Novas bio-estratégias para a destoxificação de ocratoxina A utilizando bactérias do ácido láctico

A ocorrência de bolores micotoxigénicos pertencentes aos géneros Aspergillus, Penicillium e Fusarium em alimentos para consumo Humano e animal, tem um impacto importante sobre a saúde pública e constitui também um importante problema económico. Isto é devido à síntese por este tipo de fungos filamentosos de metabolitos altamente tóxicos conhecidos como micotoxinas. A maioria das micotoxinas são substâncias cancerígenas, mutagénicas, neurotóxicas e imunossupressoras, sendo a ocratoxina A (OTA) uma das mais importantes. A OTA é uma micotoxina, tóxica para os animais e Humanos principalmente devido às suas propriedades nefrotóxicas. Alguns grupos de bactérias gram positivas nomeadamente as bactérias do ácido láctico (BAL) são capazes de controlar o crescimento de fungos, melhorando e aumentando a vida útil de muitos produtos fermentados e, assim, reduzir os riscos para a saúde provocados pela exposição às micotoxinas. Algumas BAL são, também, capazes de destoxificar certas micotoxinas. Em trabalhos anteriores do nosso grupo foi observada a biodegradação da OTA por estirpes de Pediococcus parvulus isoladas de vinhos do Douro. Assim, com este trabalho, pretendeu-se compreender com maior detalhe o processo de biodegradação da OTA pelas referidas estirpes e identificar quais as enzimas que estão associadas à sua biodegradação. Para atingir este objetivo utilizaram-se algumas ferramentas ioinformáticas (BLAST, CLUSTALX2, CLC Sequence Viewer 7, Finch TV), desenharam-se primers específicos e realizaram-se PCR específicos para os genes envolvidos. Através da utilização de ferramentas de bioinformática, foi possível identificar várias proteínas que pertencem à família das carboxipeptidases e que podem eventualmente participar no processo da degradação da OTA, tais como D-Ala-D-Ala carboxipeptidase serínica e carboxipeptidase membranar. Estas BAL podem desempenhar um papel importante na destoxificação da OTA, sendo as carboxipeptidases uma das enzimas envolvidas na sua biodegradação.

O documentário, o real e o ponto de vista

Relatório de estágio de mestrado em Ciências da Comunicação (área de especialização em Audiovisual e Multimédia)

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.

Real-Time models to predict the use of vasopressors in monitored patients

The needs of reducing human error has been growing in every field of study, and medicine is one of those. Through the implementation of technologies is possible to help in the decision making process of clinics, therefore to reduce the difficulties that are typically faced. This study focuses on easing some of those difficulties by presenting real-time data mining models capable of predicting if a monitored patient, typically admitted in intensive care, will need to take vasopressors. Data Mining models were induced using clinical variables such as vital signs, laboratory analysis, among others. The best model presented a sensitivity of 94.94%. With this model it is possible reducing the misuse of vasopressors acting as prevention. At same time it is offered a better care to patients by anticipating their treatment with vasopressors.

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%.

A Real-Time intelligent system for tracking patient condition

Hospitals have multiple data sources, such as embedded systems, monitors and sensors. The number of data available is increasing and the information are used not only to care the patient but also to assist the decision processes. The introduction of intelligent environments in health care institutions has been adopted due their ability to provide useful information for health professionals, either in helping to identify prognosis or also to understand patient condition. Behind of this concept arises this Intelligent System to track patient condition (e.g. critic events) in health care. This system has the great advantage of being adaptable to the environment and user needs. The system is focused in identifying critic events from data streaming (e.g. vital signs and ventilation) which is particularly valuable for understanding the patient’s condition. This work aims to demonstrate the process of creating an intelligent system capable of operating in a real environment using streaming data provided by ventilators and vital signs monitors. Its development is important to the physician because becomes possible crossing multiple variables in real-time by analyzing if a value is critic or not and if their variation has or not clinical importance.

Development of nanohydrogels as a vehicle for the release of bioactive compounds in food matrices

PhD in Chemical and Biological Engineering

Reúso do património do movimento moderno: a panificadora de Vila Real

Dissertação de mestrado integrado em Arquitectura (área de especialização em Cultura Arquitectónica)

The real economic effects of cross-delistingfrom the united states: evidence on post-operating performance, financial constraints and stock crash risk

Tese de Doutoramento em Ciências Empresariais.

Avaliação de projetos de eficiência energética na iluminação pública

Dissertação de mestrado em Engenharia Industrial