Ceramic Physics Laboratory, Kyoto Institute of Technology, Sakyo-ku, Matsugasaki, Kyoto 606-8585, Japan.
Department of Immunology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kamigyo-ku, 465 Kajii-cho, Kyoto 602-8566, Japan.
Int J Mol Sci. 2022 May 11;23(10):5359. doi: 10.3390/ijms23105359.
Oral candidiasis, a common opportunistic infection of the oral cavity, is mainly caused by the following four species (in decreasing incidence rate): , , , and . This study offers in-depth Raman spectroscopy analyses of these species and proposes procedures for an accurate and rapid identification of oral yeast species. We first obtained average spectra for different species and systematically analyzed them in order to decode structural differences among species at the molecular scale. Then, we searched for a statistical validation through a chemometric method based on principal component analysis (PCA). This method was found only partially capable to mechanistically distinguish among species. We thus proposed a new Raman barcoding approach based on an algorithm that converts spectrally deconvoluted Raman sub-bands into barcodes. Barcode-assisted Raman analyses could enable on-site identification in nearly real-time, thus implementing preventive oral control, enabling prompt selection of the most effective drug, and increasing the probability to interrupt disease transmission.
口腔念珠菌病是一种常见的口腔机会性感染，主要由以下四种（发病率依次降低）引起： 、 、 和 。本研究对这些物种进行了深入的拉曼光谱分析，并提出了一种准确快速鉴定口腔酵母物种的方法。我们首先获得了不同 物种的平均光谱，并对其进行了系统分析，以便从分子尺度上解码物种间的结构差异。然后，我们通过基于主成分分析（PCA）的化学计量学方法寻找统计验证。发现该方法仅部分能够从机制上区分 物种。因此，我们提出了一种新的基于算法的拉曼条形码方法，该算法将光谱解卷积的拉曼子带转换为条形码。条形码辅助的拉曼分析可以实现近实时的现场鉴定，从而实现预防性口腔控制，能够迅速选择最有效的药物，并增加中断疾病传播的概率。
