Changes in torque complexity with fatigue : unravelling the role of neuromuscular coordination mechanisms

Physiological complexity is believed to reflect a system’s adaptability to environmental challenges. Torque complexity reflects the adaptability of motor control and has been proposed as an indirect indicator of the functional capacity of the neuromuscular system. While torque complexity has been shown to decrease with aging, disease and fatigue, its underlying mechanisms are not yet fully understood. Thus, the present study aimed to investigate the neurophysiological mechanisms underlying torque complexity. Twenty-one healthy and young adults (age: 24.62 ± 3.51yrs; height: 1.77 ± 0.07m; weight: 74.57 ± 13.34kg; BMI: 23.68 ± 3.30kg/m2) took part in the present study and visited the laboratory on one occasion. Participants performed three extension and flexion Maximal Voluntary Isometric Contractions (MVIC) proceeded by two repetitions of a thirty-second long submaximal isometric contraction at 30% MVIC. Participants then performed the fatiguing protocol, which consisted of a series of concentric and eccentric knee extensions until exhaustion at 90º/s. Immediately after, participants performed the same tests as prior to the fatiguing protocol. Peak Torque (PT) and Rate of Torque Development (RTD) were determined from the MVIC trials. Torque signals were sampled continuously, and the metrics of variability and complexity were calculated based on submaximal contractions trials. The coefficient of variation (CV) was used to quantify torque variability, while torque complexity was determined through Sample Entropy (SampEn). Electromyographic (EMG) signals, specifically, motor unit-related parameters, EMG amplitude, EMG CV and EMG co-contraction index (CCi) were also extracted from the submaximal trials. Paired sampled t-tests or Wilcoxon Signed-rank tests were used to test the effect of fatigue in all the dependent variables. Additionally, a stepwise multiple linear regression analysis was conducted to examine the contribution of other parameters to explain changes in torque complexity. Torque SampEn and CV were not altered with fatigue. PT and RTD significantly decreased whereas EMG amplitude, CCi, motor unit action potential amplitude (MUAPa) and average firing rate (FRa) significantly increased with fatigue. The multiple linear regression analysis revealed that FR/MUAPslope, FR/MUAPintercept and torque’s CV significantly explained changes in torque complexity accounting for 80.5% of its variance. Interestingly, changes in torque complexity were mainly attributed to intramuscular coordination processes which should be taken into consideration when planning training process and competition cycles.A complexidade fisiológica é considerada uma medida que reflete a capacidade de adaptação de um sistema biológico a alterações no contexto em que ele está inserido. A complexidade do torque caracteriza-se como a adaptabilidade do controlo motor sendo vista como um indicador indireto da capacidade funcional do sistema neuromuscular. Os fenómenos de envelhecimento, doença e fadiga constituem-se como fatores responsáveis pela diminuição da complexidade do torque; no entanto, os mecanismos responsáveis por esta alteração ainda não são compreendidos na sua totalidade. Assim, o presento estudo tem como objetivo investigar os mecanismos responsáveis pela alteração da complexidade do torque com a fadiga. Vinte e um adultos jovens e saudáveis (idade: 24.62 ± 3.51anos; altura: 1.77 ± 0.07m; massa corporal: 74.57 ± 13.34kg; IMC: 23.68 ± 3.30kg/m2) participaram no presente estudo e visitaram o laboratório numa única ocasião. Os participantes realizaram três Contrações Isométricas Voluntárias Máximas (MVIC) de extensão e flexão, procedidas por duas contrações isométricas submáximas com a duração de trinta segundas a 30% MVIC. De seguida, os participantes realizaram o protocolo de fadiga que consistiu numa série de extensões do joelho concêntricas e excêntricas até à exaustão a 90º/s. Imediatamente após, os participantes repetiram os testes realizados antes do protocolo de fadiga. O Peak Torque (PT) e a Taxa de Produção de Torque (RTD) foram determinados a partir das tarefas MVIC. Os dados do torque foram recolhidos de forma contínua e as métricas da variabilidade e da complexidade foram calculadas a partir das contrações submáximas. O coeficiente de variação (CV) foi usado para quantificar a magnitude da variabilidade do torque e a complexidade do torque foi determinada através da Sample Entropy (SampEn). As medidas da eletromiografia (EMG), nomeadamente, os parâmetros relacionados com as unidades motoras, a amplitude do EMG, o CV do EMG e o índex de co-contração do EMG (CCi) também foram extraídos a partir das tarefas submáximas. Os testes t para amostras emparelhadas e os testes de Wilcoxon foram usados para testar o efeito da fadiga em todas as variáveis dependentes. Adicionalmente, foi realizada uma análise de regressão linear múltipla para examinar a contribuição de outros parâmetros para a explicação de alterações na complexidade do torque. Não houve diferenças na SampEn e no CV do torque com a fadiga. Com a fadiga, o PT e a RTD diminuíram significativamente. Por outro lado, a amplitude do EMG, o CCi, a amplitude dos potenciais de ação das unidades motoras (MUAPa) e a frequência de descarga média (FRa) aumentaram significativamente com a instalação da fadiga. A análise de regressão linear múltipla demonstrou que o FR/MUAPslope, o FR/MUAPintercept e o CV do torque explicam significativamente 80.5% das alterações na complexidade do torque. Deste modo, as alterações na complexidade do torque foram maioritariamente explicadas por mecanismos de coordenação intramuscular, os quais devem ser tidos em consideração no processo de planeamento do treino e dos ciclos competitivos

Analysis of possible pathways on the mechanism of action of minocycline and doxycycline against strains of Candida spp. resistant to fluconazole

Species of the genus Candida, characterized as commensals of the human microbiota, are opportunistic pathogens capable of generating various types of infections with high associated costs. Considering the limited pharmacological arsenal and the emergence of antifungal-resistant strains, the repositioning of drugs is a strategy used to search for new therapeutic alternatives, in which minocycline and doxycycline have been evaluated as potential candidates. Thus, the objective was to evaluate the in vitro antifungal activity of two tetracyclines, minocycline and doxycycline, and their possible mechanism of action against fluconazole-resistant strains of Candida spp. The sensitivity test for antimicrobials was performed using the broth microdilution technique, and the pharmacological interaction with fluconazole was also analysed using the checkerboard method. To analyse the possible mechanisms of action, flow cytometry assays were performed. The minimum inhibitory concentration obtained was 4-427 µg ml-1 for minocycline and 128-512 µg ml-1 for doxycycline, and mostly indifferent and additive interactions with fluconazole were observed. These tetracyclines were found to promote cellular alterations that generated death by apoptosis, with concentration-dependent reactive oxygen species production and reduced cell viability. Therefore, minocycline and doxycycline present themselves as promising study molecules against Candida spp.This study was supported by grants and fellowships from the research support agencies CNPq, CAPES and FUNCAP/Ceará.Peer reviewe

SARS-CoV-2 introductions and early dynamics of the epidemic in Portugal

Genomic surveillance of SARS-CoV-2 in Portugal was rapidly implemented by the National Institute of Health in the early stages of the COVID-19 epidemic, in collaboration with more than 50 laboratories distributed nationwide. Methods By applying recent phylodynamic models that allow integration of individual-based travel history, we reconstructed and characterized the spatio-temporal dynamics of SARSCoV-2 introductions and early dissemination in Portugal. Results We detected at least 277 independent SARS-CoV-2 introductions, mostly from European countries (namely the United Kingdom, Spain, France, Italy, and Switzerland), which were consistent with the countries with the highest connectivity with Portugal. Although most introductions were estimated to have occurred during early March 2020, it is likely that SARS-CoV-2 was silently circulating in Portugal throughout February, before the first cases were confirmed. Conclusions Here we conclude that the earlier implementation of measures could have minimized the number of introductions and subsequent virus expansion in Portugal. This study lays the foundation for genomic epidemiology of SARS-CoV-2 in Portugal, and highlights the need for systematic and geographically-representative genomic surveillance.We gratefully acknowledge to Sara Hill and Nuno Faria (University of Oxford) and Joshua Quick and Nick Loman (University of Birmingham) for kindly providing us with the initial sets of Artic Network primers for NGS; Rafael Mamede (MRamirez team, IMM, Lisbon) for developing and sharing a bioinformatics script for sequence curation (https://github.com/rfm-targa/BioinfUtils); Philippe Lemey (KU Leuven) for providing guidance on the implementation of the phylodynamic models; Joshua L. Cherry (National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health) for providing guidance with the subsampling strategies; and all authors, originating and submitting laboratories who have contributed genome data on GISAID (https://www.gisaid.org/) on which part of this research is based. The opinions expressed in this article are those of the authors and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government. This study is co-funded by Fundação para a Ciência e Tecnologia and Agência de Investigação Clínica e Inovação Biomédica (234_596874175) on behalf of the Research 4 COVID-19 call. Some infrastructural resources used in this study come from the GenomePT project (POCI-01-0145-FEDER-022184), supported by COMPETE 2020 - Operational Programme for Competitiveness and Internationalisation (POCI), Lisboa Portugal Regional Operational Programme (Lisboa2020), Algarve Portugal Regional Operational Programme (CRESC Algarve2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by Fundação para a Ciência e a Tecnologia (FCT).info:eu-repo/semantics/publishedVersio

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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

Outcomes from elective colorectal cancer surgery during the SARS-CoV-2 pandemic

This study aimed to describe the change in surgical practice and the impact of SARS-CoV-2 on mortality after surgical resection of colorectal cancer during the initial phases of the SARS-CoV-2 pandemic

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

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