The Trypanosoma cruzi Virulence Factor Oligopeptidase B (OPBTc) Assembles into an Active and Stable Dimer

Oligopeptidase B, a processing enzyme of the prolyl oligopeptidase family, is considered as an important virulence factor in trypanosomiasis. Trypanosoma cruzi oligopeptidase B (OPBTc) is involved in host cell invasion by generating a Ca2+-agonist necessary for recruitment and fusion of host lysosomes at the site of parasite attachment. The underlying mechanism remains unknown and further structural and functional characterization of OPBTc may help clarify its physiological function and lead to the development of new therapeutic molecules to treat Chagas disease. In the present work, size exclusion chromatography and analytical ultracentrifugation experiments demonstrate that OPBTc is a dimer in solution, an association salt and pH-resistant and independent of intermolecular disulfide bonds. The enzyme retains its dimeric structure and is fully active up to 42°C. OPBTc is inactivated and its tertiary, but not secondary, structure is disrupted at higher temperatures, as monitored by circular dichroism and fluorescence spectroscopy. It has a highly stable secondary structure over a broad range of pH, undergoes subtle tertiary structure changes at low pH and is less stable under moderate ionic strength conditions. These results bring new insights into the structural properties of OPBTc, contributing to future studies on the rational design of OPBTc inhibitors as a promising strategy for Chagas disease chemotherapy

Recent advances in lanthanide spectroscopy in Brazil

This review discusses recent advances in lanthanide spectroscopy involving luminescence applications Q2 carried out in Brazil. The revised topics include glasses, sol–gel, light-emitting diodes, nanoparticles, metal–organic frameworks, coordination polymers, thin films, energy transfer processes, upconversion and development of new theoretical tools. The important role played by Prof. Oscar L. Malta on this subject is evidenced by his many contributions to the broad range of investigations reported here and this review is dedicated to him, on the occasion of his 60th birthday

Early mobilisation in mechanically ventilated patients:A systematic integrative review of definitions and activities

From PubMed via Jisc Publications RouterHistory: received 2018-10-23, accepted 2018-12-11Publication status: epublishMechanically ventilated patients often develop muscle weakness post-intensive care admission. Current evidence suggests that early mobilisation of these patients can be an effective intervention in improving their outcomes. However, what constitutes early mobilisation in mechanically ventilated patients (EM-MV) remains unclear. We aimed to systematically explore the definitions and activity types of EM-MV in the literature. Whittemore and Knafl's framework guided this review. CINAHL, MEDLINE, EMBASE, PsycINFO, ASSIA, and Cochrane Library were searched to capture studies from 2000 to 2018, combined with hand search of grey literature and reference lists of included studies. The Critical Appraisal Skills Programme tools were used to assess the methodological quality of included studies. Data extraction and quality assessment of studies were performed independently by each reviewer before coming together in sub-groups for discussion and agreement. An inductive and data-driven thematic analysis was undertaken on verbatim extracts of EM-MV definitions and activities in included studies. Seventy-six studies were included from which four major themes were inferred: (1) , (2) , (3) and (4) . The first theme indicates that EM-MV is either not fully defined in studies or when a definition is provided this is not standardised across studies. The remaining themes reflect the diversity of EM-MV activities which depends on patients' characteristics and ICU settings; the negotiated decision-making process between patients and staff; and their interdependent relationship during the implementation. This review highlights the absence of an agreed definition and on what constitutes early mobilisation in mechanically ventilated patients. To advance research and practice an agreed and shared definition is a pre-requisite

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

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