Label-free detection of thalassemia and other ROS impairing diseases.

Pathogenesis of different diseases showed that some of them, especially thalassemia (T) and rheumatoid arthritis (RA) have an implicit association with oxidative stress and altered levels of reactive oxygen species (ROS). Introducing ROS level and the balance between ROS and antioxidants as essential metrics, an attempt was made to classify T and RA from normal individuals (treated as controls)(C) using synchronous fluorescence spectroscopy (SFS) and Raman line intensity of water. This non-invasive and label-free approach was backed up by a categorization algorithm that helped in the prediction of disease types from serum samples. The predictive system constituted principal component analysis (PCA) with four parameters, namely spectral intensity ratios of reduced nicotinamide adenine dinucleotide (NADH) to tryptophan (Trp) (NADH/Trp), kynurenine (Kyn) to tryptophan (Kyn/Trp), kynurenine to NADH (Kyn/NADH), and logarithmic changes in Raman line intensity of water (Rline), with the index headers containing the disease types. Rline has a positive correlation with both Kyn/Trp and Kyn/NADH and a negative correlation with NADH/Trp ratio, implying its direct or indirect association with oxidative stress. In addition to the classification of T, RA, and C a sub-classification of T into beta major and E-beta in their post and pre-splenectomized surgical stages could also be realized. Furthermore, receiver operating characteristic (ROC) analysis was deployed to ascertain that the misclassification error (ME) was negligible for the disease types. Graphical Abstract A schematic representation of the workflow converging into the categorical classification of disease classes.