Rapid Identification of Candida auris by Raman Spectroscopy Combined With Deep Learning

IF 2.4 3区 化学 Q2 SPECTROSCOPY
S. Kiran Koya, Michelle A. Brusatori, Sally Yurgelevic, Changhe Huang, Jake DeMeulemeester, Danielle Percefull, Hossein Salimnia, Gregory W. Auner
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Abstract

Candida auris is a multidrug-resistant yeast that can lead to outbreaks in healthcare facilities, even with strict infection prevention and control measures. Candida auris detection is challenging using standard laboratory methods. Advancements in identification methods, such as matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and polymerase chain reaction, have improved detection, though these methodologies can be costly and impractical in resource-limited settings. This study presents a practical, portable, and reagentless platform known as Counter-Propagating Gaussian Beam Raman Spectroscopy (CPGB-RS), integrated with deep learning spectral analysis for the rapid and accurate identification of C. auris. This method has shown a sensitivity of 96% and a specificity of 99% in differentiating C. auris from other highly prevalent pathogenic species, such as Candida albicans, Candida glabrata, and Candida tropicalis. The differentiation between species is based on unique variations in their Raman spectra, influenced by differences in cell wall composition (including β-glucan, chitin, and mannoprotein), cell membrane components (like ergosterol), and cellular energy states (mitochondrial cytochromes b and c). This platform allows for automated molecular screening, generating diagnostic results within 2 min, making it highly practical for clinical applications. Furthermore, this technology has the potential to evaluate the effectiveness of antifungal agents, which could significantly improve patient outcomes.

Abstract Image

结合深度学习的拉曼光谱快速鉴定耳念珠菌
耳念珠菌是一种多重耐药酵母菌,即使采取严格的感染预防和控制措施,也可能导致卫生保健机构爆发疫情。使用标准的实验室方法检测耳念珠菌具有挑战性。鉴定方法的进步,如基质辅助激光解吸/电离飞行时间质谱法和聚合酶链反应,已经改善了检测,尽管这些方法在资源有限的情况下可能成本高昂且不切实际。本研究提出了一种实用、便携、无试剂的反传播高斯束拉曼光谱(CPGB-RS)平台,该平台与深度学习光谱分析相结合,可快速准确地鉴定金黄色葡萄球菌。该方法在鉴别金黄色念珠菌与白色念珠菌、光秃念珠菌和热带念珠菌等其他高度流行的病原菌时,灵敏度为96%,特异性为99%。物种之间的区分基于其拉曼光谱的独特变化,受细胞壁组成(包括β-葡聚糖,几丁质和甘糖蛋白),细胞膜成分(如麦角甾醇)和细胞能量状态(线粒体细胞色素b和c)差异的影响。该平台允许自动分子筛选,在2分钟内生成诊断结果,使其在临床应用中非常实用。此外,这项技术有潜力评估抗真菌药物的有效性,这可以显著改善患者的预后。
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来源期刊
CiteScore
5.40
自引率
8.00%
发文量
185
审稿时长
3.0 months
期刊介绍: The Journal of Raman Spectroscopy is an international journal dedicated to the publication of original research at the cutting edge of all areas of science and technology related to Raman spectroscopy. The journal seeks to be the central forum for documenting the evolution of the broadly-defined field of Raman spectroscopy that includes an increasing number of rapidly developing techniques and an ever-widening array of interdisciplinary applications. Such topics include time-resolved, coherent and non-linear Raman spectroscopies, nanostructure-based surface-enhanced and tip-enhanced Raman spectroscopies of molecules, resonance Raman to investigate the structure-function relationships and dynamics of biological molecules, linear and nonlinear Raman imaging and microscopy, biomedical applications of Raman, theoretical formalism and advances in quantum computational methodology of all forms of Raman scattering, Raman spectroscopy in archaeology and art, advances in remote Raman sensing and industrial applications, and Raman optical activity of all classes of chiral molecules.
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