Integrating BERT and Graph Convolutional Networks for Medical Literature Mining: A Knowledge Graph Ap-proach to Pelvic Fracture Research Analysis

PurposeAsthma and depression are prevalent conditions with significant comorbidity, impacting patients’ quality of life. This bibliometric study aims to analyze research trends and hotspots in the field from 2000 to 2023, identifying key contributions and predicting future directions.MethodsWe conducted a systematic search in the Web of Science Core Collection (WoSCC) for articles on asthma and depression, published between 2000 and 2023. Bibliometrics, which involves the application of mathematical and statistical methods to analyze scholarly literature, was employed in this study to systematically assess the research trends and hotspots in the field of asthma and depression. VOSviewer and CiteSpace software were utilized for visual analysis and data visualization, enabling us to map collaboration networks and identify research hotspots and trends within the asthma and depression literature.ResultsOur analysis retrieved 3067 papers from 937 journals, involving 14,631 authors and 4006 institutions across 106 countries. The United States, Columbia University, the Journal of Asthma, and Christer Janson were the most prolific contributors. Six primary research themes emerged: quality of life, childhood asthma, primary care, substance P, intervention, and emotion. Additionally, Burst detection analysis identified emerging topics, including severe asthma, other respiratory diseases, and oxidative stress.ConclusionThis bibliometric analysis has revealed significant insights into the research trends and hotspots in the field of asthma and depression. The primary findings indicate a growing body of research highlighting the impact of depression on asthma control and patients’ quality of life, the need for psychological interventions in treating comorbid asthma and depression, and the emerging focus on severe asthma and oxidative stress mechanisms. These findings underscore the importance of continued research in these areas to advance our understanding and improve clinical outcomes for patients with these comorbid conditions.

BackgroundDiabetic nephropathy (DN) is the leading cause of end-stage renal disease, with an increasing prevalence worldwide, but its pathomechanisms remain incompletely understood. Accumulating evidence suggests that immunity plays an important role in the development of DN. Many papers have been published in the field over the last 20 years, but there has been no bibliometric review of the research hotspots and trends in the field. This study aimed to assess the current research status and future trends of the link between immune and DN using bibliometric analysis.MethodsPublications on the association between immunity and DN from 2004 to 2023 were retrieved from the Web of Science Core Collection database and screened according to inclusion criteria. VOSviewer and CiteSpace software were employed to visualize research trends and hotspots in the field. Data including author, institution, country, journal, reference, and keyword were analyzed.ResultsUltimately 1246 publications meeting the criteria were included in the bibliometric analysis, involving 838 articles (84.96%) and 408 reviews (15.04%). The literature covered 81 countries and regions, 1751 institutions, and 6584 authors. The top 2 countries in terms of the number of publications were China (435) and the United States (318), and they collaborated most frequently. The United States had the highest number of citations for published papers (18,161), far exceeding the other countries. England had 38 publications but had the highest average number of citations (92.32). The University of California system was the most prolific institution (25 papers, 1062 citations, 42.48 citations per paper). Frontiers in Immunology was the most prolific journal in the field (30 papers). The most cited journal was Kidney International (863 citations). The analysis of keywords and references showed that inflammation, ferroptosis, and lipid metabolism may be future research hotspots in this field.ConclusionsThe number of publications related to immunity and DN has increased annually over the past 20 years, with a significant increase in the last 3 years especially. Our results identified research hotspots and trends in the field. These findings provide valuable perspectives for future research, enhancing our understanding of the immune-related mechanisms of DN and exploring potential therapeutic strategies.

Biomedical knowledge graphs (KGs), which can help with the understanding of complex biological systems and pathologies, have begun to play a critical role in medical practice and research. However, challenges remain in their embedding and use due to their complex nature and the specific demands of their construction. Existing studies often suffer from problems such as sparse and noisy datasets, insufficient modeling methods and non-uniform evaluation metrics. In this work, we established a comprehensive KG system for the biomedical field in an attempt to bridge the gap. Here, we introduced PharmKG, a multi-relational, attributed biomedical KG, composed of more than 500000 individual interconnections between genes, drugs and diseases, with 29 relation types over a vocabulary of ~8000 disambiguated entities. Each entity in PharmKG is attached with heterogeneous, domain-specific information obtained from multi-omics data, i.e. gene expression, chemical structure and disease word embedding, while preserving the semantic and biomedical features. For baselines, we offered nine state-of-the-art KG embedding (KGE) approaches and a new biological, intuitive, graph neural network-based KGE method that uses a combination of both global network structure and heterogeneous domain features. Based on the proposed benchmark, we conducted extensive experiments to assess these KGE models using multiple evaluation metrics. Finally, we discussed our observations across various downstream biological tasks and provide insights and guidelines for how to use a KG in biomedicine. We hope that the unprecedented quality and diversity of PharmKG will lead to advances in biomedical KG construction, embedding and application.

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The knowledge graph, as a significant branch of Library and Information Science, has also become a vital part of Computer Science. In a bid to reveal the research trend and hot topics of the knowledge graph domain, this paper collected latest data about literature on knowledge graphs from SCI-Expanded and EI Compendex database. This paper took advantage of Citespace for visualization analyses of data, which discerned the research topics, hot issues and theme changes of the knowledge graph domain over time. Through analyses, we conclude that the knowledge graph has developed greatly in both Library and Information Science and Computer Science. In Library and Information Science domain, researches focus on the issues like the plotting of knowledge graphs and visualization of knowledge. In Computer Science domain, hot issues are the techniques of constructing knowledge graphs, which include entity recognition, knowledge integration, etc., and their applications in Semantic Web. Among all the research topics, the domain knowledge graph is one of the paramount research trends. Compared with previous researches, this paper keeps up with the times and can reveal the theme evolution of researches on knowledge graphs more precisely.

BackgroundVisceral pain is a complex and heterogeneous disorder that is considered more prominent compared to somatic pain, due to its multiple and complex causes and accompanying emotional and mood disorders. Research has become increasingly extensive over the years, but a bibliometric analysis of this field is lacking. The aim of this study was to analyze global research trends in visceral pain over the past 40 years through visual analysis.MethodsWe conducted a comprehensive search of the literature from January 1981 to December 2021 using the Web of Science core database. The medical subject term ‘visceral pain’ was searched. We used CiteSpace and VOSviewer for bibliometric analysis and network visualization, including top-ranked authors, keywords, research collaborations, and literature co-occurrence network analysis.ResultsA total of 5,047 articles were included in the analysis. The number of articles on visceral pain has continued to grow steadily over the past 40 years. The United States (1,716 articles), University of California (159 articles), and Neurogastroenterology and Motility (276 articles) were the country, institution, and journal with the most publications, respectively. Keyword analysis showed that inflammation, visceral hypersensitivity, inflammatory bowel disease (IBD), irritable bowel syndrome (IBS), anxiety, and quality of life were the research trends and priorities in this research field.ConclusionVisceral pain-related research has received increasing attention in recent decades. However, there are still many unresolved issues in the field of visceral pain, such as the specific molecular mechanisms and clinical treatments between visceral pain and inflammation, IBD, IBS, anxiety, and quality of life, which may require further exploration based on modern scientific and technological means and more basic research, especially for the therapeutic targets of visceral pain, which may become a hot spot for future research and provide guidance for the treatment of clinical diseases related to visceral pain.

Drug repurposing (DR) offers a promising alternative to the high cost and low success rate of traditional drug development, especially for complex diseases like Alzheimer’s disease (AD). This study addressed DR for AD from three key angles: (1) demonstrating how disease-specific knowledge graphs can improve DR performance, (2) evaluating the role of large language models (LLMs) in enhancing the usability and efficiency of these graphs, and (3) assessing whether Graph-of-Thoughts (GoT)-enhanced LLMs, when integrated with AD knowledge graphs, can outperform traditional machine learning and LLM-based approaches. We tested five distinct DR strategies (DR1–DR5) for AD: DR1, a machine learning method using TxGNN; DR2, a machine learning model leveraging the Alzheimer’s KnowledgeBase (AlzKB); DR3, an LLM-based chatbot built on AlzKB; DR4, our ESCARGOT framework combining GoT-enhanced LLMs with AlzKB; and DR5, a general reasoning-driven LLM approach. Results showed that AlzKB significantly improved DR outcomes. ESCARGOT further enhanced performance while reducing the need for coding or advanced expertise in knowledge graph analysis. Because the architecture of AlzKB is easily adaptable to other diseases and ESCARGOT can integrate with various knowledge graph platforms, this framework offers a broadly applicable, innovative tool for accelerating drug discovery through repurposing.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13040-025-00466-5.

Hydro-graph: A knowledge graph for hydrogen research trends and relations

BackgroundMedical entity recognition is a key technology that supports the development of smart medicine. Existing methods on English medical entity recognition have undergone great development, but their progress in the Chinese language has been slow. Because of limitations due to the complexity of the Chinese language and annotated corpora, these methods are based on simple neural networks, which cannot effectively extract the deep semantic representations of electronic medical records (EMRs) and be used on the scarce medical corpora. We thus developed a new Chinese EMR (CEMR) dataset with six types of entities and proposed a multi-level representation learning model based on Bidirectional Encoder Representation from Transformers (BERT) for Chinese medical entity recognition.ObjectiveThis study aimed to improve the performance of the language model by having it learn multi-level representation and recognize Chinese medical entities.MethodsIn this paper, the pretraining language representation model was investigated; utilizing information not only from the final layer but from intermediate layers was found to affect the performance of the Chinese medical entity recognition task. Therefore, we proposed a multi-level representation learning model for entity recognition in Chinese EMRs. Specifically, we first used the BERT language model to extract semantic representations. Then, the multi-head attention mechanism was leveraged to automatically extract deeper semantic information from each layer. Finally, semantic representations from multi-level representation extraction were utilized as the final semantic context embedding for each token and we used softmax to predict the entity tags.ResultsThe best F1 score reached by the experiment was 82.11% when using the CEMR dataset, and the F1 score when using the CCKS (China Conference on Knowledge Graph and Semantic Computing) 2018 benchmark dataset further increased to 83.18%. Various comparative experiments showed that our proposed method outperforms methods from previous work and performs as a new state-of-the-art method.ConclusionsThe multi-level representation learning model is proposed as a method to perform the Chinese EMRs entity recognition task. Experiments on two clinical datasets demonstrate the usefulness of using the multi-head attention mechanism to extract multi-level representation as part of the language model.

EcologyVolume 37, Issue 3 p. 544-550 Article Differential Attraction of Species of Drosophila to Different Species of Yeasts Th. Dobzhansky, Th. Dobzhansky Columbia University, New York City; University of California, Davis; University of Sao Paulo, Brazil; and Washington University, St. LouisSearch for more papers by this authorD. M. Cooper, D. M. Cooper Columbia University, New York City; University of California, Davis; University of Sao Paulo, Brazil; and Washington University, St. LouisSearch for more papers by this authorH. J. Phaff, H. J. Phaff Columbia University, New York City; University of California, Davis; University of Sao Paulo, Brazil; and Washington University, St. LouisSearch for more papers by this authorE. P. Knapp, E. P. Knapp Columbia University, New York City; University of California, Davis; University of Sao Paulo, Brazil; and Washington University, St. LouisSearch for more papers by this authorH. L. Carson, H. L. Carson Columbia University, New York City; University of California, Davis; University of Sao Paulo, Brazil; and Washington University, St. LouisSearch for more papers by this author Th. Dobzhansky, Th. Dobzhansky Columbia University, New York City; University of California, Davis; University of Sao Paulo, Brazil; and Washington University, St. LouisSearch for more papers by this authorD. M. Cooper, D. M. Cooper Columbia University, New York City; University of California, Davis; University of Sao Paulo, Brazil; and Washington University, St. LouisSearch for more papers by this authorH. J. Phaff, H. J. Phaff Columbia University, New York City; University of California, Davis; University of Sao Paulo, Brazil; and Washington University, St. LouisSearch for more papers by this authorE. P. Knapp, E. P. Knapp Columbia University, New York City; University of California, Davis; University of Sao Paulo, Brazil; and Washington University, St. LouisSearch for more papers by this authorH. L. Carson, H. L. Carson Columbia University, New York City; University of California, Davis; University of Sao Paulo, Brazil; and Washington University, St. LouisSearch for more papers by this author First published: 01 July 1956 https://doi.org/10.2307/1930178Citations: 48AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume37, Issue3July 1956Pages 544-550 RelatedInformation

Journal of Educational MeasurementVolume 16, Issue 1 p. 19-26 EFFECTS OF TESTING CONDITIONS ON PIAGET MATRICES AND ORDER OF APPEARANCE PROBLEMS: A STUDY OF COMPETENCE VERSUS PERFORMANCE JERRY S. CARLSON, Corresponding Author JERRY S. CARLSON University of California, Riverside School of Education, University of California, Riverside, CA 92521. Title. Professor of Education. Degrees: B. A. University of Washington, Ph.D. University of California, Berkeley. Specialization. Human Learning and Development; Psychodiagnostics. Department of Guidance and Educational Psychology, Southern Illinois University, Carbondale, IL 62901. Title: Assistant Professor of Educational Psychology. Degrees: B.A. University of California, Santa Barbara, M.A., Ph.D. University of California, Riverside. Specialization: Human Learning and Development; Psychodiagnostics.Search for more papers by this authorRONNA DILLON, Corresponding Author RONNA DILLON Southern Illinois University, Carbondale School of Education, University of California, Riverside, CA 92521. Title. Professor of Education. Degrees: B. A. University of Washington, Ph.D. University of California, Berkeley. Specialization. Human Learning and Development; Psychodiagnostics. Department of Guidance and Educational Psychology, Southern Illinois University, Carbondale, IL 62901. Title: Assistant Professor of Educational Psychology. Degrees: B.A. University of California, Santa Barbara, M.A., Ph.D. University of California, Riverside. Specialization: Human Learning and Development; Psychodiagnostics.Search for more papers by this author JERRY S. CARLSON, Corresponding Author JERRY S. CARLSON University of California, Riverside School of Education, University of California, Riverside, CA 92521. Title. Professor of Education. Degrees: B. A. University of Washington, Ph.D. University of California, Berkeley. Specialization. Human Learning and Development; Psychodiagnostics. Department of Guidance and Educational Psychology, Southern Illinois University, Carbondale, IL 62901. Title: Assistant Professor of Educational Psychology. Degrees: B.A. University of California, Santa Barbara, M.A., Ph.D. University of California, Riverside. Specialization: Human Learning and Development; Psychodiagnostics.Search for more papers by this authorRONNA DILLON, Corresponding Author RONNA DILLON Southern Illinois University, Carbondale School of Education, University of California, Riverside, CA 92521. Title. Professor of Education. Degrees: B. A. University of Washington, Ph.D. University of California, Berkeley. Specialization. Human Learning and Development; Psychodiagnostics. Department of Guidance and Educational Psychology, Southern Illinois University, Carbondale, IL 62901. Title: Assistant Professor of Educational Psychology. Degrees: B.A. University of California, Santa Barbara, M.A., Ph.D. University of California, Riverside. Specialization: Human Learning and Development; Psychodiagnostics.Search for more papers by this author First published: March 1979 https://doi.org/10.1111/j.1745-3984.1979.tb00082.xCitations: 7AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Citing Literature Volume16, Issue1March 1979Pages 19-26 RelatedInformation

Research trends and hotspots of acupuncture therapy for obesity from 2004 to 2023: A bibliometric analysis

Freshwater periphytic biofilms (FPBs), existing widely in various aquatic environments, have attracted extensive attention for many years. In the present study, a bibliometric analysis based on Web of Science Core Collection (WoSCC) was used to understand the research progress, trends, and hot topics of FPBs qualitatively and quantitatively. The results indicated that publications on FPBs have increased from 1991 to 2020 rapidly, and researchers have focused more on the areas of environmental sciences, microbiology, and marine freshwater biology. The most influential countries were mainly the USA, Spain, France, and Germany. Cooperation network analysis reflected that the USA and its affiliated institutions played crucial roles in the research of FPB cooperation, but the collaboration between core author groups still fell short. Based on the analysis of top 20 high-cited FPB documents over the last 30years, research hotspots mainly included micro-observation and assembly mechanisms of FPBs; interactions of FPBs and pollutants including heavy metals, antibiotic resistance genes, pathogens, organic pollutants, and nanoparticles; and the role of FPBs for biogeochemical cycling, especially nitrogen cycling. Additionally, future research directions were proposed. Overall, this study provides a comprehensive and systematic overview of FPBs, which is useful for research development and researchers who are interested in this area.

Global temperature change is related to the destiny of all mankind, and carbon mitigation, as well as greenhouse gases control, are key points. In order to explore the basic knowledge, research hotspots and trends in global carbon mitigation research, this paper, based on 15,304 carbon mitigation articles from Web of Science, from 1991 to 2021, conducts spatio-temporal distribution, country distribution, research hotspots and cooperation network analysis, and draws frontier knowledge graphs of carbon mitigation by using CiteSpace, Gephi and other scientific knowledge mapping and literature analysis software. The key scholars, important literature, main contribution institutions and countries/regions in the field of carbon mitigation research were extracted, and the research basis and evolution law were displayed. The study found that: (1) The research on carbon mitigation is increasing every year, which can be roughly divided into four stages: germination, low-speed development, medium-speed development and high-speed development. (2) The spatial distribution of carbon mitigation research is unbalanced, mainly showing a characteristic of “one super and many strong” centered on the United States. (3) The research hotspots of carbon mitigation have gradually evolved from phenomenon analysis, policy guidance, method exploration to mechanism improvement. Further research might focus on ocean carbon sink, carbon trading and carbon-negative technology.

Non-pharmaceutical interventions (NPI) have great potential to improve cognitive function but limited investigation to discover NPI repurposing for Alzheimer's Disease (AD). This is the first study to develop an innovative framework to extract and represent NPI information from biomedical literature in a knowledge graph (KG), and train link prediction models to repurpose novel NPIs for AD prevention. We constructed a comprehensive KG, called ADInt, by extracting NPI information from biomedical literature. We used the previously-created SuppKG and NPI lexicon to identify NPI entities. Four KG embedding models (i.e., TransE, RotatE, DistMult and ComplEX) and two novel graph convolutional network models (i.e., R-GCN and CompGCN) were trained and compared to learn the representation of ADInt. Models were evaluated and compared on two test sets (time slice and clinical trial ground truth) and the best performing model was used to predict novel NPIs for AD. Discovery patterns were applied to generate mechanistic pathways for high scoring candidates. The ADInt has 162,212 nodes and 1,017,284 edges. R-GCN performed best in time slice (MR = 5.2054, Hits@10 = 0.8496) and clinical trial ground truth (MR = 3.4996, Hits@10 = 0.9192) test sets. After evaluation by domain experts, 10 novel dietary supplements and 10 complementary and integrative health were proposed from the score table calculated by R-GCN. Among proposed novel NPIs, we found plausible mechanistic pathways for photodynamic therapy and Choerospondias axillaris to prevent AD, and validated psychotherapy and manual therapy techniques using real-world data analysis. The proposed framework shows potential for discovering new NPIs for AD prevention and understanding their mechanistic pathways.