Surface-Enhanced Raman Spectroscopy Combined with Multivariate Analysis for Fingerprinting Clinically Similar...

Area of Science:

• Biochemistry
• Spectroscopy
• Medical Diagnostics

Background:

• Fibromyalgia (FM) and Long COVID (LC) are complex syndromes with overlapping symptoms, complicating diagnosis.
• Both conditions involve chronic pain and other debilitating clinical manifestations.
• Accurate differentiation is crucial for effective patient management and treatment.

Purpose of the Study:

• To investigate the feasibility of using surface-enhanced Raman spectroscopy (SERS) with gold nanoparticles (AuNPs) to differentiate between FM and LC.
• To develop classification models using soft independent modelling of class analogies (SIMCA) for diagnostic purposes.
• To identify potential spectral biomarkers for distinguishing FM and LC.

Main Methods:

• Blood samples were collected using dried bloodspot cards (DBS) and volumetric absorptive micro-sampling (VAMS) tips.
• Low molecular fraction (LMF) was extracted from blood samples using a 10 kDa semi-permeable membrane.
• Raman spectra were acquired via SERS using AuNPs, and SIMCA models were applied for classification.

Main Results:

• SIMCA models using VAMS-collected samples achieved 100% accuracy, sensitivity, and specificity in validation.
• The models demonstrated an excellent classification accuracy of 0.86 area under the curve (AUC).
• Discrimination patterns were linked to specific metabolites, including amide groups and amino acids.

Conclusions:

• AuNP-SERS combined with SIMCA is a promising technique for differentiating FM and LC.
• The identified spectral features and metabolites may serve as potential biomarkers for these conditions.
• This approach offers a novel, non-invasive method for improving diagnostic accuracy in complex chronic illnesses.