For coronary artery diseases, imaging diagnosis is usually used to guide the treatment. However, it can only reflect the geometric characteristics of the disease but does not determine the hemodynamically significant stenosis. This study was aimed to investigate the relationship between angiographic and functional severity of coronary artery stenosis and to improve the diagnostic value of imaging. 39 patients with 55 stenosis vessels were included in this study. The correlation between FFR and stenosis rate was analyzed with the medical statistical analysis method, and the influence of the position of stenosis and coronary dominant type on the correlation was discussed. By regression analysis, the stenosis rate of left anterior descending artery of right dominant type showed a significant correlation with FFR value (r≈0.79, P < 0.000 1) after grouping with position and the dominant type. Due to the significance of a value of the FFR < 0.80 in determining inducible ischemia, the diagnostic accuracy of myocardial ischemia by the stenosis rate increased from 70.9% to 82.8% after grouping. Sensitivity (from 72.2% to 78.6%) and specificity (from 70.3% to 86.7%) were also significantly improved. This study indicates that the position of stenosis and the coronary dominant type are significant influence factors on the correlation between FFR and stenosis rate. Consideration of these two factors in the diagnosis of myocardial ischemia by imaging will be helpful to improve the effectiveness of diagnosis.
Coronary microcirculation dysfunction (CMVD) is an important risk factor for the prognosis of re-perfused ischemic heart. Recent studies showed that the evaluation of CMVD has significant impact on both the early diagnosis of heart diseases relevant to blood supply and prognosis after myocardial reperfusion. In this review, the definition of CMVD from the perspective of pathophysiology was clarified, the principles and features of the state-of-the-art imaging technologies for CMVD assessment were reviewed from the perspective of engineering and the further research direction was promoted.
ObjectiveTo summarize and explore the individualized surgical treatment strategy and prognosis of anomalous aortic origin of coronary artery (AAOCA). MethodsThe clinical data of children with AAOCA admitted to Shanghai Children's Medical Center from March 2018 to August 2021 were retrospectively analyzed. ResultsA total of 17 children were enrolled, including 13 males and 4 females, with a median age of 88 (44, 138) months and a median weight of 25 (18, 29) kg. All patients received operations. The methods of coronary artery management included coronary artery decapitation in 9 patients, coronary artery transplantation in 5 patients and coronary artery perforation in 3 patients. One patient with severe cardiac insufficiency (left ventricular ejection fraction 15%) received mechanical circulatory assistance after the operation for 12 days. No death occurred in the early postoperative period, the average ICU stay time was 4.3±3.0 d, and the total hospital stay was 14.4±6.1 d. All the children received regular anticoagulation therapy for 3 months after discharge. The median follow-up time was 15 (13, 24) months. All patients received regular anticoagulation therapy for 3 months after discharge. No clinical symptoms such as chest pain and syncope occurred again. The cardiac function grade was significantly improved compared with that before operation. Imaging examination showed that the coronary artery blood flow on the operation side was unobstructed, and no restenosis occurred. ConclusionAAOCA is easy to induce myocardial ischemia and even sudden cardiac death. Once diagnosed, operation should be carried out as soon as possible. According to the anatomic characteristics of coronary artery, the early effect of individualized surgery is satisfactory, and the symptoms of the children are significantly improved and the cardiac function recovers well in the mid-term follow-up.
Objective To identify the N6-methyladenosine (m6A)-related characteristic genes analyzed by gene clustering and immune cell infiltration in myocardial ischemia-reperfusion injury (MI/RI) after cardiopulmonary bypass through machine learning. Methods The differential genes associated with m6A methylation were screened by the dataset GSE132176 in GEO, the samples of the dataset were clustered based on the differential gene expression profile, and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the differential genes of the m6A cluster after clustering were performed to determine the gene function of the m6A cluster. R software was used to determine the better models in machine learning of support vector machine (SVM) model and random forest (RF) model, which were used to screen m6A-related characteristic genes in MI/RI, and construct characteristic gene nomogram to predict the incidence of disease. R software was used to analyze the correlation between characteristic genes and immune cells, and the online website was used to build a characteristic gene regulatory network. Results In this dataset, a total of 5 m6A-related differential genes were screened, and the gene expression profiles were divided into two clusters for cluster analysis. The enrichment analysis of m6A clusters showed that these genes were mainly involved in regulating monocytes differentiation, response to lipopolysaccharides, response to bacteria-derived molecules, cellular response to decreased oxygen levels, DNA transcription factor binding, DNA-binding transcription activator activity, RNA polymerase Ⅱ specificity, NOD-like receptor signaling pathway, fluid shear stress and atherosclerosis, tumor necrosis factor signaling pathway, interleukin-17 signaling pathway. The RF model was determined by R software as the better model, which determined that METTL3, YTHDF1, RBM15B and METTL14 were characteristic genes of MI/RI, and mast cells, type 1 helper lymphocytes (Th1), type 17 helper lymphocytes (Th17), and macrophages were found to be associated with MI/RI after cardiopulmonary bypass in immune cell infiltration. Conclusion The four characteristic genes METTL3, YTHDF1, RBM15B and METTL14 are obtained by machine learning, while cluster analysis and immune cell infiltration analysis can better reveal the pathophysiological process of MI/RI.