Traditional Chinese Medicine (TCM) has played a vital role in public health throughout history, particularly evidenced during the COVID-19 pandemic, where it demonstrated both accessibility and clinical efficacy. However, TCM faces critical challenges, including unsustainable medicinal resources, ambiguous multi-target mechanisms, and a lack of standardized clinical evaluation systems. Addressing these issues requires interdisciplinary integration, particularly between synthetic biology and artificial intelligence (AI). Synthetic biology offers solutions to resource scarcity and production standardization by enabling the sustainable biosynthesis of active compounds. Meanwhile, AI enhances TCM research through bioinformatics-driven compound prediction, machine learning-assisted quality control, and network pharmacology-based mechanism elucidation. AI also improves diagnostic reproducibility, aligning with synthetic biology’s precision-driven framework. Together, these technologies facilitate the transformation of TCM from an experience-based practice into a standardized, evidence-based public health intervention. This review highlights the synergistic potential of AI and synthetic biology in overcoming TCM’s modernization barriers. By leveraging AI for data-driven drug discovery and synthetic biology for scalable production, TCM can achieve sustainable development while retaining its therapeutic value. Future efforts should focus on enhancing AI interpretability, expanding biological databases, and optimizing cross-disciplinary collaboration to fully realize this integration.

Porcine reproductive and respiratory syndrome virus (PRRSV) is an RNA virus that induces reproductive disorders in sows and respiratory diseases in growing pigs. Recently, the NADC34-like strain of PRRSV has become more prevalent, with outbreaks occurring across pig farms in China. However, a reliable diagnostic method for the clinical detection of this strain has been absent. This study developed a TaqMan probe-based dual real-time quantitative PCR assay targeting the M and GP5 genes to specifically identify the NADC34-like PRRSV strain. The assay exhibited high specificity, detecting exclusively the NADC34-like strain without cross-reactivity with other PRRSV strains. The detection limits for pMD-M and pMD-GP5 plasmids were 2.67 × 10² and 1.35 × 10¹ copies/μL, respectively, indicating high assay sensitivity. The assay also demonstrated excellent reproducibility, with coefficient of variation (CV) values for both recombinant plasmids below 2 %. Among 251 clinical samples, 27 tested positive for NADC34-like PRRSV. This study establishes an accurate, sensitive, and reliable TaqMan dual real-time PCR assay for detecting NADC34-like PRRSV, offering a valuable tool for clinical diagnostics and outbreak management in pig farms.