Níveis de lactato sanguíneo, em futebolistas profissionais, verificados após o primeiro e o segundo tempos em partidas de futebol

O objetivo desse estudo foi verificar o envolvimento do metabolismo anaeróbio lático por meio de medidas de lactato sanguíneo em futebolistas profissionais imediatamente após o término do primeiro e do segundo tempos, em partidas de futebol. Foram avaliados 26 jogadores, todos do sexo masculino e pertencentes ao Departamento de Futebol Profissional da Associação Portuguesa de Desportos de São Paulo. O grupo foi composto por 4 laterais, 13 meio-campistas e 9 atacantes. A idade variou de 18 a 33 anos, o peso de 56,5 kg a 78,5 kg e a estatura de 164 cm a 185 cm. A escolha dos futebolistas durante os procedimentos foi aleatória. Foi utilizado um analisador de lactato portátil (modelo Accusport®, Boehringer Mannheim®, GER). Os seguintes resultados foram verificados: a média de lactato em todos os jogadores verificados ao final do primeiro e do segundo tempos das partidas analisadas foi de 7,1 mM ± 0,6 mM e 5,7 mM ± 1,3 mM (p < 0,05). Quando divididos por posição, os laterais apresentaram respectivamente: 6,9 mM ± 2,9 mM e 4,7 mM ± 3,0 mM; os meiocampistas: 6,4 mM ± 1,8 mM e 5,6 mM ± 1,2 mM e os atacantes: 7,7 mM ± 1,8 mM e 7,2 ± 2,1 mM. Quando comparamos a produção de lactato pelas posições, somente houve diferença estatística significante (p < 0,05) no segundo tempo entre os atacantes 7,2 mM ± 2,1 mM e os meio-campistas 5,6 mM ± 1,2 mM, respectivamente. Conclusão: Fatores subjetivos como grau de movimentação, recuperação rápida e disposição constante na partida, relacionados a fatores primários como atividade glicolítica reduzida e a diminuição da concentração de glicogênio muscular, motivados por pouco treinamento anaeróbio lático específico e/ou uma dieta deficiente, são evidências que podem explicar, em parte, porque o lactato sanguíneo no segundo tempo tem sido freqüentemente mais baixo quando comparado ao término do primeiro tempo em uma partida de futebol.Lactate blood levels in professional socer players after end of the first and second half in soccer matches The main purpose of this study was to assess the participation of the anaerobic lactic metabolism by measuring the lactate blood level in professional soccer players shortly after the end of the first and second half in soccer matches. The assessment was carried out in 26 male soccer players belonging to the Department of Professional Soccer at Associação Portuguesa de Desportos in São Paulo – Brazil. The group was composed of 4 full-backs, 13 midfielders and 9 forwards. Whose age, weight and stature ranged, respectively, from 18 to 33, from 56.5 kg to 78.5 kg and from 164 cm to 185 cm. The soccer players were piecked at random during the procedure. The lactate analyser used to this end was a portable (Accusport model, Boehringer Mannheim, Germany). These were the results: the average lactate level in all soccer players examined after the end of the first half and the second half of the matches analyzed was 7.1 mM ± 0.6 mM and 5.7 mM ± 1.3 mM (p < 0.05). When divided according to their position, the full-backs showed, respectively, 6.9 mM ± 2.9 mM and 4.7 mM ± 3.0 mM; the midfielders 6.4 mM ± 1.8 mM and 5.6 mM ± 1.2 mM and the forwards 7.7 mM ± 1.8 and 7.2 ± 2.1 mM. When we compared the lactate production according to the position, there was only significant statistic (p < 0.05) in the second half between the forwards 7.2 mM ± 2.1 mM and the midfielders 5.6 mM ± 1.2 mM, respectively. Conclusion: Subjective factors, such as movement rate, quick recovery and constant disposition during the match related to primary factors, such as reduced glycolitic activity and diminished muscular glycogen concentration caused by little specific lactic anaerobic training and/or poor diet, are evidences that can partly explain why the lactate blood level in the second half has often been lower when compared with the end of the first half of a soccer match

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

Updated cardiovascular prevention guideline of the Brazilian Society of Cardiology: 2019

Sem informação113478788

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