Restrição alimentar de glúten e caseína em pacientes com Transtorno do Espectro Autista

O Transtorno do Espectro Autista (TEA) é enquadrado nos transtornos do neurodesenvolvimento, e cursa com sintomas centrais no comprometimento de, principalmente, três áreas: comunicativa, social e comportamental. Trata-se de um transtorno mais prevalente no sexo masculino e geralmente manifestado até o terceiro ano de vida. O objetivo desse estudo foi abordar os aspectos da restrição alimentar de glúten e caseína em pacientes com o transtorno, tendo em vista a complexidade de manejo terapêutico do TEA desde seu diagnóstico. Foi realizada revisão de literatura, a partir da busca por artigos nas bases de dados: Biblioteca Virtual de Saúde (BVS), Scientific Eletronic Online (Scielo) e PubMed, por meio dos descritores: “Transtorno do Espectro Autista”, “Caseína”, “Glúten”, “Restrição”, e 8 artigos foram utilizados para o desenvolvimento do trabalho. Os resultados evidenciaram a falta de dados comprobatórios para a eficácia da restrição alimentar de glúten e caseína na melhora dos sintomas em pacientes com TEA. Ademais, foi visto que tal restrição só deve ser considerada em casos de intolerância ou alergia, pois as restrições alimentares sem indicação efetiva podem estar relacionadas a rejeição social, estigmatização e dificuldades de socialização e integração e potencializar efeitos do transtorno. Conclui-se, portanto, a necessidade de novos estudos com metodologia eficaz e organizada para, então, considerar tal prática como medida terapêutica

Doenças endocrinológicas com repercussões psiquiátricas: revisão sistemática / Endocrinological disorders with psychiatric repercussions: a systematic review

O objetivo do estudo foi realizar uma revisão sistemática de literatura sobre doenças endocrinológicas com repercussões psiquiátricas. Para isso, realizou-se uma revisão sistemática de literatura, através de uma busca nas bases de dados Latino-Americana e do Caribe em Ciências da Saúde, Google Scholar e Scientific Electronic Library Online, utilizando-se os descritores: Endocrinology, Mental Disorders, Psychiatric Symptoms, Cushing’s syndrome, Hyperthyroidism, Hypothyroidism, Addison disease. Através disso, foram selecionados 12 artigos que compunham os critérios de inclusão e exclusão do presente estudo. Dessa forma, destaca-se que os estudos evidenciaram que diversos são as repercussões psiquiátricas em pacientes com doenças endocrinológicas, variando desde transtornos de ansiedade, depressão, transtorno de déficit de atenção/hiperatividade e psicose, cabendo aos profissionais o diagnóstico correto e em tempo hábil para auxiliar na qualidade de vida do indivíduo. 

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