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In this study, polylactic acid (PLA) and thermoplastic polyurethane (TPU) were used to fabricate composite materials with different raster orientations by a 3D printing process. The idea of this study came from the fact that in nature, many plants have vertical or horizontal lines on their leaves or branches. The authors made use of the composite material of a material structure under a thermal effect to achieve the material concept. Finally, their characteristics were observed by applying a thermal effect, tensile tests (to evaluate toughness), and a dynamic mechanical analysis (tan [delta] to evaluate energy absorption). After a thermal effect (temperature) from a heat source was applied, it was found that the material exhibited negative Poisson's ratio (NPR) characteristic. A single material with two layers on 0[degrees] raster orientation of a material in this study had the maximum tan [delta] (energy absorption), an NPR, and toughness regardless of the kind of material. Among the composite materials, 0[degrees] raster orientation of them also had a larger NPR, and the absolute value of Poisson's ratio decreased as the temperature increased. Highlights * The innovative two-layer composite material fabricated by 3D printing. * The material characteristics with various raster orientations are discussed. * PLA0 has the largest tan [delta], toughness, and negative Poisson's ratio. KEYWORDS 3D printing, additive manufacturing, composite material, energy absorption, negative Poisson's ratio, raster orientation, thermal effect, 1 | INTRODUCTION Three-dimensional (3D) printing is a method of additive manufacturing (AM); it is also called rapid prototyping (RP) because it can quickly fabricate good finished products. The stereolithographic [...]

The skin is subjected to various external factors that contribute to aging including oxidative stress from hydrogen peroxide (H2O2). This study investigated the distribution of aquaporin-8 (AQP8), a protein that transports H2O2 across biological membranes, in skin cells, and its effects in mitigating H2O2-induced oxidative damage. Human dermal fibroblasts were treated with increasing concentrations of H2O2 to evaluate oxidative damage. Cell viability, reactive oxygen species (ROS) generation, and the expression of specific genes associated with skin aging (IL-10, FPR2, COL1A1, KRT19, and Aggrecan) were evaluated and AQP8 expression was assessed via quantitative polymerase chain reaction and western blotting. Small-interfering RNA was used to silence the AQP8 gene and evaluate its significance. The results show that H2O2 treatment reduces cell viability and increases ROS generation, leading to oxidative damage that affects the expression of target molecules. Interestingly, H2O2-treated cells exhibit high levels of AQP8 expression and gene silencing of AQP8 reverses high levels of ROS and low levels of COL1A1, KRT19, and Aggrecan expression in stressed cells, indicating that AQP8 plays a vital role in preventing oxidative damage and consequent aging. In conclusion, AQP8 is upregulated in human dermal fibroblasts during H2O2-induced oxidative stress and may help prevent oxidative damage and aging. These findings suggest that AQP8 could be a potential therapeutic target for skin aging. Further research is necessary to explore the feasibility of using AQP8 as a preventive or therapeutic strategy for maintaining skin health.

Research on the phonetic consistency effect in Chinese began in the 1980s. For nearly forty years, the consistency effect, as well as its implications for Chinese character recognition, has been frequently examined. This article presents the debate over the consistency effect in Chinese character recognition. While some research supported the involvement of prelexical phonology and sublexical processing based on the presence of consistency effects, others argued against these accounts by revealing three potential limitations, including the confounding of the task demand effect, the regularity effects and the frequency effect. A possible way to settle the debate is to shift focus to the top-down role of neighborhood characteristics underlying the consistency effect in Chinese character recognition.

Lidocaine, a local anesthetic widely used in dentistry, is esteemed for its efficacy and safety. Recent research reveals its additional role in modulating the immune system, and particularly in reducing inflammation crucial for protecting tooth-supporting tissues. Notably, monocytes and macrophages, essential cellular components overseeing various physiological and pathological processes, stand as potential mediators of lidocaine's effects. Therefore, this study aimed to investigate how lidocaine influences cell behavior using RNA sequencing. To investigate the effect of lidocaine on THP-1 cells' behavior, we performed an MTT assay and RNA-Seq along with qPCR analyses to evaluate the transcriptomic and proteomic changes in THP-1 cells. Our results showed that a high dose of lidocaine (>1 mM) had a significant cytotoxic effect on THP-1 cells. However, a lidocaine dose lower than 0.5 mM induced a mixed anti-inflammatory profile by significantly upregulating tissue remodeling (GDF15, FGF7, HGF, COL4A3, COL8A2, LAMB2, LAMC2, PDGFRA, and VEGFA) and through the resolution of inflammation (Cpeb4, Socs1, Socs2, Socs3, Dusp1, Tnfaip3, and Gata3) gene cassettes. This study explores the effect of lidocaine on the THP-1 in the M2-like healing phenotype and provides potential applications of lidocaine's therapeutic effectiveness in dental tissue repair. [ABSTRACT FROM AUTHOR]

Objective Surgical outcome prediction is challenging but necessary for postoperative management. Current machine learning models utilize pre- and post-op data, excluding intraoperative information in surgical notes. Current models also usually predict binary outcomes even when surgeries have multiple outcomes that require different postoperative management. This study addresses these gaps by incorporating intraoperative information into multimodal models for multiclass glaucoma surgery outcome prediction. Materials and methods We developed and evaluated multimodal deep learning models for multiclass glaucoma trabeculectomy surgery outcomes using both structured EHR data and free-text operative notes. We compare those to baseline models that use structured EHR data exclusively, or neural network models that leverage only operative notes. Results The multimodal neural network had the highest performance with a macro AUROC of 0.750 and F1 score of 0.583. It outperformed the baseline machine learning model with structured EHR data alone (macro AUROC of 0.712 and F1 score of 0.486). Additionally, the multimodal model achieved the highest recall (0.692) for hypotony surgical failure, while the surgical success group had the highest precision (0.884) and F1 score (0.775). Discussion This study shows that operative notes are an important source of predictive information. The multimodal predictive model combining perioperative notes and structured pre- and post-op EHR data outperformed other models. Multiclass surgical outcome prediction can provide valuable insights for clinical decision-making. Conclusions Our results show the potential of deep learning models to enhance clinical decision-making for postoperative management. They can be applied to other specialties to improve surgical outcome predictions. [ABSTRACT FROM AUTHOR]

The current digital dentistry workflow has streamlined dental restoration production, but the effectiveness of digital virtual design and 3D printing for restorations still needs evaluation. This study explores the impact of model-free digital design and 3D-printing placement angles on restorations, including single crowns and long bridges produced with and without casts. The restorations are 3D printed using resin at placement angles of 0°, 60°, and 90°. Each group of samples was replicated ten times, resulting in a total of 120 restorations. The Root Mean Square Error (RMSE) value was used to evaluate the surface integrity of the restoration. In addition, the contact space, edge gap, and occlusal space of restorations produced by different processes were recorded. The results indicate that there was no significant difference in the RMSE value of the crown group (p > 0.05). Changing the bridge restoration angle from 0° to 90° resulted in RMSE values increasing by 2.02 times (without casts) and 2.39 times (with casts). Furthermore, the marginal gaps in the crown group were all less than 60 μm, indicating good adaptation. In contrast, the bridge group showed a significant increase in marginal gaps at higher placement angles (p > 0.05). Based on the findings, virtual fabrication without casts does not compromise the accuracy of dental restorations. When the position of the long bridge exceeds 60 degrees, the error will increase. Therefore, designs without casts and parallel placement result in higher accuracy for dental restorations. [ABSTRACT FROM AUTHOR]

Previous studies about the orthographic neighborhood size (NS) in Chinese have overlooked the morphological processing, and the co-variation between the character frequency and the the NS. The present study manipulated the word frequency and the NS simultaneously, with the leading character frequency controlled, to explore their influences on word lexical decision (Experiment 1) and naming (Experiment 2). The results showed a robust effect that words with a larger NS produced shorter reaction time than those with a smaller NS, irrespective of the word frequency and the tasks. This facilitative effect may occur due to a semantic network formed by neighbor words, resulting in the semantic activation to accelerate the word recognition. Moreover, the comparison of the effect sizes of word frequency between the two tasks showed that lexical decision responses demonstrated a larger word frequency effect, indicating that the sub-word processing was involved in the multi-character word recognition.

Selecting appropriate texts for L2 (second/foreign language) learners is an important approach to enhancing motivation and, by extension, learning. There is currently no tool for classifying foreign language texts according to a language proficiency framework, which makes it difficult for students and educators to determine the precise difficulty/complexity levels of an unclassified text. Taking the Chinese language as an example, this study aimed to create a readability assessment system, called the Chinese Readability Index Explorer for Chinese as a Foreign Language (CRIE-CFL), in order to level--that is, to sort by proficiency level--texts that will be used for instructional purposes. The framework of choice in this project is the Common European Framework of Reference (CEFR). A team of expert CFL teachers first classified 1,578 CFL texts into their appropriate CEFR levels. A set of 30 CFL readability features was then developed or drawn from previous research, and sorted according to importance using F-scores. In addition, a support vector machine model was trained by sequentially integrating the features into the model to optimize accuracy. The empirical evaluation of CRIE-CFL revealed average exact- and adjacent-level accuracies of 74.97% and 99.62%, respectively, for predicting the expert classification of a text. The functionalities of CRIE-CFL are introduced and discussed.