The use of Raman spectroscopy to analyze the biochemical composition of serum samples and hence distinguish between normal and cervical cancer serum samples was investigated. The serum samples were obtained from 19 patients who were clinically diagnosed with cervical cancer, 3 precancer, and 20 healthy volunteer controls. The imprint was put under an Olympus microscope, and around points were chosen for Raman measurement.All spectra were collected at a Horiba Jobin-Yvon LabRAM HR800 Raman Spectrometer with a laser of 830-nm wavelength and 17-mW power irradiation. Raw spectra were processed by carrying out baseline correction, smoothing, and normalization to remove noise, florescence, and shot noise and then analyzed using principal component analysis (PCA). The control serum spectrum showed the presence of higher amounts of carotenoids indicated by peaks at 1,002, 1,160, and 1,523 cm(-1)and intense peaks associated with protein components at 754, 853, 938, 1,002, 1,300-1,345, 1,447, 1,523, 1,550, 1,620, and 1,654 cm(-1). The Raman bands assigned to glutathione (446, 828, and 1,404 cm(-1)) and tryptophan (509, 1,208, 1,556, 1,603, and 1,620 cm(-1)) in cervical cancer were higher than those of control samples, suggesting that their presence may also play a role in cervical cancer. Furthermore, weak bands in the control samples attributed to tryptophan (545, 760, and 1,174 cm(-1)) and amide III (1,234-1,290 cm(-1)) seem to disappear and decrease in the cervical cancer samples, respectively. It is shown that the serum samples from patients with cervical cancer and from the control group can be discriminated with high sensitivity and specificity when the multivariate statistical methods of PCA is applied to Raman spectra. PCA allowed us to define the wavelength differences between the spectral bands of the control and cervical cancer groups by confirming that the main molecular differences among the control and cervical cancer samples were glutathione, tryptophan, β carotene, and amide III. The preliminary results suggest that Raman spectroscopy could be a highly effective technique with a strong potential of support for current techniques as Papanicolaou smear by reducing the number of these tests; nevertheless, with the construction of a data library integrated with a large number of cervical cancer and control Raman spectra obtained from a wide range of healthy and cervical cancer population, Raman-PCA technique could be converted into a new technique for noninvasive real-time diagnosis of cervical cancer from serum samples.

The gene expression through microarray technique is a current topic in the field of nutrigenomics for researching the complex gene networks and relationship with nutrition. DNA Microarrays has been used to quantify and compare gene expression on a large scale and to explore a major subset or all genes of an organism. As a consequence, formal methods and computer tools for the modeling and simulation of gene networks are indispensable. We identified the genes associated to the obesity susceptible to modify their expression level in a profile of microarray data and the possible relationship with some bioactive components present in lactobacillus gasseri probiotic. Using international microarray databases, we analyzed responsible obesity genes and the role of probiotic intake in the gene expression for some specific genes (LEP, LEPR, FTO y PPARg2) to study the interaction patterns networks between them. In addition, we reviewed the clustering of gene networks by applying the mathematical procedure principal component analysis (PCA) to the gene expression data point by introducing an interaction between neighboring points. The identification and comparison of gene expression levels associated with obesity will help to guide future research work focus on effective treatments for patients with this disease. As well, these networks may help to analyze dietary variables associated with probiotics intake, to prevent health problems like obesity and type 2 diabetes and to obtain evidence of their interaction with certain human genome modules based on their analysis of the expression microarray data. This integrative analysis offers a new conceptual framework that could expand our knowledge of nutrigenomics and some human pathologies.

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In this paper, we present a different way to the standard methods to classify Raman spectra whose grouping process is based on a phenomenon of clustering observed in nature at the atomic level and correctly described by the statistical physics model known as the Potts model, which represents the interacting spins on a crystalline lattice. This clustering method is known as the super paramagnetic clustering (SPC), which allows identifying hierarchical structures in data banks. In this novel method, we assigned a Potts spin to each data point (Raman spectrum) and introduced an interaction between neighboring points whose coupling strength is a decreasing function of the distance between the nearest neighboring sites. We found a hierarchical tree structure in our data bank of Raman spectra allowing us to discriminate between the spectra from control and diabetes patients. The sensitivity and specificity of the diabetes detection technique by Raman spectroscopy were calculated directly because the SPC method achieves an accurate determination of the members of each cluster. As a cross-check, SPC results were compared with published results of multivariate analysis, observing excellent agreements; however, the SPC method allows determining the members of all identified clusters explicitly.