Stage at presentation of breast cancer in Luanda, Angola - a retrospective study

Background: It is expected that, by 2020, 15 million new cases of cancer will occur every year in the world, one million of them in Africa. Knowledge of cancer trends in African countries is far from adequate, and improvements in cancer prevention efforts are urgently needed. The aim of this study was to characterize breast cancer clinically and pathologically at presentation in Luanda, Angola; we additionally provide quality information that will be useful for breast cancer care planning in the country. Methods: Data on breast cancer cases were retrieved from the Angolan Institute of Cancer Control, from 2006 to 2014. For women diagnosed in 2009 (5-years of follow-up), demographic, clinical and pathological information, at presentation, was collected, namely age at diagnosis, parity, methods used for pathological diagnoses, tumor pathological characteristics, stage of disease and treatment. Descriptive statistics were performed. Results: The median age of women diagnosed with breast cancer in 2009 was 47 years old (range 25–89). The most frequent clinical presentation was breast swelling with axillary lymph nodes metastasis (44.9 %), followed by a mass larger than 5 cm (14.2 %) and lump (12.9 %). Invasive ductal carcinoma was the main histologic type (81.8 %). Only 10.1 % of cancer cases had a well differentiated histological grade. Cancers were diagnosed mostly at advanced stages (66.7 % in stage III and 11.1 % in stage IV). Discussion: In this study, breast cancer was diagnosed at a very advanced stage. Although it reports data from a single cancer center in Luanda, Angola it reinforces the need for early diagnosis and increasing awareness. According to the main challenges related to breast cancer diagnosis and treatment herein presented, we propose a realistic framework that would allow for the implementation of a breast cancer care program, built under a strong network based on cooperation, teaching, audit, good practices and the organization of health services. Conclusion: Angola needs urgently a program for early diagnosis of breast cancer.

O impacto das redes socias na cessação do contrato de trabalho: liberdade de expressão versus justa causa de despedimento

Dissertação de mestrado em Direito e Informática

Reconstruction of the regulatory network for Bacillus subtilis and reconciliation with gene expression data

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb. 2016.00275

Quando a luta pelos direitos sexuais se faz no feminino: um estudo de caso sobre o associativismo lésbico em Portugal

Dissertação de mestrado em Sociologia (área de especialização em Desenvolvimento e Políticas Sociais)

Tapping the wealth of microbial data in high-throughput metabolic model reconstruction

Genome-scale metabolic models are valuable tools in the metabolic engineering process, based on the ability of these models to integrate diverse sources of data to produce global predictions of organism behavior. At the most basic level, these models require only a genome sequence to construct, and once built, they may be used to predict essential genes, culture conditions, pathway utilization, and the modifications required to enhance a desired organism behavior. In this chapter, we address two key challenges associated with the reconstruction of metabolic models: (a) leveraging existing knowledge of microbiology, biochemistry, and available omics data to produce the best possible model; and (b) applying available tools and data to automate the reconstruction process. We consider these challenges as we progress through the model reconstruction process, beginning with genome assembly, and culminating in the integration of constraints to capture the impact of transcriptional regulation. We divide the reconstruction process into ten distinct steps: (1) genome assembly from sequenced reads; (2) automated structural and functional annotation; (3) phylogenetic tree-based curation of genome annotations; (4) assembly and standardization of biochemistry database; (5) genome-scale metabolic reconstruction; (6) generation of core metabolic model; (7) generation of biomass composition reaction; (8) completion of draft metabolic model; (9) curation of metabolic model; and (10) integration of regulatory constraints. Each of these ten steps is documented in detail.

An object-oriented model for rapid prototyping of data path/control systems - a case study

Publicado em "Information control in manufacturing 1998 : (INCOM'98) : advances in industrial engineering : a proceedings volume from the 9th IFAC Symposium, Nancy-Metz, France, 24-26 June 1998. Vol. 2"

An R package for the integrated analysis of metabolomics and spectral data

Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as Nuclear Magnetic Resonance, Gas or Liquid Chromatography, Mass Spectrometry, Infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.

Estimation of statistical cure from cancer using population-based data

Dissertação de mestrado em Estatística

Breastfeeding and depression: a systematic review of the literature

Background: Research has separately indicated associations between pregnancy depression and breastfeeding, breastfeeding and postpartum depression, and pregnancy and postpartum depression. This paper aimed to provide a systematic literature review on breastfeeding and depression, considering both pregnancy and postpartum depression. Methods: An electronic search in three databases was performed using the keywords: “breast feeding”, “bottle feeding”, “depression”, “pregnancy”, and “postpartum”. Two investigators independently evaluated the titles and abstracts in a first stage and the full-text in a second stage review. Papers not addressing the association among breastfeeding and pregnancy or postpartum depression, non-original research and research focused on the effect of antidepressants were excluded. 48 studies were selected and included. Data were independently extracted. Results: Pregnancy depression predicts a shorter breastfeeding duration, but not breastfeeding intention or initiation. Breastfeeding duration is associated with postpartum depression in almost all studies. Postpartum depression predicts and is predicted by breastfeeding cessation in several studies. Pregnancy and postpartum depression are associated with shorter breastfeeding duration. Breastfeeding may mediate the association between pregnancy and postpartum depression. Pregnancy depression predicts shorter breastfeeding duration and that may increase depressive symptoms during postpartum. Limitations: The selected keywords may have led to the exclusion of relevant references. Conclusions: Although strong empirical evidence regarding the associations among breastfeeding and pregnancy or postpartum depression was separately provided, further research, such as prospective studies, is needed to clarify the association among these three variables. Help for depressed pregnant women should be delivered to enhance both breastfeeding and postpartum psychological adjustment.

The alarm distress baby scale in a longitudinal Portuguese study reanalyzed with attachment data

Article first published online: 13 NOV 2013

Parents' physical victimization in childhood and current risk of child maltreatment: the mediator role of psychosomatic symptoms

Objective: To test the potential mediation effect of psychosomatic symptoms on the relationship between parents' history of childhood physical victimization and current risk for child physical maltreatment. Methods: Data from the Portuguese National Representative Study of Psychosocial Context of Child Abuse and Neglect were used. Nine-hundred and twenty-four parents completed the Childhood History Questionnaire, the Psychosomatic Scale of the Brief Symptom Inventory, and the Child Abuse Potential Inventory. Results: Mediation analysis revealed that the total effect of the childhood physical victimization on child maltreatment risk was significant. The results showed that the direct effect from the parents' history of childhood physical victimization to their current maltreatment risk was still significant once parents' psychosomatic symptoms were added to the model, indicating that the increase in psychosomatic symptomatology mediated in part the increase of parents' current child maltreatment risk. Discussion: The mediation analysis showed parents' psychosomatic symptomatology as a causal pathway through which parents' childhood history of physical victimization exerts its effect on increased of child maltreatment risk. Somatization-related alterations in stress and emotional regulation are discussed as potential theoretical explanation of our findings. A cumulative risk perspective is also discussed in order to elucidate about the mechanisms that contribute for the intergenerational continuity of child physical maltreatment.

Pervasive decision support to predict football corners and goals by means of data mining

Football is considered nowadays one of the most popular sports. In the betting world, it has acquired an outstanding position, which moves millions of euros during the period of a single football match. The lack of profitability of football betting users has been stressed as a problem. This lack gave origin to this research proposal, which it is going to analyse the possibility of existing a way to support the users to increase their profits on their bets. Data mining models were induced with the purpose of supporting the gamblers to increase their profits in the medium/long term. Being conscience that the models can fail, the results achieved by four of the seven targets in the models are encouraging and suggest that the system can help to increase the profits. All defined targets have two possible classes to predict, for example, if there are more or less than 7.5 corners in a single game. The data mining models of the targets, more or less than 7.5 corners, 8.5 corners, 1.5 goals and 3.5 goals achieved the pre-defined thresholds. The models were implemented in a prototype, which it is a pervasive decision support system. This system was developed with the purpose to be an interface for any user, both for an expert user as to a user who has no knowledge in football games.

Towards a pervasive data mining engine: architecture overview

Current data mining engines are difficult to use, requiring optimizations by data mining experts in order to provide optimal results. To solve this problem a new concept was devised, by maintaining the functionality of current data mining tools and adding pervasive characteristics such as invisibility and ubiquity which focus on their users, providing better ease of use and usefulness, by providing autonomous and intelligent data mining processes. This article introduces an architecture to implement a data mining engine, composed by four major components: database; Middleware (control); Middleware (processing); and interface. These components are interlinked but provide independent scaling, allowing for a system that adapts to the user’s needs. A prototype has been developed in order to test the architecture. The results are very promising and showed their functionality and the need for further improvements.

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.

Predicting triage waiting time in maternity emergency care by means of data mining

Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. In Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto (CHP), an intelligent pre-triage system is implemented, aiming to prioritize patients in need of gynaecology and obstetrics care in two classes: urgent and consultation. The system is designed to evade emergency problems such as incorrect triage outcomes and extensive triage waiting times. The current study intends to improve the triage system, and therefore, optimize the patient workflow through the emergency room, by predicting the triage waiting time comprised between the patient triage and their medical admission. For this purpose, data mining (DM) techniques are induced in selected information provided by the information technologies implemented in CMIN. The DM models achieved accuracy values of approximately 94% with a five range target distribution, which not only allow obtaining confident prediction models, but also identify the variables that stand as direct inducers to the triage waiting times.