Acellular Gellan-gum based bilayered structures for the regeneration of osteochondral defects: a preclinical study

 In orthopaedics, the management and treatment of osteochondral (OC) defects remains an ongoing clinical challenge. Autologous osteochondral mosaicplasty has been used as a valid option for OC treatments although donor site morbidity remains a source of concern [1]. Engineering a whole structure capable of mimicking different tissues (cartilage and subchondral bone) in an integrated manner could be a possible approach to regenerate OC defects. In our group we have been proposing the use of bilayered structures to regenerate osteochondral defects [2,3]. The present study aims to investigate the pre-clinical performance of bilayered hydrogels and spongy-like hydrogels in in vivo  models (mice and rabbit, respectively), in both subcutaneous and orthotopic models. The bilayered structures were produced from Low Acyl Gellan Gum (LAGG) from Sigma-Aldrich, USA. Cartilage-like layers were obtained from a 2wt% LAGG solution. The bone-like layers were made of 2wt% LAGG with incorporation of hydroxyapatite at 20% and 30% (w/v). Hydrogels and spongy-like were subcutaneouly implanted in mice to evaluate the inflammatory response. Then, OC defects were induced in rabbit knee to create a critical size defect (4 mm diameter and 5 mm depth), and then hydrogels and sponges implanted. Both structures followed different processing methods. The hydrogels were injected allowing in situ  crosslinking. Unlike, the spongy-like were pre-formed by freeze-drying. The studies concerning subcutaneous implantation and critical size OC defect were performed for 2 and 4 weeks time, respectively. Cellular behavior and inflammatory responses were assessed by means of histology staining and biochemical function and matrix deposition by immunohistochemistry. Additionally, both OC structures stability and new cartilage and bone formation were evaluated by using vivo- computed tomography (Scanco 80). The results showed no acute inflammatory response for both approaches. New tissue formation and integration in the adjacent tissues were also observed, which present different characteristic behaviors when comparing hydrogels and sponges response. As future insights, a novel strategy for regeneration of OC defects can be designed encompassing both, hydrogels and spongy-like structures and cellular approaches. References: 1. Espregueira-Mendes J. et al. Osteochondral transplantation using autografts from the upper tibio-fibular joint for the treatment of knee cartilage lesions. Knee Surgery, Sports Traumatology, Arthroscopy 20,1136, 2012. 2. Oliveira JM. et al, Novel hydroxyapatite/chitosan bilayered scaffold for osteochondral tissue-engineering applications: Scaffold design and its performance when seeded with goat bone marrow stromal cells. Biomaterials 27, 6123, 2006. 3. Pereira D R. et al. Gellan Gum-Based Hydrogel Bilayered Scaffolds for Osteochondral Tissue Engineering. Key Engineering Materials 587, 255, 2013.

Artificial thymus: a tissue engineering strategy

The thymus is the central organ responsible for the generation of T lymphocytes (1). Various diseases cause the thymus to produce in- sufficient T cells, which can lead to immune-suppression (2). Since T cells are essential for the protection against pathogens, it is crucial to promote de novo differentiation of T cells on diseased individuals. The available clinical solutions are: 1) one protocol involving the transplant of thymic stroma from unrelated children only applicable for athymic children (3); 2) for patients with severe peripheral T cell depletion and reduced thymic activity, the administration of stimu- lating molecules stimulating the activity of the endogenous thymus (4). A scaffold (CellFoam) was suggested to support thymus regen- eration in vivo (5), although this research was discontinued. Herein, we propose an innovative strategy to generate a bioartificial thymus. We use a polycaprolactone nanofiber mesh (PCL-NFM) seeded and cultured with human thymic epithelial cells (hTECs). The cells were obtained from infant thymus collected during pediatric cardio-tho- racic surgeries. We report new data on the isolation and characterization of those cells and their interaction with PCL-NFM, by expanding hTECs into relevant numbers and by optimizing cell seeding methods.

Bioactive nanocomposite spheres with proved shape memory capability in bone defects

Bioactive glass nanoparticles (BGNPs) promote an apatite surface layer in physiologic conditions that lead to a good interfacial bonding with bone.1 A strategy to induce bioactivity in non-bioactive polymeric biomaterials is to incorporate BGNPs in the polymer matrix. This combination creates a nanocomposite material with increased osteoconductive properties. Chitosan (CHT) is a polymer obtained by deacetylation of chitin and is biodegradable, non-toxic and biocompatible. The combination of CHT and the BGNPs aims at designing biocompatible spheres promoting the formation of a calcium phosphate layer at the nanocomposite surface, thus enhancing the osteoconductivity behaviour of the biomaterial. Shape memory polymers (SMP) are stimuli-responsive materials that offer mechanical and geometrical action triggered by an external stimulus.2 They can be deformed and fixed into a temporary shape which remains stable unless exposed to a proper stimulus that triggers recovery of their original shape. This advanced functionality makes such SMPs suitable to be implanted using minimally invasive surgery procedures. Regarding that, the inclusion of therapeutic molecules becomes attractive.  We propose the synthesis of shape memory bioactive nanocomposite spheres with drug release capability.3   1.  L. L. Hench, Am. Ceram. Soc. Bull., 1993, 72, 93-98. 2.  A. Lendlein and S. Kelch, Angew Chem Int Edit, 2002, 41, 2034-2057. 3.  Ã . J. Leite, S. G. Caridade and J. F. Mano, Journal of Non-Crystalline Solids (in Press)

Abstract computation in schizophrenia detection through artificial neural network based systems

Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information.

Thrombophilia screening: An artificial neural network approach

Thrombotic disorders have severe consequences for the patients and for the society in general, being one of the main causes of death. These facts reveal that it is extremely important to be preventive; being aware of how probable is to have that kind of syndrome. Indeed, this work will focus on the development of a decision support system that will cater for an individual risk evaluation with respect to the surge of thrombotic complaints. The Knowledge Representation and Reasoning procedures used will be based on an extension to the Logic Programming language, allowing the handling of incomplete and/or default data. The computational framework in place will be centered on Artificial Neural Networks.

Artificial neural networks in diagnosis of liver diseases

Liver diseases have severe patients’ consequences, being one of the main causes of premature death. These facts reveal the centrality of one`s daily habits, and how important it is the early diagnosis of these kind of illnesses, not only to the patients themselves, but also to the society in general. Therefore, this work will focus on the development of a diagnosis support system to these kind of maladies, built under a formal framework based on Logic Programming, in terms of its knowledge representation and reasoning procedures, complemented with an approach to computing grounded on Artificial Neural Networks.

Logic programming and artificial neural networks in breast cancer detection

About 90% of breast cancers do not cause or are capable of producing death if detected at an early stage and treated properly. Indeed, it is still not known a specific cause for the illness. It may be not only a beginning, but also a set of associations that will determine the onset of the disease. Undeniably, there are some factors that seem to be associated with the boosted risk of the malady. Pondering the present study, different breast cancer risk assessment models where considered. It is our intention to develop a hybrid decision support system under a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate the risk of developing breast cancer and the respective Degree-of-Confidence that one has on such a happening.

Electronic transport across linear defects in graphene

We investigate the low-energy electronic transport across grain boundaries in graphene ribbons and infinite flakes. Using the recursive Green’s function method, we calculate the electronic transmission across different types of grain boundaries in graphene ribbons. We show results for the charge density distribution and the current flow along the ribbon. We study linear defects at various angles with the ribbon direction, as well as overlaps of two monolayer ribbon domains forming a bilayer region. For a class of extended defect lines with periodicity 3, an analytic approach is developed to study transport in infinite flakes. This class of extended grain boundaries is particularly interesting, since the K and K0 Dirac points are superposed.

Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm

The artificial fish swarm algorithm has recently been emerged in continuous global optimization. It uses points of a population in space to identify the position of fish in the school. Many real-world optimization problems are described by 0-1 multidimensional knapsack problems that are NP-hard. In the last decades several exact as well as heuristic methods have been proposed for solving these problems. In this paper, a new simpli ed binary version of the artificial fish swarm algorithm is presented, where a point/ fish is represented by a binary string of 0/1 bits. Trial points are created by using crossover and mutation in the different fi sh behavior that are randomly selected by using two user de ned probability values. In order to make the points feasible the presented algorithm uses a random heuristic drop item procedure followed by an add item procedure aiming to increase the profit throughout the adding of more items in the knapsack. A cyclic reinitialization of 50% of the population, and a simple local search that allows the progress of a small percentage of points towards optimality and after that refines the best point in the population greatly improve the quality of the solutions. The presented method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method can be an alternative method for solving these problems.

Desenvolvimento de rede neuronal artificial para o diagnóstico de doenças neurodegenerativas

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e Computadores