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Artificially intelligent robots as teachers (AI teachers) have attracted extensive attention due to their potential to relieve the challenge of global teacher shortage and realize universal elementary education by 2030. Despite mass production of service robots and discussions about their educational applications, the study of full-fledged AI teachers and children's attitudes towards them is quite preliminary. Here, we report a new AI teacher and an integrated model to assess how pupils accept and use it. Participants included students from Chinese elementary schools via convenience sampling. Questionnaires (n = 665), descriptive statistics and structural equation modeling based on software SPSS Statistics 23.0 and Amos 26.0 were carried out in data collection and analysis. This study first developed an AI teacher by coding a lesson design, course contents and Power Point with script language. Based on the popular Technology Acceptance Model and Task-Technology Fit Theory, this study identified key determinants of the acceptance, including robot use anxiety (RUA), perceived usefulness (PU), perceived ease of use (PEOU) and robot instructional task difficulty (RITD). Moreover, this study found that pupils' attitudes towards the AI teacher, which could be predicted by PU, PEOU and RITD, were generally positive. It is also found that the relationship between RITD and acceptance was mediated by RUA, PEOU and PU. This study holds significance for stakeholders to develop independent AI teachers for students.

Aortic aneurysm is a life-threatening disease with limited interventions that is closely related to vascular smooth muscle cell (VSMC) phenotypic switching. SLC44A2, a member of the solute carrier series 44 (SLC44) family, remains undercharacterized in the context of cardiovascular diseases. Venn diagram analysis based on microarray and single-cell RNA sequencing identified SLC44A2 as a major regulator of VSMC phenotypic switching in aortic aneurysm. Screening for Slc44a2 among aortic cell lineages demonstrated its predominant location in VSMCs. Elevated levels of SLC44A2 were evident in the aorta of both patients with abdominal aortic aneurysm and angiotensin II-infused (Ang II- infused) [Apoe.sup.-/-] mice. In vitro, SLC44A2 silencing promoted VSMCs toward a synthetic phenotype, while SLC44A2 overexpression attenuated VSMC phenotypic switching. VSMC-specific SLC44A2-knockout mice were more susceptible to aortic aneurysm under Ang II infusion, while SLC44A2 overexpression showed protective effects. Mechanistically, SLC44A2's interaction with NRP1 and ITGB3 activates TGF-[beta]/SMAD signaling, thereby promoting contractile gene expression. Elevated SLC44A2 in aortic aneurysm is associated with upregulated runt-related transcription factor 1 (RUNX1). Furthermore, low- dose lenalidomide (LEN; 20 mg/kg/day) suppressed aortic aneurysm progression by enhancing SLC44A2 expression. These findings reveal that the SLC44A2-NRP1-ITGB3 complex is a major regulator of VSMC phenotypic switching and provide a potential therapeutic approach (LEN) for aortic aneurysm treatment., Introduction Aortic aneurysm is defined as a localized enlargement of the aorta by at least 50% compared with the expected diameter in age-matched and sex-matched healthy individuals (1). Patients with [...]

Content and language integrated learning (CLIL) is a booming innovation pervading many educational settings. The global spread of CLIL is being practiced in classrooms the world over. Nevertheless, there is still a lack of systematic curricula for CLIL, despite its widespread adoption. Furthermore, the majority of teachers are implementing CLIL without the support of suitable published materials or resource banks. To explore and explain the curriculum development in CLIL, 281 studies from 2009 to 2019 were reviewed systematically, focusing on peer-reviewed English-language journals. We identified studies through using 'Content and Language Integrated Learning' and CLIL as the search terms in the ProQuest Research Library and EBSCO host databases. Selected articles were organized into five inquiry areas and analyzed thematically, inspired by the curriculum development model proposed by Pawlas and Oliva: 1) CLIL philosophy; 2) CLIL goals; 3) CLIL plan; 4) CLIL implementation; and 5) CLIL evaluation. Based on these themes, Boyer's scholarship of integration was introduced to propose a holistic model for CLIL curriculum development.

Aims: Based on our previous research on the specific miRNAs identified from Gastrodia elata, we selected Gas-miR2-3p to investigate its effects on neuroinflammation via in vitro and in vivo experiments. Results: RT-qPCR analysis indicated that G. elata specific Gas-miR2-3p was detected in all murine tissues post-oral administration, suggesting their potential as orally bioavailable miRNA. The analysis of RT-qPCR, Western blotting and ELISA assays consistently demonstrate that the expression of inflammatory factors as TNF-α, IL-6, IL-1β was decreased and the expression levels of p-p65 and p-IκBα were downregulated after the action of Gas-miR2-3p in both cell and animal experiments. Conclusion: Gas-miR2-3p can attenuate neuroinflammation by regulating the inflammation factors and suppressing the activation of the NF-κB signaling pathway. Our findings indicate that G. elata miRNAs, as novel active components, perform a modulatory role in the NF-κB signaling pathway associated with neuroinflammation in a cross-species way. [ABSTRACT FROM AUTHOR]

Advancements in hydrogel bioadhesives for sealing gastrointestinal perforations (GP) face challenges due to weak mechanical properties, causing leakage, especially on uneven wound surfaces. Herein, a Janus hydrogel is introduced featuring a two‐step cross‐linking process, designed to serve as an efficient bioadhesive using a sprayable method. This Janus hydrogel formulation is achieved by modifying a photocurable hyaluronic acid hydrogel with dopamine (DA) and phenylboronic acid (PBA) groups, enabling reversible boronate ester bonds. These dynamic cross‐linking bonds facilitate shear‐thinning and self‐healing abilities, ensuring secure adhesion to complex wounds. A subsequent photocurable irreversible cross‐linking stage results in the formation of a hydrogel barrier with asymmetric adhesive properties, imparting inherent mechanical strength for postoperative anti‐adhesion. To enhance the clinical practicality of this hydrogel, an integrated sprayer device is further developed, enabling the efficient delivery of hydrogel at any angle and over long distances under minimally invasive conditions. Therefore, the HADP hydrogel, integrated with the sprayer device, successfully addressed the limitations associated with hydrogel bioadhesives in clinical applications concerning angles and terrains. This integrated approach presents a compelling solution for repairing gastrointestinal perforations in clinical settings. Moreover, it demonstrates significant potential for diverse applications in repairing complex wound surfaces. [ABSTRACT FROM AUTHOR]

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Groundwater Contamination Source Identification (GCSI) is a crucial prerequisite for conducting comprehensive pollution risk assessments, formulating effective groundwater contamination control strategies, and devising remediation plans. In previous GCSI studies, various boundary conditions were typically assumed to be known variables. However, in many practical scenarios, these boundary conditions are exceedingly complex and difficult to accurately pre-determine. This practice of presuming boundary conditions as known may significantly deviate from reality, leading to errors in identification results. Moreover, the outcomes of GCSI may be influenced by multiple factors or conditions, including the fundamental information about the contamination source boundary conditions of the polluted area. This study primarily focuses on contamination source information and unknown boundary conditions. Innovatively, three deep learning surrogate models, the Deep Belief Neural Network (DBNN), Bidirectional Long Short-Term Memory Networks (BiLSTM), and Deep Residual Neural Network (DRNN), are employed for identification and validation and to simulate the highly no-linear simulation model and directly establish a mapping relationship between the outputs and inputs of the simulation model. This approach enables the direct acquisition of the inverse identification results of the variables based on actual monitoring data, thereby facilitating rapid inverse identification. Furthermore, to account for the uncertainty of noise in monitoring data, the inversion accuracy of these three deep learning methods is compared, and the method with higher accuracy is selected for uncertainty analysis. Multiple experiments were conducted, such as accuracy identification tests, robustness tests, and cross-comparative ablation studies. The results demonstrate that all three deep learning models effectively complete the research tasks, with DBNN showing the most exceptional performance in the experiments. DBNN achieved an R2 value of 0.982, an RMSE of 3.77, and an MAE of 7.56%. Subsequent uncertainty analysis, model robustness, and ablation study further affirm DBNN adaptability to GCSI research tasks. [ABSTRACT FROM AUTHOR]