A IMPORTÂNCIA DO LEITE MATERNO PARA O CRESCIMENTO E DESENVOLVIMENTO INFANTIL

To analyze the importance of breast milk for infant growth and development using scientific evidence. Methods: This is a qualitative integrative literature review. The search for studies involved in the research was carried out in the following databases: SCIELO, LILACS, BDENF and MEDLINE, using the health sciences descriptors: "Breastfeeding", "Health promotion" and "Child health". The inclusion criteria were: published between 2014 and 2024, with free access to full texts, articles in Portuguese, English and Spanish and related to the theme. Exclusion criteria were: duplicate articles, incomplete articles, abstracts, reviews, debates, articles published in event proceedings and unavailable in full. Results: Through breastfeeding, physical well-being can be established, where the child feels cozy, in addition to this feeling of protection and skin-to-skin contact, this practice has a very positive impact. Conclusion: It can be concluded that breastfeeding is important for the child's development, especially in the first six months of life. It also provides the right nutrients in the right quantities for the child.Analisar por meio das evidências cientificas a importância do leite materno para o crescimento e desenvolvimento infantil. Métodos: Trata-se de uma revisão integrativa da literatura de caráter qualitativo. A busca dos trabalhos envolvidos na pesquisa foi realizada nas seguintes bases de dados: SCIELO, LILACS, BDENF e MEDLINE, a partir dos descritores em ciências da saúde: “Aleitamento materno”, “Promoção da saúde” e “Saúde da criança”. Os critérios de inclusão foram: publicados no período entre 2014 e 2024, cujo acesso ao periódico era livre aos textos completos, artigos em idioma português, inglês e espanhol e relacionados a temática. Critérios de exclusão foram: artigos duplicados, incompletos, resumos, resenhas, debates, artigos publicados em anais de eventos e indisponíveis na íntegra. Resultados: Por meio da amamentação pode se estabelecer o bem estar físico, onde a criança se sente aconchegado, além dessa sensação de proteção e contato pele a pele, essa prática exercer um impacto bastante positivo. Conclusão: Conclui-se que a prática do aleitamento materno é importante para o desenvolvimento da criança principalmente nos primeiros seis meses de vida. Além disso, ele possui os nutrientes corretos e em quantidades certas para a criança

Inclusão escolar de alunos portadores de Transtorno do Espectro Autista na educação infantil: uma revisão sistemática: School inclusion of students with Autistic Spectrum Disorder in early childhood education: a systematic review

Este artigo debate o problema da inclusão escolar de crianças com transtorno do espectro autista (TEA). Para estabelecer o debate, foi feito um levantamento bibliográfico e referencial para uma revisão sistemática do tema. O objetivo é clarificar os conceitos de inclusão escolar e TEA para debater como é feito o processo educacional de crianças com TEA. Sendo assim, a partir da pesquisa científica de descritores como “inclusão escolar”, “transtorno do espectro autista”, foi observado, pelos diversos autores trabalhados, que ainda há muito o que se debater e trabalhar para que ocorra a inclusão escolar de crianças com TEA. Apesar das muitas tentativas, erros e acertos, o tema ainda é pouco trabalhado e divulgado, visto que há poucos profissionais capacitados na área

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