Previous studies have reported that the long non-coding RNA SNHG12 (lncRNA SNHG12) plays a critical role in regulating the function of mesenchymal stem cells (MSCs); however, the effect of lncRNA SNHG12 on MSCs in injured brain tissue has rarely been reported. We studied the effect and mechanism of lncRNA SNHG12-modified mesenchymal stem cells (MSCs) in treating brain injuries caused by ischemia/reperfusion (I/R). I/R treated rat brain microvascular endothelial cells (BMECs) were co-cultured with MSCs or I/R pretreated MSCs. Next, BMEC proliferation was detected by using CCK-8 and EdU assays, and cell apoptosis was determined by using flow cytometry and the Hoechst staining method. Autophagy of BMECs was determined using immunofluorescence and expression of associated pathway proteins were measured by western blotting. Moreover, BMEC proliferation, apoptosis, and autophagy were also determined after the BMECs had been co-cultured with shSNHG12-MSCs. In addition, a rat model of middle cerebral artery occlusion (MCAO) was used to further confirm the findings obtained with cells. I/R treatment significantly decreased the proliferation of BMECs, but increased their levels of SNHG12 expression, apoptosis, and autophagy. However, co-culturing of BMECs with MSCs markedly alleviated the reduction in BMEC proliferation and the increases in BMEC apoptosis and autophagy, as well as the phosphorylation of PI3K, AKT, and mTOR proteins in BMECs that had been induced by I/R. Furthermore, shSNHG12 remarkably enhanced the effects of MSCs. In addition, an injection MSCs reduced the infarct areas and rates of cell apoptosis in MACO rats, and reduced the phosphorylation of PI3K, AKT, and mTOR proteins. Moreover, shSNHG12 enhanced the ameliorative effect of MSCs in treating brain injuries in the MACO rats. In conclusion, silencing of SNHG12 enhanced the effects of MSCs in reducing apoptosis and autophagy of BMECs by activating the PI3K/AKT/mTOR signaling pathway.

Inflammation is typically related to dysfunction of the blood-brain barrier (BBB) that leads to early brain injury (EBI) after subarachnoid hemorrhage (SAH). Resolvin D1 (RVD1), a lipid mediator derived from docosahexaenoic acid, possesses anti-inflammatory and neuroprotective properties. This study investigated the effects and mechanisms of RVD1 in SAH. A Sprague-Dawley rat model of SAH was established through endovascular perforation. RVD1was injected through the femoral vein at 1 and 12 h after SAH induction. To further explore the potential neuroprotective mechanism, a formyl peptide receptor two antagonist (WRW4) was intracerebroventricularly administered 1 h after SAH induction. The expression of endogenous RVD1 was decreased whereas A20 and NLRP3 levels were increased after SAH. An exogenous RVD1 administration increased RVD1 concentration in brain tissue, and improved neurological function, neuroinflammation, BBB disruption, and brain edema. RVD1 treatment upregulated the expression of A20, occludin, claudin-5, and zona occludens-1, as well as downregulated nuclear factor-κBp65, NLRP3, matrix metallopeptidase 9, and intercellular cell adhesion molecule-1 expression. Furthermore, RVD1 inhibited microglial activation and neutrophil infiltration and promoted neutrophil apoptosis. However, the neuroprotective effects of RVD1 were abolished by WRW4. In summary, our findings reveal that RVD1 provides beneficial effects against inflammation-triggered BBB dysfunction after SAH by modulating A20 and NLRP3 inflammasome.

Background: Assessment of cerebral aneurysm rupture risk is an important task, but it remains challenging. Recent works applying machine learning to rupture risk evaluation presented positive results. Yet they were based on limited aspects of data, and lack of interpretability may limit their use in clinical setting. We aimed to develop interpretable machine learning models on multidimensional data for aneurysm rupture risk assessment.Methods: Three hundred seventy-four aneurysms were included in the study. Demographic, medical history, lifestyle behaviors, lipid profile, and morphologies were collected for each patient. Prediction models were derived using machine learning methods (support vector machine, artificial neural network, and XGBoost) and conventional logistic regression. The derived models were compared with the PHASES score method. The Shapley Additive Explanations (SHAP) analysis was applied to improve the interpretability of the best machine learning model and reveal the reasoning behind the predictions made by the model.Results: The best machine learning model (XGBoost) achieved an area under the receiver operating characteristic curve of 0.882 [95% confidence interval (CI) = 0.838–0.927], significantly better than the logistic regression model (0.779; 95% CI = 0.729–0.829; P = 0.002) and the PHASES score method (0.758; 95% CI = 0.713–0.800; P = 0.001). Location, size ratio, and triglyceride level were the three most important features in predicting rupture. Two typical cases were analyzed to demonstrate the interpretability of the model.Conclusions: This study demonstrated the potential of using machine learning for aneurysm rupture risk assessment. Machine learning models performed better than conventional statistical model and the PHASES score method. The SHAP analysis can improve the interpretability of machine learning models and facilitate their use in a clinical setting.