Valor prognóstico da proteína-c reativa às 24 horas após a admissão hospitalar na pancreatite aguda: um estudo coorte retrospetivo

Introduction: C-reactive protein (CRP) and Bedside Index for Severity in Acute Pancreatitis (BISAP) have been used in early risk assessment of patients with AP. Objectives: We evaluated prognostic accuracy of CRP at 24 hours after hospital admission (CRP24) for in-hospital mortality (IM) in AP individually and with BISAP. Materials and Methods: This retrospective cohort study included 134 patients with AP from a Portuguese hospital in 2009---2010. Prognostic accuracy assessment used area under receiver---operating characteristic curve (AUC), continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results: Thirteen percent of patients had severe AP, 26% developed pancreatic necrosis, and 7% died during index hospital stay. AUCs for CRP24 and BISAP individually were 0.80 (95% confidence interval (CI) 0.65---0.95) and 0.77 (95% CI 0.59---0.95), respectively. No patients with CRP24 <60 mg/l died (P = 0.027; negative predictive value 100% (95% CI 92.3---100%)). AUC for BISAP plus CRP24 was 0.81 (95% CI 0.65---0.97). Change in NRI nonevents (42.4%; 95% CI, 24.9---59.9%) resulted in positive overall NRI (31.3%; 95% CI, − 36.4% to 98.9%), but IDI nonevents was negligible (0.004; 95% CI, − 0.007 to 0.014). Conclusions: CRP24 revealed good prognostic accuracy for IM in AP; its main role may be the selection of lowest risk patients.Introdução: A proteína-C reativa (CRP) e o Bedside Index for Severity in Acute Pancreatitis (BISAP) têm sido usados na avaliação de risco precoce de doentes com pancreatite aguda (AP). Objectivos: Nós avaliámos o valor prognóstico da CRP às 24 horas após a admissão hospitalar (CRP24) na mortalidade intrahospitalar (IM) na AP, individualmente e com o BISAP. Materiais e Métodos: Este estudo coorte retrospetivo incluiu 134 doentes com AP de um hos- pital português em 2009---2010. A acuidade prognóstica foi avaliada usando a área debaixo da receiver-operating characteristic curve (AUC), o continuous net reclassification improvement (NRI), e o integrated discrimination improvement (IDI). Resultados: Treze por cento dos doentes tiveram AP grave, 26% desenvolveram necrose pan- creática, e 7% morreram durante a hospitalização índice. As AUCs da CRP24 e do BISAP individualmente foram 0,80 (intervalo de confiança (IC) 95%, 0,65---0,95) e 0,77 (IC 95%, 0,59---0,95), respectivamente. Nenhum doente com CRP24 <60 mg/l morreu (P = 0,027; valor predictivo negativo 100% (IC 95%, 92,3---100%)). A AUC para o BISAP mais a CRP24 foi 0,81 (IC 95%, 0,65---0,97). A mudança no NRI-não-eventos (42,4%; IC 95%, 24,9---59,9%) resultou num NRI- total positivo (31,3%; IC 95%, − 36,4 a 98,9%), mas num IDI-não-eventos negligenciável (0,004; IC 95%, − 0,007 a 0,014). Conclusões: A CRP24 revelou um valor prognóstico bom para a mortalidade intrahospitalar na AP; o seu papel principal poderá ser a selecção dos doentes de menor risco

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

C-reactive protein prognostic accuracy in acute pancreatitis: timing of measurement and cutoff points.

C-reactive protein (CRP) has been used widely in the early risk assessment of patients with acute pancreatitis. This study evaluated the prognostic accuracy of CRP for severe acute pancreatitis (SAP), pancreatic necrosis (PNec), and in-hospital mortality (IM) in terms of the best timing for CRP measurement and the optimal CRP cutoff points. MATERIALS AND METHODS: This was a single-center retrospective cohort study including 379 patients consecutively admitted with acute pancreatitis. CRP determinations at hospital admission, 24, 48, and 72 h after hospital admission were collected. Discriminative and predictive abilities of CRP for SAP, PNec, and IM were assessed by the area under the receiver-operating characteristic curve and the Hosmer-Lemeshow test, respectively. To determine the optimal CRP cutoff points for SAP, PNec, and IM, the minimum P-value approach was used. RESULTS: In total, 11% of patients had SAP, 20% developed PNec, and 4.2% died. The area under the receiver-operating characteristic curves of CRP at 48 h after hospital admission for SAP, PNec, and IM were 0.81 [95% confidence interval (CI) 0.72-0.90], 0.77 (95% CI 0.68-0.87), and 0.79 (95% CI 0.67-0.91), respectively. The Hosmer-Lemeshow test P-values of CRP at 48 h after hospital admission for SAP, PNec, and IM were 0.82, 0.47, and 0.24, respectively. The optimal CRP at 48 h after hospital admission cutoff points for SAP, PNec, and IM derived were 190, 190, and 170 mg/l, respectively. CONCLUSION: CRP at 48 h after hospital admission showed a good prognostic accuracy for SAP, PNec, and IM, better than CRP measured at any other timing. The optimal CRP at 48 h after hospital admission cutoff points for SAP, PNec, and IM varied from 170 to 190 mg/l.info:eu-repo/semantics/publishedVersio

Streamflow forecasts due precipitation water in a tropical large watershed at Brazil for flood early warning, based on SWAT model

The research reported here was supported by National Counsel of Technological and Scientific Development - CNPQ, Brazil - UNIVERSAL CALL – MCTI/CNPq Nº 14/2014 and Environment and Conservation Research Laboratory - LaPMAC of Federal University of the Pará, Brazil.Federal University of Pará. Environment and Conservation Research Laboratory. Belém, PA, Brazil.Technical University of Lisbon. Environment Technology Center/MARETEC. Portugal, PTTechnical University of Lisbon. Environment Technology Center/MARETEC. Portugal, PTTechnical University of Lisbon. Environment Technology Center/MARETEC. Portugal, PTMinistério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Galileo Institute of Technology and Education of the Amazon. Manaus, AM, Brazil.Federal University of Pará. Environment and Conservation Research Laboratory. Belém, PA, Brazil.The Tocantins-Araguaia Watershed, which is distributed equivalent to 11% of Brazilian territory, conveys waters to the northern portion of Brazil with average discharge of 11000 m3 s -1 , with contribution from the Tocantins River (40%), the Araguaia River (45%), and the Itacaiúnas River (5%), making possible an intangible flood in the Marabá city and Tucuruí Hydroelectric Plant (Downstream) during periods of high rainfall within the tropical watershed without provide timely warnings. For flash flood forecasting in a tropical large watershed, streamflow forecasts due precipitation water is required for flood early warning and in this sense, numerical prediction models are fundamental to extend streamflow forecast of a watershed due to precipitation. The paper focuses on the use Soil and Water Assessment Tool (SWAT), January 2007 to December 2010 period, to comparison of streamflows obtained from the post-processed precipitation forecasts, in providing skilful flood forecasts. In this sense, the basin was divided into 109 sub-basins and 1969 HRUs, and the model was calibrated and validated based on flow rate data in three monitoring points located next of Marabá city and Tucuruí hydroelectric. Posteriorly, simulated discharges scenario due to climatic variability extreme were generated under three strategies: 10%, 50% and 100% increase in ambient temperature (24℃) due natural and/or anthropogenic events within the watershed. The model results show that stream flows obtained adds value to the flood early warning system when compared to precipitation forecasts. Considering that climate is a direct function of temperature it is obvious that all relevant phenomena undergo changes. The scenarios results show that 50% increase in ambient temperature this leads to greater and faster evaporation. Thus, the gradual increase of precipitation in tropical watershed large alters flow rates over time and increase flood potentials in areas downstream of the basins. However, the need for more detailed evaluation of the model results in the study area is highlighted, due adequately represent the convective precipitation within the large tropical watershed

Streamflow forecasts due precipitation water in a tropical large watershed at Brazil for flood early warning, based on SWAT model

The research reported here was supported by National Counsel of Technological and Scientific Development - CNPQ, Brazil - UNIVERSAL CALL – MCTI/CNPq Nº 14/2014 and Environment and Conservation Research Laboratory - LaPMAC of Federal University of the Pará, Brazil.Federal University of Pará. Environment and Conservation Research Laboratory. Belém, PA, Brazil.Technical University of Lisbon. Environment Technology Center/MARETEC. Portugal, PTTechnical University of Lisbon. Environment Technology Center/MARETEC. Portugal, PTTechnical University of Lisbon. Environment Technology Center/MARETEC. Portugal, PTMinistério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Galileo Institute of Technology and Education of the Amazon. Manaus, AM, Brazil.Federal University of Pará. Environment and Conservation Research Laboratory. Belém, PA, Brazil.The Tocantins-Araguaia Watershed, which is distributed equivalent to 11% of Brazilian territory, conveys waters to the northern portion of Brazil with average discharge of 11000 m3 s -1 , with contribution from the Tocantins River (40%), the Araguaia River (45%), and the Itacaiúnas River (5%), making possible an intangible flood in the Marabá city and Tucuruí Hydroelectric Plant (Downstream) during periods of high rainfall within the tropical watershed without provide timely warnings. For flash flood forecasting in a tropical large watershed, streamflow forecasts due precipitation water is required for flood early warning and in this sense, numerical prediction models are fundamental to extend streamflow forecast of a watershed due to precipitation. The paper focuses on the use Soil and Water Assessment Tool (SWAT), January 2007 to December 2010 period, to comparison of streamflows obtained from the post-processed precipitation forecasts, in providing skilful flood forecasts. In this sense, the basin was divided into 109 sub-basins and 1969 HRUs, and the model was calibrated and validated based on flow rate data in three monitoring points located next of Marabá city and Tucuruí hydroelectric. Posteriorly, simulated discharges scenario due to climatic variability extreme were generated under three strategies: 10%, 50% and 100% increase in ambient temperature (24℃) due natural and/or anthropogenic events within the watershed. The model results show that stream flows obtained adds value to the flood early warning system when compared to precipitation forecasts. Considering that climate is a direct function of temperature it is obvious that all relevant phenomena undergo changes. The scenarios results show that 50% increase in ambient temperature this leads to greater and faster evaporation. Thus, the gradual increase of precipitation in tropical watershed large alters flow rates over time and increase flood potentials in areas downstream of the basins. However, the need for more detailed evaluation of the model results in the study area is highlighted, due adequately represent the convective precipitation within the large tropical watershed