ABSTRACT
The ongoing pandemic of coronavirus disease 2019 (COVID-19) has a huge influence on global public health and the economy. Lung cancer is one of the high-risk factors of COVID-19, but the molecular mechanism of lung cancer and COVID-19 is still unclear, and further research is needed. Therefore, we used the transcriptome information of the public database and adopted bioinformatics methods to identify the common pathways and molecular biomarkers of lung cancer and COVID-19 to further understand the connection between them. The two RNA-seq data sets in this study-GSE147507 (COVID-19) and GSE33532 (lung cancer)-were both derived from the Gene Expression Omnibus (GEO) database and identified differentially expressed genes (DEGs) for lung cancer and COVID-19 patients. We conducted Gene Ontology (GO) functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis and found some common features between lung cancer and COVID-19. We also performed TFs-gene, miRNAs-gene, and gene-drug analyses. In total, 32 DEGs were found. A protein-protein interaction (PPI) network was constructed by DEGs, and 10 hub genes were screened. Finally, the identified drugs may be helpful for COVID-19 treatment.
ABSTRACT
OBJECTIVE: The aim of this study was to explore the role of the AI system which was designed and developed based on the characteristics of COVID-19 CT images in the screening and evaluation of COVID-19. METHODS: The research team adopted an improved U-shaped neural network to segment lungs and pneumonia lesions in CT images through multilayer convolution iterations. Then the appropriate 159 cases were selected to establish and train the model, and Dice loss function and Adam optimizer were used for network training with the initial learning rate of 0.001. Finally, 39 cases (29 positive and 10 negative) were selected for the comparative test. Experimental group: an attending physician a and an associate chief physician a read the CT images to diagnose COVID-19 with the help of the AI system. Control group: an attending physician b and an associate chief physician b did the diagnosis only by their experience, without the help of the AI system. The time spent by each doctor in the diagnosis and their diagnostic results were recorded. Paired t-test, univariate ANOVA, chi-squared test, receiver operating characteristic curves, and logistic regression analysis were used for the statistical analysis. RESULTS: There was statistical significance in the time spent in the diagnosis of different groups (P<0.05). For the group with the optimal diagnostic results, univariate and multivariate analyses both suggested no significant correlation for all variables, and thus it might be the assistance of the AI system, the epidemiological history and other factors that played an important role. CONCLUSION: The AI system developed by us, which was created due to COVID-19, had certain clinical practicability and was worth popularizing.