Contributions from specific and general factors to unique deficits: two cases of mathematics learning difficulties

Mathematics learning difficulties are a highly comorbid and heterogeneous set of disorders linked to several dissociable mechanisms and endophenotypes. Two of these endophenotypes consist of primary deficits in number sense and verbal numerical representations. However, currently acknowledged endophenotypes are underspecified regarding the role of automatic vs. controlled information processing, and their description should be complemented. Two children with specific deficits in number sense and verbal numerical representations and normal or above-normal intelligence and preserved visuospatial cognition illustrate this point. Child H.V. exhibited deficits in number sense and fact retrieval. Child G.A. presented severe deficits in orally presented problems and transcoding tasks. A partial confirmation of the two endophenotypes that relate to the number sense and verbal processing was obtained, but a much more clear differentiation between the deficits presented by H.V. and G.A. can be reached by looking at differential impairments in modes of processing. H.V. is notably competent in the use of controlled processing but has problems with more automatic processes, such as nonsymbolic magnitude processing, speeded counting and fact retrieval. In contrast, G.A. can retrieve facts and process nonsymbolic magnitudes but exhibits severe impairment in recruiting executive functions and the concentration that is necessary to accomplish transcoding tasks and word problem solving. These results indicate that typical endophenotypes might be insufficient to describe accurately the deficits that are observed in children with mathematics learning abilities. However, by incorporating domain-specificity and modes of processing into the assessment of the endophenotypes, individual deficit profiles can be much more accurately described. This process calls for further specification of the endophenotypes in mathematics learning difficulties

Déficits em funções executivas “frias” são mais salientes nos sintomas de TDAH que na dificuldade de leitura?

Introduction: Reading disability (RD) and Attention Deficit Hyperactivity Disorder (ADHD) symptoms often co-occur in school-age children. Methods: The present study evaluated the performance of 216 Brazilian children from 3rd and 4th grades on “cool” executive function (EF) abilities and phonological processing. The children were divided into three groups: those with ADHD symptoms only, those with RD only, and controls. Results: MANOVA analyses, controlling for age and nonverbal intelligence, showed worse performance for the RD group, compared to the ADHD symptoms group, on measures of phonological processing (phonemic awareness, phonological short-term memory, and lexical access) and “cool” EF components (orthographic verbal fluency and processing speed). The ADHD symptoms group did not differ from the control group on the majority of the “cool” EF tasks. Compared to the control group, the ADHD symptoms group and the RD group both showed significantly more errors in rapid automatized naming of figures, which evaluates the inhibition component of EF; performance on this task was similar for these groups. Conclusion: We conclude that children with RD have greater impairment in phonological processing and “cool” EF compared to those with ADHD symptoms. Furthermore, deficits in inhibitory control may be shared among children with both conditions.Introdução: Dificuldades de leitura (DL) e sintomas do Transtorno de Déficit de Atenção e Hiperatividade (TDAH) frequentemente coocorrem em crianças escolares. Métodos: O presente estudo comparou o desempenho em FE “frias” e processamento fonológico de 216 crianças brasileiras de 3ª e 4ª anos, que foram divididas em três grupos: apenas com sintomas de TDAH, apenas em DL e controles. Resultados: As análises de MANOVA, controlando para idade e inteligência não-verbal, indicaram que o grupo com DL apresentou desempenho significativamente inferior ao grupo com sintomas de TDAH nas medidas de processamento fonológico (consciência fonológica, memória verbal de curto prazo e acesso lexical) e em componentes das FE “frias” (fluência verbal ortográfica e velocidade de processamento). O grupo com sintomas de TDAH não se diferiram do grupo controle na maior parte das tarefas de FE “frias”. Ambos os grupos com sintomas de TDAH e DL apresentaram desempenhos significativamente menores (mais erros) em comparação às crianças de desenvolvimento típico na tarefa de Nomeação Seriada Rápida de figuras que avalia o componente de controle inibitório, e o desempenho foi semelhante entre os grupos. Conclusão: Conclui-se que crianças com DL apresentam maior comprometimento em processamento fonológico e FE “frias” em comparação àquelas com sintomas de TDAH e que déficits no controle inibitório podem ser compartilhados entre crianças com ambas as condições

O papel da semaglutida no tratamento e progressão da doença hepática gordurosa não alcoólica

The integrative review aimed to assess the efficacy and safety of semaglutide treatment in managing non-alcoholic fatty liver disease (NAFLD), addressing its impact on non-alcoholic steatohepatitis across different patient populations. Initially, 187 studies were retrieved, of which 145 were excluded for not meeting specific inclusion criteria. Following a more detailed evaluation, 5 studies were selected and included in the review. Results highlight the pleiotropic effects of semaglutide, demonstrating improvements in NASLD markers and significant reduction in cardiovascular risks. Studies in human patients showed glycemic control and weight reduction, while animal model studies revealed improvements in liver function and lipid composition. Research by Soto-Catalán et al. suggested that the effects of semaglutide may directly affect the liver, irrespective of food intake, providing a complementary perspective to human studies. The importance of these findings in clinical management of NAFLD is underscored, pointing to the need for further research to further elucidate the therapeutic effects of semaglutide in this condition.A revisão integrativa buscou avaliar a eficácia e segurança do tratamento com semaglutida na gestão da Doenças hepática gordurosa não alcoólica (DHGNA), abordando seu impacto na esteato-hepatite não alcoólica em diferentes populações de pacientes. Inicialmente, 187 estudos foram recuperados, dos quais 145 foram excluídos por não atenderem aos critérios de inclusão específicos. Após uma avaliação mais detalhada, 5 estudos foram selecionados e incluídos na revisão. Resultados destacam os efeitos pleiotrópicos do semaglutida, evidenciando melhorias nos marcadores de DHGNA e redução significativa dos riscos cardiovasculares. Estudos em pacientes humanos mostraram controle glicêmico e redução de peso, enquanto estudos em modelo animal revelaram melhorias na função hepática e composição lipídica. A pesquisa de Soto-Catalán et al. sugeriu que os efeitos do semaglutida podem ser diretos sobre o fígado, independentemente da ingestão de alimentos, fornecendo uma perspectiva complementar aos estudos em humanos. A importância desses achados na gestão clínica da DHGNA e apontam para a necessidade de mais pesquisas para elucidar ainda mais os efeitos terapêuticos do semaglutida nesta 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

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