144 resultados para GLUCURONIDE METABOLITE
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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The complexity of biological samples poses a major challenge for reliable compound identification in mass spectrometry (MS). The presence of interfering compounds that cause additional peaks in the spectrum can make interpretation and assignment difficult. To overcome this issue, new approaches are needed to reduce complexity and simplify spectral interpretation. Recently, focused on unknown metabolite identification, we presented a new approach, RANSY (ratio analysis of nuclear magnetic resonance spectroscopy; Anal. Chem. 2011, 83, 7616-7623), which extracts the signals related to the same metabolite based on peak intensity ratios. On the basis of this concept, we present the ratio analysis of mass spectrometry (RAMSY) method, which facilitates improved compound identification in complex MS spectra. RAMSY works on the principle that, under a given set of experimental conditions, the abundance/intensity ratios between the mass fragments from the same metabolite are relatively constant. Therefore, the quotients of average peak ratios and their standard deviations, generated using a small set of MS spectra from the same ion chromatogram, efficiently allow the statistical recovery of the metabolite peaks and facilitate reliable identification. RAMSY was applied to both gas chromatography/MS and liquid chromatography tandem MS (LC-MS/MS) data to demonstrate its utility. The performance of RAMSY is typically better than the results from correlation methods. RAMSY promises to improve unknown metabolite identification for MS users in metabolomics or other fields.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Aloe vera (Aloe barbadensis Miller), popularly known in Brazil as babosa, has a long history of use as medicinal plant for different therapeutic purposes. The components of the plant extract are present in various products of human use, mainly for nutritional and cosmetics purposes. However, some studies suggest that this extract might also have carcinogenic activity. The aloe vera extract is a complex mixture of bioactive compounds. The study of isolated compounds may contribute to elucidate contradictory results about the effects related to the consumption of the plant, as well as their mechanisms of action. One of the most important compound from Aloe vera is aloe-emodin, which is a secondary metabolite generated in the intestinal tract. Putative antimicrobial and antitumor effects were previously attributed to aloe-emodin. Although the exposure of urothelial cells to aloe-emodin was already reported in the literature, only one study showed its effects on urothelial cells, suggesting that aloe-emodin inhibits the viability of T24 cancer cells due to apoptosis induction. Since there is no sufficient information about the effects of aloe-emodin on urothelial cells, and low efficiency in the treatment of bladder cancer currently, the present study aims to evaluate the hypothesis that the treatment with aloe-emodin could impact the behavior of other urothelial cell lines in vitro. Therefore, the in vitro IC50 exposure of aloe-emodin to human immortalized neoplastic urothelial cells will be determinated in order to verify possible differences in the behavior of urothelial cells in vitro treated with aloe-emodin in comparison with untreated cells. Furthermore, differences between cell lines will be also evaluated
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Pós-graduação em Ciências Farmacêuticas - FCFAR
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Currently, mammalian cells are the most utilized hosts for biopharmaceutical production. The culture media for these cell lines include commonly in their composition a pH indicator. Spectroscopic techniques are used for biopharmaceutical process monitoring, among them, UV–Vis spectroscopy has found scarce applications. This work aimed to define artificial neural networks architecture and fit its parameters to predict some nutrients and metabolites, as well as viable cell concentration based on UV–Vis spectral data of mammalian cell bioprocess using phenol red in culture medium. The BHK-21 cell line was used as a mammalian cell model. Off-line spectra of supernatant samples taken from batches performed at different dissolved oxygen concentrations in two bioreactor configurations and with two pH control strategies were used to define two artificial neural networks. According to absolute errors, glutamine (0.13 ± 0.14 mM), glutamate (0.02 ± 0.02 mM), glucose (1.11 ± 1.70 mM), lactate (0.84 ± 0.68 mM) and viable cell concentrations (1.89 105 ± 1.90 105 cell/mL) were suitably predicted. The prediction error averages for monitored variables were lower than those previously reported using different spectroscopic techniques in combination with partial least squares or artificial neural network. The present work allows for UV–VIS sensor development, and decreases cost related to nutrients and metabolite quantifications.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)