Blood Biomarker Discovery by Raman Spectroscopy in Acute Aortic Dissection

Sample Collection: Blood (serum and plasma) collection from all the selected subjects at admission will be performed following the collection tube manufacturer's instructions. Then the collected samples will be stored at -80° C. Data Collection: For the Raman analysis, 5 μL of each sample will be casted on an aluminium foil in order to achieve the Surface Enhanced Raman Scattering (SERS). Raman and SERS spectra will be acquired using a WITec Alpha500 confocal micro-Raman spectroscopy system (WITec, Germany) with a 532 nm He-Ne laser (Research Electro-Optics, Inc., USA) as the excitation source, which offered a resolution of approximately 3 cm-1. The instrument will be calibrated before each analysis using the reference band of silicon at 520.7 cm-1. A 20× microscope objective (NA = 1.25, Zeiss, Germany) was used for spectral excitation and measurements. All spectral information were recorded by a back-illuminated deep-depletion charge coupled device camera (ardorTechnology, UK) working at -60°C with a 1.5 s acquisition time for a single spectrum with a spectral range of 300-4000 cm-1. The software package WITec Project spectral analysis (WITec GmbH, Germany) will be used for map design and the acquisition of spectra. Data Processing: All the acquired spectra will be fit with a nine-order polynomial baseline and normalized by unit vector using the dedicated WITec Project spectral analysis software. The statistical analysis to validate the method, will be performed using a multivariate analysis approach. Principal Component analysis (PCA) will be performed in order to reduce data dimensions and to evidence major trends. Mann-Whitney will be performed on PCs scores to verify the differences statistically relevant between the analysed groups. Correlation and partial correlation analysis will be performed using the Spearman's test, assuming as valid correlation only the coefficients with a p-value lower than 0.05. Subsequently, principal components analysis (PCA) combined with linear discriminant analysis (LDA), PCA combined with support vector machine (SVM), partial least squares-discriminant analysis (PLS-DA) and etc were tested for evaluation of diagnostic power. The statistical analysis will be performed using Origin2018 (OriginLab, USA) and MATLAB R2018b software (Mathworks. Inc., Natica, MA, USA).