Biomecânica aplicada à prevenção de traumas em dançarinos de pole dance: revisão sistemática

Introdução: Tendo o seu primeiro registro em meados do século XII na Índia, o Pole Dance ou MallaKhamb, era um exercício apenas para homens que usavam postes de madeira para obtenção de força e resistência. PD é a arte de realizar sequências de figuras plásticas estáticas e dinâmicas, usando a força dos pulsos, e o atrito das pernas e do abdômen para pendurar em um poste vertical. Tendo como principal objetivo alcançar e manter harmoniosamente tais figuras. Por possuir um nível extenso de treinamento, o PD pode e deve ser considerado sim um esporte acrobático com sério potencial de lesão. Metodologia: A pesquisa caracteriza-se como uma revisão sistemática de estudos e contempla os procedimentos metodológicos PRISMA (MOHER). Descritores utilizados: "pole dance", "Injures", "biomechanical" e, operadores booleanos “and” e “or”. Resultados: Dittrich e et al (2020), constataram uma prevalência maior de lesões na cabeça, coluna e ombro, decorrentes de quedas durante a execução de figuras suspensas devido à alta energia cinética do acidente, achados semelhantes aos de Ruscello et al que afirmam que em algumas figuras as taxas de aceleração vertical positiva e negativa podem alcançar até 2G e rotação de 400° por segundo. Já Szopa e et al (2022) descreveu achados antagônicos aos de Drittrich com uma incidência maior em extremidades inferiores (59%) à extremidades superiores (39%), sendo o menor percentual de lesões na coluna e tronco. Músculos e tendões foram os mais lesionados (25%), seguidos por articulações e ligamentos (23%). O tipo de lesão mais comum foi contusão (60%). Sendo que 86% da amostra relatou sofrer algum tipo de lesão e 56% algum tipo de relesão. Conclusão: Conclui-se que a biomecânica aplicada ao esporte é de extrema importância na prevenção e no plano terapêutico para reabilitação de lesões e acometimentos maléficos aos atletas

Os principais tipos e manifestações da Cirrose Hepática: uma atualização clínica

Introdução: A cirrose hepática é um processo patológica crônico, considerado a hepatopatia mais comum, definido como a conversão difusa morfoestrutural por nódulos de arquitetura anômalo envoltos por fibrose. Objetivou-se descrever os tipos mais relevantes de cirrose e suas devidas manifestações. Metodologia: Trata-se de uma revisão bibliográfica, fundamentada nas plataformas do SciELO, PubMed, Scopus, utilizando os termos “hepatical cirrhosis”, “liver disease” e “hepatocellular insufficiency” a qual através da revisão narrativa, abordou amplamente a respeito da contextualização da cirrose e as principais etiologias. Resultados e Discussão: Foi analisado que tal condição afeta qualquer faixa etária, sexo, etnia e independe da classe socioeconômica, mas as diversas etiologias impõem um perfil epidemiológico específico conforme a aparição. As principais origens abordam o tipo alcoólico, hepatite, aplicação crônica de alguns fármacos e esteatose gordurosa ou não. Ademais, estima-se que estas afetam a anatomofuncionalidade do órgão responsável por grande parte da homeostase, culminando em diversas manifestações clínicas.  Conclusão: A cirrose é uma consequência grave de fatores de base em estágio avançado, a qual devido ao seu curso geralmente silencioso culmina no desenvolvimento e progressão clínica. Neste contexto, a atenção aos fatores predisponentes como alimentação rica em lipídios, estilismo, negligência a exames de rotina, sedentarismo e obesidade contribuem constituem medidas eficazes de prevenção primária.&nbsp

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

Educomunicação, Transformação Social e Desenvolvimento Sustentável

Esta publicação apresenta os principais trabalhos dos GTs do II Congresso Internacional de Comunicação e Educação nos temas Transformação social, com os artigos que abordam principalmente Educomunicação e/ou Mídia-Educação, no contexto de políticas de diversidade, inclusão e equidade; e, em Desenvolvimento Sustentável os artigos que abordam os avanços da relação comunicação/educação no contexto da educação ambiental e desenvolvimento sustentável

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

Catálogo Taxonômico da Fauna do Brasil: setting the baseline knowledge on the animal diversity in Brazil

The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others

Seminário de Dissertação (2024)

Página da disciplina de Seminário de Dissertação (MPPP, UFPE, 2022) Lista de participantes == https://docs.google.com/spreadsheets/d/1mrULe1y04yPxHUBaF50jhaM1OY8QYJ3zva4N4yvm198/edit#gid=

Characterisation of microbial attack on archaeological bone

As part of an EU funded project to investigate the factors influencing bone preservation in the archaeological record, more than 250 bones from 41 archaeological sites in five countries spanning four climatic regions were studied for diagenetic alteration. Sites were selected to cover a range of environmental conditions and archaeological contexts. Microscopic and physical (mercury intrusion porosimetry) analyses of these bones revealed that the majority (68%) had suffered microbial attack. Furthermore, significant differences were found between animal and human bone in both the state of preservation and the type of microbial attack present. These differences in preservation might result from differences in early taphonomy of the bones. © 2003 Elsevier Science Ltd. All rights reserved