Background:The SYNTAX score for decision makings or outcome predictions in coronary artery disease does not account for the variations in the coronary anatomy, which is a clear fallacy for patients with less typical anatomy than suggested by the SYNTAX score. The current study aimed to derive a new coronary angiographic scoring system accommodating the variability in the coronary anatomy. Methods:The 17-myocardial segment model and laws of competitive blood supply and flow conservation were utilized to derive this new scoring system.Results: We obtained 6 types of RCA dominance, 3 types of diagonal size and 3 types of left anterior descending artery (LAD) length, which together resulted in a total of 54 patterns of coronary artery circulation to account for the variability in the coronary anatomy among individuals. A Coronary Artery Tree description and Lesion EvaluaTion (CatLet) angiographic scoring system has been designed based on the above-mentioned reclassification scheme (htpp://www.catletscore.com, IE browser is required to run this calculator). Conclusions: This new CatLet angiographic scoring system accommodated the variability in the coronary anatomy and standardized the collection of the coronary angiographic data, which could facilitate the comparison and exchange of these data between different catheter labs. Its utility for predicting the clinical outcomes and standardizing the angiographic data collection will be investigated in a series of clinical trials enrolling "all-comers" with coronary artery disease (CAD).

Background: Dilated cardiomyopathy (DCM) is currently the major cause of systolic heart failure. This study explored potential therapeutic targets and investigated the role of immune cell infiltration in DCM.Methods: Three DCM datasets (GSE3585, GSE9800, and GSE84796) from the Gene Expression Omnibus (GEO) database were merged into an integrated dataset, and batch effects were removed.Differentially expressed genes (DEGs) were screened and the associations between gene co-expression modules and clinical traits were assessed by weighted gene co-expression network analysis (WGCNA) in R software. Any DEGs from the integrated dataset overlapped with the significant module genes were defined as common genes (CGs). Enrichment analysis of the CGs was performed. The protein-protein interaction (PPI) network of the CGs was visualized and the hub gene was identified by using Cytoscape 3.8.2 software.The miRNA-transcription factor-mRNA (miRNA-TF-mRNA) network was constructed using Cytoscape to unveil the regulatory relationships in DCM. Finally, the CIBERSORT method (https://cibersort.stanford. edu/) was used to investigate immune cell infiltration in DCM.Results: A total of 53 DEGs were identified, and 5 gene co-expression modules were detected by WGCNA of the DCM and control group samples of cardiac tissue. Genes such as FRZB, ASPN, and PHLDA1were significantly upregulated, whereas IDH2 and ENDOG were significantly downregulated. Functional enrichment analysis showed that CGs were mainly enriched in the extracellular matrix (ECM) signaling pathway. ASPN was the hub gene in the PPI network. The miRNA-TF-mRNA network revealed that FRZB and ASPN were targeted by paired related homeobox 2 (Prrx2). We also found that miR-129-5p could regulate ASPN, PHLDA1, and IDH2 simultaneously. The immune infiltration analysis revealed higher levels of M1 macrophages in DCM samples than in the control samples. Conclusions:In conclusion, we speculate that miR-129-5p might target ASPN in regulating DCM via the ECM signaling pathway. Macrophage infiltration may be involved in ECM remodeling and eventually lead to DCM.

<b><i>Background:</i></b> We recently developed the Coronary Artery Tree description and Lesion EvaluaTion (CatLet) angiographic scoring system. Our preliminary study demonstrated that the CatLet score better predicted clinical outcomes than the SYNTAX score. The current study aimed at assessing whether 3 clinical variables (CVs) – age, serum creatinine, and left ventricular ejection fraction (LVEF) – improved the performance of the CatLet score in outcome predictions in patients with acute myocardial infarction (AMI). <b><i>Methods:</i></b> This study was a post hoc study of the CatLet score validation trial. Primary endpoint was major adverse cardiac or cerebrovascular events (MACCEs), and secondary endpoints were all-cause deaths and cardiac deaths. <b><i>Results:</i></b> Over 1,185 person-years (median [interquartile range], 4.3 [3.8–4.9] years), there were 64 MACCEs (20.8%), 56 all-cause deaths (18.2%), and 47 cardiac deaths (15.2%). The addition of the 3 CVs to the stand-alone CatLet score significantly increased the Harrell’s C-index by 0.0967 (<i>p</i> = 0.002) in MACCEs, by 0.1354 (<i>p</i> &#x3c; 0.001) in all-cause deaths, and by 0.1187 (<i>p</i> = 0.001) in cardiac deaths. When compared with the stand-alone CatLet score, improved discrimination and better calibration led to a significantly refined risk stratification, particularly at the intermediate-risk category. <b><i>Conclusions:</i></b> CatLet score had a predicting value for clinical outcome in AMI patients. This predicting value can be improved through a combination with age, serum creatinine, and LVEF (http://www.chictr.org.cn; unique identifier: ChiCTR-POC-17013536).

Background: Previously, we developed a novel C oronary A rtery T ree description and L esion E valua T ion (CatLet © ) angiographic scoring system, which was capable of accounting for the variability in the coronary anatomy and assisting in the risk-stratification of patients with acute myocardial infarction (AMI). Our preliminary study revealed that the CatLet score better predicted clinical outcomes for AMI patients than the Synergy between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery score. However, the reproducibility of the CatLet score in both inter- and intra-observer remains to be evaluated. Methods: A total of 30 consecutive AMI patients, admitted in September of 2015, were independently assessed by two experienced interventional cardiologists to evaluate the inter-observer reproducibility of the CatLet score. Another set of 49 consecutive AMI patients, admitted between September and October in 2014, were assessed by one of the two interventional cardiologists on two occasions 3 months apart to evaluate the intra-observer reproducibility of the CatLet score. The weighted kappa was used to express the degree of agreement. Results: The weighted kappa values (95% confidence interval) for the intra- and inter-observer reproducibility of the CatLet Score were 0.82 (0.59–1.00, Z = 7.23, P < 0.001) and 0.86 (0.54–1.00, Z = 5.20, P < 0.001), respectively, according to the tertile analysis (≤14, 15–22, >22). Regarding the adverse characteristics pertinent to lesions and dominance parameters, the kappa values for the inter-observer variability were 0.80 (0.56–1.00, Z = 6.47, P < 0.001) for total number of lesions, 0.57 (0.28–0.85, Z = 3.03, P < 0.001) for bifurcation, 0.69 (0.43–0.96, Z = 5.06, P < 0.001) for heavy calcification, 1.00 (0.72–1.00, Z = 6.93, P < 0.001) for tortuosity, 0.54 (0.26–0.82, Z = 3.78, P < 0.001) for thrombus, 0.69 (0.48–0.91, Z = 6.29, P < 0.001) for right coronary artery dominance, 0.69 (0.41–0.96, Z = 4.91, P < 0.001) for left anterior descending artery length, and 0.22 (0.06–0.51, Z = 1.56, P = 0.06) for diagonal size. Equivalent values for the intra-observer variability were moderate to almost perfect (range...

Background: Possible interaction between Lipoprotein (a) (Lp(a)) and body mass index (BMI) was investigated with regard to the risk of first incident acute myocardial infarction (AMI). Methods: Cross-sectional study of 1522 cases with initial AMI and 1691 controls without coronary artery disease (CAD) were retrospectively analyzed using logistic regression model. Subjects were categorized based on Lp(a) and BMI and compared with regard to occurrence of AMI by calculating odds ratios (ORs) with 95% confidence intervals (CIs). A potential interaction between Lp(a) and BMI was evaluated by the measures of effect modification on both additive (Relative excess risk due to interaction, RERI) and multiplicative scales. Results: Compared with reference group (BMI < 24 kg/m 2 and in the first quintile of Lp(a)), multivariable-adjusted analysis revealed that ORs(95%CI) of AMI were 2.27(1.46-3.52) for higher BMI alone; 1.79(1.11-2.90), 1.65(1.05-2.60), 1.96(1.20-3.20) and 2.34(1.47-3.71) for higher Lp(a) alone across its quintiles; and 2.86(1.85-4.40), 3.30(2.14-5.11), 4.43(2.76-7.09) and 5.98(3.72-9.60) for both higher BMI and higher Lp(a), greater than the sum of the both risks each. Prominent interaction was found between Lp(a) and BMI on additive scale (RERI = 2.45 (0.36-4.54) at the fifth quintile of Lp(a)) but not on multiplicative scale. Conclusions: This study demonstrates that BMI and Lp(a) levels are important factors affecting the risk of AMI. Significant interaction is found between Lp(a) and BMI in initial AMI on additive scale, indicating that Lp(a) confers greater risk for initial AMI when BMI is elevated. For those whose BMIs are inadequately controlled, Lp(a) lowering may be an option. Trial registration: This clinical study was not registered in a publicly available registry because this study was a retrospective study first started in 2015. Data are available via the correspondent.