A novel neuroprotective method against ischemic stroke by accelerating the drainage of brain interstitial fluid.

Ischemic stroke is a leading cause of death and disability worldwide. Inflammatory response after stroke determines the outcome of ischemic injury. A recent study has reported an efficient method, epidural arterial implantation (EAI), for accelerating interstitial fluid (ISF) drainage, which provides a promising strategy to clear pro-inflammatory cytokines in the brain extracellular space (ECS). In this study, the method of EAI was modified (m-EAI) to control its function of accelerating the ISF drainage at different time points following ischemic attack. The neuroprotective effect of m-EAI on ischemic stroke was evaluated with the transient middle cerebral artery occlusion (tMCAO) rat model. The results demonstrated the accumulation of IL-1ß, IL-6, and TNF-α was significantly decreased by activating m-EAI at 7 d before and immediately after ischemic attack in tMCAO rats, accompanied with decreased infarct volume and improved neurological function. This study consolidates the hypothesis of exacerbated ischemic damage by inflammatory response and provides a new perspective to treat encephalopathy via brain ECS. Further research is essential to investigate whether m-EAI combined with neuroprotective drugs could enhance the therapeutic effect on ischemic stroke.

Quantitative analysis of molecular transport in the extracellular space using physics-informed neural network.

The brain extracellular space (ECS), an irregular, extremely tortuous nanoscale space located between cells or between cells and blood vessels, is crucial for nerve cell survival. It plays a pivotal role in high-level brain functions such as memory, emotion, and sensation. However, the specific form of molecular transport within the ECS remain elusive. To address this challenge, this paper proposes a novel approach to quantitatively analyze the molecular transport within the ECS by solving an inverse problem derived from the advection-diffusion equation (ADE) using a physics-informed neural network (PINN). PINN provides a streamlined solution to the ADE without the need for intricate mathematical formulations or grid settings. Additionally, the optimization of PINN facilitates the automatic computation of the diffusion coefficient governing long-term molecule transport and the velocity of molecules driven by advection. Consequently, the proposed method allows for the quantitative analysis and identification of the specific pattern of molecular transport within the ECS through the calculation of the Péclet number. Experimental validation on two datasets of magnetic resonance images (MRIs) captured at different time points showcases the effectiveness of the proposed method. Notably, our simulations reveal identical molecular transport patterns between datasets representing rats with tracer injected into the same brain region. These findings highlight the potential of PINN as a promising tool for comprehensively exploring molecular transport within the ECS.