In-stent restenosis (ISR) is a fatal complication of percutaneous coronary intervention (PCI). An early predictive model with the medical history of patients, angiographic characteristics, inflammatory indicators and blood biochemical index is urgently needed to predict ISR events. We aim to establish a risk prediction model for ISR in CAD patients undergoing PCI. Methods: A total of 477 CAD patients who underwent PCI with DES (drug-eluting stents) between January 2017 and December 2020 were retrospectively enrolled. And the preoperative factors were compared between the non-ISR and ISR groups. The least absolute shrinkage and selection operator (LASSO) and multi-factor logistic regression were used for statistical analysis. The prediction model was evaluated using receiver operator characteristic (ROC) analysis, the Hosmer-Lemeshow 2 statistic, and the calibration curve. Results: In this study, 94 patients developed ISR after PCI. Univariate analysis showed that post-PCI ISR was associated with the underlying disease (COPD), higher Gensini score (GS score), higher LDL-C, higher neutrophil/lymphocyte ratio, and higher remnant cholesterol (RC). The multi-factor logistic regression analysis suggested that remnant cholesterol (odds ratio [OR] = 2.09, 95% confidence interval , P < 0.001), GS score (OR = 1.01, 95% CI [1.00, 1.02], P = 0.002), medical history of COPD (OR = 4.56, 95% CI [1.98,10.40], P < 0.001), and monocyte (OR = 1.30, 95% CI [1.04, 1.70], P < 0.001) were independent risk factors for ISR. A nomogram was generated and displayed favorable fitting (Hosmer-Lemeshow test P = 0.609), discrimination (area under ROC curve was 0.847), and clinical usefulness by decision curve analysis. Conclusion: Patients with certain preoperative characteristics, such as a history of COPD, higher GS scores, higher levels of RC, and monocytes, who undergo PCI may have a higher risk of developing ISR. The predictive nomogram, based on the above predictors, can be used to help identify patients who are at a higher risk of ISR early on, with a view to provide post-PCI health management for patients.

Objective In-stent restenosis (ISR) is regarded as a critical limiting factor in stenting for coronary heart disease (CHD). Recent research has shown that fasting residual cholesterol (RC) has been shown to have a substantial impact on coronary heart disease. Unfortunately, there have not been much data to bear out the relationship between RC and ISR. Then, the predictive value of RC for in-stent restenosis in patients with coronary heart disease was analyzed. Patients and Methods Aiming to explore the relationship between RC and ISR, we designed a retrospective study of patients with CHD after drug-eluting stent (DES) implantation, combining the data from a public database and selecting the best-fitting model by comparing the optical subset with least absolute shrinkage and selection operator (LASSO) regression. Results Analysis of the abovementioned two models showed that the optical subset optimal subset model, which was based on RC, creatine, history of diabetes, smoking, multi-vessel lesions (2 vessels or more lesions), peripheral vascular lesions (PAD), and blood uric acid, had a better fit (AUC = 0.68), and that RC was an independent risk factor for ISR in the abovementioned two models. Notwithstanding its limitation, this study does suggest that RC has good predictive value for ISR. Conclusion Remnant cholesterol is an independent risk factor for in-stent restenosis after percutaneous coronary intervention (PCI) and is a reliable predictor of ISR.

Background: ARVC is a rare genetic-related disease characterized by fibrous fat replacement in the ventricular myocardium, caused by mutations in genes encoding for the desmosomal proteins, such as the desmoglein-2 gene (DSG2). It is reported in the literature that other genetic factors may play a role in disease penetrance. Herein, we report a Chinese proband with ARVC, which was probably caused by DSG2 p.Val149Ile mutation as genetic background when carrying heterozygous PRRT2 p.Arg217ProfsTer8 mutation. Case Presentation: A 17-year-old male with a history of paroxysmal kinesigenic dyskinesia (PKD) presented to the hospital for syncope induced by ventricular tachycardia. According to relevant clinical data and the diagnostic criteria of ARVC, a precise positive diagnosis of ARVC was finally made. Gene testing revealed that the patient carried a DSG2 heterozygous missense mutation (NM_001943: exon5: c.445G>A, p.Val149Ile) as well as frameshift mutation of PRRT2 (NM_001256442: exon2: p. Arg217Profs Ter8). Conclusion: This is the first time to report a Chinese proband with ARVC and a history of PKD carrying both DSG2 p. val149ile mutation and PRRT2 p. Arg217ProfsTer8 mutation, which can provide a new direction for gene screening of patients with ARVC and further supplements for its diagnostic criteria.

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Background: Ischemic cardiomyopathy (ICM) with high mobility and mortality is closely linked to immunology, oxidative stress, inflammatory response and so on. Early diagnosis counts for the effective treatment of ICM. However, there are still no distinctive diagnostic signatures. This research aims to investigate effective signatures and build the diagnostic model for ICM. Methods: The Gene Expression Omnibus was used to retrieve the microarray data of GSE9800 and GSE580, which were obtained from tissue biopsy samples. Differentially expressed genes (DEGs), GO, and KEGG analyses were then carried out on the microarray data. The PPI network was constructed via STRING database.Following that, CIBERSORT techniques in conjunction with the LM22 feature matrix were used to assess the immune infiltration of the samples.The expression of a few chosen genes served as the predictor variable, and the occurrence of ICM served as the responder variable, in the construction of the best subset stepwise regression model. Results: A total of 28 DEGs were found. And according to the GO and KEGG studies, numerous biological processes were enriched. Patients with ICM and their normal counterparts had considerably distinct immune cell types infiltrating. For the construction of the PPI network, the top 20 most significant DEGs were selected and were used to build the original regression model. The optimal subset screened using stepwise regression analysis contained three pivotal genes (SCD, SNX25, WNT7B) and the area under the curve (AUC) values in this model was 0.891. Conclusion:We identified several possible hub genes, including SCD, SNX25, and WNT7B, which may be strongly related to the development of ICM. Based on the three genes, the logistic regression model could be used to accurately diagnose ICM patients.