A SÍNDROME DE TURNER E AS PERSPECTIVAS PARA O TRATAMENTO: UMA REVISÃO INTEGRATIVA

Turner Syndrome (TS) is a sexual chromosomal abnormality characterized by the absence of an X chromosome (45, X, 46 X karyotype iso), chromosomal mosaicism (45, X/46, XX, XY karyotype), common in women. The aim of this study was to analyze the scientific productions about the health implications of Turner Syndrome and treatment perspectives. This is an integrative literature review (ILR), conducted by two independent reviewers, using the LILACS, PubMed and Scielo databases, using the PICo search strategy. Data analysis occurred through detailed reading of categorized articles. Were selected 6 articles using the inclusion and exclusion criteria and the results showed that the treatments used for ST are mainly Growth Hormone in conjunction with Progesterone and Estrogen, ensuring the increase in stature and pubertal development. However, the importance of early treatment is visible, as is the need for further studies on the subject, in order to expand the network of treatments for TS, providing an improvement in the quality of life of patients.El síndrome de Turner (ST) es una anomalía cromosómica sexual caracterizada por la ausencia de un cromosoma X (cariotipo 45, X, 46 X iso), mosaicismo cromosómico (cariotipo 45, X/46, XX, XY), común en las mujeres. El objetivo de este estudio fue analizar las producciones científicas sobre las perspectivas de tratamiento del Síndrome de Turner. Se trata de una revisión integradora de la literatura (RIL), realizada por dos revisores independientes, en bases de datos lilas, pubmed y scielo, en el período de marzo de 2022, utilizando la estrategia de búsqueda PICo. El análisis de los datos se realizó a través de la lectura detallada de artículos categorizados.  Utilizando los criterios de inclusión y exclusión, se seleccionaron 6 artículos y los resultados mostraron que los tratamientos utilizados para el ST son principalmente hormona de crecimiento en conjunto con Progesterona y Estrógeno, asegurando el aumento de la altura y el desarrollo puberal. Sin embargo, es visible la importancia del tratamiento precoz, así como la necesidad de más estudios sobre el tema, con el fin de ampliar la red de tratamientos para el ST, proporcionando una mejora en la calidad de vida de los portadores.A Síndrome de Turner (ST) é uma anormalidade cromossômica sexual caracterizada pela ausência de um cromossomo X (cariótipo 45, X, 46 X iso), mosaicismo cromossômico (cariótipo 45, X/46, XX, XY), frequente em mulheres. O objetivo deste estudo foi analisar as produções científicas acerca das perspectivas de tratamento para a Síndrome de Turner. Trata-se de uma revisão integrativa da literatura (RIL), conduzida por dois revisores independentes, nas bases de dados LILACS, PubMed e Scielo, no período de março de 2022, utilizando-se a estratégia de busca PICo. A análise dos dados ocorreu por meio da leitura detalhada dos artigos categorizados.  Utilizando os critérios de inclusão e exclusão foram selecionados 6 artigos e os resultados mostraram que os tratamentos usados para a ST, são principalmente o Hormônio do crescimento em conjunto com a Progesterona e o Estrogênio, garantindo o aumento da estatura e o desenvolvimento puberal. Contudo, é visível a importância do tratamento precoce, como também a necessidade de novos estudos acerca da temática, a fim de ampliar a rede de tratamentos para a ST, proporcionando melhoria na qualidade de vida das portadoras

A inclusão escolar para pacientes com deficiência intelectual ou atraso cognitivo: School inclusion for patients with intellectual disability or cognitive delay

A educação inclusiva é fundamental para que crianças e adolescentes vivenciem ideias e experiências de ensino aprendizagem significativa, desenvolvam a autonomia e conquistem direitos de cidadania. No entanto, existem obstáculos que precisam ser compreendidos e superados e estratégias que podem ser adotadas para promover a inclusão de crianças com deficiência intelectual ou atraso cognitivo. Diante disso, este estudo tem como objetivo compreender o processo de inclusão escolar de alunos com deficiência intelectual ou atraso cognitivo. Para isso, trata-se de uma revisão sistemática de literatura, desenvolvida a partir da seleção de estudos nas bases de dados Scielo, Pubmed e BVS/Medline a partir do uso de descritores DeCS/MeSH e aplicação de critérios de inclusão e exclusão. Após a análise e interpretação dos dados, concluiu-se que, no processo de inclusão de alunos com deficiência intelectual ou atraso cognitivo no ambiente escolar, a educação inclusiva interfere positivamente na qualidade de vida desses. Para isso, destacam-se uma série de estratégias relevantes, tais como: envolvimento de escola como um todo, dos professores e da família; compreender a deficiência; valorizar os interesses e habilidades dos alunos com deficiência; estimular a autodeterminação desses e a convivência entre pessoas deficientes e não deficientes; promover a socialização por meio de jogos; utilizar atividades adaptadas; e cuidar da formação inicial e continuada dos professores, contemplando ideias sobre educação inclusiva

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

NEOTROPICAL ALIEN MAMMALS: a data set of occurrence and abundance of alien mammals in the Neotropics

Biological invasion is one of the main threats to native biodiversity. For a species to become invasive, it must be voluntarily or involuntarily introduced by humans into a nonnative habitat. Mammals were among first taxa to be introduced worldwide for game, meat, and labor, yet the number of species introduced in the Neotropics remains unknown. In this data set, we make available occurrence and abundance data on mammal species that (1) transposed a geographical barrier and (2) were voluntarily or involuntarily introduced by humans into the Neotropics. Our data set is composed of 73,738 historical and current georeferenced records on alien mammal species of which around 96% correspond to occurrence data on 77 species belonging to eight orders and 26 families. Data cover 26 continental countries in the Neotropics, ranging from Mexico and its frontier regions (southern Florida and coastal-central Florida in the southeast United States) to Argentina, Paraguay, Chile, and Uruguay, and the 13 countries of Caribbean islands. Our data set also includes neotropical species (e.g., Callithrix sp., Myocastor coypus, Nasua nasua) considered alien in particular areas of Neotropics. The most numerous species in terms of records are from Bos sp. (n = 37,782), Sus scrofa (n = 6,730), and Canis familiaris (n = 10,084); 17 species were represented by only one record (e.g., Syncerus caffer, Cervus timorensis, Cervus unicolor, Canis latrans). Primates have the highest number of species in the data set (n = 20 species), partly because of uncertainties regarding taxonomic identification of the genera Callithrix, which includes the species Callithrix aurita, Callithrix flaviceps, Callithrix geoffroyi, Callithrix jacchus, Callithrix kuhlii, Callithrix penicillata, and their hybrids. This unique data set will be a valuable source of information on invasion risk assessments, biodiversity redistribution and conservation-related research. There are no copyright restrictions. Please cite this data paper when using the data in publications. We also request that researchers and teachers inform us on how they are using the data