Termal ve Akışkan Bilimleri Açısından Alzheimer Hastalığının Navier–Stokes Denklemleri İle Modellenmesi, Erken Teşhis ve Tedavi Yaklaşımları
Year: 2026
Type: article
Abstract: ("Modeling of Alzheimer's Disease Using Navier–Stokes Equations in Terms of Thermal and Fluid Sciences: Early Diagnosis and Treatment Approaches") Alzheimer's disease is one of the most common neurodegenerative diseases among the elderly worldwide, characterized by progressive memory loss, cognitive dysfunction, behavioral changes, and irreversible degeneration of nerve cells. Currently, the mechanisms of Alzheimer's disease are largely explained by biochemical processes. However, recent research indicates that the disease is not merely a biological problem; it also involves significant hemodynamic, thermodynamic, and biophysical processes. Disruptions in blood flow in the cerebral vascular system, cerebral hypoperfusion, atherosclerosis, microvascular occlusions, decreased oxygen transport, and irregularities in cerebrospinal fluid circulation can play a significant role in the development of Alzheimer's. In this study, Alzheimer's disease was investigated using the Navier–Stokes equations, which form the basis of fluid mechanics. Blood flow within the brain vessels was mathematically modeled, and intravascular velocity distributions, pressure changes, shear stresses, energy losses, viscosity effects, and turbulence formations were analyzed in detail. Furthermore, potential approaches for the preonset detection of Alzheimer's disease using Computational Fluid Dynamics (CFD) methods were evaluated. MRI-based flow analyses, digital vascular modeling, drug delivery mechanisms, nanoparticle systems, and cerebrospinal fluid movement were addressed within the framework of Navier–Stokes-based differential equation systems. This study aims to demonstrate that Alzheimer's disease is not merely a neurological problem, but also a multi-physics process directly related to the disciplines of fluid mechanics, thermodynamics, biophysics, and mathematical modeling. [1-106]
Authors: Elmas, E. T., Dağ, İ.
Institution: Iğdır Üniversitesi
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7204173620