The most important factor contributing to short-term and long-term success of cementless total joint arthroplasties is osseointegration. Osseointegration leads to a direct structural and functional connection between living bone and the surface of an implant. Surface contaminants may remain on orthopaedic implants after sterilization procedures and impair osseointegration. For example, specific lots of hip replacement Sulzer Inter-OP(TM) acetabular shells that were associated with impaired osseointegration and early failure rates were found to be contaminated with both bacterial debris and machine oil residues. However, the effect of machine oil on implant integration is unknown. Therefore, the goal of this study was to determine if machine oil inhibits the osseointegration of orthopaedic implants. To test this hypothesis in vivo we used our murine model of osseointegration where titanium alloy implants are implanted into a unicortical pilot hole in the mid-diaphysis of the femur. We found that machine oil inhibited bone-to-implant contact and biomechanical pullout measures. Machine oil on titanium alloy discs inhibited early stages of MC3T3-E1 osteogenesis in vitro such as attachment and spreading. Inhibition of osteoblast attachment and spreading occurred in both areas with and without detectable oil. Osteoblast growth was in turn inhibited on discs with machine oil due to both a decrease in proliferation and an increase in cell death. Later stages of osteogenic differentiation and mineralization on titanium alloy discs were also inhibited. Thus, machine oil can inhibit osseointegration through cell autonomous effects on osteoblast cells. These results support routine testing by manufacturers of machine oil residues on orthopaedic implants.
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http://dx.doi.org/10.1002/jor.22850 | DOI Listing |
Sci Rep
January 2025
Civil and Environmental Engineering Department, Khalifa University, Abu Dhabi, UAE.
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January 2025
Young Researchers and Elite Club, Omidiyeh Branch, Islamic Azad University, Omidiyeh, Iran.
Precise estimation of rock petrophysical parameters are seriously important for the reliable computation of hydrocarbon in place in the underground formations. Therefore, accurately estimation rock saturation exponent is necessary in this regard. In this communication, we aim to develop intelligent data-driven models of decision tree, random forest, ensemble learning, adaptive boosting, support vector machine and multilayer perceptron artificial neural network to predict rock saturation exponent parameter in terms of rock absolute permeability, porosity, resistivity index, true resistivity, and water saturation based on acquired 1041 field data.
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January 2025
Department of Petroleum Engineering, Ahvaz Faculty of Petroleum, Petroleum University of Technology, Ahvaz, Iran.
Smart water injection (SWI) is a practical enhanced oil recovery (EOR) technique that improves displacement efficiency on micro and macro scales by different physiochemical mechanisms. However, the development of a reliable smart tool to predict oil recovery factors is necessary to reduce the challenges related to experimental procedures. These challenges include the cost and complexity of experimental equipment and time-consuming experimental methods for obtaining the recovery factor (RF).
View Article and Find Full Text PDFEnviron Int
December 2024
State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Science, Beijing 100012, China; State Environmental Protection Key Laboratory of Estuarine and Coastal Environment, Chinese Research Academy of Environmental Science, Beijing 100012, China; State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China. Electronic address:
Identifying and differentiating human activities is crucial for effectively preventing the threats posed by environmental pollution to aquatic ecosystems and human health. Machine learning (ML) is a powerful analytical tool for tracking human impacts on river ecosystems based on high-through datasets. This study employed an ML framework and 16S rRNA sequencing data to reveal microbial dynamics and trace human activities across China.
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December 2024
School of Oil & Natural Gas Engineering, SouthWest Petroleum University, Chengdu, 610500, China.
Unconventional gas reservoirs, characterized by their complex geologies and challenging extraction conditions, demand innovative approaches to enhance gas production and ensure economic viability. Well stimulation techniques, such as hydraulic fracturing and acidizing, have become indispensable tools in unlocking the potential of these tight formations. However, the effectiveness of these techniques can vary widely depending on the specific characteristics of the reservoir.
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