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.

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)

Tube-jack and sonic testing for the evaluation of the state of stress in historical masonry

In the investigation and diagnosis of damages to historical masonry structures, the state of stress of the masonry is an important characteristic that must be determined with as much accuracy as possible. Flat-jack testing is a traditional method used to determine the state of stress in historical masonry structures. However, when irregular masonry is tested the method can cause damage to the masonry units and the accuracy of the method is reduced. An enhanced technique, called tube-jack testing, is being developed at the University of Minho to reduce the damage caused during testing and improve the accuracy when used on irregular masonry. This method uses multiple cylindrical jacks inserted in a line of holes drilled in the mortar joints of the masonry, avoiding damage to the masonry units. Concurrently with the development of tube-jack testing, the effect of stress state on sonic testing is being studied. Sonic testing is often used to determine locations of voids and damage in masonry. The focus of these studies was to determine if the state of stress is influencing the sonic test results. In this paper the results of tube-jack testing and sonic testing on masonry walls, built for the purpose of this study in the laboratory, loaded in compression is presented. The tube-jack testing is used to estimate the state of stress in the masonry and the sonic test results are evaluated based on the effect of the applied load on the wall. Future testing and study are suggested for continued development of these test methods.

Neural correlates of face familiarity in institutionally reared children with distinctive, atypical social behavior

Although the impact of early adverse experience on neural processing of face familiarity has been studied, research has not taken into account disordered child behavior. This work compared the neural processing of familiar versus strangers' faces in 47 institutionalized children with a mean age of 54 months to determine the effects of (a) the presence versus absence of atypical social behavior and (b) inhibited versus indiscriminant atypical behavior. Results revealed a pattern of cortical hypoactivation in institutionalized children manifesting atypical social behavior and that inhibited children displayed larger neural response to a caregiver's face than to the stranger's, while indiscriminant children did not discriminate between stimuli. These findings suggest that neural correlates of face familiarity are associated with social functioning in institutionalized children.

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.

Learning joint representations for order and timing of perceptual-motor sequences: a dynamic neural field approach

Many of our everyday tasks require the control of the serial order and the timing of component actions. Using the dynamic neural field (DNF) framework, we address the learning of representations that support the performance of precisely time action sequences. In continuation of previous modeling work and robotics implementations, we ask specifically the question how feedback about executed actions might be used by the learning system to fine tune a joint memory representation of the ordinal and the temporal structure which has been initially acquired by observation. The perceptual memory is represented by a self-stabilized, multi-bump activity pattern of neurons encoding instances of a sensory event (e.g., color, position or pitch) which guides sequence learning. The strength of the population representation of each event is a function of elapsed time since sequence onset. We propose and test in simulations a simple learning rule that detects a mismatch between the expected and realized timing of events and adapts the activation strengths in order to compensate for the movement time needed to achieve the desired effect. The simulation results show that the effector-specific memory representation can be robustly recalled. We discuss the impact of the fast, activation-based learning that the DNF framework provides for robotics applications.

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.

[Letter to the editor] Sustained effects of a neural-based intervention in a refractory case of Tourette syndrome

There are only a few treatments available for Tourette syndrome (TS). These treatments frequently do notwork in patients with moderate to severe TS [1]. Neuroimaging studies show a correlation between tics severity and increased activation over motor pathways, along with reduced activation over the control areas of the cortico-striato-thalamo-cortical circuits [2]. Moreover, the temporal pattern of tic generation suggests that cortical activation especially in the SMA precedes subcortical activation [3]. Following this assumption, here we explored the brain effects of 10-daily sessions of cathodal transcranial Direct Current Stimulation (tDCS) delivered over the pre-SMA in a patient with refractory and severe TS and also assessed whether those changes were long lasting (up to 6 months).

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.

High-aspect-ratio tridimensional electrode arrays for neural applications

Tese de Doutoramento em Engenharia Biomédica.