Antioxidant, allelopathic and toxic activity of Ochna serrulata

Ochna serrulata (Ochnaceae) is an ornamental plant introduced into Brazil from Asia and Africa. Species of the Ochna genus are rich in phenolic compounds, mainly flavonoids. The biological screening of extracts and fractions showed that this plant exhibited a significant antioxidant activity, when evaluated by the DPPH and reducing potential assays. Ochna serrulata also demonstrated slight toxic activity against Artemia salina and a potential inhibitory allelopathic activity, when evaluated using the Lactuca sativa seed germination test. The ethyl acetate fraction, the most active one, was partitioned on a silica gel column to obtain epicatechin, which showed potential antioxidant activity.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Antioxidant, allelopathic and toxic activity of Ochna serrulata

Ochna serrulata (Ochnaceae) is an ornamental plant introduced into Brazil from Asia and Africa. Species of the Ochna genus are rich in phenolic compounds, mainly flavonoids. The biological screening of extracts and fractions showed that this plant exhibited a significant antioxidant activity, when evaluated by the DPPH and reducing potential assays. Ochna serrulata also demonstrated slight toxic activity against Artemia salina and a potential inhibitory allelopathic activity, when evaluated using the Lactuca sativa seed germination test. The ethyl acetate fraction, the most active one, was partitioned on a silica gel column to obtain epicatechin, which showed potential antioxidant activity.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Antioxidant, allelopathic and toxic activity of Ochna serrulata

Ochna serrulata (Ochnaceae) is an ornamental plant introduced into Brazil from Asia and Africa. Species of the Ochna genus are rich in phenolic compounds, mainly flavonoids. The biological screening of extracts and fractions showed that this plant exhibited a significant antioxidant activity, when evaluated by the DPPH and reducing potential assays. Ochna serrulata also demonstrated slight toxic activity against Artemia salina and a potential inhibitory allelopathic activity, when evaluated using the Lactuca sativa seed germination test. The ethyl acetate fraction, the most active one, was partitioned on a silica gel column to obtain epicatechin, which showed potential antioxidant activity.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Is there justification for prophylactic extraction of third molars? A systematic review

Funda??o de Amparo ? Pesquisa do Estado de Minas Gerais (FAPEMIG)Empresa de Pesquisa Agropecu?ria de Minas Gerais (EPAMIG)Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico (CNPq)Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES)The present systematic review was performed to investigate if there is evidence justifying the prophylactic extraction of third molars, one of the most frequent procedures in oral surgery. A series of searches was carried out for randomized, clinical trials and systematic reviews in seven databases (MEDLINE, BBO, LILACS, Web of Science, EMBASE, BIREME and Cochrane Library), with no restrictions regarding year or language. A supplemental manual search of the references of retrieved articles was also performed. The search strategy resulted in 260 papers. Both the data extracted and the quality of each paper were evaluated independently by two reviewers. After selection based on the preestablished eligibility criteria, four papers qualified for the final analysis. A medium degree of quality and methodological consistency was found in three studies, and low quality was found in one study. No studies showed a high degree of consistency. The most significant flaw was an inadequate sample size. The results of the present review indicate a lack of scientific evidence to justify the indication of the prophylactic extraction of third molars

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