MS Prevalence and Patients' Characteristics in the District of Braga, Portugal

Multiple Sclerosis (MS) is a chronic autoimmune disease of the Central Nervous System causing inflammation and neurodegeneration. There are only 3 epidemiological studies in Portugal, 2 in the Centre and 1 in the North, and there is the need to further study MS epidemiology in this country. The objective of this work is to contribute to the MS epidemiological knowledge in Portugal, describing the patients' epidemiological, demographic, and clinical characteristics in the Braga district of Portugal. This is a cross-sectional study of 345 patients followed in two hospitals of Braga district. These hospitals cover a resident population of 866,012 inhabitants. The data was collected from the clinical records, and 31/12/2009 was established as the prevalence day. For all MS patients, demographic characteristics and clinical outcomes are reported. We have found an incidence of 2.74/100,000 and a prevalence of 39.82/100,000 inhabitants. Most patients have an EDSS of 3 or lower and a mean age of 42 years. The diagnosis was done at mean age of 35, with RRMS being the disease type in more than 80% of patients. In this cohort, we found a female : male ratio of 1.79. More than 50% of patients are treated with Interferon β-1b IM or IFNβ-1a SC 22 μg

Unsupervised Clustering of Quantitative Imaging Phenotypes using Autoencoder and Gaussian Mixture Model

Quantitative medical image computing (radiomics) has been widely applied to build prediction models from medical images. However, overfitting is a significant issue in conventional radiomics, where a large number of radiomic features are directly used to train and test models that predict genotypes or clinical outcomes. In order to tackle this problem, we propose an unsupervised learning pipeline composed of an autoencoder for representation learning of radiomic features and a Gaussian mixture model based on minimum message length criterion for clustering. By incorporating probabilistic modeling, disease heterogeneity has been taken into account. The performance of the proposed pipeline was evaluated on an institutional MRI cohort of 108 patients with colorectal cancer liver metastases. Our approach is capable of automatically selecting the optimal number of clusters and assigns patients into clusters (imaging subtypes) with significantly different survival rates. Our method outperforms other unsupervised clustering methods that have been used for radiomics analysis and has comparable performance to a state-of-the-art imaging biomarker.Comment: Accepted at MICCAI 201

An apoplastic fluid extraction method for the characterization of grapevine leaves proteome and metabolome from a single sample

The analysis of complex biological systems keeps challenging researchers. The main goal of systems biology is to decipher interactions within cells, by integrating datasets from large scale analytical approaches including transcriptomics, proteomics and metabolomics andmore specialized ‘OMICS’ such as epigenomics and lipidomics. Studying different cellular compartments allows a broader understanding of cell dynamics. Plant apoplast, the cellular compartment external to the plasma membrane including the cell wall, is particularly demanding to analyze. Despite our knowledge on apoplast involvement on several processes from cell growth to stress responses, its dynamics is still poorly known due to the lack of efficient extraction processes adequate to each plant system.Analyzing woody plants such as grapevine raises even more challenges. Grapevine is among the most important fruit crops worldwide and awider characterization of its apoplast is essential for a deeper understanding of its physiology and cellular mechanisms. Here, we describe, for the first time, a vacuum-infiltrationcentrifugationmethod that allows a simultaneous extraction of grapevine apoplastic proteins and metabolites from leaves on a single sample, compatible with high-throughput mass spectrometry analyses. The extracted apoplast from two grapevine cultivars, Vitis vinifera cv ‘Trincadeira’ and ‘Regent’, was directly used for proteomics and metabolomics analysis. The proteome was analyzed by nanoLC-MS/MS and more than 700 common proteinswere identified, with highly diverse biological functions. The metabolome profile through FT-ICR-MS allowed the identification of 514 unique putative compounds revealing a broad spectrum of molecular classesinfo:eu-repo/semantics/publishedVersio

Museum and herbarium collections for biodiversity research in Angola

The importance of museum and herbarium collections is especially great in biodiverse countries such as Angola, an importance as great as the challenges facing the effective and sustained management of such facilities. The interface that Angola represents between tropical humid climates and semi-desert and desert regions creates conditions for diverse habitats with many rare and endemic species. Museum and herbarium collections are essential foundations for scientific studies, providing references for identifying the components of this diversity, as well as serving as repositories of material for future study. In this review we summarise the history and current status of museum and herbarium collections in Angola and of information on the specimens from Angola in foreign collections. Finally, we provide examples of the uses of museum and herbarium collections, as well as a roadmap towards strengthening the role of collections in biodiversity knowledge generationinfo:eu-repo/semantics/publishedVersio