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Background: Atrial fibrillation (AF) is the most common cardiac arrhythmia with severe clinical sequelae, but its genetic characteristic implicated in pathogenesis has not been completely clarified. Accumulating evidence has indicated that circulating exosomes and their carried cargoes, such as long non-coding RNAs (lncRNAs), involve in the progress of multiple cardiovascular diseases. However, their potential role as clinical biomarkers in AF diagnosis and prognosis remains unknown. Methods: Herein, we conducted the sequence and bioinformatic analysis of circulating exosomes harvested from AF and sinus rhythm patients. Results: A total of 53 differentially expressed lncRNAs were identified, and a total of 6 significantly changed lncRNAs (fold change > 2.0), including NR0046235, NR003045, NONHSAT167247.1, NONHSAT202361.1, NONHSAT205820.1 and NONHSAT200958.1, were verified by qRT-PCR in 215 participants. Moreover, these circulating exosome lncRNA levels were different between paroxysmal and persistent AF patients, which were dramatically associated with abnormal hemodynamics and atrial diameter. Furthermore, we observed that the area under ROC curve (AUC) of six lncRNAs combination for diagnosis of persistent AF was 80.34%. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment pathway analysis indicated these exosome lncRNAs mainly concerning response to chemokine-chemokine receptor interaction, which induced activated inflammation and structural remodeling. In addition, increased plasma levels of CXCR3 ligands, including CXCL4, CXCL9, CXCL10 and CXCL11, were accumulated in AF patient tissues. Conclusion: Our study provides the transcriptome profile revealing pattern of circulating exosome lncRNAs in atrial structural remodeling, which bring valuable insights into improving prognosis and therapeutic targets for AF.
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Background and objectives: The hemodynamic evaluation of coronary stenoses undergoes a transition from wire-based invasive measurements to image-based computational assessments. However, fractional flow reserve (FFR) values derived from coronary CT angiography (CCTA) and angiography-based quantitative flow ratio have certain limitations in accuracy and efficiency, preventing their widespread use in routine practice. Hence, we aimed to investigate the diagnostic performance of FFR derived from the integration of CCTA and invasive angiography (FFRCT-angio) with artificial intelligence assistance in patients with stable coronary artery disease (CAD). Methods: Forty stable CAD patients with 67 target vessels (50%-90% diameter stenosis) were included in this single-center retrospective study. All patients underwent CCTA followed by coronary angiography with FFR measurement within 30 days. Both CCTA and angiographic images were combined to generate a three-dimensional reconstruction of the coronary arteries using artificial intelligence. Subsequently, functional assessment was performed through a deep learning algorithm. FFR was used as the reference. Results: FFRCT-angio values were significantly correlated with FFR values (r = 0.81, P < 0.001, Spearman analysis). Per-vessel diagnostic accuracy of FFRCT-angio was 92.54%. Sensitivity and specificity in identifying ischemic lesions were 100% and 88.10%, respectively. Positive predictive value and negative predictive value were 83.33% and 100%, respectively. Moreover, the diagnostic performance of FFRCT-angio was satisfactory in different target vessels and different segment lesions. Conclusions: FFRCT-angio exhibits excellent diagnostic performance of identifying ischemic lesions in patients with stable CAD. Combining CCTA and angiographic imaging, FFRCT-angio may represent an effective and practical alternative to invasive FFR in selected patients.
