Análise clínica e terapêutica do tumor odontogênico: ameloblastoma

Introdução: O ameloblastoma é um tumor benigno de crescimento lento e invasivo, geralmente assintomático, sendo descoberto na maioria das vezes, através de exames radiográficos. Objetivo: verificar as evidências científicas acerca da análise clínica e terapêutica do tumor odontogênico ameloblastoma. Metodologia: Trata-se de uma revisão sistemática com abordagem qualitativa, realizada em setembro de 2020 com busca nas bases de dados: BVS, SCIELO, LILACS e MEDLINE, usando a estratégia PICO. Descritores selecionados: Ameloblastoma, Neoplasias Mandibulares, Tumores Odontogênicos, associados ao operador booleano And. Inclusão de estudos entre 2010 a 2022 com textos na íntegra, relevantes e disponível em português, inglês ou espanhol. Exclusão de textos incompletos, repetidos e sem relevância para temática. Resultados e discussão: Foram selecionados 24 estudos para construção da pesquisa. Os tipos de ameloblastomas são: unicístico, multicístico, periférico e o menos frequente ameloblastoma maligno. Os sintomas de apresentação incluem massa submucosa de crescimento lento, dentes com mobilidade, má oclusão, parestesia, dor e aproximadamente 35% dos pacientes podem ser assintomáticos. O diagnóstico é feito através da biópsia das células tumorais. Entretanto, o cirurgião-dentista ao suspeitar de ameloblastoma, após exames de imagem é indicado o encaminhamento para um especialista da área. Quanto ao tratamento, alguns autores indicam intervenção cirúrgica conservadora, e menos agressiva, como curetagem, enucleação, outros indicam uma cirurgia mais radical. Conclusão: O ameloblastoma é um tumor benigno, porém agressivo, de origem odontogênica, seu tratamento pode ser conservador ou radical, isso irá depender da extensão da lesão bem como da experiência do cirurgião-dentista para escolher o melhor tratamento para o paciente

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

Aspectos epidemiológicos das infecções por rota vírus no Distrito Federal, Brasil

Rotavírus foram pesquisados em 607amostrasfecais de crianças de até 6 anos de idade com quadros de diarréia aguda, no período de maio de 1986 a abril de 1990. Foi utilizada a técnica de eletroforese em gel depoliacrilamida (PAGE), sendo os rotavírus detectados em 123 amostras (20,27%) das quais 107(87,00%) apresentaram perfil eletroforético longo, compatível com o subgrupo II. Os rotavírus não foram encontrados no grupo controle constituído de crianças sadias, sendo porém detectados em 7,80% das crianças internadas por outras causas que não diarréia aguda. A maioria das crianças positivas para rotavírus encontrava-se na faixa etária de 6a24 meses (73,98%). A média depositividade nos meses chuvosos (outubro a abril) foi igual a 9,60% e no período seco, 34,48% com picos que variaram entre 53,17 e 73,27% nos meses de junho e julho, os mais frios do ano

Identification of human chromosome 22 transcribed sequences with ORF expressed sequence tags

Transcribed sequences in the human genome can be identified with confidence only by alignment with sequences derived from cDNAs synthesized from naturally occurring mRNAs. We constructed a set of 250,000 cDNAs that represent partial expressed gene sequences and that are biased toward the central coding regions of the resulting transcripts. They are termed ORF expressed sequence tags (ORESTES). The 250,000 ORESTES were assembled into 81,429 contigs. Of these, 1,181 (1.45%) were found to match sequences in chromosome 22 with at least one ORESTES contig for 162 (65.6%) of the 247 known genes, for 67 (44.6%) of the 150 related genes, and for 45 of the 148 (30.4%) EST-predicted genes on this chromosome. Using a set of stringent criteria to validate our sequences, we identified a further 219 previously unannotated transcribed sequences on chromosome 22. Of these, 171 were in fact also defined by EST or full length cDNA sequences available in GenBank but not utilized in the initial annotation of the first human chromosome sequence. Thus despite representing less than 15% of all expressed human sequences in the public databases at the time of the present analysis, ORESTES sequences defined 48 transcribed sequences on chromosome 22 not defined by other sequences. All of the transcribed sequences defined by ORESTES coincided with DNA regions predicted as encoding exons by genscan. (http://genes.mit.edu/GENSCAN.html)

A Transcript Finishing Initiative for Closing Gaps in the Human Transcriptome

We report the results of a transcript finishing initiative, undertaken for the purpose of identifying and characterizing novel human transcripts, in which RT-PCR was used to bridge gaps between paired EST clusters, mapped against the genomic sequence. Each pair of EST clusters selected for experimental validation was designated a transcript finishing unit (TFU). A total of 489 TFUs were selected for validation, and an overall efficiency of 43.1% was achieved. We generated a total of 59,975 bp of transcribed sequences organized into 432 exons, contributing to the definition of the structure of 211 human transcripts. The structure of several transcripts reported here was confirmed during the course of this project, through the generation of their corresponding full-length cDNA sequences. Nevertheless, for 21% of the validated TFUs, a full-length cDNA sequence is not yet available in public databases, and the structure of 69.2% of these TFUs was not correctly predicted by computer programs. The TF strategy provides a significant contribution to the definition of the complete catalog of human genes and transcripts, because it appears to be particularly useful for identification of low abundance transcripts expressed in a restricted set of tissues as well as for the delineation of gene boundaries and alternatively spliced isoforms