Relação entre ácidos graxos poli-insaturados do leite materno e o desenvolvimento neuropsicomotor de bebês

Introdução: O leite materno fornece fatores protetores para desenvolvimento adequado do bebê. As gorduras do leite, principalmente os ácidos graxos poli-insaturados, são fundamentais na maturação dos sistemas nervoso e visual, cruciais para os marcos motores do desenvolvimento. Ácidos graxos têm sido amplamente demonstrados como importantes para a cognição, porém, existem poucas análises sobre sua influência no desenvolvimento neuropsicomotor. Sendo assim, o presente estudo visou investigar possíveis correlações entre as medidas dos ácidos graxos e o desenvolvimento neuropsicomotor do bebê. Métodos: Estudo epidemiológico do tipo observacional prospectivo de delineamento correlacional. Variáveis coletadas a partir do banco de dados de um projeto concluído no Hospital de Clínicas de Porto Alegre (CEP/HCPA nº 13-0507). Amostra: pares de mãe-bebê, recrutados na Unidade Básica de Saúde Santa Cecília (puericultura) e Hospital Nossa Senhora da Conceição (maternidade). Questionário: Escala Motora Infantil de Alberta. Leite materno: analisado no Laboratório de Bioquímica Nutricional - Universidade Federal de Viçosa (MG). Análise estatística realizada através do coeficiente de correlação de Spearman. Resultados: Foram incluídos 27 bebês, 10 do sexo feminino e 17 do sexo masculino, que possuíam dados do conteúdo do leite materno, coletado no primeiro mês, e dados do desenvolvimento motor, avaliado no terceiro mês. Foi encontrada correlação positiva entre os ácidos eicosanoico e eisosadienoico e os escores da posição sentada e supina, respectivamente (r=0,553, p=o,005; r=0,412, p=0,033). Conclusão: Os ácidos eicosaenoico e eisocadienoico do leite materno, apresentam valores aumentados e estes expressam correlação positiva com alguns domínios avaliados pela escala de desenvolvimento motor desses bebês

Effects of an early motor intervention program for the development of infants in residential shelters center

Objetivos: Verificar os efeitos de um programa de intervenção motora precoce no desenvolvimento motor de bebês de abrigos de Porto Alegre, entre 1 e 14 meses. Métodos: Ensaio clinico randomizado com 13 bebês no Grupo Interventivo (GI) e 12 bebês no Grupo Controle (GC). Os bebês foram avaliados pela Alberta Infant Motor Scale e o GI realizou dois meses de intervenção (perseguir objeto com os olhos, manipular brinquedos e controle postural). Resultados: Nas comparações intra grupos, o GC não apresentou diferença no seu desenvolvimento pré e pós intervenção. O GI teve aumento no percentual de normalidade e redução no atraso motor no pós intervenção. Nas comparações entre os grupos, houve significância na pré-intervenção, pois os bebês do GI eram mais atrasados. Na pós intervenção a diferença não permaneceu significativa devido à melhora acentuada no GI. Considerando as posturas, o GI obteve resultados significativos em prono, supino, sedestação, ortostase. O GC obteve significância apenas em ortostase, podendo ser justificada pela maior média de idade nesse grupo. Conclusão: Os bebês do GI melhoraram sua classificação no desenvolvimento motor.Aim: To assess the effects of an early motor intervention program in the motor development of babies in Porto Alegre shelters, between 1 and 14 months. Methods: Randomized clinical trial with 13 infants in the interventional group (IG) and 12 infants in the control group (CG). Alberta Infant Motor Scale (AIMS) assessed the babies and the GI held two months of intervention (to chase object, manipulate toys and postural control).Results: Not different from the pre motor development and post-intervention in the CG. GI had an increase in the percentage of normal and reduced motor delay. In the comparison between the groups, there was significance in the pre intervention, since the GI infants were more backward. In the post intervention, the difference did not remain significant due to the marked improvement in the GI. The GI also obtained significant results in the positions prone, supine, sedestation, orthostatic. The GC obtained significance only in orthostatic, and can be justified by the highest average age in this group. Conclusion: IG Babies improved their motor development rating

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