Importância terapêutica do exercício na neuroplasticidade em adultos com patologia neurológica: revisão sistemática

Mestrado em Fisioterapia. - Área de especialização: Fisioterapia Neurológica.O exercício físico induz uma cascata de processos moleculares e celulares que sustentam a plasticidade cerebral. O fator neurotrófico derivado do cérebro (Brain Derived Neurotrofic Factor-BDNF) é uma neurotrofina essencial intimamente ligada a processos moleculares centrais e periféricos de metabolismo energético e homeostase, e pode desempenhar um papel crucial nesses mecanismos induzidos. Objetivo: Verificar os efeitos do exercício terapêutico na indução da neuroplasticidade e/ou níveis de BDNF em condições neurológicas em adultos. Metodologia: Realizou-se uma revisão sistemática da literatura recorrendo às bases de dados: PubMed, Web of Science e Scopus. Consideram-se para inclusão ensaios clínicos controlados e aleatorizados e estudos piloto, em língua inglesa e em humanos adultos com patologia neurológica. Foram considerados artigos que possuíssem qualidade moderada a alta com pontuação ≥6 na Escala PEDro, excluindo-se revisões sistemáticas da literatura, meta análises e outros artigos que não atendam aos requisitos da estratégia de pesquisa. Realizou-se a seleção com base na estratégia PICO, incluindo-se 9 estudos para análise. Resultados/Discussão: Esta revisão analisa os impactos do exercício físico na neuroplasticidade através da avaliação das redes neurais e da excitabilidade neuronal, por meio do BDNF, da avaliação cognitiva e funcional. As evidências analisadas mostram que o exercício aeróbio (EA) de intensidade moderada a elevada, no mínimo de 30 minutos, 3 vezes/semana, no decorrer de 4 semanas, interfere positivamente no nível de BDNF e nos ganhos funcionais. Conclusão: O EA de intensidade moderada a elevada mostrou potencializar a neuroplasticidade, sendo assim uma intervenção terapêutica fundamental na reabilitação de condições neurológicas.ABSTRACT - Physical exercise induces a cascade of molecular and cellular processes that support brain plasticity. Brain-Derived Neurotrophic Factor-BDNF is an essential neurotrophin closely linked to central and peripheral molecular processes of energy metabolism and homeostasis and may play a crucial role in these induced mechanisms. Objective: To verify the effects of therapeutic exercise on the induction of neuroplasticity and/or BDNF levels in neurological conditions in adults. Methodology: A systematic literature review was carried out using the following databases: PubMed, Web of Science, and Scopus. Controlled and randomized clinical trials and pilot studies, in English and in adult humans with neurological pathology, are considered for inclusion. Articles that had moderate to high quality with a score ≥6 on the PEDro Scale were considered, excluding systematic literature reviews, meta-analyses, and other articles that did not meet the requirements of the research strategy. The selection was performed based on the PICO strategy, including 9 studies for analysis. Results/Discussion: This review analyzes the impacts of physical exercise on neuroplasticity through the assessment of neural networks and neuronal excitability, through BDNF, cognitive and functional assessment. The analyzed evidence shows that moderate to high-intensity aerobic exercise (AS) for at least 30 minutes, 3 times/week, over 4 weeks, positively interfere with the level of BDNF and functional gains. Conclusion: Moderate to high-intensity AE has been shown to potentiate neuroplasticity, thus being a fundamental therapeutic intervention in the rehabilitation of neurological conditions.N/

Therapeutic importance of exercise in neuroplasticity in adults with neurological pathology: systematic review

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: this work was supported by Fundação para a Ciência e Tecnologia (FCT) in the scope of the FCT-IBEB Strategic Project UIDB/00645/2020 (Sofia R. Fernandes); Maria Teresa Tomás is affiliated in Health & Technology Research Center, ESTeSL - Escola Superior de Tecnologia da Saúde, Instituto Politécnico de Lisboa which is supported by FCT/MCTES (UIDB/05608/2020 and UIDP/05608/2020).Neuroplasticity is an essential mechanism by which the nervous system shapes and adapts according to functional requirements. Evidence suggests that physical exercise induces a cascade of cellular processes that favors brain plasticity. The Brain-Derived Neurotrophic Factor (BDNF) is a neurotrophin closely linked to neuroplasticity that can be increased due to exercise. To verify the effects of therapeutic exercise on neuroplasticity and/or peripheral BDNF levels in neurological conditions in adults, such as stroke, Parkinson’s and Alzheimer’s diseases, and mild cognitive impairment, and address its clinical relevance in the treatment of neurological dysfunctions. A systematic review was carried out using PUBMED, Web of Science, and Scopus databases. Inclusion criteria were: randomized controlled trials or pilot studies; humans age > 18 yrs with a neurological condition; English language; score ≥ 6 on the PEDro Scale (moderate to high quality). Reviews, meta-analyses, and other articles that did not meet the criteria were excluded. The PRISMA methodology was applied for the studies’ selection. A total of 9 studies were selected for a systematic and comprehensive analysis. According to these studies, moderate to high-intensity aerobic exercise (AE), increases the level of peripheral BDNF and positively influences functional gains in neurological conditions. Larger outcomes are observed in protocols with a minimum session duration of 30 minutes, frequency of 3 times/week, and intervention duration of 4 weeks. Current evidence shows that moderate to high-intensity AE induces neuroplasticity in neurological patients, thus being a fundamental therapeutic strategy to include in interventions aiming to repair/delay neurological dysfunctions.info:eu-repo/semantics/publishedVersio

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field

Erratum to: Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (Autophagy, 12, 1, 1-222, 10.1080/15548627.2015.1100356

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