Pteridines and its derivatives are considered as important cofactors participating in cellular metabolism. Studies reported that, the distribution of pteridines and its derivatives may change when monocytes and macrophages are activated under interferon- γ stimulus by cancer. Also, there is a significant variation in the concentration and conformation of pteridines under different pathological conditions. It has been reported that during the transformation of normal cells into neoplasm, the metabolic end products of the cancer cells are released into the blood, thereby changing the components and contents of the biological molecules and their local environment. In this regard, the present study is aimed to characterize pteridines and its derivatives in the blood plasma of normal subjects and patients with confirmed oral cancer using fluorescence and Raman spectroscopy. Observed Fluorescence & Raman spectral characteristics of samples and subsequent discriminant analysis predicts that 75 % of the original and cross validated groups are correctly classified.
Urine is one of the diagnostically important bio fluids, as it has many metabolites and some of them are native fluorophores. Riboflavin and its cofactors FMN and FAD which act as electron carriers participates in a diversity of redox reactions central to human metabolism. It has been reported that riboflavin plays a prominent role in progression of various cancers. It is well documented that, the fluorophore flavins that is not bound to proteins in the plasma is filtered by glomerulus and excreted in urine. Fluorescence spectroscopy has been considered as a promising tool to characterize the riboflavin present in the urine. The overall spectral data at 450 nm excitation were subjected to principal components based linear discriminant analysis. As a result, 100 % of the normal subjects and 90% of the cervical cancer subjects were correctly classified which shows that there exists significant difference between them.
Oral cancer has a poor five-year survival rate and has not improved much in the past two decades which is due to late diagnosis. In current clinical practice analysis of Haematoxylin Eosin stained tissue biopsy is considered as a golden standard which is rather painful and routine check is not possible. In this regard, native fluorescence spectroscopy has been considered to discriminate cancer tissue based on relative alterations in the level of tryptophan. To estimate relative variations of tryptophan at different layers of tissue fluorescence polarization gating technique has been adopted which is based on the principle that the light from the superficial layer of tissue partially retain the polarization plane of incident light as they are less scattered while light from the deeper layer is completely depolarized due to multiple scattering. Integrated intensity of tryptophan was quantified, and subsequent statistical analysis has been carried out to evaluate the diagnostic potentiality of the proposed technique. It was found that the fractional variation of tryptophan in the superficial layer to the deeper layer was found to be statistically more significant in discriminating oral cancer than cumulative tryptophan in both layers.
Urinary tract infections (UTI) are one of the frequently encountered infections in clinical practice. As there are different strains of bacteria responsible for UTI, the identification of types of bacterial is necessary to administer a proper antibiotic. Conventional staining and biochemical methods for the identification of bacteria are time-consuming and it usually leads to administer patients with broad-spectrum antibiotics which are less effective and expensive. In this regard, Multiphoton fluorescence imaging based on the distribution of NADH and FAD in several bacterial species isolated from UTI is carried out. Metabolic imaging based on fluorescence enables to analyze both biochemical distribution and their conformation. Spectral deconvolution method is used to isolate fluorescence emission from the coenzymes NADH and FAD to generate redox imaging. Further, redox imaging of bacteria was analyzed using different machine learning algorithms to improve the accuracy of classification. The results of this study revealed that the proposed technique of redox imaging was found to discriminate bacterial species. As the proposed method is both effective and less time consuming, the proposed method may be considered for real-time classification of bacterial species in the clinical setup.
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