Label-free SERS ultrasensitive and universal detection of low back pain fingerprint based on SERS substrate.

Abstract

Low back pain (LBP) seriously endangers human health and quality of life, and the detection of thoracolumbar fasciitis (TLF) is vital for the prevention and treatment of LBP. Surface-enhanced Raman scattering (SERS) is considered as a powerful technique for fingerprint detection due to the inherent richness of the spectral data. In this work, a novel SERS strategy based on a three-dimensional substrate was developed for fingerprint analysis for early diagnosis of TLF. A rat TLF model was established and the model was evaluated from the immunological and behavioral perspectives. Vibrational fingerprints were obtained by SERS testing of isolated fascial tissue and were used to explore the material changes during fasciitis. SERS spectra were analyzed using principal component analysis (PCA) that allowed unambiguous distinction and monitoring of component changes during TLF. Furthermore, in order to further clarify the occurrence and development of TLF, we combined clinical samples for analysis, and investigated the inflammatory factor expression levels of CRP and SAA in TLF. Our results demonstrated that tryptophan, phenylalanine and glycogen could unambiguously distinguish TLF as confirmed by SERS analysis, a method that is capable of noninvasive characterization of and diagnosis of TLF during LBP. We have provided a new tool that may promote in-depth study of the mechanism and treatment of fasciitis.