Knowledge Mapping of Inflammasome and Pyroptosis in Stroke: A Bibliometric Analysis (2007-2023)
Bibliographic record
Abstract
BACKGROUND: Stroke is a major contributor to disability and death worldwide. Studies have demonstrated that inflammasome/pyroptosis and its mediated inflammatory response are important factors aggravating brain injury after stroke. We aimed to investigate and map the knowledge structure and global trends on inflam- masome/pyroptosis in stroke. METHODS: All relevant documents were obtained from the Web of Science on 5 June 2023. Bibliometric visualization diagrams were created using VOSviewer and CiteSpace. Excel was used for statistical analysis and drawing graphs. RESULTS: A total of 1106 publications were included, with more articles published each year, especially since 2014. China (740 papers), Zhejiang University (57 papers), Wang J (25 papers), and the Journal of Neuroinflammation (45 papers) were the most productive countries, institutions, authors, and journals, respectively. The United States was the country with highest centrality (0.56) and total link strength (171), and all of the top 10 institutions were in China. China and the U.S. cooperated closely. The centralities of the top 10 authors were all lower than 0.01; no leader has yet emerged in this field. "NLRP3 inflammasome" ranked first with 447 occurrences among 2136 keywords, of which 56 terms appeared more than 10 times when categorized into four clusters: cluster 1 (inflammation), cluster 2 (pyroptosis), cluster 3 (NLRP3 inflammasome), and cluster 4 (neuroinflammation). The studies focused on the mechanisms of inflammasome/pyroptosis in stroke were mainly limited to cell and animal experiments. CONCLUSION: Interest in inflammasome/pyroptosis in stroke is progressively increasing. The NLRP3 inflammasome is the most extensively studied and has been a research hotspot. The mechanisms of cell death in stroke are complex and future studies are needed to strengthen the clinical research on the relationship between pyroptosis-related processes and stroke, determine at which stage NLRP3 inflammasome activation, and clarify the detailed mechanism of NLRP3 in stroke.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.146 | 0.210 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| 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".