Background: Acupuncture is increasingly evaluated within biomedical frameworks, yet its integration into modern practice remains limited by unclear mechanisms, heterogeneous training standards, and the lack of transparent point-selection systems. Traditional meridian models align poorly with contemporary neurophysiology, contributing to inconsistent protocols and barriers to rigorous research. This review examines the Most Useful Acupuncture Points (MUAPs) as a candidate, mechanismdriven acupoint cluster developed to address functional dysregulation and autonomic imbalance while providing a practical theoretical teaching framework. A structured narrative evidence synthesis was conducted using PubMed, Google Scholar, and backward citation tracking. Studies were included if they examined at least one prespecified candidate MUAP (LI4, PC6, ST36, SP6, LR3, GV20, GV26, GB34, TW5, CV6, or auricular vagus zone) and reported measurable physiological outcomes related to autonomic modulation, inflammatory signaling, neuroimmune pathways, brain-gut reflexes, limbic or cortical activity, or HRV (heart rate variability) parameters. A total of 71 studies met the inclusion criteria and underwent standardized mechanistic extraction. Across animal and human models, MUAPs demonstrated convergent effects across multiple physiological domains. ST36 and PC6 were repeatedly associated with vagal pathways, increased high-frequency HRV, and suppressed sympathetic activity. ST36, SP6, LI4, and GV20 reduced pro-inflammatory cytokines and modulated NF-κB and NLRP3 signaling. LI4, LR3, ST36, and GV20 influenced limbic-paralimbic and cortical networks. Multiple points improved visceral motility, analgesia, neuroprotection, and cognitive outcomes, with recurrent mechanistic support across studies. MUAPs represent a candidate, mechanistically grounded acupoint cluster with effects on autonomic, neuroimmune, and neurophysiological systems. This framework may support future educational and clinical protocol development, but requires prospective validation.

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