Biology subjects

Hesketh, M.

Publications and source records attributed to Hesketh, M..

2 recordsLinked to original sources

bioRxiv2026

A mathematical model of pathology progression in the TgF344-AD rat model of Alzheimer's disease

Alzheimer's disease (AD) is a devastating neurodegenerative disease whose etiology is poorly understood and for which current treatments provide only modest control of symptoms. To better investigate the causes and progression of the disease, the transgenic TgF344-AD rat model has emerged as a crucial tool. In this paper, we collect observations on the accumulation of amyloid-{beta}, changes in neuronal density, and a decline in cognitive performance in TgF344-AD and wild-type rats. We develop a compartmental ordinary differential equation model and determine its parameters by fitting the output to the experimental observations in the literature. Our model simulations are compatible with the hypothesis that the accumulation of amyloid-{beta} leads to a rapid decline in neuronal density followed by a significant loss in memory and learning ability. Our mathematical model can provide a bridge between AD research in rodent models and the human condition of AD.

neuroscience↗

bioRxiv2026

Probing the Glymphatic System Using Optical Imaging and Mathematical Modeling

The glymphatic system is a macroscopic waste clearance pathway in the central nervous system (CNS), crucial to maintaining neural homeostasis through the exchange of cerebrospinal fluid (CSF) and interstitial fluid (ISF). Impairments in this system have been associated with neurodegenerative diseases such as Alzheimers and Parkinsons, emphasizing the need to understand and potentially improve glymphatic clearance. We investigate tracer diffusion and transport in the glymphatic system by combining optical imaging in rats and mathematical modeling using a partial differential equation of advection-diffusion type. The experimental conditions differ in the levels of isoflurane-induced anesthesia (1.5, 2 and 3%) and the molecular weight of the tracer compounds (1 and 160 kDa). The optimal parameters show a close clustering of the diffusion coefficients and a wider spread of the flow velocities of the cerebrospinal fluid. Our work contributes to a better understanding of flow processes in the brain parenchyma during sleep.

neuroscience↗