Histopathological Study to Evaluate the Effect of Aqueous Extract of Portunuspelagicus and Mebendazole on Hydatid Cysts in Mice

المجلة: Archives of Razi Institute

سنة النشر: 2023

تاريخ النشر: 2024-08-23

Abstract Hydatid disease is a parasitic infestation by a tapeworm of the genus Echinococcus sp., which has a global distribution. The current study was conducted to evaluate the effectiveness of the crustacean aqueous extract of Portunuspelagicus for 2 weeks of treatment compared to mebendazole on hydatid cyst in laboratory mice male Balb / C strain. Mice were infected intraperitoneally with 2000 protoscolices. After 12 weeks of infection, each mouse was treated with mebendazole (50mg/kg) and the hot aqueous extract of p. pelagicus (8, 16 g/kg). Samples of infected organs (liver, spleen, and lungs) were examined under a microscope to evaluate the morphological and histopathological changes of hydatid cysts and tissues. The study confirmed macroscopically that there were a number of hydatid cysts of different sizes in the liver, spleen, and lungs, splenomegaly, and congestion of the lungs of the positive control group. The histological changes in the organs of the group treated with the crustacean extract were represented by the vacuolation of hepatocytes in the centrilobular area of the liver. At the same time, the lungs show intensive peri-bronchiolar inflammation, pulmonary vascular congestion, and in the spleen, the deposition of amyloid-like material in the white pulp, extramedullary hematopoiesis, While the histopathological changes in the organs of mice treated with mebendazole, were represented by the presence in the mild liver vacuolation of the centrilobular area. In contrast, the lungs show mild pulmonary vascular congestion and emphysema, and the spleen shows normal white pulp, the normal red pulp of mice. The aqueous extract Portunuspelagicus and mebendazole are effective in controlling the contamination in the intermediate hosts.

Biochemical and GC-Mass analysis of Echinococcus granulosus hydatid cyst fluid components for humans and sheep

المجلة: Iranian Society of Ichthyology

سنة النشر: 2022

تاريخ النشر: 2024-08-23

Abstract This study was conducted to evaluate the chemical and biochemical components of Echinococcus granulosus hydatid cyst fluid (HCF) isolated from infected humans and sheep. The study shows significant differences between the potassium, urea, cholesterol, uric acid, and magnesium in the HCF content of humans and sheep. In contrast, no significant differences were found in the total protein, glucose, triglyceride, creatinine, calcium, and sodium. The Urea, magnesium, calcium, creatinine, glucose, and potassium in HCF of sheep was higher than those of humans, whereas, the total protein, uric acid, triglyceride, cholesterol, and sodium content of HCF of humans were higher than in sheep. In (GC-MS), a difference in the number of peaks and components of HCF between humans and sheep was found, where the number of peaks in humans HCF was 25 peaks vs. 43 peaks in sheep.

Enhancing Agricultural Decision-Making through Data Analysis: Predicting Crop Health Outcomes

المجلة: BIO Web of Conferences 97

سنة النشر: 2024

تاريخ النشر: 2024-08-23

Abstract. This research employs advanced data analysis techniques to predict crop health outcomes during harvest seasons, with a focus on insect count, pesticide use, and soil type. The study encompasses two main components: feature correlation and predictive modeling. Feature engineering techniques are applied to capture variations in pesticide use and insect infestation, enhancing predictive capabilities. Ensemble methods, including Random Forest, XGBoost, and Decision Trees, are employed to forecast patterns of crop damage based on identified trends. Decision Trees exhibit robust capabilities, achieving an impressive accuracy rate of 90.03%. Random Forest excels with a robust accuracy of 90.35%, highlighting its classification abilities. XGBoost stands out with an accuracy rate of 86.51%. In contrast, Logistic Regression, Naive Bayes, and Convolutional Neural Networks face challenges, displaying lower accuracy. The evaluation further emphasizes the strength of ensemble methods and Decision Trees through precision, recall, and F1-Score metrics, providing a comprehensive understanding of relationships within pesticide damage. The framework of the study introduced in this paper can be seen as a major step forward with regard to agricultural decision-making. We present actionable strategies to enhance crop health while reducing damage through the integration of feature correlation, predictive modeling and precise evaluation metrics. The innovativeness is in the use of ensemble methods and Decision Trees that are implemented to promote informed decision-making among stakeholders through a sustainable approach to agriculture.