Artificial intelligence applicated in gastric cancer: A bibliometric and visual analysis via CiteSpace

Current perspectives and trends of the research on hypertensive nephropathy: a bibliometric analysis from 2000 to 2023

Hypertensive nephropathy continues to be a major cause of end-stage renal disease and poses a significant global health burden. Despite the staggering development of research in hypertensive nephropathy, scientists and clinicians can only seek out useful information through articles and reviews, it remains a hurdle for them to quickly track the trend in this field. This study uses the bibliometric method to identify the evolutionary development and recent hotspots of hypertensive nephropathy. The Web of Science Core Collection database was used to extract publications on hypertensive nephropathy from January 2000 to November 2023. CiteSpace was used to capture the patterns and trends from multi-perspectives, including countries/regions, institutions, keywords, and references. In total, 557 publications on hypertensive nephropathy were eligible for inclusion. China (n = 208, 37.34%) was the most influential contributor among all the countries. Veterans Health Administration (n = 19, 3.41%) was found to be the most productive institution. Keyword bursting till now are renal fibrosis, outcomes, and mechanisms which are predicted to be the potential frontiers and hotspots in the future. The top seven references were listed, and their burst strength was shown. A comprehensive overview of the current status and research frontiers of hypertensive nephropathy has been provided through the bibliometric perspective. Recent advancements and challenges in hypertensive nephropathy have been discussed. These findings can offer informative instructions for researchers and scholars.

Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis

BACKGROUND

With the rise of artificial intelligence (AI) in the field of dementia biomarker research, exploring its current developmental trends and research focuses has become increasingly important. This study, using literature data mining, analyzes and assesses the key contributions and development scale of AI in dementia biomarker research.

OBJECTIVE

The aim of this study was to comprehensively evaluate the current state, hot topics, and future trends of AI in dementia biomarker research globally.

METHODS

This study thoroughly analyzed the literature in the application of AI to dementia biomarkers across various dimensions, such as publication volume, authors, institutions, journals, and countries, based on the Web of Science Core Collection. In addition, scales, trends, and potential connections between AI and biomarkers were extracted and deeply analyzed through multiple expert panels.

RESULTS

To date, the field includes 1070 publications across 362 journals, involving 74 countries and 1793 major research institutions, with a total of 6455 researchers. Notably, 69.41% (994/1432) of the researchers ceased their studies before 2019. The most prevalent algorithms used are support vector machines, random forests, and neural networks. Current research frequently focuses on biomarkers such as imaging biomarkers, cerebrospinal fluid biomarkers, genetic biomarkers, and blood biomarkers. Recent advances have highlighted significant discoveries in biomarkers related to imaging, genetics, and blood, with growth in studies on digital and ophthalmic biomarkers.

CONCLUSIONS

The field is currently in a phase of stable development, receiving widespread attention from numerous countries, institutions, and researchers worldwide. Despite this, stable clusters of collaborative research have yet to be established, and there is a pressing need to enhance interdisciplinary collaboration. Algorithm development has shown prominence, especially the application of support vector machines and neural networks in imaging studies. Looking forward, newly discovered biomarkers are expected to undergo further validation, and new types, such as digital biomarkers, will garner increased research interest and attention.

Bibliometric analysis of the application of deep learning in cancer from 2015 to 2023

BACKGROUND

Recently, the application of deep learning (DL) has made great progress in various fields, especially in cancer research. However, to date, the bibliometric analysis of the application of DL in cancer is scarce. Therefore, this study aimed to explore the research status and hotspots of the application of DL in cancer.

METHODS

We retrieved all articles on the application of DL in cancer from the Web of Science database Core Collection database. Biblioshiny, VOSviewer and CiteSpace were used to perform the bibliometric analysis through analyzing the numbers, citations, countries, institutions, authors, journals, references, and keywords.

RESULTS

We found 6,016 original articles on the application of DL in cancer. The number of annual publications and total citations were uptrend in general. China published the greatest number of articles, USA had the highest total citations, and Saudi Arabia had the highest centrality. Chinese Academy of Sciences was the most productive institution. Tian, Jie published the greatest number of articles, while He Kaiming was the most co-cited author. IEEE Access was the most popular journal. The analysis of references and keywords showed that DL was mainly used for the prediction, detection, classification and diagnosis of breast cancer, lung cancer, and skin cancer.

CONCLUSIONS

Overall, the number of articles on the application of DL in cancer is gradually increasing. In the future, further expanding and improving the application scope and accuracy of DL applications, and integrating DL with protein prediction, genomics and cancer research may be the research trends.

Bibliometric and Visualization Analysis of Research Hotspots and Frontiers in Endoscopic Lumbar Discectomy

Background

The increasing utilization of endoscopic lumbar discectomy (ELD) in spinal surgery has sparked widespread interest and research. This study utilizes bibliometric analysis to identify current research trends and advancements in this innovative surgical technique, with the goal of informing and improving surgical practices.

Methods

We retrieved relevant literature on ELD from the Science Citation Index Expanded (SCI-Expanded) within the Web of Science Core Collection (WoSCC) database as research samples. Various visualization tools, such as VOSviewer, CiteSpace, Scimago Graphica, Pajek, and online bibliometric platform, were employed to generate scientific knowledge maps for the purpose of visual presentation and data analysis.

Results

Over the past two decades, there has been significant progress in the research related to ELD, particularly since 2016. China has emerged as the most productive country, while South Korea and the United States have exerted greater academic influence. Tongji University has contributed the highest number of research output, while academic achievements published by Wooridul Spine Hospital are highly esteemed by scholars. Lee SH and Ruetten S are the most prolific author and the most highly cited author, respectively. World Neurosurg has published the highest number of publications, while Spine has become the most influential journal. Clinical Neurology and Surgery are the primary subject categories. Research in this field primarily revolves around improving ELD techniques, evaluating postoperative efficacy and prognosis prediction, studying complications and risk factors, as well as comparative research with other surgical techniques. Keywords such as risk factors, LDH, PETD, lumbar spinal stenosis, degeneration, recurrent herniation, laminectomy, local anesthesia, and foraminoplasty highlight the current research hotspots and future cutting-edge trends.

Conclusion

This study employed bibliometric analysis to elucidate the research hotspots and frontiers in ELD. The findings have significant implications for advancing research and development in this field.

Visualization of breast cancer-related protein synthesis from the perspective of bibliometric analysis

Breast cancer, as a daunting global health threat, has driven an exponential growth in related research activity in recent decades. An area of research of paramount importance is protein synthesis, and the analysis of specific proteins inextricably linked to breast cancer. In this article, we undertake a bibliometric analysis of the literature on breast cancer and protein synthesis, aiming to provide crucial insights into this esoteric realm of investigation. Our approach was to scour the Web of Science database, between 2003 and 2022, for articles containing the keywords "breast cancer" and "protein synthesis" in their title, abstract, or keywords. We deployed bibliometric analysis software, exploring a range of measures such as publication output, citation counts, co-citation analysis, and keyword analysis. Our search yielded 2998 articles that met our inclusion criteria. The number of publications in this area has steadily increased, with a significant rise observed after 2003. Most of the articles were published in oncology or biology-related journals, with the most publications in Journal of Biological Chemistry, Cancer Research, Proceedings of the National Academy of Sciences of the United States of America, and Oncogene. Keyword analysis revealed that "breast cancer," "expression," "cancer," "protein," and "translation" were the most commonly researched topics. In conclusion, our bibliometric analysis of breast cancer and related protein synthesis literature underscores the burgeoning interest in this research. The focus of the research is primarily on the relationship between protein expression in breast cancer and the development and treatment of tumors. These studies have been instrumental in the diagnosis and treatment of breast cancer. Sustained research in this area will yield essential insights into the biology of breast cancer and the genesis of cutting-edge therapies.

Bibliometric and Visualization Analysis of Biomechanical Research on Lumbar Intervertebral Disc

Background

Biomechanical research on the lumbar intervertebral disc (IVD) provides valuable information for the diagnosis, treatment, and prevention of related diseases, and has received increasing attention. Using bibliometric methods and visualization techniques, this study investigates for the first time the research status and development trends in this field, with the aim of providing guidance and support for subsequent research.

Methods

The Science Citation Index Expanded (SCI-Expanded) within the Web of Science Core Collection (WoSCC) database was used as the data source to select literature published from 2003 to 2022 related to biomechanical research on lumbar IVD. VOSviewer 1.6.19 and CiteSpace 6.2.R2 visualization software, as well as the online analysis platform of literature metrology, were utilized to generate scientific knowledge maps for visual display and data analysis.

Results

The United States is the most productive country in this field, with the Ulm University making the largest contribution. Wilke HJ is both the most prolific author and one of the highly cited authors, while Adams MA is the most cited author. Spine, J Biomech, Eur Spine J, Spine J, and Clin Biomech are not only the journals with the highest number of publications, but also highly cited journals. The main research topics in this field include constructing and validating three-dimensional (3D) finite element model (FEM) of lumbar spine, measuring intradiscal pressure, exploring the biomechanical effects and related risk factors of lumbar disc degeneration, studying the mechanical responses to different torque load combinations, and classifying lumbar disc degeneration based on magnetic resonance images (MRI), which are also the hot research themes in recent years.

Conclusion

This study systematically reviews the knowledge system and development trends in the field of biomechanics of lumbar IVD, providing valuable references for further research.

Research hotspots and trends of brain-computer interface technology in stroke: a bibliometric study and visualization analysis

Background

The incidence and mortality rates of stroke are escalating due to the growing aging population, which presents a significant hazard to human health. In the realm of stroke, brain-computer interface (BCI) technology has gained considerable attention as a means to enhance treatment efficacy and improve quality of life. Consequently, a bibliometric visualization analysis was performed to investigate the research hotspots and trends of BCI technology in stroke, with the objective of furnishing reference and guidance for future research.

Methods

This study utilized the Science Citation Index Expanded (SCI-Expanded) within the Web of Science Core Collection (WoSCC) database as the data source, selecting relevant literature published between 2013 and 2022 as research sample. Through the application of VOSviewer 1.6.19 and CiteSpace 6.2.R2 visualization analysis software, as well as the bibliometric online analysis platform, the scientific knowledge maps were constructed and subjected to visualization display, and statistical analysis.

Results

This study encompasses a total of 693 relevant literature, which were published by 2,556 scholars from 975 institutions across 53 countries/regions and have been collected by 185 journals. In the past decade, BCI technology in stroke research has exhibited an upward trend in both annual publications and citations. China and the United States are high productivity countries, while the University of Tubingen stands out as the most contributing institution. Birbaumer N and Pfurtscheller G are the authors with the highest publication and citation frequency in this field, respectively. Frontiers in Neuroscience has published the most literature, while Journal of Neural Engineering has the highest citation frequency. The research hotspots in this field cover keywords such as stroke, BCI, rehabilitation, motor imagery (MI), motor recovery, electroencephalogram (EEG), neurorehabilitation, neural plasticity, task analysis, functional electrical stimulation (FES), motor impairment, feature extraction, and induced movement therapy, which to a certain extent reflect the development trend and frontier research direction of this field.

Conclusion

This study comprehensively and visually presents the extensive and in-depth literature resources of BCI technology in stroke research in the form of knowledge maps, which facilitates scholars to gain a more convenient understanding of the development and prospects in this field, thereby promoting further research work.