Acute effects of static stretching in dynamic force performance in young men

Introdução: O alongamento muscular é frequentemente utilizado nas práticas desportivas, com o objetivo de aumentar a flexibilidade muscular e amplitude articular, assim como diminuir o risco de lesões e melhorar o desempenho atlético. Objetivo: Analisar o efeito agudo do alongamento com diferentes tempos no desempenho da força dinâmica de membros superiores e inferiores em homens jovens. Métodos: Participaram da amostra 14 voluntários do sexo masculino com idade de 23 ± 2 anos, peso corporal de 84 ± 10kg, estatura de178 ± 7cm, IMC de 26 ± 2kg/m2 e percentual de gordura de 11 ± 3%. Eles foram avaliados com o teste de 10RM em três situações distintas: condição sem alongamento (SA), aquecimento especifico seguido do teste de 10-RM; condição com oito minutos de alongamento (AL-8), uma sessão de alongamento estático com oito minutos de duração, seguido do aquecimento e teste de 10RM; e a condição alongamento 16 minutos (AL-16), 16 minutos de alongamento seguidos dos procedimentos descritos anteriormente. Os testes foram feitos no supino reto e leg-press 45º; os alongamentos foram selecionados de forma a atingir as musculaturas solicitadas nos respectivos exercícios. Resultados: Houve redução de 9,2% da força muscular dinâmica de membros superiores em comparação dos grupos SA e AL16, e entre os grupos AL8 e AL16 (p < 0,001). Em membros inferiores essa redução de força (p < 0,001) foi de 4,8% para AL-8 e de 14,3% para AL-16 em comparação com o grupo SA. Conclusão: Sessões de alongamentos estáticos efetuados antes de atividades que envolvam força dinâmica possuem a capacidade de alterar negativamente o desempenho dessa qualidade física, acarretando pior rendimento em longos períodos de alongamento.Background: Muscular stretching is frequently used in sports practice with the aim to increase muscular flexibility and joint range of motion as well as to reduce injury risks and to improve athletic performance. Aim: To analyze the acute effect of stretching with different times in the dynamic strength performance of lower and upper extremities in young men. Methods: The sample was composed by 14 healthy male volunteers aged 23 ± 2 years, weight of 84 ± 10 Kg , height of 178 ± 7 cm, BMI of 26 ± 2 Kg/m2 and body fat of 11 ± 3 %. They were evaluated in a 10-maximum repetition test (10-RM) in three situations: no stretching (NS); after an 8-minute session of static stretching followed by specific warm-up (SS-8); and after 16-minute and specific warm-up before 10 RM test (SS-16). Tests were performed in bench press and 45º leg press exercises, and stretching was selected as to reach the musculature required in these exercises. Results: There was significant reduction (p<0.001) of dynamic muscular strength of upper extremities in comparison to NS with SS-16 (9.2%) and between SS-8 (4.2%) and SS-16 (14.3%) to lower extremities. This difference was found in all tested conditions. Conclusion: Static stretching sessions before activities involving dynamic strength are able to negatively change performance in longer stretching periods

Stroke outcome measurements from electronic medical records : cross-sectional study on the effectiveness of neural and nonneural classifiers

Background: With the rapid adoption of electronic medical records (EMRs), there is an ever-increasing opportunity to collect data and extract knowledge from EMRs to support patient-centered stroke management. Objective: This study aims to compare the effectiveness of state-of-the-art automatic text classification methods in classifying data to support the prediction of clinical patient outcomes and the extraction of patient characteristics from EMRs. Methods: Our study addressed the computational problems of information extraction and automatic text classification. We identified essential tasks to be considered in an ischemic stroke value-based program. The 30 selected tasks were classified (manually labeled by specialists) according to the following value agenda: tier 1 (achieved health care status), tier 2 (recovery process), care related (clinical management and risk scores), and baseline characteristics. The analyzed data set was retrospectively extracted from the EMRs of patients with stroke from a private Brazilian hospital between 2018 and 2019. A total of 44,206 sentences from free-text medical records in Portuguese were used to train and develop 10 supervised computational machine learning methods, including state-of-the-art neural and nonneural methods, along with ontological rules. As an experimental protocol, we used a 5-fold cross-validation procedure repeated 6 times, along with subject-wise sampling. A heatmap was used to display comparative result analyses according to the best algorithmic effectiveness (F1 score), supported by statistical significance tests. A feature importance analysis was conducted to provide insights into the results. Results: The top-performing models were support vector machines trained with lexical and semantic textual features, showing the importance of dealing with noise in EMR textual representations. The support vector machine models produced statistically superior results in 71% (17/24) of tasks, with an F1 score >80% regarding care-related tasks (patient treatment location, fall risk, thrombolytic therapy, and pressure ulcer risk), the process of recovery (ability to feed orally or ambulate and communicate), health care status achieved (mortality), and baseline characteristics (diabetes, obesity, dyslipidemia, and smoking status). Neural methods were largely outperformed by more traditional nonneural methods, given the characteristics of the data set. Ontological rules were also effective in tasks such as baseline characteristics (alcoholism, atrial fibrillation, and coronary artery disease) and the Rankin scale. The complementarity in effectiveness among models suggests that a combination of models could enhance the results and cover more tasks in the future. Conclusions: Advances in information technology capacity are essential for scalability and agility in measuring health status outcomes. This study allowed us to measure effectiveness and identify opportunities for automating the classification of outcomes of specific tasks related to clinical conditions of stroke victims, and thus ultimately assess the possibility of proactively using these machine learning techniques in real-world situations

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