Circular RNAs (circRNAs), a class of endogenous RNA molecules, are produced by alternative splicing of precursor RNA and are covalently linked at the 5′ and 3′ ends. Recent studies have revealed that dysregulated circRNAs are closely related to the occurrence and progression of gastrointestinal malignancies. Accumulating evidence indicates that circRNAs, including circPVT1, circLARP4, circ-SFMBT2, cir-ITCH, circRNA_100782, circ_100395, circ-DONSON, hsa_circ_0001368, circNRIP1, circFAT1(e2), circCCDC66, circSMARCA5, circ-ZNF652, and circ_0030235 play important roles in the proliferation, differentiation, invasion, and metastasis of cancer cells through a variety of mechanisms, such as acting as microRNA sponges, interacting with RNA-binding proteins, regulating gene transcription and alternative splicing, and being translated into proteins. With the characteristics of high abundance, high stability, extensive functions, and certain tissue-, time-and diseasespecific expressions, circRNAs are expected to provide novel perspectives for the diagnoses and treatments of gastrointestinal malignancies.

The coronary atherosclerotic heart disease is a common cardiovascular disease with high morbidity, disability, and societal burden. Early, precise, and comprehensive diagnosis of the coronary atherosclerotic heart disease is of great significance. The rise of artificial intelligence technologies, represented by machine learning and deep learning, provides new methods to address the above issues. In recent years, artificial intelligence has achieved an extraordinary progress in multiple aspects of coronary atherosclerotic heart disease diagnosis, including the construction of intelligent diagnostic models based on artificial intelligence algorithms, applications of artificial intelligence algorithms in coronary angiography, coronary CT angiography, intravascular imaging, cardiac magnetic resonance, and functional parameters. This paper presents a comprehensive review of the technical background and current state of research on the application of artificial intelligence in the diagnosis of the coronary atherosclerotic heart disease and analyzes recent challenges and perspectives in this field.

Helicobacter pylori (H. pylori) is a gram-negative microaerobic bacterium that is a common pathogen widely distributed around the world, especially in developing countries. 1 According to statistics from recent years, the H. pylori infection rate in developing countries exceeds 50%. 2 In West Asia, Africa, and South America, the H. pylori infection rate has been reported to be as high as 66.6%, 70.1%, and 69.4%, respectively. 3 H. pylori infection causes persistent damage to human gastric mucosa, leading to a variety of digestive

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Background Circular RNAs (circRNAs) are thought to be vital participants in carcinogenesis and have the characteristics of being stable, specific, and well conserved. However, their clinical significance and application value in gastric cancer (GC) are still poorly understood. Hsa_circ_0086720 was found to be a dysregulated circRNA in GC by microarray screening and was further explored for its clinical significance and application. Methods Hsa_circ_0086720 was detected in GC cell lines, tissues, and plasma, and the clinicopathological correlations were investigated. The existence, stability, origin, and change in the plasma hsa_circ_0086720 level were verified in early GC patients. Moreover, receiver operating characteristic and Kaplan–Meier survival curves were constructed to analyze the diagnostic and prognostic values, and bioinformatics analysis was used to identify the potential functions. Finally, risk factors and nomogram predicting were established. Results Hsa_circ_0086720 was found to be downregulated in gastric carcinogenesis, and tissue hsa_circ_0086720 was negatively associated with perineural invasion, Borrmann type, disease‐free survival, and overall survival. Hsa_circ_0086720 was stable in circulating plasma and was actively secreted by cells in gastric carcinogenesis. As a biomarker for early GC screening, plasma hsa_circ_0086720 had good sensitivity and specificity, and its stability met the clinical application requirements. Bioinformatics analysis suggested that dysregulated hsa_circ_0086720 has important functions in gastric carcinogenesis. Univariate Cox regression analysis identified factors associated with overall survival time and disease‐free survival time. The nomograms showed good accuracy of predicting survival time. Conclusion Hsa_circ_0086720 is a novel biomarker for screening early GC and predicting the prognosis of advanced‐stage patients.