Bridging physics and biology in acupuncture: flexible multimodal bioelectronics for decoding the deqi microenvironment

Zhanhao Wu, Yinhu Hu, Lei Song, Juan Jin, C G Zhu, Yi Chen, Y Zhang, Jie Zhuang, Xu G

Clinical standardization of acupuncture remains limited by its reliance on empirical tactile feedback and subjective patient sensations, a physiological response collectively termed Deqi. Here, the Deqi microenvironment refers to the local, time-varying tissue domain around the needle in which mechanical deformation, microvascular perfusion, biochemical mediator release and electrophysiological activity are coupled and measurable. Conventional rigid sensing architectures cannot decode this process without disturbing it, because their stiffness can create mechanical mismatch with soft tissues, distort dynamic signals and provoke non-physiological inflammatory artifacts at the biological interface. This review examines the evolution from conventional rigid instrumentation toward flexible, multimodal bioelectronics for high-fidelity quantification of needling kinematics. Sub-micron electrospun nanomeshes, liquid metals and low-dimensional carbon networks enable the decoding of manipulation dynamics without tactile interference. These conformal arrays also support real-time, in situ mapping of the Deqi microenvironment across tissue biomechanics, local hemodynamics, biochemical metabolism and electrophysiological signaling. The translation of these physical inputs into systemic physiological responses is mediated by structurally defined mechanotransduction pathways, including the peripheral force-immune axis regulated by Ptgs2+ telocytes and central neuroanatomical projections such as the PROKR2-dependent vagal-adrenal circuit. Synthesizing the resulting high-dimensional data streams requires specialized computational architectures. Graph neural networks, multimodal Transformers and related AI frameworks can synchronize heterogeneous sensor inputs and support the construction of a computable Deqi Index for cautious clinical prognostication. Future progress will depend on scalable manufacturing, chronic bio-interface stability and clinically governed closed-loop control. Taken together, the integration of intelligent robotics and Acupuncture Digital Twins may help transition acupuncture from a heuristic manual practice toward a quantifiable, data-driven medical intervention.