RESUMO
Bovine cysticercosis is categorized as a serious parasitic zoonotic infestation. The infection is mainly caused by the tapeworm Taenia saginata, which infects cattle and humans. The larval stage, Cysticercus bovis (C. bovis), is found in the skeletal and cardiac muscles of infected cattle. Despite its potential public health concern, few studies have been conducted on cardiac cysticercosis in Upper Egypt. This study investigates the prevalence, epidemiology, and impact of cardiac cysticercosis in Upper Egypt, emphasizing how histopathological changes in cardiac muscle and physiological parameters might be associated with the infection. From December 2022 to October 2023, a total of 941 animals from Assiut province, Upper Egypt, were slaughtered and their cardiac muscles were examined for the presence of C. bovis. Cysts were classified as viable or degenerated through macroscopic inspection. The overall prevalence of C. bovis infected hearts made up 10.8% of the total examined. The highest prevalence rate was in the summer season followed by spring; winter had the lowest infections. The histopathological examination of infected tissues revealed immune cell infiltration around Cysticercus-infected areas. Additionally, Bax immunostaining demonstrated the apoptotic effect of cysticercosis. Regarding the measured physiological parameters, there were non-significant changes in plasma levels of total protein and albumin in cattle infected with cysticercosis compared with control animals. Moreover, there was a significant decrease in total antioxidant capacity (TAC) combined with a significant increase in lipid peroxide (Malondialdehyde) (MDA), troponin T, and lactate dehydrogenase (LDH) activity in infected animals. The present work documented a set of epidemiological and pathological findings, revealing that C. bovis is a potentially harmful parasite and can cause significant health problems in both cattle and humans.
RESUMO
Trichuridae family has a genetic and morphological variability between species affecting rodents, but it is considerably hard to morphologically diagnose species within the genus of Trichuris and the individuals of these species are identified according to their host, as it is known that Trichuris spp. is strictly host-specific. However, some species lack host specificity. So, it is necessary to use molecular data in order to well identify the Trichuris spp. in Egyptian rodents. The host examined in the current research is Psammomys obesus and the molecularly identified species from its cecum is Trichuris arvicolae. In addition, Trichuris arvicolae was subjected to in vitro treatment with Androctonus crassicauda Crude Venom as a model of natural alternative treatment for gastrointestinal nematodes that increasingly develop anthelmintic drug resistance. The changes in Trichuris arvicolae were monitored using scanning electron microscopy, Androctonus crassicauda Crude Venom made a significant ultrastructural surface changes in Trichuris arvicolae, including marked cuticular sloughing, disintegrated bacillary glands, bursting of vulva and edema of anal region. This study was done for closer identification of Trichuris spp. infecting rodents in Egypt and evaluating the efficacy of Androctonus crassicauda Crude Venom in vitro.
RESUMO
Breast cancer (BC) is one of the most common types of cancer that affects females worldwide. It may lead to irreversible complications and even death due to late diagnosis and treatment. The pathological analysis is considered the gold standard for BC detection, but it is a challenging task. Automatic diagnosis of BC could reduce death rates, by creating a computer aided diagnosis (CADx) system capable of accurately identifying BC at an early stage and decreasing the time consumed by pathologists during examinations. This paper proposes a novel CADx system named Histo-CADx for the automatic diagnosis of BC. Most related studies were based on individual deep learning methods. Also, studies did not examine the influence of fusing features from multiple CNNs and handcrafted features. In addition, related studies did not investigate the best combination of fused features that influence the performance of the CADx. Therefore, Histo-CADx is based on two stages of fusion. The first fusion stage involves the investigation of the impact of fusing several deep learning (DL) techniques with handcrafted feature extraction methods using the auto-encoder DL method. This stage also examines and searches for a suitable set of fused features that could improve the performance of Histo-CADx. The second fusion stage constructs a multiple classifier system (MCS) for fusing outputs from three classifiers, to further improve the accuracy of the proposed Histo-CADx. The performance of Histo-CADx is evaluated using two public datasets; specifically, the BreakHis and the ICIAR 2018 datasets. The results from the analysis of both datasets verified that the two fusion stages of Histo-CADx successfully improved the accuracy of the CADx compared to CADx constructed with individual features. Furthermore, using the auto-encoder for the fusion process has reduced the computation cost of the system. Moreover, the results after the two fusion stages confirmed that Histo-CADx is reliable and has the capacity of classifying BC more accurately compared to other latest studies. Consequently, it can be used by pathologists to help them in the accurate diagnosis of BC. In addition, it can decrease the time and effort needed by medical experts during the examination.