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

Ferrous sulphate triggers ferroptosis in Candida albicans and cures vulvovaginal candidiasis in a mouse model

Candida albicans is the most leading cause of life-threatening fungal invasive infections, especially for vulvovaginal candidiasis (VVC). Resistance and tolerance to common fungicide has risen great demands on alternative strategies for treating C. albicans infections. In the present study, ferroptosis has been proven to occur in C. albicans by directly exposed to FeSO4 via induing hallmarks of ferroptosis, including Fe2+ overload burden, ROS eruption and lipid peroxidation. Transcriptomic profile gave the great hints of the possible mechanism for fungal ferroptosis that FeSO4 disturb pathways associated to ribosome, tyrosine metabolism, triglyceride metabolism and thiamine metabolism, thus mobilizing death-related gene synthesis. Inspired by the results, a FeSO4-loaded hydrogel was prepared as an antifungal agent to treat C. albicans infection. This hydrogel exhibited excellent dressing properties and maintained superior antifungal activity by characterization tests. Besides, mice treated by this composite hydrogel displayed excellent therapeutic efficacy. These results highlighted the potential therapeutic use of FeSO4 as an innovative strategy in treating C. albicans infections by targeting ferroptosis.

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

Background

Epilepsy 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.

Methods

Relevant 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).

Results

A 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.

Conclusions

This 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.

CircRNA and ferroptosis in human disease: Insights for new treatments

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.

Global biomarkers trends in acute kidney injury: a bibliometric analysis

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.