Unveiling biochemical profiles of peri-implant crevicular fluid using SERS spectroscopy

The exact identification and categorization of peri-implant diseases without the need for invasive procedures is critical for the successful clinical treatment and long-term durability of dental implants. Analysing peri-implant crevicular fluid (PICF) samples using a combination of surface-enhanced Raman scattering (SERS) spectroscopy and advanced chemometrics is a new emerging approach aimed to provide an unbiased assessment of implant health. SERS is a spectroscopic technique that offers several advantages over traditional bioanalysis for analysing biological samples ranging from in vitro cell culture models to ex vivo tissues and biofluids. SERS datasets obtained from biofluids provide a wealth of metabolic fingerprint information, however access to this information is not always straightforward. Bioanalytical SERS is a complex field requiring a complete understanding of the chemical and physical interactions between photons, nanomaterials, and biological systems. This communication will present results from a recent clinical study. A thorough investigation was conducted on PICF samples collected from a cohort of patients displaying varying degrees of peri-implant health, including implants without infection, implants affected by peri-implantitis, and implants with peri-implant mucositis. The canonical-powered partial least squares (CPPLS) method was used to analyse the acquired SERS spectra and determine the distinct biochemical features linked to each inflammatory state. Importantly, peri-implant mucositis and peri-implantitis both exhibit a comparable inflammatory SERS spectral pattern. Further, a linear discriminant analysis (LDA) classifier was used to combine the SERS-based values acquired from CPPLS with established clinical scores. The method's ability to distinguish between various implant conditions was validated using repeated double cross- validation. The integrated method showed great promise as a non-invasive diagnostic tool for early diagnosis of inflammatory diseases and real-time implant monitoring, thanks to its high sensitivity and specificity and overall balanced accuracy of 92%.