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Aims: Coronary collateral circulation (CCC) is crucial during an acute ischemic attack. Evidences showed that omentin-1 exhibited remarkable antiatherogenic effects and ischemia-induced revascularization. The aim of this study was to investigate the relationship between plasma omentin-1 levels and CCC in patients with ≥ 90% angiography-proven coronary occlusion.Methods: 142 patients with ≥ 90% luminal diameter stenosis in at least one major epicardial coronary artery were recruited. Among them, 79 patients with Rentrop 0–1 grade were classified into the poor CCC group and 63 patients with Rentrop 2–3 grade were included into the good CCC group. The association between plasma omentin-1 levels and CCC status was assessed.Results: Plasma omentin-1 level was significantly higher in patients with good CCC than those with poor CCC (566.57 ± 26.90 vs. 492.38 ± 19.70 ng/mL, p = 0.024). Besides, omentin-1 was positively correlated with total cholesterol (TC), high-density lipoprotein, and gensini score but inversely with hyperlipidemia and body mass index (all p values < 0.05). Multivariate regression analysis indicated that omentin-1 [odds ratio (OR) = 1.002, 95% confidence interval (CI): 1.000 – 1.004, p = 0.041)], TC, the number of the diseased vessels, a higher frequency of left circumflex artery and right coronary artery, chronic total occlusion, and gensini score remained as the independent predictors of good CCC.Conclusion: Higher plasma omentin-1 level was associated with better CCC development. Our findings suggest that omentin-1 may be an alternative marker for adequate CCC in patients with ≥ 90% coronary occlusion.

Background:The main purpose of the study was to develop an early screening method for aortic dissection (AD) based on machine learning. Due to the rarity of AD and the complexity of symptoms, many doctors have no clinical experience with it. Many patients are not suspected of having AD, which lead to a high rate of misdiagnosis. Here, we report the preliminary study and feasibility of rapid and accurate screening method of AD with machine learning methods. Methods: The dataset analyzed was composed by examination data provided by the Xiangya Hospital Central South University of China which include a total of 60,000 samples, including aortic patients and non-aortic ones. Each sample has 76 features which is consist of routine examinations and other easily accessible information. Since the proportion of people who are affected is usually imbalanced compared to non-diseased people, multiple machine learning models were used, include AdaBoost, SmoteBagging, EasyEnsemble and CalibratedAdaMEC. They used different methods such as ensemble learning, undersampling, oversampling, and cost-sensitivity to solve data imbalance problems. Results: AdaBoost performed poorly with an average recall of 16.1% and a specificity of 99.8%.SmoteBagging achieved a statistically significant better performance for this problem with an average recall of 78.1% and a specificity of 79.2%. EasyEnsemble reached the values of 77.8% and 79.3% for recall and specificity respectively. CalibratedAdaMEC's recall and specificity are 75.8% and 76%.Conclusions: It was found that the screening performance of the models evaluated in this paper had a misdiagnosis rate lower than 25% except AdaBoost. The data used in these methods are only routine inspection data. This means that machine learning methods can help us build a fast, cheap, worthwhile and effective early screening approach for AD.

Background: Atrial fibrillation (AF) increases the risk of stroke and heart failure. Postoperative AF (POAF) increases the risk of mortality after cardiac surgery. This study aims to explore mechanisms underlying AF, analyze infiltration of immune cells in left atrium (LA) from patients with AF, and identify potential circular RNA (circRNA) biomarkers for POAF.Methods: Raw data of GSE797689, GSE115574, and GSE97455 were downloaded and processed. AF-related gene co-expression network was constructed using weighted gene correlation network analysis and enrichment analysis of genes in relevant module was conducted. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were applied to investigate pathways significantly enriched in AF group. Infiltration of immune cells was analyzed using single-sample GSEA. Differentially expressed genes (DEGs) between patients with or without AF were identified and competing endogenous RNA (ceRNA) networks of DEGs were constructed. To screen biomarkers for POAF, differentially expressed circRNAs (DEcircRNAs) between patients with or without POAF were identified. Intersection between DEcircRNAs and circRNAs in ceRNA networks of DEGs were extracted and circRNAs in the intersection were further screened using support vector machine, random forest, and neural network to identify biomarkers for POAF.Results: Three modules were found to be relevant with AF and enrichment analysis indicated that genes in these modules were enriched in synthesis of extracellular matrix and inflammatory response. The results of GSEA and GSVA suggested that inflammatory response-related pathways were significantly enriched in AF group. Immune cells like macrophages, mast cells, and neutrophils were significantly infiltrated in LA tissues from patients with AF. The expression levels of immune genes such as CHGB, HLA-DRA, LYZ, IGKV1-17 and TYROBP were significantly upregulated in patients with AF, which were correlated with infiltration of immune cells. ceRNA networks of DEGs were constructed and has_circ_0006314 and hsa_circ_0055387 were found to have potential predictive values for POAF.Conclusion: Synthesis of extracellular matrix and inflammatory response were main processes involved in development and progression of AF. Infiltration of immune cells was significantly different between patients with or without AF. Has_circ_0006314 and hsa_circ_0055387 were found to have potential predictive values for POAF.

Background: As a particularly dangerous and rare cardiovascular disease, aortic dissection (AD) is characterized by complex and diverse symptoms and signs. In the early stage, the rate of misdiagnosis and missed diagnosis is relatively high. This study aimed to use machine learning technology to establish a fast and accurate screening model that requires only patients' routine examination data as input to obtain predictive results.Methods: A retrospective analysis of the examination data and diagnosis results of 53,213 patients with cardiovascular disease was conducted. Among these samples, 802 samples had AD. Forty-two features were extracted from the patients' routine examination data to establish a prediction model. There were five ensemble learning models applied to explore the possibility of using machine learning methods to build screening models for AD, including AdaBoost, XGBoost, SmoteBagging, EasyEnsemble and XGBF. Among these, XGBF is an ensemble learning model that we propose to deal with the imbalance of the positive and negative samples. The seven-fold cross validation method was used to analyze and verify the performance of each model. Due to the imbalance of the samples, the evaluation indicators were sensitivity and specificity.Results: Comparative experiments showed that the sensitivity of XGBF was 80.5%, which was better than the 16.1% of AdaBoost, 15.7% of XGBoost, 78.0% of SmoteBagging and 77.8% of EasyEnsemble.Additionally, XGBF had relatively high specificity, and the training time consumption was short. Based on these three indicators, XGBF performed best, and met the application requirements, which means through careful design, we can use machine learning technology to achieve early AD screening.Conclusions: Through reasonable design, the ensemble learning method can be used to build an effective screening model. The XGBF has high practical application value for screening for AD.