Abstract
The extracellular space (ECS) is an important barrier against viral attack on brain cells, and dynamic changes in ECS microstructure characteristics are closely related to the progression of viral encephalitis in the brain and the efficacy of antiviral drugs. However, mapping the precise morphological and rheological features of the ECS in viral encephalitis is still challenging so far. Here, a robust approach is developed using single-particle diffusional fingerprinting of quantum dots combined with machine learning to map ECS features in the brain and predict the efficacy of antiviral encephalitis drugs. These results demonstrated that this approach can characterize the microrheology and geometry of the brain ECS at different stages of viral infection and identify subtle changes induced by different drug treatments. This approach provides a potential platform for drug proficiency assessment and is expected to offer a reliable basis for the clinical translation of drugs.
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