Journal of Coronary Heart Diseases

Coronary artery disease is a leading cause of morbidity and mortality worldwide. Over the years, research has highlighted significant genderbased disparities in CAD presentation, diagnosis, and treatment outcomes. This contemporary analysis aims to comprehensively examine the existing literature on gender disparities in CAD, with a focus on understanding the differences in clinical presentation, diagnostic approaches, and treatment outcomes between men and women. By identifying the underlying factors contributing to these disparities, healthcare professionals and policymakers can develop targeted interventions to improve CAD management and outcomes for all individuals, regardless of gender.

Coronary artery disease remains a leading cause of mortality and morbidity worldwide. The conventional treatment approaches often face limitations such as low drug bioavailability, off-target effects and inadequate therapeutic outcomes. Nanotechnology has emerged as a promising solution to address these challenges by enabling targeted drug delivery to the affected site, thereby enhancing therapeutic efficacy while minimizing systemic side effects. This article reviews recent advancements in nanotechnology-based drug delivery systems for CAD treatment, highlighting the strategies to improve drug selectivity, stability and controlled release. Furthermore, the potential benefits and challenges of these novel approaches are discussed, along with their potential to revolutionize the management of CAD.

Coronary artery disease remains a leading cause of morbidity and mortality worldwide. With the rapid advancement of machine learning and artificial intelligence techniques, there has been an increasing interest in using these methods for CAD risk prediction. This study aims to provide a comprehensive comparative analysis of various ML and AI models for predicting CAD risk, considering both their performance and interpretability. A diverse dataset containing clinical, demographic, and diagnostic features was used to train and evaluate the models. The models' performance was assessed using standard evaluation metrics, including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve. Additionally, model interpretability was evaluated using techniques such as feature importance analysis and SHAP (SHapley Additive exPlanations). Our findings indicate that while some complex models achieve higher predictive performance, simpler models also demonstrate competitive accuracy while maintaining higher interpretability. The trade-off between performance and interpretability is crucial, as interpretable models can offer valuable insights into the factors driving CAD risk. The study underscores the need to strike a balance between model complexity and clinical interpretability in CAD risk prediction applications.

Air pollution is a pervasive environmental issue that poses significant health risks to human populations worldwide. Over the past few decades, extensive research has highlighted the association between exposure to air pollutants and various adverse health outcomes, including cardiovascular diseases. Among these, coronary artery disease stands out as a major global health concern. This research article aims to comprehensively review and analyze the existing body of knowledge from global epidemiological studies on the impact of air pollution on CAD. By synthesizing findings from diverse studies, we aim to shed light on the mechanisms linking air pollution to CAD, the contributing pollutants, and the populations most susceptible to this detrimental association. Furthermore, the article discusses potential mitigation strategies and policy implications to address this critical public health issue.