Diminuição dos níveis de BDNF em amígdala e hipocampo após a administração intracerebroventricular de ouabaína

OBJECTIVE: The present study aims to investigate the effects of ouabain intracerebroventricular injection on BDNF levels in the amygdala and hippocampus of Wistar rats. METHODS: Animals received a single intracerebroventricular injection of ouabain (10-3 and 10-2 M) or artificial cerebrospinal fluid and immediately, 1h, 24h, or seven days after injection, BDNF levels were measured in the rat's amygdala and hippocampus by sandwich-ELISA (n = 8 animals per group). RESULTS: When evaluated immediately, 3h, or 24h after injection, ouabain in doses of 10-2 and 10-3 M does not alter BDNF levels in the amygdala and hippocampus. However, when evaluated seven days after injection, ouabain in 10-2 and 10-3 M, showed a significant reduction in BDNF levels in both brain regions evaluated. DISCUSSION: In conclusion, we propose that the ouabain decreased BDNF levels in the hippocampus and amygdala when assessed seven days after administration, supporting the Na/K ATPase hypothesis for bipolar illness.OBJETIVO: O presente estudo tem como objetivo investigar os efeitos da injeção intracerebroventricular de ouabaína sobre os níveis de BDNF na amígdala e no hipocampo de ratos Wistar. MÉTODOS: Os animais receberam uma única injeção intracerebroventricular de ouabaína (10-3 and 10-2 M) ou fluido cerebroespinhal artificial e, imediatamente, 3h, 24h ou sete dias após a injeção, os níveis de BDNF foram mensurados na amígdala e hipocampo dos ratos por ELISA sandwich (n = 8 animais por grupo). RESULTADOS: Quando avaliados imediatamente após a injeção, 3h ou 24h, ouabaína nas doses 10-2 e 10-3 M não alterou os níveis de BDNF em ambas as estruturas avaliadas. Entretanto, quando avaliados sete dias após a injeção, ouabaína nas doses 10-2 e 10-3 M mostrou uma significante redução nos níveis de BDNF em amígdala e hipocampo. CONCLUSÃO: Em conclusão, propõe-se que a administração de ouabaína diminuiu os níveis de BDNF em amígdala e hipocampo quando avaliados sete dias após a injeção, suportando a hipótese da participação da Na/K ATPase no transtorno bipolar

Increasing cassava root yield: Additive-dominant genetic models for selection of parents and clones

Genomic selection has been promising in situations where phenotypic assessments are expensive, laborious, and/or inefficient. This work evaluated the efficiency of genomic prediction methods combined with genetic models in clone and parent selection with the goal of increasing fresh root yield, dry root yield, as well as dry matter content in cassava roots. The bias and predictive ability of the combinations of prediction methods Genomic Best Linear Unbiased Prediction (G-BLUP), Bayes B, Bayes Cπ, and Reproducing Kernel Hilbert Spaces with additive and additive-dominant genetic models were estimated. Fresh and dry root yield exhibited predominantly dominant heritability, while dry matter content exhibited predominantly additive heritability. The combination of prediction methods and genetic models did not show significant differences in the predictive ability for dry matter content. On the other hand, the prediction methods with additive-dominant genetic models had significantly higher predictive ability than the additive genetic models for fresh and dry root yield, allowing higher genetic gains in clone selection. However, higher predictive ability for genotypic values did not result in differences in breeding value predictions between additive and additive-dominant genetic models. G-BLUP with the classical additive-dominant genetic model had the best predictive ability and bias estimates for fresh and dry root yield. For dry matter content, the highest predictive ability was obtained by G-BLUP with the additive genetic model. Dry matter content exhibited the highest heritability, predictive ability, and bias estimates compared with other traits. The prediction methods showed similar selection gains with approximately 67% of the phenotypic selection gain. By shortening the breeding cycle time by 40%, genomic selection may overcome phenotypic selection by 10%, 13%, and 18% for fresh root yield, dry root yield, and dry matter content, respectively, with a selection proportion of 15%. The most suitable genetic model for each trait allows for genomic selection optimization in cassava with high selection gains, thereby accelerating the release of new varieties

Comparação de dois protocolos de avaliação de preensão manual em ciclistas da modalidade BMX

Objetivo: Este estudo objetivou caracterizar os parâmetros da curva de força de preensão manual ao longo do tempo, utilizando dois protocolos de avaliação (contínuo e intervalar) em atletas de BMX. Materiais e Métodos: A amostra foi composta por 10 ciclistas da modalidade BMX do sexo masculino, com idade entre 21 e 27 anos e com mais de cinco anos de experiência em competições e filiados à federação esportiva da modalidade. No protocolo intervalar, realizou-se uma contração por segundo durante 30 segundos. Os dados foram coletados de forma randomizada entre os membros dominantes e não dominantes dos voluntários, sendo feita uma única coleta em cada membro por protocolo. Para verificar a normalidade dos dados foi utilizado o teste de Shapiro-Will. Para comparar a força máxima e do tempo para atingir a força máxima entre a mão direita vs. mão esquerda, e entre os protocolos contínuo vs. intervalar, utilizou-se o teste t de Student. A análise do comportamento da força de preensão manual ao longo do tempo foi realizada por meio da ANOVA two way de medidas repetidas, seguida do post hoc de Tukey. O nível de significância adotado foi de 5%. Resultados: A mão não-dominante foi capaz de atingir a força máxima em menor período em ambos os protocolos. Não houve diferença significativa entre os membros nas forças máxima e média geradas. Conclusão: Constatou-se que a força de preensão manual máxima foi similar nos dois protocolos utilizados em comparação a mão dominante e não-dominante

Identificação de Problemas e Propostas para Melhoria

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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