A historical perspective and prospects of biomedical research on parasitic diseases

We all hope that biotechnology will answer some social and economical unavoidable requirements of the modern life. It is necessary to improve agriculture production, food abundance and health quality in a sustainable development. It is indeed a hard task to keep the progress on taking into account the rational use of genetic resources and the conservation of biodiversity. In this context, a historical perspective and prospects of the biomedical research on parasitic diseases is described in a view of three generations of investigators. This work begins with a picture of the scientific progress on biomedical research and human health over the last centuries. This black-and-white picture is painted by dissecting current advancements of molecular biology and modern genetics, which are outlined at the meaning of prospecting achievements in health science for this new millenium.

Overview of some biomedical research projects in tropical medicine conducted at the Instituto Venezolano de Investigaciones Cientificas

The Instituto Venezolano de Investigaciones Cientificas (IVIC) is a government-funded multidisciplinary academic institution dedicated to research, development and technology in many areas of knowledge. Biomedical projects and publications comprise about 40% of the total at IVIC. In this article, we present an overview of some selected research and development projects conducted at IVIC which we believe contain new and important aspects related to malaria, ancylostomiasis, dengue fever, leishmaniasis and tuberculosis. Other projects considered of interest in the general area of tropical medicine are briefly described. This article was prepared as a small contribution to honor and commemorate the centenary of the Instituto Oswaldo Cruz.

Essential diagnostics - the role of the Department of Biomedical Research of the Royal Tropical Institute in the 21st century

In the light of emerging and overlooked infectious diseases and widespread drug resistance, diagnostics have become increasingly important in supporting surveillance, disease control and outbreak management programs. In many low-income countries the diagnostic service has been a neglected part of health care, often lacking quantity and quality or even non-existing at all. High-income countries have exploited few of their advanced technical abilities for the much-needed development of low-cost, rapid diagnostic tests to improve the accuracy of diagnosis and accelerate the start of appropriate treatment. As is now also recognized by World Healt Organization, investment in the development of affordable diagnostic tools is urgently needed to further our ability to control a variety of diseases that form a major threat to humanity. The Royal Tropical Institute's Department of Biomedical Research aims to contribute to the health of people living in the tropics. To this end, its multidisciplinary group of experts focuses on the diagnosis of diseases that are major health problems in low-income countries. In partnership we develop, improve and evaluate simple and cheap diagnostic tests, and perform epidemiological studies. Moreover, we advice and support others - especially those in developing countries - in their efforts to diagnose infectious diseases.

Current millennium biotechniques for biomedical research on parasites and host-parasite interactions

The development of biotechnology in the last three decades has generated the feeling that the newest scientific achievements will deliver high standard quality of life through abundance of food and means for successfully combating diseases. Where the new biotechnologies give access to genetic information, there is a common belief that physiological and pathological processes result from subtle modifications of gene expression. Trustfully, modern genetics has produced genetic maps, physical maps and complete nucleotide sequences from 141 viruses, 51 organelles, two eubacteria, one archeon and one eukaryote (Saccharomices cerevisiae). In addition, during the Centennial Commemoration of the Oswaldo Cruz Institute the nearly complete human genome map was proudly announced, whereas the latest Brazilian key stone contribution to science was the publication of the Shillela fastidiosa genomic sequence highlythed on a Nature cover issue. There exists a belief among the populace that further scientific accomplishments will rapidly lead to new drugs and methodological approaches to cure genetic diseases and other incurable ailments. Yet, much evidence has been accumulated, showing that a large information gap exists between the knowledge of genome sequence and our knowledge of genome function. Now that many genome maps are available, people wish to know what are we going to do with them. Certainly, all these scientific accomplishments will shed light on many more secrets of life. Nevertheless, parsimony in the weekly announcements of promising scientific achievements is necessary. We also need many more creative experimental biologists to discover new, as yet un-envisaged biotechnological approaches, and the basic resource needed for carrying out mile stone research necessary for leading us to that "promised land"often proclaimed by the mass media.

Compositional Data in Biomedical Research

Modern methods of compositional data analysis are not well known in biomedical research.Moreover, there appear to be few mathematical and statistical researchersworking on compositional biomedical problems. Like the earth and environmental sciences,biomedicine has many problems in which the relevant scienti c information isencoded in the relative abundance of key species or categories. I introduce three problemsin cancer research in which analysis of compositions plays an important role. Theproblems involve 1) the classi cation of serum proteomic pro les for early detection oflung cancer, 2) inference of the relative amounts of di erent tissue types in a diagnostictumor biopsy, and 3) the subcellular localization of the BRCA1 protein, and it'srole in breast cancer patient prognosis. For each of these problems I outline a partialsolution. However, none of these problems is \solved". I attempt to identify areas inwhich additional statistical development is needed with the hope of encouraging morecompositional data analysts to become involved in biomedical research

Identifying reasons for failure in biomedical research and publishing

The regular assessment of Brazilian scientific output means that individual university departments need to constantly improve the quantity and quality of their scientific output. A significant proportion of this output involves the work of Master’s and Doctoral students, but getting this work published in a suitable journal can often prove to be a challenge. Although students’ lack of fluency in English is a contributing factor, many of the problems observed have an early origin in the formulation of the research problem and its relevance to current research trends in the international literature. In short, more time needs to be spent in the library and less in the laboratory, and more effort needs to be made in teaching students basic research skills such as the effective use of bibliographic databases like PubMed, Web of Science and Scopus.

Compositional Data in Biomedical Research

Modern methods of compositional data analysis are not well known in biomedical research. Moreover, there appear to be few mathematical and statistical researchers working on compositional biomedical problems. Like the earth and environmental sciences, biomedicine has many problems in which the relevant scienti c information is encoded in the relative abundance of key species or categories. I introduce three problems in cancer research in which analysis of compositions plays an important role. The problems involve 1) the classi cation of serum proteomic pro les for early detection of lung cancer, 2) inference of the relative amounts of di erent tissue types in a diagnostic tumor biopsy, and 3) the subcellular localization of the BRCA1 protein, and it's role in breast cancer patient prognosis. For each of these problems I outline a partial solution. However, none of these problems is \solved". I attempt to identify areas in which additional statistical development is needed with the hope of encouraging more compositional data analysts to become involved in biomedical research

TNFRSF13B Genetic variability an anthropological - evolutionary approach to Biomedical Research

In the recent years TNFRSF13B coding variants have been implicated by clinical genetics studies in Common Variable Immunodeficiency (CVID), the most common clinically relevant primary immunodeficiency in individuals of European ancestry, but their functional effects in relation to the development of the disease have not been entirely established. To examine the potential contribution of such variants to CVID, the more comprehensive perspective of an evolutionary approach was applied in this study, underling the belief that evolutionary genetics methods can play a role in dissecting the origin, causes and diffusion of human diseases, representing a powerful tool also in human health research. For this purpose, TNFRSF13B coding region was sequenced in 451 healthy individuals belonging to 26 worldwide populations, in addition to 96 control, 77 CVID and 38 Selective IgA Deficiency (IgAD) individuals from Italy, leading to the first achievement of a global picture of TNFRSF13B nucleotide diversity and haplotype structure and making suggestion of its evolutionary history possible. A slow rate of evolution, within our species and when compared to the chimpanzee, low levels of genetic diversity geographical structure and the absence of recent population specific selective pressures were observed for the examined genomic region, suggesting that geographical distribution of its variability is more plausibly related to its involvement also in innate immunity rather than in adaptive immunity only. This, together with the extremely subtle disease/healthy samples differences observed, suggests that CVID might be more likely related to still unknown environmental and genetic factors, rather than to the nature of TNFRSF13B variants only.

The Virtual Skeleton Database: An Open Access Repository for Biomedical Research and Collaboration

Background: Statistical shape models are widely used in biomedical research. They are routinely implemented for automatic image segmentation or object identification in medical images. In these fields, however, the acquisition of the large training datasets, required to develop these models, is usually a time-consuming process. Even after this effort, the collections of datasets are often lost or mishandled resulting in replication of work. Objective: To solve these problems, the Virtual Skeleton Database (VSD) is proposed as a centralized storage system where the data necessary to build statistical shape models can be stored and shared. Methods: The VSD provides an online repository system tailored to the needs of the medical research community. The processing of the most common image file types, a statistical shape model framework, and an ontology-based search provide the generic tools to store, exchange, and retrieve digital medical datasets. The hosted data are accessible to the community, and collaborative research catalyzes their productivity. Results: To illustrate the need for an online repository for medical research, three exemplary projects of the VSD are presented: (1) an international collaboration to achieve improvement in cochlear surgery and implant optimization, (2) a population-based analysis of femoral fracture risk between genders, and (3) an online application developed for the evaluation and comparison of the segmentation of brain tumors. Conclusions: The VSD is a novel system for scientific collaboration for the medical image community with a data-centric concept and semantically driven search option for anatomical structures. The repository has been proven to be a useful tool for collaborative model building, as a resource for biomechanical population studies, or to enhance segmentation algorithms.