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Copyright of Progress in Modern Biomedicine is the property of Publishing House of Progress in Modern Biomedicine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Progress in Modern Biomedicine is the property of Publishing House of Progress in Modern Biomedicine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: To investigate the efficacy of somatostatin combined with bifidobacteria tetraploid live bacteria in the treatment of acute pancreatitis and its effect on the intestinal mucosal barrier function. Methods: 98 cases of patients with acute pancreatitis admitted to our hospital from August 2015 to August 2018 were selected and divided into two groups according to the order of admission. The control group was given somatostatin, and the observation group was treated with bifidobacteria tetraplactis tablets on the basis of control group. The clinical treatment effect, clinical symptoms and indicators recovery time, gastrointestinal motility recovery, changes of D-lactic acid, DAO and L/M levels before and after treatment were compared between the two groups. Results: After treatment, the total effective rate of observation group was significantly higher than that of the control group (P<0.05). Patients in the observation group had significantly shorter recovery time for abdominal pain, abdominal distension, peritoneal irritation, blood amylase and urine amylase than those in the control group (P<0.05). After treatment, the serum D-lactic acid, DAO and L/M levels in both groups were significantly lower than those before treatment, and the above indicators in the observation group were significantly lower than those in the control group (P<0.05). In addition, the intra-abdominal pressure of patients in the observation group was significantly lower than control group, the recovery time of intestinal rumble was significantly shorter than control group, and the drainage volume of gastrointestinal decompression was significantly lower than control group (P<0.05). Conclusion: Compared with somatostatin alone, Somatostatin combined with bifidobacterium tetrabalone can more quickly relieve the clinical symptoms and indicators of patients with acute pancreatitis, restore the gastrointestinal motility and significantly improve the intestinal mucosal barrier function, which is conducive to the recovery of patients. [ABSTRACT FROM AUTHOR]

In order to improve the prediction precision of network traffic, we propose a network traf­fic prediction model based on optimized BP neural networks with an improved multi-population quantum genetic algorithm. After the neural network structure is fixed, the multi-population quantum genetic algorithm is used to optimize the initial weights and thresholds of the BP neural network. The model di­vides a population into several sub-populations by using the K-means clustering algorithm, and main­tains the diversity of the population through respective evolution of several sub-populations. Information interaction among sub-populations through immigration operation decreases the possibility of falling into local optimum. An adaptive quantum rotation gate adjustment strategy is adopted to accelerate the con­vergence rate. Simulation results show that compared with conventional models, the proposed model is of faster convergence rate and higher prediction precision in network traffic prediction. [ABSTRACT FROM AUTHOR]