Universidade de Pernambuco

About: Universidade de Pernambuco is a education organization based out in Recife, Brazil. It is known for research contribution in the topics: Population & Medicine. The organization has 6147 authors who have published 6948 publications receiving 73648 citations.

Physical therapy and anesthetic blockage for treating temporomandibular disorders: A clinical trial

Abstract: Purpose: the aim of this study was to evaluate the use of physical therapy and anesthetic blockage of the auriculotemporal nerve as a treatment for temporomandibular joint disorders. Methods: the sample comprised of twenty patients with a diagnosis of disc displacement with/ without reduction and arthralgia according to the Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD Axis I Group IIa, IIb and IIIa). Ten patients (group 1) underwent a cycle of eight anesthetic blockages of the auriculotemporal nerve with injections (1 per week) of 1 ml of bupivacaine 0.5% without vasoconstrictor for 8 weeks. The other 10 patients (group 2) received anesthetic blockage and physical therapy (massage and muscular stretching exercises). After the end of treatment all patients were evaluated at baseline, 1st week, 4th week and 2 months. The t-Student and F (ANOVA) tests were used for statistical analysis, with a significance rate of 5%. Results: there was a significant difference when both groups were compared according to VAS score (p=0.027). There was no significant difference for the other variables: MMO and jaw protrusion. Conclusion: the anesthetic blockage and physical therapy, when used together, are effective in the reduction of pain in patients with TMD.

Effect of Strong Opinions on the Dynamics of the Majority-Vote Model.

Abstract: We study how the presence of individuals with strong opinions affects a square lattice majority-vote model with noise. In a square lattice network we perform Monte-Carlo simulations and replace regular actors σ with strong actors μ in a random distribution. We find that the value of the critical noise parameter q c is a decreasing function of the concentration r of strong actors in the social interaction network. We calculate the critical exponents β/ν, γ/ν, and 1/ν and find that the presence of strong actors does not change the Ising universality class of the isotropic majority-vote model.

Use of autologous fat graft for correction of facial asymmetry stemming from Parry-Romberg syndrome

Abstract: Facial hemiatrophy is a typical manifestation of Parry-Romberg syndrome, characterized by a slow progressive atrophy that appears in early stages of life, primarily affecting the subcutaneous tissue and subjacent fat on 1 side of the face. We describe the case of a 42-year-old female patient with stabilized moderate facial hemiatrophy on the left side of the face, successfully treated with a 2-stage autologous fat transplant and the use of subcutaneous tunnels among the musculature for the placement of the graft. We also describe the principal forms of correcting facial asymmetry in patients with Parry-Romberg syndrome and demonstrate that an autologous fat graft provides good results in the correction of this deformity, with improved esthetics and patient satisfaction.

Combining Multiple Artificial Neural Networks Using Random Committee to Decide upon Electrical Disturbance Classification

Abstract: An ANN-based automatic classifier for power system disturbance waveforms was developed. Actual voltage waveforms were applied in the training process. Signals were processed in two steps: i) decomposition through wavelet transformation up to the 5th decomposition level; ii) the resultant wavelet coefficients are processed via PCA, reducing the input space of the classifier to a much lower dimension. The classification was carried out using a combination of six MLPs with different architectures: five representing the first to fifth-level details, and one representing the fifth-level approximation. The RPROP algorithm was applied for training the networks. Network combination was formed using random committee which builds an ensemble of randomized base classifiers. Experimental results with real data indicate that the random committee is clearly an effective way to improve disturbance classification accuracy when compared with the simple average and the individual models.