Currently, polymethylmethacrylate (PMMA) is the most popular denture base material. Most fractures of dentures that occur during function are due to its insufficient mechanical strength. The major drawbacks of PMMA are insufficient ductility, strength, and viscoelastic behavior. The purpose of this study was to evaluate a polymethylmethacrylate denture base material modified with TiO₂ nanoparticles in terms of nanomechanical, creep-recovery, and relaxation. Additionally, the effects of addition TiO₂ nanoparticles on the thermal and antimicrobial adhesion behaviors were investigated. Differential scanning calorimetry and thermogravimetric analysis indicated that the effect of small amounts of TiO₂ nanoparticles (1 wt. %, 2 wt. %, and 3 wt. %) on the degradation behavior of PMMA denture bases was insignificant. The nanomechanical test results of the PMMA and PMMA/TiO₂ nanocomposites indicated that the hardness and modulus in the nanoscale range improved due to TiO₂ addition. At a 1200-nm penetration depth, the modulus increased by 10%, 16%, and 29% and hardness increased by 18%, 24%, and 35% with the addition of 1 wt. %, 2 wt. %, and 3 wt. % TiO₂, respectively. Furthermore, the creep-recovery and relaxation behaviors of PMMA were significantly improved due to the addition of TiO₂. The creep strain decreased from 1.41% to 1.06%, 0.66%, and 0.49% with the addition of 1 wt. %, 2 wt. %, and 3 wt. % TiO₂, respectively. The relaxation test results showed that the initial stress under 1% strain improved to 19.9, 21.2, and 22 MPa with the addition of 1 wt. %, 2 wt. %, and 3 wt. % TiO₂, respectively. The improvement in the nanohardness, modulus, creep recovery, and relaxation behavior of PMMA due to the addition of TiO₂ nanoparticles indicated the role of the nanoparticles in increasing the PMMA matrix stiffness by reducing its mobility and free volume. TiO₂ nanoparticles also improved the antimicrobial behavior of PMMA by significantly reducing bacterial adherence with increasing TiO₂ ratio.
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http://dx.doi.org/10.3390/ma11071096 | DOI Listing |
In the context of Chinese clinical texts, this paper aims to propose a deep learning algorithm based on Bidirectional Encoder Representation from Transformers (BERT) to identify privacy information and to verify the feasibility of our method for privacy protection in the Chinese clinical context. We collected and double-annotated 33,017 discharge summaries from 151 medical institutions on a municipal regional health information platform, developed a BERT-based Bidirectional Long Short-Term Memory Model (BiLSTM) and Conditional Random Field (CRF) model, and tested the performance of privacy identification on the dataset. To explore the performance of different substructures of the neural network, we created five additional baseline models and evaluated the impact of different models on performance.
View Article and Find Full Text PDFClin Trials
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Department of Biostatistics, University of Florida, Gainesville, FL, USA.
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Front Biosci (Landmark Ed)
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Department of Food Biotechnology and Microbiology, Institute of Food Sciences, Warsaw University of Life Sciences - SGGW, 02-776 Warsaw, Poland.
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Front Biosci (Landmark Ed)
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Department of Cardiology, Affiliated Hospital of Jiangnan University, 214122 Wuxi, Jiangsu, China.
Background: Myocardial ischemia-reperfusion (I/R) injury refers to cell damage that occurs as a consequence of the restoration of blood circulation following reperfusion therapy for cardiovascular diseases, and it is a primary cause of myocardial infarction. The search for nove therapeutic targets in the context of I/R injury is currently a highly active area of research. p70 ribosomal S6 kinase (S6K1) plays an important role in I/R induced necrosis, although the specific mechanisms remain unclear.
View Article and Find Full Text PDFFront Biosci (Landmark Ed)
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Department of Cytobiology and Proteomics, Medical University of Lodz, 92-215 Lodz, Poland.
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