Zheng Zhang

Myocardial fibrosis is a key pathological process driving the progression of cardiovascular diseases toward heart failure, closely linked to persistent inflammation and immune dysregulation. Among CC chemokines, CCL17 has emerged as an important mediator connecting immune cell dynamics with fibrotic remodeling. This review outlines current understanding of the cellular sources, regulatory mechanisms, and functional roles of CCL17, with particular attention to its impact on regulatory T cell (Treg) recruitment through ligand-biased signaling. Beyond this mechanism, CCL17 likely operates within a broader inflammatory network, with potential interactions involving CCR2⁺ macrophages and IL-17-related pathways. Experimental studies show that disruption of CCL17 signaling attenuates fibrosis and improves cardiac function, while clinical data link elevated circulating CCL17 to cardiac dysfunction and adverse outcomes. However, the absence of clinical trials and the redundancy of chemokine networks remain key challenges for translation. Overall, CCL17 may serve as a biomarker and therapeutic target, although its clinical application will require a more integrated, network-based understanding.

To develop and validate a prediction model for in-hospital cardiogenic shock (CS) after percutaneous coronary intervention (PCI) in patients with acute myo

Objective: The objective of this research was to investigate the association between non-traditional lipid parameters and optical coherence tomography (OCT)-characterized high-risk plaques in patients with acute myocardial infarction (AMI). Methods: This retrospective study included 249 first-episode AMI patients admitted to the First Affiliated Hospital of Lanzhou University between January 2022 and December 2024. All patients underwent OCT-guided assessment of culprit lesions before revascularization. High-risk plaques were defined by more than two of the following features: lipid arc ≥90 °, fibrous cap thickness P < 0.05). OCT features included thinner fibrous caps, smaller lumen areas, larger lipid arcs, and higher incidences of rupture, erosion, thrombus, macrophage infiltration, cholesterol crystals, and calcification (all P < 0.05). Both ApoB/A1 (OR = 3.688, 95% CI: 1.211–11.230) and non-HDL-C (OR = 3.023, 95% CI: 1.238–7.378) were independently and linearly associated with high-risk plaques. No significant interactions were observed across clinical subgroups (all P for interaction > 0.05). The combined model incorporating the two markers achieved the highest discriminative performance (AUC = 0.696) and significantly improved the baseline model (DeLong test P < 0.05), with additional gains confirmed by IDI, NRI, and DCA (all P < 0.05). Conclusion: Both the non-traditional ApoB/A1 ratio and the traditional lipid marker non-HDL-C were independently and linearly associated with OCT-characterized high-risk plaques in AMI. Their combined assessment enhanced the identification of high-risk plaques morphology.

The aim of this study was to investigate the predictive value of the triglyceride-glucose (TyG) index to assess the severity and major adverse cardiovascular events (MACE) of patients in hospital with premature coronary artery disease (PCAD). A total of 300 patients with PCAD, diagnosed by coronary angiography (CAG), were enrolled in this study. According to the tertiles of TyG index, the 300 patients were divided into a T1 (n = 100), T2 (n = 100) and T3 group (n = 100). According to the presence or absence of MACE, the 300 patients were divided into a MACE (n = 80) and a non-MACE group (n = 220). The patients' clinical data were compared between the groups, the relationship between TyG index and the severity of PCAD and MACE were analysed through multivariable logistic regression analysis, and their predictive value was detected using receiver operating characteristic (ROC) curves. Multivariable logistic regression analysis showed that the TyG index was an independent risk factor for the severity of PCAD and MACE. The area under the ROC curve was 0.833 and 0.807, respectively (all p < 0.05). The TyG index was independently associated with the severity of PCAD and MACE, and had a good predictive value.

Objective To develop and validate a machine learning (ML) model for predicting in-hospital cardiogenic shock (CS) in acute myocardial infarction (AMI) patients undergoing percutaneous coronary intervention. Methods We included AMI patients who admitted to the First Hospital of Lanzhou University. Key predictive variables were screened using LASSO regression, and six ML predictive models were constructed. Model performance was evaluated using accuracy, sensitivity, precision, F1 score, AUROC, and AUPRC, and an online predictive application was developed. Results 851 patients were included, with 43 (5.05%) was CS. They were randomly divided into a training set (n = 595) and a test set (n = 256) at a ratio of 7:3. LASSO regression initially screened out 14 relevant variables. The LightGBM model performed optimally. After GSCV optimization, eight core modeling variables were finally determined: heart rate, D-dimer, Killip classification, creatinine, lipoprotein a, alanine aminotransferase, diastolic blood pressure, and apolipoprotein B/A. We established an ML prediction model based on LightGBM + LASSO + GSCV, Its AUROC = 0.87 (95% CI: 0.77–0.96) and AUPRC = 0.64 (95% CI: 0.36–0.86). We developed an online risk prediction application based on the Streamlit framework. Conclusion This study developed and validated a risk prediction model for in-hospital CS in AMI patients, providing a reliable risk assessment tool for clinical decision-making.

Background: Acute myocardial infarction (AMI) remains a leading cause of mortality and disability globally. Although percutaneous coronary intervention (PCI) has significantly reduced in-hospital mortality (IHM), the resultant class imbalance complicates accurate risk prediction. While machine learning (ML) demonstrates potential in predicting IHM, there is a lack of models that provide both high accuracy and personalized risk assessment. Methods: This retrospective study was conducted at the First Hospital of Lanzhou University from January 1, 2019, to December 31, 2020. We employed three data processing methods: synthetic minority over-sampling technique (SMOTE), Boruta, and grid search cross-validation (GSCV). Subsequently, six ML algorithms were implemented. Model performance was evaluated using accuracy, sensitivity, precision, F1-score, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve (AUPRC). Results: The study cohort consisted of 1693 patients diagnosed with AMI, of whom 34 (2.0%) experienced IHM following PCI. After employing SMOTE to balance the dataset, 32 independent risk factors were identified using the Boruta feature selection method. Among the evaluated ML models, ensemble algorithms demonstrated superior performance. For instance, the Light Gradient-Boosting Machine (LightGBM) framework achieved a predictive accuracy with an AUROC of 0.93 (95% confidence interval (CI): 0.82–1.00) and an AUPRC of 0.62 (95% CI: 0.17–0.96). Additional performance metrics included an accuracy of 0.988, a precision of 0.625, a sensitivity of 0.625, a specificity of 0.994, and an F1-score of 0.625. Conclusion: Utilizing SMOTE for class balancing, Boruta for feature selection, GSCV for optimal hyperparameter tuning, and LightGBM for model development achieved strong predictive performance for IHM following AMI. These findings underscore the significance of robust processing and careful algorithm selection.

Background: ST-elevation myocardial infarction (STEMI) poses a significant threat to global mortality and disability. Advances in percutaneous coronary intervention (PCI) have reduced in-hospital mortality, highlighting the importance of post-discharge management. Machine learning (ML) models have shown promise in predicting adverse clinical outcomes. However, a systematic approach that combines high predictive accuracy with model simplicity is still lacking.Methods: This retrospective study applied three data processing and ML algorithms to address class imbalance and support model development. ML models were trained to predict one-year mortality in STEMI patients post-PCI, with performance evaluated using accuracy, sensitivity, precision, F1-score, area under the receiver operating characteristic curve (AUROC), and the area under the precision-recall curve (AUPRC).Results: We analyzed data from 1,274 patients, incorporating 46 clinical and laboratory features. Using the Random Forest (RF) algorithm, we achieved an AUROC of 0.94 (95% confidence interval (CI): 0.90–0.98), an AUPRC of 0.44 (95% CI:0.15–0.76) in the internal validation set, identifying five key predictors: cardiogenic shock, creatinine, NT-proBNP, diastolic blood pressure, and left ventricular ejection fraction. By integrating risk stratification, the model’s performance improved, achieving an AUROC of 0.97 (95% CI: 0.96–0.99) and an AUPRC of 0.74 (95% CI: 0.60–0.84).Conclusion: This study highlights the feasibility of constructing accurate and interpretable ML models using a minimal set of predictors, supplemented by risk stratification, to improve long-term outcome prediction in STEMI patients.

This study aimed to employ Mendelian randomization (MR) analysis to investigate the causal relationship between sodium-glucose cotransporter 2 (SGLT2) inhibition and atrial fibrillation (AF), as well as assess the potential mediating role of inflammatory proteins in this association. A two-sample MR analysis was conducted using summary-level data from the genome-wide association study (GWAS) were used to evaluate the genetic prediction of SGLT2 inhibition and AF. Additionally, a two-step MR approach was applied to explore the causal mediation effects of inflammatory proteins in the SGLT2-AF pathway. Genetically predicted SGLT2 inhibition was associated with a significantly lower risk of AF (odds ratio [OR] = 0.51; 95% confidence interval [CI]: 0.27-0.97; p = 0.039). Mendelian randomization analysis further identified FGF-23 (OR = 0.88; 95% CI: 0.79, 0.98; p = 0.024) and PD-L1 (OR = 0.90; 95% CI: 0.82, 0.96; p = 0.023) as significant mediators, accounting for 21.28% and 17.69% of the total effect, respectively. These findings provide potential protective role of SGLT2 inhibition against AF, mediated by inflammatory proteins. Further mechanistic and clinical investigations are warranted to elucidate the underlying pathways and therapeutic implications.