A bibliometric analysis of hotpots and trends for the relationship between skin inflammation and regeneration
Bibliographic record
Abstract
Background Skin regeneration is a challenging issue worldwide. Increasing research has highlighted the role of immune cells in healing and the underlying regulatory mechanism. The purpose of this study was to identify the hotspots and trends in skin regeneration and inflammation research through bibliometrics and to provide insights into the future development of fundamental research and disease treatment. Methods Publications were collected from the Web of Science Core Collection on March 1, 2022. Articles and reviews published in English from January 1, 1999, to December 31, 2022, were selected, and statistical analyses of countries, institutions, authors, references, and keywords were performed using VOSviewer 1.6.18 and CiteSpace 5.8. Results A total of 3,894 articles and reviews were selected. The number of publications on skin inflammation and regeneration showed an increasing trend over time. Additionally, authors and institutions in the United States, United Kingdom, Canada, and China appeared to be at the forefront of research in the field of skin inflammation and regeneration. Werner Sabine published some of the most cited papers. Wound Repair and Regeneration was the most productive journal, while Journal of Investigative Dermatology was the most cited journal. Angiogenesis, diamonds, collagen, cytokine, and keratinocytes were the five most commonly used keywords. Conclusion The number of publications on skin inflammation and regeneration show an increasing trend. Moreover, a series of advanced technologies and treatments for skin regeneration, such as exosomes, hydrogels, and wound dressings, are emerging, which will provide precise information for the treatment of skin wounds. This study can enhance our understanding of current hotspots and future trends in skin inflammation and regeneration research, as well as provide guidelines for fundamental research and clinical treatment.
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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 | 0.011 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.177 | 0.253 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".