Purpose: The benefits of artificial intelligence (AI) related technologies for manufacturing firms are well recognized, however, there is a lack of industrial AI (I-AI) maturity models to enable companies to understand where they are and plan where they should go. The purpose of this study is to propose a comprehensive maturity model in order to help manufacturing firms assess their performance in the I-AI journey, shed lights on future improvement, and eventually realize their smart manufacturing visions.Design/methodology/approach: This study is based on (1) a systematic review of literature on assessing I-AI-related technologies to identify relevant measured indicators in the maturity model, and (2) semi-structured interviews with domain experts to determine maturity levels of the established model.Findings: The I-AI maturity model developed in this study includes two main dimensions, namely “Industry” and “Artificial Intelligence”, together with 12 first-level indicators and 35 second-level indicators under these dimensions. The maturity levels are divided into five types: planning level, specification level, integration level, optimization level, and leading level.Originality/value: The maturity model integrates indicators that can be used to assess AI-related technologies and extend the existing maturity models of smart manufacturing by adding specific technical and nontechnical capabilities of these technologies applied in the industrial context. The integration of the industry and artificial intelligence dimensions with the maturity levels shows a road map to improve the capability of applying AI-related technologies throughout the product lifecycle for achieving smart manufacturing.

There is accumulating evidence that microRNAs are emerging as pivotal regulators in the development and progression of neuropathic pain. MicroRNA-15a/16 (miR-15a/16) have been reported to play an important role in various diseases and inflammation response processes. However, whether miR-15a/16 participates in the regulation of neuroinflammation and neuropathic pain development remains unknown. In this study, we established a mouse model of neuropathic pain by chronic constriction injury (CCI) of the sciatic nerves. Our results showed that both miR-15a and miR-16 expression was significantly upregulated in the spinal cord of CCI rats. Downregulation of the expression of miR-15a and miR-16 by intrathecal injection of a specific inhibitor significantly attenuated the mechanical allodynia and thermal hyperalgesia of CCI rats. Furthermore, inhibition of miR-15a and miR-16 downregulated the expression of interleukin-1β and tumor-necrosis factor-αin the spinal cord of CCI rats. Bioinformatic analysis predicted that G protein-coupled receptor kinase 2 (GRK2), an important regulator in neuropathic pain and inflammation, was a potential target gene of miR-15a and miR-16. Inhibition of miR-15a and miR-16 markedly increased the expression of GRK2 while downregulating the activation of p38 mitogen-activated protein kinase and NF-κB in CCI rats. Notably, the silencing of GRK2 significantly reversed the inhibitory effects of miR-15a/16 inhibition in neuropathic pain. In conclusion, our results suggest that inhibition of miR-15a/16 expression alleviates neuropathic pain development by targeting GRK2. These findings provide novel insights into the molecular pathogenesis of neuropathic pain and suggest potential therapeutic targets for preventing neuropathic pain development.

Purpose: The purpose of this paper is to improve the understanding of the factors influencing the success of digital transformation (DT) and problems/challenges in DT as well as the communication methods used to involve citizens, based on a systematic literature review of research articles about citizen involvement in DT published between January 2010 and May 2021.Design/methodology/approach: After establishing inclusion and exclusion criteria, a systematic review of relevant studies was conducted. Out of a total of 547 articles, 33 met the paper selection criteria.Findings: The analysis of the included 33 empirical studies reveals that the factors influencing the success of DT can be described as the opposite side from challenges and problems in DT. These factors and challenges/problems all influence DT and they can be grouped into organisational values, management capabilities, organisational infrastructure, and workforce capabilities. The communication methods for citizen involvement in DT include: (1) communication mediated by human, (2) communication mediated by computers, and (3) mixed communication methods.Originality/value: The study identified specific factors that influence DT supported by citizen involvement, at a more fine-grained level. The findings concerning communication methods extend related studies for citizen involvement by adding town hall meetings and communication methods mediated by computers. Furthermore, this study links the research findings to develop a framework for citizen involvement in DT, assisting in better selecting communication methods to involve citizens for addressing problem areas in DT.Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2022-0237

Effective implementation of strategic data-driven health analysis initiatives is heavily dependent on the quality of the electronic medical records that serve as the foundation from which to improve clinical decisions and, in turn, the quality of care. Although there is a large body of research on the quality of healthcare data, a systematical understanding of the methods used to address the issues of data quality is missing. This study analyzes research articles in health information systems/healthcare informatics on data quality to derive a set of dimensions for understanding data quality. Issues related to each dimension are identified and methods used to address them summarized. The issues and methods can inform healthcare professionals of how to improve data practices.