2 resultados para Lesão músculo esquelética
em Universidade Federal de Uberlândia
Resumo:
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.
Resumo:
The coexistence of gingival recession (GR) with root coverage indication and non-carious cervical lesions (LCNC) generates the need for a protocol that respects and promotes health of dental and periodontal tissues and allows treatment predictability. The main objectives of this theses were: (1) verify, through clinical evaluations, the connective tissue graft for root coverage on direct and indirect restorations made of ceramic resin; (2) analyze the influence of the battery level of the LED curing unit in the composite resin characteristics; (3) assess the influence of restorative materials, composite resin and ceramics, on the viability of gingival fibroblasts from primary culture. Nine patients with good oral hygiene and occlusal stability diagnosed with LCNCs the anterior teeth including premolars associated with gingival recession (class I and II of Miller) and only gingival recession were selected. After initial clinical examination, occlusal adjustment was performed and the patients had their teeth randomized allocated on direct composite resin restoration of LCNC, polishing and GR treatment with connective tissue graft and advanced coronally flap CR group (n = 15); and indirect ceramic restoration of the LCNC's and GR treatment (CTG+CAF) Group C (n = 15). The GR presented teeth with no clinically formed LCNCs cavity were treated using (CTG+CAF) being the control group (n = 15). Sorption and solubility tests, analysis of the degree of conversion and diametral tensile strength were performed in composite resin samples (n = 10) photoactivated by 100, 50 and 10% battery charge LED unit. The viability of fibroblasts on composite resin, ceramics and dentin disks (n = 3) was examined. Clinical follow-up was performed for three months. The data obtained at different stages were tabulated and subjected to analysis for detection of normal distribution and homogeneity. The results showed that: the LED unit with 10% battery affects the characteristics of the composite resin; restorative materials present biocompatibility with gingival fibroblasts; and the association of surgical and restorative treatment of teeth affected by NCCL and GR presents successful results at 3-month follow-up.