p38 mitogen-activated protein kinase gene silencing rescues rat hippocampal neurons from ketamine-induced apoptosis: An in vitro study.

Ketamine (KTM) is an anesthetic drug with several advantages, including the elevation of cardiac output and blood pressure. However, KTM may also induce the apoptosis of hippocampal neurons. Notably, p38 mitogen­activated protein kinase (p38MAPK) has previously been studied for its role in neuronal injury. Therefore, the present study evaluated the effect of lentivirus­mediated p38MAPK gene silencing on KTM­induced apoptosis of rat hippocampal neurons. Hippocampal neurons were extracted from neonatal Sprague­Dawley rats, and then treated with KTM, p38MAPK­short hairpin RNA or SB203580 (an inhibitor of p38MAPK). Next, the expression levels of p38MAPK and apoptosis­associated genes, including caspase­3, B­cell lymphoma 2 (Bcl­2) and Bcl­2­associated X protein (Bax), were detected. In addition, cell viability and apoptosis were determined using an MTT assay and flow cytometry, respectively. Finally, telomerase activity of hippocampal neurons was detected by ELISA. The results revealed that silencing of p38MAPK in KTM­treated cells decreased the expression levels of p38MAPK, caspase­3 and Bax, and the extent of p38MAPK phosphorylation, while it increased the expression of Bcl­2. Furthermore, silencing p38MAPK promoted cell viability, cell cycle progression and the telomerase activity of hippocampal neurons, and inhibited the apoptosis of hippocampal neurons. Taken together, the results suggested an inhibitory role of lentivirus­mediated p38MAPK gene silencing on KTM­induced apoptosis of rat hippocampal neurons. Thus, p38MAPK gene silencing may serve as a potential target for preventing the KTM­induced apoptosis of hippocampal neurons.

Probabilistic Analysis of the (1+1)-Evolutionary Algorithm.

We give a detailed analysis of the optimization time of the [Formula: see text]-Evolutionary Algorithm under two simple fitness functions (OneMax and LeadingOnes). The problem has been approached in the evolutionary algorithm literature in various ways and with different degrees of rigor. Our asymptotic approximations for the mean and the variance represent the strongest of their kind. The approach we develop is based on an asymptotic resolution of the underlying recurrences and can also be extended to characterize the corresponding limiting distributions. While most of our approximations can be derived by simple heuristic calculations based on the idea of matched asymptotics, the rigorous justifications are challenging and require a delicate error analysis.