Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Citation - Scopus: 2The Influence of Plastic Deformation Mechanisms on the Adhesion Behavior and Collagen Formation in Osteoblast Cells(Springer International Publishing, 2018) Uzer, B.; Monte, Felipe Alves Do; Awad, Kamal R.; Aswath, Pranesh; Varanasi, Venu Gopal; Canadinc, DemircanIn many of biomedical applications, the implant might get in direct contact with the bone tissue where the osteogenesis needs to be stimulated. If osteoblasts can not successfully attach on the implant surface, the bone might resorb and implant can fail. In the current study MC3T3 cells were cultured on the 316L stainless steel samples which were deformed up to four different strain levels (5, 15, 25 and 35%) to activate plastic deformation mechanisms (slip and twinning) in different volume fractions. Scanning electron microscopy (SEM) images showed that cells adhered and spread significantly on the 25 and 35% deformed samples owing to the greater surface roughness and energy provided by the increased density of micro-deformation mechanisms which promoted the formation of focal contacts. In addition, significant amount of collagen formation was observed on the sample deformed up to 25% of strain which can be due to the ideal match of the surface roughness and collagen molecules. Overall these results show that material’s microstructure can be manipulated through plastic deformation mechanisms in order to enhance the cell response and collagen deposition. As a result long lasting implants could be obtained which would eliminate additional surgical interventions and provide a successful treatment. © 2018 Elsevier B.V., All rights reserved.Article Citation - WoS: 3Citation - Scopus: 4Prediction of Biomechanical Properties of Ex Vivo Human Femoral Cortical Bone Using Raman Spectroscopy and Machine Learning Algorithms(Elsevier, 2025-09) Unal, Mustafa; Unlu, Ramazan; Uppuganti, Sasidhar; Nyman, Jeffry S.This study applied Raman spectroscopy (RS) to ex vivo human cadaveric femoral mid-diaphysis cortical bone specimens (n = 118 donors; age range 21-101 years) to predict fracture toughness properties via machine learning (ML) models. Spectral features, together with demographic variables (age, sex) and structural parameters (cortical porosity, volumetric bone mineral density), were fed into support vector regression (SVR), extreme tree regression (ETR), extreme gradient boosting (XGB), and ensemble models to predict fracture-toughness metrics such as crack-initiation toughness (Kinit) and energy-to-fracture (J-integral). Feature selection was based on Raman-derived mineral and organic matrix parameters, such as nu 1Phosphate (PO4)/CH2-wag, nu 1PO4/ Amide I, and others, to capture the complex composition of bone. Our results indicate that ensemble models consistently outperformed individual models, with the best performance for crack initiation toughness (Kinit) prediction being achieved using the ensemble approach. This yielded a coefficient of determination (R2) of 0.623, root-mean squared error (RMSE) of 1.320, mean absolute error (MAE) of 1.015, and mean percentage absolute error (MAPE) of 0.134. For prediction of the overall energy to propagate a crack (J-integral), the XGB model achieved an R2 of 0.737, RMSE of 2.634, MAE of 2.283, and MAPE of 0.240. This study highlights the importance of incorporating mineral quality properties (MP) and organic matrix properties (OMP) for enhanced prediction accuracy. This work represents the first-ever study combining Raman spectroscopy with other clinical and structural features to predict fracture toughness of human cortical bone, demonstrating the potential of artificial intelligence (AI) and ML in advancing bone research. Future studies could focus on larger datasets and more advanced modeling techniques to further improve predictive capabilities.