Prevalence of Echinococcus spp. Infection Using Coproantigen ELISA among Canids of Moghan Plain, Iran

"nBackground: Echinococcosis is one of the most important helminthic zoonotic diseases in Iran. Intestinal Scraping Tech­nique (IST), the traditional method for diagnosis of the infection in definitive hosts, has many disadvantages including low sus­ceptibil­ity and being expensive, hazardous and laborious. Detection of coproantigens in fecal samples by enzyme-linked immu­nosorbent assay (CA-ELISA) is known as a useful tool for intravital mass-screening of definitive host populations. This study was performed to determine the prevalence of Echinococcus spp. infection among canids in Moghan plain, the only area in Iran known to be endemic for E. multilocularis."nMethods: One hundred thirty eight fecal samples were collected from red foxes and domestic dogs in three counties of Moghan plain namely Pars Abad, Bileh Savar and Germi. The samples were examined using an ELISA, designed for the detec­tion of Echinococcus-specific coproantigen and the formalin-ether concentration method as well."nResults: Totally, out of 138 fecal samples, 27 (21.6%) turned positive for Echinococcus. Coproantigen was de­tected in 16.7% and 27.1% of red foxes and domestic dogs, respectively. Formalin-ether concentration method revealed that 43 (31.2%) of samples harbored at least one parasitic helminth, but Taenia eggs were detected only in 3 fecal samples. Since coproan­tigen presence reflects current intestinal infection with adult worms, CA-ELISA can be regarded as one of the most use­ful immunological tools for diagnosis of Echinococcus infection. Besides, the high susceptibility, low cost and rapid­it

Leukocyte Classification using Adaptive Neuro-Fuzzy Inference System in Microscopic Blood Images

Microscopic pathology is still a meticulous and biased task for hematologist, which leads to the misclassification of cells and vagueness prediction of abnormal cells due to variability in the morphological structure of leukocytes. Therefore, to enhance the detection precision and diminishing the time factor, an automatic classification system for leukocytes has been proposed. In routine clinical practice, expert hematologists observed that the nucleus plays a crucial role in the identification of the blood disorders. Accordingly, in this work, the localization of leukocyte nucleus is performed by using Chan–Vase level-set method for the design of a classification framework that differentiates between four classes of the leukocytes, i.e., eosinophils, polymorphs, monocytes and lymphocytes based on the nucleus. A dataset consisting of 162 leukocyte microscopic images is used. The images in the dataset are classified on the basis of texture, shape and color features. The feature selection method based on the linguistic hedge is applied on evaluated feature space of 92. The selected features are fed to an adaptive neuro-fuzzy classifier for the classification. The proposed framework obtained an accuracy of 98.7% after applying the adaptive neuro-fuzzy classification on selected 46 informative features. The correlation of best features and data extorted from the different microscopic images may yield a dramatic increase in diagnostic consistency in clinical pathology. The results obtained by utilization of selected optimal features and adaptive neuro-fuzzy classification system indicate that it can be routinely used in clinical environment for differential diagnosis between different classes of leukocytes