Li Hongyu, Sun Xinti, Li Zesheng, Zhao Ruiping, Li Meng, Hu Taohong
Medical College of Soochow University, The People's Liberation Army of China (PLA) Rocket Force Characteristic Medical Center, Beijing, China.
Department of Cardiovascular Medicine, Baotou Central Hospital, Institute of Cardiovascular Diseases, Translational Medicine Center, Baotou, China.
Front Cardiovasc Med. 2023 Jan 4;9:1059543. doi: 10.3389/fcvm.2022.1059543. eCollection 2022.
Great strides have been made in past years toward revealing the pathogenesis of acute myocardial infarction (AMI). However, the prognosis did not meet satisfactory expectations. Considering the importance of early diagnosis in AMI, biomarkers with high sensitivity and accuracy are urgently needed. On the other hand, the prevalence of AMI worldwide has rapidly increased over the last few years, especially after the outbreak of COVID-19. Thus, in addition to the classical risk factors for AMI, such as overwork, agitation, overeating, cold irritation, constipation, smoking, and alcohol addiction, viral infections triggers have been considered. Immune cells play pivotal roles in the innate immunosurveillance of viral infections. So, immunotherapies might serve as a potential preventive or therapeutic approach, sparking new hope for patients with AMI. An era of artificial intelligence has led to the development of numerous machine learning algorithms. In this study, we integrated multiple machine learning algorithms for the identification of novel diagnostic biomarkers for AMI. Then, the possible association between critical genes and immune cell infiltration status was characterized for improving the diagnosis and treatment of AMI patients.
在过去几年中，在揭示急性心肌梗死（AMI）的发病机制方面已经取得了巨大进展。然而，其预后并未达到令人满意的预期。考虑到早期诊断在AMI中的重要性，迫切需要具有高灵敏度和准确性的生物标志物。另一方面，在过去几年中，全球范围内AMI的患病率迅速上升，尤其是在COVID-19爆发之后。因此，除了AMI的经典危险因素，如过度劳累、激动、暴饮暴食、寒冷刺激、便秘、吸烟和酗酒外，病毒感染触发因素也被考虑在内。免疫细胞在病毒感染的固有免疫监视中起关键作用。因此，免疫疗法可能作为一种潜在的预防或治疗方法，为AMI患者带来新的希望。人工智能时代催生了众多机器学习算法。在本研究中，我们整合了多种机器学习算法来识别AMI的新型诊断生物标志物。然后，对关键基因与免疫细胞浸润状态之间的可能关联进行了表征，以改善AMI患者的诊断和治疗。
