Poly lactic acid based biodegradable stents and functionalization techniques: brief review

Commercial stents, especially metallic ones, present several disadvantages, and this gives rise to the necessity of producing or coating stents with different materials, like natural polymers, in order to improve their biocompatibility and minimize the disadvantages of metallic ones. This review paper discusses some applications of natural-based polymers in stents, namely polylactic acid (PLA) for stent development and chitosan for biocompatible coatings of stents . Furthermore, some effective stent functionalization techniques will be discussed, namely Layer by Layer (LBL) technique.

Desenvolvimento de um BMS para aplicações de mobilidade elétrica

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e de Computadores

Sistema de apoio à decisão clínica: quality of life system

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

Holistic approach, tourism

Holistic refers to something that emphasizes the whole and the interdependence of its parts. It can be conceptualized as interdisciplinary: the claim that most social and economic phenomena cannot be fully understood or explained without adopting an approach that goes beyond the aims of a single scientific discipline.

A inflação pela imaginação: Estudo dos efeitos interferentes

Tese de Doutoramento em Psicologia (Especialidade de Psicologia Experimental e Ciências Cognitivas)

The diagnostic and prognostic role of liquid-based cytology: are we ready to monitor therapy and resistance?

Here, we evaluate the diagnostic and prognostic role of liquid-based cytology (LBC) in different body lesions, including thyroid, lung, effusions and malignant breast lesions. LBC has gained consensus after being applied to both non-gynecologic and fine-needle aspiration cytology. Although some remain sceptical regarding the diagnostic efficacy of LBC, mainly when used alone, in recent years, good results have been obtained as long as it showed a high diagnostic accuracy. Here, we discuss the additional possibility of storing material for the application of ancillary techniques (immunocytochemistry–molecular analysis) with several diagnostic and prognostic advantages, which may pave the way for the challenging evaluation of both monitoring responses to treatment and resistance to targeted therapies in thyroid, lung, breast carcinoma or malignant effusions. Furthermore, it provides the use of several molecular spots as specific targets for personalized therapy.

O efeito da deposição salina nas características das escorrências rodoviárias em zonas costeiras

Tese de Doutoramento em Engenharia Civil.

Flexible gastrointestinal motility pressure sensors based on aluminum thin-film strain-gauge arrays

This paper reports on an innovative approach to measuring intraluminal pressure in the upper gastrointestinal (GI) tract, especially monitoring GI motility and peristaltic movements. The proposed approach relies on thin-film aluminum strain gauges deposited on top of a Kapton membrane, which in turn lies on top of an SU-8 diaphragm-like structure. This structure enables the Kapton membrane to bend when pressure is applied, thereby affecting the strain gauges and effectively changing their electrical resistance. The sensor, with an area of 3.4 mm2, is fabricated using photolithography and standard microfabrication techniques (wet etching). It features a linear response (R2 = 0.9987) and an overall sensitivity of 2.6 mV mmHg−1. Additionally, its topology allows a high integration capability. The strain gauges’ responses to pressure were studied and the fabrication process optimized to achieve high sensitivity, linearity, and reproducibility. The sequential acquisition of the different signals is carried out by a microcontroller, with a 10-bit ADC and a sample rate of 250 Hz. The pressure signals are then presented in a user-friendly interface, developed using the Integrated Development Environment software, QtCreator IDE, for better visualization by physicians.

Real-time monitoring of progression towards renal failure in primary care patients

First published online: December 16, 2014.

Fluência no betão desde as primeiras idades: um estudo experimental

Dissertação de mestrado integrado em Engenharia Civil

Valorização patrimonial e urbana de Braga através da criação de circuitos turísticos

Projeto de mestrado em Património e Turismo Cultural

Aromatic amines sources, environmental impact and remediation

Aromatic amines are widely used industrial chemicals as their major sources in the environment include several chemical industry sectors such as oil refining, synthetic polymers, dyes, adhesives, rubbers, perfume, pharmaceuticals, pesticides and explosives. They result also from diesel exhaust, combustion of wood chips and rubber and tobacco smoke. Some types of aromatic amines are generated during cooking, special grilled meat and fish, as well. The intensive use and production of these compounds explains its occurrence in the environment such as in air, water and soil, thereby creating a potential for human exposure. Since aromatic amines are potential carcinogenic and toxic agents, they constitute an important class of environmental pollutants of enormous concern, which efficient removal is a crucial task for researchers, so several methods have been investigated and applied. In this chapter the types and general properties of aromatic amine compounds are reviewed. As aromatic amines are continuously entering the environment from various sources and have been designated as high priority pollutants, their presence in the environment must be monitored at concentration levels lower than 30 mg L1, compatible with the limits allowed by the regulations. Consequently, most relevant analytical methods to detect the aromatic amines composition in environmental matrices, and for monitoring their degradation, are essential and will be presented. Those include Spectroscopy, namely UV/visible and Fourier Transform Infrared Spectroscopy (FTIR); Chromatography, in particular Thin Layer (TLC), High Performance Liquid (HPLC) and Gas chromatography (GC); Capillary electrophoresis (CE); Mass spectrometry (MS) and combination of different methods including GC-MS, HPLC-MS and CE-MS. Choosing the best methods depend on their availability, costs, detection limit and sample concentration, which sometimes need to be concentrate or pretreated. However, combined methods may give more complete results based on the complementary information. The environmental impact, toxicity and carcinogenicity of many aromatic amines have been reported and are emphasized in this chapter too. Lately, the conventional aromatic amines degradation and the alternative biodegradation processes are highlighted. Parameters affecting biodegradation, role of different electron acceptors in aerobic and anaerobic biodegradation and kinetics are discussed. Conventional processes including extraction, adsorption onto activated carbon, chemical oxidation, advanced oxidation, electrochemical techniques and irradiation suffer from drawbacks including high costs, formation of hazardous by-products and low efficiency. Biological processes, taking advantage of the naturally processes occurring in environment, have been developed and tested, proved as an economic, energy efficient and environmentally feasible alternative. Aerobic biodegradation is one of the most promising techniques for aromatic amines remediation, but has the drawback of aromatic amines autooxidation once they are exposed to oxygen, instead of their degradation. Higher costs, especially due to power consumption for aeration, can also limit its application. Anaerobic degradation technology is the novel path for treatment of a wide variety of aromatic amines, including industrial wastewater, and will be discussed. However, some are difficult to degrade under anaerobic conditions and, thus, other electron acceptors such as nitrate, iron, sulphate, manganese and carbonate have, alternatively, been tested.

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.

Real-Time decision support using data mining to predict blood pressure critical events in intensive medicine patients

Patient blood pressure is an important vital signal to the physicians take a decision and to better understand the patient condition. In Intensive Care Units is possible monitoring the blood pressure due the fact of the patient being in continuous monitoring through bedside monitors and the use of sensors. The intensivist only have access to vital signs values when they look to the monitor or consult the values hourly collected. Most important is the sequence of the values collected, i.e., a set of highest or lowest values can signify a critical event and bring future complications to a patient as is Hypotension or Hypertension. This complications can leverage a set of dangerous diseases and side-effects. The main goal of this work is to predict the probability of a patient has a blood pressure critical event in the next hours by combining a set of patient data collected in real-time and using Data Mining classification techniques. As output the models indicate the probability (%) of a patient has a Blood Pressure Critical Event in the next hour. The achieved results showed to be very promising, presenting sensitivity around of 95%.