Differential Diagnosis Of Autoimmune Diseases Research Articles

Noninvasive in-vivo imaging of oral mucosa: state-of-the-art.

Technological development has interested most of the dentistry's branches leading to the use of other medical technologies non previously involved in dental practice. This study aims to evaluate the potential role of non-invasive imaging techniques in oral pathology workflow. Optical coherence tomography has been described by several authors as a promising aid for differential diagnosis of autoimmune diseases and to detect epithelial subversion of the oral mucosa before the clinical manifestation of oral mucositis. High-frequency ultrasound offers the chance to assess lesional dimensions both in benign and malignant lesions with a high dimensional reliability compared with histopathology. Reflectance confocal microscopy seems to be helpful in the early detection of cytological changes due to its high resolution, suggesting a more interesting role in the analysis of malignant lesions. The study presented highlighted the potential role of noninvasive in vivo imaging although further studies are needed for the further validation of these techniques.

Аутоиммунный гепатит на фоне описторхозной инвазии (клинический случай)

The article describes the case of liver cirrhosis of autoimmune origin on the background of opisthorchiasis. The relevancy of the problem is justified by complexity of early diagnosis of autoimmune liver diseases on the background of opisthorchiasis, lack of data on the characteristics of the course of the combined pathology, and probable comorbidity of these diseases. These and many other questions still need to be addressed in future researches. The article shows the difficulty of differential diagnosis of autoimmune diseases of the liver and hepatitis of parasitic etiology, which leads to untimely, inadequate treatment and disease progression.

Texture based medical image classification by using multi-scale gabor rotation-invariant local binary pattern (MGRLBP)

Texture medical image classification is a major task in many areas of computer vision and pattern recognition, including biomedical imaging. In reality, optical surfaces can be abused to recognize particular tissues or cells in a true example, to feature complex chemical reactions between molecules, and additionally to identify subcellular designs that can be confirmation of specific pathologies. It makes automated texture analysis fundamental in many applications of biomedicine, such as the accurate detection and grading of multiple types of cancer, the differential diagnosis of autoimmune diseases, or the study of physiological processes. Due to their specific characteristics and challenges, the design of texture analysis systems for biological images has attracted ever-growing attention in the last few years. In this work, approach applies the multi-scale gabor rotation-invariant local binary pattern (MGRLBP) used to analysis the texture features of a bio medicinal image and joined the weight aspect that is presented by the direct measure to obtain the last texture feature of a biomedical picture. By using multi-scale binary pattern (MSBP) classifier with the direct action and multi-scale gabor rotation-invariant local binary pattern algorithm. Both quantitative and qualitative methods are applied to assess the classification results. The simulation work does with the MATLAB2013a environment by using proposed MGRLBP and MSBP technique. The simulation results demonstrate the usefulness of the suggested technique and its ability to classify the texture medical images. Hence the proposed model produces better features for texture in medical images. Over 93% efficiency achieved by using MGRLBP and MSBP method.

Abstract 878: Identification and characterization of tumor-associated antigens as biomarkers in human prostate cancer

Abstract Autoantibodies against intracellular antigens are commonly found in a number of systemic autoimmune diseases. Their utility in providing insights into molecular and cellular biology and in the differential diagnosis of autoimmune diseases is well documented. Autoimmune phenomena manifested as antibodies to cellular components have been described in many types of cancer. Prostate cancer (PCa) is the most frequently diagnosed cancer in men and the second leading cause of male cancer deaths in the United States. PCa also presents the greatest racial disparity in this country, with an incidence and mortality in African-American men that is 3 times higher than in White and Latino men. Reducing these disparities would require a multi-faceted approach that includes developing more effective screening method for the early detection of PCa. The objective of this study is to analyze the frequency and specificity of autoantibodies in PCa patients, and further to identify and characterize the targeted tumor-associated antigens. In this study, 34 sera from PCa patients, and 30 sera from normal human individuals were examined for autoantibodies using indirect immunofluorescence (IIF) assay and western blotting. Autoantibodies were detected in 18 of 34 (52.9%) PCa sera by IIF, which was significantly higher than that in normal human sera. Of the interest is that of 34 PCa sera, 9 (26.5%) sera were identified by western blotting analysis containing antibodies against an unknown 70kDa cellular protein and 23.5% PCa patients showed antibody to 45 kDa protein. No reactivity with 70KDa and 45kDa proteins was detected in 30 normal human sera. In the further study, these cellular proteins targeted by serum antibodies in PCa were identified by using two-dimensional gel electrophoresis (2DE) and liquid chromatography-tandem-mass spectrometry (LC-MS/MS). This preliminary data supports that autoimmune responses to certain cellular proteins maybe a by-product in the transformation to PCa, and further studies of novel targeted proteins in PCa may provide insights into how these proteins might be involved in malignancy. Citation Format: Jitian Li, Liping Dai, Carlos A. Casiano, Jianying Zhang. Identification and characterization of tumor-associated antigens as biomarkers in human prostate cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 878. doi:10.1158/1538-7445.AM2014-878

Subclass Discriminant Analysis of morphological and textural features for HEp-2 staining pattern classification

Classifying HEp-2 fluorescence patterns in Indirect Immunofluorescence (IIF) HEp-2 cell imaging is important for the differential diagnosis of autoimmune diseases. The current technique, based on human visual inspection, is time-consuming, subjective and dependent on the operator's experience. Automating this process may be a solution to these limitations, making IIF faster and more reliable. This work proposes a classification approach based on Subclass Discriminant Analysis (SDA), a dimensionality reduction technique that provides an effective representation of the cells in the feature space, suitably coping with the high within-class variance typical of HEp-2 cell patterns. In order to generate an adequate characterization of the fluorescence patterns, we investigate the individual and combined contributions of several image attributes, showing that the integration of morphological, global and local textural features is the most suited for this purpose. The proposed approach provides an accuracy of the staining pattern classification of about 90%.