Mapping research trends in diabetic retinopathy from 2010 to 2019: A bibliometric analysis

Bibliometric Analysis of Research Relating to IgA Nephropathy from 2010 to 2021

BACKGROUND IgA nephropathy (IgAN), characterized by the deposition of IgA, is one of the most common forms of primary glomerulonephritis. Although bibliometrics has been popular in the field of medicine, the bibliometric analysis of research related to IgAN has not been reported in the past 10 years. Therefore, the purpose of this study was to analyze the evolution trend and hotspots of IgAN over the last 10 years. MATERIAL AND METHODS The literature data related to IgAN between 2010 and 2021 were retrieved from the Web of Science Core Collection database, a high-quality digital database that has been broadly accepted among researchers and has become a common tool for retrieving and evaluating different types of publications. VOSviewer 1.6.18 was used to analyze co-authorship, co-occurrence, citation, and co-citation. CiteSpace 5.8.R3 was used to analyze burst keywords. RESULTS According to the inclusion and exclusion criteria, 3664 papers were gathered. The country with the largest number of publications was China. Peking University was the most productive institution. The journal with the highest publications was Nephrology Dialysis Transplantation. The most prolific author was Zhang Hong. The highly cited references mainly investigated the pathology and pathogenesis of IgAN. The most frequent keywords were "IgA nephropathy", "glomerulonephritis", and "Oxford classification". CONCLUSIONS Our study provided a comprehensive overview of IgAN research and showed the development status and scientific trend of IgAN through bibliometric analysis from 2010 to 2021. Our results will allow researchers to understand the existing research quickly and get direction for future research.

Mapping the global research landscape on insulin resistance: Visualization and bibliometric analysis

BACKGROUND

Insulin resistance is a risk factor for metabolic syndromes and is associated with a wide variety of metabolic illnesses, including obesity, type 2 diabetes, and cardiovascular disease.

AIM

To investigate and map global insulin resistance studies.

METHODS

A bibliometric methodology was applied to the literature retrieved from the Scopus database and Reference Citation Analysis (https://www.referencecitationanalysis.com) by using a validated search strategy. The study period was limited from 2002 to 2021. Bibliometric indicators and mapping were presented.

RESULTS

A total of 26808 articles on the topic of insulin resistance were included in the Scopus database. The articles included research articles (n = 21918; 81.76%), review articles (n = 2641; 9.85%), and letters (n = 653; 2.44%). During the study period, 136 countries contributed to the research on insulin resistance. The highest number of articles was from the United States (n = 7360; 27.45%), followed by China (n = 3713; 13.85%), Japan (n = 1730, 6.45%), Italy (n = 1545; 5.54%), and the United Kingdom (n = 1484; 5.54%). The retrieved articles identified two main research themes: “inflammatory mechanisms in the regulation of insulin resistance” and “mechanisms linking obesity to insulin resistance”.

CONCLUSION

Our data show that insulin resistance has steadily gained interest from researchers, as evidenced by the number of citations and yearly publications. Publications have grown significantly in the last decade, while low-income countries with greater burdens continue to produce fewer publications in this field. This approach might assist researchers in choosing new research areas and recognizing research hotspots and frontiers. In the future, perhaps high-quality clinical evidence will be acquired.

Systematic Bibliometric and Visualized Analysis of Research Hotspots and Trends on the Application of Artificial Intelligence in Ophthalmic Disease Diagnosis

Background: Artificial intelligence (AI) has been used in the research of ophthalmic disease diagnosis, and it may have an impact on medical and ophthalmic practice in the future. This study explores the general application and research frontier of artificial intelligence in ophthalmic disease detection.

Methods: Citation data were downloaded from the Web of Science Core Collection database to evaluate the extent of the application of Artificial intelligence in ophthalmic disease diagnosis in publications from 1 January 2012, to 31 December 2021. This information was analyzed using CiteSpace.5.8. R3 and Vosviewer.

Results: A total of 1,498 publications from 95 areas were examined, of which the United States was determined to be the most influential country in this research field. The largest cluster labeled “Brownian motion” was used prior to the application of AI for ophthalmic diagnosis from 2007 to 2017, and was an active topic during this period. The burst keywords in the period from 2020 to 2021 were system, disease, and model.

Conclusion: The focus of artificial intelligence research in ophthalmic disease diagnosis has transitioned from the development of AI algorithms and the analysis of abnormal eye physiological structure to the investigation of more mature ophthalmic disease diagnosis systems. However, there is a need for further studies in ophthalmology and computer engineering.

The evolving field of Big Data: understanding geographic information systems analysis and its transformative potential in ophthalmic research

PURPOSE OF REVIEW

The purpose of this review to is to highlight alternative uses of Big Data in the pursuit of ophthalmologic public health. In particular, we highlight geographic information systems (GIS) analysis as a type of Big Data, summarize various GIS methods, and enumerate sources of geographic data.

RECENT FINDINGS

The recent implementation of the IRIS Registry Data, has expanded our real-world knowledge of ophthalmology in the United States. Such innovations in Big Data allow us to better define ophthalmic diseases, treatments, and outcomes for underserved individuals and subpopulations. One underutilized source of Big Data entails use of geographic information to evaluate geographic heterogeneity and access across the United States.

SUMMARY

GIS and Big Data allow for refined epidemiologic estimates of eye disease for specific communities. In particular, how GIS can enable researchers to examine disparities in access to ophthalmic care is reviewed. GIS best practices and some data sources for GIS in ophthalmology are also summarized.

Systematic bibliometric and visualized analysis of research hotspots and trends on the application of artificial intelligence in diabetic retinopathy

BACKGROUND

Artificial intelligence (AI), which has been used to diagnose diabetic retinopathy (DR), may impact future medical and ophthalmic practices. Therefore, this study explored AI's general applications and research frontiers in the detection and gradation of DR.

METHODS

Citation data were obtained from the Web of Science Core Collection database (WoSCC) to assess the application of AI in diagnosing DR in the literature published from January 1, 2012, to June 30, 2022. These data were processed by CiteSpace 6.1.R3 software.

RESULTS

Overall, 858 publications from 77 countries and regions were examined, with the United States considered the leading country in this domain. The largest cluster labeled "automated detection" was employed in the generating stage from 2007 to 2014. The burst keywords from 2020 to 2022 were artificial intelligence and transfer learning.

CONCLUSION

Initial research focused on the study of intelligent algorithms used to localize or recognize lesions on fundus images to assist in diagnosing DR. Presently, the focus of research has changed from upgrading the accuracy and efficiency of DR lesion detection and classification to research on DR diagnostic systems. However, further studies on DR and computer engineering are required.

Overview of Graves Ophthalmopathy Literature From 1999 to 2019: Bibliometric Analysis

Background

Research on Graves ophthalmopathy has increased remarkably over the last 2 decades; however, few statistical analyses of the data presented in these publications have been conducted.

Objective

This study aims to detect and analyze emerging trends and collaboration networks in Graves ophthalmopathy research.

Methods

Graves ophthalmopathy–related publications from 1999 to 2019 were collected from the Web of Science Core Collection Database. Collected publications were restricted by category (article or review) and language (English). Bibliometric analyses included changes in the annual numbers of publications, journals, authors, countries, institutions, keywords, and references.

Results

In total, 3051 publications that met the criteria were collected. The number of annual publications has exhibited an increasing trend over the last 20 years. The journal Thyroid ranked first, publishing 183 Graves ophthalmopathy–related studies. There was no evidence of a relationship between impact factor (IF) and the number of publications (P=.69). The author Smith TJ had the largest number of publications on Graves ophthalmopathy (n=83). Of the countries that had published Graves ophthalmopathy–related articles, the United States had the largest number (n=784) and the highest centrality (0.18). Among institutions, the University of Pisa (Italy) contributed the most Graves ophthalmopathy–related articles (n=114). The most recent burst keywords (proliferation, rituximab, and selenium) and references may provide clues on emerging trends in research and clinical practice.

Conclusions

This bibliometric analysis highlights countries, institutions, and authors who contributed to Graves ophthalmopathy–related publications. Emerging trends in Graves ophthalmopathy research, based on burst keywords and references, may provide clues relevant to clinical practice and future research.

Trends in HSPB5 research: a 36-year bibliometric analysis

HSPB5 (heat shock protein B5), also known as αB-crystallin, is one of the most widespread and populous of the ten human small heat shock proteins (sHsps). Over the past decades, extensive research has been conducted on HSPB5. However, few studies have statistically analyzed these publications. Herein, we conducted a bibliometric analysis to track the global research trend and current development status of HSPB5 research from the Web of Science Core Collection (WoSCC) database between 1985 and 2020. Our results demonstrate that 1220 original articles cited 54,778 times in 391 scholarly journals were published. Visualization analyses reveal that the Journal of Biological Chemistry was the most influential journal with 85 articles. The USA dominated this field with 520 publications (42.62%), followed by Japan with 149 publications (12.21%), and Kato contributed the largest number of publications. Most related publications were published in journals focusing on biochemistry molecular biology, cell biology, neurosciences neurology, and ophthalmology. In addition, keyword co-occurrence analyses identify three predominant research topics: expression of HSPB5, chaperone studies for HSPB5, and pathological studies of HSPB5. This study provides valuable guidance for researchers and leads to collaborative opportunities between diverse research interests to be integrated for HSPB5 research.