EFEITO DOS EXTRATOS DAS FOLHAS DE GUINÉ (PETIVERIA ALLIACEA L) SOBRE PARÂMETROS BIOQUÍMICOS E HISTOLÓGICOS EM RATOS DIABÉTICOS

Diabetes Mellitus (DM) is an endocrine and metabolic disorder increasingly common in the world population, lacking new therapeutic alternatives, and vegetables are a viable alternative in the search for new phytotherapics and bioactives with action to control DM. Due to the hypoglycemic activity of some bioactives of Guinea leaves (Petiveria alliacea), in addition to this being a plant widely spread throughout Brazil, easy to grow and access, the present study evaluated the influence of extracts of Guinea leaves on biochemical and morphological parameters. of Wistar rats with alloxan-induced DM. The animals were divided into three experimental groups: rats without DM (n = 10), rats with DM (n = 10) and rats with DM treated with P. alliacea leaf extract (200mg/Kg/day) (n = 10). Biochemical parameters: glucose, hepatic (TGP and TGO), renal (creatinine and urea) and lipid (total cholesterol and triglycerides) profile, as well as organ histology were evaluated. P. alliacea leaf extract, at a dose of 200mg/kg/day, was not able to prevent increases in glucose, TGP, TGO, creatinine, urea, total cholesterol, and triglycerides in animals with DM compared to the group untreated DM. There were no significant histopathological changes in the liver, kidneys, heart, and salivary glands. We emphasize the need for new studies to evaluate such preliminary findings, aiming to clarify the effects observed here.La Diabetes Mellitus (DM) es un trastorno endocrino y metabólico cada vez más frecuente en la población mundial, carente de nuevas alternativas terapéuticas, y las hortalizas son una alternativa viable en la búsqueda de nuevos fitoterapéuticos y bioactivos con acción para controlar la DM. Debido a la actividad hipoglucémica de algunos bioactivos de las hojas de Guinea (Petiveria alliacea), además de ser una planta ampliamente extendida en todo Brasil, de fácil cultivo y acceso, el presente estudio evaluó la influencia de los extractos de hoja de Guinea en los parámetros bioquímicos y morfológicos de ratas Wistar con DM inducida por aloxano. Los animales se dividieron en tres grupos experimentales: ratas sin DM (n = 10), ratas con DM (n = 10) y ratas con DM tratadas con extracto de hoja de P. alliacea (200 mg / kg / día) (n = 10). Se evaluaron los parámetros bioquímicos: glucosa, perfil hepático (TGP y GOT), renal (creatinina y urea) y lipídico (colesterol total y triglicéridos), así como la histología de órganos. El extracto de la hoja de P. alliacea, a una dosis de 200 mg/kg/día, no fue capaz de prevenir aumentos en los niveles de glucosa, TGP, GOT, creatinina, urea, colesterol total y triglicéridos en animales con DM en comparación con el grupo de DM no tratada. No hubo cambios histopatológicos significativos en el hígado, los riñones, el corazón y las glándulas salivales. Hacemos hincapié en la necesidad de estudios adicionales para evaluar estos hallazgos preliminares.O Diabetes Mellitus (DM) é um distúrbio endócrino e metabólico cada vez mais comum na população mundial, carente de novas alternativas terapêuticas, sendo os vegetais uma alternativa viável na busca por novos fitoterápicos e bioativos com ação para controlar o DM. Devido a atividade hipoglicemiante de alguns bioativos das folhas de Guiné (Petiveria alliacea), além de esta ser uma planta amplamente difundida pelo Brasil, de fácil cultivo e acesso, o presente estudo avaliou a influência dos extratos das folhas de Guiné sobre parâmetros bioquímicos e morfológicos de ratos Wistar com DM induzido por aloxano. Os animais foram divididos em três grupos experimentais: ratos sem DM (n = 10), ratos com DM (n = 10) e ratos com DM tratados com o extrato das folhas de P. alliacea (200mg/Kg/dia) (n = 10). Os parâmetros bioquímicos: glicose, perfil hepático (TGP e TGO), renal (creatinina e ureia) e lipídico (colesterol total e triglicerídeos), bem como a histologia de órgãos foram avaliadas. O extrato da folha de P. alliacea, na dose de 200mg/Kg/dia, não foi capaz de prevenir os aumentos dos níveis de glicose, TGP, TGO, creatinina, ureia, colesterol total e triglicerídeos, em animais com DM comparados ao grupo DM não tratado. Não houve alterações histopatológicas significativas no fígado, rins, coração, e glândulas salivares. Ressalta-se a necessidade de novos estudos para avaliar tais achados preliminares, visando esclarecer os efeitos aqui observados.  O Diabetes Mellitus (DM) é um distúrbio endócrino e metabólico cada vez mais comum na população mundial, carente de novas alternativas terapêuticas, sendo os vegetais uma alternativa viável na busca por novos fitoterápicos e bioativos com ação para controlar o DM. Devido a atividade hipoglicemiante de alguns bioativos das folhas de Guiné (Petiveria alliacea), além de esta ser uma planta amplamente difundida pelo Brasil, de fácil cultivo e acesso, o presente estudo avaliou a influência dos extratos das folhas de Guiné sobre parâmetros bioquímicos e morfológicos de ratos Wistar com DM induzido por aloxano. Os animais foram divididos em três grupos experimentais: ratos sem DM (n = 10), ratos com DM (n = 10) e ratos com DM tratados com o extrato das folhas de P. alliacea (200mg/Kg/dia) (n = 10). Os parâmetros bioquímicos: glicose, perfil hepático (TGP e TGO), renal (creatinina e ureia) e lipídico (colesterol total e triglicerídeos), bem como a histologia de órgãos foram avaliadas. O extrato da folha de P. alliacea, na dose de 200mg/Kg/dia, não foi capaz de prevenir os aumentos dos níveis de glicose, TGP, TGO, creatinina, ureia, colesterol total e triglicerídeos, em animais com DM comparados ao grupo DM não tratado. Não houve alterações histopatológicas significativas no fígado, rins, coração, e glândulas salivares. Ressalta-se a necessidade de novos estudos para avaliar tais achados preliminares, visando esclarecer os efeitos aqui observados.

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