Organic-inorganic nanostructured multilayers for calcium phosphate biomineralization

The weak fixation of biomaterials within the bone structure is one of the major reasons of implants failures. Calcium phosphate (CaP) coatings are used in bone tissue engineering to improve implant osseointegration by enhancing cellular adhesion, proliferation and differentiation, leading to a tight and stable junction between implant and host bone. It has also been observed that materials compatible with bone tissue either have a CaP coating or develop such a calcified surface upon implantation. Thus, the development of bioactive coatings becomes essential for further improvement of integration with the surrounding tissue. However, most of current applied CaP coatings methods (e.g. physical vapor deposition), cannot be applied to complex shapes and porous implants, provide poor structural control over the coating and prevent incorporation of bioactive organic compounds (e.g. antibiotics, growth factors) because of the used harsh processing conditions. Layer-by-layer (LbL) is a versatile technology that permits the building-up of multilayered polyelectrolyte films in mild conditions based on the alternate adsorption of cationic and anionic elements that can integrate bioactive compounds. As it is recognized in natureâ s biomineralization process the presence of an organic template to induce mineral deposition, this work investigate a ion based biomimetic method where all the process is based on LbL methodology made of weak natural-origin polyelectrolytes. A nanostructured multilayer component, with 5 or 10 bilayers, was produced initially using chitosan and chondroitin sulphate polyelectrolyte biopolymers, which possess similarities with the extracellular matrix and good biocompatibility. The multilayers are then rinsed with a sequential passing of solutions containing Ca2+ and PO43- ions. The formation of CaP over the polyelectrolyte multilayers was confirmed by QCM-D, SEM and EDX. The outcomes show that 10 polyelectrolyte bilayer condition behaved as a  better site for initiating the formation of CaP as the precipitation occur at earlier stages than in 5 polyelectrolyte bilayers one. This denotes that higher number of bilayers could hold the CaP crystals more efficiently. This work achieved uniform coatings that can be applied to any surface with access to the liquid media in a low-temperature method, which potentiates the manufacture of effective bioactive biomaterials with great potential in orthopedic applications.

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)

Adoção e difusão de aplicações das tecnologias da informação: iniciativas lançadas no âmbito de processos de implantação de aplicações em organizações – estudo de práticas profissionais e proposta de ferramenta de suporte

Tese de Doutoramento Tecnologias e Sistemas de Informação

Predicting plateau pressure in intensive medicine for ventilated patients

Barotrauma is identified as one of the leading diseases in Ventilated Patients. This type of problem is most common in the Intensive Care Units. In order to prevent this problem the use of Data Mining (DM) can be useful for predicting their occurrence. The main goal is to predict the occurence of Barotrauma in order to support the health professionals taking necessary precautions. In a first step intensivists identified the Plateau Pressure values as a possible cause of Barotrauma. Through this study DM models (classification) where induced for predicting the Plateau Pressure class (>=30 cm

Clustering barotrauma patients in ICU–A data mining based approach using ventilator variables

Lecture Notes in Computer Science, 9273

Improving quality of medical service with mobile health software

An increasing number of m-Health applications are being developed benefiting health service delivery. In this paper, a new methodology based on the principle of calm computing applied to diagnostic and therapeutic procedure reporting is proposed. A mobile application was designed for the physicians of one of the Portuguese major hospitals, which takes advantage of a multi-agent interoperability platform, the Agency for the Integration, Diffusion and Archive (AIDA). This application allows the visualization of inpatients and outpatients medical reports in a quicker and safer manner, in addition to offer a remote access to information. This project shows the advantages in the use of mobile software in a medical environment but the first step is always to build or use an interoperability platform, flexible, adaptable and pervasive. The platform offers a comprehensive set of services that restricts the development of mobile software almost exclusively to the mobile user interface design. The technology was tested and assessed in a real context by intensivists.

Step towards a patient timeline in intensive care units

In Intensive Medicine, the presentation of medical information is done in many ways, depending on the type of data collected and stored. The way in which the information is presented can make it difficult for intensivists to quickly understand the patient's condition. When there is the need to cross between several types of clinical data sources the situation is even worse. This research seeks to explore a new way of presenting information about patients, based on the timeframe in which events occur. By developing an interactive Patient Timeline, intensivists will have access to a new environment in real-time where they can consult the patient clinical history and the data collected until the moment. The medical history will be available from the moment in which patients is admitted in the ICU until discharge, allowing intensivist to examine data regarding vital signs, medication, exams, among others. This timeline also intends to, through the use of information and models produced by the INTCare system, combine several clinical data in order to help diagnose the future patients’ conditions. This platform will help intensivists to make more accurate decision. This paper presents the first approach of the solution designed

Intelligent decision support to predict patient barotrauma risk in intensive care units

The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patientswas achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: “risk” and “no risk”. Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated.

Revisões da infraestrutura viária local: o reconhecimento do lugar público no território desruralizado e extensamente urbanizadode Santa Maria da Feira

Tese de Doutoramento Arquitetura, Cidade e Território.

La formación b-learning en el máster de español segunda lengua y lengua extranjera de la Universidade do Minho: innovación pedagógica y desafíos educativos

En el actual marco de creciente innovación pedagógica, debido entre otros factores, a la irrupción de nuevas herramientas informáticas, la enseñanza a distancia (e-learning y/o b-learning) va ocupando cada vez más espacio en la oferta educativa de diversas instituciones. En esta dirección, en la Universidade do Minho, y concretamente en el Área de Estudos Espanhóis e Hispano-Americanos,[1] hemos dedicado considerables esfuerzos a la ampliación de nuestra oferta desde 2010: primero en la elaboración e implementación del Curso de Formación Especializada en Español Lengua Extranjera, modalidad b-learning (3 ediciones; 2010-2013), y, actualmente, con el Máster Universitario en Español Lengua Segunda / Lengua Extranjera (vid. www.melsle.ilch.uminho.pt), también b-learning. En las siguientes páginas, nos proponemos compartir una serie de experiencias y reflexiones que han ido surgiendo durante estos años acerca de la formación universitária de profesores de Español Lengua Extranjera, en general, con recurso a la modalidade b-learning; para ello, nos centraremos en los siguientes aspectos: (i) caracterización general y problematización de la enseñanza a distancia en la Universidade do Minho; (ii) descripción del Máster Universitario en Español Lengua Segunda / Lengua Extranjera, acerca del cual detallaremos algunas prácticas adoptadas, relacionadas com la enseñanza e-learning como, por ejemplo, (iii) la coordinación pedagógica o (iv) los enfoques metodológicos adoptados a partir de la experiencia de una Unidad Curricular concreta.

Apoio à decisão na análise inteligente de reclamações

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

Design of bio-based supramolecular structures through self-assembly of α-lactalbumin and lysozyme

Bovine α-lactalbumin (α-La) and lysozyme (Lys), two globular proteins with highly homologous tertiary structures and opposite isoelectric points, were used to produce bio-based supramolecular structures under various pH values (3, 7 and 11), temperatures (25, 50 and 75 °C) and times (15, 25 and 35 min) of heating. Isothermal titration calorimetry experiments showed protein interactions and demonstrated that structures were obtained from the mixture of α-La/Lys in molar ratio of 0.546. Structures were characterized in terms of morphology by transmission electron microscopy (TEM) and dynamic light scattering (DLS), conformational structure by circular dichroism and intrinsic fluorescence spectroscopy and stability by DLS. Results have shown that protein conformational structure and intermolecular interactions are controlled by the physicochemical conditions applied. The increase of heating temperature led to a significant decrease in size and polydispersity (PDI) of α-La–Lys supramolecular structures, while the increase of heating time, particularly at temperatures above 50 °C, promoted a significant increase in size and PDI. At pH 7 supramolecular structures were obtained at microscale – confirmed by optical microscopy – displaying also a high PDI (i.e. > 0.4). The minimum size and PDI (61 ± 2.3 nm and 0.14 ± 0.03, respectively) were produced at pH 11 for a heating treatment of 75 °C for 15 min, thus suggesting that these conditions could be considered as critical for supramolecular structure formation. Its size and morphology were confirmed by TEM showing a well-defined spherical form. Structures at these conditions showed to be stable at least for 30 or 90 days, when stored at 25 or 4 °C, respectively. Hence, α-La–Lys supramolecular structures showed properties that indicate that they are a promising delivery system for food and pharmaceutical applications.

Critical events in mechanically ventilated patients

Mechanical Ventilation is an artificial way to help a Patient to breathe. This procedure is used to support patients with respiratory diseases however in many cases it can provoke lung damages, Acute Respiratory Diseases or organ failure. With the goal to early detect possible patient breath problems a set of limit values was defined to some variables monitored by the ventilator (Average Ventilation Pressure, Compliance Dynamic, Flow, Peak, Plateau and Support Pressure, Positive end-expiratory pressure, Respiratory Rate) in order to create critical events. A critical event is verified when a patient has a value higher or lower than the normal range defined for a certain period of time. The values were defined after elaborate a literature review and meeting with physicians specialized in the area. This work uses data streaming and intelligent agents to process the values collected in real-time and classify them as critical or not. Real data provided by an Intensive Care Unit were used to design and test the solution. In this study it was possible to understand the importance of introduce critical events for Mechanically Ventilated Patients. In some cases a value is considered critical (can trigger an alarm) however it is a single event (instantaneous) and it has not a clinical significance for the patient. The introduction of critical events which crosses a range of values and a pre-defined duration contributes to improve the decision-making process by decreasing the number of false positives and having a better comprehension of the patient condition.

Pervasive patient timeline for intensive care units

This research work explores a new way of presenting and representing information about patients in critical care, which is the use of a timeline to display information. This is accomplished with the development of an interactive Pervasive Patient Timeline able to give to the intensivists an access in real-time to an environment containing patients clinical information from the moment in which the patients are admitted in the Intensive Care Unit (ICU) until their discharge This solution allows the intensivists to analyse data regarding vital signs, medication, exams, data mining predictions, among others. Due to the pervasive features, intensivists can have access to the timeline anywhere and anytime, allowing them to make decisions when they need to be made. This platform is patient-centred and is prepared to support the decision process allowing the intensivists to provide better care to patients due the inclusion of clinical forecasts.

Optimization techniques to detect early ventilation extubation in intensive care units

The decision support models in intensive care units are developed to support medical staff in their decision making process. However, the optimization of these models is particularly difficult to apply due to dynamic, complex and multidisciplinary nature. Thus, there is a constant research and development of new algorithms capable of extracting knowledge from large volumes of data, in order to obtain better predictive results than the current algorithms. To test the optimization techniques a case study with real data provided by INTCare project was explored. This data is concerning to extubation cases. In this dataset, several models like Evolutionary Fuzzy Rule Learning, Lazy Learning, Decision Trees and many others were analysed in order to detect early extubation. The hydrids Decision Trees Genetic Algorithm, Supervised Classifier System and KNNAdaptive obtained the most accurate rate 93.2%, 93.1%, 92.97% respectively, thus showing their feasibility to work in a real environment.