Bibliometric analysis on the structure and function of IL17
Bibliographic record
Abstract
Abstract Background Interleukin17 (IL17) is an important cytokine in host defense at mucosal surfaces and also mediates many autoimmune diseases, including rheumatoid arthritis (RA). In recent years, many types of research relevant to the structure and function of IL17 have been identified. However, there is no bibliometric analysis in this research field. This study aims to explore the history, research hotspots, and emerging trends of IL17 from the perspective of the structure and function dynamics. Methods Articles relevant to IL17 in the last two decades were retrieved through the Web of Science Core Collection (WoSCC) database. The bibliometric analysis was performed by VOSview. Results A total of 882 papers in this research were analyzed from 65 countries, and the rate of published articles has increased from 2008 annually, with the USA, China, and Germany leading the research effort. Frontiers in Immunology has significantly impacted research in this field and the University of Pittsburgh was the leading institution. Gaffen, Sarah L. from the University of Pittsburgh was the most productive researcher in this field and Papp Ka from the Probity Medical Research Incorporate of Canada is the most co-cited author. The analysis of keywords showed that inflammation, expression, Th17 cells, and cytokines were the main hotspots and frontier directions of IL17. The trend of clinical application in the future is the development of new therapy drugs based on the structure of IL17 or IL17 signaling pathway molecular. Conclusions Our research summarized the developments and research trends of IL17 and would help researchers understand the research status of IL17 and provide a reference for future researchers as soon as possible.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".