness of Raman-based bacterial classification in a clinical sample, human middle ear effusion (MEE) from patients affected by recurrent AOM was collected, cultured, and measured using RS. The probability of bacterial involvement from each of the three main bacteria that cause AOM was determined from the clinical MEE samples. These results suggest the potential of utilizing RS to aid in accurately diagnosing AOM and providing physicians with bacterial identification to guide treatment.
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