ABSTRACT
Introduction: The emergence of multidrug-resistant Klebsiella pneumoniae (K. pneumoniae) and the decline of effective antibiotics lead to the urgent need for new antibacterial agents. The aim of this study is to investigate the therapeutic effect of antimicrobial peptides against gentamicin-resistant (RT) K. pneumoniae and to screen effective antimicrobial peptides. Methods: In this study, the RT strains were induced by gradient gentamicin, and the RT strains were selected by detecting the expression levels of efflux pump genes, porin genes, and biofilm formation genes of the strains combined with their effects on the cells. Then the effects of four antimicrobial peptides on the efflux pump activity, biofilm formation level and cell condition after infection were detected to explore the effects of antimicrobial peptides on RT strains. Finally, the RT strain was used to induce a mouse model of pneumonia, and the four antimicrobial peptides were used to treat pneumonia mice for in vivo experiments. The pathological changes in lung tissues in each group were detected to explore the antimicrobial peptide with the most significant effect on the RT strain in vivo. Results: The results showed that the minimal inhibitory concentrations of the RT strains (strain C and strain I) were significantly higher than those of the wild-type strain, and the expression of efflux pump, porin and biofilm formation genes was significantly increased. The antimicrobial peptides could effectively inhibit the biofilm formation and efflux pump protein function of the RT strains. In addition, the antimicrobial peptides showed promising antibacterial effects both in vitro and in vivo. Discussion: Our study provided a theoretical basis for the treatment of gentamicin resistant K. pneumoniae infection with antimicrobial peptides, and found that KLA was significantly superior to LL37, Magainin I, KLA and Dermaseptin (10 µg/mL in cells, 50 µg in mice).
ABSTRACT
The Internet of Vehicle (IoV) technology is one of the most important technologies of modern intelligent transportation. The data transmission scheduling method is a research hotspot in the technology of IoV. It is a challenge to ensure the stability of data transmission due to fast network topology changes, high data transmission delays, and some other reasons. Aiming at the above problems, a multi-channel data transmission cooperative scheduling algorithm is proposed. First, construct a feasible interference map based on the data items sent and received by vehicles in the road scene. Second, assign channels to the nodes in the interference map based on the Signal-to-Interference-Noise-Ratio (SINR). Finally, the optimal multi-channel data transmission cooperative scheduling scheme is achieved through the ISing model. Simulation results show that compared with the traditional algorithm, the network service capacity is increased by about 31% and the service delay is reduced by about 20%. It ensures that emergency data is preferentially transmitted to the target vehicle and the maximum weighted service capacity of the network.
