Artificial intelligence (AI) holds immense promise for K-12 education, yet understanding the factors influencing students’ engagement with AI courses remains a challenge. This study addresses this gap by extending the technology acceptance model (TAM) to incorporate cognitive factors such as AI intrinsic motivation (AIIM), AI readiness (AIRD), AI confidence (AICF), and AI anxiety (AIAX), alongside human–computer interaction (HCI) elements like user interface (UI), content (C), and learner-interface interactivity (LINT) in the context of using generative AI (GenAI) tools. By including these factors, an expanded model is presented to capture the complexity of student engagement with AI education. To validate the model, 210 Chinese students spanning grades K7 to K9 participated in a 1 month artificial intelligence course. Survey data and structural equation modeling reveal significant relationships between cognitive and HCI factors and perceived usefulness (PU) and ease of use (PEOU). Specifically, AIIM, AIRD, AICF, UI, C, and LINT positively influence PU and PEOU, while AIAX negatively affects both. Furthermore, PU and PEOU significantly predict students’ attitudes toward AI curriculum learning. These findings underscore the importance of considering cognitive and HCI factors in the design and implementation of AI education initiatives. By providing a theoretical foundation and practical insights, this study informs curriculum development and aids educational institutions and businesses in evaluating and optimizing AI4K12 curriculum design and implementation strategies.

The potential impact of microplastics (MPs) on health has caused great concern, and a toxicology platform that realistically reproduces the system behaviour is urgently needed to further explore and validate MP-related health issues. Herein, we introduce an optically assisted thrombus platform to reveal the interaction of MPs with the vascular system. The risk of accumulation has also been evaluated using a mouse model, and the effect of MPs on the properties of the thrombus are validated via in vitro experiments. The microfluidic system is endothelialized, and the regional tissue injury-induced thrombosis is then realized through optical irradiation. Whole blood is perfused with MPs, and the invasion process visualized and recorded. The mouse model shows a cumulative risk in the blood with continuous exposure to MPs (P-value < 0.0001). The on-chip results show that MP invasion leads to decreased binding of fibrin to platelets (P-value < 0.0001), which is consistent with the results of the in vitro experiments, and shows a high risk of thrombus shedding in real blood flow compared with normal thrombus. This work provides a new method to further reveal MP-related health risks.

It is significant for the machinery fault diagnosis to extract the fault information from the multi-component vibration signal. Feature mode decomposition (FMD) which can accurately separate the fault feature has been widely verified and accepted in this field. However, it greatly limited its application that FMD cannot be directly applied under the speed-varying condition (SVC) since its decomposition objective, correlated kurtosis, is only defined in the stationary condition. Motivated by this, a new angle-domain feature mode decomposition (AFMD) is tailored under the speed-varying condition. In this paper, average kurtosis (AK) which inherently highlights the periodic impulses from the angle domain is firstly used as the decomposition objective. Subsequently, a finite impulse response filter bank initialized by the Hanning window is designed to provide a direction for the decomposition. Then, the adaptive filtering decomposition is iteratively performed constrained by the objective AK. Finally, the redundant and mixing modes are removed by the mode dropout strategy with correlation coefficient as its criterion. The simulations and experiments validate the effectiveness of the proposed method AFMD under SVC and its success in extending FMD to the speed-varying condition.

Pathological cardiac hypertrophy is an independent risk factor for heart failure and is considered a target for the treatment of heart failure. However, the mechanisms underlying pathological cardiac hypertrophy remain largely unknown. We aimed to investigate the role of angiopoietin-like protein 8 (ANGPTL8) in pathological cardiac hypertrophy. We found that serum ANGPTL8 levels were significantly increased in hypertensive patients with cardiac hypertrophy and in mice with cardiac hypertrophy induced by Ang II or TAC. Furthermore, the secretion of ANGPTL8 from the liver was increased during hypertrophic processes, which were triggered by Ang II. In the Ang II- and transverse aortic constriction (TAC)-induced mouse cardiac hypertrophy model, ANGPTL8 deficiency remarkably accelerated cardiac hypertrophy and fibrosis with deteriorating cardiac dysfunction. Accordingly, both recombinant human full-length ANGPTL8 (rANGPTL8) protein and ANGPTL8 overexpression significantly mitigated Ang II-induced cell enlargement in primary neonatal rat cardiomyocytes (NRCMs) and H9c2 cells. Mechanistically, the antihypertrophic effects of ANGPTL8 depended on inhibiting Akt and GSK-3β activation, and the Akt activator SC-79 abolished the antihypertrophic effects of rANGPTL8 in vitro. Moreover, we demonstrated that ANGPTL8 directly bound to the paired Ig-like receptor PIRB (LILRB3) by RNA-seq and immunoprecipitation-mass screening. Remarkably, the antihypertrophic effects of ANGPTL8 were largely blocked by anti-LILRB3 and siRNA-LILRB3. Our study indicated that ANGPTL8 served as a novel negative regulator of pathological cardiac hypertrophy by binding to LILRB3 (PIRB) and inhibiting Akt/GSK3β activation, suggesting that ANGPTL8 may provide synergistic effects in combination with AT1 blockers and become a therapeutic target for cardiac hypertrophy and heart failure.

Purpose: Previous studies have suggested that obesity defined by body mass index(BMI) is a protective factor for bone mineral density(BMD), but have overlooked the potential influence of different types of obesity. This study aims to evaluate the correlation between abdominal obesity index A Body Shape Index(ABSI) and adolescent bone density, and analyze the relationship between abdominal obesity and bone metabolism. Methods: A total of 1557 adolescent participants were included in NHANES from 2007 to 2018. Calculate the ABSI using a specific formula that takes into account waist circumference and BMI. A weighted multiple linear regression model is used to evaluate the linear correlation between ABSI and BMD. Forest plots are used to analyze the correlations between subgroups, and cubic splines are limited to evaluate the nonlinear correlations and saturation effects between ABSI and BMD. Results: After adjusting for confounding factors, there was a significant linear correlation (P < 0.01) between ABSI and femoral BMD, both as a continuous variable and an ordered categorical variable. The restrictive cubic spline curve indicates a significant nonlinear correlation and saturation effect between adolescent ABSI and BMD. Conclusion: Research has shown a significant negative correlation between ABSI and BMD at the four detection sites of the femur, and this correlation may vary slightly due to age, race, family income, and different detection sites. The research results indicate that compared to overall body weight, fat distribution and content may be more closely related to bone metabolism.