Os pesquisadores desenvolveram um modelo matema¡tico que pode prever o regime de exercacios ideal para construir maºsculos.
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)
Mulher levantando peso - Crédito: John Arano em Unsplash
"Surpreendentemente, não se sabe muito sobre por que ou como o exercacio fortalece os maºsculos: hámuito conhecimento aneda³tico e sabedoria adquirida, mas muito pouco em termos de dados concretos ou comprovados"
Eugene Terentjev
Os pesquisadores, da Universidade de Cambridge, usaram manãtodos da biofasica tea³rica para construir o modelo, que pode dizer quanto uma quantidade especafica de esfora§o fara¡ com que um maºsculo cresa§a e quanto tempo levara¡. O modelo pode formar a base de um produto de software, onde os usuários podem otimizar seus regimes de exercacios inserindo alguns detalhes de sua fisiologia individual.
O modelo ébaseado em um trabalho anterior da mesma equipe, que descobriu que um componente do maºsculo chamado titina éresponsável por gerar os sinais químicos que afetam o crescimento muscular.
Os resultados , relatados no Biophysical Journal , sugerem que existe um peso ideal para fazer o treinamento de resistência para cada pessoa e cada alvo de crescimento muscular. Os maºsculos são podem ficar pra³ximos de sua carga máxima por um período muito curto, e éa carga integrada ao longo do tempo que ativa a via de sinalização celular que leva a santese de novas proteanas musculares. Mas abaixo de um certo valor, a carga éinsuficiente para causar muita sinalização, e o tempo de exercacio teria que aumentar exponencialmente para compensar. O valor dessa carga cratica provavelmente depende da fisiologia particular do indivaduo.
Todos nossabemos que o exercacio constra³i maºsculos. Ou não? “Surpreendentemente, não se sabe muito sobre por que ou como o exercacio constra³i maºsculos: hámuito conhecimento aneda³tico e sabedoria adquirida, mas muito pouco em termos de dados concretos ou comprovadosâ€, disse o professor Eugene Terentjev, do Laborata³rio Cavendish de Cambridge , um dos os autores do artigo.
Durante o exercacio, quanto maior a carga, mais repetições ou maior a frequência, maior seráo aumento no tamanho do maºsculo. No entanto, mesmo olhando para todo o maºsculo, não se sabe por que ou quanto isso acontece. As respostas a ambas as perguntas ficam ainda mais complicadas a medida que o foco desce para um aºnico maºsculo ou suas fibras individuais.
Os maºsculos são compostos de filamentos individuais, que tem apenas 2 micra´metros de comprimento e menos de um micra´metro de dia¢metro, menores que o tamanho da canãlula muscular. “Por isso, parte da explicação para o crescimento muscular deve estar na escala molecularâ€, disse o co-autor Neil Ibata. “As interações entre as principais moléculas estruturais do maºsculo são foram reunidas hácerca de 50 anos. Como as proteanas menores e acessãorias se encaixam na imagem ainda não estãototalmente claro. â€
Isso ocorre porque os dados são muito difaceis de obter: as pessoas diferem muito em sua fisiologia e comportamento, tornando quase impossível conduzir um experimento controlado sobre asmudanças no tamanho dos maºsculos em uma pessoa real. “Vocaª pode extrair células musculares e examina¡-las individualmente, mas isso ignora outros problemas como os naveis de oxigaªnio e glicose durante o exercacioâ€, disse Terentjev. “a‰ muito difacil olhar para tudo junto.â€
Terentjev e seus colegas começam a examinar os mecanismos de mecanossensibilização - a capacidade das células de detectar sinais meca¢nicos em seu ambiente - hávários anos. A pesquisa foi notada pelo English Institute of Sport , que se interessou em saber se ela poderia estar relacionada a s suas observações na reabilitação muscular. Juntos, eles descobriram que a hiper / atrofia muscular estava diretamente ligada ao trabalho de Cambridge.
Em 2018, os pesquisadores de Cambridge iniciaram um projeto sobre como as proteanas nos filamentos musculares mudam com a força. Eles descobriram que os principais constituintes musculares, actina e miosina, não tem locais de ligação para moléculas de sinalização, então deve ser o terceiro componente muscular mais abundante - a titina - responsável por sinalizar asmudanças na força aplicada.
Sempre que parte de uma molanãcula fica sob tensão por um tempo suficientemente longo, ela muda para um estado diferente, expondo uma regia£o anteriormente oculta. Se essa regia£o puder se ligar a uma pequena molanãcula envolvida na sinalização celular, ela ativa essa molanãcula, gerando uma cadeia de sinalização química. A titina éuma proteana gigante, grande parte da qual se estende quando um maºsculo éalongado, mas uma pequena parte da molanãcula também fica sob tensão durante a contração muscular. Essa parte da titina contanãm o chamado domanio da titina quinase, que gera o sinal quamico que afeta o crescimento muscular.
A molanãcula tera¡ maior probabilidade de se abrir se estiver sob mais força, ou quando mantida sob a mesma força por mais tempo. Ambas as condições aumentara£o o número de moléculas de sinalização ativadas. Essas moléculas então induzem a santese de mais RNA mensageiro, levando a produção de novas proteanas musculares, e o corte transversal da canãlula muscular aumenta.
Essa constatação deu origem ao trabalho atual, iniciado pelo Ibata, ele pra³prio um grande atleta. “Fiquei animado para entender melhor o porquaª e o como do crescimento muscularâ€, disse ele. “Muito tempo e recursos poderiam ser economizados evitando regimes de exercacios de baixa produtividade e maximizando o potencial dos atletas com sessaµes regulares de alto valor, dado um volume especafico que o atleta écapaz de atingirâ€.
Terentjev e Ibata se propuseram a restringir um modelo matema¡tico que pudesse fornecer previsaµes quantitativas sobre o crescimento muscular. Eles começam com um modelo simples que monitorava as moléculas de titina se abrindo sob força e iniciando a cascata de sinalização. Eles usaram dados de microscopia para determinar a probabilidade dependente da força de que uma unidade de titina quinase se abrisse ou fechasse sob força e ativasse uma molanãcula de sinalização.
Eles então tornaram o modelo mais complexo incluindo informações adicionais, como troca de energia metaba³lica, bem como duração da repetição e recuperação. O modelo foi validado usando estudos anteriores de longo prazo sobre hipertrofia muscular.
“Embora existam dados experimentais mostrando crescimento muscular semelhante com cargas de até30% da carga máxima, nosso modelo sugere que cargas de 70% são um manãtodo mais eficiente de estimular o crescimentoâ€, disse Terentjev, que émembro do Queens 'College . “Abaixo disso, a taxa de abertura da titina quinase cai vertiginosamente e impede que a sinalizaçãosensívela meca¢nica ocorra. Acima disso, o esgotamento rápido impede um bom resultado, que nosso modelo previu quantitativamente. â€
“Um dos desafios na preparação de atletas de elite éo requisito comum para maximizar as adaptações enquanto equilibra as compensações associadas, como custos de energia,†disse Fionn MacPartlin, Treinador Saªnior de Fora§a e Condicionamento do Instituto Inglaªs de Esporte. “Este trabalho nos da¡ mais informações sobre os mecanismos potenciais de como os maºsculos sentem e respondem a carga, o que pode nos ajudar a projetar intervenções mais especificamente para atender a esses objetivos.â€
O modelo também aborda o problema da atrofia muscular, que ocorre durante longos períodos de repouso na cama ou para astronautas em microgravidade, mostrando quanto tempo um maºsculo pode se permitir permanecer inativo antes de comea§ar a se deteriorar e qual poderia ser o regime de recuperação ideal.
Eventualmente, os pesquisadores esperam produzir um aplicativo baseado em software amiga¡vel que possa fornecer regimes de exercacios individualizados para objetivos específicos. Os pesquisadores também esperam melhorar seu modelo estendendo sua análise com dados detalhados para homens e mulheres, já que muitos estudos de exercacios são fortemente voltados para atletas do sexo masculino.