Candida tropicalis biofilms: biomass, metabolic activity and secreted aspartyl proteinase production

According to epidemiological data, Candida tropicalis has been related to urinary tract infections and haematological malignancy. Several virulence factors seem to be responsible for C. tropicalis infections, for example: their ability to adhere and to form biofilms onto different indwelling medical devices; their capacity to adhere, invade and damage host human tissues due to enzymes production such as proteinases. The main aim of this work was to study the behaviour of C. tropicalis biofilms of different ages (24120 h) formed in artificial urine (AU) and their ability to express aspartyl proteinase (SAPT) genes. The reference strain C. tropicalis ATCC 750 and two C. tropicalis isolates from urine were used. Biofilms were evaluated in terms of culturable cells by colony-forming units enumeration; total biofilm biomass was evaluated using the crystal violet staining method; metabolic activity was evaluated by XTT assay; and SAPT gene expression was determined by real-time PCR. All strains of C. tropicalis were able to form biofilms in AU, although with differences between strains. Candida tropicalis biofilms showed a decrease in terms of the number of culturable cells from 48 to 72 h. Generally, SAPT3 was highly expressed. C. tropicalis strains assayed were able to form biofilms in the presence of AU although in a strain- and time-dependent way, and SAPT genes are expressed during C. tropicalis biofilm formation.

Pervasive adaptive data acquisition gateway for critical healthcare

The data acquisition process in real-time is fundamental to provide appropriate services and improve health professionals decision. In this paper a pervasive adaptive data acquisition architecture of medical devices (e.g. vital signs, ventilators and sensors) is presented. The architecture was deployed in a real context in an Intensive Care Unit. It is providing clinical data in real-time to the INTCare system. The gateway is composed by several agents able to collect a set of patients’ variables (vital signs, ventilation) across the network. The paper shows as example the ventilation acquisition process. The clients are installed in a machine near the patient bed. Then they are connected to the ventilators and the data monitored is sent to a multithreading server which using Health Level Seven protocols records the data in the database. The agents associated to gateway are able to collect, analyse, interpret and store the data in the repository. This gateway is composed by a fault tolerant system that ensures a data store in the database even if the agents are disconnected. The gateway is pervasive, universal, and interoperable and it is able to adapt to any service using streaming data.

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.

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

Relationship between estimation and real motor performance in school-age children

The relationship between estimated and real motor competences was analyzed for several tasks. Participants were 303 children (160 boys and 143 girls), which had between 6 and 10 years of age (M=8.63, SD=1.16). None of the children presented developmental difficulties or learning disabilities, and all attended age-appropriate classes. Children were divided into three groups according to their age: group 1 (N= 102; age range: 6.48-8.01 years); group 2 (N= 101; age range: 8.02-9.22 years); and group 3 (N=100; age range: 9.24-10.93 years). Children were asked to predict their maximum distance for a locomotor, a manipulative, and a balance task, prior to performing those tasks. Children’s estimations were compared with their real performance to determine their accuracy. Children had, in general, a tendency to overestimate their performance (standing long jump: 56.11%, kicking: 63.37%, throwing: 73.60%, and Walking Backwards (WB) on a balance beam: 45.21%), and older children tended to be more accurate, except for the manipulative tasks. Furthermore, the relationship between estimation and real performance in children with different levels of motor coordination (Köperkoordinationstest für Kinder, KTK) was analyzed. The 75 children with the highest score comprised the Highest Motor Coordination (HMC) group, and the 78 children with the lowest score were placed in the Lowest Motor Coordination (LMC) group. There was a tendency for LMC and HMC children to overestimate their skills at all tasks, except for the HMC group at the WB task. Children with the HMC level tended to be more accurate when predicting their motor performance; however, differences in absolute percent error were only significant for the throwing and WB tasks. In conclusion, children display a tendency to overestimate their performance independently of their motor coordination level and task. This fact may be determinant to the development of their motor competences, since they are more likely to engage and persist in motor tasks, but it might also increase the occurrence of unintended injuries.

Demonstração do mecanismo da terapia de ressincronização ventricular com ecocardiografia tridimensional em tempo real em paciente com insuficiência cardíaca

Relatamos caso de homem de 66 anos de idade portador de insuficiência cardíaca classe funcional (NYHA) IV que foi submetido a terapia de ressincronização cardíaca por implante de marcapasso biventricular. O paciente foi avaliado antes e 48 horas após o implante do marcapasso com o emprego da ecocardiografia tridimensional transtorácica em tempo real. A utilização da ecocardiografia tridimensional contribuiu para o entendimento do mecanismo envolvido na ressincronização cardíaca através da demonstração da melhor sincronização dos segmentos cardíacos, o que resultou em melhora clínica do paciente.