Health isn't maintained by occasional supplements—it's preserved through consistent nutrient intake across 6 biological levels. Research in Nut… #Bioactivecompounds #Healthmaintenance #Micronutrients #Nutrition #Preventivemedicine

Background: Plant-derived exosome-like nanovesicles (P-ELNs) effectively deliver bioactive compounds due to their high biocompatibility and low immunogenicity. While LC-MS profiles compounds in complex samples, its analysis of large datasets remains limited by traditional methods. Recent advances in large language models (LLMs) and domain-specific systems now enhance Chinese biomedical data processing and cross-modal pharmaceutical research. Objective: To create a multimodal framework of liquid chromatography-mass spectrometry (LC-MS) combined with DeepSeek models for data mining of compounds with wound-healing properties from exosome-like nanovesicles derived from Cayratia japonica (CJ-ELNs). Methods: LC-MS identified compounds enriched in CJ (N=3) and CJ-ELNs (N=3), then compounds specifically enriched in CJ-ELNs were filtered via a four-step filtering workflow. The CJ-ELNs-specific compounds were processed by DeepSeek models for screening naturally active compounds with targeted functions of antioxidation, anti-inflammation, anti-cellular damage, anti-apoptosis, wound healing and tissue regeneration, and cell proliferation. Results: A multimodal framework of LC-MS combined with the DeepSeek-DF model was created. With the assistance of artificial intelligence (AI), a total of 46 naturally active compounds derived from CJ-ELNs with targeted functions were identified. Conclusions: A self-designed multimodal framework of LC-MS combined with DeepSeek models rapidly and accurately identifies naturally active compounds from CJ-ELNs. This AI-powered system innovatively integrates the traditional analytical technique with modern large language models, thus greatly favoring data mining of active ingredients in traditional Chinese medicine (TCM) herbs.