Early diagnosis of colorectal cancer in rats with DMH induced carcinogenesis by means of urine autofluorescence analysis

Screening of neoplastic diseases by statistical analysis of urine fluorescence spectroscopic data. Application of multivariate techniques for enhancing classification

The composition of human fluids is modified during the course of neoplastic diseases. Urine analysis offers the advantage of being a noninvasive method for which samples are easily and routinely collected from patients. In this work, urine fluorescence spectra recorded upon excitation at 405 nm were obtained from healthy volunteers and individuals with different oncologic pathologies. A large number of indexes, i.e., parameters obtained from spectral data which assist spectral features characterization, were developed to classify healthy and pathological populations. The discrimination ability of simple predictive indexes, obtained from spectra pretreated with different normalization procedures and by taking their derivatives, was statistically assessed. In addition, multivariate methods, such as principal component analysis and multivariate curve resolution by alternating least squares, were used to develop more elaborate indexes for distinguishing between healthy and pathological populations. All indexes were systematically evaluated on a statistical basis by in lab-developed routines capable of detecting outliers, judging the normal distribution of the indexes, evaluating variance homogeneity, testing the difference between the means of healthy and pathological populations, as well as performing a receiver operator curve analysis to assess the classification power of each index. Those indexes with the best performances were further combined to perform a linear discriminant analysis, which yielded a powerful classification algorithm with an area under the receiver operator curve of 0.986, a sensitivity of 97.7%, a specificity of 100%, and an overall accuracy of 98.8%. The present study shows that the statistical analysis of urine fluorescence data with a proper combination of multivariate techniques bears a high potential to develop massive screening tests for the early detection of oncologic pathologies.

Differential Laser-Induced Perturbation Spectroscopy for Analysis of Mixtures of the Fluorophores l-Phenylalanine, l-Tyrosine and l-Tryptophan Using a Fluorescence Probe

Quantitative detection of common endogenous fluorophores is accomplished using differential laser-induced perturbation spectroscopy (DLIPS) with a 193-nm UV fluorescence probe and various UV perturbation wavelengths. In this study, DLIPS is explored as an alternative to traditional fluorescence spectroscopy alone, with a goal of exploring natural fluorophores pursuant to biological samples and tissue analysis. To this end, aromatic amino acids, namely, l-phenylalanine, l-tyrosine and l-tryptophan are mixed with differing mass ratios and then classified with various DLIPS schemes. Classification with a traditional fluorescence probe is used as a benchmark. The results show a 20% improvement in classification performance of the DLIPS method over the traditional fluorescence method using partial least squares (PLS) analysis. Additional multivariate analyses are explored, and the relevant photochemistry is elucidated in the context of perturbation wavelengths. We conclude that DLIPS is a promising biosensing approach with potential for in vivo analysis given the current findings with fluorophores relevant to biological tissues.