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.