ABSTRACT
Queimaduras de segundo grau são especialmente dolorosas em crianças. A cada dia que passa, a pele queimada tende a cicatrizar, mas há momentos em que a área lesada fica pior devido a curativos inadequados ou à infecção. A utilização de substitutivos dérmicos deve ser considerada. Relatamos a utilização de aloenxerto de pele como um substitutivo dérmico transitório, a fim de diminuir a dor e promover a reepitelização em queimaduras dolorosas de segundo grau. A redução da dor foi completa após o uso do aloenxerto de pele. A criança obteve reepitelização completa da área lesada, não necessitando de internação e enxerto autólogo. Aloenxertos podem representar uma possibilidade de cobertura temporária para queimaduras dolorosas de segundo grau em crianças.
Second-degree burns are especially painful in children. With each passing day, the burned skin tends to heal, but there are times when the injured area gets worst due to inadequate dressing or infection. The use of dermal substitutes must be considered. We report the use of skin allograft as a transitory dermal substitute in order to decrease pain and promote re-epithelialization in painful second-degree burns. The child got complete re-epithelialization of the injured area, not requiring hospitalization and skin grafts. The pain reduction was complete after using skin allograft. Allografts may represent a possibility of temporary coverage in painful second-degree burns in children.
Subject(s)
Humans , Infant , Burns/therapy , Pain Management , Vascularized Composite Allotransplantation/methods , Infections/etiology , Tissue and Organ ProcurementABSTRACT
INTRODUCTION: Face recognition, one of the most explored themes in biometry, is used in a wide range of applications: access control, forensic detection, surveillance and monitoring systems, and robotic and human machine interactions. In this paper, a new classifier is proposed for face recognition: the novelty classifier. METHODS: The performance of a novelty classifier is compared with the performance of the nearest neighbor classifier. The ORL face image database was used. Three methods were employed for characteristic extraction: principal component analysis, bi-dimensional principal component analysis with dimension reduction in one dimension and bi-dimensional principal component analysis with dimension reduction in two directions. RESULTS: In identification mode, the best recognition rate with the leave-one-out strategy is equal to 100%. In the verification mode, the best recognition rate was also 100%. For the half-half strategy, the best recognition rate in the identification mode is equal to 98.5%, and in the verification mode, 88%. CONCLUSION: For face recognition, the novelty classifier performs comparable to the best results already published in the literature, which further confirms the novelty classifier as an important pattern recognition method in biometry.