Autophagy is associated with secondary injury following traumatic brain injury (TBI) and is expected to be a therapeutic target. Baicalin, a neuroprotective agent, has been proven to exert multi-functional bioactive effects in brain injury diseases. However, it is unknown if Baicalin influences autophagy after TBI. In the present study, we aimed to explore the effects that Baicalin had on TBI in a mice model, focusing on autophagy as a potential mechanism. We found that Baicalin administration significantly improved motor function, reduced cerebral edema, and alleviated disruption of the blood-brain barrier (BBB) after TBI in mice. Besides, TBI-induced apoptosis was reversed by Baicalin evidenced by Nissl staining, terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay, and the level of cleaved caspase-3. More importantly, Baicalin enhanced autophagy by detecting the autophagy markers (LC3, Beclin 1, and p62) using western blot and LC3 immunofluorescence staining, ameliorating mitochondrial apoptotic pathway evidenced by restoration of the TBI-induced translocation of Bax and cytochrome C. However, simultaneous treatment with 3-MA inhibited Baicalin-induced autophagy and abolished its protective effects on mitochondrial apoptotic pathway. In conclusion, we demonstrated that Baicalin enhanced autophagy, ameliorated mitochondrial apoptosis and protected mice brain in TBI mice model.

There often exist multiple equilibria in the algorithm results of metagame theory and conflict analysis, and some of the equilibria may be non-Pareto optimal or low stable. Thus, it is still a problem for decisionmakers to choose among the equilibria. On the basis of the traditional metagame algorithm, this paper develops a new algorithm by taking "foresight incentive" into consideration, so as to obtain more terse and stable equilibria. The analysis steps of the foresight incentive algorithm include generating a route for each player in each scenario, identifying the check-point in each route, examining if each check-point is foresight incentive point, and analyzing the stability of each scenario for each player. Finally, through the case study of a real construction conflict, the effectiveness and superiority of the foresight incentive algorithm are verified.