Deteção remota na avaliação das alterações da cobertura do solo no Parque Nacional da Peneda-Gerês

Dissertação de mestrado integrado em Engenharia Civil

Novas funcionalidades para dispositivos médicos invasivos com imagem

Tese de Doutoramento em Engenharia Eletrónica e de Computadores.

In vivo imaging of glycol chitosan-based nanogel biodistribution

The preclinical development of nanomedicines raises several challenges and requires a comprehensive characterization. Among them is the evaluation of the biodistribution following systemic administration. In previous work, the biocompatibility and in vitro targeting ability of a glycol chitosan (GC) based nanogel have been validated. In the present study, its biodistribution in the mice is assessed, using near-infrared (NIR) fluorescence imaging as a tool to track the nanogel over time, after intravenous administration. Rapid whole body biodistribution of both Cy5.5 labeled GC nanogel and free polymer is found at early times. It remains widespreadly distributed in the body at least up to 6 h postinjection and its concentration then decreases drastically after 24 h. Nanogel blood circulation half-life lies around 2 h with the free linear GC polymer presenting lower blood clearance rate. After 24 h, the blood NIR fluorescence intensity associated with both samples decreases to insignificant values. NIR imaging of the organs shows that the nanogel had a body clearance time of 48 h, because at this time point a weak signal of NIR fluorescence is observed only in the kidneys. Hereupon it can be concluded that the engineered GC nanogel has a fairly long blood circulation time, suitable for biomedical applications, namely, drug delivery, simultaneously allowing efficient and quick body clearance.

An R package for the integrated analysis of metabolomics and spectral data

Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as Nuclear Magnetic Resonance, Gas or Liquid Chromatography, Mass Spectrometry, Infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.