Long Non-Coding RNA PCAT19 Suppresses Cell Proliferation and Angiogenesis in Coronary Artery Disease through Interaction with GCNT2.

LncRNAs involvement in heart disease, however, the effect of lncRNA prostate cancer-associated transcript 19 (PCAT19) in coronary artery disease (CAD) remains unclear. In the current study, we aimed to verify the role of PCAT19 in CAD. We first investigated the differentially expressed lncRNAs in different Genes Expression Omnibus (GEO) database. We then detected lncRNAs expression in healthy volunteers and acute myocardial infarction (AMI) patients by qRT­PCR. The correlation of PCAT19 and Glucosaminyl (N-Acetyl) Transferase 2 (GCNT2) was analyzed. Human coronary artery endothelial cells (HCAECs) was used to conduct cell hypoxia-reoxygenation (H/R) injury model to imitate AMI injury. CCK8, BrdU, tube formation assay were used to detect cell viability, proliferation, and angiogenesis. Immunofluorescence, western blotting were used to detect ki67, VEGFA, PCNA, CD31, and GCNT2 expression, respectively. We obtained six different lncRNAs from GEO database and identified PCAT19 high expression in AMI patients. PCAT19 was positive correlation to GCNT2. Further experiments presented that PCAT19 knockdown promoted cell viability, proliferation and angiogenesis, GCNT2 knockdown also promoted cell viability, proliferation, and angiogenesis. These results confirmed by the inhibition of Ki67 and VEGFA. Importantly, PCAT19 overexpression suppressed cell proliferation and angiogenesis, these results also confirmed by the inhibition of PCNA and CD31. However, the inhibitory effect of PCAT19 overexpression was reversed by GCNT2 knockdown. Our study indicated that PCAT19 plays an important role in the CAD disease, its effects was related to GCNT2. Our research provides a novel sight for the effect of PCAT19 on CAD.

Construction and validation of nomogram model for predicting the risk of ventricular arrhythmia after emergency PCI in patients with acute myocardial infarction.

OBJECTIVE: To make predictions about the risk of MVA (Malignant Ventricular Arrhythmia) after primary PCI (Percutaneous Coronary Intervention) in patients with AMI (Acute Myocardial Infarction) through constructing and validating the Nomogram model. METHODS: 311 AMI patients who suffered from emergency PCI in Hefei Second People's Hospital from January 2020 to May 2023 were selected as the training set; 253 patients suffering from the same symptom in Hefei First People's Hospital during the same period were selected as the validation set. Risk factors were further screened by means of multivariate logistic and stepwise regression. The nomogram model was constructed, and then validated by using C-index, ROC curve, decision curve and calibration curve. RESULTS: Multivariate logistic analysis revealed that urea, systolic pressure, hypertension, Killip class II-IV, as well as LVEF (Left Ventricular Ejection Fraction) were all unrelated hazards for MVA after emergency PCI for AMI (P<0.05); a risk prediction nomogram model was constructed. The C-index was calculated to evaluate the predictive ability of the model. Result showed that the index of the training and the validation set was 0.783 (95% CI: 0.726-0.84) and 0.717 (95% CI: 0.65-0.784) respectively, which suggested that the model discriminated well. Meanwhile, other tools including ROC curve, calibration curve and decision curve also proved that this nomogram plays an effective role in forecasting the risk for MVA after PCI in AMI patients. CONCLUSIONS: The study successfully built the nomogram model and made predictions for the development of MVA after PCI in AMI patients.

Association of systemic inflammatory response index with ST segment elevation myocardial infarction and degree of coronary stenosis: a cross-sectional study.

BACKGROUND: Systemic Inflammatory Response Index (SIRI), a composite inflammatory marker encompassing neutrophils, monocytes, and lymphocytes, has been recognized as a reliable marker of systemic inflammation. This article undertakes an analysis of clinical data from ST-segment Elevation Myocardial Infarction (STEMI) patients, aiming to comprehensively assess the relationship between SIRI, STEMI, and the degree of coronary stenosis. METHODS: The study involved 1809 patients diagnosed with STEMI between the years 2020 and 2023. Univariate and multivariate logistic regression analyses were conducted to evaluate the risk factors for STEMI. Receiver operating characteristic (ROC) curves were generated to determine the predictive power of SIRI and neutrophil-to-lymphocyte ratio (NLR). Spearman correlation analysis was performed to assess the correlation between SIRI, NLR, and the Gensini score (GS). RESULTS: Multivariate logistic regression analysis showed that the SIRI was the independent risk factor for STEMI (adjusted odds ratio (OR) in the highest quartile = 24.96, 95% confidence interval (CI) = 15.32-40.66, P < 0.001). In addition, there is a high correlation between SIRI and GS (ß:28.54, 95% CI: 24.63-32.46, P < 0.001). The ROC curve analysis was performed to evaluate the predictive ability of SIRI and NLR for STEMI patients. The area under the curve (AUC) for SIRI was 0.789. The AUC for NLR was 0.754. Regarding the prediction of STEMI in different gender groups, the AUC for SIRI in the male group was 0.771. The AUC for SIRI in the female group was 0.807. Spearman correlation analysis showed that SIRI exhibited a stronger correlation with GS, while NLR was lower (SIRI: r = 0.350, P < 0.001) (NLR: r = 0.313, P < 0.001). CONCLUSION: The study reveals a strong correlation between the SIRI and STEMI as well as the degree of coronary artery stenosis. In comparison to NLR, SIRI shows potential in predicting acute myocardial infarction and the severity of coronary artery stenosis. Additionally, SIRI exhibits a stronger predictive capability for female STEMI patients compared to males.

Development and Validation of a New Nomogram Model for Predicting Acute Ischemic Stroke in Elderly Patients with Non-Valvular Atrial Fibrillation: A Single-Center Cross-Sectional Study.

Background and Aims: Non-valvular atrial fibrillation (NVAF) patients face a 3-5 times greater risk of acute ischemic stroke (AIS) compared to those without NVAF. This study aims to establish a novel clinical prediction model for AIS in elderly patients with NVAF by incorporating relevant biomarker indicators. Methods: A total of 301 individuals diagnosed with NVAF were selected for this investigation at the Third Affiliated Hospital of Anhui Medical University. Based on the presence of AIS, patients were categorized into two groups: the Stroke Cohort and the Non-Stroke Cohort. Predictor screening was performed using the least absolute shrinkage and selection operation (LASSO) regression algorithm. The binary logistic regression equation was applied to fit the model, followed by internal validation using the bootstrap resampling method (1000 times). Receiver operating characteristic (ROC) curve, calibration degree curve plots, and clinical decision curve analysis (DCA) were generated, respectively. Finally, a nomogram was constructed to present the prediction model. Results: The final results of this study revealed that neutrophil-to-lymphocyte ratio (NLR), red cell distribution width (RDW), lipoprotein(a) (Lp(a)), systolic pressure, history of stroke, hyperlipidemia were independent risk factors for AIS in elderly patients with NVAF (P<0.05). And the high-density lipoprotein cholesterol (HDL-C) was an independent protective factor (P<0.05). By incorporating these indicators, a nomogram prediction model for predicting AIS in elderly patients with NVAF was constructed. Comparative analysis between the nomogram predictive model and CHA2DS2-VASc score revealed that the AUC of the nomogram predictive model surpassed that of the CHA2DS2-VASc score (AUC: 0.881vs 0.850). Conclusion: NLR, RDW, Lp(a), SP, history of stroke, hyperlipidemia, and HDL-C emerge as independent prognostic factors for acute ischemic stroke in elderly patients with non-valvular atrial fibrillation. The predictive utility of the nomogram model may potentially surpass that of the CHA2DS2-VASc scoring system.

Comparison of the Predicting Value of Neutrophil to high-Density Lipoprotein Cholesterol Ratio and Monocyte to high-Density Lipoprotein Cholesterol Ratio for in-Hospital Prognosis and Severe Coronary Artery Stenosis in Patients with ST-Segment Elevation Acute Myocardial Infarction Following Percutaneous Coronary Intervention: A Retrospective Study.

Background: Neutrophil to high-density lipoprotein cholesterol ratio (NHR) has demonstrated predictive value for coronary artery disease (CAD). However, few research has been conducted on the predictive capacity of NHR for Major Adverse Cardiovascular Events (MACE) following Percutaneous Coronary Intervention (PCI) or the degree of coronary artery stenosis in hospitalized ST-segment elevation myocardial infarction (STEMI) patients. Methods: The study involved 486 patients diagnosed with STEMI between the years 2020 and 2023. Univariate and multivariate logistic regression analyses were conducted to evaluate the risk factors for MACE after PCI and severe coronary artery stenosis during hospitalization. Receiver operating characteristic (ROC) curves were generated to determine predictive power of NHR and MHR. Spearman correlation analysis was performed to assess the correlation between NHR, MHR and the Gensini score (GS). Results: Multivariate logistic regression analysis showed that the NHR and MHR were the independent risk factor for MACE during hospitalization in STEMI patients (MHR: the odds ratio (OR)=2.347, 95% confidence interval (CI)=1.082-5.089, P=0.031) (NHR: OR=1.092, 95% CI=1.025-1.165, P=0.004). In addition, NHR was also an independent risk factor for high GS (NHR: OR=1.103, 95% CI=1.047-1.162, P<0.001), and the MHR was not an independent risk factor. The ROC curve analysis was performed to evaluate the predictive ability of NHR and MHR for in-hospital MACE in STEMI patients after primary PCI. The area under the curve (AUC) for NHR was 0.681. The AUC for MHR was 0.672. Regarding the prediction of high GS, the AUC for NHR was 0.649. The AUC for MHR was 0.587. Spearman correlation analysis showed that NHR exhibited stronger correlation with GS, while MHR was lower (NHR: r=0.291, P<0.001) (MHR: r=0.156, P<0.001). Conclusion: These findings highlight the potential clinical utility of NHR as a predictive indicator in STEMI patients after PCI during hospitalization, both for MACE events and the degree of coronary artery stenosis.

Predictive value of atherogenic index of plasma and atherogenic index of plasma combined with low-density lipoprotein cholesterol for the risk of acute myocardial infarction.

Background and aims: Acute myocardial infarction (AMI) is a prevalent medical condition associated with significant morbidity and mortality rates. The principal underlying factor leading to myocardial infarction is atherosclerosis, with dyslipidemia being a key risk factor. Nonetheless, relying solely on a single lipid level is insufficient for accurately predicting the onset and progression of AMI. The present investigation aims to assess established clinical indicators in China, to identify practical, precise, and effective tools for predicting AMI. Methods: The study enrolled 267 patients diagnosed with acute myocardial infarction as the experimental group, while the control group consisted of 73 hospitalized patients with normal coronary angiography. The investigators collected general clinical data and relevant laboratory test results and computed the Atherogenic Index of Plasma (AIP) for each participant. Using acute myocardial infarction status as the dependent variable and controlling for confounding factors such as smoking history, fasting plasma glucose (FPG), low-density lipoprotein cholesterol (LDL-C), blood pressure at admission, and diabetes history, the researchers conducted multivariate logistic regression analysis with AIP as an independent variable. Receiver operating characteristic (ROC) curves were employed to determine the predictive value of AIP and AIP combined with LDL-C for acute myocardial infarction. Result: The results of the multivariate logistic regression analysis indicated that the AIP was an independent predictor of acute myocardial infarction. The optimal cut-off value for AIP to predict AMI was -0.06142, with a sensitivity of 81.3%, a specificity of 65.8%, and an area under the curve (AUC) of 0.801 (95% confidence interval [CI]: 0.743-0.859, P < 0.001). When AIP was combined with LDL-C, the best cut-off value for predicting acute myocardial infarction was 0.756107, with a sensitivity of 79%, a specificity of 74%, and an AUC of 0.819 (95% CI: 0.759-0.879, P < 0.001). Conclusions: The AIP is considered an autonomous determinant of risk for AMI. Utilizing the AIP index alone, as well as in conjunction with LDL-C, can serve as effective predictors of AMI.