3D Superclusters with Hybrid Bioinks for Early Detection in Breast Cancer.

Area of Science:

• Nanotechnology
• Biomedical Engineering
• Analytical Chemistry

Background:

• Surface-enhanced Raman scattering (SERS) offers high sensitivity for disease diagnosis.
• Ideal SERS platforms require simplicity, low analyte consumption, and uniformity for practical applications.
• Integrating machine learning with SERS enhances classification of molecular fingerprints in biological samples.

Purpose of the Study:

• To develop a flexible, simple, three-dimensional (3D) plasmonic cluster SERS platform for early breast cancer detection.
• To integrate this platform with a deep learning algorithm for enhanced diagnostic accuracy.
• To assess the platform's performance using patient plasma samples.

Main Methods:

• A bottom-up strategy was employed to construct a 3D plasmonic cluster (3D-PC) SERS platform.
• The platform's sensitivity was evaluated using p-nitrophenol (PNP) with a detection limit down to 10⁻⁶ M.
• Patient plasma from cancer and healthy subjects was used to create bioink for fabricating 3D-PC structures.

Main Results:

• The 3D-PC platform demonstrated significantly enhanced Raman intensity.
• A detection limit of 10⁻⁶ M (femtomole range) was achieved for PNP molecules.
• SERS data from patient plasma successfully classified cancer and healthy subjects with 93% accuracy.

Conclusions:

• The developed 3D plasmonic cluster SERS platform shows significant potential for early breast cancer detection.
• The integration with deep learning improves the classification accuracy of subtle molecular differences.
• This approach offers a promising avenue for developing practical, sensitive diagnostic tools.