Effects of high-impact exercise on the physical properties of bones of ovariectomized rats fed to a high-protein diet.

The aim of this study was to evaluate the effects of high-impact physical exercise as a prophylactic and therapeutic means in osteopenic bones of rats submitted to ovariectomy and protein diet intake. A total of 64 Wistar rats were divided into eight groups (n = 8 each), being: OVX, ovx, standard diet and sedentary; OVXE, ovx, standard diet and jump; OVXP, ovx, high-protein diet and sedentary; and OVXEP, ovx, high-protein diet and jump; SH, sham, standard diet and sedentary; SHE, sham, standard diet and jump; SHP, sham, high-protein diet and sedentary; and SHEP, sham, high-protein diet and jump. OVX surgery consists of ovariectomy, and sham was the control surgery. The jumping protocol consisted of 20 jumps/day, 5 days/week. The bone structure was evaluated by densitometry, mechanical tests, histomorphometric, and immunohistochemical analyses. A high-protein diet resulted in increased bone mineral density (P = .049), but decreased maximal load (P = .026) and bone volume fraction (P = .023). The benefits of physical exercise were demonstrated by higher values of the maximal load in the trained groups compared to the sedentary groups (P < .001). The sham groups had decreased immunostaining of osteocalcin (P = .004) and osteopontin (P = .010) compared to ovx groups. However, the high-protein diet (P = .005) and jump exercise (P = .017) resulted in lower immunostaining of osteopontin compared to the standard diet and sedentary groups, respectively. In this experimental model, it was concluded that ovariectomy and a high-fat diet can negatively affect bone tissue and the high-impact exercise was not enough to suppress the deleterious effects caused by the protein diet and ovariectomy.

A robust computational solution for automated quantification of a specific binding ratio based on [123i]fp-cit SPECT images.

AIM: The purpose of the current paper is to present a computational solution to accurately quantify a specific to a non-specific uptake ratio in [123I]fP-CIT single photon emission computed tomography (SPECT) images and simultaneously measure the spatial dimensions of the basal ganglia, also known as basal nuclei. A statistical analysis based on a reference dataset selected by the user is also automatically performed. METHODS: The quantification of the specific to non-specific uptake ratio here is based on regions of interest defined after the registration of the image under study with a template image. The computational solution was tested on a dataset of 38 [123I]FP-CIT SPECT images: 28 images were from patients with Parkinson's disease and the remainder from normal patients, and the results of the automated quantification were compared to the ones obtained by three well-known semi-automated quantification methods. RESULTS: The results revealed a high correlation coefficient between the developed automated method and the three semi-automated methods used for comparison (r ≥0.975). The solution also showed good robustness against different positions of the patient, as an almost perfect agreement between the specific to non-specific uptake ratio was found (ICC=1.000). The mean processing time was around 6 seconds per study using a common notebook PC. CONCLUSION: The solution developed can be useful for clinicians to evaluate [123I]FP-CIT SPECT images due to its accuracy, robustness and speed. Also, the comparison between case studies and the follow-up of patients can be done more accurately and proficiently since the intra- and inter-observer variability of the semi-automated calculation does not exist in automated solutions. The dimensions of the basal ganglia and their automatic comparison with the values of the population selected as reference are also important for professionals in this area.

Using statistical deformable models to reconstruct vocal tract shape from magnetic resonance images.

The mechanisms involved in speech production are complex and have thus been subject to growing attention by the scientific community. It has been demonstrated that magnetic resonance imaging (MRI) is a powerful means in the understanding of the morphology of the vocal tract. Over the last few years, statistical deformable models have been successfully used to identify and characterize bones and organs in medical images and point distribution models (PDMs) have gained particular relevance. In this work, the suitability of these models has been studied to characterize and further reconstruct the shape of the vocal tract in the articulation of Portuguese European (EP) speech sounds, one of the most spoken languages worldwide, with the aid of MR images. Therefore, a PDM has been built from a set of MR images acquired during the artificially sustained articulation of 25 EP speech sounds. Following this, the capacity of this statistical model to characterize the shape deformation of the vocal tract during the production of sounds was analysed. Next, the model was used to reconstruct five EP oral vowels and the EP fricative consonants. As far as a study on speech production is concerned, this study is considered to be the first approach to characterize and reconstruct the vocal tract shape from MR images by using PDMs. In addition, the findings achieved permit one to conclude that this modelling technique compels an enhanced understanding of the dynamic speech events involved in sustained articulations based on MRI, which are of particular interest for speech rehabilitation and simulation.

New computational solution to quantify synthetic material porosity from optical microscopic images.

This paper presents a new computational solution to quantify the porosity of synthetic materials from optical microscopic images. The solution is based on an artificial neuronal network of the multilayer perceptron type and a backpropagation algorithm is used for training. To evaluate this new solution, 40 sample images of a synthetic material were analysed and the quality of the results was confirmed by human visual analysis. In addition, these results were compared with ones obtained with a commonly used commercial system confirming their superior quality and the shorter time needed. The effect of images with noise was also studied and the new solution showed itself to be more reliable. The training phase of the new solution was analysed confirming that it can be performed in a very easy and straightforward manner. Thus, the new solution demonstrated that it is a valid and adequate option for researchers, engineers, specialists and other professionals to quantify the porosity of materials from microscopic images in an automatic, fast, efficient and reliable manner.

Acquisition and analysis of 3D mandibular movement using a device based on electromagnetic sensors and a neural network.

In dental medicine, the study of mandibular movement has an important role in oral rehabilitation as it allows diagnosis of pathologies in the temporomandibular joint and the definition of adequate treatment plans. In this paper, a new device specially developed for the acquisition and analysis of 3D mandibular movement is presented. A facial arc is adopted as its main support structure and electromagnetic sensors are employed to acquire the mandibular movement. A neural network is used to transform the electrical signals output by the sensors into 3D Cartesian coordinates. A custom-made computer application is developed to display and analyse the movement acquired. The device is shown to be easy to use, comfortable for patients and capable of being produced at an affordable price.