Macrophages in ulcerative colitis: A perspective from bibliometric and visual analysis

Burnout among Nurses: A Bibliometric Analysis of the Global Publications

Objective

To investigate the current situation, trending subjects, and future directions in the field of burnout among nurses, and to serve as a resource for researchers conducting related research.

Methods

The bibliometric analysis was carried out using R package "bibliometrix", bibliometric online analysis platform (https://bibliometric.com/) and VOSviewer (1.6.18).

Results

The leading countries that had a significant impact on this field were the USA and China. University of Pennsylvania was the most influential institution. Journal of Nursing Management was the top productive journal. Critical care, oncology care, acute care, and infectious disease care were more likely to lead to symptoms of burnout among nurses. "Mental health", "job satisfaction", "stress", and "COVID-19" were the current hot topics in this field.

Conclusion

Our study not only provides a thorough outline to assist researchers in understanding the leading countries, institutions, journals, and potential collaborators, but it also examines the current and upcoming trends in this field and inspires researchers to select research directions.

Artificial intelligence in cardiology: a bibliometric study

OBJECTIVES

To perform a comprehensive bibliometric analysis of global publications on the applications of artificial intelligence (AI) in cardiology.

METHODS

Documents related to AI in cardiology published between 2002 and 2022 were retrieved from Web of Science Core Collection. R package "bibliometrix", VOSviewers and Microsoft Excel were applied to perform the bibliometric analysis.

RESULTS

A total of 4332 articles were included. United States topped the list of countries publishing articles, followed by China and United Kingdom. The Harvard University was the institution that contributed the most to this field, followed by University of California System and University of London. Disease risk prediction, diagnosis, treatment, disease detection, and prognosis assessment were the research hotspots for AI in cardiology.

CONCLUSIONS

Enhancing cooperation between different countries and institutions is a critical step in leading to breakthroughs in the application of AI in cardiology. It is foreseeable that the application of machine learning and deep learning in various areas of cardiology will be a research priority in the coming years.

Emerging role of exosomes in ulcerative colitis: Targeting NOD-like receptor family pyrin domain containing 3 inflammasome

Ulcerative colitis (UC) is a chronic recurrent inflammatory bowel disease. Despite ongoing advances in our understanding of UC, its pathogenesis is yet unelucidated, underscoring the urgent need for novel treatment strategies for patients with UC. Exosomes are nanoscale membrane particles that mediate intercellular communication by carrying various bioactive molecules, such as proteins, RNAs, DNA, and metabolites. The NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome is a cytosolic tripartite protein complex whose activation induces the maturation and secretion of proinflammatory cytokines interleukin-1β (IL-1β) and IL-18, triggering the inflammatory response to a pathogenic agent or injury. Growing evidence suggests that exosomes are new modulators of the NLRP3 inflammasome, with vital roles in the pathological process of UC. Here, recent evidence is reviewed on the role of exosomes and NLRP3 inflammasome in UC. First, the dual role of exosomes on NLRP3 inflammasome and the effect of NLRP3 inflammasome on exosome secretion are summarized. Finally, an outlook on the directions of exosome-NLRP3 inflammasome crosstalk research in the context of UC is proposed and areas of further research on this topic are highlighted.

Research hotspots and trends of artificial intelligence in rheumatoid arthritis: A bibliometric and visualized study

Artificial intelligence (AI) applications on rheumatoid arthritis (RA) are becoming increasingly popular. In this bibliometric study, we aimed to analyze the characteristics of publications relevant to the research of AI in RA, thereby developing a thorough overview of this research topic. Web of Science was used to retrieve publications on the application of AI in RA from 2003 to 2022. Bibliometric analysis and visualization were performed using Microsoft Excel (2019), R software (4.2.2) and VOSviewer (1.6.18). The overall distribution of yearly outputs, leading countries, top institutions and authors, active journals, co-cited references and keywords were analyzed. A total of 859 relevant articles were identified in the Web of Science with an increasing trend. USA and China were the leading countries in this field, accounting for 71.59% of publications in total. Harvard University was the most influential institution. Arthritis Research & Therapy was the most active journal. Primary topics in this field focused on estimating the risk of developing RA, diagnosing RA using sensor, clinical, imaging and omics data, identifying the phenotype of RA patients using electronic health records, predicting treatment response, tracking the progression of the disease and predicting prognosis and developing new drugs. Machine learning and deep learning algorithms were the recent research hotspots and trends in this field. AI has potential applications in various fields of RA, including the risk assessment, screening, early diagnosis, monitoring, prognosis determination, achieving optimal therapeutic outcomes and new drug development for RA patients. Incorporating machine learning and deep learning algorithms into real-world clinical practice will be a future research hotspot and trend for AI in RA. Extensive collaboration to improve model maturity and robustness will be a critical step in the advancement of AI in healthcare.