Bibliometric analysis and mapping knowledge domain of pterygium: 2000-2019

Research progress and challenges in stem cell therapy for diabetic foot: Bibliometric analysis and perspectives

BACKGROUND

Stem cell therapy has shown great potential for treating diabetic foot (DF).

AIM

To conduct a bibliometric analysis of studies on the use of stem cell therapy for DF over the past two decades, with the aim of depicting the current global research landscape, identifying the most influential research hotspots, and providing insights for future research directions.

METHODS

We searched the Web of Science Core Collection database for all relevant studies on the use of stem cell therapy in DF. Bibliometric analysis was carried out using CiteSpace, VOSviewer, and R (4.3.1) to identify the most notable studies.

RESULTS

A search was conducted to identify publications related to the use of stem cells for DF treatment. A total of 542 articles published from 2000 to 2023 were identified. The United States had published the most papers on this subject. In this field, Iran's Shahid Beheshti University Medical Sciences demonstrated the highest productivity. Furthermore, Dr. Bayat from the same university has been an outstanding researcher in this field. Stem Cell Research & Therapy is the journal with the highest number of publications in this field. The main keywords were "diabetic foot ulcers," "wound healing," and "angiogenesis."

CONCLUSION

This study systematically illustrated the advances in the use of stem cell therapy to treat DF over the past 23 years. Current research findings suggested that the hotspots in this field include stem cell dressings, exosomes, wound healing, and adipose-derived stem cells. Future research should also focus on the clinical translation of stem cell therapies for DF.

Bibliometric analysis of COVID-19 publications shows the importance of telemedicine and equitable access to the internet during the pandemic and beyond

BACKGROUND

Pandemics highlight the increasing role of information and communications technology for improving access to health care. This study aimed to present a bibliometric analysis of the concept of digital divide reported in the published articles concerning the coronavirus disease 2019 (COVID-19) pandemic.

METHODS

To conduct this bibliometric analysis of research topics and trends, we used VOSviewer software. We developed a search strategy to retrieve peer-reviewed publications related to 'digital divide in the COVID-19 era' from the Scopus database.

RESULTS

In total, 241 publications on the topic of digital divide and COVID-19 were retrieved from Scopus database between 2020 and 2021. The analysis of keywords co-occurrence of research topics revealed four main clusters including: 'telemedicine', 'Internet access and Internet use', 'e-learning' and 'epidemiology'. Seven characteristic categories were examined in these research topics, including: sociodemographic, economic, social, cultural, personal, material and motivational.

CONCLUSION

'Telemedicine' and 'Internet access and Internet use' as the largest clusters are connected to topics addressing inequalities in online health care access. Thus, policymakers should develop or modify policies in more egalitarian Internet access for all community members not only during a pandemic like the COVID-19 but also at regular times.

Global trends in research related to functional dyspepsia and anxiety or depression over the past two decades: a bibliometric analysis

Background and purpose

Functional dyspepsia (FD) is a prevalent global disorder of the upper digestive tract characterized by functional impairment. It often coexists with anxiety/depression, significantly impairing occupational productivity and overall quality of life. This study aimed to identify emerging patterns and prominent themes within FD and anxiety/depression research through bibliometric analysis to help explore new innovative avenues for investigating this type of FD.

Methods

A comprehensive review of literature encompassing FD and anxiety/depression was conducted using the Science Citation Index Extension of the Web of Science Core Collection from 2003 to 2023. Information extracted comprised "Full Record and Cited References." Bibliometric analysis of relevant publications, including country, institution, author, journal, citations, and keywords, was conducted using CiteSpace, VOSviewer, and Bibliometrix package in R and Excel.

Results

Studies related to FD and anxiety/depression have demonstrated an ascending trajectory since 2003. Our bibliometric analysis identified 338 studies published by 2023. NEUROGASTROENTEROLOGY AND MOTILITY emerged as the most prolific journal, while GASTROENTEROLOGY retained pre-eminence within the top 10 published journals. China emerged as the most prolific country, with two institutions within the top 10 in terms of volume of publications. The Mayo Clinic stood as the foremost institution in terms of publication volume, with the Chengdu University of Traditional Chinese Medicine exhibiting robust collaborative engagement. Eminent author influence was attributed to Talley NJ of Newcastle University, Australia. Clusters of extensively cited papers and prevalent keywords delineate the status and trend of FD and anxiety/depression research. This encompasses FD, anxiety, depression, sleep disorders, and functional gastrointestinal disorders. Furthermore, the timeline view map or trend-term analysis suggested that duodenal low-grade inflammation ("duodenal eosinophilia" and "mast cells") might be a new concern associated with FD and anxiety/depression.

Conclusion

Employing bibliometric analysis, this study revealed prevalent focal areas and new trends within FD and anxiety/depression research. These insights serve as valuable guidance for scholars seeking to delve into new research avenues.

Research landscape and emerging trends of diabetes-associated cognitive dysfunction: a bibliometric analysis

Background

Diabetes-associated cognitive dysfunction (DACD) is a common and serious complication in diabetes and has a high impact on the lives of both individuals and society. Although a number of research has focused on DACD in the past two decades, there is no a study to systematically display the knowledge structure and development of the field. Thus, the present study aimed to show the landscape and identify the emerging trends of DACD research for assisting researchers or clinicians in grasping the knowledge domain faster and easier and focusing on the emerging trends in the field.

Methods

We searched the Web of Science database for all DACD-related studies between 2000 and 2022. Bibliometric analysis was conducted using the VOSviewer, CiteSpace, Histcite, and R bibliometric package, revealing the most prominent research, countries, institutions, authors, journals, co-cited references, and keywords.

Results

A total of 4,378 records were selected for analysis. We found that the volume of literature on DACD has increased over the years. In terms of the number of publications, the USA ranked first. The most productive institutions were the University of Washington and the University of Pittsburgh. Furthermore, Biessels GJ was the most productive author. Journal of Alzheimers Disease, Diabetes Care, and Frontiers in Aging Neuroscience had the most publications in this field. The keywords"dementia," "alzheimers-disease," "cognitive impairment" and "diabetes" are the main keywords. The burst keywords in recent years mainly included "signaling pathway" and "cognitive deficit."

Conclusion

This study systematically illustrated advances in DACD over the last 23 years. Current findings suggest that exploring potential mechanisms of DACD and the effect of anti-diabetes drugs on DACD are the hotspots in this field. Future research will also focus on the development of targeted drugs that act on the DACD signaling pathway.

Publication trends in the field of the cornea in the last 4 decades: a bibliometric study

PURPOSE

The objective of this bibliometric study was to identify the top 100 most-cited articles on the cornea published in the English language between 1980 and 2021 using multidimensional citation analysis.

METHODS

The data were obtained from the Thomson Reuters Web of Science Core Collection and the PubMed databases. The top 100 articles in terms of citation number were identified and analyzed.

RESULTS

A total of 40,792 articles related to the cornea were retrieved. The 100 most-cited articles were published between 1995 and 2000. The average time since publication was 19.64 ± 5.75 years. The mean impact factor of the journals was 10.27 ± 17.14 and the Q category of most journals was Q1. Ophthalmology was the journal with the most published articles (n = 10), which represented level 3 evidence. The three most common topics among the top 100 articles were treatment modality, histopathology, and diagnostic imaging. The most frequently mentioned treatments were related to limbal stem cell failure, crosslinking, and lamellar keratoplasty. We observed a negative correlation between the average number of citations per year and the time passed since publication (r = - 0.629; p = 0.001).

CONCLUSION

Our analysis of the top 100 most-cited articles on the cornea revealed scientific contributions, vital current data related to clinical implementations, and valuable insights into the current developments in ophthalmology. To our knowledge, this is the first study to evaluate the most influential papers on the cornea, and our findings highlight the research quality and latest discoveries and trends in the management cornea diseases.

Emerging trends and hotspots evolution in cardiotoxicity: A bibliometric and knowledge-Map analysis From 2010 to 2022

Background

There is growing emphasis on the cardiotoxicity research over the past 12 years. To look for the hotspots evolution and to explore the emerging trends in the field of cardiotoxicity, publications related to cardiotoxicity were acquired from the Web of Science Core Collection on August 2, 2022.

Methods

We used the CiteSpace 5.8 R3 and VOSviewer 1.6.18 to perform bibliometric and knowledge-map analysis.

Results

A total of 8,074 studies by 39,071 authors from 6,530 institutions in 124 countries or regions were published in different academic journals. The most productive country was absolutely the United States, and the University of Texas MD Anderson Cancer Center was the institution with the largest output. Zhang, Yun published the most articles, and the author who had the most frequent co-citations was Moslehi, Javid. New England Journal of Medicine was the most frequently cited journals in this field. Mechanisms of cardiotoxicity have received the most attention and was the main research directions in the field. The disease of cardiotoxicity together with the related risk factors are potential research hotspots. Immune checkpoint inhibitor and myocarditis are two recently discussed and rapidly expanding research topic in the areas of cardiotoxicity.

Conclusions

This bibliometric analysis provided a thorough analysis of the cardiotoxicity, which would provide crucial sources of information and concepts for academics studying this area. As a rapidly expanding field in cardiology, the related field of cardiotoxicity will continue to be a focus of research.

Advances in artificial intelligence applications for ocular surface diseases diagnosis

In recent years, with the rapid development of computer technology, continual optimization of various learning algorithms and architectures, and establishment of numerous large databases, artificial intelligence (AI) has been unprecedentedly developed and applied in the field of ophthalmology. In the past, ophthalmological AI research mainly focused on posterior segment diseases, such as diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, retinal vein occlusion, and glaucoma optic neuropathy. Meanwhile, an increasing number of studies have employed AI to diagnose ocular surface diseases. In this review, we summarize the research progress of AI in the diagnosis of several ocular surface diseases, namely keratitis, keratoconus, dry eye, and pterygium. We discuss the limitations and challenges of AI in the diagnosis of ocular surface diseases, as well as prospects for the future.

Pterygium Screening and Lesion Area Segmentation Based on Deep Learning

A two-category model and a segmentation model of pterygium were proposed to assist ophthalmologists in establishing the diagnosis of ophthalmic diseases. A total of 367 normal anterior segment images and 367 pterygium anterior segment images were collected at the Affiliated Eye Hospital of Nanjing Medical University. AlexNet, VGG16, ResNet18, and ResNet50 models were used to train the two-category pterygium models. A total of 150 normal and 150 pterygium anterior segment images were used to test the models, and the results were compared. The main evaluation indicators, including sensitivity, specificity, area under the curve, kappa value, and receiver operator characteristic curves of the four models, were compared. Simultaneously, 367 pterygium anterior segment images were used to train two improved pterygium segmentation models based on PSPNet. A total of 150 pterygium images were used to test the models, and the results were compared with those of the other four segmentation models. The main evaluation indicators included mean intersection over union (MIOU), IOU, mean average precision (MPA), and PA. Among the two-category models of pterygium, the best diagnostic result was obtained using the VGG16 model. The diagnostic accuracy, kappa value, diagnostic sensitivity of pterygium, diagnostic specificity of pterygium, and F1-score were 99%, 98%, 98.67%, 99.33%, and 99%, respectively. Among the pterygium segmentation models, the double phase-fusion PSPNet model had the best results, with MIOU, IOU, MPA, and PA of 86.57%, 78.1%, 92.3%, and 86.96%, respectively. This study designed a pterygium two-category model and a pterygium segmentation model for the images of the normal anterior and pterygium anterior segments, which could help patients self-screen easily and assist ophthalmologists in establishing the diagnosis of ophthalmic diseases and marking the actual scope of surgery.

Digital Health in Children’s Oral and Dental Health: An Overview and a Bibliometric Analysis

Digital health technologies can widely increase access to oral health solutions and can make them easier to use and more accessible at all primary, secondary, and tertiary levels. This study aims to present a bibliometric analysis of published literature to identify the content, trends, and context of digital health technology use in children’s oral and dental health. After finalising the research question, the Scopus database was used to search systematically for related keywords from 1997 to 2022. The PRISMA methodology applied for systematic reviews was adopted to refine search results. VOS viewer software was applied to illustrate the topics and trends of digital health technology involved in children’s oral and dental health. An increase in use of the digital technologies was appeared in the index keywords after 2005. Computer-assisted therapy/surgery, computer simulation, computer program, image processing, nuclear magnetic resonance (NMR) imaging, and audio-visual equipment were more used index keywords in children’s dental care re-search from 2005–2015. Telemedicine, mobile application, virtual reality, and medical information were reported with the index keywords of dental caries, dental procedures, and dental anxiety after 2015. The study also identified a gap in the published literature in applying newer digital technologies, such as the Internet of Things (IoT) and gamification, in oral and dental health research and practice. There is a growing tendency to use digital technologies in children’s oral and dental health in recent years. Although the types and categorisations of the technology are typically diverse during the timeframe and by the area of dental services and oral health, identifying and categorizing these technologies based on oral health services could familiarise oral health policymakers with the application of the technology and help them design technology-based interventions to improve children’s oral health.

The future Landscape of macrophage research in cardiovascular disease: a bibliometric analysis

Cardiovascular disease (CVD) refers to a group of diseases involving the heart or blood vessels and is currently the leading cause of morbidity and mortality in many countries around the world and poses a serious economic burden. Macrophages are key effectors of inflammatory and innate immune responses, and their aberrant expression contributes to the development of various types of CVD.This study retrieved articles published from 1990-2022 on macrophages in CVD from the Web of Science core collection, based on CiteSpace and VOSviewer on these literature The annual output, countries and regions, institutions, authors, core journals, keywords and co-cited literature were analyzed. A total of 7,197 articles and reviews were retrieved, with a general upward trend despite slight fluctuations in annual publications. Europe, the United States and Asia are the main countries and regions publishing articles, especially the United States, with the highest number of articles (2,581), citations (173,692) and H-index (197), which also has the world's largest number of elite institutions, professional The country also has the world's largest number of elite institutions, professional researchers and high-impact journals, and is the leading country in this field of research. Keywords "inflammation", "immunology", "autophagy", "lipid-peroxidation" are the main pathogenesis of CVD caused by macrophages. "NLRP3", "nf kappa b" and "TNF-α" are the most frequently studied signalling pathways. Atherosclerosis, myocarditis and myocardial injury are the most studied disease types in this field. In addition, the study of macrophage-related CVD induced by COVID-19 seems to be a recent hot topic, and the mechanisms involved are mainly macrophage polarization, inflammatory factor storm, ACE2 and so on. The present study reveals hot spots and new trends in research on macrophages in CVD, which can provide scholars with key information in this field of research and help further explore new research directions.

A Novel System for Measuring Pterygium's Progress Using Deep Learning

Pterygium is a common ocular surface disease. When pterygium significantly invades the cornea, it limits eye movement and impairs vision, which requires surgery to remove. It is medically recognized that when the width of the pterygium that invades the cornea is >3 mm, the patient can be treated with surgical resection. Owing to this, this study proposes a system for diagnosing and measuring the pathological progress of pterygium using deep learning methods, which aims to assist doctors in designing pterygium surgical treatment strategies. The proposed system only needs to input the anterior segment images of patients to automatically and efficiently measure the width of the pterygium that invades the cornea, and the patient's pterygium symptom status can be obtained. The system consists of three modules, including cornea segmentation module, pterygium segmentation module, and measurement module. Both segmentation modules use convolutional neural networks. In the pterygium segmentation module, to adapt the diversity of the pterygium's shape and size, an improved U-Net++ model by adding an Attention gate before each up-sampling layer is proposed. The Attention gates extract information related to the target, so that the model can pay more attention to the shape and size of the pterygium. The measurement module realizes the measurement of the width and area of the pterygium that invades the cornea and the classification of pterygium symptom status. In this study, the effectiveness of the proposed system is verified using datasets collected from the ocular surface diseases center at the Affiliated Eye Hospital of Nanjing Medical University. The results obtained show that the Dice coefficient of the cornea segmentation module and the pterygium segmentation module are 0.9620 and 0.9020, respectively. The Kappa consistency coefficient between the final measurement results of the system and the doctor's visual inspection results is 0.918, which proves that the system has practical application significance.