Nano-enhanced immunity: A bibliometric analysis of nanoparticles in vaccine adjuvant research
Bibliographic record
Abstract
This study analyzed the growth, collaboration, citation trends, and emerging topics in nanoparticle-based vaccine and adjuvant research (NVAR) from 1977 to 2023, using data from the Scopus database. The field showed a steady growth rate of 7.53% per year. Leading researchers Jaafari, M.R. and Alving, C.R. contributed significantly to the field, with 24.22% of publications and 38.92% of total citations coming from the United States. International collaboration was very strong, particularly between the US, UK, Germany, China, and France. Key research topics include nanoparticles, immunotherapy, COVID-19, and vaccines with a focus on SARS-CoV-2 and malaria. Emerging topics include vaccine adjuvants, mRNA, and neutralizing antibodies. This study emphasizes the importance of ongoing collaboration and interdisciplinary efforts to advance the field of NVAR.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.107 | 0.151 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".