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Introduction Ultrasound biomicroscopy (UBM) is a technique for generating high-resolution images, with frequencies from 20 MHz to 100 MHz. For example, it has been used in animal research related to models of injury and diseases that mimic human conditions. With a three-dimensional ultrasound (3D) image system, an organ can be viewed at various angles and the volume estimated, contributing to an accurate diagnosis. This work refers to the generation of 3D-UBM images, employing a 35 MHz ultrasound system, from multiple two-dimensional (2D) images. Phantoms were used to validate the technique and to determine its reliability of volume measurements. Additionally, the technique was used to obtain 3D images of the rat gastrocnemius muscle. Methods Four different phantoms were used and ten acquisition sequences of 2D-images acquired for each one. Thereafter, 5 volume segmentations were performed for each acquisition sequence, resulting in 50 measured volumes for each phantom. The physical volumes of all phantoms were used to validate the technique based on the coefficient of variation (CV) and the intraclass correlation coefficient (ICC). Images of the gastrocnemius muscle were acquired and the partial volume quantified. Results The CV and ICC confirmed the reliability of volume measurements obtained by segmentation. Moreover, cross-sectional 2D images of rat hindlimb were obtained, allowing to identify the gastrocnemius muscle and to partially quantify the muscle volume from 3D images. Conclusion The results indicated that the technique is valid to generate 3D images and quantify the volume of a muscle compatible with the dimensions of a small animal.

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Introduction Diabetes patients can benefit significantly from early diagnosis. Thus, accurate automated screening is becoming increasingly important due to the wide spread of that disease. Previous studies in automated screening have found a maximum accuracy of 92.6%. Methods This work proposes a classification methodology based on efficient coding of the input data, which is carried out by decreasing input data redundancy using well-known ICA algorithms, such as FastICA, JADE and INFOMAX. The classifier used in the task to discriminate diabetics from non-diaibetics is the one class support vector machine. Classification tests were performed using noninvasive and invasive indicators. Results The results suggest that redundancy reduction increases one-class support vector machine performance when discriminating between diabetics and nondiabetics up to an accuracy of 98.47% while using all indicators. By using only noninvasive indicators, an accuracy of 98.28% was obtained. Conclusion The ICA feature extraction improves the performance of the classifier in the data set because it reduces the statistical dependence of the collected data, which increases the ability of the classifier to find accurate class boundaries.

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Introduction In this paper we propose a promising new technique for drowsiness detection. It consists of applying the best m-term approximation on a single-channel electroencephalography (EEG) signal preprocessed through a discrete wavelet transform. Methods In order to classify EEG epochs as awake or drowsy states, the most significant m terms from the wavelet expansion of an EEG signal are selected according to the magnitude of their coefficients related to the alpha and beta rhythms. Results By using a simple thresholding strategy it provides hit rates comparable to those using more complex techniques. It was tested on a set of 6 hours and 50 minutes EEG drowsiness signals from PhysioNet Sleep Database yielding an overall sensitivity (TPR) of 84.98% and 98.65% of precision (PPV). Conclusion The method has proved itself efficient at separating data from different brain rhythms, thus alleviating the requirement for complex post-processing classification algorithms.

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Introduction Saliva is the most promising biofluid to monitor the physiological state of athletes, because this method is not invasive and has low contamination risks. The characterization of saliva by Fourier transform infrared spectroscopy (FT-IR) has been studied as an alternative technique to the standard clinical analysis. However, methodological procedures for saliva analysis are not completely clear, especially in terms of influence of storage conditions and sample preparations for infrared analysis. Thawed saliva includes a precipitate, which may influence the infrared spectral analysis. Thus, the purpose of this study was to show the spectral differences of the precipitate, supernatant, and a combo, as well as the best way to classify the physiological state of the athletes by FT-IR. Methods The saliva collection was performed before, immediately after, and two hours after a handball match. After the storage of samples at –20 ○C, it was possible to identify two phases (precipitate and supernatant) and to determine the biochemical differences between the spectra of each phase, which were distinctly analyzed by the second derivative and deconvolution bands. Results The precipitate and supernatant results showed characteristic bands, especially in the protein regions. All FT-IR spectra were also statistically classified by linear discriminant analysis (LDA), using principal component analysis (PCA). The LDA precipitate and supernatant had lower value when compared to combo spectra (Combination of precipitate and supernatant) with 82%, showing that this combination is the best way to discriminate spectra of saliva samples collected before, immediately after, and 2 h after physical effort. Discussion The results showed that it is possible to differentiate biochemically the two salivary phases, as well as the importance of the homogenization process of saliva samples to classify the physiological status of athletes using FT-IR.

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Introduction Left ventricle hypertrophy (LVH) is an important risk factor for cardiovascular morbidity and mortality. It is characterized by a thickening of the walls of the left ventricle. The transthoracic echocardiogram is a very accurate method for LVH detection. However, the electrocardiogram (ECG) offers an alternative method in diagnosing LVH, besides being less expensive and easier to obtain. In this context, this study proposes an ECG based approach for left ventricle hypertrophy (LVH) classification. Methods According to the literature, several indexes have so far been proposed that suggest specific changes in cardiac structure, however, generally speaking there is no consensus about the best criteria. This way, instead of considering only one LVH criterion, a score derived from electrocardiographic traces was employed which explores the complementarity of the best criteria through a fusion strategy. The best criteria are those which discriminate normal and LVH ECGs. Results The experiments were performed in the Monica database with a group of fifty men. Half of the individuals had LVH diagnosed by calculating the left ventricular mass index measured by transthoracic echocardiography. The score fusion proposed achieved a sensitivity of 78.3% and specificity of 91.3%, outperforming all isolated LVH criteria. Discussion Unlike the other methods, our score must be estimated within a computer because of its high complexity. Even with this limitation it is much less expensive than using the echocardiography.

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Introduction It has been reported that inhibitory control at the superficial dorsal horn (SDH) can act in a regionally distinct manner, which suggests that regionally specific subpopulations of SDH inhibitory neurons may prevent one specific neuropathic condition. Methods In an attempt to address this issue, we provide an alternative approach by integrating neuroanatomical information provided by different studies to construct a network-model of the SDH. We use Neuroids to simulate each neuron included in that model by adapting available experimental evidence. Results Simulations suggest that the maintenance of the proper level of pain sensitivity may be attributed to lamina II inhibitory neurons and, therefore, hyperalgesia may be elicited by suppression of the inhibitory tone at that lamina. In contrast, lamina III inhibitory neurons are more likely to be responsible for keeping the nociceptive pathway from the mechanoreceptive pathway, so loss of inhibitory control in that region may result in allodynia. The SDH network-model is also able to replicate non-linearities associated to pain processing, such as Aβ-fiber mediated analgesia and frequency-dependent increase of the neural response. Discussion By incorporating biophysical accuracy and newer experimental evidence, the SDH network-model may become a valuable tool for assessing the contribution of specific SDH connectivity patterns to noxious transmission in both physiological and pathological conditions.

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Introduction Crackles are discontinuous, non-stationary respiratory sounds and can be characterized by their duration and frequency. In the literature, many techniques of filtering, feature extraction, and classification were presented. Although the discrete wavelet transform (DWT) is a well-known tool in this area, issues like signal border extension, mother-wavelet selection, and its subbands were not properly discussed. Methods In this work, 30 different mother-wavelets 8 subbands were assessed, and 9 border extension modes were evaluated. The evaluations were done based on the energy representation of the crackle considering the mother-wavelet and the border extension, allowing a reduction of not representative subbands. Results Tests revealed that the border extension mode considered during the DWT affects crackle characterization, whereas SP1 (Smooth-Padding of order 1) and ASYMW (Antisymmetric-Padding (whole-point)) modes shall not be used. After DWT, only 3 subbands (D3, D4, and D5) were needed to characterize crackles. Finally, from the group of mother-wavelets tested, Daubechies 7 and Symlet 7 were found to be the most adequate for crackle characterization. Discussion DWT can be used to characterize crackles when proper border extension mode, mother-wavelet, and subbands are taken into account.

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Introduction Raman spectroscopy may become a tool for the analysis of glucose and triglycerides in human serum in real time. This study aimed to detect spectral differences in lipid and glucose components of human serum, thus evaluating the feasibility of Raman spectroscopy for diagnostic purposes. Methods A total of 44 samples of blood serum were collected from volunteers and submitted for clinical blood biochemical analysis. The concentrations of glucose, cholesterol, triglycerides, and low-density and high-density lipoproteins (LDL and HDL) were obtained using standard biochemical assays. Serum samples were placed in Eppendorf tubes (200 µL), kept cooled (5 °C) and analyzed with near-infrared Raman spectroscopy (830 nm, 250 mW, 50 s accumulation). The mean spectra of serum with normal or altered concentrations of each parameter were compared to determine which Raman bands were related to the differences between these two groups. Results Differences in peak intensities of altered sera compared to normal ones depended on the parameter under analysis: for glucose, peaks were related to glucose; for lipid compounds the main changes occurred in the peaks related to cholesterol, lipids (mainly triolein) and proteins. Principal Components Analysis discriminated altered glucose, cholesterol and triglycerides from the normal serum based on the differences in the concentration of these compounds. Conclusion Differences in the peak intensities of selected Raman bands could be seen in normal and altered blood serum samples, and may be employed as a means of diagnosis in clinical analysis.

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Introduction Although there has been significant progress in the design of implants for osteosynthesis, the occurrence of failures in these medical devices are still frequent. These implants are prone to suffer from fretting corrosion due to micromotion that takes place between the screw heads and plate holes. Consequently, fretting corrosion has been the subject of research in order to understand its influence on the structural integrity of osteosynthesis implants. The aim of this paper is to correlate the surface finish characteristics of bone plate-screw systems with fretting corrosion. Methods The surface finish (machined and polished) of five specimens taken from three commercial dynamic compression plates (DCP) were evaluated. For testing, the specimens were fixed with bone screws, immersed in a solution of 0.90% NaCl and subjected to a rocking motion with an amplitude of 1.70 mm and frequency of 1.0 Hz for 1.0 × 106 cycles, according to the ASTM F897 standard. Both, plate and screws were manufactured in Brazil with ASTM F138 stainless steel. Results Flaws on the hole countersink area and on the screw thread of some specimens were identified stereoscopically. At the end of the test all the specimens showed evidence of fretting corrosion with an average metal loss of 4.80 mg/million cycles. Conclusion An inadequate surface finish in some areas of the plates and screws may have favored the incidence of damage to the passive film, accelerating the fretting corrosion at the interfaces between the plate hole countersink and the screw head. Keywords Osteosynthesis, DCP, Bone plate, Screw, Fretting corrosion, Stainless steel.

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Introduction This study aims at investigating the coupling of the cardiac and locomotor systems during running in trained and untrained males. Methods Sixteen healthy young males subjects were submitted to an anthropometric evaluation, followed by a treadmill test at 70% to 75% of heart rate reserve. Based on the average velocity, they were divided into two groups of eight, trained group (TG) and untrained group (UT). The electrocardiogram and the electromyogram of the vastus lateralis muscle of the right thigh were digitized at a sampling rate of 1000 Hz, and processed off-line. Each cardiac and electromyographic cycle was detected to further investigate the coupling between cardiac and running rates in time domain, using the cross-correlation, and in the frequency domain, using a phase synchronization measure based on the Hilbert transform. A Shannon entropy index and magnitude squared coherence were also applied to improve analysis. Results Both groups presented low cross-correlation (0.18 ± 0.07 TG and 0.15 ± 0.08 UG) values between these signals, and only four from 16 subjects presented short epochs of phase synchronization (4.1 ± 8.6% TG and 3.2±7.3% UG) between signals, occurring at a low frequency band and random phase differences. The low to moderate coherence (0.67 ± 0.16 TG and 0.64 ± 0.16 UG) observed at 0.1 Hz appears to be an effect of the simultaneous action of sympathetic system over both cardiac and muscular rhythms. Conclusion The combined results suggest that the chosen exercise protocol was not able to cause cardiolocomotor synchronization.