Nanomaterials and clinical SERS technology: broad applications in disease diagnosis

纳米技术 风险分析(工程) 纳米材料 疾病 医疗保健 重症监护医学 材料科学 系统工程 医学 工程类 病理 政治学 法学
作者
Ifeanyichukwu Michael Onyemaobi,Yujiao Xie,J.Y. Zhang,Lei Xu,Lingchao Xiang,Jie Lin,Aiguo Wu
出处
期刊:Journal of Materials Chemistry B [Royal Society of Chemistry]
卷期号:13 (9): 2890-2911 被引量:19
标识
DOI:10.1039/d4tb02525c
摘要

The critical need for rapid cancer diagnosis and related illnesses is growing alongside the current healthcare challenges, unfavorable prognosis, and constraints in diagnostic timing. As a result, emphasis on surface-enhanced Raman spectroscopy (SERS) diagnostic methods, including both label-free and labelled approaches, holds significant promise in fields such as analytical chemistry, biomedical science, and physics, due to the user-friendly nature of SERS. Over time, the SERS detection sensitivity and specificity with nanostructured materials for SERS applications (NMs-SERS) in different media have been remarkable. An investigation into electronic dynamics and interactions has revealed a seemingly fair result regarding the complementary effects of electromagnetic (EM) and chemical enhancements (CM), underscoring the operational principles of SERS. Nevertheless, the focus on translational SERS applications, especially beyond preliminary proof-of-concept research, remains limited. This review focuses on the advancements made in clinical SERS diagnostics and the essential role of NMs-SERS, ranging from plasmonic to non-plasmonic materials and other related advancements. Furthermore, it outlines the significant achievements of biomedical SERS in tumor diagnosis, particularly in identifying circulating tumor cells (CTCs), alongside a clear focus on NMs-SERS characteristics such as surface charge, shape, size, detection sensitivity, specificity, signal reproducibility, and recyclability. Finally, it underscores the use of microfluidic chips within the labelled SERS strategy for isolating CTCs, the concept of Ramanomics, and the integration of artificial intelligence (AI) to strengthen SERS data analysis. We hope that this review will help guide and expedite the potential for precise SERS diagnosis of key chronic diseases.
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