Diabetes mellitus and bell's palsy in Iranian population

During last decades many researchers have focused on the conditions associated with Bell's palsy including diabetes mellitus, hypertension, and viral infections. This study was performed to evaluate correlation of diabetes mellitus and Bell's palsy and some relevant features not discussed in the literature in an Iranian population. The presence of diabetes mellitus was evaluated in a total number of 275 subjects (75 patients with Bell's palsy and 200 control subjects). Diabetes mellitus was noted in 10 (13.3) patients with Bell's palsy among which 6 case were diagnosed as new cases of diabetes. Previous history of Bell's palsy was present in 10.67 of the subjects with Bell's palsy. This study confirms the correlation of diabetes mellitus and Bell's palsy for the first time in an Iranian population. We suggest screening tests for diabetes mellitus to be a routine part in the management of patients with Bell's palsy, especially in developing countries. © 2008 Tehran University of Medical Sciences. All rights reserved

Barriers to Family Caregivers’ Coping With Patients With Severe Mental Illness in Iran

The broad spectrum of problems caused by caring for a patient with mental illness imposes a high burden on family caregivers. This can affect how they cope with their mentally ill family members. Identifying caregivers’ experiences of barriers to coping is necessary to develop a program to help them overcome these challenges. This qualitative content analysis study explored barriers impeding family caregivers’ ability to cope with their relatives diagnosed with severe mental illness (defined here as schizophrenia, schizoaffective disorders, and bipolar affective disorders). Sixteen family caregivers were recruited using purposive sampling and interviewed using a semi-structured in-depth interview method. Data were analyzed by a conventional content analytic approach. Findings consisted of four major categories: the patient’s isolation from everyday life, incomplete recovery, lack of support by the mental health care system, and stigmatization. Findings highlight the necessity of providing support for caregivers by the mental health care delivery service system.The study was supported by Grant TBZMED·REC.5825 from the deputy of research in Tabriz University of Medical Sciences

Classification of Camellia (Theaceae) Species Using Leaf Architecture Variations and Pattern Recognition Techniques

Leaf characters have been successfully utilized to classify Camellia (Theaceae) species; however, leaf characters combined with supervised pattern recognition techniques have not been previously explored. We present results of using leaf morphological and venation characters of 93 species from five sections of genus Camellia to assess the effectiveness of several supervised pattern recognition techniques for classifications and compare their accuracy. Clustering approach, Learning Vector Quantization neural network (LVQ-ANN), Dynamic Architecture for Artificial Neural Networks (DAN2), and C-support vector machines (SVM) are used to discriminate 93 species from five sections of genus Camellia (11 in sect. Furfuracea, 16 in sect. Paracamellia, 12 in sect. Tuberculata, 34 in sect. Camellia, and 20 in sect. Theopsis). DAN2 and SVM show excellent classification results for genus Camellia with DAN2's accuracy of 97.92% and 91.11% for training and testing data sets respectively. The RBF-SVM results of 97.92% and 97.78% for training and testing offer the best classification accuracy. A hierarchical dendrogram based on leaf architecture data has confirmed the morphological classification of the five sections as previously proposed. The overall results suggest that leaf architecture-based data analysis using supervised pattern recognition techniques, especially DAN2 and SVM discrimination methods, is excellent for identification of Camellia species

An efficient airway tree segmentation method robust to leakage based on shape feature optimization

The main problem with most of 3D image segmentation methods which significantly influences their accuracy is the leakage occurred during the process of segmentation. In the case of airway tree segmentation, due to an imaging artifact or a thin airway wall, the contrast between the air and the airway wall can locally decrease which leads to allow the region-growing approach to move from the inside of the airway to the pulmonary parenchyma. This begins the leaks phenomena to build and large parts of the lungs can be erroneously marked as the airway tree. A user intervention is required in this case to detect the leakage point and restart the segmentation on the whole or part of the segmented area with new parameters. This makes the algorithm very exhaustive and more dependent on the user interaction. The new strategy presented here is to prevent the leakage from its origination by taking the advantage of the fact that the airway branches are cylindrically shaped objects. This has been achieved by employing a mathematical shape optimization approach based on the radial gradient of voxels along the cylindrical axes to retain cylindrical properties of the airway branches during the process of segmentation. The proposed cost function consists of two parts named cylindricalshape feature extraction and error smoothness term. The first term approaches to its minimum when underlying voxels are arranged on a cylindrical shape. The role of the second term is to control and smooth the final error and simultaneously to overcome the local minima's problem. We first applied the cost function on the simulated cylindrical objects with spongy structure to model the leakage with holes and tunnel. The impact of each term on the final error and the convergence of the algorithm while approaching to its global minimum are evaluated. © 2009 Springer Berlin Heidelberg

Development of a Nanoparticle-Labeled Microfluidic Immunoassay for Detection of Pathogenic Microorganisms

The light-scattering properties of submicroscopic metal particles ranging from 40 to 120 nm in diameter have recently been investigated. These particles scatter incident white light to generate monochromatic light, which can be seen either by the naked eye or by dark-field microscopy. The nanoparticles are well suited for detection in microchannel-based immunoassays. The goal of the present study was to detect Helicobacter pylori- and Escherichia coli O157:H7-specific antigens with biotinylated polyclonal antibodies. Gold particles (diameter, 80 nm) functionalized with a secondary antibiotin antibody were then used as the readout. A dark-field stereomicroscope was used for particle visualization in poly(dimethylsiloxane) microchannels. A colorimetric quantification scheme was developed for the detection of the visual color changes resulting from immune reactions in the microchannels. The microchannel immunoassays reliably detected H. pylori and E. coli O157:H7 antigens in quantities on the order of 10 ng, which provides a sensitivity of detection comparable to those of conventional dot blot assays. In addition, the nanoparticles within the microchannels can be stored for at least 8 months without a loss of signal intensity. This strategy provides a means for the detection of nanoparticles in microchannels without the use of sophisticated equipment. In addition, the approach has the potential for use for further miniaturization of immunoassays and can be used for long-term archiving of immunoassays