Development of an AI-derived, non-invasive, label-free 3D-printed microfluidic SERS biosensor platform utilizing Cu@Ag/carbon nanofibers for the detection of salivary biomarkers in mass screening of oral cancer

• Published in: Journal of materials chemistry. B, Materials for biology and medicine Vol. 13; no. 1; pp. 345 - 3419
• Main Authors: Sunil, Navami, Unnathpadi, Rajesh, Seenivasagam, Rajkumar Kottayasamy, Abhijith, T, Latha, R, Sheen, Shina, Pullithadathil, Biji
• Format: Journal Article
• Language: English
• Published: England Royal Society of Chemistry 05.03.2025
• Subjects: Algorithms; Amino acids; Biomarkers; Biomarkers, Tumor - analysis; Biosensing Techniques; Biosensors; Cancer; Carbon; Carbon - chemistry; Carbon fibers; Classification; Copper; Copper - chemistry; Core-shell particles; Finite difference time domain method; Free surfaces; Humans; Invasiveness; Labels; Mass Screening; Medical screening; Metal Nanoparticles - chemistry; Microfluidics; Mouth Neoplasms - diagnosis; Nanofibers; Nanofibers - chemistry; Nanoparticles; Oral cancer; Particle Size; Principal components analysis; Printing, Three-Dimensional; Raman spectroscopy; Rhodamine 6G; Saliva; Saliva - chemistry; Silver; Silver - chemistry; Spectrum Analysis, Raman; Surface Properties; Thiocyanates; Three dimensional printing
• ISSN: 2050-750X; 2050-7518; 2050-7518
• DOI: 10.1039/d4tb02766c

Developing a non-invasive and reliable tool for the highly sensitive detection of oral cancer is essential for its mass screening and early diagnosis, and improving treatment efficacy. Herein, we utilized a label-free surface enhanced Raman spectroscopy (SERS)-based biosensor composed of Cu@Ag core-shell nanoparticle anchored carbon nanofibers (Cu@Ag/CNFs) for highly sensitive salivary biomarker detection in oral cancer mass screening. This SERS substrate provided a Raman signal enhancement of up to 10 7 and a detection limit as low as 10 −12 M for rhodamine 6G molecules. Finite-difference time-domain (FDTD) simulation studies on Cu@Ag/CNFs indicated an E -field intensity enhancement factor (| E | 2 /| E 0 | 2 ) of 250 at the plasmonic hotspot induced between two adjacent Cu@Ag nanoparticles. The interaction of this strong E -field along with the chemical enhancement effects was responsible for such huge enhancement in the Raman signals. To realize the real capability of the developed biosensor in practical scenarios, it was further utilized for the detection of oral cancer biomarkers such as nitrate, nitrite, thiocyanate, proteins, and amino acids with a micro-molar concentration in saliva samples. The integration of SERS substrates with a 3D-printed 12-channel microfluidic platform significantly enhanced the reproducibility and statistical robustness of the analytical process. Moreover, AI-driven techniques were employed to improve the diagnostic accuracy in differentiating the salivary profiles of oral cancer patients ( n 1 = 56) from those of healthy controls ( n 2 = 60). Principal component analysis (PCA) was utilized for dimensionality reduction, followed by classification using a random forest (RF) algorithm, yielding a robust classification accuracy of 87.5%, with a specificity of 92% and sensitivity of 88%. These experimental and theoretical findings emphasize the real-world functionality of the present non-invasive diagnostic tool in paving the way for more accurate and early-stage detection of oral cancer in clinical settings. An AI-driven non-invasive and label-free 3D-printed SERS biosensor based on Cu@Ag integrated carbon nanofibers for detection of salivary biomarkers has been developed for mass screening, early diagnosis, and improving treatment efficacy of oral cancer.