Department of Biotechnology, Beijing Institute of Radiation Medicine, Beijing Key Laboratory of New Molecular Diagnosis Technologies for Infectious Diseases, Beijing, 100850, People's Republic of China.
Department of Laboratory Medicine, Xuzhou Tumor Hospital, Xuzhou, 221005, People's Republic of China.
Sci Rep. 2020 Jul 27;10(1):12480. doi: 10.1038/s41598-020-68978-0.
There are approximately 1 million cryptococcal infections per year among HIV+ individuals, resulting in nearly 625,000 deaths. Cryptococcus neoformans and Cryptococcus gattii are the two most common species that cause human cryptococcosis. These two species of Cryptococcus have differences in pathogenicity, diagnosis, and treatment. Cryptococcal infections are usually difficult to identify because of their slow growth in vitro. In addition, the long detection cycle of Cryptococcus in clinical specimens makes the diagnosis of Cryptococcal infections difficult. Here, we used positively charged silver nanoparticles (AgNPs) as a substrate to distinguish between C. neoformans and C. gattii in clinical specimens directly via surface-enhanced Raman scattering (SERS) and spectral analysis. The AgNPs self-assembled on the surface of the fungal cell wall via electrostatic aggregation, leading to enhanced SERS signals that were better than the standard substrate negatively charged silver nanoparticles (AgNPs). The SERS spectra could also be used as a sample database in the multivariate analysis via orthogonal partial least-squares discriminant analysis. This novel SERS detection method can clearly distinguish between the two Cryptococcus species using principal component analysis. The accuracy of the training data and test data was 100% after a tenfold crossover validation.
每年约有 100 万例 HIV 阳性个体感染隐球菌,导致近 62.5 万人死亡。新型隐球菌和格特隐球菌是引起人类隐球菌病的两种最常见的物种。这两种隐球菌在致病性、诊断和治疗方面存在差异。由于隐球菌在体外生长缓慢,通常难以识别。此外,临床标本中隐球菌的检测周期较长,使得隐球菌感染的诊断变得困难。在这里,我们使用带正电荷的银纳米粒子(AgNPs)作为基底,通过表面增强拉曼散射(SERS)和光谱分析直接在临床标本中区分新型隐球菌和格特隐球菌。AgNPs 通过静电聚集自组装在真菌细胞壁表面,导致增强的 SERS 信号优于标准带负电荷的银纳米粒子(AgNPs)基底。SERS 光谱也可以作为多元分析中的样本数据库,通过正交偏最小二乘判别分析进行分析。这种新型的 SERS 检测方法可以通过主成分分析清楚地区分两种隐球菌。经过十次交叉验证,训练数据和测试数据的准确率达到 100%。