Bibliometric analysis of ferroptosis in acute kidney injury from 2014 to 2022

Liu, Chao; Zhou, Wuhong; Mao, Zhi; Li, Xiaoming; Meng, Qingcai; Fan, Rui; Zhou, Yibo; Zhang, Li; Hong, Quan; Sun, Xuefeng; Zhou, Feihu

doi:10.1007/s11255-022-03456-2

Cited by 6 publications

(6 citation statements)

References 68 publications

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“…The trend topic map showed that frontiers of AKI biomarkers were concentrated on ferroptosis, renal fibrosis, COVID-19, and machine-learning-based prediction models. Congruently, a recent bibliometric analysis of ferroptosis in AKI showed that the study of ferroptosis in AKI is still in its infancy, and the crosstalk between ferroptosis and other types of programmed cell death would be a hotspot for future research [ 32 ]. Moreover, ferroptosis has also been implicated in the transition from AKI to chronic kidney disease, and regulating ferroptosis would be a promising strategy to halt this transition [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

Global biomarkers trends in acute kidney injury: a bibliometric analysis

Fan,

2023

Renal Failure

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Objectives Acute kidney injury (AKI) is a common global condition with high morbidity and mortality rates. Biomarkers can aid in the diagnosis, prediction, intervention, and outcome assessment of AKI. This study aimed to summarize the current research status and identify research hotspots for AKI biomarkers using bibliometric analysis. Methods Relevant original English language articles were retrieved from the Science Citation Index Expanded of the Web of Science Core Collection database, from inception to 31 December 2022. Full records and related cited references from all the documents were collected and analyzed. Results A total of 16368 authors from 3379 institutions in 83 countries/regions contributed to 2916 documents that were published in 712 academic journals. Annual publication output followed exponential growth since 2008. The United States, the University of Pittsburgh, and the American Journal of Physiology-Renal Physiology were the most productive countries, institutions, and journals in terms of research outputs, respectively. The area of interest has shifted from neutrophil gelatinase-associated lipocalin, cell cycle, and tubular damage toward sepsis and COVID-19. Apoptosis, inflammation, and chronic kidney disease have become popular in recent years, and studies on ferroptosis, machine learning, COVID-19, and renal fibrosis will be the focus of future research. Implications This bibliometric study suggests that future research on AKI biomarkers would focus on ferroptosis, renal fibrosis and COVID-19. Artificial intelligence, such as machine learning, maybe the most promising direction for the discovery and validation of AKI biomarkers.

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Section: Discussionmentioning

confidence: 99%

Global biomarkers trends in acute kidney injury: a bibliometric analysis

Fan,

2023

Renal Failure

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“…Table 3 shows the top 10 authors in terms of number of publications and co-citation frequency. The author with the highest number of publications and cocitation frequency in the field is the same author Acharya, U. Rajendra of Ngee Ann Polytechnic, Singapore (17,489), which is a good indication of his academic standing in the field of ML and epilepsy. This is followed by Loddenkemper, Tobias of Harvard Medical School, Boston, Massachusetts, USA (13) and Brinkmann, Benjamin H. of Mayo Clinic, Rochester, MN, USA (13), who rank second in terms of number of publications.…”

Section: Distribution Of Authorsmentioning

confidence: 94%

“…It not only provides a basis for researchers to analyze the hotspots and development trends of the discipline and predict the direction of development of the discipline, but also provides a reference for the construction of the discipline and training of talents in the hospital. It further provides active research institutions and groups that can facilitate potential international or national research collaborations (15)(16)(17)(18). Despite the rapid development of research related to the application of ML in epilepsy, there is still a lack of bibliometric analysis related to this field.…”

Section: Introductionmentioning

confidence: 99%

Machine learning applied to epilepsy: bibliometric and visual analysis from 2004 to 2023

Huo,

Luo,

et al. 2024

Front. Neurol.

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BackgroundEpilepsy is one of the most common serious chronic neurological disorders, which can have a serious negative impact on individuals, families and society, and even death. With the increasing application of machine learning techniques in medicine in recent years, the integration of machine learning with epilepsy has received close attention, and machine learning has the potential to provide reliable and optimal performance for clinical diagnosis, prediction, and precision medicine in epilepsy through the use of various types of mathematical algorithms, and promises to make better parallel advances. However, no bibliometric assessment has been conducted to evaluate the scientific progress in this area. Therefore, this study aims to visually analyze the trend of the current state of research related to the application of machine learning in epilepsy through bibliometrics and visualization.MethodsRelevant articles and reviews were searched for 2004–2023 using Web of Science Core Collection database, and bibliometric analyses and visualizations were performed in VOSviewer, CiteSpace, and Bibliometrix (R-Tool of R-Studio).ResultsA total of 1,284 papers related to machine learning in epilepsy were retrieved from the Wo SCC database. The number of papers shows an increasing trend year by year. These papers were mainly from 1,957 organizations in 87 countries/regions, with the majority from the United States and China. The journal with the highest number of published papers is EPILEPSIA. Acharya, U. Rajendra (Ngee Ann Polytechnic, Singapore) is the authoritative author in the field and his paper “Deep Convolutional Neural Networks for Automated Detection and Diagnosis of Epileptic Seizures Using EEG Signals” was the most cited. Literature and keyword analysis shows that seizure prediction, epilepsy management and epilepsy neuroimaging are current research hotspots and developments.ConclusionsThis study is the first to use bibliometric methods to visualize and analyze research in areas related to the application of machine learning in epilepsy, revealing research trends and frontiers in the field. This information will provide a useful reference for epilepsy researchers focusing on machine learning.

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Section: Role Of Ferroptosis In Diseasementioning

confidence: 99%

“…Ferroptosis has been implicated in the progression of various diseases, including cardiovascular diseases, neurodegenerative diseases, chronic kidney diseases, skin diseases, and others. 17 , 18 , 19 DAZ‐associated protein 1 (DAZAP1) is recognized as a potent suppressor of ferroptosis. In hepatocellular carcinoma (HCC) cells, DAZAP1 was shown to bind to SLC7A11 mRNA, positively regulating SLC7A11 expression and inhibiting ferroptosis.…”

Section: Role Of Ferroptosis In Diseasementioning

confidence: 99%

CircRNA and ferroptosis in human disease: Insights for new treatments

Liu,

Zhou,

Cao

2023

Anim Models and Exp Med

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Circular RNA (circRNA), classified as a type of non‐coding RNA, has gained significant attention in the field of biology due to its distinctive ring structure and functional properties. Recent research has provided evidence that specific circRNAs have the ability to modulate disease progression through diverse mechanisms, one of which is by regulating cellular ferroptosis. Ferroptosis is a form of regulated cell death that is driven by iron dependency and lipid peroxidation, and extensive investigations have revealed a relationship between ferroptosis and disease development. In addition to evidence that both circRNAs and ferroptosis exert critical roles in disease progression, circRNAs have also been shown to actively mediate the process of ferroptosis. The relationship between circRNAs and ferroptosis therefore influences disease progression and offers novel targets for disease treatment. By directly or indirectly modulating the expression of circRNAs that regulate the expression of ferroptosis‐related proteins, it may be possible to impact disease progression by promoting or inhibiting ferroptosis. Current research indicates such approaches may hold significant value in a wide variety of common diseases across physiological systems. This review comprehensively summarizes the findings of recent studies investigating the roles of circRNAs in the regulation of ferroptosis in various diseases.

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Bibliometric analysis of ferroptosis in acute kidney injury from 2014 to 2022

Cited by 6 publications

References 68 publications

Global biomarkers trends in acute kidney injury: a bibliometric analysis

Global biomarkers trends in acute kidney injury: a bibliometric analysis

Machine learning applied to epilepsy: bibliometric and visual analysis from 2004 to 2023

CircRNA and ferroptosis in human disease: Insights for new treatments

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