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Hepatocellular carcinoma (HCC) is a common malignant tumor of the digestive system, and its early asymptomatic characteristic increases the difficulty of diagnosis and treatment. This study is aimed at obtaining some novel biomarkers with diagnostic and prognostic meaning and may find out potential therapeutic targets for HCC. We screen differentially expressed genes (DEGs) from the HCC gene expression profile GSE14520 using GEO2R. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted by using the clusterProfiler software while a protein-protein interaction (PPI) network was performed based on the STRING database. Then, prognosis analysis of hub genes was conducted using The Cancer Genome Atlas (TCGA) database. Quantitative real-time polymerase chain reaction (qRT-PCR) was utilized to further verify the expression of hub genes and explore the correlation between gene expression and clinicopathological parameters. A total of 1053 DEGs were captured, containing 497 upregulated genes and 556 downregulated genes. GO and KEGG analysis indicated that the downregulated DEGs were mainly enriched in the fatty acid catabolic process while upregulated DEGs were primarily enriched in the cell cycle. Simultaneously, ten hub genes (CYP3A4, UGT1A6, AOX1, UGT1A4, UGT2B15, CDK1, CCNB1, MAD2L1, CCNB2, and CDC20) were identified by the PPI network. Five prognosis-related hub genes (CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20) were uncovered by the survival analysis based on TCGA database. The ten hub genes were further validated by qRT-PCR using samples obtained from our hospital. The prognosis-related hub genes such as CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20 could be considered potential diagnosis biomarkers and prognosis targets for HCC. We also use Oncomine for further verification, and we found CCNB1, CCNB2, CDK1, and CYP3A4 which were highly expressed in HCC. Meanwhile, CCNB1, CCNB2, and CDK1 are highly expressed in almost all cancer types, which may play an important role in cancer. Still, further functional study should be conducted to explore the underlying mechanism and biological effect in the near future. [ABSTRACT FROM AUTHOR]

Objective. To investigate the clinical study of intravitreal injection of anti-VEGF drugs combined with intravitreal injection of tretinoin for the treatment of Coats disease. Methods. The medical records of 90 patients (120 eyes) with Coats disease admitted to our hospital from April 2010 to June 2021 were selected as retrospective study subjects and divided into control and treatment groups according to the numerical table method. There were 45 cases and 60 eyes in each group. Among them, intravitreal tretinoin drug was injected into the control group, and anti-VEGF drug was injected into the vitreous sclera of the treatment group on the basis of the control group. Results. Before treatment, the differences in visual acuity changes between the two groups were not statistically significant (P > 0.05). After treatment, the light perception, manipulation, and indexes of both groups were significantly improved, and the treatment group was significantly better than the control group. This difference was statistically significant (P < 0.05) for statistical study comparison. The effective rate of 95.56% in the treatment group was significantly higher than that of 86.67% in the control group, and the complication rate was significantly lower than that of the control group, with statistically significant differences (P < 0.05). Conclusion. Intravitreal injection of anti-VEGF drugs combined with tretinoin injection can significantly improve the clinical efficacy of patients with Coats disease and improve visual acuity and central retinal thickness, which has certain reference value for the clinical treatment of Coats disease. [ABSTRACT FROM AUTHOR]

Blade icing problems are ubiquitous for wind turbines located in cold climate zones. Data-driven indirect icing detection methods based on supervisory control and data acquisition system have shown strong potential recently. However, the supervisory control and data acquisition data is annotated through manual observation, which will cause the data between normal condition and icing condition to be unlabeled. In addition, the amount of normal data is far more than icing data. The above two issues restrict the performance of most current data-driven models. In order to solve the label missing problem, this article proposes a Pearson correlation coefficient–based algorithm for measuring the degree of blade icing, which calculates the similarity between the unlabeled data and the icing data as its label. Aiming at the class-imbalance problem, this article constructs multiple class-balanced subsets from the original dataset by under-sampling the normal data. Temporal convolutional networks are trained to extract features and make predictions on each subset. The final prediction result is obtained by ensembling the prediction results of all temporal convolutional network models. The proposed model is validated using the actual supervisory control and data acquisition data collected from a wind farm in northern China, and the results indicate that ensuring the consecutiveness and class-balance of the data are quite advantageous for improving the detection accuracy. [ABSTRACT FROM AUTHOR]