Multiphasic, multistructured and hierarchical strategies for cartilage regeneration

Cartilage tissue is a complex nonlinear, viscoelastic, anisotropic, and multiphasic material with a very low coefficient of friction, which allows to withstand millions of cycles of joint loading over decades of wear. Upon damage, cartilage tissue has a low self-reparative capacity due to the lack of neural connections, vascularization, and a latent pool of stem/chondroprogenitor cells. Therefore, the healing of articular cartilage defects remains a significant clinical challenge, affecting millions of people worldwide. A plethora of biomaterials have been proposed to fabricate devices for cartilage regeneration, assuming a wide range of forms and structures, such as sponges, hydrogels, capsules, fibers, and microparticles. In common, the fabricated devices were designed taking in consideration that to fully achieve the regeneration of functional cartilage it is mandatory a well-orchestrated interplay of biomechanical properties, unique hierarchical structures, extracellular matrix (ECM), and bioactive factors. In fact, the main challenge in cartilage tissue engineering is to design an engineered device able to mimic the highly organized zonal architecture of articular cartilage, specifically its spatiomechanical properties and ECM composition, while inducing chondrogenesis, either by the proliferation of chondrocytes or by stimulating the chondrogenic differentiation  of stem/chondro-progenitor cells. In this chapter we present the recent advances in the development of innovative and complex biomaterials that fulfill the required structural key elements for cartilage regeneration. In particular, multiphasic, multiscale, multilayered, and hierarchical strategies composed by single or multiple biomaterials combined in a welldefined structure will be addressed. Those strategies include biomimetic scaffolds mimicking the structure of articular cartilage or engineered scaffolds as models of research to fully understand the biological mechanisms that influence the regeneration of cartilage tissue.

Evaluating biomaterial-and microfluidic-based 3D tumor models

Cancer is a major cause of morbidity and mortality worldwide, with a disease burden estimated to increase in the coming decades. Disease heterogeneity and limited information on cancer biology and disease mechanisms are aspects that 2D cell cultures fail to address. We review the current "state-of-the-art" in 3D Tissue Engineering (TE) models developed for and used in cancer research. Scaffold-based TE models and microfluidics, are assessed for their potential to fill the gap between 2D models and clinical application. Recent advances in combining the principles of 3D TE models and microfluidics are discussed, with a special focus on biomaterials and the most promising chip-based 3D models.

Colaboração em inovação: parceiros ou concorrentes?

Dissertação de mestrado em Economia Industrial e da Empresa

Column-based databases: estudo exploratório no âmbito das bases de dados NoSQL

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Modelação de processos de negócio: análise comparativa de linguagens

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Estudo das necessidades de médicos especialistas na Região Norte de Portugal

Dissertação de mestrado em Engenharia Industrial

Single line for assembly just-in-sequence multiple models

Programa Doutoral em Líderes para as Indústrias Tecnológicas

Biomaterials for cartilage tissue engineering under mechanical stimulus

Tese de Doutoramento em Ciências (Especialidade de Física)

Impact of mutated KRAS signalling on autophagy regulation in colorectal carcinoma

Programa Doutoral em Biologia Molecular e Ambiental

Metabolic engineering with multi-objective optimization of kinetic models

Kinetic models have a great potential for metabolic engineering applications. They can be used for testing which genetic and regulatory modifications can increase the production of metabolites of interest, while simultaneously monitoring other key functions of the host organism. This work presents a methodology for increasing productivity in biotechnological processes exploiting dynamic models. It uses multi-objective dynamic optimization to identify the combination of targets (enzymatic modifications) and the degree of up- or down-regulation that must be performed in order to optimize a set of pre-defined performance metrics subject to process constraints. The capabilities of the approach are demonstrated on a realistic and computationally challenging application: a large-scale metabolic model of Chinese Hamster Ovary cells (CHO), which are used for antibody production in a fed-batch process. The proposed methodology manages to provide a sustained and robust growth in CHO cells, increasing productivity while simultaneously increasing biomass production, product titer, and keeping the concentrations of lactate and ammonia at low values. The approach presented here can be used for optimizing metabolic models by finding the best combination of targets and their optimal level of up/down-regulation. Furthermore, it can accommodate additional trade-offs and constraints with great flexibility.

Stress-triggered synaptic malfunction: a gate along the path from depressionto dementia

Tese de Doutoramento em Ciências da Saúde.

Planeamento e controlo de projetos de construção com recurso ao BIM

Dissertação de mestrado integrado em Engenharia Civil

Development of computational and experimental methods for biomass composition and evaluation of its impact in genome-scale models prediction

The use of genome-scale metabolic models has been rapidly increasing in fields such as metabolic engineering. An important part of a metabolic model is the biomass equation since this reaction will ultimately determine the predictive capacity of the model in terms of essentiality and flux distributions. Thus, in order to obtain a reliable metabolic model the biomass precursors and their coefficients must be as precise as possible. Ideally, determination of the biomass composition would be performed experimentally, but when no experimental data are available this is established by approximation to closely related organisms. Computational methods however, can extract some information from the genome such as amino acid and nucleotide compositions. The main objectives of this study were to compare the biomass composition of several organisms and to evaluate how biomass precursor coefficients affected the predictability of several genome-scale metabolic models by comparing predictions with experimental data in literature. For that, the biomass macromolecular composition was experimentally determined and the amino acid composition was both experimentally and computationally estimated for several organisms. Sensitivity analysis studies were also performed with the Escherichia coli iAF1260 metabolic model concerning specific growth rates and flux distributions. The results obtained suggest that the macromolecular composition is conserved among related organisms. Contrasting, experimental data for amino acid composition seem to have no similarities for related organisms. It was also observed that the impact of macromolecular composition on specific growth rates and flux distributions is larger than the impact of amino acid composition, even when data from closely related organisms are used.

Predicting SO2 pollution incidents by means of additive models with optimum variable selection

The aim of this paper is to predict time series of SO2 concentrations emitted by coal-fired power stations in order to estimate in advance emission episodes and analyze the influence of some meteorological variables in the prediction. An emission episode is said to occur when the series of bi-hourly means of SO2 is greater than a specific level. For coal-fired power stations it is essential to predict emission epi- sodes sufficiently in advance so appropriate preventive measures can be taken. We proposed a meth- odology to predict SO2 emission episodes based on using an additive model and an algorithm for variable selection. The methodology was applied to the estimation of SO2 emissions registered in sampling lo- cations near a coal-fired power station located in Northern Spain. The results obtained indicate a good performance of the model considering only two terms of the time series and that the inclusion of the meteorological variables in the model is not significant.

A Responsabilidade Civil dos Gerentes e Administradores pelo pagamento das coimas e multas imputáveis à sociedade

Dissertação de mestrado em Direito dos Contratos e da Empresa