Gold nanoparticles functionalised with fast water exchanging Gd3+ chelates: Linker effects on the relaxivity

The relaxivity displayed by Gd3+ chelates immobilized onto gold nanoparticles is the result of complex interplay between nanoparticle size, water exchange rate and chelate structure. In this work we study the effect of the length of -thioalkyl linkers, anchoring fast water exchanging Gd3+ chelates onto gold nanoparticles, on the relaxivity of the immobilized chelates. Gold nanoparticles functionalized with Gd3+ chelates of mercaptoundecanoyl and lipoyl amide conjugates of the DO3A-N-(-amino)propionate chelator were prepared and studied as potential CA for MRI. High relaxivities per chelate, of the order of magnitude 28-38 mM-1s-1 (30 MHz, 25 ºC) were attained thanks to simultaneous optimization of the rotational correlation time and of the water exchange rate. Fast local rotational motions of the immobilized chelates around connecting linkers (internal flexibility) still limit the attainable relaxivity. The degree of internal flexibility of the immobilized chelates seems not to be correlated with the length of the connecting linkers. Biodistribution and MRI studies in mice suggest that the in vivo behavior of the gold nanoparticles is determined mainly by size. Small nanoparticles (HD= 3.9 nm) undergo fast renal clearance and avoidance of the RES organs while larger nanoparticles (HD= 4.8 nm) undergo predominantly hepatobiliary excretion. High relaxivities, allied to chelate and nanoparticle stability and fast renal clearance in vivo suggests that functionalized gold nanoparticles hold great potential for further investigation as MRI Contrast Agents. This study contributes to understand the effect of linker length on the relaxivity of gold nanoparticles functionalized with Gd3+ complexes. It is a relevant contribution towards “design rules” for nanostructures functionalized with Gd3+ chelates as Contrast Agents for MRI and multimodal imaging.

Development of advanced cell/tissue culture systems, based on enhanced polymeric scaffolds and sophisticated bioreactors, for tissue engineering applications

Programa Doutoral em Engenharia Biomédica

Presence of starch enhances in vitro biodegradation and biocompatibility of a gentamicin delivery formulation

The effect of α-amylase degradation on the release of gentamicin from starch-conjugated chitosan microparticles was investigated up to 60 days. Scanning electron microscopic observations showed an increase in the porosity and surface roughness of the microparticles as well as reduced diameters. This was confirmed by 67% weight loss of the microparticles in the presence of α-amylase. Over time, a highly porous matrix was obtained leading to increased permeability and increased water uptake with possible diffusion of gentamicin. Indeed, a faster release of gentamicin was observed with α-amylase. Starch-conjugated chitosan particles are non-toxic and highly biocompatible for an osteoblast (SaOs-2) and fibroblast (L929) cell line as well as adipose-derived stem cells. When differently produced starch-conjugated chitosan particles were tested, their cytotoxic effect on SaOs-2 cells was found to be dependent on the crosslinking agent and on the amount of starch used.

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.

From perfect strangers to research partners: a study of relationships, resource sharing and perceived benefits within R&D cooperation networks

Doctoral thesis in Marketing and Strategy.