Chagas disease and HIV co-infection: genetic analyses of two Trypanosoma cruzi strains under experimental immunosuppression

BACKGROUND: Recently new aspects of the immunopathology of Chagas disease have been described in patients infected with HIV and unusual clinical manifestations such as cutaneous lesions, involvement of central nervous system and/or serious cardiac lesions related to the reactivation of the parasite have been reported. Two uncloned Trypanosoma cruzi strains previously isolated from chronic chagasic patients with HIV co-infection were studied in order to evaluate the impact of the immunosuppression on the genetic diversity of the parasite. RESULTS: We have exploited an experimental model to determine whether genetically distinct populations appear after immunosuppression as a consequence of in vivo selection or in vitro propagation. The in vitro and in vivo conditions have allowed us to study the selected populations. The first strain was isolated from a case of reactivation of Chagas disease in a patient which presented four cerebral lesions. It was possible to demonstrate that the patient was infected with at least three distinct populations of T. cruzi. The population, recovered after immunosuppression, in mice was genetically divergent from the primary human isolate. The second strain, isolated from a hemophiliac/HIV positive patient presenting cardiac manifestation of Chagas disease showed no marked genetic difference after experimental immunosuppression. CONCLUSION: The immunological condition of the patient, associated or not to the reactivation of the infection, and also the strain of the parasite may have an important role during the course of the disease. The in vivo mechanism that generates parasite genetic variability or the participation of the selection under stress conditions will require further investigation

Niobium and niobium-iron coatings on API 5LX 70 steel applied with HVOF

The present study aimed to create and characterize niobium and niobium-iron60% coatings applied to steel API 5L X70 using the hypersonic thermal spray process (HVOF). The morphologies of the coatings were analyzed using scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), X-ray diffraction (XRD) and profilometry, while the coatings’ hardnesses was evaluated using the Vickers hardness test. The coatings’ corrosion resistance was evaluated by monitoring their open circuit potential and potentiodynamic polarization and performing electrochemical impedance spectroscopy in a 0.05 M NaCl solution. The results showed that the niobium-iron coating contained minor porosity regions, while such defects occurred over large regions of the niobium coating. In terms of corrosion resistance, the coatings obtained in this work promoted a reduction in the substrate’s corrosion rate, but the presence of discontinuities such as porosity compromised the barrier effects of these coatings

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Educomunicação, Transformação Social e Desenvolvimento Sustentável

Esta publicação apresenta os principais trabalhos dos GTs do II Congresso Internacional de Comunicação e Educação nos temas Transformação social, com os artigos que abordam principalmente Educomunicação e/ou Mídia-Educação, no contexto de políticas de diversidade, inclusão e equidade; e, em Desenvolvimento Sustentável os artigos que abordam os avanços da relação comunicação/educação no contexto da educação ambiental e desenvolvimento sustentável

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Characterisation of microbial attack on archaeological bone

As part of an EU funded project to investigate the factors influencing bone preservation in the archaeological record, more than 250 bones from 41 archaeological sites in five countries spanning four climatic regions were studied for diagenetic alteration. Sites were selected to cover a range of environmental conditions and archaeological contexts. Microscopic and physical (mercury intrusion porosimetry) analyses of these bones revealed that the majority (68%) had suffered microbial attack. Furthermore, significant differences were found between animal and human bone in both the state of preservation and the type of microbial attack present. These differences in preservation might result from differences in early taphonomy of the bones. © 2003 Elsevier Science Ltd. All rights reserved