ESS - DM - Bioestatística e Bioinformática Aplicadas à Saúde
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Browsing ESS - DM - Bioestatística e Bioinformática Aplicadas à Saúde by advisor "Baylina, Pilar"
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- In silico screening of antibiotic resistance genes in a genomics library of bacteriophagesPublication . Aguiar, Sara Catarina Leal; Fernandes, Ruben; Baylina, Pilar; Sá, VitorAntibiotic resistance (AR) is a worldwide concern that threatens the effective treatment of infectionsusing antibiotics (1-5). It is known that AR is often mobile between bacteria of different taxonomic and ecological groups by either transformation, conjugation or tranduction (6,7). Here is address the controversial dissemination of AR through the mechanism of transduction by determining the presence of antibiotic resistance genes in bacteriphages genomes through an in silico approach. As such this dissertation presentes the sreening of 4789 antibiotic resistance genes (ARGs) in all 2051 bacteriophages genomes of the order Caudovirales available online using an in silico method of choise, and further intends to discuss the chalanges in the choice as also discuss the pros and limitations of the method chosen. Results determinated the presence of 32 different antibiotic resistance gene famalies in 16 different phage hosts at a 99% confidence interval. Although the results showed strong diversity among the phage genomes where ARGs were found, the impact oh the spread of AR through the transduction mechanism remains unclear. In order to address these questions, further future work can be carried out, although the releance might be also questioned.
- Mathematical modeling for pharmacological approaches directed to metabolic pathways in "diabetic paradox" in prostate cancerPublication . Santos, Inês Ribeiro da Silva de Lima; Fernandes, Rúben; Alves, Marco; Baylina, PilarObesity and diabetes are two metabolic risk factos for cancer. However, there is a metabolic paradox in prostate cancer in which diabetes appears to protect the patient form this type of cancer. The current study aims to develop explanatory models of this contradiction utilizing prostate cancer cell lines, PC3 and LNCaP, in contrast to the metabolismo of normal prostate cells, using bioinformatics methods (HPEpiC). Two of the major routes of prostate metabolism, glycolysis and gluconeogensis, were mathematically manipulated in this study. This mathematical model offers unique and revolutionary implications in personalized medicine since it predicts the Effect, therapeutic dose, and efficacy of medications in varied conditions of the tumor microenvironment and the patient’s metabolismo. As na illustration od the model’s usefulness, a novel anti-tumor drug in the clinical trials phase, 3-bromopyruvate, which has the modeled metabolic pathways as a therapeutic target, was employed. The efficacy od 3-bromopyruvate was investigated, and the IC50 was found to be capable of significantly inhibiting tumor cell lines. When compared to basal metabolismo, its IC50 delayed glycolytic metabolismo by 12 minutes. As a result, the diabetic environment has a slowing Effect on glycolytic metabolismo. The obese environment had no significant diferences in this form os cancer as compared to the healthy environment. Tha value of mathematical modeling is clear, as the Effect of anew drug on metabolismo may be computer evaluated and used as a novel tool to provide a tailored approach to each patient.