Resumo em Inglês:

Abstract: Introduction The biomechanical changes that arise after breast cancer increase the need for new rehabilitation programs. The aim of this study was to evaluate medium- and long-term effects of vibration therapy on pain intensity, range of motion, myoelectric activity, and muscle strength of post-surgical breast cancer women. Methods This controlled longitudinal clinical study was composed of 14 breast cancer women, who underwent vibration therapy treatment (VTG), and 14 healthy women, who constituted the control group (CG). The VTG performed ten 15-minutes sessions of vibration therapy on their affected upper limb. The volunteers were evaluated before and after treatment protocol, and three months later. Results We observed an attenuation of pain intensity after vibration therapy (p < 0.0001) and significant increase in range of motion during extension, abduction, and adduction movements of the horizontal shoulder. We noticed a trend in the reduction of compensatory movements, which activated the muscle contraction mechanism. The scapular dynamometer values for shoulder strength were significant. The VTG had less muscle strength than the CG in all situations: before treatment (p < 0.0001), after treatment (p = 0.0024), and 3 months later (p = 0.0008). The VTG increased muscle strength after treatment (p = 0.0005) and 3 months later (p = 0.0006). Conclusion Vibration therapy attenuated pain symptoms, improved shoulder movements, activated muscle contraction mechanism, and increased shoulder strength, which may be benefits of the conducted physical therapy.

Resumo em Inglês:

Abstract Introduction Moving the arm towards an object is a complex task. Movements of the arm joints must be well coordinated in order to obtain a smooth and accurate hand trajectory. Most studies regarding reaching movements address young subjects. Coordination differences in the neural mechanism underlying motor control throughout the life stages is yet unknown. The understanding of these changes can lead to a better comprehension of neuromotor pathologies and therefore to more suitable therapies. Methods Our purpose was to investigate interjoint coordination in three different aging groups (children, young, elderly). Kinematics and kinetics specific variables were analyzed focusing on defined parameters to get insight into arm coordination. Intersegmental dynamics was used to calculate shoulder and elbow torques assuming a 2-link segment model of the upper extremity (upper arm and forearm) with two friction-less joints (shoulder and elbow). A virtual reality environment was used to examine multidirectional planar reaching in three different directions (randomly presented). Results Seven measures were computed to investigate group interlimb differences: shoulder and elbow muscle torques (peak and impulse), work performed by shoulder and elbow joints, maximum velocity, movement distance, distance error at final position, movement duration and acceleration duration. Our data analysis showed differences between movement performances for all analyzed variables, at all ages. Conclusion We found that the intersegmental dynamics for the interlimb (left/right) comparisons were similar for the elderly and children groups as compared to the young. In addition, the coordination and control of motor tasks changes during life, becoming less effective in old age.

Resumo em Inglês:

Abstract Introduction Various signal-processing techniques have been proposed to extract quantitative information about internal structures of tissues from the original radio frequency (RF) signals instead of an ultrasound image. The quantifiable parameter called the mean scatterer spacing (MSS) can be useful to detect changes in the quasi-periodic microstructure of tissues such as the liver or the spleen, using ultrasonic signals. Methods We evaluate and compare the performance of three classic methods of spectral estimation to calculate the MSS without operator intervention: Tufts-Kumaresan, SAC (Spectral Autocorrelation) and MUSIC (MUltiple SIgnal Classification). Initially the evaluations were performed with 10,000 signals simulated from a model in which the variables of interest are controlled, and then, real signals from sponge phantoms were used. Results For the simulated signals, the performance of all three methods decreased with increasing Ad or jitter levels. For the sponges, none of the methods accurately estimated the pore size. Conclusion For the simulated signals, Tufts-Kumaresan had the lowest performance, whereas SAC and MUSIC had similar results. For sponges, only Tufts-Kumaresan was able to detect the increase in the size of the pores of the sponge, although most often, it estimated sizes larger than expected.

Resumo em Inglês:

Introduction: Ingredients of oily fish include a large amount of polyunsaturated fatty acids, which are important elements in various metabolic processes of humans, and have also been used to prevent diseases. However, in an attempt to reduce cost, recent developments are starting a replace the ingredients of fish oil with products of microalgae, that also produce polyunsaturated fatty acids. To do so, it is important to closely monitor morphological changes in algae cells and monitor their age in order to achieve the best results. This paper aims to describe an advanced vision-based system to automatically detect, classify, and track the organic cells using a recently developed SOPAT-System (Smart On-line Particle Analysis Technology), a photo-optical image acquisition device combined with innovative image analysis software. Methods The proposed method includes image de-noising, binarization and Enhancement, as well as object recognition, localization and classification based on the analysis of particles’ size and texture. Results The methods allowed for correctly computing cell’s size for each particle separately. By computing an area histogram for the input images (1h, 18h, and 42h), the variation could be observed showing a clear increase in cell. Conclusion The proposed method allows for algae particles to be correctly identified with accuracies up to 99% and classified correctly with accuracies up to 100%.

Resumo em Inglês:

Abstract Introduction The discrete wavelet transform is used in many studies as signal preprocessor for EEG spike detection. An inherent process of this mathematical tool is the recursive wavelet convolution over the signal that is decomposed into detail and approximation coefficients. To perform these convolutions, firstly it is necessary to extend signal borders. The selection of an unsuitable border extension algorithm may increase the false positive rate of an EEG spike detector. Methods In this study we analyzed nine different border extensions used for convolution and 19 mother wavelets commonly seen in other EEG spike detectors in the literature. Results The border extension may degrade an EEG spike detector up to 44.11%. Furthermore, results behave differently for distinct number of wavelet coefficients. Conclusion There is not a best border extension to be used with any EEG spike detector based on the discrete wavelet transform, but the selection of the most adequate border extension is related to the number of coefficients of a mother wavelet.

Resumo em Inglês:

Abstract Introduction Lung cancer remains the leading cause of cancer mortality worldwide, with one of the lowest survival rates after diagnosis. Therefore, early detection greatly increases the chances of improving patient survival. Methods This study proposes a method for diagnosis of lung nodules in benign and malignant tumors based on image processing and pattern recognition techniques. Taxonomic indexes and phylogenetic trees were used as texture descriptors, and a Support Vector Machine was used for classification. Results The proposed method shows promising results for accurate diagnosis of benign and malignant lung tumors, achieving an accuracy of 88.44%, sensitivity of 84.22%, specificity of 90.06% and area under the ROC curve of 0.8714. Conclusion The results demonstrate the promising performance of texture extraction techniques by means of taxonomic indexes combined with phylogenetic trees. The proposed method achieves results comparable to those previously published.

Resumo em Inglês:

Abstract Introduction In this work, the effect of a dynamic visual stimulation (DS) protocol was used to induce egomotion, the center of pressure (COP) displacement response. Methods DS was developed concerning the scenario structure (chessboard-pattern floor and furniture) and luminance. To move the scenario in a discrete forward (or backward) direction, the furniture is expanded (or reduced) and the black and white background is reversed during floor translation while the luminance is increased (or reduced) by steps of 2 cd/m2. This protocol was evaluated using COP signals from 29 healthy volunteers: standing on a force platform observing the virtual scene (1.72 × 1.16 m) projected 1 m ahead (visual incidence angle: θl = 81.4° and θv = 60.2°), which moves with constant velocity (2 m/s) during 250 ms. A set of 100 DS was applied in random order, interspersed by a 10 s of static scene. Results The Tukey post-hoc test (p < 0.001) indicated egomotion in the same direction of DS. COP displacement increased over stimulation (8.4 ± 1.7 to 22.6 ±5.3 mm), as well as time to recover stability (4.1 ± 0.4 to 7.2 ± 0.6 s). The peak of egomotion during DSF occurred 200 ms after DSB (Wilcoxon, p = 0.002). Conclusion The dynamic configuration of this protocol establishes virtual flow effects of linear egomotion dependent on the direction of the dynamic visual stimulation. This finding indicates the potential application of the proposed virtual dynamic stimulation protocol to investigate the cortical visual evoked response in postural control studies.

Resumo em Inglês:

Abstract Introduction The use of tools for computer-aided diagnosis (CAD) has been proposed for detection and classification of breast cancer. Concerning breast cancer image diagnosing with ultrasound, some results found in literature show that morphological features perform better than texture features for lesions differentiation, and indicate that a reduced set of features performs better than a larger one. Methods This study evaluated the performance of support vector machines (SVM) with different kernels combinations, and neural networks with different stop criteria, for classifying breast cancer nodules. Twenty-two morphological features from the contour of 100 BUS images were used as input for classifiers and then a scalar feature selection technique with correlation was used to reduce the features dataset. Results The best results obtained for accuracy and area under ROC curve were 96.98% and 0.980, respectively, both with neural networks using the whole set of features. Conclusion The performance obtained with neural networks with the selected stop criterion was better than the ones obtained with SVM. Whilst using neural networks the results were better with all 22 features, SVM classifiers performed better with a reduced set of 6 features.

Resumo em Inglês:

Abstract Introduction Fourier-transform infrared (FT-IR) spectroscopy is a technique with great potential for body fluids analyses. The aim of this study was to examine the impact of session training on cortisol concentrations in rugby players by means of infrared analysis of serum. Methods Blood collections were performed pre, post and 24 hours after of rugby training sessions. Serum cortisol was analyzed by FT-IR spectroscopy and chemiluminescent immunoassay. Results There was a significant difference between the integrated area, in the region of 1180-1102 cm-1, of the spectra for pre, post and post 24 h serums. The cortisol concentration obtained by chemiluminescent immunoassay showed no significant difference between pre, post and post 24 h. Positive correlations were obtained between the techniques (r = 0.75), post (r = 0.83) and post 24 h (r = 0.73). Conclusion The results showed no increase in cortisol levels of the players after the training sessions, as well as positive correlations indicating that FT-IR spectroscopy have produced promising results for the analysis of serum for diagnosis of stress.

Resumo em Inglês:

Abstract Introduction Relaxometry images are an important magnetic resonance imaging (MRI) technique in the clinical routine. Many diagnoses are based on the relaxometry maps to infer abnormal state in the tissue characteristic relaxation constant. In order to study the performance of these image processing approaches, a controlled simulated environment is necessary. However, a simulated relaxometry image tool is still lacking. This study proposes a computational anatomical brain phantom for MRI relaxometry images, which aims to offer an easy and flexible toolkit to test different image processing techniques, applied to MRI relaxometry maps in a controlled simulated environment. Methods A pipeline of image processing techniques such as brain extraction, image segmentation, normalization to a common space and signal relaxation decay simulation, were applied to a brain structural ICBM brain template, on both T1 and T2 weighted images, in order to simulate a volumetric brain relaxometry phantom. The FMRIB Software Library (FSL) toolkits were used here as the base image processing needed to all the relaxometry reconstruction. Results All the image processing procedures are performed using automatic algorithms. In addition, different artefact levels can be set from different sources such as Rician noise and radio-frequency inhomogeneity noises. Conclusion The main goal of this project is to help researchers in their future image processing analysis involving MRI relaxometry images, offering reliable and robust brain relaxometry simulation modelling. Furthermore, the entire pipeline is open-source, which provides a wide collaboration between researchers who may want to improve the software and its functionality.