Giardiasis is the major water-borne diarrheal disease present worldwide caused by the common intestinal parasite, Giardia duodenalis. This work aims to investigate the effect of G. duodenalis infection pathogenicity in immunosuppressed animals through histopathological examination. A total of 45 BALB/c mice were divided into four groups; G1 (negative control), G2 (healthy animals exposed to Giardia); G3 (immunosuppressed animals exposed to Giardia), and G4 (non-exposed immunosuppressed animals). Our study revealed that G3 was the most affected group with an infection rate of 100%. The animals showed general weakness, soft stool, and high death rate with severe histopathological changes in the duodenum and mild degenerative changes in hepatic tissues. In G2, the maximal lesions in both duodenum and liver were on the 11th day. We spotted damage in the villi, edema in the central core, and submucosa, in addition to increased cellular infiltration with inflammation in lamina propria. The presence of the parasites within the villi and the lumen was clear. Most of the hepatocytes revealed hydropic and fatty changes, also dilated congested central veins and edema were observed. G3 changes were more intense than G2 with massive Giardia trophozoites between the intestinal villi, lumen, and extensive fatty liver degeneration. Immune suppression plays a significant role in the severity of injury with the Giardia parasites in duodenum and liver cells.
Predicting Unified Parkinson's Disease Rating Scale (UPDRS) in Total- UPDRS and Motor-UPDRS clinical scales is an important part of controlling PD. Computational intelligence approaches have been used effectively in the early diagnosis of PD by predicting UPDRS. In this research, we target to present a combined approach for PD diagnosis using an ensemble learning approach with the ability of online learning from clinical large datasets. The method is developed using Deep Belief Network (DBN) and Neuro-Fuzzy approaches. A clustering approach, Expectation-Maximization (EM), is used to handle large datasets. The Principle Component Analysis (PCA) technique is employed for noise removal from the data. The UPDRS prediction models are constructed for PD diagnosis. To handle the missing data, K-NN is used in the proposed method. We use incremental machine learning approaches to improve the efficiency of the proposed method. We assess our approach on a real-world PD dataset and the findings are assessed compared to other PD diagnosis approaches developed by machine learning techniques. The findings revealed that the approach can improve the UPDRS prediction accuracy and the time complexity of previous methods in handling large datasets.
Healthcare professionals consider predicting heart disease an essential task and deep learning has proven to be a promising approach for achieving this goal. This research paper introduces a novel method called the asynchronous federated deep learning approach for cardiac prediction (AFLCP), which combines a heart disease dataset and deep neural networks (DNNs) with an asynchronous learning technique. The proposed approach employs a method for asynchronously updating the parameters of DNNs and incorporates a temporally weighted aggregation technique to enhance the accuracy and convergence of the central model. To evaluate the effectiveness of the proposed AFLCP method, two datasets with various DNN architectures are tested, and the results demonstrate that the AFLCP approach outperforms the baseline method in terms of both communication cost and model accuracy.