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

Programa Doutoral em Engenharia Biomédica

Algorithms and computational tools for metabolic flux analysis

PhD thesis in Biomedical Engineering

Chondrogenic potential of injectable κ-carrageenan hydrogel with encapsulated adipose stem cells for cartilage tissue-engineering applications

Due to the limited self-repair capacity of cartilage, regenerative medicine therapies for the treatment of cartilage defects must use a significant amount of cells, preferably applied using a hydrogel system that can promise their delivery and functionality at the specific site. This paper discusses the potential use of k-carrageenan hydrogels for the delivery of stem cells obt ained from adipose tissue in the treatment of cartilage tissue defects. The developed hydrogels were produced by an ionotropic gelation met hod and human adipose stem cells (hASCs) were encapsulated in 1.5% w/v k-carrageenan solution at a cell density of 5  10 6 cells/ml. The results from the analysis of the cell-encapsulating hydrogels, cultured for up to 21 days, indicated that k-carrageenan hydrogels support the viability, proliferation and chondrogenic differentiation of hASCs. Additionally, the mec hanical analysis demonstrated an increase in stiffness and viscoelastic properties of k-carrageenan gels with their encapsulated cells with increasing time in culture with chondrogenic medium. These results allowed the conclusion that k-carrageenan exhibits properties t hat enable the in vitro functionality of encapsulated hASCs and thus may provide the basis for new successful approaches for the treatment of cartilage defects.

Bioactive ceramics for tissue engineering and regenerative medicine derived from marine sponges

Publicado em "Journal of tissue engineering and regenerative medicine". Vol. 8, suppl. s1 (2014)

Marine Collagen/Apatite scaffolds envisaging tissue engineering applications

Despite the vast investigation and the large amount of products already available in the market to treat the different bone defects there is still a growing need to develop more advanced and complex therapeutic strategies. In this context, a mixture of Marine Hydroxyapatite-Fluorapatite:Collagen (HA-FP:ASC) seems to be a promising solution to overcome these bone defects, specifically, dental defects. HA-FP particles (20–63 μm) were obtained through pyrolysis (950°C, 12 h) of shark teeth (Isurus oxyrinchus, P. glauca), and Type I collagen was isolated from Prionace glauca skin as previously described (1). After the steps of purification, collagen was solubilized in 0.5 M acetic acid and HA-FP added producing three different formulations: were produced, 30:70, 50:50 and 70:30 of HA-FP:ASC, respectively. EDC/NHS and HMDI binding agents were used to stabilize the produced scaffolds. Mechanical properties were evaluated by compression tests. SEM analysis allowed observing the mineral deposition, after immersion in simulated body fluid and also permitted to evaluate how homogenous was the distribution of HA-FP in the different scaffold formulations, also confirmed by μ-CT assay. It was readily visible by Cytotoxicity and life/dead CLSM assays that cells were able to adhere and proliferate in the produced scaffolds. Scaffolds crosslinked with EDC/NHS showed lower cytotoxicity, being the ones chosen for further cellular evaluation.

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.