Instruments for assessing back pain in athletes: A systematic review

Back pain in athletes varies with sport, age, and sex, which can impair athletic performance, thereby contributing to retirement. Studies on back pain in this population use questionnaires to assess components, such as pain intensity and location and factors associated with pain, among others. This study aimed to review validated questionnaires that have assessed back pain in athletes. This systematic review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) by searching the databases Embase, MEDLINE, SPORTDiscus, CINAHL, and Scopus. The articles were selected regardless of language and date of publication. Titles and abstracts were independently selected by two reviewers; disagreements were resolved by a third reviewer. All the steps were conducted using the software Rayyan. The methodological quality of the questionnaire validation articles was assessed using a critical appraisal tool checklist proposed by Brink and Louw. The search returned 4748 articles, of which 60 were selected for this review, including 5 questionnaire validation studies. These articles were published between 2004 and 2022, which were performed in more than 20 countries, particularly Germany (14) and Sweden (5). Thirteen different instruments were identified, of which 46.1% were developed in Europe. The most commonly used questionnaires were the Oswestry Disability Index and Nordic Standardized Questionnaire. In addition, five questionnaire validation studies were selected for methodological quality assessment, with only two studies demonstrating high methodological quality. The following three instruments were identified for assessing back pain specifically in athletes: Micheli Functional Scale, Persian Functional Rating Index, and Athlete Disability Index. This review confirmed that all three instruments were specifically designed to assess this condition

EVIDÊNCIAS QUE APONTAM A IMPORTÂNCIA DO USO DA ESPIRITUALIDADE COMO TERAPIA ALTERNATIVA COMPLEMENTAR NO ENFRENTAMENTO DOS EFEITOS DA COVID-19 – REVISÃO INTEGRATIVA

<p>Diante da imprevisível pandemia do COVID-19, que iniciou em dezembro de 2019 em Wuhan na China que acabou restringindo os delineamentos do viver humano, despertando assim a espiritualidade de muitos. O presente estudo objetivou refletir sobre o poder da espiritualidade como terapia complementar face aos desafios da COVID-19. A Metodologia aplicada nessa pesquisa trata-se de uma revisão integrativa da literatura, com abordagem qualitativa do tipo descritiva- exploratória, fundamentada em uma análise integrativa , desenvolvida através de pesquisas nas bases de dados online, BIREME/BVS, PUBMED e CINAHL, nos idiomas Português, Inglês e Espanhol com palavras chaves pré-selecionadas, com pesquisas indexadas no período de 2020 a 2021, sendo selecionados dez estudos para esta revisão. Este estudo evidenciou a espiritualidade como estratégia de enfrentamento terapêutico complementar da pandemia do COVID-19 e revelou que os efeitos de incorporá-los ao estilo de vida dos pacientes no cenário pandêmico, são gerais, vistos em todas as áreas da vida do indivíduo, afetando principalmente sua mente, sua maneira de lidar com os outros e sua própria vida, apresentando- se assim, como uma importante estratégia para mitigar e / ou eliminar os efeitos nocivos</p&gt

Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine

Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil

Plasticity of the pilus gene clusters <i>spaA</i> and <i>spaD</i> in <i>C. pseudotuberculosis</i>.

<p>A1 and B1, PiCp15 harboring the <i>spaA</i> cluster of genes; A2 and B2, PiCp7 harboring the <i>spaD</i> cluster of genes. A, all the <i>C. pseudotuberculosis</i> strains were aligned using <i>C. pseudotuberculosis</i> strain 1002 as a reference. From the inner to outer circle on A1 and A2: the biovar <i>equi</i> strains Cp31, Cp1/06-A, CpCp162, Cp258, Cp316, CpCIP52.97; and, the biovar <i>ovis</i> strains CpC231, CpP54B96, Cp267, CpPAT10, CpI19, Cp42/02-A, Cp3/99-5, CpFRC41 and Cp1002. B, all the <i>C. pseudotuberculosis</i> strains were aligned using <i>C. pseudotuberculosis</i> strain CIP52.97 as a reference. From the inner to outer circle on B1 and B2: the biovar <i>ovis</i> strains CpC231, Cp1002, CpPAT10, Cp267, CpP54B96, CpI19, Cp42/02-A, CpFRC41, Cp3/99-5, Cp1/06-A; and, the biovar <i>equi</i> strains Cp31, CpCp162, Cp316, Cp258 and CpCIP52.97. CDS, coding sequences; tRNA, transfer RNA; rRNA, ribosomal RNA; and PAI, pathogenicity island.</p

Venn diagram representing the core genomes of the <i>C. pseudotuberculosis</i> strains.

<p>All genomes, the number of genes composing the core genome of all the strains; <i>equi</i>, the number of genes of the core genome of the <i>C. pseudotuberculosis</i> biovar <i>equi</i> strains, which were absent in one or more of the <i>C. pseudotuberculosis</i> biovar <i>ovis</i> strains; <i>ovis</i>, the number of genes of the core genome of the <i>C. pseudotuberculosis</i> biovar <i>ovis</i> strains, which were absent in one or more of the <i>C. pseudotuberculosis</i> biovar <i>equi</i> strains.</p

Phylogenomic tree and heatmap analyses of the genus <i>Corynebacterium</i>.

<p>All the complete genomes from the genus <i>Corynebacterium</i> were retrieved from the NCBI ftp site. Comparisons between the variable content of all the strains were plotted as percentages of similarity on the heatmap using Gegenees (version 1.1.4). The percentage of similarity was used to generate a phylogenomic tree with SplitsTree (version 4.12.6). Numbers from 1 to 39 (upper-left to upper-right corner) represent species from <i>Corynebacterium aurimucosum</i> ATCC 70097 to <i>Corynebacterium variable</i> DSM 44702 (upper-left to lower-left corner). Percentages were plotted with a spectrum ranging from red (low similarity) to green (high similarity). On the heatmap, the upper portion is not symmetrical to the lower portion because the variable contents of all genomes present different sizes. Therefore, considering a scenario where the variable content from genomes A and B are composed of 100 and 80 genes, respectively, with a common repertoire of 40 genes, genome A will present 40% of similarity to genome B and genome B will present 50% of similarity to genome A.</p

General information about the 15 <i>C. pseudotuberculosis</i> strains used in this work.

<p>General information about the 15 <i>C. pseudotuberculosis</i> strains used in this work.</p

Comparative genomic maps of the <i>C. pseudotuberculosis</i> biovar <i>equi</i> and <i>ovis</i> strains.

<p>A, all the <i>C. pseudotuberculosis</i> strains were aligned using <i>C. pseudotuberculosis</i> strain 1002 as a reference. From the inner to outer circle on A: the biovar <i>equi</i> strains Cp31, Cp1/06-A, CpCp162, Cp258, Cp316 and CpCIP52.97; and, the biovar <i>ovis</i> strains CpC231, CpP54B96, Cp267, CpPAT10, CpI19, Cp42/02-A, Cp3/99-5, CpFRC41 and Cp1002. B, all the <i>C. pseudotuberculosis</i> strains were aligned using <i>C. pseudotuberculosis</i> strain CIP52.97 as a reference. From the inner to outer circle on B: the biovar <i>ovis</i> strains CpC231, Cp1002, CpPAT10, Cp267, CpP54B96, CpI19, Cp42/02-A, CpFRC41, Cp3/99-5; and, the biovar <i>equi</i> strains Cp1/06-A Cp31, CpCp162, Cp316, Cp258 and CpCIP52.97. CDS, coding sequences; tRNA, transfer RNA; rRNA, ribosomal RNA; and PAI, pathogenicity island.</p