The current study was designed to investigate the effect of Tilletia smut spores on histopathological changes in liver and kidney in mice. Twenty animals were divided into two equal groups, 10 mice each, control group fed on normal diet and the treated groups were fed on a mixture of 50% normal diet with 50% wheat infected with Tilletia for 30 days. Histopathological sections taken from liver and kidney treated with Tilletia revealed several alterations. The changes in liver included, multiple granulomatous lesions, area of coagulation necrosis, vacuolar degeneration in the cytoplasm of hepatocytes, proliferation of hepatocytes with formation of pseudolobull which initiates for procancer. Whereas in the kidney, the changes included

The aim of this study to conduct the effects of fimbrial and lipopolysacchride (LPS) immunization is on the pathohistological changes in rabbits, Fifteen rabbits of both sexes (Weight 1500-2000 gm) divided into three groups (5 animals of each group). The first group was immunized by 1ml (200µg /animal) of fimbrial subcutaneously the second group gave 1 ml ( 200 µg /animal) LPS while the third group was left as negative control group that injected 1 ml phosphate buffer control subcutaneously. First and second groups recived the same dose after two weeks give as booster dose. All animals challenged after 5 weeks of immunization by5X107CFU/ml Proteus vulgaris intra peritoneally .After 7 days from challenge all the animals, sacrificed for hi

KE Sharquie, AA Noaimi, E Abdulqader, WK Al-Janabi, J Dermatol Venereol, 2020 - Cited by 6

Interleukin-38 (IL-38), an inflammatory cytokine discovered in recent years, has been implicated in the pathogenesis of systemic lupus erythematosus (SLE). IL-38 is encoded by the IL1F10 (interleukin 1 family member 10) gene. Genetic variants of this gene have been associated with susceptibility to a number of autoimmune and inflammatory diseases, while their association with SLE risk has not been explored. In this case–control study, two novel variants of the 5 prime untranslated region (5′UTR) of the IL1F10 gene, rs3811050 C/T and rs3811051 T/G, were investigated

Amis: NAFLD is considered to be the most common cause of liver conditions worldwide. Also, it is a primary reason that leads to coronary artery diseases, limiting blood flow to the heart. Therefore, This study aimed to evaluate the serum level of Nesfatin-1 and its ability to indicate the prognosis of CAD in patients with NAFLD. Material & Methods: one-hundred eighty Individuals were enrolled in the study, including In both genders, blood was collected from each Individual and sent to the laboratory for biochemical tests. Findings: Data from the current study showed a significant increase in Nesfatin-1 in the CAD group and a significant decrease in Nesfatin-1 in the NAFLD group compared to the control group. In addition, there w

The Internet of Things (IoT) has significantly transformed modern systems through extensive connectivity but has also concurrently introduced considerable cybersecurity risks. Traditional rule-based methods are becoming increasingly insufficient in the face of evolving cyber threats.  This study proposes an enhanced methodology utilizing a hybrid machine-learning framework for IoT cyber-attack detection. The framework integrates a Grey Wolf Optimizer (GWO) for optimal feature selection, a customized synthetic minority oversampling technique (SMOTE) for data balancing, and a systematic approach to hyperparameter tuning of ensemble algorithms: Random Forest (RF), XGBoost, and CatBoost. Evaluations on the RT-IoT2022 dataset demonstrat

Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature