Integrative transcriptomic and genomic analyses unveil the IFI16 variants and expression as MASLD progression markers.

BACKGROUND AND AIMS: Metabolic dysfunction-associated steatotic liver disease (MASLD) encompasses a broad and continuous spectrum of liver diseases ranging from fatty liver to steatohepatitis. The intricate interactions of genetic, epigenetic, and environmental factors in the development and progression of MASLD remain elusive. Here, we aimed to achieve an integrative understanding of the genomic and transcriptomic alterations throughout the progression of MASLD. APPROACH AND RESULTS: RNA-Seq profiling (n = 146) and whole-exome sequencing (n = 132) of MASLD liver tissue samples identified 3 transcriptomic subtypes (G1-G3) of MASLD, which were characterized by stepwise pathological and molecular progression of the disease. Macrophage-driven inflammatory activities were identified as a key feature for differentiating these subtypes. This subtype-discriminating macrophage interplay was significantly associated with both the expression and genetic variation of the dsDNA sensor IFI16 (rs6940, A>T, T779S), establishing it as a fundamental molecular factor in MASLD progression. The in vitro dsDNA-IFI16 binding experiments and structural modeling revealed that the IFI16 variant exhibited increased stability and stronger dsDNA binding affinity compared to the wild-type. Further downstream investigation suggested that the IFI16 variant exacerbated DNA sensing-mediated inflammatory signals through mitochondrial dysfunction-related signaling of the IFI16-PYCARD-CASP1 pathway. CONCLUSIONS: This study unveils a comprehensive understanding of MASLD progression through transcriptomic classification, highlighting the crucial roles of IFI16 variants. Targeting the IFI16-PYCARD-CASP1 pathway may pave the way for the development of novel diagnostics and therapeutics for MASLD.

I know my teammates: the role of Group Member Familiarity in Computer-Supported and face-to-face collaborative learning.

Considering the importance of group member familiarity in collaborative learning in classroom learning environments, this study examined the impact of group member familiarity on CSCL (computer-supported collaborative learning) in a networked setting. Also, the differences between CSCL in the online environments and FtF (face-to-face) collaborative learning were also compared. An analysis using structural equation modeling revealed that group member familiarity increased teamwork satisfaction, which in turn increased student engagement and perceived knowledge construction. A multi-group analysis revealed that while FtF collaborative learning exhibits higher levels of group member familiarity, teamwork satisfaction, student engagement, and perceived knowledge construction, the mediating effect of teamwork satisfaction was more pronounced in online settings. The study findings provided insights for teachers to improve collaborative learning experiences and adapt different teaching strategies.

Effects of postpolymerization conditions on the physical properties, cytotoxicity, and dimensional accuracy of a 3D-printed dental restorative material.

STATEMENT OF PROBLEM: Although the introduction of high-speed 3-dimensional (3D) printing technology has significantly reduced printing time, the time required for postpolymerization is a speed-determining step because of the long wait time. How postpolymerization conditions affect material properties is unclear. PURPOSE: The purpose of this in vitro study was to assess the physical properties, accuracy, and biosafety of a 3D-printed dental restorative material according to postpolymerization conditions. MATERIAL AND METHODS: Specimens were prepared by 3D printing with a digital light processing 3D printer with 1 interim dental material (C&B MFH). All printed specimens underwent a postpolymerization process with 5 different postpolymerization devices and were designated as groups D1 (D102H), FO (Form Cure), LC (LC-3DPrintBox), ME (Medusa), and MP (MP100). The light intensity and temperature of each device were measured, and the Vickers hardness, flexural strength and modulus, degree of conversion (DC), cytotoxicity, and polymerization shrinkage were analyzed. Statistical analyses were conducted with 1-way analysis of variance, the Tukey post hoc test, and regression testing (α=.05). Scanning electron microscopy was used to assess the fracture surface characteristics of the specimens. RESULTS: Light intensity was strongest with the ME device, and the temperature inside the device during postpolymerization showed the highest increase with the LC device and the lowest increase with the D1 device. The LC group specimens showed the highest mean Vickers hardness, and the MP group showed the lowest. The flexural strength was ≥100 MPa in all groups, with a flexural modulus ranging from 1.17 to 1.5 GPa. The DC results were similar to the physical properties test results. The D1, FO, LC, and ME groups all showed ≥70% cell viability, indicating no toxicity. The FO group showed the highest shrinkage rate of 0.52%. CONCLUSIONS: When the light intensity was strong, the surface was sufficiently hard, and toxic substances were not eluted even after a short postpolymerization time, suggesting that light intensity modulation and time management can be used to improve the postpolymerization process.

Dental students' perceptions on a simulated practice using patient-based customised typodonts during the transition from preclinical to clinical education.

PURPOSE/OBJECTIVES: Dental students experience difficulties during the transition from preclinical to clinical curriculum. In order to help the students to adapt to the clinical education programme, a simulated practice using patient-based customised models was introduced in this study to prepare for their first clinical practice. METHODS: This study included 45 third-year predoctoral students (D3 students) who were about to perform the preparation of a single crown abutment on their first patient. After practicing abutment preparation using simulated models and providing the actual treatment to their own patient, the students were surveyed to investigate their perceptions on the simulated practice using the 3D-printed customised typodont model. The statistical analysis of the quantitative data and the thematic analysis of the qualitative data were conducted. RESULTS: Regarding this simulation, more than 80% of the students gave positive feedback on their practice of (a) operative positions and postures, (b) finger rest, (c) occlusal reduction, (d) axial reduction and (e) proximal reduction. Student responses on the open-ended questions about how they perceived the usefulness of this simulation were categorised as "First clinical case," "Patient-based model" and "Realistic simulation environment." In addition, a number of improvements of the simulation were also suggested by the students including the typodont and the manikin. CONCLUSIONS: This study gives insights into the significance of simulated practice using patient-based customised typodonts as a transitional education tool and its direction of development in the field of restorative treatments accompanied by irreversible tooth preparations.

My video game console is so cool! A coolness theory-based model for intention to use video game consoles.

With the outbreak of COVID-19, the video game console market is thriving again. In this study, we attempted to explore users' intention to use video game consoles by developing a causal model mainly based on coolness theory and the technology acceptance model. To better illustrate user experience for video game consoles, we added several concepts to the causal model, including hedonic motivation, system and service quality, perceived cost, and game variety. Through examining survey-based data from 360 Koreans, we discovered that the model had a high explanatory power for users' intention to use video game consoles. The key findings were as follows: First, among the components of coolness theory, individuals' attitude toward consoles was significantly related to subcultural appeal and originality, but not to attractiveness. Second, originality positively influenced subcultural appeal significantly. Overall, this study implied that the novel coolness theory is effective for exploring user experience regarding of specific devices and services.

Changes in mucogingival junction after an apically positioned flap with collagen matrix at sites with or without previous guided bone regeneration: A prospective comparative cohort study.

AIM: To assess changes in the position of the mucogingival junction (MGJ) after an apically positioned flap (APF) with collagen matrix performed at sites with or without previous guided bone regeneration (GBR). MATERIALS AND METHODS: Dental implants were placed with or without GBR (group GBR or non-GBR) depending on the available ridge width in 30 patients with a limited width of keratinized mucosa (MGJ placed more coronally than the expected prosthetic margin). An apically positioned flap with collagen matrix was performed in both groups. Changes in the position of the MGJ from the day of an apically positioned flap up to 1, 3, and 12 months thereafter were assessed on digital scans (primary endpoint). Secondary endpoints were the width and thickness of the keratinized mucosa, and the position of the mucosal margin. RESULTS: The position of the MGJ changed significantly from baseline to the first month, by 5.25 ± 2.10 and 4.40 ± 1.41 mm in groups GBR and non-GBR, respectively. Thereafter, the position remained stable in both groups up to 1 year (changes from baseline of 5.46 ± 2.28 and 4.58 ± 1.92 mm, respectively; p = .34). The position of the mucosal margin did not differ between groups GBR and non-GBR (-1.57 ± 2.04 and -1.75 ± 2.08 mm, respectively; p = .84), nor did the width of the keratinized mucosa (1.20 ± 1.03 and 0.99 ± 0.66 mm, p = .91) or its thickness (1.28 ± 0.44 and 1.40 ± 0.78 mm, p = .87). CONCLUSION: Apically positioned flap combined with a collagen matrix results in a more apical position of the MGJ at sites with or without GBR. Following a coronal shift during the first month after the apical positioning of the flap, the level of the MGJ remained stable.

Clinical factors influencing implant positioning by guided surgery using a nonmetal sleeve template in the partially edentulous ridge: Multiple regression analysis of a prospective cohort.

OBJECTIVE: To determine the positional accuracy of implants placed with a three-dimensionally printed template having nonmetal sleeves and to determine the contributing factors to observed deviations. MATERIALS AND METHODS: One hundred and eighty-seven implants placed in 72 patients were analyzed. Presurgical intraoral scans and cone-beam computed tomography images obtained before and after surgery were superimposed, and vertical, angular, platform, and apex deviations were measured between the virtually planned and actually placed positions. A multiple linear regression model was designed for identifying the contributing factors. Statistical significance was set at p < .05, with Bonferroni correction if necessary (p < .0167). RESULTS: A total of 187 implants demonstrated deviations of 0.65 [0.56, 0.75] mm (mean [95% confidence interval]) vertically, 3.59° [3.30°, 3.89°] angularly, 1.16 [1.04, 1.28] mm at platform, and 1.50 [1.36, 1.65] mm at apex. Implants placed in the mandible showed larger angular, platform, and apex deviations compared with those in the maxilla (p = .049, p = .014 and p = .003, respectively). Implants placed at the third or fourth nearest sites from the most-distal tooth had larger deviations than those placed at the first or second nearest sites, in vertical, platform, and apical aspects (p = .015, p = .011 andp = .018, respectively). This was only applicable to free-ending-supported templates (p < .0167), and anchor pin-supported free-ending templates (p < .0167). CONCLUSION: Using a three-dimensionally printed surgical template with a nonmetal sleeve in the partial edentulous ridge resulted in larger deviations in implants placed in the mandible or distal free-end third or fourth nearest site.

Transient Analysis of Tunnel Field-Effect Transistor with Raised Drain.

This paper investigates the electrical performance of a proposed tunnel field-effect transistor (TFET) via transient analysis. The proposed TFET has three features: a SiGe channel, a fin structure, and an elevated drain. As the SiGe channel and fin structure make a small tunnel resistance, the ONstate current can be increased. In addition, an elevated drain can suppress an ambipolar current (IAMB). The transient characteristics should be confirmed in terms of inverter switching for the high applicability of the proposed device to the logical circuit. The analysis is verified through technology computer-aided design (TCAD) simulations calibrated with rigorously fabricated devices. Based on the simulation results, we conclude that the proposed TFET shows better ON/OFF transient characteristics when compared to conventional TFETs and the small gate-to-drain capacitance (CGD) can improve the transient characteristics in TFET.

Extraction of recombination coefficients and internal quantum efficiency of GaN-based light emitting diodes considering effective volume of active region.

An improved rate equation model for GaN-based LEDs considering the effective volume of the active region is proposed. Through numerical simulations, it is confirmed that the IQE, especially efficiency droop is related with small effective volume. Also, we confirmed that the effective volume is controlled by polarization charge, the barriers between the quantum wells, and current density. We also developed a fast and reliable method for extracting the recombination coefficients and the IQE of the GaN-based LEDs by measuring transient characteristics and considering the effective volume.

Social media use, body image, and psychological well-being: a cross-cultural comparison of Korea and the United States.

This study examined the relationships among social media use for information, self-status seeking and socializing, body image, self-esteem, and psychological well-being, and some cultural effects moderating these relationships. Americans (n = 502) and Koreans (n = 518) completed an online survey. The main findings showed that (a) social media use for information about body image is negatively related to body satisfaction in the United States and Korea, while social media use for self-status seeking regarding body image is positively related to body satisfaction only in Korea; and (b) body satisfaction has direct and indirect positive effects on psychological well-being manifested in similar ways in the United States and Korea. Implications and future research directions are discussed.

Inhibition of TNF-α-induced MUC5AC mucin gene expression and production by wogonin through the inactivation of NF-κB signaling in airway epithelial cells.

In this study, we investigated whether wogonin significantly affects MUC5AC mucin gene expression and production in human airway epithelial cells. Confluent NCI-H292 cells were pretreated with wogonin for 30 min and then stimulated with tumor necrosis factor-α (TNF-α) for 24 h or the indicated periods. The MUC5AC mucin gene expression and mucin protein production were measured by RT-PCR and ELISA, respectively. We found that incubation of NCI-H292 cells with wogonin significantly inhibited mucin production and down-regulated MUC5AC gene expression induced by TNF-α in a dose-dependent fashion. To elucidate the action mechanism of wogonin, effect of wogonin on TNF-α-induced NF-κB signaling pathway was investigated by western blot analysis. Wogonin inhibited NF-κB activation induced by TNF-α. Inhibition of IKK by wogonin led to the suppression of IκB phosphorylation and degradation, p65 nuclear translocation and NF-κB-regulated gene expression. This, in turn, led to the down-regulation of MUC5AC protein production in NCI-H292 cells. Wogonin also inhibited the gene products involved in cell survival (Bcl-2) and proliferation (cyclooxygenase-2). These results suggest that wogonin inhibits the NF-κB signaling pathway, which may explain its role in the inhibition of MUC5AC mucin gene expression and production.

Deep-learning-based stock market prediction incorporating ESG sentiment and technical indicators.

As sustainability emerges as a crucial factor in the development of modern enterprises, integrating environmental, social, and governance (ESG) information into financial assessments has become essential. ESG indicators serve as important metrics in evaluating a company's sustainable practices and governance effectiveness, influencing investor trust and future growth potential, ultimately affecting stock prices. This study proposes an innovative approach that combines ESG sentiment index extracted from news with technical indicators to predict the S&P 500 index. By utilizing a deep learning model and exploring optimal window sizes, the study explores the best model through mean absolute percentage error (MAPE) as an evaluation metric. Additionally, an ablation test clarifies the influence of ESG and its causality with the S&P 500 index. The experimental results demonstrate improved predictive accuracy when considering ESG sentiment compared to relying solely on technical indicators or historical data. This comprehensive methodology enhances the advantage of stock price prediction by integrating technical indicators, which consider short-term fluctuations, with ESG information, providing long-term effects. Furthermore, it offers valuable insights for investors and financial market experts, validating the necessity to consider ESG for financial assets and introducing a new perspective to develop investment strategies and decision-making processes.

Expansive data, extensive model: Investigating discussion topics around LLM through unsupervised machine learning in academic papers and news.

This study presents a comprehensive exploration of topic modeling methods tailored for large language model (LLM) using data obtained from Web of Science and LexisNexis from June 1, 2020, to December 31, 2023. The data collection process involved queries focusing on LLMs, including "Large language model," "LLM," and "ChatGPT." Various topic modeling approaches were evaluated based on performance metrics, including diversity and coherence. latent Dirichlet allocation (LDA), nonnegative matrix factorization (NMF), combined topic models (CTM), and bidirectional encoder representations from Transformers topic (BERTopic) were employed for performance evaluation. Evaluation metrics were computed across platforms, with BERTopic demonstrating superior performance in diversity and coherence across both LexisNexis and Web of Science. The experiment result reveals that news articles maintain a balanced coverage across various topics and mainly focus on efforts to utilize LLM in specialized domains. Conversely, research papers are more concise and concentrated on the technology itself, emphasizing technical aspects. Through the insights gained in this study, it becomes possible to investigate the future path and the challenges that LLMs should tackle. Additionally, they could offer considerable value to enterprises that utilize LLMs to deliver services.

ESG2PreEM: Automated ESG grade assessment framework using pre-trained ensemble models.

Incorporating environmental, social, and governance (ESG) criteria is essential for promoting sustainability in business and is considered a set of principles that can increase a firm's value. This research proposes a strategy using text-based automated techniques to rate ESG. For autonomous classification, data were collected from the news archive LexisNexis and classified as E, S, or G based on the ESG materials provided by the Refinitiv-Sustainable Leadership Monitor, which has over 450 metrics. In addition, Bidirectional Encoder Representations from Transformers (BERT), Robustly optimized BERT approach (RoBERTa), and A Lite BERT (ALBERT) models were trained to accurately categorize preprocessed ESG documents using a voting ensemble model, and their performances were measured. The accuracy of the ensemble model utilizing BERT and ALBERT was found to be 80.79% with batch size 20. Additionally, this research validated the performance of the framework for companies included in the Dow Jones Industrial Average (DJIA) and compared it with the grade provided by Morgan Stanley Capital International (MSCI), a globally renowned ESG rating agency known for having the highest creditworthiness. This study supports the use of sophisticated natural language processing (NLP) techniques to attain important knowledge from large amounts of text-based data to improve ESG assessment criteria established by different rating agencies.

Madecassoside inhibits melanin synthesis by blocking ultraviolet-induced inflammation.

Madecassoside (MA), a pentacyclic triterpene isolated from Centella asitica (L.), is used as a therapeutic agent in wound healing and also as an anti-inflammatory and anti-aging agent. However, the involvement of MA in skin-pigmentation has not been reported. This study was conducted to investigate the effects of MA on ultraviolet (UV)-induced melanogenesis and mechanisms in a co-culture system of keratinocytes and melanocytes. MA significantly inhibited UVR-induced melanin synthesis and melanosome transfer in the co-culture system. These effects were further demonstrated by the MA-induced inhibition of protease-activated receptor-2 expression and its signaling pathway, cyclooxygenase-2, prostaglandin E2 and prostaglandin F2 alpha in keratinocytes. The clinical efficacy of MA was confirmed on artificially tanned human skin. MA significantly reduced UV-induced melanin index at 8 weeks after topical application. Overall, the study demonstrated significant benefits of MA use in the inhibition of hyperpigmentation caused by UV irradiation.

Sentiment Impact of Public Health Agency communication Strategies on TikTok under COVID-19 Normalization: Deep Learning Exploration.

Aim: The accessibility of social media data has allowed researchers to measure official-public interactions during COVID-19. However, previous work analyzing official posts or public comments has failed to explore the link between the two. Therefore, this study investigates the relationship between the communication strategies of public health agencies (PHAs) on TikTok and public emotional/sentiment tendencies in COVID-19 normalization. Subject and methods: This study uses the 2022 Shanghai city closure event as a public health communication case study in the context of COVID-19 normalization, using TikTok as a data source. We first analyze the communication strategies adopted by the PHA based on the Crisis and Emergency Risk Communication (CERC) model. Then, we classify the sentiment of public comments using the Large-Scale Knowledge Enhanced Pre-Training for Language Understanding and Generation (ERNIE) pre-training model. Finally, we explore the connection between PHA communication strategies and public sentiment tendencies. Results: First, the public's sentiment tendencies differ at different stages. Therefore, appropriate communication strategies should be developed stage-by-stage. Second, the public's emotional disposition to different communication strategies varies: government statements, vaccines, and prevention and control programs are more likely to produce a friendly comment environment, while policy and new cases per day are more likely to produce unfavorable comment content. However, this does not mean that policy and new cases per day should be avoided; the judicious use of these two strategies can help PHAs understand the current issues causing public dissatisfaction. Third, videos with celebrity appearances can significantly increase positive public sentiment and, thereby, public participation. Conclusion: We propose an improved CERC guideline for China based on the Shanghai lockdown case.

Effect of daily new cases of COVID-19 on public sentiment and concern: Deep learning-based sentiment classification and semantic network analysis.

Aim: This study explored the influence of daily new case videos posted by public health agencies (PHAs) on TikTok in the context of COVID-19 normalization, as well as public sentiment and concerns. Five different stages were used, based on the Crisis and Emergency Risk Communication model, amidst the 2022 Shanghai lockdown. Subject and Methods: After dividing the duration of the 2022 Shanghai lockdown into stages, we crawled all the user comments of videos posted by Healthy China on TikTok with the theme of daily new cases based on these five stages. Third, we constructed the pre-training model, ERNIE, to classify the sentiment of user comments. Finally, we performed semantic network analyses based on the sentiment classification results. Results: First, the high cost of fighting the epidemic during the 2022 Shanghai lockdown was why ordinary people were reluctant to cooperate with the anti-epidemic policy in the pre-crisis stage. Second, Shanghai unilaterally revised the definition of asymptomatic patients led to an escalation of risk levels and control conditions in other regions, ultimately affecting the lives and work of ordinary people in the area during the initial event stage. Third, the public reported specific details that affected their lives due to the long-term resistance to the epidemic in the maintenance stage. Fourth, the public became bored with videos regarding daily new cases in the resolution stage. Finally, the main reason for the negative public sentiment was that the local government did not follow the central government's anti-epidemic policy. Conclusion: Our results suggest that the methodology used in this study is feasible. Furthermore, our findings will help the Chinese government or PHAs improve the possible behaviors that displease the public in the anti-epidemic process.

How does online social interaction promote students' continuous learning intentions?

Learning from online videos using synchronized Danmu comments provides a co-learning experience. To explore the factors influencing learning with or without Danmu videos, an initial set of reasons and impediments was compiled based on a pilot study of 24 Chinese university students with learning experience using Danmu videos. Three hundred such students were surveyed to determine the factors that influence their motivations and hindrances with regard to using Danmu videos. The potential predictors of users' continuance intentions were also examined. The results showed that the frequency of using Danmu videos is related to the continuous learning intention. Seeking information, social presence, and perceived entertainment motivate learners and positively impact their intention to continue learning using Danmu videos. Hindrances such as information pollution, attention failure, and visual obstacle were found to be negatively associated with learners' continuance intention in the long run. Our findings provided constructive suggestions for addressing the issue of dropout rates, and novel ideas for future studies were proposed.

Correction to: Effect of daily new cases of COVID-19 on public sentiment and concern: Deep learning-based sentiment classification and semantic network analysis.

[This corrects the article DOI: 10.1007/s10389-023-01833-4.].

Machine Learning Algorithm for Efficient Design of Separated Buffer Super-Junction IGBT.

An improved structure for an Insulated Gate Bipolar Transistor (IGBT) with a separated buffer layer is presented in order to improve the trade-off between the turn-off loss (Eoff) and on-state voltage (Von). However, it is difficult to set efficient parameters due to the increase in the new buffer doping concentration variable. Therefore, a machine learning (ML) algorithm is proposed as a solution. Compared to the conventional Technology Computer-Aided Design (TCAD) simulation tool, it is demonstrated that incorporating the ML algorithm into the device analysis could make it possible to achieve high accuracy and significantly shorten the simulation time. Specifically, utilizing the ML algorithm could achieve coefficients of determination (R2) of Von and Eoff of 0.995 and 0.968, respectively. In addition, it enables the optimized design to fit the target characteristics. In this study, the structure proposed for the trade-off improvement was targeted to obtain the minimum Eoff at the same Von, especially by adjusting the concentration of the separated buffer. We could improve Eoff by 36.2% by optimizing the structure, which was expected to be improved by 24.7% using the ML approach. In another way, it is possible to inversely design four types of structures with characteristics close to the target characteristics (Eoff = 1.64 µJ, Von = 1.38 V). The proposed method of incorporating machine learning into device analysis is expected to be very strategic, especially for power electronics analysis (where the transistor size is comparatively large and requires significant computation). In summary, we improved the trade-off using a separated buffer, and ML enabled optimization and a more precise design, as well as reverse engineering.