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Ergothioneine (ERG) is a natural sulfur-containing amino acid found in many organisms, including humans. It accumulates at high concentrations in red blood cells and is distributed to various organs, including the brain. ERG has numerous health benefits and antioxidant capabilities, and it has been linked to various human physiological processes, such as anti-inflammatory, neuroprotective, and anti-aging effects. Accurate, rapid, and cost-effective quantification of ERG levels in human biofluids is crucial for understanding its role in oxidative stress-related diseases. Surface-enhanced Raman scattering (SERS) is an effective approach for measuring compounds at concentrations similar to those at which ERG is present in serum. However, while SERS has been used to characterize or detect ERG, quantification has not yet been achieved due to the variability in the signal enhancement that can arise during sample preparation and analysis. This study introduces a highly efficient and reliable technique for quickly (20 min is typical per sample) measuring ERG levels in human serum using SERS. This employs an internal standard highly specific for ERG which resulted in limit of quantification values of 0.71 μM. To validate this approach, we analysed real human serum with unknown ERG levels as a blind test set and primary reference levels of ERG were produced using a targeted UHPLC-MS/MS reference method.

The precise identification and differentiation of peri-implant diseases, without the need for intrusive procedures, is crucial for the successful clinical treatment and overall durability of dental implants. This work introduces a novel approach that combines surface-enhanced Raman scattering (SERS) spectroscopy with advanced chemometrics to analyse peri-implant crevicular fluid (PICF) samples. The primary purpose is to offer an unbiased evaluation of implant health. A detailed investigation was performed on PICF samples obtained from a cohort of patients exhibiting different levels of peri-implant health, including those with healthy implants, implants impacted by peri-implantitis, and implants with peri-implant mucositis. The obtained SERS spectra were analysed using canonical-powered partial least squares (CPPLS) to identify unique chemical characteristics associated with each inflammatory state. Significantly, our research findings unveil the presence of a common inflammatory SERS spectral pattern in cases of peri-implantitis and peri-implant mucositis. Furthermore, the SERS-based scores obtained from CPPLS were combined with established clinical scores and subjected to a linear discriminant analysis (LDA) classifier. Repeated double cross-validation was used to validate the method's capacity to discriminate different implant conditions. The integrated approach showcased high sensitivity and specificity and an overall balanced accuracy of 92%, demonstrating its potential to serve as a non-invasive diagnostic tool for real-time implant monitoring and early detection of inflammatory conditions.

Gingival crevicular fluid (GCF) is an interesting biofluid reflecting the physiological and pathological states of a single dental element. Due to this unique feature, in recent years, metabolomic analysis of GCF has gained attention as a biometric tool for the diagnosis and therapy of periodontal disease. Traditional methods are, however, too slow, cumbersome and expensive for a health-care routine. Surface enhanced Raman scattering (SERS) can offer rapid and label-free detailed molecular fingerprints that can be used for biofluid analysis. Here we report the first SERS characterization of GCF using an easy and quick sample preparation. The dominant features in the SERS spectrum of GCF are ascribed to very few metabolites, in particular to uric acid, hypoxanthine, glutathione and ergothioneine. Additionally, we succeeded in differentiating between the SERS signal of GCF collected from healthy volunteers and the one collected from patients with periodontal disease.

A bacterial di-heme cytochrome c binds electrostatically to a gold electrode surface coated with a negatively charged COOH-terminated SAM adopting a sort of 'perpendicular' orientation. Cyclic voltammetry, Resonance Raman and SERRS spectroscopies indicate that the high-potential C-terminal heme center proximal to the SAM's surface undergoes an adsorption-induced swapping of one axial His ligand with a water molecule, which is probably lost in the reduced form, and a low- to high-spin transition. This coordination change for a bis-His ligated heme center upon an electrostatically-driven molecular recognition is as yet unprecedented, as well as the resulting increase in reduction potential. We discuss it in comparison with the known methionine ligand lability in monoheme cytochromes c occurring upon interaction with charged molecular patches. One possible implication of this finding in biological ET is that mobile redox partners do not behave as rigid and invariant bodies, but in the ET complex are subjected to molecular changes and structural fluctuations that affect in a complex way the thermodynamics and the kinetics of the process.

Raman mapping in combination with uni- and multi-variate methods of data analysis is applied to articular cartilage samples. Main differences in biochemical composition and collagen fibers orientation between superficial, middle and deep zone of the tissue are readily observed in the samples. Collagen, non-collagenous proteins, proteoglycans and nucleic acids can be distinguished on the basis of their different spectral characteristics, and their relative abundance can be mapped in the label-free tissue samples, at so high a resolution as to permit the analysis at the level of single cells. Differences between territorial and inter-territorial matrix, as well as inhomogeneities in the inter-territorial matrix, are properly identified. Multivariate methods of data analysis prove to be complementary to the univariate approach. In particular, our partial least squares regression model gives a semiquantitative mapping of the biochemical constituents in agreement with average composition found in the literature. The combination of hierarchical and fuzzy cluster analysis succeeds in detecting variations between different regions of the extra-cellular matrix. Because of its characteristics as an imaging technique, Raman mapping could be a promising tool for studying biochemical changes in cartilage occurring during aging or osteoarthritis.

Congenital cystic adenomatoid malformations (CCAMs) are benign masses of non-functional lung tissue developing from an overgrowth of the terminal bronchioles with subsequent suppressing of alveolar growth. For the first time CCAMs are studied by Raman mapping and Fourier transform infrared (FTIR) imaging. Both vibrational spectroscopic methods are able to analyze the biochemical composition of tissues and their pathological changes at the molecular level. Cryosections were prepared on calcium fluoride substrates from CCAMs and from normal lung tissue of two infant patients who underwent surgery. Raman maps were collected at a step size of 100 microm in order to assess the whole tissue section and at a smaller step size of 10 microm in order to resolve details in selected areas. FTIR images were collected in the macroscopic and microscopic modes. Data sets were segmented by cluster analysis and the mean spectra of each cluster were compared. At low lateral resolution a lower red blood cell content and higher lipid content were found in CCAMs than in normal lung tissue. At higher lateral resolution, accumulations of lipids and glycogen were identified in CCAMs. The lipid aggregates contain a high concentration of phosphatidylcholine. It is discussed how the combined application of Raman mapping and FTIR imaging might improve the differential diagnosis of lung malformations and how both modalities might be applied to other bioanalytical and biomedical problems in the future.