ObjectiveTo explore the current status and tendency of the application of CT or MRI in the pancreatic pseudocyst using bibiometric analysis for relative documents, and provide reference information for the future research of radiology. MethodsBibliographies from research literatures of CT or MRI application in the pancreatic pseudocyst from January 1, 2003 to September 20, 2014 in PubMed database were downloaded.The publication years, journals, the first authors, and the frequency of subject headings and subheadings were extracted from them by Bicomb 2.0 software.The subject headings and subheadings appeared more than five times were intercepted as high frequency ones, then created the high frequency subject headings and subheadings co-occurrence matrix.SPSS 22.0 statistical software was applied for clustering analysis with this matrix, then got the major hotspots. ResultsA total of 342 literatures were screened out.The research of CT or MRI application in the pancreatic pseudocyst increased slowly year by year in recent 10 years, then slowly decreased after 2008 year.The related literatures were published in the 164 journals, in which 16 journals (115 literatures were published) were core area distribution according to the Bradford law.There were 10 authors at least 2 published literatures, in who Bhasin DK in USA published 7 literatures, was the most active researcher in this field.The number of high frequency subject headings and subheadings was 33 and among which 5 research hotspots were clustered. ConclusionResearch hotspots about CT or MRI application in pancreatic pseudocyst mainly focuses on five aspects below:pathology, diagnosis, therapy, complications, and etiology.
Objective To analyze the hot spot and future application trend of artificial intelligence technology in the field of intensive care medicine. Methods The CNKI, WanFang Data, VIP and Web of Science core collection databases were electronically searched to collect the related literature about the application of artificial intelligence in the field of critical medicine from January 1, 2013 to December 31, 2022. Bibliometrics was used to visually analyze the author, country, research institution, co-cited literature and key words. Results A total of 986 Chinese articles and 4 016 English articles were included. The number of articles published had increased year by year in the past decade, and the top three countries in English literature were China, the United States and Germany. The predictive model and machine learning were the most frequent key words in Chinese and English literature, respectively. Predicting disease progression, mortality and prognosis were the research focus of artificial intelligence in the field of critical medicine. ConclusionThe application of artificial intelligence in the field of critical medicine is on the rise, and the research hotspots are mainly related to monitoring, predicting disease progression, mortality, disease prognosis and the classification of disease phenotypes or subtypes.
Due to the high dimensionality and complexity of the data, the analysis of spatial transcriptome data has been a challenging problem. Meanwhile, cluster analysis is the core issue of the analysis of spatial transcriptome data. In this article, a deep learning approach is proposed based on graph attention networks for clustering analysis of spatial transcriptome data. Our method first enhances the spatial transcriptome data, then uses graph attention networks to extract features from nodes, and finally uses the Leiden algorithm for clustering analysis. Compared with the traditional non-spatial and spatial clustering methods, our method has better performance in data analysis through the clustering evaluation index. The experimental results show that the proposed method can effectively cluster spatial transcriptome data and identify different spatial domains, which provides a new tool for studying spatial transcriptome data.