Background Abdominal aortic aneurysm (AAA), a disease with high mortality, is limited by the current diagnostic methods in the early screening. This study aimed to construct a diagnostic model for AAA by using a novel machine learning method, i.e., an ensemble of the random forest (RF) algorithm and an artificial neural network (ANN) (RF-ANN), to identify potential AAA-associated genetic biomarkers. Methods Through a search of the Gene Expression Omnibus (GEO) database, two large-sample gene expression datasets (GSE57691 and GSE47472) were identified and downloaded. The differentially expressed genes (DEGs) between the AAA and normal control samples were identified, followed by Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Then, RF-ANN was used to identify the key genes from the DEGs, and an AAA diagnostic model was established. Finally, the diagnostic performance of the model was assessed using the area under the receiver operating characteristic curve (AUC) with GSE47472 as a test dataset. Results Using GSE57691, we obtained 2486 DEGs, 52 biological process annotations, 17 cellular component annotations, 17 molecular function annotations, and 13 significantly enriched KEGG pathways. Out of these DEGs, we further identified 74 key candidate feature genes by using the RF machine learning algorithm. The weight of each key gene was calculated by the ANN with GSE57691 as a training dataset to construct an AAA diagnostic model. A transcription factor (TF) regulatory network of key genes was constructed. Finally, GSE47472 was used to validate the model. The AUC value was 0.786, indicating that the model had a highly satisfactory diagnostic performance. Conclusion Potential AAA-associated gene biomarkers were identified, and a diagnostic model of AAA was established. This study may provide a valuable reference for early clinical diagnosis and the search for therapeutic targets of AAA.

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Abstract. Nowadays, spoken English teaching in China on the improvement of students' oral ability does not meet the expectation. "Dumb English" is still prominent in English-language learners. This paper develops a research using questionnaire survey on students' spoken abilities and needs. An efficient curriculum setting is put forward by combining chunk theory with input and output strategies. It consists of accumulative input, collaborative dialogue and automatic output. As the desired outcome of the curriculum setting, the automaticity in the third part can facilitate the fluency of learners' spoken English.