Nanomaterials and clinical SERS technology: broad applications in disease diagnosis.

J Mater Chem B

Ningbo Key Laboratory of Biomedical Imaging Probe Materials and Technology, Laboratory of Advanced Theranostic Materials and Technology, Chinese Academy of Sciences (CAS) Key Laboratory of Magnetic Materials and Devices, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China.

Published: January 2025

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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Source
http://dx.doi.org/10.1039/d4tb02525cDOI Listing

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