Pathogenic pathogens invade the human body through various pathways, causing damage to host cells, tissues, and their functions, ultimately leading to the development of diseases and posing a threat to human health. The rapid and accurate detection of pathogenic pathogens in humans is crucial and pressing. Nucleic acid detection offers advantages such as higher sensitivity, accuracy, and specificity compared to antibody and antigen detection methods. However, conventional nucleic acid testing is time-consuming, labor-intensive, and requires sophisticated equipment and specialized medical personnel. Therefore, this review focuses on advanced nucleic acid testing systems that aim to address the issues of testing time, portability, degree of automation, and cross-contamination. These systems include extraction-free rapid nucleic acid testing, fully automated extraction, amplification, and detection, as well as fully enclosed testing and commercial nucleic acid testing equipment. Additionally, the biochemical methods used for extraction, amplification, and detection in nucleic acid testing are briefly described. We hope that this review will inspire further research and the development of more suitable extraction-free reagents and fully automated testing devices for rapid, point-of-care diagnostics.

Percutaneous coronary angiography remains the diagnostic gold standard for coronary artery disease. However, the complex and high-volume nature of the imaging data renders the clinical interpretation of coronary lesions a time-consuming, labor-intensive, and inherently subjective process. This retrospective study collected and preprocessed Coronary artery angiography (CAG) image data from 408 patients with acute myocardial infarction (AMI). An improved YOLOv4 algorithm was developed, validated on standard VOC datasets, and subsequently calibrated via transfer learning on the CAG training set for automated lesion detection and classification. The model-derived lesion characteristics were then statistically correlated with the occurrence of Major Adverse Cardiovascular Events (MACEs) during patient follow-up. The improved model achieved a post-modification mean Average Precision (mAP) of 84.72% (95% CI: 83.44–85.99%) on the VOC dataset. For coronary lesion detection, the model yielded an overall mean Average Precision (mAP) of 55.01%. Importantly, lesion characteristics automatically detected by the model—specifically completely occluded lesions (Log-rank p = 0.003) and multibranching lesions (Log-rank p = 0.033)—demonstrated a significant association with the cumulative incidence of MACEs. The innovative, improved YOLOv4 model exhibits robust performance in effectively and accurately detecting and classifying coronary lesions within AMI patient angiography imagery. This study provides a valuable AI-assisted diagnostic tool and offers preliminary insights for long-term prognostic assessment by seamlessly integrating deep learning-derived anatomical features with MACEs prediction.