Cuproptosis and ferroptosis represent copper- and iron-dependent forms of cell death, respectively, and both are known to play pivotal roles in head and neck squamous cell carcinoma (HNSCC). However, few studies have explored the prognostic signatures related to cuproptosis and ferroptosis in HNSCC. Our objective was to construct a prognostic model based on genes associated with cuproptosis and ferroptosis. We randomly assigned 502 HSNCC samples from The Cancer Genome Atlas (TCGA) into training and testing sets. Pearson correlation analysis was utilized to identify cuproptosis-associated ferroptosis genes in the training set. Cox proportional hazards (COX) regression and least absolute shrinkage operator (LASSO) were employed to construct the prognostic model. The performance of the prognostic model was internally validated using single-factor COX regression, multifactor COX regression, Kaplan–Meier analysis, principal component analysis (PCA), and receiver operating curve (ROC) analysis. Additionally, we obtained 97 samples from the Gene Expression Omnibus (GEO) database for external validation. The constructed model, based on 12 cuproptosis-associated ferroptosis genes, proved to be an independent predictor of HNSCC prognosis. Among these genes, the increased expression of aurora kinase A (AURKA) has been implicated in various cancers. To further investigate, we employed small interfering RNAs (siRNAs) to knock down AURKA expression and conducted functional experiments. The results demonstrated that AURKA knockdown significantly inhibited the proliferation and migration of HNSCC cells (Cal27 and CNE2). Therefore, AURKA may serve as a potential biomarker in HNSCC.

The calcium channels are the main pathogenesis and therapeutic target for post-traumatic epilepsy (PTE). However, differentially expressed miRNAs (DEMs) and mRNAs associated with calcium channels in PTE and their interactions are poorly understood. We produced a PTE model in rats and conducted RNA-seq in PTE rats. Gene annotation was used to verify differentially expressed mRNAs related to calcium channels. RNAhybrid, PITA, and Miranda prediction were used to build the miRNA–mRNA pairs. Furthermore, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used for the functional enrichment analysis of DEMs. The quantification changes of mRNA and miRNA were verified by RT-qPCR. There were 431 identified differentially expressed genes (DEGs) in PTE rats compared with the sham group, of which five mRNAs and 7 miRNAs were related to calcium channels. The miRNA–mRNA network suggested a negative correlation between 11 pairs of miRNA–mRNA involved in the p53 signaling pathway, HIF-1 signaling pathway. RT-qPCR verified three upregulated mRNAs in PTE rats, associated with 7 DEMs negatively related to them, respectively. This study has revealed the changes in miRNA–mRNA pairs associated with calcium channels in PTE, which might contribute to the further interpretation of potential underlying molecular mechanisms of PTE and the discovery of promising diagnostics.

Artificial intelligence (AI)–assisted medical imaging technology is a new research area of great interest that has developed rapidly over the last decade. However, there has been no bibliometric analysis of published studies in this field. The present review focuses on AI-related studies on computed tomography imaging in the Web of Science database and uses CiteSpace and VOSviewer to generate a knowledge map and conduct the basic information analysis, co-word analysis, and co-citation analysis. A total of 7265 documents were included and the number of documents published had an overall upward trend. Scholars from the United States and China have made outstanding achievements, and there is a general lack of extensive cooperation in this field. In recent years, the research areas of great interest and difficulty have been the optimization and upgrading of algorithms, and the application of theoretical models to practical clinical applications. This review will help researchers understand the developments, research areas of great interest, and research frontiers in this field and provide reference and guidance for future studies.