Bibliometric and visual analysis of spinal cord injury-associated macrophages from 2002 to 2023

Digestive Amyloidosis Trends: Clinical, Pathological, and Imaging Characteristics

Amyloidosis is a group of diseases characterized by the extracellular deposition of abnormally folded, insoluble proteins that lead to organ dysfunction. While it commonly affects the cardiovascular system, gastrointestinal (GI) tract involvement is undetermined. Recent research has focused on understanding the pathophysiology, diagnostic challenges, and therapeutic approaches to GI amyloidosis, particularly in systemic amyloid light-chain (AL) and amyloid A (AA) forms. GI manifestations can include motility disorders, bleeding, and, in severe cases, bowel obstruction. This review highlights the importance of the early recognition of digestive symptoms and associated imagistic findings in GI amyloidosis by analyzing the research that included clinical, pathological, and endoscopic approaches to amyloidosis. A systematic search of the PubMed and Scopus databases identified 19 relevant studies. Our findings showed that amyloid deposits commonly affect the entire GI tract, with AL amyloidosis being the most predominant form. Endoscopic evaluations and biopsy remain key diagnostic tools, with Congo Red staining and mass spectrometry being used to confirm amyloid type. Although progress has been made in diagnosis, the absence of targeted therapies and the indistinct nature of GI symptoms continue to be challenging.

Bibliometric analysis of electroencephalogram research in Parkinson's disease from 2004 to 2023

Background

Parkinson's disease (PD) is a prevalent neurodegenerative disorder affecting millions globally. It encompasses both motor and non-motor symptoms, with a notable impact on patients' quality of life. Electroencephalogram (EEG) is a non-invasive tool that is increasingly utilized to investigate neural mechanisms in PD, identify early diagnostic markers, and assess therapeutic responses.

Methods

The data were sourced from the Science Citation Index Expanded within the Web of Science Core Collection database, focusing on publications related to EEG research in PD from 2004 to 2023. A comprehensive bibliometric analysis was conducted using CiteSpace and VOSviewer software. The analysis began with an evaluation of the selected publications, identifying leading countries, institutions, authors, and journals, as well as co-cited references, to summarize the current state of EEG research in PD. Keywords are employed to identify research topics that are currently of interest in this field through the analysis of high-frequency keyword co-occurrence and cluster analysis. Finally, burst keywords were identified to uncover emerging trends and research frontiers in the field, highlighting shifts in interest and identifying future research directions.

Results

A total of 1,559 publications on EEG research in PD were identified. The United States, Germany, and England have made notable contributions to the field. The University of London is the leading institution in terms of publication output, with the University of California closely following. The most prolific authors are Brown P, Fuhr P, and Stam C In terms of total citations and per-article citations, Stam C has the highest number of citations, while Brown P has the highest H-index. In terms of the total number of publications, Clinical Neurophysiology is the leading journal, while Brain is the most highly cited. The most frequently cited articles pertain to software toolboxes for EEG analysis, neural oscillations, and PD pathophysiology. Through analyzing the keywords, four research hotspots were identified: research on the neural oscillations and connectivity, research on the innovations in EEG Analysis, impact of therapies on EEG, and research on cognitive and emotional assessments.

Conclusion

This bibliometric analysis demonstrates a growing global interest in EEG research in PD. The investigation of neural oscillations and connectivity remains a primary focus of research. The application of machine learning, deep learning, and task analysis techniques offers promising avenues for future research in EEG and PD, suggesting the potential for advancements in this field. This study offers valuable insights into the major research trends, influential contributors, and evolving themes in this field, providing a roadmap for future exploration.