79 research outputs found

    Expressão e distribuição da conexina 32 em fígados de ratos com fibrose induzida experimentalmente

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    The connexin 32 (Cx32) is a protein that forms the channels that promote the gap junction intercellular communication (GJIC) in the liver, allowing the diffusion of small molecules through cytosol from cell-to-cell. Hepatic fibrosis is characterized by a disruption of normal tissue architeture by cellular lesions, and may alter the GJIC. This work aimed to study the expression and distribution of Cx32 in liver fibrosis induced by the oral administration of dimethylnitrosamine in female Wistar rats. The necropsy of the rats was carried out after five weeks of drug administration. They presented a hepatic fibrosis state. Sections from livers with fibrosis and from control livers were submitted to immunohistochemical, Real Time-PCR and Western-Blot analysis to Cx32. In fibrotic livers the Cxs were diffusely scattered in the cytoplasm, contrasting with the control livers, where the Cx32 formed junction plaques at the cell membrane. Also it was found a decrease in the gene expression of Cx32 without reduction in the protein quantity when compared with controls. These results suggest that there the mechanism of intercellular communication between hepatocytes was reduced by the fibrotic process, which may predispose to the occurrence of a neoplastic process, taken in account that connexins are considered tumor suppressing genes.A conexina 32 (Cx32) é uma proteína que constitui os canais que promovem as comunicações intercelulares via junções comunicantes (CIJC) no fígado, permitindo difusão de pequenas moléculas citoplasmáticas de uma célula à outra. A fibrose hepática caracteriza-se pela alteração da arquitetura normal do fígado e podem alterar as CIJCs. O objetivo deste trabalho foi estudar a expressão e distribuição de Cx32 na fibrose hepática. O objetivo do presente trabalho foi estudar a expressão e distribuição da Cx32 em fígados com fibrose induzida pela administração oral de dimetilnitrosamina em fêmeas de ratos Wistar. A necropsia foi realizada após cinco semanas da última administração da droga e observou-se um quadro de fibrose hepática. Amostras dos fígados com fibrose e de animais controle foram submetidas à análise imunoistoquímica, por Real Time-PCR e por Western-Blot verificando-se a presença de Cx32 difusa e dispersa no citoplasma dos fígados com fibrose. No grupo controle a Cx32 localizou-se na membrana citoplasmática com a formação de placas juncionais. O fígado com fibrose também revelou diminuição da expressão gênica de Cx32, embora sem a redução da quantidade do produto protéico, quando comparado ao grupo controle. Estes resultados sugerem que o mecanismo de comunicação intercelular entre os hepatócitos reduziu-se durante o processo fibrótico, o que pode predispor a ocorrência de processos neoplásicos, uma vez que as conexinas são consideradas genes supressores de tumores.FAPESPCNP

    SARS-CoV-2 uses CD4 to infect T helper lymphocytes

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    The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the agent of a major global outbreak of respiratory tract disease known as Coronavirus Disease 2019 (COVID-19). SARS-CoV-2 infects mainly lungs and may cause several immune-related complications, such as lymphocytopenia and cytokine storm, which are associated with the severity of the disease and predict mortality. The mechanism by which SARS-CoV-2 infection may result in immune system dysfunction is still not fully understood. Here, we show that SARS-CoV-2 infects human CD4+ T helper cells, but not CD8+ T cells, and is present in blood and bronchoalveolar lavage T helper cells of severe COVID-19 patients. We demonstrated that SARS-CoV-2 spike glycoprotein (S) directly binds to the CD4 molecule, which in turn mediates the entry of SARS-CoV-2 in T helper cells. This leads to impaired CD4 T cell function and may cause cell death. SARS-CoV-2-infected T helper cells express higher levels of IL-10, which is associated with viral persistence and disease severity. Thus, CD4-mediated SARS-CoV-2 infection of T helper cells may contribute to a poor immune response in COVID-19 patients.</p

    SARS-CoV-2 uses CD4 to infect T helper lymphocytes

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    The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the agent of a major global outbreak of respiratory tract disease known as Coronavirus Disease 2019 (COVID-19). SARS-CoV-2 infects mainly lungs and may cause several immune-related complications, such as lymphocytopenia and cytokine storm, which are associated with the severity of the disease and predict mortality. The mechanism by which SARS-CoV-2 infection may result in immune system dysfunction is still not fully understood. Here, we show that SARS-CoV-2 infects human CD4+ T helper cells, but not CD8+ T cells, and is present in blood and bronchoalveolar lavage T helper cells of severe COVID-19 patients. We demonstrated that SARS-CoV-2 spike glycoprotein (S) directly binds to the CD4 molecule, which in turn mediates the entry of SARS-CoV-2 in T helper cells. This leads to impaired CD4 T cell function and may cause cell death. SARS-CoV-2-infected T helper cells express higher levels of IL-10, which is associated with viral persistence and disease severity. Thus, CD4-mediated SARS-CoV-2 infection of T helper cells may contribute to a poor immune response in COVID-19 patients.</p

    LHCb calorimeters: Technical Design Report

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    LHCb magnet: Technical Design Report

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    LHCb RICH: Technical Design Report

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    Geography and ecology shape the phylogenetic composition of Amazonian tree communities

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    Aim: Amazonia hosts more tree species from numerous evolutionary lineages, both young and ancient, than any other biogeographic region. Previous studies have shown that tree lineages colonized multiple edaphic environments and dispersed widely across Amazonia, leading to a hypothesis, which we test, that lineages should not be strongly associated with either geographic regions or edaphic forest types. Location: Amazonia. Taxon: Angiosperms (Magnoliids; Monocots; Eudicots). Methods: Data for the abundance of 5082 tree species in 1989 plots were combined with a mega-phylogeny. We applied evolutionary ordination to assess how phylogenetic composition varies across Amazonia. We used variation partitioning and Moran\u27s eigenvector maps (MEM) to test and quantify the separate and joint contributions of spatial and environmental variables to explain the phylogenetic composition of plots. We tested the indicator value of lineages for geographic regions and edaphic forest types and mapped associations onto the phylogeny. Results: In the terra firme and várzea forest types, the phylogenetic composition varies by geographic region, but the igapó and white-sand forest types retain a unique evolutionary signature regardless of region. Overall, we find that soil chemistry, climate and topography explain 24% of the variation in phylogenetic composition, with 79% of that variation being spatially structured (R2^{2} = 19% overall for combined spatial/environmental effects). The phylogenetic composition also shows substantial spatial patterns not related to the environmental variables we quantified (R2^{2} = 28%). A greater number of lineages were significant indicators of geographic regions than forest types. Main Conclusion: Numerous tree lineages, including some ancient ones (>66 Ma), show strong associations with geographic regions and edaphic forest types of Amazonia. This shows that specialization in specific edaphic environments has played a long-standing role in the evolutionary assembly of Amazonian forests. Furthermore, many lineages, even those that have dispersed across Amazonia, dominate within a specific region, likely because of phylogenetically conserved niches for environmental conditions that are prevalent within regions

    Geographic patterns of tree dispersal modes in Amazonia and their ecological correlates

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    Aim: To investigate the geographic patterns and ecological correlates in the geographic distribution of the most common tree dispersal modes in Amazonia (endozoochory, synzoochory, anemochory and hydrochory). We examined if the proportional abundance of these dispersal modes could be explained by the availability of dispersal agents (disperser-availability hypothesis) and/or the availability of resources for constructing zoochorous fruits (resource-availability hypothesis). Time period: Tree-inventory plots established between 1934 and 2019. Major taxa studied: Trees with a diameter at breast height (DBH) ≥ 9.55 cm. Location: Amazonia, here defined as the lowland rain forests of the Amazon River basin and the Guiana Shield. Methods: We assigned dispersal modes to a total of 5433 species and morphospecies within 1877 tree-inventory plots across terra-firme, seasonally flooded, and permanently flooded forests. We investigated geographic patterns in the proportional abundance of dispersal modes. We performed an abundance-weighted mean pairwise distance (MPD) test and fit generalized linear models (GLMs) to explain the geographic distribution of dispersal modes. Results: Anemochory was significantly, positively associated with mean annual wind speed, and hydrochory was significantly higher in flooded forests. Dispersal modes did not consistently show significant associations with the availability of resources for constructing zoochorous fruits. A lower dissimilarity in dispersal modes, resulting from a higher dominance of endozoochory, occurred in terra-firme forests (excluding podzols) compared to flooded forests. Main conclusions: The disperser-availability hypothesis was well supported for abiotic dispersal modes (anemochory and hydrochory). The availability of resources for constructing zoochorous fruits seems an unlikely explanation for the distribution of dispersal modes in Amazonia. The association between frugivores and the proportional abundance of zoochory requires further research, as tree recruitment not only depends on dispersal vectors but also on conditions that favour or limit seedling recruitment across forest types

    Pervasive gaps in Amazonian ecological research

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    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
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