Galectin plays an important role in host-parasite interactions. In this study, we identified a novel gene encoding galectin-10 (AcGal-10) from the cDNA library of Angiostrongylus cantonensis and characterized its biological role in the parasite. Sequence and phylogeny analysis showed that AcGal-10 is related to other galectin family members with the conserved loci (H(84)-D(86)-R(88)-V(96)-N(98)-W(105)-E(108)-R(110)). The mRNA level of AcGal-10 was expressed in reactive oxygen stress radicals. We have identified two proteins of A. cantonensis galectin-10 gene, one of which was reported (AcGAL10-W) and the others is AcGAL-10-M. In addition, recombinant AcGal-10 (rAcGal-10) was constructed into the pGEX-4T-1 plasmid, purified, and finally confirmed by SDS-PAGE and LC-MS. Hemagglutination assay showed that the minimum concentration of rAcGAL10-W and rAcGAL10-M required for the hemagglutination of BALB/c mice erythrocyte was 25 μg/mL, and the carbohydrate-binding ability showed no difference between rAcGAL10-W and rAcGAL10-M. The mRNA levels of AcGal-10 were indeed expressed higher after stimulation with H(2)O(2) and recombinant A. cantonensis galectin-10. A mutation of AcGal-10 was also found, but there was no significant difference compared with the wild type. Furthermore, we also confirmed that recombinant AcGal-10 plays a role in the activation of the microglia. In conclusion, the report here showed that AcGal-10 may be an important molecule related to infection of A. cantonensis.

Liu et al.: Effect Dezocine and Dexmedetomidine combination on Postoperative PainTo investigate the effect of dezocine injection combined with dexmedetomidine on pain and quality of life in patients with advanced hepatocellular carcinoma. 64 patients with advanced liver cancer undergone surgery in the Central Hospital of EnshiTujia and Miao Autonomous centre from January 2016 to January 2019 were selected and randomly assigned to the experimental group and the control group. The control group was anesthetized with remifentanil during the operation and the experimental group was given remifentanil along with a combination of dezocine combined with dexmedetomidine. After the anesthesia, the time of extubating and the recovery time of the 2 groups were monitored, the visual analogue scale was used to evaluate the degree of pain in both the groups of patients at 2, 6 and 24 h, respectively and the SF-36 dimensions of 7D before and after the surgery were scored for both the groups. The incidence of adverse reactions in both the groups of patients post-surgery were monitored and compared. The visual analogue scale scores of the experimental group were lower than those of the control group at 2, 6 and 24 h after surgery (p<0.05). The visual analogue scale scores at 6 and 24 h after surgery were statistically lower than those at 2 h after surgery. The extubation time, recovery time and orientation recovery time in the experimental group were all lower than those in the control group, the SF-36 dimension scores in both groups before the surgery were not significantly different. The scores of all dimensions in the experimental group were significantly higher than those in the control group post-surgery. The scores of physical function, mental function and life function significantly (p<0.05) increased after intervention in both the groups. The incidence of adverse reactions was lower in the experimental group compared to the control group (p<0.05). Dezocine injection combined with dexmedetomidine could significantly relieve postoperative pain in patients with advanced hepatocellular carcinoma, shorten the recovery time and improve the quality of life.

The exponential growth of digital media content has introduced new challenges in managing and classifying internet traffic. Digital media traffic is composed of various applications such as video, audio, social media, and search, and its data structure is complex, incorporating a vast array of features. The classification of traffic data is a crucial aspect of internet traffic management and network security, and it forms the basis for several scenarios, including content distribution, advertising recommendations, and data analysis. Traditional classification methods rely mainly on deep packet inspection and port-based techniques, which have become increasingly ineffective due to the rapid evolution of network traffic. To address this issue, this study proposes a machine learning-based traffic classification method aimed at enhancing the accuracy and efficiency of digital media traffic classification to meet the current needs of traffic management and network security. The paper also analyzes and evaluates the classification effect and prediction capability of various algorithms under different training set sizes to validate the feasibility and effectiveness of the proposed method. The result demonstrates that the neural network algorithm has superior classification and prediction capabilities compared to the decision tree and support vector machine algorithms. Furthermore, our proposed method achieves the highest accuracy of 96.88% with a large training sample of 40,000 data streams, proving its superiority in handling high-dimensional data and complex datasets. The research results are significant for the development of digital media traffic classification and prediction methods and are expected to be applied in practical scenarios.