Purpose/Significance To explore the characteristics and clinical significance of differentially expressed genes closely re-lated to HPV E6/E7 by using bioinformatics.Method/Process The cervical tissue and clinical information of cervical cancer in TCGA and GTEx of UCSC are used as the training set.The expression profile chip GSE63514 related to cervical cancer in GEO is used as the validation set.Firstly,the limma package of R software is used to screen DEGs of tumor and normal samples,and Venn map of genes re-lated to E6/E7 protein in MigDB is made.Survival analysis is performed by survival kit and verified by ROC and protein expression lev-els.Secondly,key genes are obtained by copy number variation and methylation correlation.Finally,the specific co-expression network is constructed and enrichment analysis and immune infiltration analysis are performed.Result/Conclusion There are 101 differentially expressed genes related to HPV E6/E7 protein,and three genes are found to have significance after screening,namely E2F1,MCM4 and PCNA.At the same time,it is found that the genes in the specific coexpression network are significantly enriched in the DNA replication and chromosome organization pathways.Immune correlation analysis shows that key genes are significantly associated with CD4 T cells,B cells and neutrophils.DNA replication,chromosome organization,etc.,are the molecular mechanisms and key genes significantly related to the development of cervical squamous cell carcinoma and HPV E6/E7 encoded proteins.

In order to better assist doctors in the diagnosis of dry eye and improve the ability of ophthalmologists to recognize the condition of meibomian gland, a meibomian gland image segmentation and enhancement method based on Mobile-U-Net network was proposed. Firstly, Mobile-Net is used as the coding part of U-Net for down sampling, and then features are extracted and fused with the features in decoder to guide image segmentation. Secondly, the segmentation of meibomian gland region is enhanced to assist doctors to judge the condition. Thirdly, a large number of meibomian gland images are collected to train and verify the semantic segmentation network, and the clarity evaluation index is used to verify the meibomian gland enhancement effect. The experimental results show that the similarity coefficient of the proposed method is stable at 92.71%, and the image clarity index is better than the similar dry eye detection instruments on the market.
Deep Learning ; Diagnostic Imaging ; Dry Eye Syndromes ; Humans ; Image Processing, Computer-Assisted ; Meibomian Glands/diagnostic imaging*