Investigation of the effects of porosity and volume fraction on the atomic behavior of cancer cells and microvascular cells of 3DN5 and 5OTF macromolecular structures during hematogenous metastasis using the molecular dynamics method.

BACKGROUND AND OBJECTIVE: The molecular dynamics (MD) simulation is a powerful tool for researching how cancer patients are treated. The efficiency of many factors may be predicted using this approach in great detail and with atomic accuracy. METHODS: The MD simulation method was used to investigate the impact of porosity and the number of cancer cells on the atomic behavior of cancer cells during the hematogenous spread. In order to examine the stability of simulated structures, temperature and potential energy (PE) values are used. To evaluate how cell structure has changed, physical parameters such as gyration radius, interaction force, and interaction energy are also used. RESULTS: The findings demonstrate that the samples' gyration radius, interaction energy, and interaction force rose from 41.33 Å, -551.38 kcal/mol, and -207.10 kcal/mol Å to 49.49, -535.94 kcal/mol, and -190.05 kcal/mol Å, respectively, when the porosity grew from 0% to 5%. Also, the interaction energy and force in the samples fell from -551.38 kcal/mol and -207.10 kcal/mol to -588.03 kcal/mol and -237.81 kcal/mol Å, and the amount of gyration radius reduced from 41.33 to 37.14 Å as the number of cancer cells rose from 1 to 5 molecules. The strength and stability of the simulated samples will improve when the radius of gyration is decreased. CONCLUSIONS: Therefore, high accumulation of cancer cells will make them resistant to atomic collapse. It is expected that the results of this simulation should be used to optimize cancer treatment processes further.

Statistical modeling and investigation of thermal characteristics of a new nanofluid containing cerium oxide powder.

In this paper, the thermal conductivity (knf) of cerium oxide/ethylene glycol nanofluid is extracted for different temperatures (T = 25, 30, 35, 40, 45, and 50 °C) and the volume fraction of nanoparticles ( φ = 0, 0.25, 0.5, 0.75, 1, 1.5, 2 and 2.5%) and then knf is predicted by two methods including Artificial Neural Network (ANN) and fitting method. For both methods, the results have been presented and compared. The experiments showed that with increasing φ and temperature, the thermal conductivity ratio (TCR) of nanofluid increases. It was also observed that when the experiments are performed at high temperatures, the rate of increase in knf is much higher than the change in the same amount of φ change at low temperatures. An ANN with 7 neurons has a correlation coefficient very close to 1 and this proves that the outputs are compatible with experimental results. Also, it can be seen that the ANN could predict the thermal behavior of cerium oxide/ethylene glycol nanofluid more accurately.