Investigating the role of oxytocic microorganisms in cancer therapy: artificial neural network analysis of local thermal non-equilibrium in hybrid nanofluid

In the present study examined the effects of local thermal non-equilibrium on the bioconvection flow of a hybrid nanofluid containing oxytactic microbes via three different geometries using an artificial neural network. Oxytocic bacteria are used in cancer treatment to activate the immune system and prevent the growth of cancer. As a targeted medication delivery vehicle for infected cells, oxygen-repellent bacteria appear to be a viable weapon in the battle against tumours. Using modified Hamilton–Crosser models, the outcomes of the Soret and Dufour impacts on hybrid nanofluid is examined. By precisely replicating the intricate heat and mass transport within biological tissues, the model can improve the administration of Oxytocic bacteria as targeted medication carriers or direct therapeutic agents. The local thermal non-equilibrium feature is critical because it compensates for the temperature difference between the injected hybrid nanofluid (including magnetic nanoparticles and bacteria) and the surrounding tumour tissue, ensuring an accurate thermal dose. The Soret and Dufour impact model includes mass and heat diffusion, which was crucial in forecasting the dispersion of nanoparticles and bacteria in the tumor microenvironment's temperature gradient. The Levenberg‒Marquardt technique is applied to the generated synthetic data in order to minimize error and obtain approximation results for different hybrid nanofluid system scenarios. The microorganism profile reduces as the Peclet number increases.