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Machine Learning-Assisted Development of Injectable, Mechanically Robust, and Energy Metabolism-Modulating Brushite
Dachuan Liu1, Jiaxu Shi1, Youhao Ni2
1Orthopedic Institute, Department of Orthopedic Surgery, Medical 3D Printing Center, The First Affiliated Hospital, MOE Key Laboratory of Geriatric Diseases and Immunology, School of Basic Medical Sciences, Suzhou Medical College, Soochow University, Suzhou, Jiangsu 215000, China.
None:
In orthopedic minimally invasive surgeries (MIS) such as percutaneous vertebroplasty (PVP) and percutaneous kyphoplasty (PKP), calcium phosphate cements (CPCs) are an attractive alternative to bioinert polymethyl methacrylate (PMMA) due to their superior biocompatibility and osteoconductivity. However, the mechanical strength and injectability of CPCs often remain insufficient for load-bearing applications, limiting their broader use in these critical procedures. To address this challenge, we introduce a machine learning-assisted approach to enhance both the mechanical strength and injectability of CPCs by identifying specific polymers as superplasticizers. By optimizing its concentration and the liquid-to-powder (L/P) ratio, we developed an injectable brushite-based cement with an exceptional compressive strength of 79.5 ± 4.3 MPa, surpassing both traditional CPCs and PMMA in orthopedic applications. Zeta potential and adsorption studies reveal that these superplasticizers enhance cement paste dispersion via electrostatic repulsion. In vitro assays demonstrate excellent biocompatibility and osteogenic properties, while in vivo experiments further confirm the cement's superior osteoinductive capability. The brushite cement regulates cellular metabolism and stem cell differentiation by enhancing energy metabolism and activating key signaling pathways such as phosphatidylinositol 3-kinase-AKT and mitogen-activated protein kinase-extracellular signal-regulated kinase. These findings offer a novel approach to fabricating CPCs with enhanced mechanical strength and osteogenic potential, addressing long-standing challenges in orthopedic MIS.

