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Collaborative problem-solving (CPS) engages students in solving ill-structured problems, creating group knowledge, and developing self-regulation and collaboration skills. Different scaffoldings, such as minimal-guided, task-oriented, and idea-oriented, can be used to facilitate students' CPS activities, but their effects have not been comprehensively explored. In this research, we use minimally-guided, task-oriented, and idea-oriented scaffoldings to promote Chinese university students' online CPS activities and use a multi-method approach to analyze the effects of three scaffolding on collaboration. The results indicate relatively complicated collaborative processes and outcomes supported by three scaffoldings. It is initially shown that the idea-centered scaffolding strengthens students' connections between idea contribution, metacognitive regulation, and knowledge artifact behaviors, which are critical factors for improving the CPS quality. Based on the empirical research results, we conclude that future instructional design should carefully consider the educational culture, time constraint, and student regulation to better facilitate CPS practices.

Aim: This study aims to investigate the influence of medical explainable artificial intelligence (XAI) on the innovation behaviour of nurses, as well as explore the dual‐pathway mediating effect of AI self‐efficacy and AI anxiety and organizational ethical climate as the moderating effect. Background: To address the practical application of medical AI technology, alleviate the scarcity of medical resources and fulfil the medical and health demands of the public, it is crucial to improve the innovation behaviour of nurses through the use of medical XAI. Methods: A cross‐sectional survey was conducted involving 368 Chinese nurses working at tertiary and secondary hospitals in Anhui Province, Jiangsu Province, Zhejiang Province and Shanghai. Results: Implementing medical XAI significantly enhanced the innovation behaviour of nurses. Anxiety and self‐efficacy regarding AI mediated the connection between medical XAI and the innovation behaviour of nurses. Furthermore, the organizational ethical climate positively moderated the relationship between medical XAI and AI self‐efficacy. Conclusion: Medical XAI helps to enhance nurses' AI self‐efficacy and reduce AI anxiety, thereby enhancing nurses' innovation behaviour. An organizational ethical climate enhances the positive relationship between medical XAI and AI self‐efficacy. Implications for Nursing Management: Organizations and technology developers must augment the study about XAI and the system design of human‐centred AI technology. The organizations aim to enhance the education and training of nurses in AI, specifically focussing on boosting nurses' self‐efficacy in utilizing AI technology. Moreover, they want to alleviate nurses' fear of new technological advancements. Hospital administrators and leaders develop strategies to address the ethical atmosphere inside their organization. [ABSTRACT FROM AUTHOR]

Mirabegron and vibegron, both newly identified beta-3 adrenergic agonists, have significantly improved the quality of life for patients suffering from overactive bladder. In order to comprehensively assess the plasma exposure levels of these agents, the development of a rapid and highly sensitive bioanalytical method becomes imperative. The primary objective of this study was to establish a robust high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method for the concurrent quantification of mirabegron and vibegron in human plasma. The analytes were extracted from a 100 μL plasma sample through protein precipitation, employing 300 μL of methanol. Subsequently, samples underwent separation and quantification using a Waters XBridge C 18 column (2.1 × 100 mm, 3.5 µm), with a mobile phase consisting of 0.1% formic acid in water and 0.1% formic acid in acetonitrile. The mass analysis was conducted using positive electrospray ionization (ESI+) operated in a multiple reaction monitoring (MRM) mode. The proposed method was meticulously validated in accordance with the guidelines set forth by the U.S. Food and Drug Administration (FDA) for bioanalytical method validation. The regression equations demonstrated exceptional linearity for both mirabegron (r² ≥ 0.994) and vibegron (r² ≥ 0.996) across the concentration range of 0.5 – 200 ng/mL. Furthermore, the assay exhibited accuracy (inter-day relative error ≤ 6.90%) and precision (inter-day coefficient of variation ≤ 8.88%). The average recoveries of the analytes were found to range from 81.94% to 102.02%, with mean matrix effects falling within the range of 89.77% to 110.58%. As a result, this method was deemed highly suitable for the precise determination of the concentrations of both mirabegron and vibegron in the context of therapeutic drug monitoring and bioequivalence studies. ● First simultaneous determination of two beta-3 adrenergic agonists in human plasma. ● A rapid, robust, and sensitive method for the quantification of mirabegron and vibegron by HPLC-MS/MS. ● Simple protein precipitation method with short run time suitable for high-throughput analysis of biological samples. ● The proposed method was successfully implemented for therapeutic drug monitoring in patients with overactive bladder. [ABSTRACT FROM AUTHOR]

Graphene nanoribbon is a novel variety of graphene with high length-to-width ratio and straight edges. Herein, we report an improved method for the synthesis of graphene oxide nanoribbons (GONRs) from longitudinal unraveling of multiwalled carbon nanotubes by means of a one-step, one-pot pressurized oxidation reaction. The obtained GONRs were characterized by different techniques. Furthermore, owing to their unique properties such as strong optical absorption and good water dispersibility, we show that GONRs can be used as an excellent matrix or probe in matrix-assisted or surface-enhanced laser desorption/ionization mass spectrometry (MALDI or SELDI MS) for the first time. In MALDI MS, GONRs generated significantly higher signals than conventional organic matrix and other graphene-based matrices in the detection of low-mass compounds. We also demonstrate the use of GONRs as a sensitive SELDI probe for simultaneous detection of multiple small molecules and profiling of small molecules in complex environmental samples, thus revealing its application potential in rapid screening of low-mass pollutants in complex media., (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)