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Updated: May 7, 2026
Exploring the Application of Surface-enhanced Raman Scattering-based Biosensing of Individual sEVs in Disease Diagnosis and Therapeutics
Published on: March 13, 2026
Protocol for cerebrospinal fluid analysis using enrichment-enhanced surface-enhanced Raman spectroscopy and
Zhaoyi Li1, Dongjie Zhang2, Zhaoyang Cheng1
1Center for Biomedical-photonics and Molecular Imaging, Advanced Diagnostic-Therapy Technology and Equipment Key Laboratory of Higher Education Institutions in Shaanxi Province, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China; Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education & Xi'an Key Laboratory of Intelligent Sensing and Regulation of Trans-Scale Life Information, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China.
This study introduces a new method for rapid detection and classification of acute leukemia using deep learning with surface-enhanced Raman spectroscopy (DL-SERS). This approach offers highly sensitive and efficient identification of patients with acute leukemia.
Area of Science:
- Biomedical Engineering
- Analytical Chemistry
- Computational Biology
Background:
- Accurate detection of cerebrospinal fluid (CSF) biomarkers is critical for diagnosing acute leukemia.
- Current diagnostic methods can be time-consuming and require specialized expertise.
- There is a need for rapid, sensitive, and precise analytical techniques for CSF analysis in leukemia detection.
Purpose of the Study:
- To develop and present a novel protocol for the detection and classification of acute leukemia using cerebrospinal fluid.
- To integrate deep learning with enrichment-enhanced surface-enhanced Raman spectroscopy (DL-SERS) for enhanced sensitivity and specificity.
- To utilize transformer-enabled spectral classification for precise identification of acute leukemia patients.
Main Methods:
- Preparation of SERS-active materials and cerebrospinal fluid samples.
- Acquisition of SERS spectral data using advanced spectroscopy techniques.
- Application of deep learning algorithms, specifically transformer models, for spectral data analysis and classification.
Main Results:
- The developed DL-SERS protocol demonstrates high sensitivity and efficiency in identifying acute leukemia.
- Transformer-enabled spectral classification achieves precise categorization of patients.
- The method facilitates rapid analysis of cerebrospinal fluid for clinical decision-making.
Conclusions:
- The presented DL-SERS protocol offers a powerful tool for the rapid and accurate diagnosis of acute leukemia.
- This integrated approach combines advanced spectroscopy with sophisticated deep learning for improved clinical outcomes.
- The protocol holds significant potential for routine clinical application in acute leukemia diagnostics.
