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Life sciences · Journal article
Journal of Taibah University for Science · August 10, 2026
Raises a question worth testing. It does not answer one.
This is a computational study that models the thermal and mass transport properties of hybrid nanofluids containing oxytactic microorganisms in cancer treatment using artificial neural networks. The work is purely theoretical and exploratory, examining how local thermal non-equilibrium, Soret and Dufour effects, and the Peclet number influence microorganism dispersion in simulated tumour tissue; it does not present experimental or clinical evidence of therapeutic efficacy.
Computational modelling study using artificial neural networks. Intervention: Hybrid nanofluid containing oxytactic microorganisms, magnetic nanoparticles, and models of local thermal non-equilibrium..
The microorganism profile reduces as the Peclet number increases
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This is a computational modelling study using artificial neural networks to simulate heat and mass transport of hybrid nanofluids with oxytactic bacteria; it presents no experimental validation, clinical trial, or empirical evidence of therapeutic effect in cancer.
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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.
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