Study on the university students' satisfaction of the wisdom tree massive open online course platform based on parameter optimization intelligent algorithm.

INTRODUCTION: Curriculum learning through the wisdom tree massive open online course platform not only gets rid of the limitations of specialty, school and region, eliminates the limitations of time and space in traditional teaching, but also effectively solves the problem of educational equity. OBJECTIVES: This paper proposes an intelligent algorithm combining decision tree, support vector machine, and simulated annealing to obtain the best classification accuracy and decision rules for university students' satisfaction with the wisdom tree massive open online course platform. METHODS: This study takes the university students in Fuzhou city information management department as the survey object, and adopts the electronic questionnaire survey method. A total of 1136 formal questionnaires were responded, and 1028 valid questionnaires were obtained after data cleaning and deleting invalid questionnaires (the effective rate was 90.49%). In this paper, the reliability and validity of the questionnaire were tested by IBM SPSS-20.0 software, and six explanatory variables including function, achievement, exercise, quality, richness, and interaction were obtained by principal component analysis. Then, the questionnaire data is converted to CSV (comma separated values) format for analysis. This paper proposes an intelligent algorithm combining decision tree, support vector machine, and simulated annealing to obtain the best classification accuracy and decision rules for university students' satisfaction with the wisdom tree massive open online course platform. In this paper, the proposed algorithm is compared with decision tree, random forest, k-nearest neighbor, and support vector machine to verify its performance. RESULTS: The experimental results show that training set classification accuracy of decision tree, random forest, k-nearest neighbor, only support vector machine and the proposed algorithm (simulated annealing + support vector machine) are 92.21%, 96.10%, 95.67%, 97.29%, and 99.58%, respectively. CONCLUSION: The proposed algorithm simulated annealing + support vector machine does increase the classification accuracy. At the same time, the 11 decision rules generated by simulated annealing + decision tree can provide useful information for decision makers.

Protective effect of Bifidobacterium infantis CGMCC313-2 on ovalbumin-induced airway asthma and ß-lactoglobulin-induced intestinal food allergy mouse models.

AIM: To determine whether oral administration of Bifidobacterium infantis CGMCC313-2 (B. infantis CGMCC313-2) inhibits allergen-induced airway inflammation and food allergies in a mouse model. METHODS: Ovalbumin (OVA)-induced allergic asthma and ß-lactoglobulin-induced food allergy mouse models were used in this study. Following oral administration of B. infantis CGMCC313-2 during or after allergen sensitization, histopathologic changes in the lung and intestine were evaluated by hematoxylin and eosin (HE) staining. In the allergic asthma mouse model, we evaluated the proportion of lung-infiltrating inflammatory cells. OVA-specific IgE and IgG1 levels in serum and cytokine levels in bronchoalveolar lavage fluid (BALF) were also assessed. In the food allergy mouse model, the levels of total IgE and cytokines in serum were measured. RESULTS: Oral administration of B. infantis CGMCC313-2 during or after allergen sensitization suppressed allergic inflammation in lung and intestinal tissues, while the proportion of infiltrating inflammatory cells was significantly decreased in the BALF of allergic asthma mice. Moreover, B. infantis CGMCC313-2 decreased the serum levels of total IgE in food allergy mice, and reductions in IgE and IgG1 were also observed in OVA-induced allergic asthma mice. The expression of interleukin-4 (IL-4) and IL-13 in both serum and BALF was suppressed following the administration of B. infantis CGMCC313-2, while an effect on serum IL-10 levels was not observed. CONCLUSION: B. infantis CGMCC313-2 inhibits the secretion of allergen-induced IgE, IL-4 and IL-13, and attenuates allergic inflammation.

Loop-mediated isothermal amplification of Neisseria gonorrhoeae porA pseudogene: a rapid and reliable method to detect gonorrhea.

BACKGROUND AND OBJECTIVE: Gonorrhea is a sexually transmitted disease caused by the bacterium Neisseria gonorrhoeae. Rapid detection is crucial for effective prevention and treatment. This study developed and tested a low-cost effective method for detecting N. gonorrhoeae, especially in developing countries. METHODS: DNA from a N. gonorrhoeae standard strain, as well as from 26 genital secretion samples of gonorrhea patients, were isolated and used for loop-mediated isothermal amplification (LAMP) assay, which was conducted using either an automatic real-time PCR analyzer or a water bath. The amplified porA pseudogene sequence was compared with the NCBI database and the LAMP results were compared with that of the traditional culture method for its sensitivity and specificity. RESULTS: LAMP was able to detect Neisseria DNA at a concentration as low as 1 pg/µL (1 × 103 CFU/mL cells). The LAMP assay results obtained using an automatic real-time PCR analyzer was similar to that of the water bath. Relative to traditional culture, the sensitivity and specificity of the LAMP assay were 94.7 and 85.7%, respectively. CONCLUSION: LAMP was sensitive and reliable for detecting the porA gene of N. gonorrhoeae. It could be used as a rapid, low cost, and effective method for detecting N. gonorrhoeae.