Segredo médico e o direito humano à privacidade: uma abordagem jurídica / Secreto médico y el derecho humano a la privacidad: un abordaje jurídico / Medical confidentiality and the human right to privacy: a legal approach

Este artigo apresenta uma análise crítica do segredo médico. Busca compreendê-lo de maneira ampla, desde sua origem e respaldo ético no juramento de Hipócrates. Passa pela normatização disciplinar e penal para chegar à concepção do direito humano à privacidade. O objetivo é localizar as diferentes abordagens jurídicas do segredo médico e demonstrar os diversos aspectos de que se reveste e que culminam atualmente com sua colocação como parte do direito à privacidade.

Este articulo presenta un análisis crítico del secreto médico. Trata de entender de una manera amplia, desde sus inicios y la ética de apoyo en el juramento hipocrático. Por medio de las normas penales y disciplinarias para llegar a la idea de que el derecho humano a la privacidad. Su objetivo es encontrar los diferentes enfoques legales del secreto médico y demostrar los diferentes aspectos que contiene y que culminó con suubicación actual como parte del derecho a la privacidad.

This paper presents a critical analysis of medical confidentiality. It attempts to understand it in a wide way, from its inception and ethics base that can be found in the Hippocratic oath. It goes through criminal and disciplinary norms in order to reach the concept of the human right to privacy. The aim is to find the different legal approaches of medical confidentiality and to demonstrate the various aspects it consists of, currently making it as a part of the right to privacy.

Using linear algebra for protein structural comparison and classification

In this article, we describe a novel methodology to extract semantic characteristics from protein structures using linear algebra in order to compose structural signature vectors which may be used efficiently to compare and classify protein structures into fold families. These signatures are built from the pattern of hydrophobic intrachain interactions using Singular Value Decomposition (SVD) and Latent Semantic Indexing (LSI) techniques. Considering proteins as documents and contacts as terms, we have built a retrieval system which is able to find conserved contacts in samples of myoglobin fold family and to retrieve these proteins among proteins of varied folds with precision of up to 80 percent. The classifier is a web tool available at our laboratory website. Users can search for similar chains from a specific PDB, view and compare their contact maps and browse their structures using a JMol plug-in.