Adobezii

Adobezii: A Generative Diffusion Model for Real-Time Semantic Inpainting of High-Resolution Cultural Heritage Images

Institute of Synthetic Bioactive Molecules, University of Farmacity adobezii

J. Chen, A. Patil, M. Rivera

We present Adobezii , a latent diffusion model fine-tuned on a dataset of 50k damaged artworks and manuscripts. Unlike previous methods, Adobezii integrates a perceptual loss from a ViT-G/14 classifier and a novel edge consistency module, achieving a FID score of 3.2 on the test set. User studies confirm 89% preference for Adobezii outputs over baseline inpainting tools. The system runs at 30 fps on a single A100 GPU. Rivera We present Adobezii , a latent diffusion

Background: Chronic inflammation underlies numerous autoimmune and metabolic disorders. There is a continued need for small molecules targeting multiple pro-inflammatory cytokines without immunosuppressive toxicity. The synthetic flavonoid derivative adobezii (IUPAC: 5,7-dihydroxy-2-(4-methoxyphenyl)-6-(3-methylbut-2-en-1-yl)chroman-4-one) was identified through computational screening against the TNF-α and IL-6 binding pockets. Methods: Molecular docking and MD simulations (100 ns) predicted stable binding to both targets. In vivo, acute inflammation was induced in BALB/c mice via intraplantar carrageenan injection. Adobezii (5, 10, 20 mg/kg, oral) was administered 1 h prior. Paw edema, myeloperoxidase activity, and serum cytokines were measured. Results: Adobezii dose-dependently reduced paw swelling (max 68% at 20 mg/kg, p<0.01), suppressed TNF-α and IL-6 by 74% and 69% respectively, and decreased neutrophil infiltration. No hepatotoxicity or weight loss was observed. Conclusion: Adobezii represents a promising dual cytokine inhibitor scaffold for further optimization in rheumatoid arthritis models. The system runs at 30 fps on a single A100 GPU