Performance of patients with refractory obsessive-compulsive disorder in the Frontal Systems Behavior Scale

Objective: To compare the performance of patients with obsessive-compulsive disorder (OCD) refractory to conventional treatments to healthy controls according to the Frontal Systems Behaviour Scale (FrSBe), comparing the scale scores within each group (Self or Family) and correlating FrSBe with Y-BOCS, DY-BOCS, tic disorder and age of first symptoms. Method: Twenty OCD patients and 20 healthy controls were assessed using the FrSBe, a scale designed to evaluate frontal syndromes. Results: The patients had higher scores when compared with the control group (p value .001) in terms of total score on the scale for both profile forms (Self and Family). In addition, there was a significant difference between the scores reported by the patients and their respective relatives. However, no correlation was observed between the scale and the other variables. Conclusions: The scale was able to clearly differentiate patients with OCD from healthy controls. This finding suggests that the FrSBe can be used not only in neurologic patients but also in psychiatric cases such as refractory OCD.

Comparison of three systems of classification in predicting the outcome of diabetic foot ulcers in a Brazilian population

Objective: The aim was to compare there ulcer classification systems as predictors of the outcome of diabetic foot ulcers; the Wagner, the University of Texas (UT) and the size (area, depth), sepsis, arteriopathy, denervation system (S(AD)SAD) systems in specialist clinic in Brazil. Methods: Ulcer area, depth, appearance, infection and associated ischaemia and neuropathy were recorded in a consecutive series of 94 subjects. A novel score, the S(AD)SAD score, was derived from the sum of individual items of the S(AD)SAD system, and was evaluated. Follow-up was for at least 6 months. The primary outcome measure was the incidence of healing. Results: Mean age was 57.6 years; 57 (60.6%) were made. Forty-eight ulcers (51.1%) healed without surgery; 11 (12.2%) subjects underwent minor amputation. Significant differences in terms of healing were observed for depth (P = 0.002), infection (P = 0.006) and denervation (P = 0.002) using the S(AD)SAD system, for UT grade (P = 0.002) and stage (P = 0.032) and for Wagner grades (P = 0.002). Ulcers with an S(AD)SAD score of <= 9 (total possible 15) were 7.6 times more likely to heal than scores >= 10 (P < 0.001). Conclusions: All three systems predicted ulcer outcome. The S(AD)SAD score of ulcer severity could represent a useful addition to routine clinical practice. The association between outcome and ulcer depth confirms earlier reports. The association with infection was stronger than that reported from the centres in Europe or North America. The very strong association with neuropathy has only previously been observed in Tanzania. Studies designed to compare the outcome in different countries should adopt systems of classification, which are valid for the populations studied.

Toxoplasma gondii isolates: Multi locus RFLP-PCR genotyping from human patients in Sao Paulo State, Brazil identified distinct genotypes

This study investigated the genetic characteristics of Toxoplasma gondii samples collected from 62 patients with toxoplasmosis in Sao Paulo State, Brazil. DNA samples were isolated from blood, cerebrospinal fluid and amniotic fluids of 25 patients with cerebral toxoplasmosis and AIDS, two patients with acute toxoplasmosis, 12 patients with ocular toxoplasmosis, six newborns with congenital toxoplasmosis and 17 pregnant women with acute infection. Diagnosis of toxoplasmosis was based in clinical, radiological and laboratory features. Genotyping was performed using multilocus PCR-RFLP genetic markers including SAG1, SAG2, 5`- and 3`-SAG2, alt.SAG2, SAG3, BTUB, GRA6, C22-8, c29-2, L358, PK1 and Apico. Among the 62 clinical samples, 20 (32%) were successfully genotyped at eight or more genetic loci and were grouped to three distinct genotypes. Eighteen samples belonged to ToxoDB Genotype #65 and the other two samples were identified as ToxoDB Genotypes #6 and #71, respectively (http://toxodb.org/toxo/). Patients presenting Genotypes #6 and #71 had severe and atypical cerebral toxoplasmosis, characterized by diffuse encephalitis without extensive brain lesions. These results indicate that T. gondii Genotype #65 may have a high frequency in causing human toxoplasmosis in Sao Paulo State, Brazil. This unusual finding highlights the need to investigate the possible association of parasite genotypes with human toxoplasmosis. (C) 2011 Elsevier Inc. All rights reserved.

Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques

In this work, we take advantage of association rule mining to support two types of medical systems: the Content-based Image Retrieval (CBIR) systems and the Computer-Aided Diagnosis (CAD) systems. For content-based retrieval, association rules are employed to reduce the dimensionality of the feature vectors that represent the images and to improve the precision of the similarity queries. We refer to the association rule-based method to improve CBIR systems proposed here as Feature selection through Association Rules (FAR). To improve CAD systems, we propose the Image Diagnosis Enhancement through Association rules (IDEA) method. Association rules are employed to suggest a second opinion to the radiologist or a preliminary diagnosis of a new image. A second opinion automatically obtained can either accelerate the process of diagnosing or to strengthen a hypothesis, increasing the probability of a prescribed treatment be successful. Two new algorithms are proposed to support the IDEA method: to pre-process low-level features and to propose a preliminary diagnosis based on association rules. We performed several experiments to validate the proposed methods. The results indicate that association rules can be successfully applied to improve CBIR and CAD systems, empowering the arsenal of techniques to support medical image analysis in medical systems. (C) 2009 Elsevier B.V. All rights reserved.