Research Trends and Hotspots of Q Fever Research: A Bibliometric Analysis 1990-2019

Artificial intelligence and machine learning trends in kidney care

BACKGROUND

The integration of artificial intelligence (AI) and machine learning (ML) in kidney care has seen a significant rise in recent years. This study specifically analyzed AI and ML research publications related to kidney care to identify leading authors, institutions, and countries in this area. It aimed to examine publication trends and patterns, and to explore the impact of collaborative efforts on citation metrics.

METHODS

The study used the Science Citation Index Expanded (SCI-EXPANDED) of Clarivate Analytics Web of Science Core Collection to search for AI and machine learning publications related to nephrology from 1992 to 2021. The authors used quotation marks and Boolean operator "or" to search for keywords in the title, abstract, author keywords, and Keywords Plus. In addition, the 'front page' filter was applied. A total of 5425 documents were identified and analyzed.

RESULTS

The results showed that articles represent 75% of the analyzed documents, with an average author to publications ratio of 7.4 and an average number of citations per publication in 2021 of 18. English articles had a higher citation rate than non-English articles. The USA dominated in all publication indicators, followed by China. Notably, the research also showed that collaborative efforts tend to result in higher citation rates. A significant portion of the publications were found in urology journals, emphasizing the broader scope of kidney care beyond traditional nephrology.

CONCLUSIONS

The findings underscore the importance of AI and ML in enhancing kidney care, offering a roadmap for future research and implementation in this expanding field.

Publication performance and trends in temporomandibular disorders research: A bibliometric analysis

BACKGROUND

Temporomandibular disorders (TMD) are common, can be both painful and non-painful, and encompass various conditions affecting the temporomandibular joint, the masticatory muscles or both TMD. Therefore, the purpose of this bibliometric analysis was to synthetically analyze citation performance in TMD, to address a more innovative method including details of article title, author keyword, KeyWords Plus, and abstracts.

MATERIAL AND METHODS

Data used in this study were retrieved from the Clarivate Analytics Web of Science Core Collection, the online version of the Science Citation Index Expanded (SCI-EXPANDED) between 1992 and 2021. The distribution of key words in the article title and author‑selected keywords were used to evaluate research trends.

RESULTS

Of the 7,228 documents in SCI-EXPANDED, 6,138 documents met all inclusion criteria and were included in the final analysis, of which 4,945 were articles. The present bibliometric analysis of the articles published in the research filed of TMD revealed that orofacial pain, bruxism, chronic pain, and myofascial pain are the most commonly used keywords by the authors. Further, over the last 30 years 4,945 articles are published in the field of TMD, and the far most frequently cited study was published 8 years ago and handles the diagnostic criteria of TMD. The USA and Brazil were top two ranking productive countries of publication on TMD. The most productive journal was Journal of Oral Rehabilitation, followed by Cranio-The Journal of Craniomandibular & Sleep Practice and Journal of Oral & Facial Pain and Headache. The most productive authors were P. Svensson, R. Ohrbach, and F. Lobbezooas. The most productive institutes were Sao Paulo University (Brazil), Malmo University (Sweden), and Washington university (USA) CONCLUSION: Based on the outcome of this bibliometric study, the authors hope that both clinicians and researchers will have information to shape their future research focus, finding prominent institutions in their nearby area, or even to be stimulated to initiate new international or even multinational collaborations.

The successful use of -omic technologies to achieve the 'One Health' concept in meat producing animals

Human health and wellbeing are closely linked to healthy domestic animals, a vital wildlife, and an intact ecosystem. This holistic concept is referred to as 'One Health'. In this review, we provide an overview of the potential and the challenges for the use of modern -omics technologies, especially transcriptomics and proteomics, to implement the 'One Health' idea for food-producing animals. These high-throughput studies offer opportunities to find new potential molecular biomarkers to monitor animal health, detect pharmacological interventions and evaluate the wellbeing of farm animals in modern intensive livestock systems.