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dc.contributor.advisorSaraiva, Paulo Jaconipt_BR
dc.contributor.authorCruz, Giuliano Netto Florespt_BR
dc.date.accessioned2019-06-13T02:30:26Zpt_BR
dc.date.issued2018pt_BR
dc.identifier.urihttp://hdl.handle.net/10183/195681pt_BR
dc.description.abstractIdiopathic Pulmonary Fibrosis (IPF) is a chronic, progressive, irreversible lung disease. After diagnosis, the interstitial condition commonly presents 3-5 years of life expectancy if untreated. Despite the limited capacity of recapitulating IPF, animal models have been useful for identifying related pathways relevant for drug discovery and diagnostic tools development. Using these techniques, several immune-related mechanisms have been implicated to IPF. For instance, subpopulations of macrophages and monocytes-derived cells are recognized as centrally active in pulmonary immunological processes. One of the most used technologies is high-throughput gene expression analysis, which has been available for almost two decades now. The “omics” revolution has presented major impacts on macrophage and pulmonary fibrosis research. The present study aims to investigate macrophage dynamics within the context of IPF at the transcriptomic level. Using publicly available gene-expression data, we applied modern data science approaches to (1) understand longitudinal profiles within IPF models; (2) investigate correlation between macrophage genomic dynamics and IPF development; and (3) apply longitudinal profiles uncovered through multivariate data analysis to the development of new sets of predictors able to classify IPF and control samples accordingly. Principal Component Analysis and Hierarchical Clustering showed that our pipeline was able to construct a complex set of biomarker candidates that together outperformed gene expression alone in separating treatment groups in an IPF animal model dataset. We further assessed the predictive performance of our candidates on publicly available gene expression data from IPF patients. Once again, the constructed biomarker candidates were significantly differentiated between IPF and control samples. The data presented in this work strongly suggest that longitudinal data analysis holds major unappreciated potentials for translational medicine research.en
dc.format.mimetypeapplication/pdfpt_BR
dc.language.isoengpt_BR
dc.rightsOpen Accessen
dc.subjectFibrose pulmonar idiopáticapt_BR
dc.subjectMacrófagospt_BR
dc.titleGenomic analysis of macrophage gene signatures during idiopathic pulmonary fibrosis developmentpt_BR
dc.typeTrabalho de conclusão de graduaçãopt_BR
dc.contributor.advisor-coFuentefria, Alexandre Meneghellopt_BR
dc.contributor.advisor-coSaraiva, Otavio Jaconipt_BR
dc.identifier.nrb001094762pt_BR
dc.degree.grantorUniversidade Federal do Rio Grande do Sulpt_BR
dc.degree.departmentFaculdade de Farmáciapt_BR
dc.degree.localPorto Alegre, BR-RSpt_BR
dc.degree.date2018pt_BR
dc.degree.graduationFarmáciapt_BR
dc.degree.levelgraduaçãopt_BR


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