Effects of simvastatin on cytokines secretion from mononuclear cells from critically ill patients with acute kidney injury

Purpose: To assess the in vitro effects of simvastatin on IL-10 and TNF-alpha secretion from peripheral blood mononuclear cells (PBMC) of critically ill patients with and without acute kidney injury (AKI).Methods: PBMC were collected from 63 patients admitted to the intensive care unit (ICU) and from 20 healthy controls. Patients were divided in 3 subgroups: with AKI, with sepsis and without AKI and with AKI and sepsis. After isolation by ficoll-gradient centrifugation cells were incubated in vitro with LPS 1 ng/mL, simvastatin (10(-8)M) and with LPS plus simvastatin for 24 h. TNF-alpha and IL-10 concentrations on cells surnatant were determined by ELISA.Results: Cells isolated from critically ill patients showed a decreased spontaneous production of TNF-alpha and IL-10 compared to healthy controls (6.7(0.2-12) vs 103(64-257) pg/mL and (20 (13-58) vs 315(105-510) pg/mL, respectively, p < 0.05). Under LPS-stimulus, IL-10 production remains lower in patients compared to healthy control (451 (176-850) vs 1150(874-1521) pg/mL,p < 0.05) but TNF-alpha production was higher (641 (609-841) vs 406 (201-841) pg/mL, p < 0.05). the simultaneous incubation with LPS and simvastatin caused decreased IL-10 production in cells from patients compared to control (337 (135-626) vs 540 (345-871) pg/mL, p < 0.05) and increased TNF-alpha release (711 (619-832) vs 324 (155-355) pg/mL, p < 0.05). Comparison between subgroups showed that the results observed in TNF-alpha and IL-10 production by PBMC from critically ill patients was independent of AKI occurrence.Conclusions: the PBMC treatment with simvastatin resulted in attenuation on pro-inflammatory cytokine spontaneous production that was no longer observed when these cells were submitted to a second inflammatory stimulus. Our study shows an imbalance between pro and anti-inflammatory cytokine production in PBMC from critically ill patients regardless the presence of AKI. (C) 2011 Elsevier B.V. All rights reserved.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Instituto de Ensino e Pesquisa do Hospital Israelita Albert EinsteinUniversidade Federal de São Paulo, Div Nephrol, Dept Med, São Paulo, BrazilIAEH IEP Hosp Israelita Albert Einstein Inst Ensi, São Paulo, BrazilUniversidade Federal de São Paulo, Div Nephrol, Dept Med, São Paulo, BrazilWeb of Scienc

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