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Objective: Notable progress has been made in "ferroptosis-based nano drug delivery systems (NDDSs)" over the past 11 years. Despite the ongoing absence of a comprehensive scientometric overview and up-to-date scientific mapping research, especially regarding the evolution, critical research pathways, current research landscape, central investigative themes, and future directions. Methods: Data ranging from 1 January 2012, to 30 November 2023, were obtained from the Web of Science database. A variety of advanced analytical tools were employed for detailed scientometric and visual analyses. Results: The results show that China significantly led the field, contributing 82.09% of the total publications, thereby largely shaping the research domain. Chen Yu emerged as the most productive author in this field. Notably, the journal ACS Nano had the greatest number of relevant publications. The study identified liver neoplasms, pancreatic neoplasms, gliomas, neoplasm metastases, and melanomas as the top five crucial disorders in this research area. Conclusion: This research provides a comprehensive scientometric assessment, enhancing our understanding of NDDSs focused on ferroptosis. Consequently, it enables rapid access to essential information and facilitates the extraction of novel ideas in the field of ferroptotic nanomedicine for both experienced and emerging researchers. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis (OA) is a disabling and highly prevalent condition affecting millions worldwide. Recently discovered, disulfidptosis represents a novel form of cell death induced by the excessive accumulation of cystine. Despite its significance, a systematic exploration of disulfidptosis-related genes (DRGs) in OA is lacking. Methods: This study utilized three OA-related datasets and DRGs. Differentially expressed (DE)-DRGs were derived by intersecting the differentially expressed genes (DEGs) from GSE114007 with DRGs. Feature genes underwent screening through three machine learning algorithms. High diagnostic value genes were identified using the receiver operating characteristic curve. Hub genes were confirmed through expression validation. These hub genes were then employed to construct a nomogram and conduct enrichment, immune, and correlation analyses. An additional validation of hub genes was performed through in vitro cell experiments. Results: SLC3A2 and PDLIM1 were designated as hub genes, displaying excellent diagnostic performance. PDLIM1 exhibited low expression in early chondrocyte differentiation, rising significantly in the late stage, while SLC3A2 showed high overall expression, declining in the late differentiation stage. Cellular experiments corroborated the correlation of SLC3A2 and PDLIM1 with chondrocyte inflammation. Conclusions: Two hub genes, SLC3A2 and PDLIM1, were identified in relation to disulfidptosis, providing potential directions for diagnosing and treating OA. [ABSTRACT FROM AUTHOR]

Purpose: Over the past 24 years, significant advancements have been made in applying artificial intelligence (AI) to musculoskeletal (MSK) diseases. However, there is a lack of analytical and descriptive investigations on the trajectory, essential research directions, current research scenario, pivotal focuses, and future perspectives. This research aims to provide a thorough update on the progress in AI for MSK diseases over the last 24 years. Methods: Data from the Web of Science database, covering January 1, 2000, to March 1, 2024, was analyzed. Using advanced analytical tools, we conducted comprehensive scientometric and visual analyses. Results: The findings highlight the predominant influence of the USA, which accounts for 28.53% of the total publications and plays a key role in shaping research in this field. Notable productivity was seen at institutions such as the University of California, San Francisco, Harvard Medical School, and Seoul National University. Valentina Pedoia is identified as the most prolific contributor. Scientific Reports had the highest number of publications in this area. The five most significant diseases are joint diseases, bone fractures, bone tumors, cartilage diseases, and spondylitis. Conclusion: This comprehensive scientometric assessment benefits both experienced researchers and newcomers, providing quick access to essential information and fostering the development of innovative concepts in this field. [ABSTRACT FROM AUTHOR]

[Purpose/significance] The period of big data has brought changes to all industries and fields of the society, as well as challenges to traditional knowledge management research. In order to adapt to the new era environment better, knowledge management needs to be re-recognized and re-positioned, and to meet and embrace new technologies on the challenges and changes it faces. [Method/process]This paper analyzed the role of knowledge management in the era of big data, discussed the methods and challenges of knowledge management in the era of big data, and put forward the development direction of knowledge management in the era of big data based on the reality. [Result/conclusion] Knowledge management has not disappeared in the data age. The development of big data has promoted the transformation of knowledge management process from traditional to technical. Although big data has made a major breakthrough in explicit knowledge management, it is still a problem needs to be solved in tacit knowledge management. Knowledge management in the new era emphasizes technology application and the ability of extracting the value of knowledge from the data.