Fuente: PubMed "honey" J Control Release. 2026 May 5;395:114983. doi: 10.1016/j.jconrel.2026.114983. Online ahead of print.ABSTRACTChronic diabetic foot ulcers (DFUs) are plagued by irregular geometries, persistent inflammation, and poor angiogenesis, demanding personalized therapeutic strategies. Herein, we developed a closed-loop platform integrating AI multi-scale fusion segmentation, 3D bioprinting drug-stratified hydrogels and diabetic-specific wound modeling to address DFU management. The AI workflow combined YOLOv8 for wound segmentation and Depth Anything V2 for depth estimation, optimized via multi-scale prediction fusion to enhance robustness. A refined dataset (210 lean pork wound images +180 clinical DFU images) replaced non-physiological fatty tissue, with external validation on 50 clinical wound samples (Dice coefficient: 0.93 ± 0.02). Chitosan methacrylate (CsMeA) hydrogels loaded with tailored drug combinations (VEGF/PDGF for angiogenesis, AgSD/honey for infection control, LIDHCl/LVX for pain/inflammation) were 3D-bioprinted with layer-specific drug deposition. In vitro, the hydrogels exhibited sustained release (up to 14 days), >85% cell viability, and synergistic antibacterial activity (inhibition zone increase: 61%and 81%for LVX against S. aureus and P. aeruginosa). In vivo studies on diabetic pigs showed VEGF/honey hydrogels achieved 93.62% ± 1.83 wound closure at Day 21, significantly higher than non-personalized controls (72.3 ± 3.5%), with enhanced angiogenesis and reduced inflammation. This platform advances personalized DFU care by bridging AI-driven diagnostics and targeted therapeutics, with improved translational potential.PMID:42097229 | DOI:10.1016/j.jconrel.2026.114983