A document by Ruxin ZHANG. Click on the document to view its contents.

Backgroud: In order to study how to reduce the edema of CRSwNP, we tried to co-cultured CRSwNP with glucocorticoid, predicted and verified possible mechanisms through gene sequencing and CRISPR-Case9 gene editing technology Method：We co-cultured CRSwNP with glucocorticoid , and selected normal CRSwNP as control group. Then analyzed the differentially expressed genes(DEGs), and the RPL26 gene was found through bioinformatics analysis. CRISPR-Case9 was used to establish the RPL26 gene silencing and overexpression cells system; Immunofluorescence staining and flow cytometry analysis were used to observe changes in cell morphology and apoptosis. Results: Pearson Correlation to analyze the correlation of the probe signal values of all specimens and found that the correlation between the two sets of experimental data was high, R value was close to 1. A total of 12336 genes were detected, and 5026 genes were differentially expressed. Then screened sequence length of ≤1000, 916 of DEGs were obtained, including 340 up-regulated genes and 576 down-regulated genes. Via bioinformatics analysis, the Ribosome pathway is most relevant, and the RPL26 plays an important role. The results of immunofluorescence staining and apoptosis experiments showed that high expression of RPL26 can effectively reduce the degree of edema of CRSwNP cells, and has little effect on apoptosis. Conclusion: Highly expressed RPL26 can effectively reduce the edema of CRSwNP cells, proving that RPL26 plays an important role in this mechanism, and this role of RPL26 may be highly correlated with the ribosome signaling pathway.

Background and purpose: Artificial intelligence is an important product of the rapid development of computer technology today. This study intends to propose an intelligent diagnosis and detection method for AR based on ensemble learning. Method: This study collectedAR cases and other 7 types of diseases with similar symptoms：Rhinosinusitis, Chronic rhinitis, upper respiratory tract infection etc.) and collected clinical data such as medical history, clinical symptoms, allergen detection and imaging. Multiple models are used to train the classifier for the same batch of data, and the final ensemble classifier is obtained by using the ensemble learning algorithm. 5 common machine learning classification algorithms were selected for comparative experiments, including Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), Multilayer Perceptron (MLP), Deep Forest (GCForest), eXtreme Gradient boosting (XGBoost). In order to evaluate the prediction results of AR samples, parameters such as Precision, Sensitivity, Specificity, G-Mean, F1-Score, and AUC under the ROC curve are jointly used as prediction evaluation indicators. Results: 7 classification models are used for comparison, covering probability model, tree model, linear model, ensemble model and neural network models, and the comprehensive classification evaluation index is lower than the ensemble classification algorithms ARF-OOBEE and GCForest. Compared with other algorithms, the accuracy of G-Mean and AUC parameters is improved nearly 2%, and it has good comprehensive classification characteristics for massive large data and unbalanced samples. Conclusion: The ensemble learning ARF-OOBEE model has good generalization performance and comprehensive classification ability to be used for diagnosis of AR.