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Calcium-phosphate cement (CPC), a paste-like artificial bone, is a material form that allows minimally invasive treatment. However, CPC is not infection resistant, which may lead to surgical site infections. We recently developed a paste-like organic/inorganic hybrid artificial bone that is compatible with the bone remodeling cycle. In this study, we added silver-loaded tricalcium phosphate, which has antibacterial properties, to the hybrid CPC and fabricated a prototype "antibacterial CPC". Antibacterial and non-antibacterial CPCs were implanted into a rabbit jaw defect model in which infection could occur, and the in vivo responses were compared. In cement specimens retrieved from rabbit jaws, residual material was observed with the non-antibacterial CPC, whereas with the antibacterial CPC, almost all of the material was resorbed and replaced with host bone. These results suggest that placement of antibacterial CPC in a rabbit jaw bone defect model susceptible to bacterial infection promotes material resorption and bone formation. The antibacterial CPC developed in this study is thus a novel paste artificial bone exhibiting good bioresorption and osteogenic potential in biological hard tissues.
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http://dx.doi.org/10.1007/s10856-025-06932-0 | DOI Listing |
Osteoarthr Cartil Open
December 2025
Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China.
Objective: We developed and validated an artificial intelligence pipeline that leverages diffusion models to enhance prognostic assessment of knee osteoarthritis (OA) by analyzing longitudinal changes in patella shape on lateral knee radiographs.
Method: In this retrospective study of 2,913 participants from the Multicenter Osteoarthritis Study, left-knee weight-bearing lateral radiographs obtained at baseline and 60 months were analyzed. Our pipeline commences with an automatic segmentation for patella shapes, followed by a diffusion model to predict patella shape trajectories over 60 months.
Cureus
August 2025
Department of Orthopaedic Surgery, Teikyo University School of Medicine, Tokyo, JPN.
Osteoporosis is a common condition, and treatment can reduce the risk of fracture and extend healthy life expectancy, but most cases go undiagnosed and untreated. Dual-energy X-ray absorptiometry (DXA), the gold standard for diagnosing osteoporosis, is costly, time-consuming, and labor-intensive, with limited availability in low-resource settings and small clinics, so it is not suitable for screening for potential osteoporosis. To address this problem, in recent years, some studies have attempted to screen for osteoporosis by estimating DXA bone mineral density (BMD) from chest radiographs (CR), which are frequently used in daily clinical practice, by applying deep learning technology.
View Article and Find Full Text PDFIEEE Trans Autom Sci Eng
January 2025
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
Cone beam computed tomography (CBCT) is a widely-used imaging modality in dental healthcare. It is an important task to segment each 3D CBCT image, which involves labeling lesions, bone, teeth, and restorative material on a voxel-by-voxel basis, as it aids in lesion detection, diagnosis, and treatment planning. The current clinical practice relies on manual segmentation, which is labor-intensive and demands considerable expertise.
View Article and Find Full Text PDFBone Rep
September 2025
Department of Orthopaedic Surgery, Vanderbilt University Medical Center, 1215 21st Ave. S., Suite 4200, Nashville, TN 37232, USA.
This study applied Raman spectroscopy (RS) to ex vivo human cadaveric femoral mid-diaphysis cortical bone specimens ( = 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 (K) and energy-to-fracture (J-integral). Feature selection was based on Raman-derived mineral and organic matrix parameters, such as νPhosphate (PO)/CH-wag, νPO/Amide I, and others, to capture the complex composition of bone.
View Article and Find Full Text PDFConnect Tissue Res
September 2025
Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland.
Osteoarthritis (OA) is a multifactorial, mechano-inflammatory joint disorder characterized by cartilage degradation, synovial inflammation, and subchondral bone remodeling. Despite its high prevalence and significant impact on quality of life, no disease-modifying treatments have been approved. In many other disease areas, advanced omics technologies are impacting the development of advanced therapies.
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