Knowledge domain and emerging trends in beta‐cell research: A bibliometric and knowledge‐map analysis
Bibliographic record
Abstract
Background Up to now, the physiology, pathology, and recovery of beta-cells have been intensively studied and made great progress, and these are of major significance for the treatment of related diseases. Nevertheless, a comprehensive and objective report on the status of beta-cell research is lacking. Therefore, this study aims to conduct a bibliometric analysis to quantify and identify the current status and trending issues in beta-cell research. Methods The articles and reviews related to beta-cell were obtained from the Web of Science Core Collection on August 31, 2022. Two scientometric software (CiteSpace 6.1.R3 and VOSviewer 1.6.18) were used to perform bibliometric and knowledge-map analysis. Results A total of 4098 papers were published in 810 academic journals in 2938 institutions from 83 countries/regions. The number of beta-cell-related publications was increasing steadily. The United States was the most productive country, while Universite libre de Bruxelles, University of Toronto and University of Geneva were the most active institutions. Diabetes published the most beta-cell studies and received the largest number of co-citations. Decio I Eizirik published the most papers and had the most co-citations. Twelve references on reviews and mechanisms were regarded as the knowledge base. Four major aspects of beta-cell research included the pathological mechanism of beta-cell failure, the recovery of beta cells, the risk factor related to beta cells, and the physiology of beta cells. Endoplasmic reticulum stress and oxidative stress have been core elements throughout the research in this field. In addition, beta-cell dedifferentiation, inflammation, autophagy, miRNA, and lncRNA are hot topics nowadays. Additionally, stem cell replacement therapies might be the alternative way to reverse beta-cell failure. Restoring beta-cell mass and function will remain a research goal in the future. Conclusion This study provided a comprehensive overview of beta-cell research through bibliometric and visual methods. The information would provide helpful references for scholars focusing on beta cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.164 | 0.184 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".