Navegando por Autor "Lima, Kássio M.G."
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Artigo Environmentally compatible bioconjugated gold nanoparticles as efficient contrast agents for colorectal cancer cell imaging(Elsevier, 2014-06) Araújo, Aurigena Antunes de; Lima, Kássio M.G.; Araújo Junior, Raimundo F.; Oliveira, Ana Luiza C.S. Leitão; Gasparotto, Luiz H.S.In this study we show, for the first time, that gold nanoparticles (AuNPs) synthesized by a simple, inexpensive, and environmentally-correct method can be easily conjugated with the antibodies anti-β-catenin and anti-E-cadherin to specifically target colorectal carcinoma cells. The antibody/AuNPs conjugates were then successfully applied for imaging cancerous cells with fluorescence confocal microscopy. The AuNPs as well as the conjugates were very stable in high-salinity medium, a pre-requisite for application in physiological-like environments. Fluorescence results suggest that conjugation was achieved by direct adsorption of antibodies on the AuNPs surface. Finally, compared with a standard method of cell staining, our method is less laborious and the preparation time (from immobilization of cells onto glass cover slips until observation by confocal microscopy) decreased from 27 h to about 1 h, which makes the method eligible for colorectal cancer diagnostic.Artigo LDA vs QDA for FT-MIR prostate cancer tissue classification(Elsevier, 2017-03-15) Araújo, Aurigena Antunes de; Siqueira, Laurinda F.S.; Araújo Júnior, Raimundo F.; Morais, Camilo L.M.; Lima, Kássio M.G.Discrimination/classification of biological material a ta molecular level is one of the key aims of chemometrics applied to biospectroscopic data. Two discriminant functions, namely Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA), were considered in this study for prostate cancer classification based on FT-MIR data, and illustrated graphically as boundary methods. Principal Component Analysis (PCA) was applied as a variable/dimensionality reduction method and Genetic Algorithm (GA) as variable selection method, followed by LDA and QDA. The performance of each method was determined using 40–100 MIR spectra per tissue sample (n=45), previously classified according to Gleason traditional grading by pathologists. The methods were used to separate the two-category of prostate cancer: Low grade (Gleason grade 2) vs. High grade (Gleason grade 3 and 4). The models were optimized using a training set and their performance was evaluated using a test set. Classification rates and quality metrics (Sensitivity, Specificity, Positive (or Precision) and Negative Predictive Values, Youden's index, and Positive and Negative Likelihood Ratios) were computed for each model. QDA-based models obtained higher classification rates and quality performance than LDA-based models. The models studied identify that secondary protein structure variations and DNA/RNA alterations are the main biomolecular ‘difference markers’ for prostate cancer grades.