Seleção de características para identificação de diferentes proporções de tipos de fibras musculares por meio da eletromiografia de superfície

Skeletal muscle consists of muscle fiber types that have different physiological and biochemical characteristics. Basically, the muscle fiber can be classified into type I and type II, presenting, among other features, contraction speed and sensitivity to fatigue different for each type of muscle fiber. These fibers coexist in the skeletal muscles and their relative proportions are modulated according to the muscle functionality and the stimulus that is submitted. To identify the different proportions of fiber types in the muscle composition, many studies use biopsy as standard procedure. As the surface electromyography (EMGs) allows to extract information about the recruitment of different motor units, this study is based on the assumption that it is possible to use the EMG to identify different proportions of fiber types in a muscle. The goal of this study was to identify the characteristics of the EMG signals which are able to distinguish, more precisely, different proportions of fiber types. Also was investigated the combination of characteristics using appropriate mathematical models. To achieve the proposed objective, simulated signals were developed with different proportions of motor units recruited and with different signal-to-noise ratios. Thirteen characteristics in function of time and the frequency were extracted from emulated signals. The results for each extracted feature of the signals were submitted to the clustering algorithm k-means to separate the different proportions of motor units recruited on the emulated signals. Mathematical techniques (confusion matrix and analysis of capability) were implemented to select the characteristics able to identify different proportions of muscle fiber types. As a result, the average frequency and median frequency were selected as able to distinguish, with more precision, the proportions of different muscle fiber types. Posteriorly, the features considered most able were analyzed in an associated way through principal component analysis. Were found two principal components of the signals emulated without noise (CP1 and CP2) and two principal components of the noisy signals (CP1 and CP2 ). The first principal components (CP1 and CP1 ) were identified as being able to distinguish different proportions of muscle fiber types. The selected characteristics (median frequency, mean frequency, CP1 and CP1 ) were used to analyze real EMGs signals, comparing sedentary people with physically active people who practice strength training (weight training). The results obtained with the different groups of volunteers show that the physically active people obtained higher values of mean frequency, median frequency and principal components compared with the sedentary people. Moreover, these values decreased with increasing power level for both groups, however, the decline was more accented for the group of physically active people. Based on these results, it is assumed that the volunteers of the physically active group have higher proportions of type II fibers than sedentary people. Finally, based on these results, we can conclude that the selected characteristics were able to distinguish different proportions of muscle fiber types, both for the emulated signals as to the real signals. These characteristics can be used in several studies, for example, to evaluate the progress of people with myopathy and neuromyopathy due to the physiotherapy, and also to analyze the development of athletes to improve their muscle capacity according to their sport. In both cases, the extraction of these characteristics from the surface electromyography signals provides a feedback to the physiotherapist and the coach physical, who can analyze the increase in the proportion of a given type of fiber, as desired in each case.

Prospecção química e avaliação biológica de Banisteriopsis laevifolia (Malpighiaceae)

The Banisteriopsis genus is widespread in traditional medicine. This work aims to contribute with information about the chemical composition and on the evaluation of the biological activity of the essential oil, the ethanol extract of the leaves and partitions of the Banisteriopsis laevifolia. The phytochemical screeningtest of ethanol extract and partitions of leaves indicated the presence of flavonoids, terpenoids, saponins, phenols and steroids compounds. Nitrogenous compounds, characteristic of some species of this family, were not detected. Flavonoids were the predominant metabolite, with the highest concentrations on the partitions ethyl acetate and n-butanol. The antibacterial activity, antifungal and cytotoxicity of the essetial oil, ethanol extract and partitions were assyed by microdilution broth method (MBM), where the minimum inhibitory concentrations (MIC) were calculated. The ethanol extract and partitions did not inhibit growth against to Gram positive bacteria tested, with MIC less than 400 mg L-1. For the Gram negative bacteria tested, the hexane and hydroethanol partitios were more effective against F. nucleatum bacteria (MIC 100 ug mL-1). The ethanol extract showed antifungal activity with MIC of 31.2 mg L-1. Ethyl acetate and n-butanol partitions showed MIC 187.5 mg L-1 and 93.7 mg L-1, respectively, arousing interest for isolation studies. The antioxidant activity was evaluated by the DPPH free radical method. The ethanolic extract, ethyl acetate and n-butanol partitions were active, since they showed EC50 values (4.53 ug mL-1, 4.07 and 8.39 ug mL-1, respectively), values equivalent to the BHT (7.3 mg L-1). The analysis by HPLC-MS/MS of the most active fractions (ethyl acetate and n-butanol) identified phenolic compounds (flavonols and phenolic acids) which exert recognized biological activity. The GC-MS analysis of the essential oils from leaves collected in two periods studied (dry and wet), showed a small variation in the number of compounds. The major classes identified for the oil collected in the dry period were aliphatic alcohols (23,4%), terpenoids (18.7%), sterols (10.4%) and long-chain alkanes (9.2%) compounds. Terpenoids (26.8%) were the major class for the rain season. The major compounds (3Z) -hexenol, phytol and untriacontano are present in the two seasons but in different amounts (19.4%, 9.8% and 7.5% during the dry season, and 17.0 %, 14.9% and 15.3% in the rainy season, respectively). The essential oil from rainy season was not effective against to the oral bacteria Gram positive and Gram negative tested. However, showed significant antifungal activity with MIC 1000 mg L-1 against Candidas. Thus, the promising results with respect to biological assays of ethanolic extract and partitions from B. laevifolia contributed to the chemical and biological knowledge of the species B. laevifolia.