Network pharmacology (NP) provides a new methodological perspective for understanding traditional medicine from a holistic perspective, giving rise to frontiers such as traditional Chinese medicine network pharmacology (TCM-NP). With the development of artificial intelligence (AI) technology, it is key for NP to develop network-based AI methods to reveal the treatment mechanism of complex diseases from massive omics data. In this review, focusing on the TCM-NP, we summarize involved AI methods into three categories: network relationship mining, network target positioning and network target navigating, and present the typical application of TCM-NP in uncovering biological basis and clinical value of Cold/Hot syndromes. Collectively, our review provides researchers with an innovative overview of the methodological progress of NP and its application in TCM from the AI perspective.

Aims. Metabolic syndrome (MetS) affects approximately one-third of the global population. Visit-to-visit variability of blood pressure (VVV-BP) constitutes a substantial risk factor for numerous chronic conditions. Thus, this study aimed to assess the relationship between VVV-BP and MetS and identify potential moderating factors between these. Methods. Data were obtained from the China Health and Retirement Longitudinal Study, a nationally representative study. Multiple logistic regression analyses were utilized to explore the association between VVV-BP and MetS while incorporating moderation analyses. MetS was defined according to the criteria outlined in the Joint Interim Statement. VVV-BP was expressed by the standard deviation, coefficient of variation, average real variability, and root mean square error. Results. Individuals with the highest levels of VVV of systolic blood pressure (SBP) exhibited a 70 increased risk of MetS compared to those with the lowest levels (OR=1.70, 95 CI=1.312.21). In addition, they had a 41 increased risk of VVV of diastolic blood pressure (DBP) (OR=1.41, 95 CI=1.091.81). Notably, weight change status significantly influenced the relationship between VVV-BP and MetS (P_interaction=0.01). Conclusions. VVV-BP is a significant contributor to the risk of developing MetS. Importantly, individuals who experienced weight loss during the follow-up period did not face a significantly higher risk of developing MetS.