Human malarial disease: a consequence of inflammatory cytokine release

Malaria causes an acute systemic human disease that bears many similarities, both clinically and mechanistically, to those caused by bacteria, rickettsia, and viruses. Over the past few decades, a literature has emerged that argues for most of the pathology seen in all of these infectious diseases being explained by activation of the inflammatory system, with the balance between the pro and anti-inflammatory cytokines being tipped towards the onset of systemic inflammation. Although not often expressed in energy terms, there is, when reduced to biochemical essentials, wide agreement that infection with falciparum malaria is often fatal because mitochondria are unable to generate enough ATP to maintain normal cellular function. Most, however, would contend that this largely occurs because sequestered parasitized red cells prevent sufficient oxygen getting to where it is needed. This review considers the evidence that an equally or more important way ATP deficency arises in malaria, as well as these other infectious diseases, is an inability of mitochondria, through the effects of inflammatory cytokines on their function, to utilise available oxygen. This activity of these cytokines, plus their capacity to control the pathways through which oxygen supply to mitochondria are restricted (particularly through directing sequestration and driving anaemia), combine to make falciparum malaria primarily an inflammatory cytokine-driven disease

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

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

New insights into the in silico prediction of HIV protease resistance to nelfinavir.

The Human Immunodeficiency Virus type 1 protease enzyme (HIV-1 PR) is one of the most important targets of antiretroviral therapy used in the treatment of AIDS patients. The success of protease-inhibitors (PIs), however, is often limited by the emergence of protease mutations that can confer resistance to a specific drug, or even to multiple PIs. In the present study, we used bioinformatics tools to evaluate the impact of the unusual mutations D30V and V32E over the dynamics of the PR-Nelfinavir complex, considering that codons involved in these mutations were previously related to major drug resistance to Nelfinavir. Both studied mutations presented structural features that indicate resistance to Nelfinavir, each one with a different impact over the interaction with the drug. The D30V mutation triggered a subtle change in the PR structure, which was also observed for the well-known Nelfinavir resistance mutation D30N, while the V32E exchange presented a much more dramatic impact over the PR flap dynamics. Moreover, our in silico approach was also able to describe different binding modes of the drug when bound to different proteases, identifying specific features of HIV-1 subtype B and subtype C proteases

Temporal dynamics of HIV-1 circulating subtypes in distinct exposure categories in southern Brazil

Abstract Background The HIV-1 epidemic in Brazil is predominantly driven by subtype B. However, in Brazilian Southern region subtype C prevails and a relatively high AIDS incidence rate is observed. The aim of the present study was to assess the temporal dynamics of HIV-1 subtypes circulating in patients from distinct exposure categories in Southern Brazil. For this purpose 166 HIV-1 samples collected at the years of 1998 (group I) and 2005–2008 (group II) were analyzed. Results Analysis of group I revealed statistically significant (p Conclusions The present study shows an association between HIV subtypes and exposure categories at the middle 1990s in Southern Brazil. Our findings suggest that MSM and IDU populations might have played a major role in the introduction and initial dissemination of subtypes B and C, respectively, in Southern Brazil. This study also suggests a trend towards homogenization of HIV-1 strains across distinct exposure categories as a consequence of an overall increase in the prevalence of subtype C and BC recombinants in both HET and MSM populations.</p

Introductions of Human-Origin Seasonal H3N2, H1N2 and Pre-2009 H1N1 Influenza Viruses to Swine in Brazil

In South America, the evolutionary history of influenza A virus (IAV) in swine has been obscured by historically low levels of surveillance, and this has hampered the assessment of the zoonotic risk of emerging viruses. The extensive genetic diversity of IAV in swine observed globally has been attributed mainly to bidirectional transmission between humans and pigs. We conducted surveillance in swine in Brazil during 2011–2020 and characterized 107 H1N1, H1N2, and H3N2 IAVs. Phylogenetic analysis based on HA and NA segments revealed that human seasonal IAVs were introduced at least eight times into swine in Brazil since the mid-late 1980s. Our analyses revealed three genetic clades of H1 within the 1B lineage originated from three distinct spillover events, and an H3 lineage that has diversified into three genetic clades. The N2 segment from human seasonal H1N2 and H3N2 viruses was introduced into swine six times and a single introduction of an N1 segment from the human H1N1 virus was identified. Additional analysis revealed further reassortment with H1N1pdm09 viruses. All these introductions resulted in IAVs that apparently circulate only in Brazilian herds. These results reinforce the significant contributions of human IAVs to the genetic diversity of IAV in swine and reiterate the importance of surveillance of IAV in pigs

Temporal dynamics of HIV-1 circulating subtypes in distinct exposure categories in southern Brazil

Submitted by Sandra Infurna ([email protected]) on 2016-09-15T15:14:30Z No. of bitstreams: 1 caroline2_passaes_etal_IOC_2012.pdf: 375073 bytes, checksum: 0a8be9d439c43d6fb830774c41d06c6a (MD5)Approved for entry into archive by Sandra Infurna ([email protected]) on 2016-09-15T15:26:39Z (GMT) No. of bitstreams: 1 caroline2_passaes_etal_IOC_2012.pdf: 375073 bytes, checksum: 0a8be9d439c43d6fb830774c41d06c6a (MD5)Made available in DSpace on 2016-09-15T15:26:39Z (GMT). No. of bitstreams: 1 caroline2_passaes_etal_IOC_2012.pdf: 375073 bytes, checksum: 0a8be9d439c43d6fb830774c41d06c6a (MD5) Previous issue date: 2012Fundação Estadual de Produção e Pesquisa em Saúde – FEPPS. Centro de Desenvolvimento Técnico e Científico – CDCT. Porto Alegre, RS, Brasil.Fundação Estadual de Produção e Pesquisa em Saúde – FEPPS. Centro de Desenvolvimento Técnico e Científico – CDCT. Porto Alegre, RS, Brasil / Universidade Federal do Rio Grande do Sul (UFRGS). Centro de Ciências Biológicas. Departamento de Genética. Programa de Pós-Graduação em Genética e Biologia Molecular. Porto Alegre, RS, Brasil.Fundação Estadual de Produção e Pesquisa em Saúde – FEPPS. Centro de Desenvolvimento Técnico e Científico – CDCT. Porto Alegre, RS, Brasil / Universidade Federal do Rio Grande do Sul (UFRGS). Centro de Ciências Biológicas. Departamento de Genética. Programa de Pós-Graduação em Genética e Biologia Molecular. Porto Alegre, RS, Brasil.Fundação Estadual de Produção e Pesquisa em Saúde – FEPPS. Centro de Desenvolvimento Técnico e Científico – CDCT. Porto Alegre, RS, Brasil / Universidade Federal de Santa Catarina (UFSC). Centro de Ciências Biológicas. Departamento de Microbiologia, Imunologia e Parasitologia. Programa de Pós-Graduação em Biotecnologia e Biociências. Florianópolis, SC, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de AIDS e Imunologia Molecular. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de AIDS e Imunologia Molecular. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de AIDS e Imunologia Molecular. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Laboratório de AIDS e Imunologia Molecular. Rio de Janeiro, RJ, Brasil