Tactile threshold detection in leprosy patients with an electronic algometer

Objective: To propose an electronic method for sensitivity evaluation in leprosy and to compare it to the Semmes-Weinstein monofilaments. Methods:Thirty patients attending the Dermatology outpatient clinic of HCFMRP-USP were consecutively evaluated by both the electronic aesthesiometer and Semmes-Weinstein monofilaments on hand and foot test points. The intraclass correlation coefficient (ICC) was calculated to determine the variability of the electronic measures and the Kappa coefficient was calculated to determine the agreement between methods according to their categories (altered and non-altered tactile sensitivity). Results: The ICC was approximately 1, demonstrating repeatability. The Kappa coefficient showed more than 75 and 63% agreement on the hand and foot points, respectively. The mean agreement between the 2 methods for the 7 points of the right and left hand was 77.14 and 75.71%, respectively. The mean agreement for all 10 points was 74.33 and 63.66% on the right and left foot, respectively. In cases of disagreement the detection of altered tactile sensitivity by the electronic esthesiometer on the right and left foot was 90.91 and 84.25%, respectively, with no detection by the monofilaments. Conclusion: The results suggest that the electronic esthesiometer is a reliable and easy application, capable of evaluating alterations of tactile sensitivity in leprosy patients. (C) 2009 Elsevier B.V. All rights reserved.

Magnetic Resonance Angiography in Extracranial Giant Cell Arteritis

Background: Noninvasive diagnosis of giant cell arteritis (GCA) remains challenging, particularly with regard to evaluation of extracranial arterial disease. Objectives: The objective of the study was to retrospectively review extracranial involvement in patients with GCA and/or polymyalgia rheumatica (PMR), evaluated with magnetic resonance imaging (MRI), especially 3-dimensional contrast-enhanced magnetic resonance angiography images of the aortic arch and its branches. Methods: Clinical information, biopsy status, and MRI examinations of 28 patients with GCA/PMR were reviewed. Patient images were mixed randomly with 20 normal control images and were independently reviewed by 2 radiologists. Interobserver agreement for detection of arterial stenosis was determined by the k coefficient. Results: Both readers described vascular alterations in keeping with extracranial GCA in 19 of 28 patients (67%) with good interobserver agreement (k = 0.73) and with even higher agreement on diagnosing nonocclusive versus occlusive disease (k = 1.00). The most common lesions were bilateral axillary stenosis or obstructions, observed by both readers in 8 patients (28%). Among the 19 patients with magnetic resonance angiography lesions in the subclavian/axillary arteries, 12 (75%) had biopsy-proven GCA, but only 5 (41%) of these patients had clinical features of large artery disease. Conclusions: In our series review, MRI could provide accurate information on involvement of the aortic arch and its branches in extracranial GCA, depicting different degrees of stenosis. Our analysis also illustrates that occult large artery vasculitis should be considered in patients without biopsy-proven GCA, patients with classic GCA but without clinical signs of large artery disease, and in patients initially diagnosed as having PMR.

Topological triangle characterization with application to object detection from images

A novel mathematical framework inspired on Morse Theory for topological triangle characterization in 2D meshes is introduced that is useful for applications involving the creation of mesh models of objects whose geometry is not known a priori. The framework guarantees a precise control of topological changes introduced as a result of triangle insertion/removal operations and enables the definition of intuitive high-level operators for managing the mesh while keeping its topological integrity. An application is described in the implementation of an innovative approach for the detection of 2D objects from images that integrates the topological control enabled by geometric modeling with traditional image processing techniques. (C) 2008 Published by Elsevier B.V.