Trends in the Use of Sphingosine 1 Phosphate in Age-Related Diseases: A Scientometric Research Study (1992-2020)
Bibliographic record
Abstract
Objectives. This study was designed to explore the intellectual landscape of research into the application of sphingosine 1 phosphate (S1P) in age-related diseases and to identify thematic development trends and research frontiers in this area. Methods. Scientometric research was conducted by analyzing bibliographic records retrieved from the Web of Science (WOS) Sci-Expanded Database dated between 1900 and 2020. Countries, institutions, authors, keyword occurrence analysis, and cooperation network analysis were performed using the CiteSpace and VOSviewer software. Results. A total of 348 valid records were included in the final dataset, and the number of publications and the frequency of citations have grown rapidly over the last ten years. The USA ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>n</mi> <mo>=</mo> <mn>175</mn> </math> ), China ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>n</mi> <mo>=</mo> <mn>42</mn> </math> ), and Germany ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>n</mi> <mo>=</mo> <mn>37</mn> </math> ) were the three largest contributors to the global publications on S1P and aging, while the Medical University of South Carolina ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>n</mi> <mo>=</mo> <mn>15</mn> </math> ), University of California, San Francisco ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <mi>n</mi> <mo>=</mo> <mn>13</mn> </math> ), and University of Toronto ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mi>n</mi> <mo>=</mo> <mn>13</mn> </math> ) were the leading institutions in this field. Analysis showed that early studies primarily focused on the mechanism of S1P intervention in AD. While S1P and its relevant metabolites have remained a long-term active area of research, recent studies have focused more on interventions aimed at improving retinal degeneration, cardiomyopathy, multiple sclerosis, and diabetes, among others. Conclusions. It is worth mentioning that this manuscript is the first to describe any bibliometric analysis of S1P and its application in age-related interventions. This study includes a discussion of the (1) historical overview of the topic; (2) main contributors: journals, countries, institutes, funding agencies, and authors; (3) collaboration between institutes and authors; (4) research hot spots and zones; and 5) research trends and frontiers. This will enable scholars to understand the current status of S1P research in age-related diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".