KoreaBio

Craniofacial bone reconstruction presents significant clinical challenges due to the region's complex anatomy and the need to restore both structural integrity and aesthetic function. This study aimed to enhance bone regeneration at graft sites through the development of an osteoconductive bioactive glass-polymer composite for patient-specific implant (PSI) applications. Composite constructs were fabricated via extrusion-based 3D printing using varying weight ratios of bioactive glass ceramic (BGS-7) and poly(ε-caprolactone) (PCL): 2:8, 4:6, 4.5:5.5, and 5:5. Printing parameters were optimized for each formulation to ensure consistent material flow and structural fidelity. Comprehensive characterization included morphological and elemental analysis via scanning electron microscopy/energy-dispersive X-ray spectroscopy, mechanical testing (compressive, flexural, and tensile), and biological evaluation and osteogenic differentiation of human placental stem cells. Higher BGS-7 content correlated with increased incorporation of phosphate, silicon, and calcium, contributing to enhanced mechanical properties and osteogenic potential. The constructs supported high cell viability, promoted cell adhesion and spreading, and induced osteogenic differentiation, as evidenced by calcium deposition and upregulation of key markers. The high-content BGS-7/PCL (5:5) formulation demonstrated optimal printability and bioactivity and was successfully used to fabricate anatomically accurate, human-scale structure. These findings highlight the potential of BGS-7/PCL composites as scalable, biocompatible, and osteoconductive platforms for craniofacial PSI applications.

Neoantigens are tumor-derived peptides and are biomarkers that can predict prognosis related to immune checkpoint inhibition by estimating their binding to major histocompatibility complex (MHC) proteins. Although deep neural networks have been primarily used for these prediction models, it is difficult to interpret the models reported thus far as accurately representing the interactions between biomolecules. In this study, we propose the GraphMHC model, which utilizes a graph neural network model applied to molecular structure to simulate the binding between MHC proteins and peptide sequences. Amino acid sequences sourced from the immune epitope database (IEDB) undergo conversion into molecular structures. Subsequently, atomic intrinsic informations and inter-atomic connections are extracted and structured as a graph representation. Stacked graph attention and convolution layers comprise the GraphMHC network which classifies bindings. The prediction results from the test set using the GraphMHC model showed a high performance with an area under the receiver operating characteristic curve of 92.2% (91.9-92.5%), surpassing a baseline model. Moreover, by applying the GraphMHC model to melanoma patient data from The Cancer Genome Atlas project, we found a borderline difference (0.061) in overall survival and a significant difference in stromal score between the high and low neoantigen load groups. This distinction was not present in the baseline model. This study presents the first feature-intrinsic method based on biochemical molecular structure for modeling the binding between MHC protein sequences and neoantigen candidate peptide sequences. This model can provide highly accurate responsibility information that can predict the prognosis of immune checkpoint inhibitors to cancer patients who want to apply it.