The gut microbiota is widely recognized as a central regulator of human health and disease. Medicine-food homologous resources, leveraging their inherent safety and multi-target characteristics, serve as pivotal modulators for intervening in metabolic, inflammatory, and immune-related disorders via microbiota regulation. However, the inherent complexity, substantial interindividual variability, and dynamic nature of the gut microbiome remain major hurdles to achieving precise interventions. This perspective delineates a novel paradigm for precision medicine-food intervention, built upon three interconnected and cutting-edge directions: (1) targeting key microbial metabolites, (2) advancing targeted delivery technologies for beneficial microbes, and (3) implementing artificial intelligence (AI)-assisted personalized microbiome functional profiling. This triad synergistically addresses the challenge of individual variability and paves the way for highly effective and precise interventions.

Antimicrobial resistance has become one of the top global public health and development threats due to the misuse and overuse of antimicrobials in humans, animals, and plants. Researchers are leveraging artificial intelligence and interdisciplinary approaches to design antimicrobial peptides (AMPs) that show a reduced risk of inducing resistance. Precise targeting design makes AMPs more efficient for combating drug-resistant bacteria and fungi, with applications spanning medicine, agriculture, and food safety.

This article discusses the transformative role of spatial metabolomics in advancing research on "food-medicine homology." By integrating metabolomics with spatial analysis technologies, this approach preserves the original spatial distribution information of metabolites within tissues, enabling a paradigm shift from mere component identification to precise localization. The paper highlights that food-medicine homology substances exhibit multi-component synergies, spatiotemporal dynamics, and strong environmental dependencies. Spatial metabolomics allows visual tracking of the absorption, distribution, and metabolic pathways of these components in vivo, reveals interaction mechanisms among components, gut microbiota, and the host, and provides chemical evidence for evaluating the geo-authenticity of medicinal materials. Despite challenges such as high detection costs and a lack of technical standardization, spatial metabolomics is poised to transition food-medicine research from macroscopic effect evaluation to microscopic spatial resolution. It holds promise for supporting personalized dietary recommendations, intelligent cultivation technologies, and the modernization of traditional medicine, ultimately contributing to global health innovation under initiatives like "Healthy China 2030."

Plant-derived Extracellular Vesicles (PDEVs)—nanoscale vesicles packed with bioactive molecules from food-medicine homology plants—offer promising applications in anti-inflammatory therapy, bone regeneration, and targeted drug delivery. However, traditional production methods suffer from severe quality fluctuations and batch-to-batch inconsistencies, limiting their use. A new study published in Food & Medicine Homology demonstrates that the Temporary Immersion Bioreactor System (TIBS) solves these critical issues through precise environmental control, enabling standardized PDEV production. This innovation paves the way for PDEVs’ industrialization and clinical translation in biomedicine.

Inspired by the natural spider web structure, this study innovatively designed an omnidirectional strain sensor array with a bioinspired spider web configuration. Using Ti₃C₂T_x (MXene) conductive ink and 3D printing technology, the sensor array was successfully fabricated. The strain sensor array leverages the isotropic strain response characteristics of the spider web structure, combined with a multi-class multi-output neural network, to achieve signal decoupling of the sensor array, enabling accurate identification and differentiation of both strain direction and magnitude. Within the 0-10% strain range, the sensor demonstrated a gauge factor (GF) of 26.3, with an identification accuracy of approximately 97% for strain magnitude and direction under various surface stimuli. This research provides a novel approach for achieving both high sensitivity and reliability in strain detection, demonstrating potential applications in human motion monitoring and multi-directional stress sensing. Furthermore, it offers promising prospects for applications in intelligent robotics and wearable health monitoring devices.