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Wael Abdo Hassan,Ahmed Kamal ElBanna,Noha Noufal,Mohamed El-Assmy,Hany Lotfy,Rehab Ibrahim Ali 대한병리학회 2023 Journal of Pathology and Translational Medicine Vol.57 No.2
Background: Tumor-infiltrating neutrophils and lymphocytes play essential roles in promoting or combating various neoplasms. This study aimed to investigate the association between tumor-infiltrating neutrophils and lymphocytes and the neutrophil-to-lymphocyte ratio in the progression of urothelial carcinoma. Methods: A total of 106 patients diagnosed with urothelial carcinoma were was. Pathological examination for tumor grade and stage and for tumor-infiltrating neutrophils, both CD4 and CD8+ T lymphocytes, as well as the neutrophil- to-lymphocyte ratio were evaluated. Results: The presence of neutrophils and the neutrophil-to-lymphocyte ratio correlated with high-grade urothelial neoplasms. In both low- and high-grade tumors, the lymphocytes increased during progression from a non-invasive neoplasm to an early-invasive neoplasm. CD8+ T lymphocytes increased in low-grade non–muscle-invasive tumors compared to non-invasive tumors. Additionally, there was a significant decrease in CD8+ T lymphocytes during progression to muscle-invasive tumors. Conclusions: Our results suggest that tumor-infiltrating neutrophils and CD8+ T lymphocytes have a significant effect on tumor grade and progression.
Gaurav Sharma,Ankit Kotia,Subrata Kumar Ghosh,Prashant Singh Rana,Seema Bawa,Mohamed Kamal Ahmed Ali 한국정밀공학회 2020 International Journal of Precision Engineering and Vol.21 No.10
Recent researchers widely used nanoparticle additives for improving thermal and rheological properties of machine lubricant. In present study the effect of Al2O3 and CeO2 nanoparticles on transmission oil (SAE30), hydraulic oil (HYDREX100) and gear oil (EP90) of heavy earth moving machinery is investigated. Nano-lubricant samples are prepared in 0.01–4% nanoparticle volume fraction range. Four machine learning techniques namely decision tree (DT), random forest (RF), generalized linear models and neural network (NN) have been used to predict the kinematic viscosity for Al2O3 and CeO2 nanolubricants. Further, multi-criteria decision-making technique named technique for order of preference by similarity to ideal solution have been used to find the best predictive method in each category of the nanolubricants. DT, RF and NN methods are found to be most accurate in kinematic viscosity prediction of transmission oil (R 2 = 0.861), hydraulic oil (R 2 = 0.971) and gear oil (R 2 = 0.973), respectively.