Diffusion data obtained from multiple particle tracking of nanotherapeutically-relevant platforms can predict nanoparticle transport in living tissue.

Brain extracellular matrix (ECM) structure mediates many aspects of neural development and function. Probing structural changes in brain ECM could thus provide insights into mechanisms of neurodevelopment, the loss of neural function in response to injury, and the detrimental effects of pathological aging and neurological disease. We demonstrate the ability to probe changes in brain ECM microstructure using multiple particle tracking (MPT). We performed MPT of colloidally stable polystyrene nanoparticles in organotypic rat brain slices collected from rats aged 14−70 days old. Our analysis revealed an inverse relationship between nanoparticle diffusive ability in the brain extracellular space and age. Additionally, the distribution of effective ECM pore sizes in the cortex shifted to smaller pores throughout development. We used the raw data and features extracted from nanoparticle trajectories to train a boosted decision tree capable of predicting chronological age with high accuracy. Collectively, this work demonstrates the utility of combining MPT with machine learning for measuring changes in brain ECM structure and predicting associated complex features such as chronological age. This will enable further understanding of the roles brain ECM play in development and aging and the specific mechanisms through which injuries cause aberrant neuronal function. Additionally, this approach has the potential to develop machine learning models capable of detecting the presence of injury or indicating the extent of injury based on changes in the brain microenvironment microstructure.

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Brain extracellular matrix (ECM) structure mediates many aspects of neuronal function. Probing changes in ECM structure could provide insights into aging and neurological disease. Herein, we demonstrate the ability to characterize changes in brain ECM structure using multiple particle tracking (MPT). MPT was carried out in organotypic rat brain slices to detect induced and naturally occurring changes in ECM structure. Induced degradation of neural ECM led to a significant increase in nanoparticle diffusive ability in the brain extracellular space. For structural changes that occur naturally during development, an inverse relationship existed between age and nanoparticle diffusion. Using the age-dependent dataset, we applied extreme gradient boosting (XGBoost) to generate models capable of classifying nanoparticle trajectories.Collectively, this work demonstrates the utility of MPT combined with machine learning for measuring changes in brain ECM structure and predicting associated complex features such as developmental age.

The progression of a severe accident in nuclear reactors is composed by a diversity of phenomena that areBeyond Design Basis, such as Direct Containment Heating (DCH), Molten Corium Concrete Interaction(MCCI), hydrogen detonation, and others. Currently, there are several devices and systems that allowmitigating the progression of this events, avoiding the failure of the physical barriers between the nuclearpower plant and the environmental. In this context, the present work aims to reproduce the HR-14experiment carried out at the Thermal-hydraulic, Hydrogen, Aerosols and Iodine (THAI) test facility throughthe Passive Autocatalytic Recombiners (PAR) performance assessment with the COCOSYS code. The analysisof the convergence of the results was performed using the Fast Fourier Transform Based Method (FFTBM)and showed that the results had sufficient accuracy with the experimental data.