Avaliação da cultura de segurança do paciente em um hospital geral

Objective: to assess patient safety culture (PSC) from the perspective of the multidisciplinary team working at a general hospital. Method: this a cross-sectional study with a quantitative approach, based on the application of the Brazilian electronic version of the Hospital Survey on Patient Safety Culture from the Agency for Healthcare Research and Quality. Data collection took place in August 2022 through E-questionário de PSC. Results: a total of 236 nursing professionals, doctors and multidisciplinary teams responded to the questionnaire. Of the 12 PSC dimensions assessed, seven stood out with percentages between 63.3% and 95%, considered strong, one with 50% positivity, indicating a growing dimension, and four with lower percentages, with 50% of positive answers identified as weak areas. Conclusion: it was possible to assess PSC in the hospital environment and perceive the strong, weak and growing dimensions. Progressing in this practice is challenging for teams looking for a reliable organization

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

Dissipation of excess photosynthetic energy contributes to salinity tolerance: A comparative study of salt-tolerant Ricinus communis and salt-sensitive Jatropha curcas

The relationships between salt tolerance and photosynthetic mechanisms of excess energy dissipation were assessed using two species that exhibit contrasting responses to salinity, Ricinus communis (tolerant) and Jatropha curcas (sensitive). The salt tolerance of R. communis was indicated by unchanged electrolyte leakage (cellular integrity) and dry weight in leaves, whereas these parameters were greatly affected in J. curcas. The leaf Na+ content was similar in both species. Photosynthesis was intensely decreased in both species, but the reduction was more pronounced in J. curcas. In this species biochemical limitations in photosynthesis were more prominent, as indicated by increased Ci values and decreased Rubisco activity. Salinity decreased both the Vcmax (in vivo Rubisco activity) and Jmax (maximum electron transport rate) more significantly in J. curcas. The higher tolerance in R. communis was positively associated with higher photorespiratory activity, nitrate assimilation and higher cyclic electron flow. The high activity of these alternative electron sinks in R. communis was closely associated with a more efficient photoprotection mechanism. In conclusion, salt tolerance in R. communis, compared with J. curcas, is related to higher electron partitioning from the photosynthetic electron transport chain to alternative sinks

Predictive factors for red blood cell transfusion requirements in liver transplantation

sem informação573CP303SI181A181AAABB Annual Meeting2017-10Estados UnidosAABBSan Diego, C