ANÁLISE QUALITATIVA E AVALIAÇÃO ANTIMICROBIANA DO ÓLEO DE ARGAN (Argania spinosa L.) COMERCIALIZADO EM BARBACENA - MG

Argania spinosa L. é uma árvore pertencente à família Sapotaceae que cresce endemicamente no sudoeste marroquino. Seu óleo possui diversos constituintes como compostos fenólicos, saponinas, triterpenos que conferem diversas atividades biológicas. O objetivo deste estudo foi avaliar a qualidade e o potencial antioxidante e antibacteriano das amostras de óleo de argan comercializadas em farmácias de Barbacena - MG, por meio de testes de análise organoléptica, densidade relativa, saponinas de busca, atividade antioxidante e atividade antibacteriana contra Staphylococcus aureus, uma bactéria que pode colonizar o couro cabeludo. Foram utilizadas três amostras de óleo de argan denominadas A, B e C. Essas amostras apresentaram características consistentes ao óleo puro, exceto a amostra C. No teste de atividade antioxidante com DPPH, as três amostras apresentaram potencial antioxidante, sendo a amostra A mais satisfatória. Em relação à atividade antibacteriana, a amostra A na inibição do crescimento bacteriano em teste por contato mostrou atividade antimicrobiana contra cepas de S. aureus e pode ser utilizada no tratamento tópico de infecções por essas bactérias. As amostras B e C não inibiram o crescimento da espécie em nenhuma metodologia satisfatória. Foi demonstrado uma diversidade de qualidade em relação às amostras de argan comercializadas em Barbacena. Uma melhor fiscalização poderia auxiliar no controle da qualidade dos produtos comercializados. A amostra A demonstrou ser uma boa alternativa para o desenvolvimento de produtos contendo óleo de argan no combate à bactéria Staphylococcus aureus

Associação entre a ooforectomia bilateral precoce e o desenvolvimento do parkinsonismo e Doença de Parkinson em mulheres na pré-menopausa

O parkinsonismo é um distúrbio do sistema nervoso de maior incidência masculina do que feminina, visto que, por mecanismos fisiológicos, o estrogênio possui efeitos neuroprotetores, com funções como aumento da dopamina, um neurotransmissor essencial para o controle das funções motoras. Além disso, previne a formação dos corpúsculos de Lewy e da agregação da α-sinucleína, responsáveis pela progressão da Doença de Parkinson. Por isso, a doença se apresenta diferentemente nas mulheres. A remoção cirúrgica de ambos os ovários em mulheres na pré-menopausa para a prevenção do câncer de ovário parece favorecer o surgimento da doença, tendo em vista a perda da produção do hormônio protetor. Assim, o objetivo do estudo é analisar a associação entre a ooforectomia bilateral precoce e o desenvolvimento de parkinsonismo e Doença de Parkinson em mulheres na pré-menopausa. Trata-se de uma revisão bibliográfica sistemática, do tipo quantitativa, que utilizou as plataformas do PubMed, SciELO e Cochrane Library como bases de dados para seleção dos artigos, todos na língua inglesa. Foram utilizadas literaturas publicadas com recorte temporal de 2017 a 2022. De acordo com as literaturas analisadas, a ooforectomia bilateral precoce em mulheres na pré-menopausa aumenta o risco do desenvolvimento de parkinsonismo. Desse modo, a diminuição dos procedimentos cirúrgicos profiláticos para câncer de ovário nas pacientes com risco médio de malignidade reduziria o risco dessa condição

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

Neotropical freshwater fisheries : A dataset of occurrence and abundance of freshwater fishes in the Neotropics

The Neotropical region hosts 4225 freshwater fish species, ranking first among the world's most diverse regions for freshwater fishes. Our NEOTROPICAL FRESHWATER FISHES data set is the first to produce a large-scale Neotropical freshwater fish inventory, covering the entire Neotropical region from Mexico and the Caribbean in the north to the southern limits in Argentina, Paraguay, Chile, and Uruguay. We compiled 185,787 distribution records, with unique georeferenced coordinates, for the 4225 species, represented by occurrence and abundance data. The number of species for the most numerous orders are as follows: Characiformes (1289), Siluriformes (1384), Cichliformes (354), Cyprinodontiformes (245), and Gymnotiformes (135). The most recorded species was the characid Astyanax fasciatus (4696 records). We registered 116,802 distribution records for native species, compared to 1802 distribution records for nonnative species. The main aim of the NEOTROPICAL FRESHWATER FISHES data set was to make these occurrence and abundance data accessible for international researchers to develop ecological and macroecological studies, from local to regional scales, with focal fish species, families, or orders. We anticipate that the NEOTROPICAL FRESHWATER FISHES data set will be valuable for studies on a wide range of ecological processes, such as trophic cascades, fishery pressure, the effects of habitat loss and fragmentation, and the impacts of species invasion and climate change. There are no copyright restrictions on the data, and please cite this data paper when using the data in publications

ABC-SPH risk score for in-hospital mortality in COVID-19 patients : development, external validation and comparison with other available scores

The majority of available scores to assess mortality risk of coronavirus disease 2019 (COVID-19) patients in the emergency department have high risk of bias. Therefore, this cohort aimed to develop and validate a score at hospital admission for predicting in-hospital mortality in COVID-19 patients and to compare this score with other existing ones. Consecutive patients (≥ 18 years) with confirmed COVID-19 admitted to the participating hospitals were included. Logistic regression analysis was performed to develop a prediction model for in-hospital mortality, based on the 3978 patients admitted between March-July, 2020. The model was validated in the 1054 patients admitted during August-September, as well as in an external cohort of 474 Spanish patients. Median (25-75th percentile) age of the model-derivation cohort was 60 (48-72) years, and in-hospital mortality was 20.3%. The validation cohorts had similar age distribution and in-hospital mortality. Seven significant variables were included in the risk score: age, blood urea nitrogen, number of comorbidities, C-reactive protein, SpO/FiO ratio, platelet count, and heart rate. The model had high discriminatory value (AUROC 0.844, 95% CI 0.829-0.859), which was confirmed in the Brazilian (0.859 [95% CI 0.833-0.885]) and Spanish (0.894 [95% CI 0.870-0.919]) validation cohorts, and displayed better discrimination ability than other existing scores. It is implemented in a freely available online risk calculator (https://abc2sph.com/). An easy-to-use rapid scoring system based on characteristics of COVID-19 patients commonly available at hospital presentation was designed and validated for early stratification of in-hospital mortality risk of patients with COVID-19