Enhancing Prediction of Myocardial Recovery After Coronary Revascularization: Integrating Radiomics from Myocardial Contrast Echocardiography with Machine Learning.

Introduction: Chronic coronary artery disease (CAD) management often relies on myocardial contrast echocardiography (MCE), yet its effectiveness is limited by subjective interpretations and difficulty in distinguishing hibernating from necrotic myocardium. This study explores the integration of machine learning (ML) with radiomics to predict functional recovery in dyskinetic myocardial segments in CAD patients undergoing revascularization, aiming to overcome these limitations. Methods: This prospective study enrolled 55 chronic CAD patients, dividing into training (39 patients, 205 segments) and testing sets (16 patients, 68 segments). Dysfunctional myocardial segments were identified by initial wall motion scores (WMS) of ≥2 (hypokinesis or higher). Functional recovery was defined as a decrease of ≥1 grade in WMS during follow-up echocardiography. Radiomics features were extracted from dyssynergic segments in end-systolic phase MCE images across five cardiac cycles post- "flash" impulse and processed through a five-step feature selection. Four ML classifiers were trained and compared using these features and MCE parameters, to identify the optimal model for myocardial recovery prediction. Results: Functional improvement was noted in 139 out of 273 dyskinetic segments (50.9%) following revascularization. Receiver Operating Characteristic (ROC) analysis determined that myocardial blood flow (MBF) was the most precise clinical predictor of recovery, with an area under the curve (AUC) of 0.770. Approximately 1.34 million radiomics features were extracted, with nine features identified as key predictors of myocardial recovery. The random forest (RF) model, integrating MBF values and radiomics features, demonstrated superior predictive accuracy over other ML classifiers. Validation of the RF model on the testing dataset demonstrated its effectiveness, evidenced by an AUC of 0.821, along with consistent calibration and clinical utility. Conclusion: The integration of ML with radiomics from MCE effectively predicts myocardial recovery in CAD. The RF model, combining radiomics and MBF values, presents a non-invasive, precise approach, significantly enhancing CAD management.

cGAS-STING signaling in cardiovascular diseases.

Sterile inflammation, characterized by a persistent chronic inflammatory state, significantly contributes to the progression of various diseases such as autoimmune, metabolic, neurodegenerative, and cardiovascular disorders. Recent evidence has increasingly highlighted the intricate connection between inflammatory responses and cardiovascular diseases, underscoring the pivotal role of the Stimulator of Interferon Genes (STING). STING is crucial for the secretion of type I interferon (IFN) and proinflammatory cytokines in response to cytosolic nucleic acids, playing a vital role in the innate immune system. Specifically, research has underscored the STING pathway involvement in unregulated inflammations, where its aberrant activation leads to a surge in inflammatory events, enhanced IFN I responses, and cell death. The primary pathway triggering STING activation is the cyclic GMP-AMP synthase (cGAS) pathway. This review delves into recent findings on STING and the cGAS-STING pathways, focusing on their regulatory mechanisms and impact on cardiovascular diseases. It also discusses the latest advancements in identifying antagonists targeting cGAS and STING, and concludes by assessing the potential of cGAS or STING inhibitors as treatments for cardiovascular diseases.