Toxicity of plant extracts to Scutellonema bradys

O objetivo deste trabalho foi avaliar o efeito nematostático e nematicida de extratos aquosos de bulbilhos de alho (Allium sativum L.), folhas de mandioca (Manihot esculenta Crantz), folhas e sementes de mamão (Carica papaya L.), folhas de hortelã (Mentha piperita L.) e casca de gliricídia (Gliricidia sepium (Jacq.) Steud.) em Scutellonema bradys, agente causal da casca-preta do inhame (Dioscorea cayennensis Lam.). Todos os extratos vegetais inibiram a mobilidade e causaram mortalidade ao fitonematóide. Os extratos de hortelã e de mandioca causaram menos de 45% de mortalidade a S. bradys. As maiores porcentagens de mortalidade são causadas pelos extratos de sementes e folhas do mamoeiro e pelos bulbilhos de alho.The objective of this work was to evaluate the nematostatic and nematicide effect of aqueous extracts from garlic bulbs (Allium sativum L.), cassava (Manihot esculenta Crantz) leaves, papaya (Carica papaya L.) leaves and seeds, mentha (Mentha piperita L.) leaves, and gliricídia (Gliricidia sepium (Jacq.) Steud.) tree bark to Scutellonema bradys, the causal agent of yam (Dioscorea cayennensis Lam.) dry rot. All plant extracts inhibited the mobility and caused mortality to S. bradys. Mentha and cassava extracts cause less than 45% mortality to S. bradys. The highest percentages of mortality are caused by extracts from papaya seeds and leaves, and garlic bulbs

Central and mixed venous oxygen saturation in septic shock: is there a clinically relevant difference?

INTRODUCTION: Central venous oxygen saturation (SvcO2) has been proposed as an alternative for mixed venous oxygen saturation (SvO2), with a variable level of acceptance according to available data. This study aimed to evaluate possible differences between SvO2 and SvcO2 or atrial venous saturation (SvaO2), with emphasis on the role of cardiac output and their impact on clinical management of the septic patient. METHODS: This is an observational, prospective study of patients with septic shock monitored by pulmonary artery catheter. Blood was obtained simultaneously for SvcO2, SvO2 and SvaO2 determination. Linear correlation (significant if p<0.05) and agreement analysis (Bland-Altman) were performed with samples and subgroups according to cardiac output. Moreover, agreement about clinical management based on these samples was evaluated. RESULTS: Sixty one measurements from 23 patients were obtained, median age of 65.0 (49.0-75.0) years and mean APACHE II of 27.7±6.3. Mean values of SvO2, SvcO2 and SvaO2 were 72.20±8.26%, 74.61±7.60% and 74.64±8.47%. Linear correlation test showed a weak correlation between SvO2 and SvcO2 (r=0.61, p<0.0001) and also between SvO2 and SvaO2 (r=0.70, p<0.0001). Agreements between SvcO2/SvO2 and SvaO2/SvO2 were -2.40±1.96 (-16.20 and 11.40) and -2.40±1.96 (-15.10 and 10.20), respectively, with no difference in the cardiac output subgroups. No agreement was found in clinical management for 27.8% of the cases, both for SvcO2/SvO2 and for SvaO2/SvO2. CONCLUSION: This study showed that the correlation and agreement between SvO2 and SvcO2 is weak and may lead to different clinical management.INTRODUÇÃO: A medida da saturação venosa central de oxigênio (SvcO2) tem sido proposta como alternativa a saturação venosa mista (SvO2), com grau de concordância variável nos dados atualmente disponíveis. Esse estudo objetivou avaliar as possíveis diferenças entre a SvO2 e a SvcO2 ou saturação venosa atrial de oxigênio (SvaO2), com ênfase na interferência do débito cardíaco, e o impacto delas no manejo clínico do paciente séptico. MÉTODOS: Estudo prospectivo observacional em pacientes com choque séptico monitorizados com cateter de artéria pulmonar. Foi obtido sangue simultaneamente para determinação da SvcO2, SvO2 e SvaO2. Realizado testes de correlação linear (significativos se p<0,05) e análise de concordância (Bland-Altman) entre as amostras e nos subgupos de débito cardíaco. Além disso, foi avaliada a concordância entre condutas clínicas baseadas nessas medidas. RESULTADOS: Foram obtidas 61 medidas de 23 pacientes, mediana de idade de 65,0 (49,0-75,0) anos, APACHE II médio de 27,7±6,3. Os valores médios encontrados foram 72,20±8,26%, 74,61±7,60% e 74,64±8,47% para SvO2, SvcO2 e SvaO2. O teste de correlação linear mostrou baixa correlação tanto entre a SvO2 e a SvcO2 (r=0,61, p<0,0001) quanto entre a SvO2 e a SvaO2 (r=0,70, p<0,0001). As concordâncias entre SvcO2/SvO2 e SvaO2/SvO2 foram, respectivamente, de -2,40±1,96 (-16,20 e 11,40) e -2,40±1,96 (-15,10 e 10,20), sem diferença nos subgrupos de débito cardíaco. Não houve concordância na conduta clínica em 27,8% dos casos, tanto entre SvcO2/SvO2 como de SvaO2/SvO2. CONCLUSÃO: Esse estudo mostra que a correlação e a concordância entre SvO2 e SvcO2 é baixa e pode levar a condutas clínicas diferentes.Universidade Federal de São Paulo (UNIFESP)Universidade Federal de São Paulo (UNIFESP) Setor de Terapia Intensiva da Disciplina de Anestesiologia, Dor e Terapia IntensivaUNIFESP, Setor de Terapia Intensiva da Disciplina de Anestesiologia, Dor e Terapia IntensivaSciEL

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

Sensitivity of South American tropical forests to an extreme climate anomaly

The tropical forest carbon sink is known to be drought sensitive, but it is unclear which forests are the most vulnerable to extreme events. Forests with hotter and drier baseline conditions may be protected by prior adaptation, or more vulnerable because they operate closer to physiological limits. Here we report that forests in drier South American climates experienced the greatest impacts of the 2015–2016 El Niño, indicating greater vulnerability to extreme temperatures and drought. The long-term, ground-measured tree-by-tree responses of 123 forest plots across tropical South America show that the biomass carbon sink ceased during the event with carbon balance becoming indistinguishable from zero (−0.02 ± 0.37 Mg C ha −1 per year). However, intact tropical South American forests overall were no more sensitive to the extreme 2015–2016 El Niño than to previous less intense events, remaining a key defence against climate change as long as they are protected

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