Abstract
Glioblastoma multiforme(GBM)is the most common malignant primary brain tumor in adults.The pre-cise identification and distinction of GBM heterogeneity from surrounding brain parenchyma at the cel-lular level and even at the tissue level are important for GBM therapy.In this study,GBM cells are distin-guished from normal astrocytes and non-central nervous system(CNS)tumor cells by surface-enhanced Raman scattering(SERS)based on gold nanoshell(SiO2@Au)particles and support vector machine(SVM)algorithm.In addition,the gold nanoisland(AuNI)SERS substrates are further developed and explored for accurate detection of GBM at the tissue level.The distinction between glioma and trauma tissues,identification of different tumor grades,and IDH mutation are realized with the assistance of orthogonal partial least squares discriminant analysis(OPLS-DA)in a rapid,non-invasive,and convenient method.The results show that the developed SERS-based analytical method has the potential for practical appli-cation for the detection of GBM at the single-cell and tissue levels and even for real-time intraoperative diagnosis.
基金项目
Key Research and Development Program:Social Development Project(BE2021653)
江苏省自然科学基金(BK20201172)
Key Program of Health Commission of Jiangsu Province(ZBD2020016)
浙江省自然科学基金(LR19H180001)
维普
万方数据