Background The acute inhibition of glymphatic after stroke has been shown to aggravate post-stroke inflammation and apoptosis; however, the related mechanisms remain ambiguous. This study aimed to assess the specific mechanism of inflammation and apoptosis after cerebral ischemia-reperfusion (I/R) injury by improving glymphatic dysfunction. Materials and Methods Ischemic stroke was induced using the mice middle cerebral artery occlusion (MCAO) model. The C57/BL6 mice were randomly divided into three groups as follows: sham operation, Ischemia-reperfusion (I/R) 48 hours, and N-(1,3,4-thiadiazol-2-yl) pyridine-3-carboxamide dihydrochloride (TGN-020) + I/R 48 hours treatment. Neurological examination, TTC, fluorescence tracer, western blot, and immunofluorescence staining were performed in all mice in sequence. The glymphatic function in the cortex surrounding cerebral infarction was determined using tracer, glial fibrillary acid protein (GFAP), aquaporin-4 (AQP4) co-staining, and beta-amyloid precursor protein (APP) staining, differential genes were detected using RNA-seq. Iba-1, IL-1β, TNF-α, cleaved caspase 3, and tunel staining were used to verify inflammation and apoptosis after TGN-020 treatment. Results Compared with I/R group, the degree of neurological deficit was alleviated in TGN-020 group. TGN-020 alleviated glymphatic dysfunction by improving astrocyte proliferation and reducing tracer accumulation in the peri-infarct area. RNA-seq showed that the differentially expressed genes were mainly involved in the activation of astrocytes and microglia, and involved in the ERK pathway. RNA-seq was verified by western blot and immunofluorescence. Conclusions The inflammation of astrocytes and microglia after cerebral ischemia-reperfusion (I/R) is closely related to the glymphatic system. The improvement of glymphatic function may play a neuroprotective role after cerebral I/R by inhibiting inflammation through ERK pathway.

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Steam coal is the blood of China industry. Forecasting steam coal prices accurately and reliably is of great significance to the stable development of China’s economy. For the predictive model of existing steam coal prices, it is difficult to dig the law of nonlinearity of power coal price data and with poor stability. To address the problems that steam coal price features are highly nonlinear and models lack robustness, Laplacian kernel–log hyperbolic loss–Ridge regression (LK-LC-Ridge-Ensemble) model is proposed, which uses ensemble learning model for steam coal price prediction. First, in each sliding window, two kinds of correlation coefficient are employed to identify the optimal time interval, while the optimal feature set is selected to reduce the data dimension. Second, the Laplace kernel functions are adopted for constructing kernel Ridge regression (LK-Ridge), which boosts the capacity to learn nonlinear laws; the logarithmic loss function is introduced to form the LK-LC-Ridge to enhance the robustness. Finally, the prediction results of each single regression models are utilized to build a results matrix that is input into the meta-model SVR for ensemble learning, which further develops the model performance. Empirical results from three typical steam coal price datasets indicate that the proposed ensemble strategy is reliable for the model performance enhancement. Furthermore, the proposed model outperforms all single primitive models including accuracy of prediction results and robustness of model. Grouping cross-comparison between the different models suggests that the proposed ensemble model is more accurate and robust for steam coal price forecasting.