Expression of nonclassical molecule human leukocyte antigen-G in oral lesions

Introduction: Human leukocyte antigen (HLA)-G is a nonclassic class I molecule that acts as a modulator of immune responses, and the expression of these molecules in virus-infected cells has been associated with subversion of the immune response. Objective: In this study, we performed a cross-sectional study, systematically comparing the expression of the HLA-G in benign, premalignant, and malignant oral lesions and correlating it with the presence of high-risk and low-risk human papillomavirus (HPV) types. Specimens and Methods: Oral biopsies were collected from 51 patients and analyzed by immunohistochemistry using anti HLA-G antibody. Human papillomavirus detection and typing from oral biopsies were obtained by polymerase chain reaction using GP5+/GP6+ and specific primers. Results: The 51 biopsies were stratified into 3 groups according to lesion grade: oral benign lesions (oral hyperplasia and papilloma, n = 16), oral premalignant lesions (oral leukoplakia with dysplasia and lichen planus, n = 17), and malignant lesions (oral squamous cell carcinoma, n = 18). Human leukocyte antigen G overexpression was mainly observed in benign and premalignant oral lesions but was not related to HPV infection (P>.05). On the other hand, HPV DNA was detected in 24 (47%) oral lesions, mainly in benign and premalignant lesions, with the most frequent type detected being high-risk HPV type. Conclusion: The HLA-G molecule was expressed in a significant number of benign oral lesions and was not correlated with HPV infection or oral cancer. Crown Copyright (C) 2012 Published by Elsevier Inc. All rights reserved.

Ecosystem approach and the Fuzzy logic: a dialectical proposal for information on Environmental Health

The ever-growing production and the problematization of Environmental Health have shown the need to apprehend complex realities and deal with uncertainties from the most diversified instruments which may even incorporate local aspects and subjectivities by means of qualitative realities, while broadening the capacity of the information system. This paper presents a view on the reflection upon some challenges and possible convergences between the ecosystemic approach and the Fuzzy logic in the process of dealing with scientific information and decision-making in Environmental Health.

Classification of the severity of diabetic neuropathy: a new approach taking uncertainties into account using fuzzy logic

OBJECTIVE: This study proposes a new approach that considers uncertainty in predicting and quantifying the presence and severity of diabetic peripheral neuropathy. METHODS: A rule-based fuzzy expert system was designed by four experts in diabetic neuropathy. The model variables were used to classify neuropathy in diabetic patients, defining it as mild, moderate, or severe. System performance was evaluated by means of the Kappa agreement measure, comparing the results of the model with those generated by the experts in an assessment of 50 patients. Accuracy was evaluated by an ROC curve analysis obtained based on 50 other cases; the results of those clinical assessments were considered to be the gold standard. RESULTS: According to the Kappa analysis, the model was in moderate agreement with expert opinions. The ROC analysis (evaluation of accuracy) determined an area under the curve equal to 0.91, demonstrating very good consistency in classifying patients with diabetic neuropathy. CONCLUSION: The model efficiently classified diabetic patients with different degrees of neuropathy severity. In addition, the model provides a way to quantify diabetic neuropathy severity and allows a more accurate patient condition assessment.

Link prediction using a probabilistic description logic

Due to the growing interest in social networks, link prediction has received significant attention. Link prediction is mostly based on graph-based features, with some recent approaches focusing on domain semantics. We propose algorithms for link prediction that use a probabilistic ontology to enhance the analysis of the domain and the unavoidable uncertainty in the task (the ontology is specified in the probabilistic description logic crALC). The scalability of the approach is investigated, through a combination of semantic assumptions and graph-based features. We evaluate empirically our proposal, and compare it with standard solutions in the literature.

Programmable logic design of a compact Genetic Algorithm for phasor estimation in real-time

The main objective of this work is to present an efficient method for phasor estimation based on a compact Genetic Algorithm (cGA) implemented in Field Programmable Gate Array (FPGA). To validate the proposed method, an Electrical Power System (EPS) simulated by the Alternative Transients Program (ATP) provides data to be used by the cGA. This data is as close as possible to the actual data provided by the EPS. Real life situations such as islanding, sudden load increase and permanent faults were considered. The implementation aims to take advantage of the inherent parallelism in Genetic Algorithms in a compact and optimized way, making them an attractive option for practical applications in real-time estimations concerning Phasor Measurement Units (PMUs).