Pyroptosis is a form of cell death mediated by inflammasomes and gasdermins, and the relevance of pyroptosis to neurodegenerative diseases is currently receiving increasing attention. Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that is closely associated with neuroinflammation. Its main pathological features include β-amyloid (Aβ) deposition, Tau protein hyperphosphorylation and neuronal loss. Aβ, tau-induced microglia pyroptosis and polarization leading to neuroinflammation play an important role in the pathogenesis of AD. Studying the pathogenesis and treatment of AD based on cellular pyroptosis has become a new direction in AD research. In this paper, we review the research progress of pyroptosis and will focus on the pathogenic roles of pyroptosis in AD and the role of targeted inhibition of inflammasome-dependent pyroptosis in AD treatment. These results deepen our understanding of the pathogenesis of AD and provide ideas for the development of new drugs based on the regulation of pyroptosis in AD patients.
"Like poles repel, unlike poles attract" is a fundamental principle of magnetism commonly used in instantaneous haptic interaction. Through the assembly design of basic five magnets, MagneChase creates a potential barrier between repulsion and attraction. This allows MagneChase modules to change the direction of their interacting force when brought closer. Applying this interaction principle, we provide application examples of kinetic roleplay to enhance tangible play experiences. A preliminary user study suggests that children were captivated by the magnetic phenomenon and derived pleasure from engaging with MagneChase. We also discuss the potential for MagneChase as a tangible kit to promote enlightenment learning in gameplay.
Abstract Motor imagery electroencephalogram (MI-EEG) is becoming increasingly important. This paper solves the problem of online signal recognition for motor imagery across subjects by finding common features across multiple subjects to improve the generality of the classification model. We analyzed the EEG data from left/right-hand motor imagery of eight subjects and proposed a weighted time-domain (WTD) feature extraction method based on a weighted channel screening method. The classification model constructed by combining this feature extraction method with the support vector machine (SVM) classification method was faster in classification and achieved good cross-subject classification accuracy (The average offline classification accuracy was 91.39%). In this paper, an online control system for asynchronous brain-controlled wheelchairs was built with good performance. The online average motor imagery classification accuracy was 81.67%, and the average response time was 1.36s. This method contributes to bringing the online Brain-computer interface (BCI) system out of the laboratory and into wider application.
As a repetition of cognitive activities occurring in designers' thinking process, mental iteration is considered to be a natural feature of a designer's competency. Although the utilization of mental iteration is believed to increase the efficiency of the design process and lead to better quality solutions, the current understanding of it is still limited. This paper presents coding categories that were developed from a study on graphic design behavior. An experiment is conducted to study the effect of AI-augmented on the behavior of mental iteration. The results indicate AI-augment increases the frequency of cognitive activities in design iterations.
Nursing research training is important for improving the nursing research competencies of clinical nurses. Rigorous development of such training programs is crucial for ensuring the effectiveness of these research training programs. Therefore, the objectives of this study are: (1) to rigorously develop a blended emergent research training program for clinical nurses based on a needs assessment and related theoretical framework; and (2) to describe and discuss the uses and advantages of the ADDIE model (Analysis, Design, Development, Implementation, Evaluation) in the instructional design and potential benefits of the blended emergent teaching method.
Museum exhibitions on traditional Chinese paintings are gaining popularity for educational and cultural value. Chinese paintings are characterized by a long history and implicit emotional expression, and it is challenging for non-professional and non-Chinese visitors to understand. To enhance museum visitors' interest and comprehension of Chinese artworks, we design an EEG-based interactive installation. The installation simulates the process of creation of a work of art, in this case a painting. Visitors can control the generation of lines, colors, and movements of characters by wearing a commercial EEG headset. Our interactive design contributes a novel experience of 'painting with your mind' and at the same time transform the exhibition into an enjoyable game experience.
Motor imagery electroencephalogram (MI-EEG) is becoming increasingly important. This paper solves the problem of online signal recognition for motor imagery across subjects by finding common features across multiple subjects to improve the generality of the classification model. We analysed the EEG data from left/right-hand motor imagery of eight subjects and proposed a weighted time-domain (WTD) feature extraction method based on a weighted channel screening method. The classification model constructed by combining this feature extraction method with the support vector machine (SVM) classification method was faster in classification and achieved good cross-subject classification accuracy (The average offline classification accuracy was 91.39%). In this paper, an online control system for asynchronous brain-controlled wheelchairs was built with good performance. The online average motor imagery classification accuracy was 81.67%, and the average response time was 1.36s. This method contributes to bringing the online Brain-computer interface (BCI) system out of the laboratory and into wider application.
With the emergence of Artificial Intelligence (AI) 2.0, computers are now equipped with new creative capabilities and are playing an increasingly significant role in design. The use of AI augmentation has the potential to enhance design performance, however, there is limited research on the acceptance of AI-augmented design. The research gap under consideration in this study is addressed by presenting an acceptance model designed for AI-augmented design. This model integrates a range of variables including perceived privacy risk, enjoyment, perceived value, perceived usefulness, perceived ease of use, perceived behavioral control, social influence, and behavioral intention. The proposed model was validated through a questionnaire survey of 249 designers in China.The results reveal that enjoyment, perceived value, perceived ease of use, perceived behavioral control, and social influence have a significant positive impact on users' intention to use AI-augmented design, while perceived privacy risk has a significant negative impact. Perceived value was found to mediate the relationship between enjoyment and behavioral intention, while perceived behavioral control play a mediation role in the relationship between social influence and behavioral intention.In conclusion, this study highlights the variables that influence the acceptance of AI-augmented design and provides valuable insights into the potential benefits and drawbacks of integrating AI technologies in design. The proposed acceptance model serves as a framework for future research in this area and can guide the development of more user-friendly and effective AI-augmented design tools and technologies.
Abstract Background: Evidence is scarce on the trend in prevalence of physical frailty in China; the primary purpose of this study was to identify the prevalence and correlates of physical frailty among older nursing home residents in China. Methods: Cross-sectional study in 20 nursing homes in Changsha, China. Physical frailty was defined based on the frailty phenotype including weight loss, low grip strength, exhaustion, slow gait speed, and low physical activity. Participants with at least three affected criteria were defined as being frail. Participants with one or two affected criteria were considered as pre-frail, and those with no affected criteria were considered as robust. A total of 1004 nursing home residents aged 60 and over were included in this study. A multinomial logistic regression model was used to analyze the associations of physical frailty with its potential risk factors, including age, sex, education levels, marital status, type of institution, living status, current drinking, current smoking, regular exercise, and self-reported health. Results: The overall prevalence of physical frailty and prefrailty was 55.6%, and 38.5%, respectively. The rate of physical frailty substantially increased with age, and was higher in women than in men (69.5% vs. 30.5%). The multinomial logistic regression analysis showed that older age, being women, living in a private institution, living alone or with unknown person, having no regular exercise (≤ 2 times/week), and poor self-reported health were significantly associated with increased odds of being physically frail. Conclusion: We demonstrated physical frailty is highly prevalent among older residents in nursing homes in China, especially in women. The potential role of those associated factors of physical frailty warrant further investigations to explore their clinical application among elderly nursing home residents.
Abstract Background Evidence is scarce on the trend in prevalence of physical frailty in China; the primary purpose of this study was to identify the prevalence and correlates of physical frailty among older nursing home residents in China. Methods Cross-sectional study in 20 nursing homes in Changsha, China. Physical frailty was defined based on the frailty phenotype including weight loss, low grip strength, exhaustion, slow gait speed, and low physical activity. Participants with at least three affected criteria were defined as being frail. Participants with one or two affected criteria were considered as pre-frail, and those with no affected criteria were considered as robust. A total of 1004 nursing home residents aged 60 and over were included in this study. A multinomial logistic regression model was used to analyze the associations of physical frailty with its potential risk factors, including age, sex, education levels, marital status, type of institution, living status, current drinking, current smoking, regular exercise, and self-reported health. Results The overall prevalence of physical frailty and prefrailty was 55.6, and 38.5%, respectively. The rate of physical frailty substantially increased with age, and was higher in women than in men (69.5% vs. 30.5%). The multinomial logistic regression analysis showed that older age, being women, living in a private institution, living alone or with unknown person, having no regular exercise (≤ 2 times/week), and poor self-reported health were significantly associated with increased odds of being physically frail. Conclusion We demonstrated physical frailty is highly prevalent among older residents in nursing homes in China, especially in women. The potential role of those associated factors of physical frailty warrant further investigations to explore their clinical application among elderly nursing home residents.
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