PurposeTo propose a dynamic feature-enhanced TabTransformer framework that providing a more effective and accurate tool for predicting Contrast-Induced Nephropathy (CIN).MethodsThis study proposes CIN-RiskNet, a dynamic feature-enhanced TabTransformer model integrated with a hybrid SMOTE-Noise augmentation strategy. The approach includes adaptive feature gating to suppress noise, synthetic minority oversampling to address class imbalance, and multi-head self-attention to capture complex feature interactions. The model was trained and evaluated under a leakage-free stratified five-fold cross-validation protocol, where SMOTE and Gaussian noise were applied only to the training split within each fold trained on a clinical dataset from Tianjin University Chest Hospital that including a total of 1,679 patients who underwent percutaneous coronary intervention for coronary heart disease.ResultsUnder leakage-free five-fold evaluation, CIN-RiskNet achieved strong performance with an accuracy of 95.40%, a recall of 95.40%, and an F1-score of 95.42%. It attained the highest F1-score and recall among all evaluated configurations. It outperformed not only traditional machine learning models including XGBoost, Random Forest, and Support Vector Machine, but also the Mehran risk score, a widely used clinical scoring system for CIN prediction. Ablation studies confirmed the contributions of each module, demonstrating improved robustness and generalization.ConclusionThe proposed model effectively addresses key challenges in CIN prediction, including class imbalance and feature noise, through an integrated deep learning framework. It shows promising potential as a decision-support tool, while external multicenter validation remains necessary before broad clinical deployment.

To evaluate the potential of computed tomography (CT) radiomics, based on high-resolution large matrix target reconstruction images, in predicting the invasiveness of lung adenocarcinoma in pure ground-glass nodules (pGGNs) with a diameter ≤ 1.5 cm. The clinical and imaging data of 297 patients with pGGNs, confirmed by pathology, were collected between March 2021 and June 2024. Pathological diagnoses included atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC). The patients were divided into non-invasive (AAH and AIS) and invasive (MIA and IAC) groups based on pathology. Radiomics features were extracted using ITK-SNAP software, and a predictive model was built using Python 3.9.7, with feature selection based on least absolute shrinkage and selection operator regression. Receiver operating characteristic analysis, area under the curve (AUC), sensitivity, specificity and clinical decision curve analysis were used to assess model performance. Multivariate logistic regression revealed that the maximum lesion diameter, median CT value and solid component ratio were significant predictors of invasiveness (P < 0.05). The CT radiomics model achieved AUC values of 0.861 (95

BackgroundSubclinical pulmonary congestion may occur after ST-segment elevation myocardial infarction (STEMI) before overt heart failure (HF) develops. However, its clinical significance remains unclear. This study aimed to investigate the clinical correlates and in-hospital prognostic significance of computed tomography (CT)-detected subclinical pulmonary congestion in acute STEMI patients without overt HF.MethodsWe retrospectively enrolled 276 consecutive patients with acute STEMI classified as Killip class I who underwent chest CT at admission. Quantitative CT-derived mean lung density was used to assess subclinical pulmonary congestion. Patients were stratified into subgroups using K-means clustering based on mean lung density. A logistic regression model was constructed to discriminate the two subgroups, and the importance of clinical features was ranked accordingly. For prognostic analysis, univariable and multivariable logistic regression analyses were performed to evaluate the independent association between mean lung density and in-hospital major adverse cardiovascular events (MACEs).ResultsTwo distinct subgroups were identified. The median (IQR) mean lung density was −806 [−826 to −785] HU in subgroup 1 and −727 [−747 to −704] HU in subgroup 2 (P < 0.001). Compared with subgroup 1, patients in the higher mean lung density phenotype (subgroup 2) exhibited significantly higher levels of cardiac enzymes, increased neutrophil and monocyte counts, and worse left ventricular function. Feature importance analysis identified markers of myocardial injury as the strongest correlates of increased mean lung density, followed by inflammatory cell counts and cardiac functional parameters. During hospitalization, 56 patients (20.3%) experienced MACEs, with a higher incidence in subgroup 2 than in subgroup 1 (30.4% vs. 14.4%). Increased mean lung density was associated with in-hospital MACEs in univariable analysis (OR per 1-SD increment, 1.92; 95% CI, 1.41–2.62; P < 0.001) and remained independently associated after adjustment for CK, LVEF, and neutrophil count (adjusted OR, 1.75; 95% CI, 1.24–2.47; P = 0.002).ConclusionThe higher mean lung density phenotype on CT, suggestive of subclinical pulmonary congestion, is associated with greater myocardial injury, heightened inflammatory activation, and impaired left ventricular function in patients with acute STEMI without overt HF. Multivariable analysis showed an independent association between increased mean lung density and in-hospital MACEs, supporting its potential role in early risk stratification among Killip class I patients.

BACKGROUND:We aimed to develop a novel cardiac magnetic resonance (CMR)-based method for quantifying myocardial synchrony and evaluate its diagnostic value in detecting myocardial dysfunction of coronary artery disease (CAD). METHODS:Consecutive participants with anatomically/angiographically obstructive CAD (n = 112) and healthy participants (n = 87) undergoing CMR imaging were prospectively enrolled. Myocardial strain was analyzed using feature-tracking, and myocardial synchrony was quantified via Pearson correlation coefficients of segmental strain time series across the cardiac cycle. Machine learning models (strain-only, synchrony-only, combined) were developed and validated in an independent external cohort. RESULTS:Healthy participants exhibited high left ventricular myocardial synchrony (radial: 0.91 [IQR: 0.88, 0.93]; circumferential: 0.90 ± 0.04; longitudinal: 0.97 ± 0.02), significantly reduced in participants with CAD (radial: 0.84 [IQR: 0.75, 0.89]; circumferential: 0.81 ± 0.12; longitudinal: 0.90 ± 0.08), including those with preserved left ventricular ejection fraction (LVEF ≥50%) (radial: 0.86 [IQR: 0.82, 0.90]; circumferential: 0.86 ± 0.07; longitudinal: 0.91 ± 0.07), all p < 0.001. In model analysis, the combined model significantly outperformed individual models (AUC: 0.94 [95% CI: 0.89-1.00] vs. 0.84 [0.75-0.94] for strain model, p = 0.037; vs. 0.79 [0.68-0.90] for synchrony model, p = 0.001). Superiority persisted in CAD with preserved LVEF (AUC: 0.91 [95% CI: 0.83-1.00]) and external validation (AUC: 0.93 [95% CI: 0.84-1.00]). CONCLUSIONS:This CMR-derived approach demonstrated the high degree of left ventricular synchrony in healthy populations and significant dyssynchrony in CAD, even in those with preserved LVEF. Integrating myocardial synchrony with strain significantly enhanced CAD myocardial dysfunction detection relative to strain alone, with robust diagnostic performance maintained in CAD with preserved LVEF.

Background:Coronary heart disease (CHD) remains a major global cause of morbidity and mortality. Identifying reliable and non-invasive biochemical markers that reflect CHD severity may improve early diagnosis and risk stratification. This study investigated the associations of homocysteine (Hcy), high-density lipoprotein cholesterol (HDL-C), and uric acid (UA) with CHD severity, and evaluated the diagnostic value of their combined detection. Methods:A total of 100 participants undergoing coronary angiography were enrolled, including 58 patients with CHD and 42 controls. Serum Hcy, HDL-C, and UA were measured, and coronary stenosis burden was quantified using the Gensini score. Spearman correlation and multivariable linear regression analyses were performed to identify independent predictors of Gensini score. Receiver operating characteristic (ROC) curves and reclassification indices-including net reclassification improvement (NRI), integrated discrimination improvement (IDI), and ΔAUC-were used to assess diagnostic performance. Best subset regression evaluated biomarker selection. Results:Hcy, UA, and HDL-C were significantly altered in CHD patients compared with controls (all p < 0.001). Gensini scores were positively correlated with Hcy (r = 0.314), UA (r = 0.307), Scr (r = 0.411), NT-proBNP (r = 0.294), and fasting glucose (r = 0.252), but negatively correlated with HDL-C (r = -0.324; all p < 0.01). In multivariable models, adding Hcy, UA, and HDL-C to clinical predictors increased the explained variance of the Gensini score from 31% to 48% (Model 2 R2 = 0.48, p < 0.001). Individually, Hcy showed the highest diagnostic value (AUC = 0.794), followed by HDL-C (AUC = 0.767) and UA (AUC = 0.749). The combined biomarker model achieved the highest discriminative performance (AUC = 0.803; sensitivity 69.8%, specificity 74.2%). Reclassification metrics demonstrated modest improvement with the combined model (NRI = 0.24, IDI = 0.065, ΔAUC = 0.084; all p < 0.01). Best subset regression and BIC criteria consistently supported Hcy, UA, and HDL-C as optimal predictors. Conclusion:Serum Hcy, UA, and HDL-C are significantly associated with both the presence and severity of CHD. Their combined detection provides a modest improvement in diagnostic accuracy and improves risk stratification beyond traditional clinical variables. These biomarkers may aid early assessment of coronary stenosis burden.

Background Contrast-Induced Nephropathy (CIN) is a serious complication following the use of contrast media in cardiovascular interventions, with no effective treatment available. Early prediction is crucial for prevention, but existing models often struggle with class imbalance, feature redundancy, and noise in clinical data. Methods This study proposes CIN-RiskNet, a dynamic feature-enhanced TabTransformer model integrated with a hybrid SMOTE-Noise augmentation strategy. The approach includes adaptive feature gating to suppress noise, synthetic minority oversampling to address class imbalance, and multi-head self-attention to capture complex feature interactions. The model was trained and evaluated using five-fold cross-validation on a clinical dataset from Tianjin University Chest Hospital. Results CIN-RiskNet achieved state-of-the-art performance with an accuracy of 99.0%, recall of 99.0%, and an F1-score of 99.0%, outperforming traditional machine learning models such as XGBoost, Random Forest, and support vector machine. Ablation studies confirmed the contributions of each module, demonstrating improved robustness and generalization. Conclusions The proposed model effectively addresses key challenges in CIN prediction, including class imbalance and feature noise, through an integrated deep learning framework. It shows strong potential for clinical application, though further validation on multi-center datasets is recommended to enhance generalizability.

Background:Quantitative assessment of macrophage accumulation is appealing in evaluating plaque inflammation. In optical coherence tomography (OCT) imaging, local macrophage clusters may be a feasible marker for macrophage quantification. Methods:404 patients presenting with acute coronary syndrome who underwent OCT evaluation were included. This study aims to assess the relationships between systemic inflammatory biomarkers [including monocytes, high-density lipoprotein cholesterol (HDL-C), and monocyte-to-HDL ratio (MHR)], plaque characteristics, and local macrophage clusters in coronary plaque. Results:Macrophage clusters were present in 218 patients, with a median arc value of 72° (50°-163°). Patients with macrophage clusters showed markedly higher levels of inflammatory biomarkers and plaque vulnerability. Multivariate logistic regression analysis demonstrated that MHR, lipid index, and microchannel were independently associated with the presence of macrophage clusters. The DeLong test showed the area under the curve of the above three combined indicators was significantly larger than that of single indicators (0.774 vs. 0.692, 0.665, 0.624, respectively, p < 0.001). The macrophage cluster arc correlated positively with MHR and lipid index (r = 0.219, p = 0.001; and r = 0.229, p = 0.001, respectively). More superficial macrophage infiltration, thin cap fibroatheromas, plaque rupture, and thinner fibrous cap thickness were observed in the large macrophage cluster group (>72°) compared to the small macrophage cluster group (50°-72°). The macrophage cluster arc in the low MHR + lipid index group was significantly lower than that in the high MHR + lipid index group (68° ± 17° vs. 84° ± 26°, p = 0.001). Multiple linear regression analysis demonstrated that MHR, age, and lipid index were independently associated with macrophage cluster arc. In subgroup analysis stratified by clinical presentation and high-sensitivity C-reactive protein level, higher MHR and lipid index levels were observed in large macrophage clusters than in the non-macrophage cluster group, irrespective of the inflammation background. Conclusions:The macrophage cluster was a valuable index for quantifying local plaque inflammation. MHR, lipid index, and microchannel were independently associated with macrophage clusters. Large macrophage clusters were independently associated with high MHR and high lipid plaque burden.

The present study explored the potential protective effects of a proprotein-converting enzyme subtilisin/kexin 9 inhibitor (evolocumab) against contrast-associated acute kidney injury (CA-AKI) in high-risk patients with atherosclerotic cardiovascular disease(ASCVD). This retrospective cohort study included patients who underwent percutaneous coronary intervention at Tianjin Chest Hospital between January 2020 and December 2021. The endpoint was the incidence of CA-AKI. Bias matching was used to mitigate the impact of selection bias and other potential confounding factors. This study included 1642 patients, with 821 receiving evolocumab treatment (subcutaneous injection of 140 mg evolocumab within 2 days before undergoing PCI) before contrast agent administration and 821 not receiving this treatment. The incidences of CA-AKI were 6.21% and 8.04% in the evolocumab and control groups, respectively (P = .150). After propensity score matching, the incidence was 4.52% and 8.47% in the evolocumab and control groups (P = .009), respectively. These findings suggest that evolocumab treatment significantly reduced the occurrence of CA-AKI. These results were consistent across the subgroups of individuals with varying risk scores. Evolocumab administration effectively reduces the incidence of CA-AKI in inpatients with ASCVD, with a notable effect in high-risk individuals with CA-AKI.

Background Coronary artery calcification (CAC) is an established hallmark of advanced atherosclerosis. The detailed relationship between CAC and plaque vulnerability remains incompletely understood. We aim to investigate the relationship between calcification burden, calcification patterns, and plaque instability features. Methods A total of 540 patients with established coronary artery disease who underwent optical coherence tomography imaging before percutaneous coronary intervention were included. Calcified plaques were investigated using both qualitative (microcalcification, spotty calcification, and macrocalcification) and quantitative (non-calcified, low, intermediate, and high calcified plaque burden (CPB) stratified by CPB tertiles) assessments across the culprit vessel. The vulnerable features were compared between the groups. Results The prevalence of thin-cap fibroatheroma (TCFA), layered plaque, cholesterol crystal, microchannel, macrophage cluster, plaque rupture, plaque erosion, and eruptive calcified nodules differed significantly in different CPB groups. Intermediate CPB was associated with a significantly higher prevalence of TCFA and plaque rupture in these groups. The prevalence of TCFA, plaque rupture, layered plaque, and microchannel was lowest in the high CPB group. Macrophage clusters and cholesterol crystals were associated with a higher number of spotty calcifications, especially in young patients. Microchannels were associated with more microcalcifications. High CPB and macrocalcifications showed the lowest level of inflammation. In the patients with calcification, age, multivessel disease, macrophage cluster, and lipid index were independently associated with CPB. Conclusions Intermediate CPB was associated with a significantly higher prevalence of TCFA and plaque rupture, while spotty calcifications correlated with macrophage clusters and cholesterol crystals.

Hypertension constitutes a major risk factor for cardiovascular diseases. Globally, the management and control of hypertension remain suboptimal. At present, pharmacological intervention is a critical strategy for patients with hypertension to achieve blood pressure regulation. Nevertheless, inadequate adherence to prescribed medication significantly impedes effective blood pressure control. Renal denervation (RDN) has emerged as a minimally invasive intervention that has the potential to achieve long-term reduction in blood pressure. Many preclinical and clinical trials have validated the effects of RDN. While trials such as SYMPLICITY HTN-3 raised questions about the efficacy of RDN, the majority of subsequent clinical trials have demonstrated benefits in patients with resistant hypertension. Presently, traditional RDN therapy is characterized by invasiveness and technical complexity, so the utilization of noninvasive high-intensity focused ultrasound (HIFU) has been incorporated into RDN procedures. This review is aimed at consolidating data on the efficacy and safety of various RDN modalities, examining the implementation of noninvasive HIFU in RDN practices, and suggesting potential avenues for future advancements and opportunities.

The data on prediction for early mortality in patients with acute myocardial infarction (AMI) caused by total left main (LM) occlusion are limited. We aim to evaluate predictors for early mortality in these extremely high-risk patients. In this retrospective study, all consecutive patients with total occlusive LM-AMI were included between January 1997 and October 2023. The ECG and clinical data were compared between the in-hospital mortality and survival groups. The receiver operating characteristic curve (ROC) was created to identify the best predictors of in-hospital mortality. The primary endpoint was in-hospital mortality. Secondary outcomes included major adverse cardiovascular events. A total of 116 patients were included. The in-hospital mortality was 47% (54/116), and the 5-year MACE-free survival rate was 44%. ST-elevation myocardial infarction (STEMI), cardiogenic shock, STEMI plus shock, STEMI plus left anterior fascicular block (LAFB) + right bundle branch block (RBBB), post-procedural TIMI I to II flow, and collateral flow absence were associated with in-hospital death, with the area under ROC (AUC) of 0.726, 0.704, 0.782, 0.605, 0.633, and 0.671, respectively. ST-elevation in aVR and ST-elevation in aVR + aVL were more common in the survival group. STEMI plus shock showed a significantly greater AUC than the other predictors (82% specificity and 74% sensitivity, Z = 1.980, P < .05). STEMI plus LAFB + RBBB predicted in-hospital mortality with a specificity of 95% and a sensitivity of 26%. ECG features were associated with shock and collateral circulation. Patients with shock presented with more STEMI, LAFB + RBBB, STEMI plus LAFB + RBBB, collateral flow absence, prolonged QRS interval, and less ST-elevation in aVR ( P < .05). STEMI plus shock is a valuable predictor for in-hospital mortality in patients with acute total LM occlusion. STEMI plus LAFB + RBBB predicted a fatal in-hospital outcome in LM occlusion with high specificity.

Background: Recently, the combination of rotational atherectomy (RA) with intravascular lithotripsy (IVL), known as "RotaTripsy," has been employed in the treatment of coronary lesions with severe calcification. In this article, we provide an overview of the current evidence regarding this technique, emphasizing the importance of appropriate patient and lesion selection to achieve optimal clinical outcomes, including the primary goal of improvement of stenting and enhancement of short and long-term prognosis. Methods: We performed a systematic literature search using PubMed, Embase, Web of Science, and Cochrane library up to July 2024 for studies that combined RA and IVL for coronary artery calcification lesions that were included. The retrieved articles and references of the primary articles were used to collect the basic information. SPSS 20.0 and Excel statistic software were used to conduct this scoping review. Results: A total of 25 studies consisting of 259 patients were identified. Of all the patients, 208 (80.3%) were male. Patients had an average age of 68.31 years, and 119 (45.95) patients had acute coronary syndrome. In addition, 218 (84.17%) had hypertension, 128 (49.42%) had diabetes mellitus, and 48 (18.53%) had chronic kidney disease. In the ultimate analysis, 252 patients (97.3%) successfully underwent the "RotaTripsy" procedure, with a minimal mortality rate of only 7 individuals (2.7%) during the follow-up period. Conclusions: "RotaTripsy," as an efficacious therapeutic modality, shows its unique potential for severe calcified coronary artery lesions resistant to dilation. Our research findings substantiate its feasibility, safety, and effectiveness in clinic.

Objective The aim of this paper is to discover differentially expressed genes related to ferroptosis (DEFRGs) in patients with ST-segment elevation myocardial infarction (STEMI) and to construct a reliable prognostic signature that incorporates key DEFRGs and easily accessible clinical factors. Methods We did a systematic review of Gene Expression Omnibus datasets and picked datasets SE49925, GSE60993, and GSE61144 for analysis. We applied GEO2R to find DEFRGs and overlapped them among the picked datasets. We performed functional enrichment analysis to explore their biological functions. We built an optimal model with least absolute shrinkage and selection operator (LASSO) penalized Cox proportional hazards regression. We tested the clinical value of the signature with survival analysis, ROC curve, decision curve analysis and a prognostic nomogram. We also confirmed the model externally with plasma samples from our center’s patients. Results A prognostic signature combining three overexpressed DEFRGs (ACSL1, ACSL4, TSC22D3) and two clinical variables (serum creatinine level, Gensini score) was established. The signature effectively classified patients into low- and high-risk groups. Survival analysis, ROC curve analysis, and DCA showed its robust predictive performance and clinical utility of the signature within two years after the onset of the disease. The external validation cohort confirmed the significant difference in major adverse cardiovascular events (MACEs) between the low- and high-risk groups. Conclusion This study revealed DEFRGs in patients with STEMI and developed a prognostic signature that integrates gene expression levels and clinical factors for stratifying patients and predicting the risk of MACEs.

Abstract Heart failure with preserved ejection fraction (HFpEF) is a mortal clinical syndrome without effective therapies. Empagliflozin (EMPA) improves cardiovascular outcomes in HFpEF patients, but the underlying mechanism remains elusive. Here, mice were fed a high-fat diet (HFD) supplemented with L-NAME for 12 weeks and subsequently intraperitoneally injected with EMPA for another 4 weeks. A 4D-DIA proteomic assay was performed to detect protein changes in the failing hearts. We identified 310 differentially expressed proteins (DEPs) (ctrl vs. HFpEF group) and 173 DEPs (HFpEF vs. EMPA group). The regulation of immune system processes was enriched in all groups and the interferon response genes (STAT1, Ifit1, Ifi35 and Ifi47) were upregulated in HFpEF mice but downregulated after EMPA administration. In addition, EMPA treatment suppressed the increase in the levels of aging markers (p16 and p21) in HFpEF hearts. Further bioinformatics analysis verified STAT1 as the hub transcription factor during pathological changes in HFpEF mice. We next treated H9C2 cells with IFN-γ, a primary agonist of STAT1 phosphorylation, to investigate whether EMPA plays a beneficial role by blocking STAT1 activation. Our results showed that IFN-γ treatment caused cardiomyocyte senescence and STAT1 activation, which were inhibited by EMPA administration. Notably, STAT1 inhibition significantly reduced cellular senescence possibly by regulating STING expression. Our findings revealed that EMPA mitigates cardiac inflammation and aging in HFpEF mice by inhibiting STAT1 activation. The STAT1–STING axis may act as a pivotal mechanism in the pathogenesis of HFpEF, especially under inflammatory and aging conditions. Graphical abstract The schematic figure depicts a mechanism model of the STAT1–STING axis in HFpEF (this figure was drawn using FigDraw software).

Abstract Aims A high red blood cell distribution width (RDW) at admission or discharge is associated with a worse prognosis in hospitalized patients with heart failure (HF), and the prognostic value of the in‐hospital change in RDW (∆RDW) remains debatable. Methods and results We included 5514 patients with critical illness and HF from the MIMIC‐IV database. The ΔRDW was calculated by the RDW at discharge minus that at admission. Clinical outcomes included all‐cause mortality at 90 day, 180 day, and 1 year after discharge. The median age of the patients was 73.91 years, and 46.37% were women. Kaplan–Meier curve and Cox regression analyses were used to examine the association between the ΔRDW and all‐cause mortality at different time points. A multivariable Cox proportional hazard model showed that the ΔRDW (per 1% increase) was independently associated with all‐cause mortality at 90 day, 180 day, and 1 year after adjusting for confounding factors (hazard ratio [HR] = 1.17, 95% confidence interval [CI] = 1.13–1.21, P < 0.001; HR = 1.17, 95% CI = 1.14–1.20, P < 0.001; and HR = 1.18, 95% CI = 1.15–1.20, P < 0.001, respectively). Restricted cubic splines showed a non‐linear relationship between the ΔRDW and the risk of clinical outcomes. High ΔRDW was associated with a high risk of mortality at different time points. A subgroup analysis showed that this positive association remained consistent in pre‐specified subgroups. Conclusions Our study suggests that an increased RDW during hospitalization is independently associated with short‐ or long‐term all‐cause mortality in critical‐ill patients with HF.

The dysregulation of adenylate cyclase-associated protein 1 (CAP1) is associated with a variety of inflammatory conditions. Here, we aimed to assess the role of serum CAP1 protein in predicting acute myocardial infarction (AMI), and to explore its effect and mechanism in vascular endothelial cells injury. ELISA was utilized to detected CAP1 protein expression in serum from 70 patients with first-time AMI at 0, 6, 12, 24, 48 hours and 7 days of the onset of chest pain. Receiver operating characteristic (ROC) curve analysis was administered to analyze the diagnostic power of CAP1 for AMI. The CCK-8 and 5-BrdU assays were applied to measure cell proliferation and inflammation in a model of oxidized low-density lipoprotein (ox-LDL) induced human umbilical vein endothelial cells (HUVEC). Luciferase reporter gene assay and Western blotting were used to assess the activity of NF-κB pathway. Results showed that serum CAP1 protein expression was upregulated in patients with first-time AMI, its expression was highest at 12 hours of the onset of chest pain. CAP1 protein was positively associated with the levels of cTnI and ox-LDL. CAP1 showed a relatively high diagnostic accuracy in patients with first-time AMI compared with cTnI, and CAP1 combined with cTnI had superior diagnostic value than CAP1 and cTnI alone. The expression of CAP1 protein was increased in supernatants of ox-LDL induced HUVEC in a dose- and time-dependent manner. CAP1 inhibited cell proliferation but promoted inflammation, and induced the activation of NF-κB pathway in vitro. To sum up, increased serum CAP1 expression might serve as a novel diagnostic biomarker for patients with first-time AMI, the mechanism might be related to its induction of NF-κB pathway activation causing abnormal proliferation and inflammation and thus mediating vascular endothelial cell injury.

Abstract. Background:. Right ventricular (RV)-arterial uncoupling is a powerful independent predictor of prognosis in heart failure with preserved ejection fraction (HFpEF). Coronary artery disease (CAD) can contribute to the pathophysiological characteristics of HFpEF. This study aimed to evaluate the prognostic value of RV-arterial uncoupling in acute HFpEF patients with CAD. Methods:. This prospective study included 250 consecutive acute HFpEF patients with CAD. Patients were divided into RV-arterial uncoupling and coupling groups by the optimal cutoff value, based on a receiver operating characteristic curve of tricuspid annular plane systolic excursion to pulmonary artery systolic pressure (TAPSE/PASP). The primary endpoint was a composite of all-cause death, recurrent ischemic events, and HF hospitalizations. Results:. TAPSE/PASP ≤0.43 provided good accuracy in identifying patients with RV-arterial uncoupling (area under the curve, 0.731; sensitivity, 61.4%; and specificity, 76.6%). Of the 250 patients, 150 and 100 patients could be grouped into the RV-arterial coupling (TAPSE/PASP >0.43) and uncoupling (TAPSE/PASP ≤0.43) groups, respectively. Revascularization strategies were slightly different between groups; the RV-arterial uncoupling group had a lower rate of complete revascularization (37.0% [37/100] vs. 52.7% [79/150], P <0.001) and a higher rate of no revascularization (18.0% [18/100] vs. 4.7% [7/150], P <0.001) compared to the RV-arterial coupling group. The cohort with TAPSE/PASP ≤0.43 had a significantly worse prognosis than the cohort with TAPSE/PASP >0.43. Multivariate Cox analysis showed TAPSE/PASP ≤0.43 as an independent associated factor for the primary endpoint, all-cause death, and recurrent HF hospitalization (hazard ratios [HR]: 2.21, 95% confidence interval [CI]: 1.44–3.39, P <0.001; HR: 3.32, 95% CI: 1.30–8.47, P = 0.012; and HR: 1.93, 95% CI: 1.10–3.37, P = 0.021, respectively), but not for recurrent ischemic events (HR: 1.48, 95% CI: 0.75–2.90, P = 0.257). Conclusion:. RV-arterial uncoupling, based on TAPSE/PASP, is independently associated with adverse outcomes in acute HFpEF patients with CAD.

Objective: To explore the relationship between serum lipoprotein (a) levels and acute myocardial infarction (AMI) and aortic dissection in athletic patients and those with optimal physical health. Methods: This study involved 216 athletic patients admitted to a Chinese hospital for AMI who underwent Percutaneous Coronary Intervention (PCI) between 2018 and 2019. These patients, characterized by their athletic background and optimal physical health, were divided based on their serum lipoprotein (a) levels: 133 in the low-lipoprotein (a) group (<300 mg/L) and 83 in the high-lipoprotein (a) group (>= 300 mg/L). Data including baseline demographics, laboratory tests, and details of interventional treatment were collected from medical records. All patients were followed up for two years post-discharge to record Major Adverse Cardiac Events (MACE). Factors influencing MACE were analyzed using univariate and multivariate logistic regression. Results: The low lipoprotein (a) group exhibited lower age, reduced Killip grades III-IV, lower LDL-C levels, and fewer diseased vessels than the high lipoprotein (a) group (P<0.05). The incidence of MACE was significantly lower in the low lipoprotein (a) group (5.3%, 7/133) compared to the high lipoprotein (a) group (27.87%, 51/183) (P<0.05). Univariate analysis identified significant differences in age, post-surgery beta-blocker use, LDL-C levels, serum lipoprotein (a) levels, revascularization strategies, and the number of diseased vessels (P<0.05). Multivariate analysis revealed serum lipoprotein(a) as an independent predictor of MACE in athletic patients post-PCI (OR=1.010, 95%CI: 1.007-1.013, P=0.000). Conclusion: Serum lipoprotein (a) levels are significantly associated with the incidence and progression of AMI and aortic dissection in athletic patients and those with optimal physical health. Athletic patients with low pre-PCI lipoprotein (a) levels had a reduced risk of MACE during the two-year follow-up. This suggests that serum lipoprotein (a) could be a valuable prognostic marker for these patients, aiding in the prediction and management of post-PCI outcomes.