Adoecimento docente e sofrimento psíquico em tempos de Pandemia de Covid 19 / Teacher Illness and psychic suffering in times of the Covid 19 Pandemic

O trabalho pretende discutir o adoecimento docente e o sofrimento psíquico causado pelas condições de trabalho. Para tanto foi realizada uma revisão bibliográfica sobre o assunto e um levantamento de dados publicados pela Organização Mundial de Saúde (OMS, 2017), Confederação Nacional dos Trabalhadores da Educação (CNTE, 2018), OCDE (Organização para a Cooperação e Desenvolvimento Econômico, 2019), a fim de discutir as condições adversas que têm culminado nas patologias que afastam, impossibilitam ou prejudicam o trabalho docente a fim de colaborar para a construção de políticas públicas eficazes que melhorem as condições do trabalho docente

Healing incisional surgical wounds using Rose Hip oil in rats

Purpose: To evaluate incisional surgical wound healing in rats by using Rose Hip (Rosa rubiginosa L.) oil. Methods: Twenty-one days after the oophorectomy procedure, twenty-seven female, adult, Wistar rats were distributed into three groups: Control group (wound treatment with distilled water); Collagenase group (treatment with collagenase ointment); and Rose Hip group (wound treatment with Rose Hip oil). Each group was distributed according to the date of euthanasia: 7, 14 and 21 days. The wound was evaluated considering the macroscopic and microscopic parameters. Results: The results indicated differences in the healing of incisional wounds between treatments when compared to control group. Accelerated wound healing was observed in the group treated with Rose Hip oil in comparison to the control and collagenase, especially after the 14th day. Morphometric data confirmed the structural findings. Conclusion: There was significant effect in topical application of Rose Hip oil on incisional surgical wound healing

Tomografia de corpo todo no trauma e seus desfechos na mortalidade: uma revisão sistemática: Whole body tomography in trauma and its outcomes in mortality: a systematic review

A tomografia computadorizada de corpo inteiro é altamente sensível e representa o padrão-ouro no cenário de diagnóstico da sala de trauma. WBCT fornece uma ferramenta de diagnóstico rápido, que reduz a mortalidade em pacientes gravemente feridos. A lesão traumática é a terceira principal causa de morte em geral. Para otimizar os resultados nesses pacientes, os hospitais empregam imagens de tomografia computadorizada de corpo inteiro (WBCT) devido ao alto rendimento diagnóstico e potencial para identificar lesões perdidas. No entanto, isso atrasa intervenções de tempo crítico. Atualmente, há uma ausência de qualquer evidência de alto nível para apoiar ou refutar qualquer visão. Uma busca sistemática da literatura foi realizada nas bases de dados MEDLINE, Embase, Web of Science, Cochrane Library e demais bases dedados eletrônicas. As publicações eram elegíveis se contivessem dados originais comparando TC de corpo total imediata em pacientes com trauma e associação com a mortalidade. A análise mostra que a TC está associada a melhores resultados, incluindo uma menor taxa de mortalidade geral, entretanto estudos randomizados e controlados merecem ser realizados para que se possa estabelecer de forma fidedigna essa relação

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