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Objective To investigate the impact of Atractylodin on lung tissue damage in young asthmatic rats by regulating the CXC chemokine ligand 12 (CXCL12)/CXC chemokine receptor 4 (CXCR4) signaling pathway. Methods Twelve young SD rats were randomly selected from 60 rats as the control group (CON group), while the remaining 48 rats were used to construct asthma models using ovalbumin (OVA). Successfully modeled asthma rats were randomly separated into Model group, Atractylodin group (50 mg/kg Atractylodin), and CXCL-12 group (5 µg/kg recombinant CXCL-12 protein) and Atractylodin+CXCL-12 group (50 mg/kg Atractylodin+5 µg/kg recombinant CXCL-12 protein), with 12 in each group, continuously administered for 14 days. The CON and Model groups were given equal amounts of physiological saline. The percentages of neutrophils and eosinophils in bronchoalveolar lavage fluid (BALF) were detected. ELISA method was applied to detect cytokine levels in serum and BALF fluid; HE staining was applied to detect pathological changes in lung tissue; Western blot was applied to detect the levels of CXCL12/CXCR4 pathway related proteins. Results Compared with the CON group, the pathological score of lung tissue, percentage of neutrophils, percentage of eosinophils, the levels of IL-17, IL-4, IL-5, IL-13, IgE, OVA sIgE, and the protein levels of CXCL12 and CXCR4 in Model group were obviously increased (P < 0.05); compared with the Model group, the pathological score of lung tissue, percentage of neutrophils, percentage of eosinophils, the levels of IL-17, IL-4, IL-5, IL-13, IgE, OVA sIgE, and the protein levels of CXCL12 and CXCR4 in the Atractylodin group were obviously reduced (P < 0.05), the results in the CXCL-12 group were opposite to those in the Atractylodes group; CXCL-12 eliminated the improvement effect of atractylodes on asthma rats. Conclusion Atractylodin may improve lung tissue damage in asthmatic rats by down-regulating the CXCL12/CXCR4 signaling pathway. [ABSTRACT FROM AUTHOR]

In modern intelligent algorithms and real-industrial applications, there are many fields involving multi-objective particle swarm optimization algorithms, but the conflict between each objective in the optimization process will easily lead to the algorithm falling into local optimal. In order to prevent the algorithm from quickly falling into local optimization and improve the robustness of the algorithm, a multi-objective particle swarm optimization algorithm based on grid distance (GDMOPSO) was proposed, which has to improve the diversity of the algorithm and the search ability. Based on the MOPSO algorithm, a new external archive control strategy was established by using the grid technology and Pareto-dominant ordering principle, and the learning samples were improved. The proposed GDMOPSO is compared with a group of benchmark function tests and four classical algorithms. The results of experiment show that our proposed algorithm can effectively avoid premature convergence in terms of generational distance and hyper-volume (HV) indicator compared with other four classical MOPSO algorithms. [ABSTRACT FROM AUTHOR]