Analysis of research hotspots and trends in pediatric ophthalmopathy based on 10 years of WoSCC literature.

Background: Ophthalmopathy occurring in childhood can easily lead to irreversible visual impairment, and therefore a great deal of clinical and fundamental researches have been conducted in pediatric ophthalmopathy. However, a few studies have been performed to analyze such large amounts of research using bibliometric methods. This study intended to apply bibliometric methods to analyze the research hotspots and trends in pediatric ophthalmopathy, providing a basis for clinical practice and scientific research to improve children's eye health. Methods: Publications related to pediatric ophthalmopathy were searched and identified in the Web of Science Core Collection (WoSCC) database. Bibliometric and visualized analysis was performed using the WoSCC analysis system and CiteSpace.6.2.6 software, and high-impact publications were analyzed. Results: This study included a total of 7,177 publications from 162 countries and regions. Of these, 2,269 from the United States and 1,298 from China. The centrality and H-index were highest in the United States at 0.27 and 66, respectively. The University of London and Harvard University had the highest H-index at 37. Freedman,Sharon F published 55 publications, with the highest H-index at 19. The emerging burst keyword in 2020-2023 was "eye tracking," and the burst keywords in 2021-2023 were "choroidal thickness," "pediatric ophthalmology," "impact" and "childhood glaucoma." Retinopathy of prematurity, myopia, retinoblastoma and uveitis in juvenile idiopathic arthritis were the main topics in the high-impact publications, with clinical studies in the majority, especially in retinopathy of prematurity. Conclusion: Eye health in children is a research hotspot, with the United States publishing the largest number of papers and having the greatest influence in research on pediatric ophthalmopathy, and China coming in second. The University of London and Stanford University had the greatest influence. Freedman, Sharon F was the most influential author. Furthermore, "choroidal thickness," "pediatric ophthalmology," "impact," "childhood glaucoma" and "eye tracking"are the latest hotspots in the field of pediatric ophthalmopathy. These hotspots represent hot diseases, hot technologies and holistic concepts, which are exactly the research trends in the field of pediatric ophthalmopathy, providing guidance and grounds for clinical practice and scientific research on children's eye health.

Tumor microenvironment-responsive size-changeable and biodegradable HA-CuS/MnO2 nanosheets for MR imaging and synergistic chemodynamic therapy/phototherapy.

Tumor microenvironment (TME)-responsive size-changeable and biodegradable nanoplatforms for multimodal therapy possess huge advantages in anti-tumor therapy. Hence, we developed a hyaluronic acid (HA) modified CuS/MnO2 nanosheets (HCMNs) as a multifunctional nanoplatform for synergistic chemodynamic therapy (CDT)/photothermal therapy (PTT)/photodynamic therapy (PDT). The prepared HCMNs exhibited significant NIR light absorption and photothermal conversion efficiency because of the densely deposited ultra-small sized CuS nanoparticles on the surface of MnO2 nanosheet. They could precisely target the tumor cells and rapidly decomposed into small sized nanostructures in the TME, and then efficiently promote intracellular ROS generation through a series of cascade reactions. Moreover, the local temperature elevation induced by photothermal effect also promote the PDT based on CuS nanoparticles and the Fenton-like reaction of Mn2+, thereby enhancing the therapeutic efficiency. Furthermore, the T1-weighted magnetic resonance (MR) imaging was significantly enhanced by the abundant Mn2+ ions from the decomposition process of HCMNs. In addition, the CDT/PTT/PDT synergistic therapy using a single NIR light source exhibited considerable anti-tumor effect via in vitro cell test. Therefore, the developed HCMNs will provide great potential for MR imaging and multimodal synergistic cancer therapy.

Systematic bibliometric and visualized analysis of research hotspots and trends in artificial intelligence in autism spectrum disorder.

Background: Artificial intelligence (AI) has been the subject of studies in autism spectrum disorder (ASD) and may affect its identification, diagnosis, intervention, and other medical practices in the future. Although previous studies have used bibliometric techniques to analyze and investigate AI, there has been little research on the adoption of AI in ASD. This study aimed to explore the broad applications and research frontiers of AI used in ASD. Methods: Citation data were retrieved from the Web of Science Core Collection (WoSCC) database to assess the extent to which AI is used in ASD. CiteSpace.5.8. R3 and VOSviewer, two online tools for literature metrology analysis, were used to analyze the data. Results: A total of 776 publications from 291 countries and regions were analyzed; of these, 256 publications were from the United States and 173 publications were from China, and England had the largest centrality of 0.33; Stanford University had the highest H-index of 17; and the largest cluster label of co-cited references was machine learning. In addition, keywords with a high number of occurrences in this field were autism spectrum disorder (295), children (255), classification (156) and diagnosis (77). The burst keywords from 2021 to 2023 were infants and feature selection, and from 2022 to 2023, the burst keyword was corpus callosum. Conclusion: This research provides a systematic analysis of the literature concerning AI used in ASD, presenting an overall demonstration in this field. In this area, the United States and China have the largest number of publications, England has the greatest influence, and Stanford University is the most influential. In addition, the research on AI used in ASD mostly focuses on classification and diagnosis, and "infants, feature selection, and corpus callosum are at the forefront, providing directions for future research. However, the use of AI technologies to identify ASD will require further research.