Salvia Miltiorrhizae Bge, a popular Chinese herb has been widely adopted for use in Chinese hospitals for both the prevention and the active management of cardiovascular disease. there is no study on its cardioprotective effects against HF and more importantly, the underlying mechanisms of its beneficial effects by SDF-1/CXCR4 axis remain poorly understood. This study was performed to investigate the protective effects and possible mechanism of aqueous extract of Salvia miltiorrhiza (AESM) on HF rats. In the study, cardioprotective effects of AESM on HF rats was evaluated by herat function, myocardial pathology, myocardial cell proliferation, SDF-1, CXCR4 and Bcl-2 family mRNA expressions. In the experiment, we found that AESM exerted beneficially protective effects on the HF rats, mainly recoverying normal heart function, myocardial pathology and myocardial cell proliferation. The underlying mechanism of these protective effects of AESM appeared to involve improving the SDF-1/CXCR4 axis and Bcl-2 family expressions.
Saponins from Rhizoma Panacis Majoris (SRPM), the bioactive component in Rhizoma Panacis Majoris, were reported to possess protective effects on brain injury, but the underlying mechanisms remain poorly understood. This study was performed to investigate the protective effects and possible mechanism of SRPM on cerebral ischemia/reperfusion (CI/R) injury. Neuroprotective effects of SPRM in CI/R mice was evaluated by infarct size, biochemical values, Nuclear factor erythroid 2-related factor 2 (Nrf2) and Bcl-2 family expressions. In the study, we found that SRPM exerted beneficially protective effects on CI/R injury, mainly scavenging oxidative stress-triggered overgeneration and accumulation of reactive oxygen species, improving the Nrf2-mediated antioxidant response: role of Nrf2 and Bcl-2 family expressions, and alleviating CI/R injury and cerebral cell death.
Recently, the ant colony algorithm has been widely used in the field of path planning, which is a key technology required for ship operations in offshore wind farms to improve navigation efficiency and power generation. However, the ant colony algorithm has the defects of a long search time and stagnation in the early stage of the operation and maintenance path planning of offshore wind farms, and it easily falls into the problem of local optima; furthermore, the ant colony algorithm uses incremental construction to build a complete itinerary path, it takes a lot of time to search the path, and only the suboptimal solution is obtained. To address the above problems, in this paper, we propose a multi-agent-based operation and maintenance model for offshore wind farms. Specifically, the introduction of the heuristic factor can optimize the local optimal solution and make the ant colony algorithm clearer when searching for the target. Based on this, increasing the pheromone adjustment factor can eliminate the invalid path from searching and select a high-quality path. In addition, by integrating the genetic algorithm, it is possible to select, cross, and mutate to simulate the natural evolution process to search for the optimal solution, reduce the similarity of the paths constructed by the ant colony, reduce the probability of algorithm stagnation, improve the convergence speed, and improve the time efficiency and solution accuracy of the algorithm. Simulation experiments on a series of benchmark datasets show that the proposed GA-PACO algorithm achieves better performance in global search and path planning than the existing three algorithms.
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