Chronic diabetic wounds remain a major clinical challenge because persistent inflammation, hypoxia, and immune dysregulation prevent the transition of macrophages from pro‐inflammatory M1 states to pro‐regenerative M2 phenotypes. Here, we report an immunomodulatory colloidal bioink that integrates zein‐based oxygen‐generating microparticles and human mesenchymal stem cells within a porous, mechanically robust matrix for adaptive in situ bioprinting. Implemented with the autonomous, intelligent, visually guided in situ robotic bioprinting platform, this bioink enables patient‐specific deposition directly onto wound defects with high spatial conformity and stable tissue integration. Sustained oxygen release alleviates local hypoxic stress, enhances stem cell survival, and reshapes the wound microenvironment to favor regenerative immune responses. In diabetic wound models, the printed constructs promote angiogenesis, accelerate wound closure, and improve tissue regeneration. Spatial transcriptomic analysis further reveals macrophage heterogeneity across wound compartments and identifies a coordinated M1‐to‐M2 transition spatially and transcriptionally associated with the combined contribution of oxygen modulation and stem cell‐derived paracrine signaling. Together, this study establishes a therapeutic strategy that combines intelligent biomaterial design, controlled hypoxic conditioning, and adaptive robotic bioprinting to achieve precision immunomodulation and personalized regenerative treatment for chronic diabetic wounds with clinical translational potential and broad future applicability.
Seol‐Ha Jeong, Eleftheria‐Angeliki Valsami, Kitae Kim et al.· Advanced Functional Material...· 0 citations
Depression is a prevalent mental health disorder that often remains unrecognized in real-world settings, and although artificial intelligence approaches using digital signals show promise for screening, many lack interpretability and rely on cloud-based processing that limits clinical use. This study developed and evaluated V3-Gemma, an on-device multimodal framework for depression screening based on a Clinical-Computational Alignment (CCA) approach that integrates visual, vocal, and verbal cues within a structured clinical reasoning architecture. A total of 130 adults (65 with depression and 65 controls) completed a one-minute picture-description task; 20 observable multimodal features were defined by clinicians and refined to a final set of 19, then implemented as structured prompts for a vision-audio-language model with hierarchical agent-based orchestration, with core inference running locally on-device. In the feature-based analysis, the random forest performed best on an independent test set (50 participants; AUC 0.779, sensitivity 0.84, specificity 0.52). In the criterion-level analysis, features mapped to DSM-5 symptom domains and combined using the DSM-5 rule yielded a test-set accuracy of 0.64 with high sensitivity (0.88) but limited specificity (0.40). Given the small test set, these results represent exploratory feasibility evidence. This proof-of-concept demonstrates a privacy-preserving, interpretable screening approach aligned with DSM-5 symptom domains, pending validation in larger, more diverse samples.
M. Jhon, Eunkyoung Jeon, Dae-Kwang Kim et al.· Scientific Reports· 0 citations
A Mixture of Multicenter Experts (MoME) framework to address AI bias in the medical domain without requiring data sharing across institutions is proposed and validated using a multimodal target volume delineation model for prostate cancer radiotherapy.
Yujin Oh, Sangjoon Park, Xiang Li et al.· 0 citations
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