Global Trends and Frontier in Research on Pancreatic Alpha Cells: A Bibliometric Analysis from 2013 to 2023
Bibliographic record
Abstract
PURPOSE: Over the past 20 years, much of the research on diabetes has focused on pancreatic beta cells. In the last 10 years, interest in the important role of pancreatic alpha cells in the pathogenesis of diabetes, which had previously received little attention, has grown. We aimed to summarize and visualize the hotspot and development trends of pancreatic alpha cells through bibliometric analysis and to provide research direction and future ideas for the treatment of diabetes and other islet-related diseases. METHODS: We used two scientometric software packages (CiteSpace 6.1.R6 and VOSviewer1.6.18) to visualize the information and connection of countries, institutions, authors, and keywords in this field. RESULTS: A total of 532 publications, published in 752 institutions in 46 countries and regions, were included in this analysis. The United States showed the highest output, accounting for 39.3% of the total number of published papers. The most active institution was Vanderbilt University, and the authors with highest productivity came from Ulster University. In recent years, research hotspots have concentrated on transdifferentiation, gene expression, and GLP-1 regulatory function. Visualization analysis shows that research hotspots mainly focus on clinical diseases as well as physiological and pathological mechanisms and related biochemical indicators. CONCLUSIONS: This study provides a review and summary of the literature on pancreatic alpha cells through bibliometric and visual methods and shows research hotspot and development trends, which can guide future directions for research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.098 | 0.178 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".