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Carbamathione (Carb), an NMDA glutamate receptor partial antagonist, has potent neuroprotective functions against hypoxia- or ischemia-induced neuronal injury in cell- or animal-based stroke models. We used PC-12 cell cultures as a cell-based model and bilateral carotid artery occlusion (BCAO) for stroke. Whole-cell patch clamp recording in the mouse retinal ganglion cells was performed. Key proteins involved in apoptosis, endoplasmic reticulum (ER) stress, and heat shock proteins were analyzed using immunoblotting. Carb is effective in protecting PC12 cells against glutamate- or hypoxia-induced cell injury. Electrophysiological results show that Carb attenuates NMDA-mediated glutamate currents in the retinal ganglion cells, which results in activation of the AKT signaling pathway and increased expression of pro-cell survival biomarkers, e.g., Hsp 27, P-AKT, and Bcl2 and decreased expression of pro-cell death markers, e.g., Beclin 1, Bax, and Cleaved caspase 3, and ER stress markers, e.g., CHOP, IRE1, XBP1, ATF 4, and eIF2α. Using the BCAO animal stroke model, we found that Carb reduced the brain infarct volume and decreased levels of ER stress markers, GRP 78, CHOP, and at the behavioral level, e.g., a decrease in asymmetric turns and an increase in locomotor activity. These findings for Carb provide promising and rational strategies for stroke therapy. 

Abstract Predicting future discontinuous phenomena that are unobservable from training data sets has long been a challenging problem in scientific machine learning. We introduce a novel paradigm to predict the emergence and evolution of various discontinuities of hyperbolic partial differential equations (PDEs) based on given training data over a short window with limited discontinuity information. Our method is inspired by the classical Roe solver [P. L. Roe, J Comput Phys., vol. 43, 1981], a basic tool for simulating various hyperbolic PDEs in computational physics. By carefully designing the computing primitives, the data flow, and the novel pseudoinverse processing module, we enable our data‐driven predictor to satisfy all the essential mathematical criteria of a Roe solver and hence deliver accurate predictions of hyperbolic PDEs. We demonstrate through various examples that our data‐driven Roe predictor outperforms original human‐designed Roe solver and deep neural networks with weak priors in terms of accuracy and robustness.